Caving 2014, Chile

MASS MINING PROJECTS AND KNOWLEDGE FOR THE FUTUREEDITOR: RAÚL CASTRO

5-6 JUNE 2014SANTIAGO - CHILE

3RD INTERNATIONALSYMPOSIUM ON BLOCK AND SUBLEVEL CAVING


Proceedings of the Third InternationalSymposium on Block and Sublevel Caving

5-6 June 2014, Santiago, Chile

EDITORRaúl Castro

Universidad de Chile

Av. Libertador Bernardo O’Higgins 1058, Santiago de Chile | Teléfono: (56 2) 29782000

© Copyright 2014. Universidad de Chile. All rights reserved. No part of this publication may be reproduced, stored or transmitted in any form without the prior permission of The Universidad de Chile.

Disclaimer

The information contained in this publication is for general education and informative purposes only. Except to the extent required by law, the Universidad de Chile makes no representations or warranties express or implied as to the accuracy, reliability or completeness of the information stored therein. To the extent permitted by law, the Universidad de Chile excludes all liability for loss or damage of any kind at all (including indirect or consequential loss or damage) arising from the information in this publication or use of such information. You acknowledge that the information provided in this publication is to assist you with undertaking your own enquires and analyses and that you should seek independent professional advice before acting in reliance on the information contained therein. While all care has been taken in presenting this information herein, no liability is accepted for errors or omissions. The views expressed in this publication are those of the authors and may not necessarily reflect those of the Universidad de Chile.

The papers contained in this publication are for general information only, and readers are cautioned to take advice on cave mine projects.

Photographs courtesy of Codelco.

ISBN 978-956-19-0857-4


The Universidad de Chile was founded in 1842 being the oldest higher education institution in Chile. Generating, developing, integrating and communicating knowledge in all the areas of knowledge and culture are the mission and basis of the activities of the University.

The Universidad de Chile (UCH) has also a 160-year tradition of educating mining engineers. The first mining engineering program was created under the leadership of Andrés Bello in 1853, during the presidency of Manuel Montt. Since then, several hundred mining engineers have been trained at the UCH contributing greatly to the development of the Chilean mining industry. Mining engineers from the UCH have lead important technological changes, institution and to open new horizons in the mining and metallurgy industry. Examples of the involvement of UCH graduates are numerous including technological developments in block caving, the generation of El Teniente´s convertor and the development of heap leaching technologies.

Today the mining training activities at the UCH are multiple and largest than the first Bello´s dream. The Mining Engineering Department is in charge of delivering undergraduates, postgraduates (master and doctorate) and continuous mining education programs. Fundamental and applied research in mining is achieved through the Advanced Mining Technology Center (AMTC) a multidisciplinary center aimed to develop technology-based applied solutions for the industry. In terms of underground mining, block caving research is conducted at the Block Caving Laboratory, where the next generation of underground mining specialists is being trained.

The communication of knowledge is one of the missions of the University of Chile. Therefore, seminars and publications in mining are the platforms through which we present and discuss the latest advancements in mining related technology and knowledge. The Universidad de Chile is honored to be the organizer of Caving 2014 and to host it here in Santiago.

TECHNICAL REVIEWERS

The editor thanks the following people who contributed their time and expertise as reviewers of manuscripts for the Third International Symposium on Block and Sublevel Caving held in Santiago, Chile.

Dr. Eleonora Widzyk-Capehart, Universidad de Chile, Chile

Prof. Juan Pablo Vargas, Universidad de Santiago de Chile, Chile

Prof. Javier Vallejos, Universidad de Chile, Chile

Prof. Nelson Morales, Universidad de Chile, Chile

Prof. Xavier Emery, Universidad de Chile, Chile

Prof. Yves Potvin, Australian Centre for Geomechanics, Australia

Prof. Hans Göpfert, Universidad de Chile, Chile

Dr. Matthew Pierce, Itasca, United States

Prof. Italo Onederra, University of Queensland, Australia

Prof. Leandro Alejano, Universidad de Vigo, Spain

Dr. Enrique Rubio, REDCO, Chile

Local Organizing Committee – Universidad de Chile

Carolina Bahamondez

Sebastián Valerio

María Elena Valencia

Verónica Moller

Paula Alfaro

Marcela Muñoz

Bernardita Ponce

Javier Gutiérrez International Organizing Committee

Andrzej Zablocki Atlas Copco

Dr. Matthew Pierce Itasca Consulting Group

Prof. Gideon Chitombo University of Queensland

Gustavo Reyes Hatch

Danie Burger Sandvik

Jarek Jakubec SRK

Victor Encina JRI

Alfonso Ovalle AMEC

Mauricio Larraín Codelco

PREFACE

To be profitable, the extraction of large amounts of valuable minerals from the ground requires the use of efficient mine technologies. Equally important is the sustainability of the operations and high safety standards. Underground mining methods produce less impact on the environment than open pit practices. Caving methods are also the natural replacement for open pit operations as the ore reserves near the surface become depleted. Mine caving offers the lowest cost and highest production, provided that this method is correctly selected and implemented for the orebody’s geotechnical and geological conditions.

Australia, Canada, Chile, Indonesia, Mongolia, China, Sweden, South Africa and the USA all have cave mines. Currently, worldwide mine caving research is being pursued within mining companies, universities and research centers. Current research is analyzing some of the technical challenges that the block caving industry faces, including:

• Large amount of development required in a short period of time.

• Scarcity of highly qualified people.

• Need for high productivity material handling systems.

• Understanding and tracking of the cave and the material flow.

• Mud rush and rockburst prediction and control, especially when the mud has a high grade content.

• Mine costs and dilution control.

• High stress conditions.

• Ventilation and high temperature conditions.

• Stability of the mine infrastructure.

Many operations are considering, or have decided, to use block caving as their preferred mining method. Currently, about 400,000 ton per day are extracted by caving methods. It is estimated that this figure will increase to a rate of 1 M ton per day by 2018. Production rates will also increase. This will present new and exciting challenges and opportunities for the mining industry and for the R&D community.

I would like to present to you the proceedings of the Third International Symposium on Block and Sublevel Caving, which will be held in Santiago on the 5th and 6th of June 2014 in Chile. In this Proceedings, you would find two key notes and sixty eight technical articles written by people from all over the globe. Technical topics include innovation, mine planning, mine geomechanics gravity flow, seismicity, production and development planning, ventilation, blasting and case studies.

I would also like to acknowledge the people that believed in the dream of making Chile not only a center of copper production but also a center of knowledge production: Fidel Baez, Sergio Fuentes, Ernesto Arancibia, Gideon Chitombo, Octavio Araneda, Mauricio Larraín, Marko Didyk and to the many other professionals and friends that have contributed to the dream.

We hope that this book, the presentations and the workshops would contribute to define the state of the art of caving and to help us think about our future, the future of mining.

Prof. Raúl CastroCo-chairman Caving 2014


SPONSORS

The Universidad de Chile proudly thanks and acknowledges the Principal and Major Sponsors of the Third International Symposium on Block and Sublevel Caving

PRINCIPAL SPONSOR

ORGANIZING INSTITUTIONS

Caving 2014, Santiago, Chile

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TABLE OF CONTENTS

KEYNOTE SPEAKERS

Future Challenges and Why Cave Mining Must ChangeGerman Flores, Newcrest Mining Limited, Australia 23

It’s Not Mine Safety But Mind Safety - A Henderson ApproachGK Carlson Climax Molybdenum Company, USA 53

CASE STUDIES

Fracturing in the footwall at the Kiirunavaara mine, Sweden M Nilsson Luleå University of technology, Sweden D Saiang SRK Consulting (Sweden) AB, SwedenE Nordlund Luleå University of technology, Sweden 63

Draw control strategy at the New Gold New Afton MineA Chaudhary New Gold, Canada K Keskimaki New Gold, CanadaS Masse New Gold, Canada 72

Caving experiences in Esmeralda Sector, El Teniente MineM Orellana Codelco, ChileC Cifuentes Codelco, ChileJ Díaz Codelco, Chile 78

Undercut advance direction management at the North 3rd Panel, Rio Blanco Mine, División Andina Codelco ChileL Quiñones Codelco, ChileC Lagos Codelco, Chile F Ortiz Codelco, ChileE Farías Codelco, ChileL Toro Codelco, ChileD Villegas Codelco, Chile 91

New growth strategy in Esmeralda MineN Jamett Codelco, ChileRQ Alegría Codelco, Chile 98

CAVING MECHANICS

Assessment of broken ore density variations in a block cave draw column as a function of fragment size distributions and fines migrationL Dorador University of British Columbia, Canada E Eberhardt University of British Columbia, Canada D Elmo University of British Columbia, Canada B Norman University of British Columbia, Canada A Aguayo Codelco, Chile 109

Assessing the state of the rock mass in operating block caving mines: A reviewD Cumming-Potvin, University of Western Australia, AustraliaJ Wesseloo, University of Western Australia, Australia 118


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Influence of secondary fragmentation and column height on block size distribution and fines migration reaching drawpointsL Dorador University of British Columbia, CanadaE Eberhardt University of British Columbia, CanadaD Elmo University of British Columbia, CanadaB Norman B. University of British Columbia, CanadaA Aguayo Codelco, Chile 128

Analysis of hangup frequency in Bloque 1-2, Esmeralda Sur MineE Viera Codelco, ChileE Diez Codelco, Chile 138

A 3DEC-FLAC3D Model to predict primary fragmentation distribution in Cave MinesT V Garza-Cruz Itasca Consulting Group, Inc., USA M Fuenzalida Itasca Consulting Group, Inc., USAM Pierce Itasca Consulting Group, Inc., USAP Andrieux Itasca Consulting Group, Inc., USA 146

ALCODER, challeges of paradigms in caving methodsGl Krstulovic Geomecánica Ltda., ChileGA Bagioli Tetra Tech Metálica, Chile 159

Characterization and synthetic simulations to determine rock mass behaviour at the El Teniente Mine, Chile. Part IA Brzovic Codelco, ChileP Schachter Codelco, ChileC de los Santos Codelco, ChileJA Vallejos, University of Chile, ChileD Mas Ivars Itasca Consultans AB, Sweden 171

Characterization and synthetic simulations to determine rock mass behaviour at the El Teniente mine, Chile. Part IIJA Vallejos University of Chile, ChileK Suzuki University of Chile, ChileA Brzovic Codelco Chile, ChileD Mas Ivars Itasca Consultans AB, Sweden 179

FRAGMENTATION

Fragmentation estimates using BCF software – Experiences and pitfallsJ Jakubec, SRK Consulting Ltd., Canada 191

An alternative approach to verifying predicted fragmentation in weak rockRN Greenwood SRK Consulting Inc., CanadaBN Viljoen SRK Consulting (Canada) Inc., Canada 201 FUTURE PROJECTS

Block Caving using Macro BlocksS Fuentes Codelco, ChileF Villegas Codelco, Chile 211

La Encantada: An inclined cave design J Valencia NCL Ingeniería y Construcción, ChileP Paredes NCL Ingeniería y Construcción, ChileF Macías First Majestic Silver Corporation, Mexico 217


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GEOMECHANIC DESIGN

Considerations for designing a geomechanics monitoring plan for each engineering stageAE Espinosa Codelco, ChileP Jorquiera, Codelco, ChileJ Glötzl, Glötzl GmbH, Germany 227

Integrated support quality system at El Teniente MineMS Celis, Codelco, ChileRA Parraguez, Codelco, ChileE Rojas, Codelco Chile, Chile 234

Management indicators for the cave geometry control, El Teniente mine J Cornejo Codelco, ChileC Pardo Codelco, Chile 243

Geomechanical issues and concepts associated with scoping study and prefeasibility stage of a Block/Panel Caving ProjectJ Díaz DERK Ltda., Chile P Lledó DERK Ltda., Chile F Villegas Codelco, Chile 250

GEOMECHANICAL CHARACTERIZATION

Ciresata geotechnical evaluation and caving study, RomaniaN Burgio Stratavision Pty Ltd, Australia 263

Identification of different geomechanics zones in panel caving- application to Reservas Norte El Teniente P Landeros Codelco, ChileJ Cornejo Codelco, ChileJ Alegría Codelco, ChileE Rojas Codelco, Chile 271

Geostatistical evaluation of fracture frequency and crushingSA Séguret MINES ParisTech, FranceC Guajardo Codelco, ChileR Freire Rivera Codelco, Chile 280

Geomechanical ground control in block/panel cavingJ Díaz DERK Ltda., Chile Y Sepúlveda DERK Ltda., Chile P Lledó DERK Ltda., Chile 289

GRAVITY FLOW

Use of experiments to quantify the flow-ability of caved rock for block caving RE Gómez, University of Chile, ChileR Castro, University of ChileD Olivares, University of Chile, Chile 299

An analysis of the lateral dilution entry mechanisms in Panel CavingPS Paredes University of Chile, Chile MF Pineda University of Chile, Chile 307


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Application of a methodology for secondary fragmentation prediction in cave mines MA Fuenzalida Itasca Consulting Group, Inc., USAT Garza-Cruz Itasca Consulting Group, Inc., USAM Pierce Itasca Consulting Group, Inc., USAP Andrieux Itasca Consulting Group, Inc., USA 318

Case Study: Improving SLC recovery by measuring ore flow with electronic markersS Steffen Elexon Mining, AustraliaP Kuiper Elexon Mining, Australia 328

Stochastic models for gravity flow: numerical considerationsWH Gibson SRK Consulting (Australasia) Pty Ltd, Australia 337

First steps in monitoring gravity flow at El Teniente Mine: installagion stage in Block-2, Esmeralda MineE Viera Codelco, ChileM Montecino Codelco, Chile M Meléndez Codelco, Chile 348

Experimental study of fines migration for caving minesF Armijo BCTEC Engineering and Technology, ChileS Irribarra Universidad de Chile, ChileR Castro Universidad de Chile, Chile 356

Towards an understanding of mud rush behaviour in block-panel caving minesME Valencia University of Chile, ChileK Basaure University of Chile, ChileR Castro University of Chile, ChileJ Vallejos University of Chile, Chile 363

Statistical analyses of mud entry at Diablo Regimiento sector-El Teniente’s MineIM Navia Universidad de Chile, ChileRL Castro Universidad de Chile, ChileMA Valencia, Universidad de Chile, Chile 372 INNOVATION

Hybrid composite, a way to enhance the mechanical properties of breakable ground supportV Barrera Mining and Metallurgy Innovation Institute IM2 – Codelco, ChileP Lara Mining and Metallurgy Innovation Institute IM2 – Codelco, ChileG Pinilla Codelco, ChileF Báez Codelco, Chile 381

Pilot tests as a tool for the design of autonomous mining systems J Riquelme University of Chile, ChileR Castro University of Chile, ChileS Valerio University of Chile, ChileJ Baraqui Codelco Chile, Chile 386

Implementation of LiDAR technology to evaluate deformation field induced by panel caving exploitation, Codelco Chile El Teniente DivisionAE Espinosa Codelco, ChileP Landeros Codelco, Chile 394


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Semi-autonomous Mining ModelM Fishwick Codelco, ChileM Telias IM2-Codelco, Chile 403

Future automated mine operation: Synergistic collaboration between humans and automated systemsJ Ruiz-del-Solar University of Chile, Electrical Engineering Dept-AMTC, ChileE Widzyk-Capehart University of Chile-AMTC, ChileP Vallejos University of Chile-AMTC, ChileR Asenjo University of Chile-AMTC, Chile 415

MINE PLANNING

Mine sequence optimization for Block Caving using concept of ‘best and worst case’D Villa, DASSAULT SYSTEMS GEOVIA, Canada 426

Fast-track Detailed Engineering for Panel CavingJC Vienne, AMEC Internacional, Chile 437

Optimizing Hill of Value for Block CavingA Ovalle, AMEC International, ChileM Vera, AMEC International, Chile 442

Footprint and economic envelope calculation for Block/Panel Caving Mines under geological uncertaintyE Vargas University of Chile, ChileN Morales University of Chile, ChileX Emery University of Chile, Chile 449

Determination of the best height of draw in block cave sequence optimizationF Khodayari University of Alberta, CanadaY Pourrahimian University of Alberta, Canada 457

Block Caving strategic mine planning using Risk-Return Portfolio OptimizationE Rubio REDCO Mining Consultants, Chile 466

NUMERICAL MODELLING

Numerical modelling of Pilar Norte Mine development using AbaqusR Cabezas MVA Geoconsulta, ChileF García MVA Geoconsulta, ChileM Van Sint Jan MVA Geoconsulta, ChileR Zepeda CODELCO, Chile 479

Geomechanical evaluation of large excavations at the New Level Mine - El TenienteE Hormazabal SRK Consulting, ChileJ Pereira Codelco,ChileG Barindelli, Codelco, ChileR Alvarez SRK Consulting, Chile 486

Design of 3-D models in miningE Córdova Codelco, ChileP González, Codelco, ChileC Pardo Codelco, Chile 501


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PRECONDITIONING

Study of the impact of rock mass preconditioning on a Block Caving Mine OperationC Castro IM2-Codelco, ChileF Báez Codelco, ChileE Arancibia Codelco, ChileV Barrera, Im2-Codelco, Chile 515

Pre-conditioning with hydraulic fracturing — when and how much?C Valderrama Pontificia Universidad Católica de Chile-IM2 Codelco, ChileF Báez Codelco, ChileE Arancibia Codelco, ChileV Barrera IM2-Codelco, Chile 525

Caving propagation and dilution control through the preconditioning technologyV Barrera Codelco, ChileC Valderrama Codelco, ChileP Lara IM2 Codelco, ChileE Arancibia Codelco, ChileF Báez Codelco, ChileE Molina Codelco, Chile 532

Numerical analysis of preconditioning using blasting and its relationship with the geomechanical properties of the rock mass and its interaction with Hydraulic fracturingF Báez Codelco, ChileE Arancibia Codelco, ChileI Piñeyro IM2 S.A., ChileJ León IM2 S.A., Chile 538

Intensity rock mass preconditioning and fragmentation performance at the El Teniente Mine, ChileA Brzovic Codelco, ChileJP Hurtado Universidad de Santiago de Chile, ChileN Marín Codelco, Chile 547

SEISMICITY

Improved microseismic event hypocentre location in Block Caving Mines using local earthquake tomography J Philippe Mercier, Golder Associates, CanadaW de Beer, Golder Associates, CanadaJ Pascal Mercier, Advanced GeoScience Imaging Solutions, Canada 559

Seismic risk management for underground miningprojects - Codelco Chile División El TenienteAE Espinosa CODELCO Chile División El Teniente, ChileRA Fuentes CODELCO Chile División El Teniente, ChileEG Moscoso ERDBEBEN Ltda, Chile 567

Seismic hazard analysis at the El Teniente Mine ising a clustering approachJ Cornejo Codelco, ChileJ Vallejos University of Chile, ChileX Emery University of Chile, ChileE Rojas Codelco, Chile 575


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Modeling induced seismicity in 4DE Cordova Codelco, ChileM Nelson University of Utah, USA 586

SUBSIDENCE

Application of InSAR technologies to measure the subsidence at El Teniente´s MineAE Espinosa Codelco, ChileO Mora Altamira Information, España F Sánchez Altamira Information, España 603

Chuquicamata Underground Project subsidence analysisA Aguayo Codelco, ChileD Villegas Codelco, Chile 611

UNIT MINING OPERATIONS

Methodology for up-hole drilling accuracy measurements at Kiruna SLC mineM Wimmer LKAB, SwedenAA Nordqvist LKAB, SwedenD Billger Inertial Sensing One AB, Sweden 625

Analysis of geometric design in ventilation raises for Block Cave production level driftsJP Hurtado, Universidad de Santiago de Chile, ChileYH San Martín, Universidad de Santiago de Chile, Chile 638

Simulating the logistic of an underground mineM Moretti Paragon Decision Science, BrazilL Franzese Paragon Decision Science, BrazilM Capistran Paragon Decision Science, BrazilJ Cordeiro Alkmim/AngloGold Ashanti, BrazilB Penna Alkmim/AngloGold Ashanti, BrazilG Mendes Alkmim/AngloGold Ashanti, Brazil 647

Engineering approach for the design and analysis of drawbell blasting in block and panel cavingÁ Altamirano BCTEC Ingeniería y Tecnología SpA, ChileR Castro Universidad de Chile, ChileI Onederra University of Queensland, Australia 656

Analysis of induced damage due to undercut blasting D Morales Hatch, ChileR Olivares Codelco, Chile 665

How high a draw column in Block Caving?C Cerrutti AMEC International, ChileA Ovalle AMEC International, ChileY Vergara Universidad de Chile, Chile 674


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Keynote Speakers

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Keynote Speakers

Future Challenges and Why Cave Mining Must Change

German Flores, Newcrest Mining Limited, Australia

Abstract

The evolution of the cave mining industry has been driven by the requirement to adapt to change. In the late 70’s, the driver for change was when lower grade and hard ore rock was encountered, after easier caving in near surface weaker rocks. Arguably, this major change was first introduced at Codelco’s El Teniente mine in order to mine the hard rock efficiently, safely and economically. This step change was the introduction of mechanised panel caving based on load, haul and dump (LHDs) machines subsequently resulting in the development of different cave mining layouts. Given the hard rock and the size of the drives needed to accommodate the LHDs at that time, jumbos and new rock support systems were also introduced. Since then, incremental changes have been introduced into the cave mining industry primarily to increase safety, mining efficiencies and reduce mining costs. These have included increasing LHD capacity to handle up to 2 m3 rocks and increasing productivity, electric LHDs to improve underground environment, and rapid development technology in order to increase development rates and access orebodies quicker. During this same period, semi- autonomous technology has been introduced for the purposes of increasing productivity, safety and further reducing mining costs. Preconditioning techniques were introduced with the view to change the characteristics of the rockmass in order to enhance the caving process, especially the cavability and fragmentation.

The cave mining industry is now moving rapidly into a new and less certain environment where arguably, another revolutionary change is required in order to continue sustaining the industry. The potential challenges include technical, economical, licence to operate and human capital issues. As it was the case in the late 70’s when hard ore rock was first encountered, the industry must now change in order to sustain itself technically and economically.

This paper, which supplements a keynote address by the author, argues that in some geotechnical environments, future cave mining may not be effectively applied with today’s practice and technology that has evolved in the last 30 years. It is also argued that the development of future cave mining systems can be accelerated covering a much wider range of mining conditions, requirements and even mining philosophies. Revolutionary changes are required in order for the industry to sustain its future. This means that the cave mining industry must change.

1 Introduction

Caveminingmethodshavebecomeviableandpreferredmassminingoptionswhere theobjectivesarelowcostandhighproductionrates.However,thecaveminingindustryisnowenteringapotentiallylesscertainperiodwherecurrentcaveminingmethodsmaynotbesuitabletoachievethelowcostandhighproductivityobjectives.Thisenvironmentincludesgreaterdepths,loweraveragegradedeposits,demandforincreasedproductivity,escalatingminingcost(capitalandoperating),harderandheterogeneousrockmasses,higherstressand,insomecases,highertemperatureenvironments.Inaddition,thereisincreasingshortageof technicalskills,becomingmoredifficult toaccesscapitalandcommunitiesareafterhigherenvironmentalstandards.Radicalchangestocurrentpracticesarethusneeded.

In the 70s,Codelcowas successfully applying block cavingmethods designed forweak rockmass inlow stress environment andwith relatively high grades (Ovalle 1981;Baeza et al. 1987;Kvapil et al.

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1989). During this period, Codelco then encountered hard ore rock withmuch lower average grades.Theconsequencesof these twoissueswere:veryslowcavepropagation,coarsefragmentationandlowproductivityleadingtohigherminingcosts.

Inordertocontinueachievingsimilarlevelsofproductivityandprofitability,stepchangeswererequiredtotheblockcavingpracticesatthattime.Chacónetal.(2004)discussedthesestepchanges,whichincludedtheintroductionofthemechanisedpanelcavingmethodusingLHDs,suitablelayouts,newsupportsystems,alternativeundercuttingsequenceandundergroundprimarycrushers.

Historically,blockcavinghadbeenthepreferredmethodbecauseoftheweakrockmassesbeingcavedatthetime.Thearearequiredtoachievecontinuouscavinginsuchrockmasseswassmall(e.g.90mx60m=5,400m2)makingblockcavingsuitableforawiderangeofconditions(Figure1).Becausehardorerockrequiredmuchbiggerfootprinttoachievecaving(e.g.15,000m2),theconceptofpanelcavingwas introduced (Chacón et al. 2004).Larger 3.5 t capacityLHDswere introduced for thefirst time inundergroundcaveminingasameanstohandlethecoarsefragmentation(Haley1982).Inordertoincreasethe productivity fromLHDs, a different horizontal extraction level layoutwas developed.Following anumberoftrials,the“ElTenientelayout”wascreated(Figure2).TheintroductionoflargerLHDsrequiredthedevelopmentofbiggerdrivesofupto3.6mx3.6m.Thedevelopmentofthesesizedrivesresultedintheintroductionofdevelopmentjumbosandalternativesupportsystemsincludinggroutedrebars,meshandshotcreteasshownin(Figure3).

InadditiontotheuseofLHDs,partof thestrategytomanagethebigrockswastheintroductionofanundergroundgyratorycrushertoimprovetheefficienciesofsubsequentmaterialhandling.

Theabovechangesformedthebasisofcurrentmechanisedcaving.Sincethen,therehavebeenanumberofincrementalchangesthathavebeenintroducedtofurtherincreaseminingefficiencies,safetyandreduceminingcosts.Suchchanges,whichincluderapiddevelopment,undercuttingstrategiesandgeometriesandmaterialhandlingsystems,arediscussedinthispaper.However,inthemselves,theyarenotexpectedtoeffectivelydealwiththefuturechallengeslistedearlier.

Additionally, the relatively near surface orebodies where current mechanised caving techniques weredevelopedarenowbeingexhaustedandneworebodiesareincreasinglybeenfoundatmuchgreaterdepththancurrent.Suchorebodiesarebringingwiththemnewchallenges.

Thispaperdiscusses the futurechallengesandwhycavemining, inparticular,mustchange inorder toexploitthefutureorebodiesefficientlyandeconomically.TheneedfortheindustrytochangeisreinforcedinrecentpublishedworkbyErnst&Young(2014)andDeloitte(2013).Theydiscussbusinessriskfacingminingandmetalsduring2013-2014and the top ten issues that theminingcompanieswill face in thecomingyear,respectively.Therisksdiscussedbytheseauthorsareconsistentwiththosepresentedinthispaper.

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Keynote Speakers

Figure 1 Block caving method used in weak ore rock at El Teniente mine (Sisselman 1978)

Figure 2 Panel caving method used in hard ore rock at El Teniente mine (Hartman 1992)


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Figure 3 Ground support for hard ore rock (Ovalle & Albornoz 1981)

2 Incremental changes in the last 30 years

Followingengineeringstudies,thekeyprocessesincaveminingincludedesign,caveestablishmentandproduction.Numerousandsignificantincrementalchangesinthelast30yearshavemainlybeenfocussedin these areas. In this context, incremental changes refer to improvements but still within the currentpracticeumbrellaorchangesthatonlyaffectacomponentoftheentirecaveminingprocess.Inspiteoftheirsignificance,theydonotnecessarilyresultinacompletetransformationofthecavingpracticessuchaswhencaveminingmovedfromweakorerocktohardorerock.

2.1 Design

Oncethecavabilityandfragmentationhavebeenassessed,thekeydesignfeaturesincaveminingpracticesareminingstrategy,blockheight,extractionlevellayoutandundercutting(strategyandgeometry).

Theeffectivedesignofthecavingoperationispivotaltothesuccessofanyminingbusiness.Itiscrucialthatproperorebodyknowledge(geological,geotechnical,hydrogeological,metallurgicalandenvironmental)including associated uncertainties is collected early in the design process.This should be followed byaproper and rigorous analysis inorder to establishmost appropriatedesignparameters to suit agivenorebody. This is instead of simply adopting parameters from existing operations which unfortunatelyremainscommonpractice.

Oftheabovedesignactivitiesthekeyincrementalchangeshavebeeninthefollowingareas:

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Keynote Speakers

2.1.1 Mining strategy

ThemovefromweaktohardorerockmassesresultedinthechangefromblocktopanelcavingstrategyasshowninFigure4.Thiswasduetothesizeofthearearequiredtoinitiatethecavinginharderorerock.Asindicatedearlier,thearearequiredtoachievecontinuouscavinginweakrockmasseswassmallandgeneral less than10,000m2 (Flores&Karzulovic2004).Forharder rockmasses, the area required toinitiatecontinuouscavinghasbeenreportedtobeashighas25,000m2(Catalánetal.2010).However,morerecentlyandgivenanumberoftechnicalandoperationalproblemsassociatedwithlargepanelssuchasdiscussedbyAraneda&Sougarret(2008),therearenowdesignsandoperationsutilisingblockcavingstrategiesbutatamuchlargerscale.Thesearereferredtoasmacroblocks(Aguayoetal.2012;Madrid&Constanzo2013;Villegas&Fuentes2014).Theadvantagesofthismovebacktoblockstrategyincludesbettermanagementofcaveestablishment,productionandpanelcavefrontstabilityand, insomecases,bettermanagementofpotentialoperationalhazards(e.g.collapses).Anaddedadvantageofthemacroblockconcept,incaseswheretheorebodyfootprintisverylarge,istheabilitytodevelopaminingstrategyorsequencetoreducethepaybackperiodoftheprojecttherebymaximisingthereturnoftheentiredeposit.Somerefertothisstrategyas“valueengineering”.Figures5and6areillustrationsofthisconceptusingtheCadiaEastdeposit(Manca&Flores2013).

Blockcavingforweakorerock Panelcavingforhardorerock

Figure 4 Cave mining step change from block caving to panel caving method (Chacón et al. 2004)

2.1.2 Block heights

Blockheightisdefined,inthiscontext,astheheightoftheblocktobecavedfromtheextractionleveltothesurface,thebaseofapre-existingopenpit,aleveloramined-outareaabove(afterBrown2003&2007).Blockheightshavetodayrangedfrom150mtoapproximately500m(Floresetal.2004).However,morerecentlyblockheightsofupto1,000morslightlygreaterhavebeendesignedsuchasshowninFigure7(Manca&Flores2013).Themaindriverbehindthisincreaseinblockheighthasbeentherequirementtoexploitlowgradeorebodiesprofitably(i.e.economicconsideration).


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Figure 5 Value engineering – vertical section (Manca & Flores 2013)

Figure 6 Value engineering – plan view (after Manca & Flores 2013)

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Keynote Speakers

Figure 7 Block height (Manca & Flores 2013)

2.1.3 Extraction layout

InapaperbyChacónet al. (2004)on“ThirtyYearsEvolutionofblockcaving inChile”, thedifferentextraction layouts established since caving of hard rock was encountered are discussed. These wereprimarilydesignedtoimprovetheefficiencyofLHDsinhardrockminingwithcoarsefragmentation.ThelayoutincludedtheherringbonedesignspecificallyadoptedforSalvadormine(Figure8)andlatermodifiedforAndinamine(Figure9).Atthesametime,HendersonoperationsintheUSAintroducedtheherringbonelayout(Figure10).Basedondetailedanalysisofthesegeometries,the“ElTenientelayout”wasintroducedforthefirsttimeintheElTeniente-4Southproductionsector.Theadvantageofthisnewlayoutwasanincreaseinthe5tLHDproductivityfromapproximately100to150tph(Chacónetal.2004).

Inthelast30years,themostcommonlyusedlayoutsaretheherringboneandtheElTenienteasshowninFigures11and12(Leachetal.2000;Bothaetal.2008).AnadvantageoftheherringbonelayoutistheLHDmanoeuvrabilitywhenelectrictetheredmachinesareused.InthecaseoftheElTenientelayout,theadvantages are the easiness of construction, the effectiveness ofLHDdigging (attacking themuckpilehead-on)resultinginabetteroreflowintothedrawpointandthestabilityoftheextractionlevelpillars.Theextractionlevelgeometry(grid)haschangedfromtheoriginal24mx12.5mgrid(Chacónetal.2004)to34mx20m(Castroetal.2012),withthemostcommonbeing30mx15m(Chitombo2010).Laubscher(1994),howeverstressesthatthegridsizeshouldbeafunctionoffragmentationandtherequirementtoachieveflowinteractionbetweenadjacentdrawpoints.


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Figure 8 Herringbone layout adapted at Salvador mine (Chacón et al. 2004)

Figure 9 Herringbone layout adapted at Andina mine (Chacón et al. 2004)

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Figure 10 Herringbone layout adapted at Henderson mine (Chacón et al. 2004)

Figure 11 Typical Herringbone layout (after Brown 2007)

Figure 12 Typical El Teniente layout (after Brown 2007)


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2.1.4 Undercutting strategy

One of themost critical processes in cavemine design is undercutting.This is in terms of effectivelyinitiating the caving of a block or panel, ensuring earlier start of production and depending on theundercuttingsequenceused,managingofstresses.Brown(2003&2007)emphasisesthattheundercuttingstrategyadoptedcanhaveasignificantinfluenceoncavepropagationandonthestressesinducedin,andtheperformanceof, the extraction level installations.The threemostlyusedundercutting strategies arepost,preandadvancedundercuttingasshowninFigure13(Rojasetal.2000;Barraza&Crorkan2000;Barberetal.2000).Historically,thepost-undercutwasusedandlaterthemostcommonlyusedbecametheadvanced.Currently,thereisanincreasinginterestinapplyingpost-undercuttingstrategy(Manca&Flores2013).Thereasonforthisshiftismainlytoreducetheinteractionbetweentheactivitiesassociatedwithcavepreparation(i.e.undercutandextractionleveldevelopment)andthoseassociatedwithproduction.Themainbenefitofthisisthereductionoftheoverallcaveestablishmenttime.

Figure 13 Undercutting strategies – post, pre and advanced undercutting (after Brown 2007)

2.1.5 Undercutting Geometry

Withrespecttogeometry,thehighundercut(sublevelcavingringgeometry),narrowandflatand,narrowand inclined options have beenused as shown inFigures 14, 15 and16 (Jofré et al. 2000;Barraza&Crorkan2000;Floresetal.2004;Silveira2004).Theadvantagesanddisadvantagesoftheseoptionshavebeendebatedwidelyintheindustryandhaveincludedthefollowing:

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1. High undercuts (up to 20 m) have historically been the most common geometry used forundercuttingasshowninFigure14(Ovalle&Albornoz1981;Manca&Flores2013).Theyarerelativelyflexibleinthattheycanbedrilledwitharangeofdrillingequipmentandholediameters.Inaddition,highundercutcanbeusedtoproducetonnagewithfinerfragmentationforthemillatthebeginningofthecavingprocess.However,acommonproblemassociatedwithhighundercutsisblastholedeviationandholelossresultinginpoorringbreakagenecessitatingredrillingoftheundercutrings.

2. Asthetermimplies,aflatundercut isformedbyusingflat lyingdrillholesrather thanfansorsteeply inclined holes (Figure 15).As a result, the undercut is narrowwith a height equal orslightlygreaterthanthatofthedrilldrives(e.g.4m).TheadvantagesofnarrowflatundercutasreportedbyButcher(2000a)includethattheyproducehigheradvanceratesbecauselessdrillingandchargingisrequiredandreducethemagnitudesoftheinducedstresseswhichmayotherwisecauseproblems.However,themajordisadvantageofnarrowflatundercutisthepotentialoftheformationof pillars (remnants) due to either blast hole loss anddeviationor confinedblastingconditionsarisingfrominadequatecleaningofthepreviouslyblastedundercutrings.Inaddition,coarsecavefragmentationisgenerallyencounteredearlierinthenaturalcavingprocess(Leiva&Duran2003).

3. Inorder tooffsetoneof thedisadvantagesof thenarrowflatgeometry (i.e. assuringcompletebreakage), the narrow inclined undercutwas introduced (Figure 16).This allowed easier flowof theblastedmaterial in the inclinedsectionof thisgeometry. Inspiteof this, theproblemofencounteringcoarsecavefragmentationduringearliercavingstillremains.(Calderetal.2000).

Becauseofthedisadvantagesassociatedwithflatundercutdiscussedearlierandinparticularpoorundercutbreakageandformationofremnantpillarswiththepotentialofcausingcollapsesintheextractionlevelbelow, some operations are now implementing and/or reconsidering high undercuts but utilising betterdrillingandblastingpracticesandtechnologies(Manca&Flores2013;Manca&Dunstan2013).

Figure 14 High undercut geometry (after Manca & Flores 2013)


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Figure 15 Narrow and flat undercut geometry (after Brown 2007)

Figure 16 Narrow and inclined undercut geometry (after Brown 2007)

2.2 Cave establishment

Caveestablishmentincludesactivitiesassociatedwithmineset-upwiththoseassociatedwithcaveset-upasdescribedinManca&Flores(2013)andshowninFigures17and18.Morespecifically,theassociatedkeyminingactivitiesincaveestablishmentincludeaccessdevelopmentaswellasassociatedmaterialhandlingsystems;miningservices(ventilation,power,water);extraction,undercutandhaulageleveldevelopment;civilworks(permanentgroundsupportandconcreteroadways);drawbellopeningandundercuttingrate.Wherepreconditioningisapplied,thisactivitybecomesintegralpartofthecaveestablishment.

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Figure 17 Mine set-up strategies (after Manca & Flores 2013)

Figure 18 Cave establishment (after Manca & Flores 2013)

Oftheabovecaveestablishmentactivitiesthekeyincrementalchangeshavebeeninthefollowingareas:


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2.2.1 Access Development

Inthelast10years,thefocusonaccessdevelopmenthasbeenonsingleheadingrapiddevelopmentbutstillusingconventionaldrillingandblastingmethods.Theadvanceratesusingsuchmethodshavebeenoftheorderof160m/month(Willcox2008).Aspartofthisfocus,rapiddevelopmenttechnologyhasbeenintroducedleadingtoadvancerateofupto265m/monthwitharecordof311m/monthinasingleheading(accessdecline)fora5.5mx6.0mdeclineaccessminedatagradientof1:7(Willcox2008;Zablocki&Nord2012)asshowninFigures19and20.Theuseofmechanicalexcavators(TBM,roadheader)forrapidaccessdevelopmentcontinuestobeanactiveareaofR&D.

Figure 19 Rapid development (Flores & Logan 2008)

Figure 20 Rapid development (Willcox 2008)

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2.2.2 Undercut, extraction and haulage level development

Therapiddevelopmenttechnologyhasbeenextendedtofootprintdevelopment,whichincludesmultipleheadingundercut,extractionandhaulageleveldevelopment.Anaveragedevelopmentrateof540m/monthforasinglelongroundjumbo,hasbeenachievedforsuchheadings(Manca&Flores2013).

2.2.3 Civil Works

Incavemining,civilworksrefersmainlytopermanentgroundsupportandconcretingofroadways.Withrespect topermanentgroundsupportassociatedwithdrawpoint support, thechangeshavemainlybeenassociatedwiththenumberandtypeofsteelsetsusedinconjunctionwithcablesupportandconcreting.The number of steel sets have been reduced from asmany as 7 down to two for a single drawpoints,howevertherehavebeencaseswherenosteelsetshavebeenusedasshowninFigure21(Bartlett1992;Rojasetal.1992;Golden&Fronapfel2008;Dunstan&Popa2012).Recently,AndinaandElTenienteoperationshavetrialledtheuseofpre-fabricatedsupportsystemsfordrawpointtoreducetheinstallationtimeby50%asshowninFigure22(Fuenzalida&Baraqui2012).

Figure 21 Drawpoint support with and without steel sets

Figure 22 Drawpoint using prefabricated support (Fuenzalida & Baraqui 2012)


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Shotcretewas introducedaspart of the support system in the late70s (Wilson2000). Inmost cases itwasusedinconjunctionwithmesh(Dolipas2000).Theincrementalchangeassociatedwithshotcretingincludedthethickness50mmto100mmandtheintroductionofsteelfibrereinforcement.Atthesametime,thereweresignificantimprovementinthedeliveryandsprayingsystems.

Inthe80s,thepracticeofconcretingextractionlevelroadways(extractiondrivesanddrawpointdrives)wasintroduced.ThiswaspurelytoachievehighspeedtrammingandthereforehighLHDproductivityaswellasoperatorcomfort(Butcher2000b;Duffield2000).Theincrementalchangeinconcretingofroadwayswasthenumberoflayersandthestrengthoftheconcreteused.Currentpracticeincludesabottomlayerof25to30MPaandanupperlayerof70to85MPaasshowninFigure23.

Figure 23 Roadways design (Duffield 2000)

2.2.4 Drawbell opening

Theopeningofthedrawbellswastraditionallyaverylengthyprocessrequiringupto3to4blastingstages(Music&SanMartin2012).However, therehavebeencaseswhere theprocesshasbeen significantlylonger.

With the introductionofaccurate smalldiameterdrill rigs, theonsetofelectronicdetonators,emulsionproductsandlargediameterblindholedrilling,drawbellblastinghasnowbeenreducedtoasinglestepblastingasshowninFigure24(Silveiraetal.2005;Dunstan&Popa2012).Asaresultofthesechanges,drawbellheightshavebeenincreasedfromaround10mtoupto18mandopeningratesfromaround3toupto12drawbells/montharebeingachieved(Silveira2004;Castenetal.2008;Manca&Flores2013).

2.2.5 Undercutting Rate

Theincrementalchangeassociatedwithundercuttinghasbeentherateofundercuttingexpressedinm2/monthduetotheimprovementsindrillingandblastingtechnologies.Themostrecentindustrybenchmarkforblockandpanelcavesindicatedthattheundercuttingrateisintherangeof2,000to4,000m2/monthforlowundercuts(Chitombo2010).Someofthecurrentoperationshaveachievedundercuttingratesintherangeof4,000to6,000m2/month.Thishasenabledrapidcaveestablishmentandreductionoftheramp-uptime(Silveira2004;Manca&Flores2013).

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Figure 24 Drawbell fired in a single blasting

2.2.6 Preconditioning

Preconditioningwasan incrementalchange introducedtobettermanage thecavingprocess.The intentwastoalterthecharacteristicsofhardrockmasssuchthatitbehavessimilartoaweakrockmass(vanAs&Jeffrey2000;Chacónetal.2004;vanAsetal.2000).Incavemining,theprocessesofinterestarecavabilityandfragmentation(Sougarretetal.2004,Catalánetal.2010;2012).Inadditiontotheenhancementofthecavingprocesses,preconditioningisalsobeingusedtomanageseismicity(Araneda&Sougarret2008).Thetechniquescurrentlyusedarehydraulicfracturing,confinedblastingandcombinationofthetwoasshowninFigure25.Catalánetal.2012referstothelatterasintensivepreconditioning.

Figure 25 Intensive preconditioning (after Manca & Flores 2013)


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Preconditioning for caving applications remains a very active area of research. From technical andoperationalperspectives,theobjectiveistoengineerthevolumeofhardrocktobecavedinordertoachievefastercavepropagationratesandthereforehigherdrawrates,finerfragmentationresultinginlesshang-upsandsecondarybreakageactivities,reducearearequiredtoinitiatecavingandreducethemagnitudeoftheseismicityduetocaving.Fromabusinessperspective,thesebenefitswilltranslateintoshorterrampuptimes,morecontinuousproductionprocess,smallerundergroundprimarycrushersandthereforelowerminingcosts.Fromasafetypointofview,preconditioningshouldenablebettermanagementofthestressesresultinginsaferworkingconditions.

2.3. Production

With respect to production, the most significant incremental changes have been associated with thefollowing:

2.3.1 LHD capacity and type

TheLHDcapacityhasprogressivelybeenincreasedfrom3.5tinthe80stocurrent21tasshowninFigures26.Thiswasforthepurposesofhandlingcoarsefragmentationandachievinghigherproductivity(Stevens&Acuña1982).

3.5tLHD(http://www.slideshare.net/smhhs/lhd)17tLHD(CadiaValleyOperationsFebruary2013)

Figure 26 LHD capacities from 3.5 tonnes to 21 tonnes

ThetwoLHDtypes,currentlyusedaredieselandelectricsasshowninFigure27.ThedieselLHDsarethemostcommonlyusedandtheelectric,whichwasintroducedinthelast10to15years,aremoresuitedtotheherringbonelayoutandarebecomingmorepopulargiventheminingenvironmentalrestrictions(i.e.dieselparticulate).ElectricLHDs are currently all tetheredbut there is active research to developuntetheredmachines.

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Figure 27 Diesel 21 t and electric 14 t Sandvik LHDs (www.miningandconstruction.sandvik.com)

2.3.2 Automation

TheideaofautomatedLHDs(semiautonomous)wasintroducedintheearly2000sinordertoreducetheexposureofundergroundworkerstosevereandunsafeminingenvironmentorconditionsincludinghotter,wetmuckormudrushconditions,dusty,noisyandseismicproneareas(Gustafsonetal.2013;Schunnessonetal.2009;Metsänen2004;Schweikart&Soikkeli2004;Varas2004).Inaddition,automatedLHDswereintroducedasapotentialmeansofachievingconsistentproductivityhoweverthisremainsanactiveareaofR&D.TheapplicationoffullyautomatedLHDsishoweveryettobeachievedandremainsanareaofactiveresearchbydifferentsuppliers.

Withrespecttoundergroundautomatedtrucks,thereisonlyoneknownanddocumentedcase(Burger&Cook2008;Cooketal.2008).Figure28showsthesemiautonomousLHDandtruckusedincavemining.

Figure 28 Semiautonomous underground LHD and truck (Burger 2006; Cook et al. 2008)

2.3.3 Drawpoint secondary breakage

Anumberofincrementalchangeshavebeenmadeinthisareaandhaverangedfromtheuseofasingleboomjumboincombinationwitheitherexplosivesorpenetratingconefracture(PCF),specialisedhighreachdrillrig,watercannontomobilerockbreakers(seeFigure29).Additionalresearchisbeencarriedoutontheuseothermoreexotictechniquessuchasplasmarockbreakageandpulsewaterjet.Thegoalinthisareaistodevelopsystems,whichcanbedeployedrapidlywithminimalevacuation,ventilationandthereforemuchlessproductioninterruptions(Singh1998;Mossetal.2004).


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Jumbowithasingleboom Commando

Mobilerockbreaker Watercannon

Figure 29 Drawpoint secondary breakage systems used in caving operations

2.3.4 Crushing and tipping arrangements

Theconventionalprimarycrushingsystemsinhardrockcaveminingaregyroandjaw(Calderetal.2000;Castenetal.2000;Bothaetal.2008).Inthelast10years,jaw-gyrocrushers(Duffield2000;Manca&Dunstan2013) andmineral sizershavebeen introducedas shown inFigure30 (Arancibia et al. 2012;Fuenzalidaetal.2012).Withrespecttomineralsizers,thebiggestchangehasbeentheabilitytocrushrockabove200MPa.Inaddition,developmentsarebeingmadetoachievethroughputshigherthan2,500tphusingjaw-gyrocrushers.

Gyratorycrusher Jawcrusher(Floresetal.2007)

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Jaw-gyratorycrusher Sizer(CadiaValleyOperationsApril2013) (L’Estrange2009)

Figure 30 Crushing systems

ThetippingarrangementshaveevolvedfromsingletomultipleasshowninFigure31andfromtippingintoahopper-feeder-crushertotippingdirectlyintothecrusher(Kreketal.2008;Manca&Dunstan2013).

Figure 31 Tipping arrangements at Cadia East mine with 4-tipping points (Manca & Flores 2013; Cadia Valley Operations September 2013)

2.3.5 Draw Rate

Draw rate is the rate at which caved ore is drawn from individual drawpoints or a group of adjacentdrawpointsanditisexpressedinmillimetresperunittimeortonnesperareapertimeperiod(mm/dayort/m2-day).Theincrementalchangehasbeenanincreaseofdrawratefrom25mm/dayto115mm/dayatcaveinitiation.ThedrawratesestablishedforCadiaEastduringthecaveinitiation(upto30%oftheblockheight)varyfrom115mm/dayto280mm/daywithanaverageof190mm/day.Higherthan30%tothetopoftheblock,thedrawratesvaryfrom280mm/dayto400mm/daywithanaverageof320mm/day.Thisincreaseindrawratesisbeingattributedbysometoimpactofpreconditioning(Manca&Flores2013).

2.3.6 Main Material Handling System

Themainmaterialhandlingsystemstothesurfacestockpileortothemillusedincaveminingaretrucks,trains,shaftsandmorerecentlyconveyorsasshowninFigure32(Tyleretal.2004;Bothaetal.2008;Brannon et al. 2008;Ferguson et al. 2008;Pinochet, et al. 2012;Sinuhaji et al. 2012).The associated


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incrementalchangeswithrespecttothemainmaterialhandlingsystemshavebeenmainlyintheareasofcapacity,depthandspeed(Taljaard&Stephenson2000;Brannonetal.2012).

Trainsystem(Floresetal.2007)Trucksystem(Willcox2008)

Conveyorbeltsystem Shaftsystem(CadiaValleyOperationsApril2013) (Moss2004)

Figure 32 Material handling systems used in cave mining

2.4. The impact of the incremental changes

Theincrementalchangesthathaveoccurredinthelast30yearsanddiscussedabove,havearguablybeensignificantintermsofincreasingsafety,miningefficienciesandproductivityaswellasreducingcostsduringcaveminingofhardrock.Collectively,thesechangeshavealsoenabledtheindustrytoeffectivelymineinconditionsthatwouldotherwisehavebeenuneconomicusingconventionalmethodsandpractices.Inaddition,themoderatedepthsorebodieswherecurrentmechanisedcavingtechniqueshavebeendevelopedarenowbeingexhaustedandneworebodiesareincreasinglybeenfoundatdepthmuchgreaterthancurrent.Suchorebodiesarebringingwiththemnewchallenges.

Theincrementalchangesdesignedtogetmoreefficientlyoutofthe1970-80sstepchangehaveneverthelessnotresultedinagenerationaltransformationincavemining.Thefuturechallengesconstitutearadicalshiftfromcurrentexperiencesandthereforenecessitateradicalchangesinordertoeffectivelymitigatetherisksthatthecaveminingbusinessmayfaceinthefuture.Thesechallengescanbecategorisedunderthebroadtopicsoftechnical,economical,licencetooperateandhumancapital.Nowisthetimetomakefundamentalanddramaticchanges.

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3 Future Challenges

Thefuturehighriskenvironmentsorchallengesidentifiedinthispaperandwiththepotentialtosignificantlyimpactontheefficiency,economicandsustainabilityofcaveminingincludethefollowing:

3.1. Challenge 1: technical

The technical challenges that need tobeovercome include those associatedwithgreater depths, loweraveragegradedepositsandmeetingthedemandforincreasedproductivity.

3.1.1 Greater depths

Theissuesimpactedbydepthsandthatneedtobeovercomeareacquisitionofreliabledepositknowledge,access to the orebody, harder rock, higher rock stresses, extreme work environment (e.g. ventilation,temperatureandhumidity),higherdemandforpower,longerdistancesformaterialtransporttosurfaceandeffectiveworkinghoursduetothetransportthepersonnelfromandtosurface.Engineeringsolutionsarerequiredinordertobeabletomineunderthesenewconditionswhichareoutsidecurrentpractices.

3.1.2 Lower average grade deposits

It iswidelyrecognisedthat thefuturewillpredominantlybeassociatedwiththeminingof lowergradedeposits.Caveminingoperatingcostsareoftennotreportedinthepublicdomain,howeverthesecostshavearguablybeenescalatinginrecenttimesusingcurrentpracticesandestimatedtobeintherangetoUSD7/ttoUSD12/t.Inordertoeconomicallyminefuturelowergradedeposits,willnecessitatethedevelopmentandapplicationoftechnologies,suchasdiscusslater, inordertoensurethatoperationally,caveminingremainslowcosts(e.g.<USD5.0/t).Suchlowcostwillenableminingoflowgradedepositswhichwouldotherwisebeconsidereduneconomicusingtodayoperatingpractices.

3.1.3 Demand for increased productivity

Caveminingwillcontinueexperiencingthedemandforincreasedproductivity(Ernst&Young,2013).Thiswillbeexacerbatedbytheminingoflowergradedeposits.InordertobeeconomicalundertheseconditionswillrequiretheindustrytoatleastdoubleitscurrentbestproductivityfromasinglefootprintasdiscussedinpaperbyWellmanetal.(2012).Suchproductivitylevelscanonlybeachievedthroughapplicationofnewtechnologiesforcontinuousandautomatedproductionsystems.

Broadlyspeaking, thetechnicalchallengesassociatedwithdepth, lowgradesanddemandfor increasedproductivity;suggestthatcaveminingcannotbebusinessasusual.Fundamentalchangesarerequiredinordertoeffectivelyaddressthesechallenges.Assuch,itisproposedthat:

1 Methods are developed to enable safe and rapid access to the deep orebodies aswell as rapidfootprintestablishment.With respect tosingleheadingaccessdevelopment, thecurrentminingindustry’sbestusingdrillingandblastingtechniquesassistedbylongrundrillingjumboshasbeenreportedtoaverage265m/month(Willcox2008).Duringthesametime,tunnelsconstructedusingcivilengineeringmechanicalexcavatorshavereportedaveragesofupto670m/month(Ciglaetal. 2001).Whilst the conditions and requirement associatedwith civil tunnels are significantlydifferentfromtheminingtunnellingrequirements,considerableeffortneedstobemadetodevelopmechanical excavation technology for mining to approach similar development rates as civiltunnels.Itshouldbepossibletoalsoadaptthesametechnologiesforrapidfootprintestablishmentpurposes.


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2 The current industry’s practices to enhance cavability and fragmentation include the use ofhydrofracturingandconfinedblastingtechniquesandmorerecentlythecombinationofthetwo(Catalán2012).Thetrueeffectsofthesetechniquescontinuetobeasubjectofresearchinmining.Moreeffectivepreconditioningmethodsshouldbedevelopedinordertoachieveunconstrainedcaving rates, better fragmentation and therefore better cave drawpoint flowwithmuch higherdegreeofconfidence.Thesemethodscouldincludeultrapreconditioningandunconfinedblasting.

3ThecurrentproductionsystemsarebatchinnaturebecauseoftheuseofLHDsasbothadiggingandtrammingmachine.Inaddition,withcurrentlayouts,theLHDspendsmoretimetrammingthandigging.Thisresultsinabatchprocesswithlowproductivity(e.g.120to150tph).Toachievethedemandforhigherproductivity,continuousratherthanbatchsystemsneedtobedeveloped.This could include the use of compact, flexible and mobile machines integrating the loadingandcrushingprocesseslinkedtoconveyorsystems.Alternatively,systemssuchastherockflowcontinuousproductionsystemcouldbeadopted(Steinbergetal.2012).

4 The current layouts such as El Teniente and Herringbone were developed to improve LHDproductivity(Ovalle1981,Chacón2004).Alternativelayoutstoenableeffectiveimplementationofcontinuousminingsystemsneedtobedeveloped.

TheabovechangescombinedareshowninFigure33andtheyshouldhelpreduceescalatingminingcoststherebyimprovingthecaveminingbusinessofthefutureinspiteofthenewtechnicalchallengesdiscussed.

Figure 33 Conceptual future cave mining – continuous mining system 3.2. Challenge 2: economical

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Both,capitalandoperatingcaveminingcostshavebeenescalating in the last fewyearsand this trendis expected to continue in the future. The result has been a reduction in themargin due to the lowercommodities prices and the increasingmining costs.This challenge needs to be given priority if largescaleandlowgradecavingoperationsaretobeviable.Ernst&Young2013discussstrategiestomanagecostescalation thatcouldequallybeconsidered for thecaveminingbusiness.Thesuggestedstrategiesincludereviewingthesoundnessandprobabilisticriskofdifferentcaveminingprojects,betterdefinitionoforebodycharacteristics,greateranalysisofthereturnofinvestmentfromindividualprojects,buildingminesinphasesandinsmallervolumeswhencommoditypricesarelowandthenscalingupasdemandfundamentalsshift,andshiftingfromassetexpansiontooperationalexcellencetherebymaximizingcashflowfromexistingassets.

3.3. Challenge 3: social license to operate

Public focusonminingactivitiesand themanner inwhichcommunityandenvironmentalconcernsareaddressed continues to increase. This increases the importance of including social licence to operateconsiderationsinthedevelopmentandassessmentofminingprojectproposals.Asuccessfulprojectshouldincludecommunityandenvironmentalconsiderationsinanenterpriseriskmanagementframeworkwithclearandproactiveriskmitigationstrategies.Abalancedassessmentofaproject,whichidentifiesboththepotentialimpactsofaprojectdevelopmentandtheprojectbenefits,combinedwithearlyandcontinuingengagementwithstakeholdersatalllevels,willhaveasignificantimpactonprojectoutcomes.

3.4. Challenge 4: skills shortage

Inthelast10yearstherehasbeenanincreasingnumberofexperiencedcaveminingtechnicalandoperatorseitheratornearingretiringage.Atthesametime,therehasbeenanincreasingnumberofyoungpeoplepursuingcareersotherthaninmining(e.g.informationtechnology,health,business,socialservices).Thesetwotrendshaveresultedinminingskillsshortageinalmostallmajorminingcountries.Thisisanissuethattheindustryneedstoaddressnotonlyintermoffillingthecurrentskillsshortagebutalsotopreparethenewgenerationofminingtalentsequippedtoadequatelyaddressthechallengesdiscussedearlier.Someofthestrategiesdiscussedintheliteratureincludebettertrainingorreskillingofexistingtalents,retainingmoderntalents,flexibilityandmobilityoftheworkforce,adoptingnewtechnologyandcloserlinkswitheducationalandtraininginstitutions.

4 Conclusions

Caveminingwillcontinuebeinganattractivemethodforquitesometimemainlyduetothefactthatthemethodcanbe lowcost andhighproductivity.However, anumberof the futurechallenges,whicharesignificantlyoutsidepastandorpresentexperience,dictatethatsignificantchangestocurrentpracticesarerequired.Itisarguedinthispaperthatthenumberofincrementalchangesintroducedtotheindustryinthelast30yearsmaynotnecessarilybeadequateinthemselvestoeffectivelyaddressthefuturechallenges.Someoftheenvisagedstepchangesforfuturechallengesarediscussedinthepaper.

Whilstsomemajorminingcompaniesmayhavesufficientresourcestoaddressanumberofthechallengesin isolation,aglobalandcollaborativeapproach is required inorder toaccelerateandmoreeffectivelydevelop new technologies, practices and skills required to successfullymanage the challenges therebysustainingthecaveminingindustryglobally.


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Acknowledgements

TheauthorwouldespeciallyliketoacknowledgeAndrewLoganofNewcrest,ProfessorGideonChitomboofBryanResearchCentre,TheUniversityofQueensland,Australiaand,JockMacneishandRobertBlackofStrategicImageswhohavecontributedtothispaper.

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Brunton,I,Sharrock,G&Lett,J2012,‘FullScaleNearFieldFlowBehaviourat theRidgewayDeepsBlockCaveMine’,ProceedingsofMassMin2012,Sudbury,Paper6826,CanadianInstituteofMining,MetallurgyandPetroleum:Ontario.

Burger,DandCook,B2008,‘Equipmentautomationformassiveminingmethods’,ProceedingsMassMin2008, Lulea, (Eds: H Schunnesson and E Nordlund), pp. 493-498, Luleå University ofTechnology:Sweden.

Butcher,R2000a, ‘Blockcaveundercutting -aims, strategies,methodsandmanagement’,ProceedingsMassMin2000,Brisbane,(Ed:GChitombo),pp.405-411,AustralasianInstituteofMiningandMetallurgy:Melbourne.

Butcher,R2000b,‘Theroleofmassconcreteinsoftrockblockcavemines’,ProceedingsMassMin2000,Brisbane,(Ed:GChitombo),pp.422-428,AustralasianInstituteofMiningandMetallurgy:Melbourne.

CadiaValleyOperations,2013.Acrossthevalleymagazine.NewcrestMiningLimited,Internalmagazine,EditionFebruary2013.

CadiaValleyOperations,2013,Acrossthevalleymagazine,NewcrestMiningLimited,Internalmagazine,EditionApril2013.

CadiaValleyOperations,2013,Acrossthevalleymagazine,NewcrestMiningLimitedInternalmagazine,EditionSeptember2013.

Calder,K,Townsend,P&Russell,F2000,‘PC-BC:ablockcavedesignanddrawcontrolsystem’,ProceedingsMassMin2000,Brisbane,(Ed:GChitombo),pp.469-484,AustralasianInstituteofMiningandMetallurgy:Melbourne.

Casten,T,Golden,R,Mulyadi,A&Barber,J2000,‘Excavationdesignandgroundsupportofthegyratorycrusher installationat theDOZmine,PTFreeport Indonesia’,ProceedingsMassMin2000,Brisbane,(Ed:GChitombo),pp.295-299,AustralasianInstituteofMiningandMetallurgy:Melbourne.

Casten,T,Rachmad,L,Arkadius,T,Osborne,K&Johnson,M2008,‘P.T.FreeportIndonesia’sDeepOreZonemine-expandingto80,000tonnesperday’,ProceedingsMassMin2008,Lulea,(Eds:HSchunnessonandENordlund),pp.265-274,LuleåUniversityofTechnology:Sweden.

Castro,R,Vargas,R&HuertaF2012,‘Determinationofdrawpointspacinginpanelcaving:acasestudyattheElTenienteMine’,TheJournaloftheSouthernAfricanInstituteofMiningandMetallurgy,vol.112.

Catalan, A, Sinaga, F & Qudraturrahman, I 2010, ‘The role of geotechnical engineering during theprefeasibilitystudiesandearlyworksofCadiaEastpanelcavingproject,NewSouthWales,Australia’,ProceedingsofCaving2010Conference,Perth,Australia.

Catalan,A,Dunstan,G,Morgan,M,Green,S,Jorquera,S,Thornhill,T,Onederra,I&Chitombo,G2012,‘How can an intensive preconditioning concept be implemented at mass miningmethod?Application to Cadia East panel caving project’, Proceedings 46th Congress, US RockMechanics/Symposium,PaperNoARMA12-681,July24-27,Chicago,IL,USA.

Chacón,J,Göepfert,H,Ovalle,A2004,‘ThirtyyearsevolutionofblockcavinginChile’,ProceedingsMassMin2004,Santiago,(Eds:AKarzulovicandMAlfaro),pp.387-392,ChileanEngineeringInstitute:Santiago.


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Chacón,E,Barrera,V,Jeffrey,R&vanAs,A2004,‘Hydraulicfracturingusedtopreconditionoreandreduce fragment size for block caving’, Proceedings MassMin 2004, Santiago, (Eds: AKarzulovicandMAlfaro),pp.529-534,ChileanEngineeringInstitute:Santiago.

Chitombo,GP2010,‘Cavemining-16yearsafterLaubscher’s1994paper‘Cavemining–stateoftheart’’,CavingConference2010.

Cigla, M, Yagiz, S, Ozdemer, L 2001, ‘Application of tunnel boring machines in underground minedevelopment’,17thInternationalMiningCongress,Ankara,Turkey.

Cook,B,Burger,D,Alberts,L&Grobler,R2008,‘AutomatedloadingandhaulingexperiencesatDeBeersFinschmine’,ProceedingsTenthUndergroundOperators’Conference2008,Launceston,231-238,AustralasianInstituteofMiningandMetallurgy:Melbourne.

Deloitte,2013,‘Trackingthetrends2014,Thetop10issuesminingcompanieswillfaceinthecomingyear’,Accessibleathttp://www.deloitte.com/assets/Dcom-Australia/LocalAssets/Documents/Industries/Energyandresources/Mining/Deloitte Tracking_the_trends_2014_final_Dec2013.pdf.

Dolipas,R2000,‘RockmechanicsasappliedinPhilexblockcaveoperations’,ProceedingsMassMin2000,Brisbane,(Ed:GChitombo),pp.395-404,AustralasianInstituteofMiningandMetallurgy:Melbourne.

Duffield,S2000,‘DesignofthesecondblockcaveatNorthparkesE26Mine’,ProceedingsMassMin2000,Brisbane,(Ed:GChitombo),pp.335-346,AustralasianInstituteofMiningandMetallurgy:Melbourne.

Dunstan,G&Popa,L2012,‘InnovativecaveestablishmentpracticesatRidgewayDeeps’,ProceedingsMassMin2012,Sudbury,Paper6792,CanadianInstituteofMining,MetallurgyandPetroleum:Ontario.

Ernst&Young, 2013, ‘Business risks facingmining andmetals 2013–2014.The business risk report.Miningandmetals2013–2014’,Accessibleathttp://www.ey.com/Publication/vwLUAssets/Business_risks_facing_mining_and_metals_2013–2014_ ER0069/ $FILE/Business_risks_facing_mining_and_metals_2013–2014_ER0069.pdf.

Ferguson,W,Keskimaki,K,Mahon,J&Manuel,S2008,‘Henderson2000conveyorupdate’,ProceedingsMassMin2008,Lulea,(Eds:HSchunnessonandENordlund),pp.575-584,LuleåUniversityofTechnology:Sweden.

Flores,G,Karzulovic,A&Brown,ET2004,‘Currentpracticesandtrendsincavemining’,ProceedingsMassMin2004,Santiago,(Eds:AKarzulovicandMAlfaro),pp.83-90,ChileanEngineeringInstitute:Santiago.

Flores, G, Logan,A& Cuthbert, B 2007, ‘Codelco November 2006Visit Lessons Report’, NewcrestTechnicalVisitreporttoCodelcooperations,April2007.

Flores,G&Logan,A2008,‘CavingtechnologydevelopmentanditsapplicationatCadiaEastproject’,Proceedings 3rd International Conference on Innovation inMine Operations,Minin 2008(Eds:JArias,RCastroTadeuszGolosinski),pp.257-269,Santiago.

Fuenzalida,P,Baraqui,J&Castro,R2012,‘Miningwithlowprofilecruhsers:TheexperienceatCodelcoChile’,Proceedings5ThInternationalConferenceonInnovationinMineOperations,Minin2012(Eds:RKuyvenhove,JMoralesandCVega),pp.90-91,Santiago.

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Keynote Speakers

Fuenzalida,P&Baraqui,J2012,‘Precaststructurefordrawpointinblock/panelcaving’,Proceedings5thInternationalConferenceonInnovationinMineOperations,Minin2012(Eds:RKuyvenhove,JMoralesandCVega),pp.94-95,Santiago.

GoldenJr,R&Fronapfel,L2008,‘EvolutionofgroundsupportpracticesonHenderson’slowerlevels’,ProceedingsMassMin2008,Lulea,(Eds:HSchunnessonandENordlund),717-728,LuleåUniversityofTechnology:Sweden.

Gustafson,A,Schunnesson,H,Galar,D&Kumar,U2013,‘Theinfluenceoftheoperatingenvironmentonmanualandautomaticload-haul-dumpmachines:afaulttreeanalysis’,InternationalJournalofMining,ReclamationandEnvironment,vol.27Iss2,2013,pp.75-87.

Haley,W1982,‘Adaptationofsurfaceminingmachinestoundergroundmining’,DesignandOperationofCavingandSublevelStopingMines, (Ed:DRStewart), 1198-1219,SocietyofMiningEngineers,AIME:NewYork.

Jofre,J,Yáñez,P&Ferguson,G1992,‘Evolutioninpanelcavingundercuttinganddrawbellexcavation,ElTenienteMine’,ProceedingsMassmin1992.

Krek, R, Leonforte, A, Pratt, A & Dunstan, G 2008, ‘Underground infrastructure requirements forundergroundcaveminingoperations’,ProceedingsTenthUndergroundOperators’Conference2008,Launceston,pp.205-217,AustralasianInstituteofMiningandMetallurgy:Melbourne.

Kvapil,R,Baeza,L,Rosenthal,J&Flores,G1989,‘BlockcavingatElTenienteMine,Chile’,TransInstnMinMetall,SectA:MinIndustry,98:A43-56.

Laubscher,DH1994,Cavemining–thestateoftheart.JSAfrInstMinMetall,94(10):pp.279-293.

Leach,A,Naidoo,K&Bartlett,P2000,‘Considerationsfordesignofproductionleveldrawpointlayoutsforadeepblockcave’,ProceedingsMassMin2000,Brisbane,(Ed:GChitombo),pp.356-366,AustralasianInstituteofMiningandMetallurgy:Melbourne.

Leiva,EandDuran,L,2003.Pre-caving,drillingandblastingintheEsmeraldasectoroftheElTenientemine.Fragblast2003,Vol7,No.2,pp.87-104.

L’Estrange,H2009,‘CadiaEastTechnology,ChileVisitReport–SizerInvestigation’,NewcrestTechnicalVisitreporttoCodelcooperations,October2009.

Madrid,A&Constanzo,H2013,‘OperationaldefinitionofthreeundercuttingfrontsfortheNewMineLevelproject’,Proceedingsof3rdInternationalSeminaronMinePlanning,Santiago(Eds:JBeniscelli,CBottinelli,JCárdenas,HConstanzo,HGöpfertandEHenríquez),pp.171-179,Chile.

Manca,L&Dunstan,G2013,‘CadiaEast–acasestudyinappliedinnovativedesign’,Proceedingsof3rdInternationalSeminaronMinePlanning,Santiago(Eds:JBeniscelli,CBottinelli,JCárdenas,HConstanzo,HGöpfertandEHenríquez),181-190,Chile.

Manca,L&Flores,G2013,‘ModernPlanningPracticesforCaveMining’,Proceedingsof3rdInternationalSeminaronMinePlanning,Santiago(Eds:JBeniscelli,CBottinelli,JCárdenas,HConstanzo,HGöpfertandEHenríquez),pp.191-204,Chile.

Metsänen,A2004,‘Supplierassolutionproviderfortheminingindustry,SandvikMiningandConstructionvisionofthefutureinmining’,ProceedingsMassMin2004,Santiago,(Eds:AKarzulovicandMAlfaro),pp.659-661,ChileanEngineeringInstitute:Santiago.


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Moss,A,Russell,F&Jones,C2004,‘CavingandfragmentationatPalabora:predictiontoproduction’,ProceedingsMassMin2004,Santiago,(Eds:AKarzulovicandMAlfaro),585-590,ChileanEngineeringInstitute:Santiago.

Music,A&SanMartinJ2012,‘GreatvolumedrawbellsblastatElTeniente’,ProceedingsMassMin2012,Sudbury,Paper6779,CanadianInstituteofMining,MetallurgyandPetroleum:Ontario.

Ovalle,AW1981,‘AnalysisandconsiderationsforminingtheElTenienteorebody’,DesignandOperationofCavingandSublevelStopingMines,(Ed:DRStewart),pp.195-208,SocietyofMiningEngineers,AIME:NewYork.

Ovalle,AW&Albornoz, HR 1981, ‘Block caving with LHD equipment at El Teniente’, Design andOperationofCavingandSublevelStopingMines,(Ed:DRStewart),pp.355-361,SocietyofMiningEngineers,AIME:NewYork.

Pinochet,A,Constanzo,H&Larraín,M2012,‘ValuegenerationatElTenienteminebyusingthemaintransport system flexibility’, ProceedingsMassMin 2012, Sudbury, Paper 6979, CanadianInstituteofMining,MetallurgyandPetroleum:Ontario.

Rojas,E,Cuevas,J&Barrera,V1992,‘AnalysisofthewearindrawpointatElTenientemine’,ProceedingsMassmin1992,Johannesburg,(Ed:HGlea),pp.303-310,SAIMM:SouthAfrica.

Rojas,E,Molina,R,Bonani,A&Constanzo,H2000,‘Thepre-undercutcavingmethodattheElTenienteMine,Codelco–Chile’,ProceedingsMassMin2000,Brisbane,(Ed:GChitombo),pp.261-266,AustralasianInstituteofMiningandMetallurgy:Melbourne.

Silveira,AC2004,‘UndercuttingatE26lift2Northparkes’,ProceedingsMassMin2004,Santiago,(Eds:AKarzulovicandMAlfaro),pp.410-414,ChileanEngineeringInstitute:Santiago.

Silveira, C, Lovitt, M& Hewitt, T 2005, ‘Off to a Good Start with Lift #2: Drawbell Extraction –Northparkes’,ProceedingsNinthUndergroundOperators’Conference2005,Perth,pp.75-80,AustralasianInstituteofMiningandMetallurgy:Melbourne.

Schunnesson,H,Gustafson,A&Kumar,U2009,‘PerformanceofautomatedLHDmachines:AReview’,ProceedingsofInternationalSymposiumonMinePlanningandEquipmentSelection,Banff,Canada,pp.773-782.

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Keynote Speakers

It’s Not Mine Safety But Mind Safety - A Henderson Approach

GK Carlson Climax Molybdenum Company, USA

Abstract

As all industries have come to realize the implications and value of a safe workplace, and while it continues to be a topic that dominates conversation, we continue to have injuries. Henderson is no exception. Safe Production is our number one priority and Henderson has improved its safety performance; but we still have not achieved the ultimate goal of no accidents and incidents. With all the rules, programs, tools, experience, and studies at our disposal today, the question of why they have not been eliminated comes down to one thing: not using the greatest tool at our disposal, OUR BRAINS!

Henderson has been working for years to get all employees, top to bottom, to understand that Safe Production is truly the number one priority every minute of every day and tries to create an atmosphere that encourages people to make good decisions in all they do at work. To succeed at this, all employees must feel comfortable with the concept that safety has priority over production. It can also be summed up by a single statement from our General Manager “I want you all to be intensely selfish when it comes to making safe choices. After all, you and your family depend on you to exercise your intelligence to that end. You are the only one that has complete control.” We all know the weakest link in a chain will be the failure point. As in all workforces, from the top down, anyone not intensely selfish about safety becomes the weak link, and the chain will break. This paper will present some of the structure and belief systems Henderson is employing toward the ultimate goal of no accidents and incidents. To achieve this everyone must use the best tool available, their brains!

1 Introduction

TheHendersonMineisapost-undercut,panelcavingoperationlocatednearEmpire,Colorado,USA.Themineproduceshighqualityprimarymolybdenumfromacomplexofgraniticintrusives.Minesafetyisnotaseparatetopicfromgeneralsafeworkpractices.Maintainingasafeworkplaceisrequiredofallindustriesandbusinesses. Pigeonholingourselves intoa specific industry rather focusingon the root causes andmitigationofinjuriesdoesnotassistineliminationofaccidentsandincidents.Miningdoeshavecertainhazards that are somewhatunique,but asan industryweshouldbe focusingon safeworkpractices ingeneral. Anopenholeisanopenhole,whether it isanorepassoracatwalkwithasectionofgratingremoved.Electricitycankillifitisanenergizedcableinamineoranimproperlywiredoutletinahouse.Confinedspacesarefoundinminesaswellasacity’ssewersystems.Trippinghazardscanbeanywhere.Henderson,likeallotherminesandmills,worksdiligentlytomitigateandminimizeallhazardsthatmayaffectourworkers.However,withoutthefullandcompletebuy-inofemployeesintoatruesafetyculture,injuriesandincidentswillcontinue.

Hendersonhasrules, lotsandlotsofrules.CoupledwithHenderson’srequirementsarethegovernmentauthorities’rulesandregulationsconcerningareasnotnecessarilydirectlyinvolvedwithsafety.Howcananyonepossiblykeepallthisinformationattheforefrontoftheirmindsandperformdailywork?Sowhydowehavesomanyrules?Obviously,theseruleshavebeendevelopedinordertogiveworkersareferenceandguidanceonsafeworkpracticesasallwerelikelycreatedduetosomehistoricinjuryorfatality.Therulesareintendedtohelppreventrepetitionofpastincidentsbyprovidingtrainingsopeoplecanlearn,andhopefully,actaccordingly.


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Thus,howdowegetworkerstofollowalltheserules?Manyprograms,incentives,studies,andsystemshavebeendevelopedtoaccomplishthisgoal.Thequestionis:dotheywork?Theansweris:sortof.Nomatterhowmuchbusinessestry,individualsactaccordingtothewaytheythinkandtheirperceptionoftheworldaroundthem.Thatperceptionandmethodofprocessinginputshasbeendevelopinginapersonfromthedaytheywereborn.Itisinfluencedbythepeople,experiences,andbeliefstheyhavebeeninteractingwiththroughouttheirlives.Changingalifetimeofreasoningisadauntingtask.However,inmostcases,somefacetofaperson’slifelearningsmustbeovercomeinordertodevelopaworkerwhoconsistentlymakessafechoices.Itisvitalthatwediligentlypursuethere-educationandtrainingofpeopletobecomesafeandproductiveemployees.Althoughonewouldthinkjustgoinghometoourlovedonesuninjuredwouldbeincentiveenough,itappearstoneedreinforcement.Furthermore,positivefeedbackorrecognitionforgooddecisionscanbeamorepowerful incentive toreinforceabehaviouralchange.Hendersonhasfoundthataccountabilityforone’sactionsisakeyelementtochangingbehaviour.Ifpeoplearenotheldaccountableformakingpoordecisions,thereislittleexternalincentiveforthemtochangetheirbehaviours.Thismustbeconsistentforallorthesystemwillfail.Inotherwords,weneedtodoallwecantogetpeopletousethebesttoolavailabletothem,theirbrains.

Henderson continues to re-examine its safety culture, programs, systems, and behavioural evaluations.HendersonutilizesanOHSAS18001certifiedsafetysystemtomanagealltrainingdocuments,allholdSafeOperatingProcedures(SOP’s),developandclassifyallcompliancedocumentationandassistHendersoninauditingsystemcompliance.Sub-categoriesofthissysteminclude;taskriskassessment,consequencethinking,andengineeredoradministrativecontrolstoreducerelianceonbehaviouraldecisionmakingforanyjob.Allof theseareelements thatmustbeusedas training,enforcementandrewardopportunities.Hendersonbelievesithastheappropriateproceduresandcontrolsin-placeforallouremployeestocompletealltaskssafely,aslongastheyfollowthe“rules.”Moreimportantly,however,Hendersonisattemptingtoredirectandgivepositiveinfluencetothewayapersonthinks,reasonsandreacts.Untilpeoplebelievepassionatelyintheirownwellbeingasthenumberonepriority,fullyunderstandthecombinedknowledgethathascontributedtoalltherulesthatareinplace,andusethatknowledgetothinkbeforeacting,wewillcontinuetohaveaccidents.Wemuststriveforallemployeestoexercisetheirmindsandusetheirbrains,thebesttooloutthere.

2 Beyond the System

Henderson’sOHSAS18001certifiedsafetysystemisthebasisforitssafetyprogram.ThissystemisusedtoensureHendersondoeswhatitsaysitisdoingaswellastrackallrelevantdocumentation.Thescopeofthispaperthoughisnot,however,todiscusstheOHSASsystem.ThispaperisintendedtogobeyondtherulesandfocusonotherattributesthatHendersonbelievesarethecoreofasuccessfulsafetyculture.

Let’sfirstfocusisonsomeoftheverybasics.Aclean,well-organizedworkplace:Tothisend,Hendersonhasembracedahigherlevelofhousekeepingcomplianceandworkplaceinspections,makingitincumbentuponfrontlinesupervisorsandemployeestoadheretothisprinciple.Personalaccountabilityisparamountinmaintainingthishighstandard.Throughouttheoperation,thereisnotoleranceforsaying“it’snotmyarea,sonotmyproblem.”Allpersonnelaretoreportanyissuesobservedandiftheycanfixit,thenfixit!Thisincludessimplethingslikepickingupanystraytrash;makingsureallequipmentiskeptclean,cleanedforinspection,andreadyforthenextuser;keepingallstorageareaorderlyandsuppliessecured;andbarricadinganyareas thatmayhavesafety issues(andpromptlyreportingsuch issues). Ingeneral,Hendersonencouragesitsemployeestotakeprideintheoperation.

Inordertoachievesuchhighstandards,theworkforcemustactasacohesiveunit.Individualscannotbeworking inmultipledirectionsbasedon theirownattitudesandpre-conditions.Henderson relieson itsfrontlinesupervisorstobefullyengagedwiththeoperationandtheirowncrewtomakesurethecompanygoalofSafeProductionisattheforefrontofallwedo,andisnotcompartmentalized.Thisrequiresconstantcommunicationsandfurthermakesthefrontlinesupervisorshavetothinkandunderstandtheirrolesand

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Keynote Speakers

responsibilities,aswellashowtheyinteractwiththosedownstream.Hendersonfurtherconducts“mockaudits”inwhichtheSeniorSupervisorofeachdepartmentroutinelyauditstheirworkareas,tryingtothinkasaFederalinspectormight.Thisallowssupervisorstofindanyissuesandmitigatethembeforetheissuesbecomeenforcementcitations.Allsupervisorscarrycamerasfordocumentingobservations(bothgoodandbad)tosharewithothers.Equipmentpre-operationalchecksareexaminedtoprovetheyhavebeenproperlydoneandallsupervisorsandemployeesareheldresponsiblefortheirworkareas.

Thebenefitsofthisefforthavebeenimpressive.EmployeesaretakingownershipandthelevelofissuedcitationsfromFederalinspections,despiteincreasedscrutiny,isfalling(Figure1).Equipmentavailabilitieshaverisenasemployeestakegreatercareoftheirmachinesandsubsequentlysohasproductivity.Thisdidnothappenovernight.Ithastakenmanyyearsofperseveranceandenforcementbyalllevelsofsupervisionto“re-train”themselvesandtheworkforcetoreachtheselevels.

Figure 1. Henderson Quarterly Issued Citations (Mine and Mill)

3 Accountability

Itisprobablywellacceptedinourindustrythatwehavelotsofrules.Somanysothatit isbeyondthehumancapacitytorememberallofthem.However,mostrulesarearesultofsomepoorindividualbeinginjuredorkilledbeforetherulebecamearule.Certainly,theserulesaregood,butiftheyarenotfollowedorfullyunderstood,itcanleadtoaccidents.Accountabilityandimpartialenforcementismandatoryifaneffectivesafetyculture is to takeroot.Theexcuse”Ididn’tknow”isnotacceptableatHenderson. Ifaworkerdoesnotknowtheprocedures,theyshouldnotbedoingthetask.Iftheyareunsure,theyshouldgetconfirmationand/ortrainingbeforeproceeding.

Henderson takes the approach that individuals must be fully engaged in their own personal safety toappreciate thegravityanddimensionsof thedecisions thatgovern theirworkethic.Concurrently,howthatindividualrespondstotheirsupervisor’sdirectionsandhowthesupervisorreflectsthesafetyattitudewilldictatehowachoice ismadebyeachindividual.Hendersonhasplacedparticularemphasisonthesupervisors’rolesandresponsibilitiestoensurethattheyarefullyengagedandthatcommunicationstotheworkforceareinlinewithSafeProduction.Thiscontinuesupthechainofcommandtotheverytop.


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In this way, all are held accountable and responsible for the actions of those below them. ContinueddiscussionsbetweensupervisorsandsubordinateshaveslowlybeguntomovethependulumtowardsthesafetycultureHendersonwants.Itisnowconsideredalrightforanemployeetorefusetodoajobtheyfeelisunsafe,butitisnotacceptabletojustsaysomethingisunsafe.Theemployeemustidentifytheissuesthattheybelievemakeitunsafe,whetheritisinsufficienttraining,specificcontrolsarenotinplace,theequipmenttobeusedisunsatisfactoryforperformingthetask,orwhatevertheissueis.Supervisorsworkwithemployeestothedegreenecessarytoensurethatthetaskhasbeenproperlyvettedandriskhasbeenminimizedtoanacceptablelevel.Ifthiscannotbeagreedupon,thetaskwillbereviewedbyanappropriateteamuntilallagreetheriskhasreachedacceptablelevels.Thisrarelyoccurs,however,sincetheOHSAS18001systemdictatesthatcrewshavereviewedtheirtasks,assessedrisks,applypropercontrols,andhaveasafeprocedureinplacebeforeanyworkbegins.ThesearetheSOP’sthathavebeendevelopedandsupplythe“rules”tobefollowedinordertocompleteatasksafely.Again,thisisusingbrainpowerupfrontasanefforttostemthepossibilityofunintendedconsequencesinthefuture.

Accountabilitydoesnotalwayshavetobenegative.Whenweobservepeoplemakinggoodchoices(likesteppingbackandthinkingwhenthingsarenotproceedingcorrectly)andpubliclyacknowledgingitasadesiredbehaviour,thiscanpotentiallybemorepowerfulandlastingthanpunishmentforanundesirablebehaviour.

4 The Frontline Offense

SafeProductionisnotadefensivestrategyandshouldbeproactiveratherthanreactive.Itisalloffense.Wemustmakethefirstmoves,berelentlessinourefforts,andcontinuallycommunicatetheexpectationsandgoalstobeachieved.

Hendersonisverysuccessfulinsafelybringinglargeprojectstocompletion,yethastendedtohaveless-favorableexperiencesonnormalday-to-daytasks.Thismaybeduetotherecognitionthatlargeprojectshavelargepotentialforaccidents.Wefocusourmindsandeffortsduringtheselargeprojectsinahigherfashiontoensurethattheprojectisdonesafely.Thehumanbrainhasatendencytowardrunninginautopilot.Thus,anindividual’sattentiontendsnottobefocusedtothedegreenecessarytoproducethesameresultsaslargeprojects.Obviously,thepeoplewhodotheworkarethosewiththebestknowledgeofhowtodoitsafelyandefficiently,butforsomereasonmaymakepoorchoices.

Henderson’sapproachistoplaceahighdegreeofresponsibilityonthefrontlinesupervisorstohaveamorethorough knowledge of their personnel to properly engage them, knowwhatmotivates or demotivatesthem,whatlifeissuesmightaffecttheirworkfocus,anddirectthemsothattheirchoicesareinlinewiththeappropriategoals.Itisincumbentonthefrontlinesupervisorstohavethegreatestawarenessofthoseunderthemtoensurethattheyaretherightpeopleforthejob,arefocusedandreadytoperformthework,andarenotbeingdistractedbyotherfactorsthatcouldputthemorothersatrisk.Hendersonrequiresthefrontlinesupervisorstospend80%ormoreoftheirtimevisitingtheirworkareasandcommunicatingwiththeirpersonnel,keepingapulseonanythingthatmightdetractfromtheworkfocus.Toassistinthiseffort,Hendersonsetslimitsoncrewsizestoallowthefrontlinesupervisortohavetheextratimetotrulyengagetheirsubordinates.Thisengagementfosterscommunications,thinking(usingthemind)andexaminingandanalysinganypertinentissuestocreatetheintendedresultofworkinginasafeandproductivemanner.Similarly,thisallowsmoreone-on-oneinteractionbetweenthesupervisorandsubordinate,givingeachabetterunderstandingofexpectationsanddistractionsthatmightbebarrierstoreachingthedesiredresults.

5 It’s Not Just Us

Another aspect that Henderson has changed in recent years is how we work with and manage ourcontractorson-site.ContractorsareconsideredanadditiontotheworkforceandassuchmustabidebyallthevaluesandrulesofHenderson.Therearenoexceptionsandcontractorworkhoursandincidentsare

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Keynote Speakers

compiledwiththesites’reportedsafetynumbers.Notonlydocontractorshavetosubmitasitesafetyplanprior to commencinganyworkon theproperty,butHendersonhasplacedadditional requirements andaccountabilityonprojectmanagerstoensurefullcompliancewiththeirplanandHenderson’sregulations.SincecontractorsafetyperformanceimpactstheHendersonsafetystatistics,everyemployeeisempoweredtoquestionacontractor’sperceivedunsafeactstoensureweareallmovingforwardinasafemanner.Itisnotjusttheprojectmanager’sresponsibility;everyoneintheareaisresponsibletoensureallworkisbeingperformedsafely.Accountability, asmentionedbefore, iskey forbothHendersonandcontractorpersonnel,andthosepeoplewhofailtomeetthestandardshavebeenremoved.Again,thishastakentimeanddiligence,andtheimprovementisdemonstratedbytheincidentgraph(Figure2)showingaprogressivedeclineinrecordableincidents.

Onenormallythinksofcontractorsasthosedoing“heavy”work,butatHendersonitappliestoallcontractors,including,forexample,thejanitorialstafftofreighthaulers.Allaresubjecttothesamerulesandstandards.ThisalsorequiresadditionaldiligenceasmanysmallercompaniesmaynothavetakenmeasurestoensuresafeworkpracticestomeetHenderson’sexpectations.Theeducationalandcommunicationdemandstogeteveryoneutilizingtheirbrainsandactingaccordinglyisenormous,butmustbedoneifweexpecttohaveallworkersmakingsafechoicesonaroutinebasis.

Figure 2 Henderson Total Recordable Incident Rate(TRIR)

6 Incentives

Overtheyearsmanyideashavebeentriedtoincentivizethesafetyculture.Historicapproachesusuallyincludedextrinsicmotivatorssuchasawards,bonusesandgifts.Thesemotivatorsonlyseemedtohaveworkedforsome.Thequestionweneedtoaskis:Whatbetterincentiveistherethantonotbehurt,disabledorfatallyinjuredwhileatwork?Howdoyouteachsomeonethattheendofafinger,oratoe,oraleg,oranarmisworththinkingabouteveryminuteofeveryday?Hendersonhascometorecognizethatintrinsicmotivationisthekey.Wearetryingtohelppeopleunderstandthattheyneedtobefocusedonthetaskathandfortheirownwellbeingandtheirlovedonesratherthantoearnsometrinketortopreventdisciplinaryactions.

Mostwouldagreethatwhenataskbecomesroutine,theperson(s)doingtheworkwillbecomecomplacentinperformancesincetheyhavedoneitmanytimeswithoutinjury.Thisisoneofourgreatestchallenges.Whenthemindgoesintoautopilot,theconsequencesofactionsandchoicesbecomelessattheforefrontofthework,untilsomethinggoeswrong.


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What is themeaningof “SafeProduction?” Is it justwordsor is therea truecommitment?HendersonbelievesthatSafeProductionisjustasitsays.Henderson’sbonusstructureisbasedontheentireoperation’sperformanceandcorporategoals.Initially,thiswasstructuredasabreakdownof35%ofthetotalbonus(paidquarterly) basedon theoperation’s safetyperformance.This placed theburdenon all employeestoworksafelyorallcouldpotentiallyloseasignificantpercentageof thebonus.Thismayseemunfairtosome,punishingtheinnocent,howeverit isalsomeanttoactasanincentivetohaveemployeespayattentiontoeachother’sworkpractices,speakupandcorrectinappropriatebehavioursandactions.Howwouldyoufeelifyouchosetoignoreanactionyouobserved,knowingitwasunsafe,onlytofindoutlaterthatthepersonwasinjuredorkilled?Gettingtheworkforcetoacceptcriticalcoachingfrompeersand/orsubordinateaswellasovercomingtheintimidationonemayfeelinapproachingapeeroracontractor,ischallenging.Thisrequiresgettingallindividualstoacceptcoachingnotasacriticismbutratherasanopportunitytothinkandanalysewhattheyaredoingandmakesureitisthesafestandbestapproach.Thishastakenalotoftimeandefforttochangethewaytheworkforcebehavesandisbynomeanscomplete.However,thereisnoticeablepositivechangetakingplaceandHendersoniscommittedtopursuingthisactaswebelievethenewcultureistakingroot.

Recently,thebonusstructurewasmodifiedtoallowfor50%ofthebonuspaybebasedontheoperation’ssafety performance,with the caveat that any employee injured in an accident and found culpable in asafetyviolation forfeits their safetyportionof thebonus. Inotherwords,Henderson is holdingpeopleaccountableforpoordecisions.Somemightsaythiswillmakeemployeesnotreportinjuriesand,insomerelativelyminorincidents,thismightbetrue.However,amajoraccidentwithinjurieswillgetreported.The subsequent investigationwill also reveal the decision processes leading up to the occurrence andthisinformationisthensharedthroughoutthecompany.Additionally,contractorhoursandincidentsareincludedinthecalculationsforbonuspay.Althoughtheyarenotparticipantsintheprogram,theirsafetyrecordcanaffectthebonuscalculations.

Supervisorsarealsoratedontheircrews’performancewhichcanhaveimplicationstowardsadvancementandmeritpay,andonupthechain.

7 Consequence Thinking

Nowthatwehaverules,controls,accountability,enforcementandincentives,whydowecontinuetohaveaccidentsandincidents?Astheoldsayinggoes,youcanleadahorsetowaterbutyoucannotmakehimdrink.

Eachpersonhasadifferentperspectiveandattitudeabouttheworldaroundthem.Thewaytheyprocessinformation,weighalternativesandmakedecisionsarealldifferent.Likewise, thewayeach individualcommunicatesdesiresorconcernsisequallyunique.

It is impractical and impossible to have a supervisorwith every employee at everymoment.Wemustrelyontheemployeestounderstandwhatisexpectedofthemandtrustthemtoactaccordingly.Enterthedemon;thehumancapacitytonotusethebesttoolinourarsenal,ourbrains.

Consequencethinkinginvolvesusingone’sbraintoanalysewhatmighthappenandweightherisksifaparticular choice ismadebeforeproceedingwith the action/decision.Henderson calls it the15 secondrule–whatcouldhappeninthenext15secondsofwork.Thissoundssimplebutasamatterofpracticeitgoesagainsthumannaturetoengagethebrainthatmuch.Sincethehumanbrainpreferstoruninautopilot,foreachindividualtothinkbeforeeachactionwithhundredsofactionstakingplaceoverthecourseofaworkdayischallenging.However,thisiswhatmustbedoneinordertotrulyhaveSafeProduction.

Noonewants tobe injuredandconsciouslydecides toproceedwitha taskknowing theywillbehurt.Unfortunately,peoplefacedwithachoicecangamblethattheywillnotbehurt(“itwon’thappentome”).Thismaybeduetoseveralfactors:itisaroutinetaskdonemanytimebeforewithoutinjury(complacency),

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theyignoreordisregardsomeaspectoftheprocedurethusthejobwillbedonemorequickly(shortcuts),itismoreofabothertogettherighttoolthanusewhatisathand(improvising/laziness),theybelievetheycanhandle the loadwithoutassistance(bulletproof),andonandon. Ineach instance, there isprobablysomesmallvoiceinthebraintellingthemnottoproceed,butitisoftenignored,sometimeswithseriousconsequences.IntheinjuriesHendersonhassufferedoverthepastfewyears,thefollowupinvestigationdemonstratesacommonelementofhumanbehaviourthathascausedapoorchoicetobemade.Howdowegetpeopletoengagetheirbrainsandreallylistentothatlittlevoice?

8 Managing Production Pressure, Frustration, Upset Conditions

InkeepingSafeProductionasthepriority,Hendersontriestoconveytoemployeesthatproductionpressureis really an individual choice.Although theremaybeemphasis communicated toget a taskdone, it isHenderson’spolicythatitisnottobeconstruedasanordertoshortcutanyprocessandputoneselfatriskinordertocompletethetaskquickly.Whatitismeanttobeconveyedisthatalleffortsshouldbetakentoensurethetaskiscompletedcorrectly,intheshortestpossibletimeframe.Productionpressureatthispointbecomesanindividual’spersonaldecision.Knowingthatthetaskistobedonecorrectly,employeesneedtoexercisetheirminds,usetheirbrains,todeterminethemosteffectiveandsafemannerinwhichtoproceedtominimizetaskcompletiontime.Itisalsorequiredthatthesupervisorbeengagedwith,andunderstand,theirpeopletomakesureallworkisbeingdoneappropriately.

Likewise,ifthingsarenotgoingwellastasksarebeingperformed,itisbettertotakethetimetostop,re-examinetheissues,getadditionalhelpifneeded(morebrains),developaplanmovingforward,andthenbeginagain.Takingthetimetoincorporateallthesestepsshould,ifthebrainisused,resultintheissuebeingresolvedandthetaskbeingcompletedsafelyandcorrectlyinapracticaltimeframe.Itiswhenfrustrationisallowed toovershadowconsequence thinking, ignoring that littlevoiceand forcinga resolution, thataccidentsarelikelytooccur.

9 Core Values

WhatHendersonwantsisforsafetytobecomeacorevalueofeachperson’slife.Ifapersonfullyembracessafetyaspartofhisorhertrueself,thenthisattitudeiscarriedbeyondtheworkplacetohomelifeaswell.Anemployeeseriouslyinjuredathome,sufferinglostworktime,impactsthecompanyjustasmuchasoneinjuredatwork.Theresultisthesame,losttimeandproductivity.Atruebeliefinsafetyisnotcheckedinandoutatthegatetowork.Whatdoesittaketomakegoodbehavioursbecomegoodhabits?Constantandconcisetraining,reminders,andcontinualre-enforcementofgoodbehavioursareakeyelement.Trainingisnotjusthowtooperateorworkonapieceofequipment,itisalsothesafetyaspectsofthatoperation.Wemustbeginbytakingthetimetofullytrainourworkforceinthesafetyaspectsoftheirjobs.Wemusthaveconstantremindersofthattrainingandonaday-to-daybasisfollowuptomakesurethesepracticesarebeingutilized.Thiscannotbedonebyonlyafewindividuals,butmustbeapartoftheday-to-dayworkbyall.

Hendersonhasbeencultivatingaculturewhereallworkerscanandshouldquestionanotherwhentheyseesomethingthatdoesnotappeartobesafe.Likewise,itisobligatoryfortheemployeebeingquestionedtonotbeintimidatingordismissive,butinsteadtoacceptandexamineorexplaintheirownpractices.Callitforcedre-focusing,areminderofconsequencethinking,usingthebrain.

Hendersonalsohelpspeoplefocusonsafetybyhavinganygroupmeetingbeginwithasafetyshare.Thismaybeamanagementmeeting,crewtailgates,oranyothermeeting.Peopleareexpectedtocometomeetingswithsomethingtoshareonsafety.Managementalsoholdsquarterlysupervisormeetingsinordertobringallfrontlinesupervisorsandseniorsupervisorstogetherwiththesuperintendentstodiscusssafetyvaluesandissuesinadditiontoproductionmatterssoallcanshareexperiencesandlearnfromeachother.Thishelpsdrive,fromthetopdown,thecorevalueandexpectationthatSafeProductionisthefirstpriority.Ithelps


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break down the barriers to a true safety culture that can then be communicated further down to the workforce. It also reinforces the attitude that supervisors will support good decisions with regards to safety, even if it disrupts or hampers production. Good decisions are recognized and shared throughout the operation.

10 Conclusions

As a business, we lead people toward the results we desire and must utilize our best efforts to influence and positively redirect a person’s thoughts, attitudes and actions to consistently make safe choices. Henderson is continually trying to engage each employee, from top to bottom, to encourage the belief that Safe Production is not only in the company’s best interest, but in each individual’s best interest. We provide guidance and set examples for others to follow. Holding people accountable for their poor decisions, as well as holding supervisors accountable for the actions of their employees, is mandatory if an effective safety culture is to take root. If a person does not hold safety as a core value in their life, then true safety is not an intrinsic value and the person is gambling with their body and/or life and potentially the lives of their fellow employees. Recognizing and communicating the safe choices that have been made is a positive and potentially greater motivator that all must share.

Safety is a mind game. We must continually focus on each individual, day by day, to reinforce the belief that safety is a core value they need to adopt. We cannot let up; to do so invites the inevitable accident. This is not easy and will never be complete because the brain wants to run on autopilot. But in order to have Safe Production it must be continually active and focused. Safety is uncompromising, repetitious and demands lots of continuous effort, and the very best safety tool provided to everyone is their brain. We must be relentless in the pursuit that all need to use it!


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Case Studies

Fracturing in the footwall at the Kiirunavaara mine, Sweden

M Nilsson Luleå University of technology, Sweden D Saiang SRK Consulting (Sweden) AB, SwedenE Nordlund Luleå University of technology, Sweden

Abstract

The Kiirunavaara mine is a large scale sub level caving (SLC) mine located near the city of Kiruna in northern Sweden. It is owned and operated by LKAB (Luossavaara-Kiirunavaara AB). The mine produces approximately 28 million tonnes of iron ore annually. Over the last 30 years the mine has experienced a slow but progressive fracturing and movement in the footwall rock mass induced by the SLC operations. The footwall contact which assumes a “slope-like” geometry is partially supported by the caved material from the hangingwall. However, since the late 1980s damage has been observed on the footwall crest as well as within the footwall. Progressive rock mass movement in the footwall is indicated by surface subsidence and visual observations underground. The extent of the damage has traditionally been estimated using empirical relations. Most of the current long term underground infrastructure within the footwall is located at a considerable distance from the ore contact. However, for new developments on deeper levels it is imperative to predict the future extent of the damage volume. Approximating the position of the damage boundary in the footwall at the current state of mining would assist in predicting the extent and characteristics of the damage volume as the mine deepens. LKAB and LTU (Lulea University of Technology) have therefore initiated a joint research project to study the long term stability of the footwall at the Kiirunavaara mine. This paper constitutes part of the work in this research.

The paper describes a damage mapping campaign and subsequent analysis of the Kiirunavaara mine footwall to approximate the outer boundary of the damage. The footwall was systematically mapped on 6 levels between 320 and 800 m. The mapping results were then used to interpolate damage lines on the respective levels. The damage lines were used to construct a continuous damage surface between the studied levels. Existing records of damage mapping, monitoring and predictions were reviewed and compared to the results from the current campaign. The new results show that, the outer damage surface appears to remain stationary on the upper levels while new damage was observed on the deeper levels. At levels above 740 m the damage is judged to be mainly controlled by movements along natural discontinuities. At levels below 740 m the majority of the damage seems to be stress induced.

1 Introduction

TheKiirunavaaramine,locatednearthecityofKirunainnorthernSweden,isalargescalesublevelcavingoperationproducing28Mt(millionmetrictons)ofironoreperyear.Originallyanopenpitoperationtheminelatertransitedtoundergroundoperationsinthelate1950-s.TodaythemainorebodyisminedusingSLCandthemineiscurrentlytransitioningfromamainhaulageatlevel1045tothenewoneatlevel1365situatedatadepthofroughly1100m(actualdepthfromgroundsurface).

ThemainhostrockinthefootwallisthePrecambrianagedlowquartzsyeniteporphyry.Theporphyryissubsequentlyreplacedbyotherrocktypesfartherintothefootwall.Thefootwallporphyryissubdividedinto5categories;denotedSP1-SP5accordingtostrengthproperties.TheRMRforthefootwallwascompiledandreferencedbySandström(2003)torangebetween49and68.


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Inprinciplethemineisorientednorth-south,withthefootwallonthewestsideandtheorebodydippingaround 60˚ towards the east (see Figure 1). Orientation and naming of objects and infrastructure arereferencedtoalocal3Dcoordinatesystemwithverticalz-axisoriginatingatthepre-miningpeakoftheKiirunavaaramountain.Thelocaly-axisisroughlyorientednorthtosouthandfollowsthegeneralstrikeoftheorebody,thex-axisisorientedroughlywesttoeast,z-coordinatesincreaseswithdepth,y-coordinatesincreasessouthwardsandx-coordinatesincreaseseastwardsintoandbeyondthehangingwall.

Figure 1 Coordinate orientation of the Kiirunavaara mine

ThesurfaceandundergroundmininginfrastructuresatKiirunavaaraarelocatedonandwithinthefootwall.Thegenerallayoutoftheundergroundinfrastructureisalignedparalleltothestrikeoftheorebody.Mostofthepermanentinfrastructuressuchascrushers,skipshaftsandworkshopsarelocatedatarelativelylargedistancefromthefootwall-orecontact.Theinfrastructureclosetothecontactconsistsmainlyofroads,orepassesandfootwalldrifts.

Overthelast30yearstheminehasexperiencedaslowbutprogressivefracturinganddeformationinthefootwallrockmass.Thismovementoftherockmassisdirectlyrelatedtothesequentialsub-levelcaving(SLC)operations.AstheorebodyisremovedthroughSLCthefootwallcontactbecomesde-stressedandassumesaslope-likegeometry,seeFigure2.Thefootwall“slope”ispartiallysupportedbythecavedrockmassesfromthehangingwall(Villegas&Nordlund2008;Stöckeletal.2013).

Damage has been observed since the late 1980s both on the footwall crest and at the undergroundinfrastructure.Surface crackshavebeen systematicallymappedand tracked invarying regularity since1992.Themappingwasfirst focusedwithin theextentof theopenpit areabuthas later shifted to thenorthernmostpartsofthecrestwherecrackshavebeenobservedoutsidetheopenpitarea(Lupo1996).

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Figure 2 Concept of the footwall slope

Even thoughmost of the production critical infrastructure (skip shafts, crushers, etc.) is located at aconsiderabledistancefromtheorecontactalargescalemovement/failureinthefootwallcoulddrasticallyimpedetheminingoperationsduetodamageonroads,orepassesandfootwalldrifts.IthasthusbeenafocusofLKABsincetheearly1990stoaccuratelyforecasttheglobalstabilityofthefootwallwithincreasingmining depth. Several short studies have already been publishedwith this aim.However,thesestudieshaveproduced inconsistentand inconclusive results.LKABandLTU(LuleaUniversityofTechnology)have therefore initiateda joint researchproject tostudy the long termstabilityof thefootwallattheKiirunavaaramine.

Thispaperconstitutestheinitialphaseofthisresearchprojectandaimstodeterminethepresentextentofthefootwallfracturing(damageline)and,ifpossible,confirmtheassumedfailuremodes.

2 Kiirunavaara footwall fracture studies

2.1 Previous studies

Inthe1990sseveralstudieswerepublishedonthelargescalefootwallstabilityattheKiirunavaaramine.Thesestudieswereaimedatidentifyingthefootwallfailuremechanisms.Dahner-Lindkvist(1992)analysedtheobserveddamagebyusingtheslopestabilitychartsdevelopedbyHoek&Bray(1981).Singhetal.(1993)evaluated theprogressive failureof thehangingand footwallatKiirunavaaramineandRajpuraDaribaSLCmine inIndia.They interpreted tensile failureas themainmechanismdriving thefootwallinstabilityandhenceforthpredictedformationoftensilecracksatthegroundsurface.

Lupo(1996)assumedthatthefailuresurfaceinthefootwallwasplanar,eithercomprisingofapre-existingstructureoracombinationofgeologicalstructuresandfailuresurfacesthroughintactrockincombinationwithasurfacetensioncrack.Withthisassumptionthesurfacedeformationsandthecorrelatedundergrounddamagewerepredictedtofollowtheinclinationoftheore/hostrockcontactandnottopropagatesignificantlywestwardastheminingdepthincreased.


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Sjöberg (1999) performed numerical and limit equilibrium analyses of the Kiirunavaara footwall andidentifiedcircularshearfailureastheprimaryfailuremechanism.

ThesubjectoffootwallstabilitywasrevisitedbyHenry&Dahner-Lindqvist(2000)inwhichthesubjectofpredominant failuremechanismswasbrieflydiscussedbutnotexamined indepth.Later,Villegas&Nordlund(2008)modelledtheprogressivefailureinKiirunavaarausingthecodePFC2D.Boththerockmassandthecavedmaterialweremodelled,butmajorstructureswerenotconsideredandthesizeoftheparticlesconstitutingthecavedmaterialhadaradiusof1m.Themodelshowedthatthecavedmaterialsupports the footwall even during ore draw.The effect of the traction from the cavedmaterial on thefootwallinducedonlylocalfailuresonthefootwallfaceclosetotheundercutlevel.Theselocalfailuresdidnotprogresssignificantlyintothewall.ThiscontradictssomeindicationsbyLupo(1996)thatthecavedmassesaddedtotheshearforcesinthefootwallduringoredraw.

3 Current monitoring

3.1 Surface monitoring

Quantitative deformationmonitoring is performed almost exclusively on the ground surface. Both thefootwallandhangingwallaremonitoredbyGPSalongpredeterminedmonitoringlines(Stöckeletal.2013.Thefootwallmonitoringlinesincludesatotalof84measurementhubsofwhichthemajorityaremeasuredonceayear.Somespecificpointsofspecialinterestwherelargerdeformationsareexpectedaremeasuredquarterly.InSARtechnologyhasbeenusedsince2009butisprimarilyevaluatedonlyforthehangingwallgroundsurface(Stöckeletal.2013).Inaddition,aerialphotographsbyhelicopterover-flightshavebeencapturedyearlysince2008.Sofarthefootwallcrestsuffersonlyminorandcontinuousdeformations.

3.2 Underground monitoring

Deformationsaremoreapparent in theunderground infrastructure than theyareon the surface. Largescalefootwallfracturingwasfirstobservedundergroundinthe1980s.TomonitorthisdeformationTimeDomainReflectometry (TDR) cableswereutilized.However, due to the early installationmanyof themeasuring points are now inside the failure volume and can no longer be accessed. The high cost ofinstallingnewTDRcablesledtoLKABnotreplacingthissystemasmeasuringpointswerelost.Insteadamacro-seismicsystemfromCANMETwasbroughtinaroundtheyear2000totrackvolumessufferingrockmassdeformation.Thissystemwassubsequentlyreplacedbyalocalmicro-seismicsystemfromIMSin2003constitutingaround10geophones.The2003systemwaslaterexpandedtoamine-widesystemin2008whichincludedaround220geophonesinlate2013(Stöckeletal.2013).

ThemajorityofthecurrentIMSgeophonesareinstalledinthefootwallclosetotheactiveminingareas,whileonlyafewarelocatedatthehigherlevels.Duetotheresultinglowazimuthalcoverageoftheupperlevelsthesystemhasofyetnotbeenevaluatedformonitoringthefootwallfailureinanylargerextentusingthecurrentanalysismethods.

Theseismicsystemiscurrentlytheonlymethodthatisquantitativelymonitoringthefootwallunderground.Qualitativemeasurements are performedby routine damagemapping concentratednear the productionareas.The upper decommissioned areas aremapped only in relation to specific projects,meaning thatseveralyearsmaypassbetweenthemappingcampaigns.InternalLKABmemosdocumentseveralofthesecampaigns,thetwomostrecentwereperformedin2004and2012respectivelybuttheyonlycoveredtheminesectionY22toY24.

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3.3 Quantitative damage mapping

InthecampaignreportedinthispaperdamagemappingwasperformedbetweenthecoordinatesY15andY45(roughly3kmalongthestrikeoftheorebody),seeFigure3.Theaimwastoupdatetheundergroundobservationsondecommissionedlevelsandtopresenttheminthecontextoflargescalefootwallfracturing.

Toachievethebestoverviewanumberofevenlyspacedlevelswerestudied.OldhaulagelevelswerefoundtobebothquiteeasilyaccessibleonmostY-coordinatesandevenlyspacedindepth.Thelevels320,420,509,540and775mwerethereforeinitiallyconsideredformapping.Inaddition,level740mwasincludedas the2004and2012campaigns (betweenY22andY24) indicatedabreak in trendof the locationofdamageontheoverlyinglevels.

Inordertosingleoutspecificregionstoinvestigatepreviousdamagemappingprotocolsfortherespectivelevelswereused.Alinewasdrawnoneachlevelfollowingtheoutlinedcontouroftheoutermostpreviouslymappeddamage.Areaswherethecontourlinesintersectedexistingdriftsrunningsemi-perpendiculartotheorestrikeweremarkedascandidatesformapping.Areashostingknownlargescalediscontinuitieswheremovementhadpreviouslybeenrecordedwerealsoincluded.

Figure 3 Sketch of the footwall damage mapping area (adapted with courtesy of LKAB)

Insomeareas,suchaspartsoflevel740m,damagewasalreadymappedintheoutermost(farthestfromtheorecontact)partsoftheinfrastructure.Intheseareas,thepositionoftheouterdamagesurfacecannotbeexplicitlydeterminedutilizingdamagemappingalonebutneedstobeinterpolatedbetweenmappedpoints.


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4 Results

Byinterpolatingdamagecontourlinesbetweenobservedpointsontherespectivelevelsadamagesurfacegeometry was approximated (Figure 4). The damage contour lines were then updated based on newobservationswhendamagewas confirmedoutside the lines derived from theprevious campaigns (i.e.,fartherintothefootwall).

Figure 4 Plan map showing contour lines for mapped damage on the respective levels

Theobservedfalloutsassociatedwithfootwalldamagewerepredominantlystructurallycontrolledabovelevel740m.Atlevel740mthemajorityoftheobserveddamageswerestressinduced.Atthisleveltheinitiationandpropagationofnewlocalfracturesalsobecamemoreapparent.

Figure4outlinesafailuresurfacethatroughlyfollowsthegeneraldipandstrikeoftheorebody.Forfurtheranalysisallpartsofthecontourlinesweregiventhesamecredibilityregardlessofifthepointswerederivedfromdirectobservationorfrominterpolation.Onthebasisofthisassumptionadamagesurfacecanbeinterpolatedalsobetweenlevels.

An“averaged”damageplanewasgeneratedbytracingtheprojectededgesofthedamagecontourlinesatY12andY49betweenlevels320mand740musingstraightlines.The“averagedamageplane”obtainedwasaflatsurfacedipping56˚andstrikingparalleltotheorebody.Theplanewasusedasareferenceforsettingthecolourschemeofarefineddamagesurfacegeneratedfromthefullcontourlines.Therefineddamagesurfaceishereafterreferredtoassimply“thedamagesurface”.Darkcolourmeansthatthedamagesurface lieseast fromthe“averageplane”,while lightcolourmeans that it lieswest fromthe“averageplane”, black line indicates the reference “average plane” (Figure 5). The reason behind the differentcolouringwastosimplifyvisualisationofthecomplexsurfacegeometry.

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Figure 5 Profile view of the damage surface with colour reference plane (left) and with levels 320 and 775 m (right)

FromthecolourschemeinFigure5itisclearthattheshapeofthedamagesurfaceiscomplex.Theearlierpostulatedcircularshearsurfacesarenotdiscerniblenorcananyotherclearmechanismbeidentifiedfromthedelineatedsurface.

Domainsareestablishedtoprepareforfuturenumericalmodelsofthefootwall.Thevolumeisseparatedintodomainswithsimilardamagesurfacegeometry.Thismeansthata2Dnumericalmodelcanbecalibratedwithrespecttoeachdomainusingthetraceofthedamagesurfaceandanassumptionofplanestrain.InFigure6thedamagesurfacehasbeendividedinto7domains.Anarbitraryverticalprofilewithinadomainshouldcapturetherepresentativebehaviourofthedamagesurfacewithinthatdomain.

Figure 6 Footwall domains with respect to the damage surface geometry, front view from the east (upper image) and top view (lower image) along the “average plane”


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ThefootwallwasdividedintodomainsbasedontheorientationofthemajorjointsetsbyRådberg(1991).Incomparisonwiththejointdomainforlevel795mpresentedbyRådberg(1991)anumberofsimilaritiesappeartothedomainsproposedinthispaper,seeFigure6andFigure7.

Figure 7 Joint set domains on level 795 m by Rådberg (1991), top view

TheresultsfrombothRådberg(1991)andthecurrentinvestigationindicatedomainboundariesatroughlyY33,Y39andY44.Theagreementbetweenthetwostudiesisanindicationofachangeintherockmasspropertiesatthesesections.Thenorthernpartoftheminei.e.betweenY15andY33wasnotdividedintodomainsbyRådberg(1991).

5 Conclusions

Usingdamageobservationsconnectedbycontourlinesonmultiplelevelsa3Drepresentationofalargescaledamagezonecanbemade.Astudyofthis3Drepresentationallowedanumberofconclusionsrelatedtothedamagebehaviourofthefootwalltobemade:

• Acontinuousbutcomplexdamagesurfacecanbeapproximatedfrommappedundergrounddamage.ThemovementsinthefootwallcausingthisdamagedoesnotdirectlytransfertothegroundsurfaceassurfacedeformationmeasurementsbyGPSindicatesmallandcontinuousdeformationonly.

• Comparingwithpreviousmappingitisclearthattheprogressionofthedamagesurfaceintothefootwalldoesnotfollowtheminingdepthlinearly.Therateofprogressionseemstobeloweronshallowlevelsthanonlevelsclosertotheexcavationlevel(i.e.deeperlevels).

• Atlevelsabove740mtheobservedfalloutsappearedtobepredominantlystructurallycontrolled.Below740mthedamageseemedtobemainlystressinduced.

• No single failuremode could be discerned from the damage surface.The derived geometricalshapewascomplexwhichwouldindicatethattwoormoremechanismsareactingincombination.

Thesurfaceapproximationshowsaplausiblepositionofanestimateddamagesurface.Thatis,itindicatesthe boundary betweenmobilised damaged de-stressed rockmass and non-mobilised load bearing rockmass. The fact that the delineation results in a relatively smooth surface suggests that this boundaryrepresentsanexplicit zoneof fracturing.Thezoneof fracturing is indicated tobecontinuousandsub-paralleltotheorestrikeanddip.Perpendiculartotheorestrikethezoneseemstobelimitedinwidthwiththeactivityconcentratedinanarrowregion.Itmightthusbedetectablethroughdeformationmonitoring.Amonitoring/measuringsystemshouldbedesigned toconfirmandmonitor thepositionof thedamageboundaryin-situby.

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Acknowledgements

TheauthorsacknowledgethefundinganddataaccessgrantedforthisstudybyLKAB.Thanksarealsodue to Centre ofMining andMetallurgy (CAMM) at LTU.The authors also acknowledge the on-sitecontributionsduringmappingbyLKABstaff, especiallyKarolaMäkitaavola,HåkanKrekula andÅkeÖhrn.FinallytheauthorswishtothankJonnySjöberg(Itasca/LTU)forvaluablecommentsduringthestudyduration.

References

Dahner-Lindkvist,C1992,LiggväggstabiliteteniKiirunavaara(inSwedish).Bergmekanikdagen,pp.37-52),Stockholm:BeFo.

Henry,E,&Dahner-Lindkvist,C2000,‘FootwallstabilityattheLKAB’sKirunasublevelcavingoperation,Sweden’,Massmin 2000, pp. 527-532, Brisbane, Queensland,Australia: TheAustralasianinstituteofminingandmetallurgy.

Hoek,E,&Bray,JW1981,Rockslopeengineering.London:InstitutionofMiningandMetallurgy,358p.

Lupo,JF1996,‘Evaluationofdeformationsresultingfrommassminingofaninclinedorebody’,ColoradoSchoolofMines:DoctoralThesis.

Rådberg, G 1991, Strukturkarteringar i Kiirunavaaragruvans liggvägg, Nivå 795m avv (in Swedish).TechnicalReport:TekniskaHögskolaniLuleå.

Sandström,D2003,‘AnalysisofthevirginstateofstressattheKiirunavaaramine’,LuleåUniversityofTechnology:LicentiateThesis2003:02.

Singh,UK,Stephansson,OJ,&Herdocia,A1993,‘Simulationofprogressivefailureinhangingwallandfootwallforminingwithsub-levelcaving’,Transactions-InstitutionofMiningandMetallurgy,volA102;pp.A188-A194.

Sjöberg, J 1999,Analysis of large scale rock slopes.LuleåUniversity ofTechnology:DoctoralThesis1999:01.

Stöckel, BM,Mäkitaavola,K& Sjöberg, J 2013, ‘Hanging-wall and footwall slope stability issues insublevelcaving’,SlopeStability2013,pp.1045-1060,Brisbane,Australia:AustralianCentreforGeomechanics.

Villegas,T&Nordlund,E2008, ‘Numerical simulationof thehangingwall subsidenceusingPFC2D’,Massmin 2008: Proceedings of the 5th International Conference and Exhibition onMassMining,pp.907-916,Luleå:LuleåUniversityofTechnology.


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Draw control strategy at the New Gold New Afton Mine

A Chaudhary New Gold, Canada K Keskimaki New Gold, CanadaS Masse New Gold, Canada

Abstract

The New Gold New Afton mine is a 4.5 Million tonne per year operating block cave mine located 8 km outside of Kamloops, British Columbia. The current production level at New Afton is the 5070m level and has a mineable reserves base of 48.8 Mtonnes at 0.56 g/t Au and 0.84% Cu, with a lower production level in the planning stage. At its size, New Afton is one of the smaller producing block caving operations in the world, with a long and narrow footprint atypical of most existing caving operations. In 2012, to further optimize the project’s economics, the decision was made to separate the New Afton cave into three distinct areas; west, east and central. The first drawbell in the east cave was blasted in June 2013 forcing the transition from a panel cave mentality in the west cave to a self-contained block cave. This transition was necessary to manage the cave back profile, minimize the potential for rilling of broken cave material and to establish an even draw profile to reduce or delay the effects of dilution entry. Key tools used at New Afton to manage this change in philosophy and to monitor results include: extensive draw point grade sampling and trend analysis, cave monitoring systems such as micro-seismic system and time-domain reflectometry (TDR) cables, and weekly height of draw (HOD) sections developed from daily drawpoint production reporting. A key challenge encountered while transitioning to a block cave draw strategy was the balancing of grade and production tonnes to ensure consistent mill feed material, as well as maintaining draw focus to minimize stress generation on the production level. Verification of cave performance compared to PCBC modeling, tracking drawpoint grade changes vs. column height, and evaluating for dilution entry is still in its infancy at New Afton. These areas continue to be studied to verify cave performance and for model calibration. New Afton’s draw strategy and adjustments so far have proven to be successful. Learnings from the west cave are currently being applied to the east cave in order to ensure healthy cave growth, as well as maximizing resource recovery and project value.

1 Introduction

NewGold’sNewAftonmineisa4.5Milliontonneperyearoperatingblockcaveminelocated8kmoutsideofKamloops,BritishColumbia.The2013yearendreservebaseisestimatedat48.8Mtonneswithaveragegradesof0.56g/tAuand0.84%Cu.NewAftonisatraditionalrubbertiredcavingoperationwith4CATR1600scoopsoperatingontheextractionlevel.Materialfromthecaveisdumpeddownorepassestothehaulage levelwhere7.6m3scoopsand45 tonne trucks transfer it to theundergroundgyratorycrusher.Materialisthenconveyedbya4.2kmlongconveyingsystemtothesurfacestockpile.

In2012theNewAftoncavewasseparatedintothreedistinctareas,thewest,east,andcentralcaves,eachhaving130,203,and56planneddrawpointsrespectively.Theseparationofthethreecavesrequiredtheadaptationofdrawstrategyfromapanel-cavingscenariointhewestcavewheretheoldestdrawpointsarepulledthehardest,toablockcavingstrategywherethecaveisdrawndownevenly.Inordertoachievethis,acompleteswitchindrawstrategywasrequiredtocatchuptheeastportionofthecavetopromotecavepropagationandlocatetheheightofdraw(HOD)peakclosertothecenterofthefootprint.

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2 Cave development

ThewestcaveatNewAftonwasconstructedfromwesttoeast(Figure1),andasdrawbellswereblastedthetonnesproducedfromeachdrawpointsteadilyincreasedovertimetoreachsuitableproductiontargets.Thismethodofcaveadvancementnaturallyproducesa skewedcaveprofileandan inclinedadvancingcaveface.Thiscanbebeneficialandispreferableforlargercavesasolderdrawpointsaredepletedsooner.Therefore,asthecaveadvancesthereisnotanoversupplyofdrawpointsthatcannotbemaintained.ItwasrealizedatNewAftonthatacontinuedunevendrawprofileforsmallercavingfootprintscanpromoterillingofmaterialdownthesteepcaveface,andearlydilutionentrybydrawingoverlyingwastezonessoonerthanotherpartsofthecave.Unevendrawcouldpotentiallyaffectexpectedcaveperformanceandrecoveryoftheoredeposit.Aproactiveapproachwastakentocontinuouslymonitorandmanagethecavebackprofileaswellashavingoutsideconsultantsanalyzedrawcontrolstrategiestoensureoptimumcavegrowth.

Figure 1 New Afton cave footprint (as of April 1, 2014)

3 Draw control strategy

Thedecisiontoadjustdrawstrategytoablockcavescenariowasmadetobalancethecavebackshapeandpromoteevenverticaldrawdownofcolumns.Thedecisionwasmadeafterthecavehadbrokentosurfaceandsubsidencewasobservedonsurfacetoconfirmcavebreach.Atthispointtheentirewestcavefootprintwasconstructedandactivelycaving.Toincreasecavegrowthintheeast,evenoutthesteepcaveface,andminimizepotentialdilutionentry,thedecisionwasmadetobalancethecavebackbyfocusingdrawontheeastside.Attypicaldrawratesrangingfrom15to20buckets/day(126to168tonnes/day)perdrawpoint,severalmonthsofdrawwouldberequiredtobalancethecaveshape.

Key rules followed during draw strategy adjustment included: 1) ensuring even grade and tonnagedistributionbetweenshifts2)ensuringthedifferentialindailydrawbetweenadjacentdrawpointsdoesnotexceed50%,and3)maintainingdrawemphasisonhigherthannormalconvergencedrawpoints

EvengradedistributionwasmanagedbyutilizingdatafromtheextensivedrawpointsamplingprograminplaceatNewAfton.Grabsamplesaretakenfromdrawpointsatvaryingfrequenciestodevelopadatabase


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ofassayresults.Thesegradesaretonnageweightedbetweensamplestoprovideactualdrawpointgradeperformance eachmonth.Draw rateswere adjusted to balance grade and provide consistentmill feedmaterial.Thiswasakeyrequestfromthemillasaconsistentfeedmaterialisbeneficialtomaintainsteadystateoperationsandimprovedrecoveryrates.

AkeyfocusatNewAftonisensuringneighboringdrawpointsdonothavelargedailydrawratedifferentials.Highdrawdifferentialsbetweenadjacentdrawpointscanresultinlocalproblemsofunevenmixingandhasthepotentialtopromotepackingandconvergenceinweakerground.Therefore,maintainingastrictruleforamaximumdailydrawdifferentialof50%betweenadjacentdrawpointshelpsalleviatesuchproblemsandensurestheprogressionofaconsistentcaveshape.

Agroupof6drawpointsatNewAftonhaveexperiencedgreater thannormalconvergenceratesat1 to1.5mm per day ofmovement. It was seen that vertical convergence in drawpoints can be controlledwith increasing therateofdrawbyamagnitudeof50%-100%in thatparticulardrawpoint foraweek.However,thepresenceofhorizontalconvergenceandveryminorverticalconvergencewasseeninthese6drawpoints.Experimentationwithmaintainingsteadydrawrates,increasingdrawintheaffectedareaforvaryingperiodsoftime,andcontinuousdrawonbothdayandnightshiftswasconductedforthisscenario.Itwasnotedthatcontinuousdrawprovedtobethemosteffectiveincontrollinghorizontalconvergence.

4 Cave profile tracking

AtNewAfton,visualinterpretationsofthecavebackarecompletedonaweeklybasisbyutilizingcavegrowthdatafromTDRcablesandseismicactivitydetectedbymicroseismicsensorsinstalledaroundthecave.Datafromtheseinstrumentsgivesindicationsofwheretheseismogenicandfracturezonesofthecavearelocated.TheplottingofinstrumentationdataalongwithHODprofilesallowsforvisualinterpretationofthecavebackandcavegrowthratescanbedeterminedasshowninFigure2.

Figure 2 Cave profile section view

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Throughanalysisof thisdata, itwas found theNewAftoncave followsa4:1growth rate.Thismeansthecavebackgrowsatroughly4xtheHODheight.Locallythiscavegrowthratemayvary,howeveronaglobalscale thisrelationshipgivesagoodindicationofexpectedcavegrowth,approximate timingtobreachsurface,andtimingforinteractionwithanyinfrastructurelocatedabovethegrowingcave.

A4:1cavegrowthfactorisequivalentto25%swellingofthecavematerialasitisbrokenandisindicativeoffracturedandfragmentedcavematerial.NewAftonhasarockmassrating(RMR76)of35-50withsomeareas significantlyweakerdue to clay infill.Themain rock types encountered includeBXF,Mozonite,andDiorite.Thefinefragmentationhasbeenobservedthroughtheoperationof theNewAftoncaveasdrawpointavailabilityhasbeenexcellent.Itisveryseldomadrawpointisunavailableforproductionduetooversizedmaterialrequiringremediationworklastinglongerthanasingleshift.ThishighavailabilityhasmadeitpossibleforNewAftontoreachhigherthanplannedproductionratesastheabilitytomovematerialfromthecavehasnotbeenthelimitingfactor.

In addition, the Cave Management System (CMS) application in PCBC allows for the utilization ofdailydrawdatatodevelopcaveHODmaps.SectionscutalongeachstrikedriveareanalyzedforHODprogression,cavebackestimations,aswellascalculationsforpotentialairgapgeneration.Thesemetricsareanalyzedweeklyanddrawisadjustedinareasofthecavethathasbeenunderoroverdrawn.Weeklydrawcompliancetoplanisalsomeasuredandadjustmentsaremadebasedonperformance.Heightofdrawcontoursandpercentoftotalcolumndrawncontoursarevaluabletoolstoassessoverallcaveshapeandtoplanlong-termdrawstrategies.Figure3showstheprogressionoftheNewAftonHODprofileovertime.

Figure 3 HOD progression tracking

5 Reconciliation process

ThereconciliationprocessisstillfairlyyoungatNewAftonafteronlyoneyearoffullproduction.Thecavehasproducedasplannedinitsfirstyearwhichcanbeexpectedinyoungercavingoperationsasthereislittleopportunitytoseedilutionentry.Coppergradeshavetrackedwellagainstexpectationswhilegoldgradeshaveoutperformedexpectations in2013.The reconciliationprocessgoing forwardwillbecomemore crucial to track cave performance as the potential formixing and dilution entrymay affect caveperformance.PCBCisusedtoreconcilemineassaygradestothepredictedblockmodelgradesandmillfeedgradesshowninFigure4.Toaccomplishthisreconciliationprocess,accuraterecordsoftonnesproducedperdrawpointandassaydataarerequiredtohaveagoodunderstandingofactualperformanceovertime.


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Figure 4 Cave grade performance over time

Overallcavegradeperformancecanbeanalyzedagainstplanandmillperformance.Acloserelationshipbetweenthethreemeasuresisidealtoconfirmcaveperformancetoplan.Ifthesemeasuresbegintodivergethanfurtherinvestigationwillberequired.Issuessuchasunplannedmixing,blockmodeluncertaintyandgradesamplingproceduresaresomeoftheissuesthatcanleadtodivergingtrends.

Anotherkeygraphicdevelopedtoanalyzedrawpointperformanceisagradedifferentialtoplanvs.columnheightgraphshowninFigure5.Thesegraphicsaredevelopedtotrackdrawpointperformanceovertimeandobservetrendsasdrawpointsmatureandclimbtohigherheightsofdraw.

Figure 5 Drawpoint grade performance vs. HOD

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6 Lessons learned

ThroughtheoperationofthewestcaveatNewAfton,manylessonshavebeenlearnedthatwillbeappliedtotheoperationoftheeastcave.Theachievementoftheminimumnumberofdrawpointsrequiredtoreachmaximumproductionratesfromthecaveisakeylearningtobetransferredtotheeastcave.84drawpoints(42drawbells)hasbeendeterminedasthecriticalvaluestoproduce9,000to11,000tonnesperdayfromthecave.Atanaverageexpecteddrawbellconstructionrateof3bellspermonth,earlierblasteddrawpointswillhavearamp-upperiodof14monthstoachievepeakproductionratesof15to20bucketsperday(or128to170tonnesperday).Thisdrawrateisequivalentto450-500mmofcolumndrawdownperday.Asthecaveisdevelopedfromwesttoeast,earlierblasteddrawbellswillhaveahigherHODthanthecenterofthecaveandthenewlyblastedbellsintheeast.Thedrawrateswillbereversedtocatch-uptheHODintheeastoncethenewerblastedbellsreachtheramp-upperiod.Thisstrategywillcontinueuntilabalancedcaveangleisreached.

7 Conclusion

TheNewAftonblockcavehasbeenverysuccessfulthroughitsfirstfullyearofproduction.Thecavehasperformed as expected based onmetal production and as operational efficiencies continue to increase,greaterthannameplatedailythroughputsarebeingachieved.TheNewAftonwestcavewastransitionedfromapanelcavetoablockcavedrawstrategyinordertobalancethecavebackprofile.Thiswasakeymeasuretomaintainlong-termcaveperformanceandtoworkonensuringcavegrowthacrossthefootprint.TheoperationofthewestcavehasprovidedvaluableexperienceonhowtheNewAftonrockmassrespondstocavingandhasprovedtobeverysuitableforblockcaving.Eastcavedevelopmentcontinuesasplannedandamillexpansionto14,000tonnesperdayisunderwaytobettermatchthemineproductioncapabilities.

References

Diering, T 2004, ‘Combining long term scheduling and daily draw control for block cave mines’, inProceedingsMassMin2004,AKarzulovicandMAlvaroeds,22–25August,Santiago,Chile,InstitutodeIngenierosdeChile,Santiago,pp.486–490.

Laubscher,DH1994,‘Cavemining–thestateoftheart’,JournaloftheSouthAfricanInstituteofMiningandMetallurgy,vol.94(10),pp.279–293.


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Caving experiences in Esmeralda Sector, El Teniente Mine

M Orellana Codelco, ChileC Cifuentes Codelco, ChileJ Díaz Codelco, Chile

Abstract

Collapse processes occurred in the Esmeralda sector, particularly those located ahead of the undercutting front during years 2009-2010, did not allowed the mining advance. A new exploitation strategy was created, and two new sector Block 1 and Block 2 were developed as virgin caving. Mining method was defined as conventional Panel caving with hydro-fracturing preconditioning.

To deal with the collapse and rockburst risk due to the stress redistribution during the connection stage, a new operational strategy was designed. New rates for drawbell opening, a new extraction policy and the undercut front advanced taking into account the geological features were established.

Currently, Block 1 is in permanent caving regime and Block 2 is in the connection process. From these two experiences, it is possible to highlight some results related to the mining management under different geological settings. The seismic response and the duration of the connection processes have been modified by the different mining strategies. For instance, a distinct result is the different seismic response in both blocks due to the differences in geological setting and stress field.

1 Introduction

TheEsmeraldaMinecurrentlyextractsa totalof25,000 tpd from theexploitationof threemainareas:Block1,Block2andPanel1.Blocks1and2werestartedaspartofanewexploitationstrategydesignedforEsmeraldaafterthemostrecentcollapsesin2010,andPanel1isbeingworkedtorecoverthereservesfromthecentralcollapsearea.Blocks1and2areindependentsectorsdesignedtostartnewcavingprocessesawayfromtheoldEsmeraldacavity(Figure1).TheexploitationsequencestartswithBlock1,whichcoversroughly43,000m2,andthencontinueswithBlock2,withanareaof41,000m2.

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Figure 1 Historic collapses in Esmeralda Mine. Block 1 and 2 locations

Conventionalpanelcavingistheexploitationmethodusedinbothareas,withpreconditioningofthefirst100mofrockabovetheUndercutLevel.Themethodfirstcompletelydevelopsproductionandundercutlevels,followedbythefiringofdrawbellsand,finally,advancingbyblastingattheundercutlevel(Figure2).

Figure 2 Conventional Panel Caving sequence

ExploitationofBlock1beganinJuneof2011withthefirstdrawbellblasting.CavingstartedapproximatelyoneyearlaterafterconnectionwasmadewiththeupperlevelofTeniente4.ExploitationofBlock2startedinJulyof2012,anditsconnectionprocessiscurrentlybeingcompleted.Thetwoblocks,beingofdifferentgeologicalandstructuralconditionsandwithdifferentstressfields,producedifferentseismicresponsesateachstageoftheconnectionprocess.Theseresponseswillbeexplainedinthispaper.


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2 Geology and Geotechnical Conditions

ThecolumnheightinthesectorwhereBlocks1and2arelocatedvariesfrom650matthewestendto1,000mattheeastend.Thiscolumnheighthasfirst160metersofinsitucolumn,andtherestisbrokenmaterialuptothesurface.

ThelithologyofBlock1isprimarilycompetentrockmassmadeupprincipallyoftheElTenienteMaficoComplex (CMET)anddioriteporphyry.Themain structuresof thisBlock1 faceNEandNNW; thesestructuresareFaultPandFaultsHandJ,respectively.ThequalityofthisrockmassisRegular-GoodontheIRMRscale.

Block2,whichconsistsmainlyofaBrechasunitandCMET,hassmallerstructures.TheLamprofidodikecrossesitanditfacesNE.ThegeotechnicalqualityoftheCMETportionoftherockisRegular,andtheBrechasComplexportionisclassifiedasGood.

Table1showstheaveragevalueofthesector’sstressfieldsthemajorstressinpre-miningconditionandrockmasscharacterization.

Table 1 Geology and geotechnical conditions, Block 1 and Block 2 (Quiroz et al. 2010)

Parameter Block 1 Block 2

LithologyCMET-TenienteMaficComplex

(60%)DioritePorhyry(40%)

CMET-TenienteMaficComplex(55%)Brecciacomplex(40%)

Tonalite(5%)

UCS[Mpa] 130 145Majorgeological

structures PFault,JFault,HFault LamprofidDike

Structurefrecuency(ff/m) 0.28-0.29 0.37inCMET

0.26-0.31inBrecciaGeotechnicalquality

(IRMR) 1-3 (Good-Regular) 3CMET(Regular)2Breccia(Buena)

Columnheight 650-750 800-1000S1/S2/S3

Dip/dipdirectionS140/36/21353/30

43/34/20202/20

3 Block cave parameters

3.1 Mine design

Thetwoblocksareverysimilarintermsofdesign.Thedrawlayoutis15x20mthroughoutBlock1,whilethenorthhalfofBlock2hasa15x20mandthatofthesouthhalfis15x24m.Thereasonforthisdifferenceindesignistoincreasethesafetyfactorandstrengthofthepillars.Bothblockshaveasimilarfootprint:280x200minBlock1and240x200minBlock2.

The undercut level is 14m above the extraction level.The exploitationmethod usedwith the blocks,conventionalpanelcaving,useshigh16mundercuttinganda2mburden.Thedrawbellsareopenedintwostages,andtheundercuttingblastsaredonethreeringsatatime.

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Thepreconditioningdesignusesascendinghydraulicfracturingoneachblock’sentirefootprinttomitigateseismicriskandtopromotecavingpropagation;theperforationsaremadeina40x35mmesh.Downwardpreconditioningisalsobeingdoneonanexperimentalbasis,with70mholesdrilledfromtheproductionleveldownwardsinordertodiminishtheseismicresponseduringthecavingprocess.

3.2 Mining sequence and extraction strategies

InBlockCavinginprimaryrock,theinitialexploitationphaseisoneofthemostimportantintheprojectsince it influences themacrosequenceof theexploitationof thearea,cavingpropagation,and the timeneededtoconnecttoupperlevels.ExploitationofbothblocksbeganattheNEendandprogressestowardtheNWsothat theundercutfrontadvancesperpendicular to thestructuresandmajorfaults(FaultP inBlock1andtheLamprofidoDikeinBlock2).

Asminingprogresses,thestrategyistoopen4x5drawbellsoveranareaofapproximately12,000m2thatisopenandavailableforextractiontobeginofcavingpropagation.

WhenBlock1exploitationbegan,theaveragemonthlyadvanceratewas1,800m2/month,asshowninFigure3.InJune2012,thisratedroppedtothecurrentaverageadvancerateof1,000m2/monthinordertominimizetheseismicriskgeneratedbytheincreaseinseismicfrequencyandthehigh-magnitude,high-energyeventsthatoccurredatthebeginningofthecavingprocess.InBlock2,incontrast,areaswerefirstopenedatadvanceratesofroughly700m2/monthsinceitwasexpectedthatgreaterstressfieldswouldbeproducedintheareaduetothehighercolumnandthepresenceoflithologicalcontacts.Sincelittleseismicactivitywasrecordedinthearea,thisadvanceratewasincreasedto1,200m2/monthinApril2013.

Different extraction strategieswere established for the twoblocksbasedon the caving stages: prior toreachingthecriticalareatostartcaving,andoncethecriticalareahasbeenreached.Thedrawbellsareincorporatedintoproductionataextractionrateof0.1or0.2tonsperm2perday.Oncethecriticalareatostartcavinghasbeenreached,therateincreasesbasedontheheightatwhichextractionistakingplace.

Dailyextractionfrombothblocksincreasesastheamountofproductiveareaavailableincreases.Block1currentlyhasadailyextractionrateof16,000tons,withanopenareaofsome40,000m2available,whileBlock2extracts3,000tpdwithanopenareaof10,000m2available.

3.3 Cave initiation

Therearevarioustheoriesastohowthedifferentconfigurationsofstressmagnitudeanddirectioncausecaving(cavingmechanisms).Coates(1981)suggeststhattherearetwodifferentmechanismsthat,actingindependentlyor together,causecaving tostart:horizontalstress traction in thecenterof theundercut,andhighsubverticalcompressionstressatthecornersoftheundercut.Cavingbeginswhenthesestressesexceedtherock’sresistance.HeslopandLaubscher(1981),ontheotherhand,proposethattherearetwofaultmechanismspresentincavingpropagation.Thefirstisknownas“stresscaving,”whichinvolvesthecombinationofflatdippingdiscontinuitiesthatcaveduetoshearinginhighcompressionstressfields.Inthesecondmechanism,called“subsidencecaving,”asolidrockmassquicklycavesinduetoshearstressneartheverticaledgesoftheblock.Thisoccursbecausethenormalstressactingontheedgesoftheblockislowerthantheslideresistancecreatedalongthelengthoftheseedgesandisnothighenoughtosupporttheblock.

Thesephenomenaareseenindirectlyinseismicactivity,asseismiceventsinvolvethebreakageofrockmassesduetoconcentrationofstress.Forexample,whenminingontheseblocksbegan,intenseseismicactivitywasseenneartheproductionlevelandaroundtheundercutfrontasstressaccumulatedatthebaseoftheblockswhenasmall,unstable,dome-shapedcavitywasformed.


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Then,whenhigh-levelcavingpropagationbegan,theconnectionprocesswasaccompaniedbyanincreaseinseismicactivityinthepillar,whosethicknessdecreasedasthecavitygrew.Stresswasredistributedoverthebasesofthecavityasaresultoftheconnection,makingitimpossibleforthestresstocontinuetobetransmittedthroughtherockmass.

3.4 Seismicity induced by the caving process

Thestagesofacavingprocessmaybeidentifiedfromthefrequencyofseismicactivity,thelocationoftheseismicevents,andthecharacteristicsofrelevantevents.Thefollowingtopicsdetailthebehaviorofeachoftheseparametersforthedifferentstagesofcaving.

3.4.1 Seismic frecuency

TherewasrelativelylittleseismicactivitywhenminingbeganatBlock1,averaging10eventsperdayfromAugust2011toFebruary2012,asshowninFigure4.Theactivitywaslocatedchieflyneartheproductionlevelandaroundtheundercutfrontasaresultofabutmentstress.Aslittleseismicactivitywasrecordedathighaltitude,anextractiontestwasdoneonceanareaof4,700m2hadbeenopenedup.Extractionratewasfirstincreasedto0.4m2/dayandwaslaterhaltedtoavoidtheriskofairblastsincetheslightseismicactivityrecorded,evenwithincreasedextraction,indicatedthattherewasnotyetenoughopenareatocausecaving.

Figure 3 Cumulative area, monthly area and extraction per day, Block 1 and Block 2

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AseismicactivationprocessbeganinApril2012,withfrequencyincreasingtoanaverageof20eventsperdayandpeakingat30to40seismiceventsperday.Thisactivationwasassociatedwithanincreaseinthecavedareaand,therefore,toanincreaseinthedestabilizedarea.Duringthispre-cavingstage,whichoccurredfromApriltoJune2012,theseismicactivitywaslocatedatthecavefrontand,particularly,clustersbegantoappearinthewesternsectorofBlock1,specificallybetweenfaultsJandH,whichmanifestedinhigh-altitudeseismicevents.

Thefirstoverallpeakofseismicfrequencyof200eventsperdaywasobservedattheendofJuly2012,withanextractionareaof13,500m2andacavedareaof16,000m2.Itwasatthistimethatthefirstevidenceof breakthroughwith the upper level ofTeniente 5was observed, 90m above the production level atEsmeralda.

Seismicactivityremainedhighaftertheblock’sfirstbreakthroughwithTeniente5,withsome100eventsoccurring per day.The second overall peak of 350 events/daywas reached inOctober 2012when theconnection wasmade between Block 1 and the Teniente 4 level located 160m above the Esmeraldaproduction level.At that time, the seismic activity was occurring chiefly between the Teniente 5 andTeniente4levels.Block1wasdeclaredconnectedwithatotalofalmost24,000m2cavedandanestimatedconnectiontimeof15months.

AftertheconnectionwithTeniente4wasmade,seismicactivitybegantodeclineconsiderably,downtoanaverageof40events/dayduringDecember2012.

Figure 4 Total seismic frequency, seismic frequency above UCL and seismic frequency below UCL in Block 1 and Block 2


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MiningatBlock2beganinJuly2012withthesameseismicfrequencyasatthestartofBlock1,anaverageof10eventsperday.However, inOctoberof2011,beforeminingstarted in thissector,seismiceventsreachedapeakof50events/dayasaresultoftheconnectionmadeinBlock1.Thisindicatedthatthetwoblocksarenotcompletelyindependentfromeachotherdespitethefactthattheyaresome200mapart.

Thefirstsignofhighaltitudecavingpropagationwasapeakof40seismiceventsperdayrecordedattheendofJuly2013.ThefirstsignsofconnectionwiththeTeniente5levelwereobservedonthisdate,withapproximately10,000m2caved.

Finally,theconnectionwiththeTeniente4leveloccurredinOctober2013with14,000m2cavedandanestimatedconnectiontimeof15months.

AsshowninFigure5,Blocks1and2underwentsimilarprocessesintermsoffrequencyofevents,withbothconnectionprocessesexperiencing increasedseismicactivity throughout thebreakupof thecrownpillar.Afterwards,seismicactivitydecreasedbacktoastateofequilibrium.However,seismicactivityinBlock1wasmuchgreaterthaninBlock2,probablybecausethehighlevelofstressduetoagreatercolumnheightandincreasedfracturingfrequencygaverisetoconditionsfavorableforcaving.

Ittookapproximatelyfourmonthstobreakupthecrownpillarineachofthetwoblocks,startingwiththetimethefirstpeakinseismicactivitywasrecordeduntiltheblocks’seismicactivityreturnedtoequilibrium.

3.4.2 Rock bust and event magnitude greater than 1.

Regardingtherockburststhatoccurredwhileconnectingthetwoblocks,itisimportanttonotethatthereweretworockburstsatBlock1:thefirstwhileexpandingacavity,andthesecondwhileconnectingacavitytotheupperlevelofSouthTeniente4.OnerockburstoccurredatBlock2whileconnectingthatblocktotheupperlevel.

Table2presentsasummaryofthelinearmetersdamagedbytherockbursts.Thedamagecausedbyrockburstsrangesfromminorspallingofshotcretetoprojectionofrockmass.Block1suffereddamageintheventilationlevelandhaulinglevel,whiletheBlock2burstcauseddamageattheproductionlevel,butnotatthelowerlevels.

Table 2 Lineal Damage in meters by Rock Burst, Block 1 and Block 2

LevelBlock 1 15-05-12 Block 1 29-09-12 Block 2 22-10-13

Heavy Moderate Minor Heavy Moderate Minor Heavy Moderate MinorUndercutProduction 45m 10m 70,5m 22m 103m 125mVentilation 103mBegincavingexperiencesinEsmeraldaMine,DivisionElTeniente.Transporting

7m 284m 12m 40m

Total 439m Total 133m Total 250m

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Theseismiceventsthatdamagedthetunnel’ssupportandrockmassweretriggeredbyminingblasts.Table3showsthedateandtimeoftheblastsandtheseismiceventsaswellasthetimelapsebetweentheblastsandtheseismiceventsconnectedwiththerockbursts.

Table 3 Date and time after blasting to triggers the rock burst, Block 1 and Block 2

Block Date Magnitude Distance to blasting Time since blasting1 15/05/2012 1,7 29 0:001 29/09/2012 2,1 71 3:38

2 22/10/2013 1,9 29 0:00

Figure5showtheblastinglocationsandtheseismiceventsofeachrockburst.

Figure 5 Event and blast location that triggers the rock burst

Eventswithmagnitudesgreaterthan1occurredinbothblocks,35inBlock1and26inBlock2,triggeredbyundercutanddrawbellblastsandbycavingpropagation,80%inBlockand46%inBlock2wasregisteredinthefirst24hourssincetheblast.Figure6showsplanandcross-sectionviewsoftheseismiceventsequaltoorgreaterthan1inBlocks1and2.


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Figure 6 Event magnitude great tan 1 Plant View and cross cut

3.4.3 Breakthrough and cave back

The cave back is estimated using information of seismic activity, inspections drillholes, subsidenceinspections,andthelithologyandgranulometryatextractionpoints.

ThefirstevidenceofexpansioninBlock1cavingwasrecordedattheendofMay2012.ThisexpansionwasassociatedwithanincreasedfrequencyofseismiceventsandwasfurtheredbythefactthattheHandJstructuresaresituatedsemi-paralleltotheundercutfrontofBlock1.

TheseismicactivityrecordedatBlock1afterthefirstevidenceofgrowthinthecavitymanifestsincreasedcollapse,spreadinginaltitude,andanincreaseinthecollapsedareaatthealtitudeofTeniente5.Figure7showstheestimatedcavebackattheendofMay2012.

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Figure 7 Estimated cave back of Block 1 at May 2012.

AfterthisexpansionoftheBlock1cavity,seismicactivityincreasedfrom10to20events/day.Afirstpeakof200events/daywasreached,followedbyanewpeakof350events/day,evidenceoftheincreasedheightoftheBlock1cavity,whichwasconnectedinNovember2012(Figure8).

Figure 8 Estimated Cave Back of Block 1 at November 2012


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Exploitation ofBlock 2 began in July 2012,with the opening of thefirst drawbell.The block did notgenerallyprovideevidenceofhigh-altitudecavingthroughseismicityuntiltherecordsbegantoincreaseinJuly2013,whenavarietyofdifferentseismicpeaksoccurred;thiscontinueduntilNovember2013.Aftertheconnectiontotheupperlevelwasmade,seismicactivityreturnedtoequilibrium(Figure9).

Figure 9 Estimated Cave Back of Block 1 at November 2013

Ittookatotalof15monthsforbothblockstobeconnectedtotheupperlevel,andthedifferencesbetweentheareasthatcavedinduringthisprocessarerelevant.AtBlock1,anareaof24,000m2wascavedtomaketheconnectionwiththeupperlevel,while14,000m2wascavedatBlock2.Thesedifferencesinrateofcavingpropagationhavetodowiththeblocks’stressconditionsandthequalityoftheirrockmass;Block2hadmoresoftfracturesandgreaterinsitustresses,resultingincavingpropagationoverasmallerarea.

Figure10showthedifferencesbetweenBlocks1and2regardingthecavebackheightsandthetonnageextracted,aswellastheopenareaneededtoreachcavebackheightsofapproximately160m.Theyalsoreflectthedifferencesbetweenmayorandminorprincipalstressof19MPatoBlock1and31MPatoBlock2.

Figure 10 Caving area and draw tones vs Cave Height, Block 1 and Block 2

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4 ConclusionsSeismicactivity,intermsoffrequencyandrelevancyofevents,reflectsthedifferentfeaturesoftheinitialprocesses,breakthroughs,andtheregimesforcavinginvirginareas.TherewasseismicactivitybeforetheconnectionwasmadewiththeTeniente4andTeniente5levels,reflectedinfrequencypeaksofroughly200events/dayinBlock1and40events/dayinBlock2.Rockburstsalsooccurredpriortoconnectionduetotheconcentrationofstressesgeneratedbytheconnectionprocessintheareaoftheundercutfrontandunderneaththeproductionlevel.Therefore,itisimportantthat,priortoandduringtheinitiationofthecavingconnection,miningactivitybedecreasedintermsofm2blastedpermonth.AnexampleofthisiswhatoccurredinBlock1,wheretheundercuttingratehadtobereducedfrom1,800m2to1,200m2permonth.

Different area were needed for the two blocks to be connected due to the geological and structuralcharacteristicsofeachsector.InBlock1,theHandJstructureswereinstrumentalinbeginningthecavingpropagationprocesssincethereleaseofthesestructuresproducedafreefaceformaterialtocollapse,thusgeneratingasortof“topplingfailure”towardtheeastsector.Thesestructureswereactivatedandbehavedastheydidduetotheeffectofthefront’sadvancewithrespecttothesefailures(sub-parallel)combinedwiththedirectionofthemainstressesthatcausedtherelease.ThePfault,incontrast,didnotinfluencethecavingpropagationprocesssincetheundercutfrontadvancedperpendiculartothefaultand,togetherwiththedirectionofthelargestmainstress,keptthisstructureconfined,whichcausedittorespondwithsignificantseismicevents.

ThecavingprocessinBlock2wasmorebenignthanthatofBlock1intermsoffrequencyandrelevanceofseismicevents,probablybecausetheintensestressesgeneratedbythehighercolumnandmorefrequentfracturescreatedfavorablecavingconditions.

Bothcavingprocessestook15monthsfromthefirstdrawbellblastuntilconnectionwasmadewiththeupperlevel,comparedtothepreviousexperienceatNorthEsmeralda,whichtook46months.Thistimedifferenceinconnectionmaybeexplainedbythefactthathydraulicfracturingwasusedwiththeblocks.

InbothBlock1andBlock2,fourmonthsofseismicactivitywasrecordedthatreflectedtheincreasingheightof the cavity as the crownpillarwasbroken,withpeaksof200 to350eventsperday.Seismicactivitylaterreturnedtoafrequencyoflessthan30eventsperday.

Thethreerockburstsintheblocksweretriggeredbythecavingorundercuttingblasts,andtwoofthethreerockburststhatoccurredduringtheblocks’cavingconnectionprocesswererecordedduringtheseismiceventpeaksand,therefore,whileconnectionwasbeingmadewiththeuppercavity.WhileblastingwashaltedatBlock1duringthisprocess,thiswasonlydoneduringtwoweeksandaftertherockburstthathadoccurredinBlock1.Duringseismicpeakscausedbycavingpropagation,theundercuttingratemustbedecreasedtokeepfromtriggeringmajorseismicevents.

ReferencesQuiroz, R,VegaH,CuelloD,CifuentesC,QuezadaO,Millán J&BarrazaM 2010, ‘Esmeralda Sur

definicionesdecrecimiento’,InformeInternoDPL-I-2010.

Coates, DF 1981, Rock Mechanics Principles. Monograph 874, pp. 5-1 to 5-37, Energy, Mines andResources,Canada.

Cuello,D,CavieresP,CifuentesC2011,‘InformeLineamientosGeomecánicosparaPlanificaciónMineraBloque1MóduloA,ProyectoEsmeraldaSur’,InformeInternoSGM-I-006/2011.

Diaz,J,CifuentesC,OrellanaM2013,‘Backanálisisconexióndebloque1,minaesmeraldasur’,InformeInternoSGM-NI-10-2013.

Diaz,J,CifuentesC,OrellanaM2013,‘TTAB,EsmeraldaBloque1’,PresentaciónInterna.


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Heslop,TG&Laubscher,D1981,‘DrawControlinCavingOperationsonSouthernAfricanChrysotileAsbestosMines’, Design and operation of caving and sublevel stoping mines, NewYork,(Ed(s):D.Stewart),755-774,SocietyofMiningEngineers-AIME.

Millán,J2010,‘Antecedentesgeológico-geotécnicoentreXC-Acceso3yXC-Acceso4,MinaEsmeralda’,SGL-I-083-2010.

Millán,J&GonzalezF2011,‘Antecedentesgeológicosygeotécnicosdeláreaaincorporarelaño2012(P0)’,SGL-I-072-2011.

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Undercut advance direction management at the North 3rd Panel, Rio Blanco Mine, División Andina Codelco Chile

L Quiñones Codelco, ChileC Lagos Codelco, Chile F Ortiz Codelco, ChileE Farías Codelco, ChileL Toro Codelco, ChileD Villegas Codelco, Chile

Abstract

The Rio Blanco Mine, property of Codelco Chile and one of the largest copper deposits in the world began the mining operation of the 3rd Panel in 1997 by Panel Caving with conventional undercutting and LHD extraction. This panel has an overburden of 500 meters and a low-medium stress regime. The main topic treated in this document is the Undercut Advance Direction Management at the North 3rd Panel, where the Design and Ground Control of front cave are the key factors for a successful operation.

1 Introduction

TheRíoBlancoUndergroundMineislocated50kmNorthofSantiago.Theextractionprocessbeganin1970withthe1stPanel,whichwasmineduntil1982usingBlockCavingwithgrizzlytreatment.Between1982and1997the2ndPanelwasextractedusingthesameminingmethod.Bothpanelsweresituatedinsecondaryrock.

Themineoperationofthe3rdPanelstartedin1997byPanelCavingwithconventionalundercuttingandLHDextraction.Itisplacedinprimaryrock,withanoverburdenof500metersandalow-mediumstressregime.Subsequenttoasouthwestcavingadvance(1997to2004),theminingprocessstoppedforeightyearsinthearea,continuingattheNorthzone.Thereareplanstocontinuewiththeexploitationinthissectoruntil2017.

ThemaintopictreatedinthisdocumentisthefrontcaveorientationcontrolattheUndercutLevelanditsusageasaninstrumenttoreducegeotechnicalproblems,emphasizingDesignandGroundControloffrontcaveaskeyfactors.

2 Risk reduction in Panel Caving

With theacquiredexperienceand thesituations thatoccurredduring theyearsofexploitationof the3ºPanel,therearefactsthatmustbetakenintoaccountwhenitcomestoreducingriskintheexploitationofaPanelCaving.Theseare:

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2.1 Front Cave orientation considering the most important geological structures.

Inorder toorientate theFrontCave in relationshipwith thegeological structures, twoaspectsmustbeconsidered:

1. TheFrontCavemustnotbeorientatedparalleltothemainstructures.InthecaseoftheIIIPanel,anangleofN40ºEhasbeendefined,asshowninFigureN°1.

Figure 1 Front Cave orientation in relation to main structures, Production and Undercut levels

2. Faults or discontinuitiesmust not be left immediately after the FrontCave because this couldcausecollapsingofthebrow.Inthiscase,twooptionscouldbeconsidered;eitherthecavinghastobestoppedatareasonabledistancebeforethediscontinuitytoavoidactivation,orithastobecontinuedbeyondthediscontinuityinordertohavetheproblematicareablasted,thuspreventingtheactivationofthestructure(Figure2).

Figure 2 Structural condition in the blast N°11, GH-81, March 2014

2.2 Principal stresses orientations

Themainstress(s1)isorientatedinanE-Wdirection,whichisrelatedtothepresenttectonicregime.FrontCavingparalleltos1hasnotbeendoneandthusitsmaximumaugmentationhasnotbeenobserved.

Ontheotherhand,ifblastingisdonewithlongspacingbetweenfronts,aconcentrationofstressintheedgesmaybeobserved.

Toavoidtheseconcentrations,whichcouldendangerthestabilityofthefront,thespacingmustbenomorethan5ringholes(Figure3).

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Figure 3 Spacing between Ring Holes

2.3 Rate of the undercut advance

Theundercutadvanceratedependsonthemonthlyareatobeblasted.Thisratevariesbetween1,000m2a1,500m2,allowinganannualundercutadvanceof10,000to15,000m2,alwaysmaintainingthedefinedangle(Figure4).

Figure 4 Layout of undercut from January to December (2014)

2.4 Rock mass Preconditioning of primary rock

TheuseofHydraulicFracturingandConfinedBlasting(DDE)methods,allowsdecreasingoftheprimaryfragmentationsizeandincreasingofthecavingpropagationspeed.


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2.4.1 Safety zone built behind the front cave

In2004,therewasacollapseinthesouthern3rdPanel.Afteranalyzingthecauses,asecuritybandwasdefinedatacertaindistancefromdeFrontCave.Thelengthofthisbandwasdeterminedbyconsideringtheabutmentstresseffect,whichinthiscasehasbeenestimatedin50m.ThebandneedstobecompletelybuiltandfortifiedbeforethebeginningofthecavingprocessanditmustbemaintainedduringtheadvanceoftheFrontCaving.ThisproceduremustbecontrolledmonthlyasshowninFigure5.

Figure 5 Monthly safety zones in the Production Level

2.5 Support design for Production and Undercut Level

AsupportsystemdesignforthePanelCavingmethodisdefinedforthemostcriticalcondition,whicharetheFrontCavingadvanceandtheabutmentstresseffectonthedriftintheProductionLevel.

Forthispurpose,astandardfortificationhasbeendefinedforthecavingandProductionLevels,aspresentedinFigureN°6.Thisfortificationisdeterminedbasedonthegeomechanicalevaluationofthebehaviorofthesupportingstructureduringthecavingprocess.

AlthoughthedesignconsiderationsareimportantforsuccessfulcavinginaFrontCavingadvancewithcontrolledrisk,groundcontrolisofvitalimportance.Forthisparameter,thefollowingaspectsneedtobeconsidered:

2.6 Brow damage at the Undercut Level

The damage produced in the brow by the detonation is evaluated after every blast with the objectiveof eliminating the risk of falling blocks,which could endanger operators.This evaluation is informedaccompaniedbyroofcontrolanddamagedsupportingstructurereplacementrecommendations.

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2.7 Detentions of the extraction in the front cave

Onceanareahas started itsproduction,extractionmustgoonaccording to theproductionprogram. Iftheprogramisnot followed,pressurepointsmightbeoriginated,whichwouldresult incollapse in theProductionLevel.Tocontrolthisrisk,theProductionLeveliscontinuallyinspected,amonthlycontrolofextractionandextensometermonitoringareusedasanalertsystem(FigureN°7).

Figure 7 Accumulated extraction rate, January 2014. The black line indicates the advance of the front cave

2.8 Extensometer monitoring.

Presently,theProductionLevelhasamonitoringsystembasedonextensometers,eachonewithmeasuringpointsatadistanceof1,3,5and7m.Theextensometersareinstalledatproductiondrift.Atotalamountof10extensometersaredistributedinapproximately10,000m2.Theseareconnectedtoadataloggerfordatatransmission(FigureN°8).

Figure 6 Support design for Production Level and Undercut Level


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Figure 8 Displacement of extensometer by Undercut advance

2.9 Seismic activity in the front cave

Since2005,inducedseismicityismonitoredbyanISS30channelseismicsystem.Thismethodhasbeenusedtosupportoperationaldecisionsinfrontcaving.Thesystemismodifiedannually,asthefrontadvanceswiththeinstallationofnewsensorsatamaximumheightof40m.Resultsindicatethatseismicityismainlyassociatedtostructureactivationandcavingpropagationattheundercut,asshowninFigureN°9.

Figure 9 Seismicity in structures at the Undercut Level

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Themagnitudeof the seismicmomentmeasuredup tonow showsvalues in the rangeof -0.2 and0(Figure9).

2.10 Crater advance

Thecrateradvanceismonitoredeveryyearbyaerialphotography.Thissystemallowscalibrationofthesubsidencemodelusedtoestimatethecrateradvanceinthefutureyears.

3 Conclusion

Undercutmanagement,withreductionoftheassociatedrisk,isacomplextaskthatrequirestheconsiderationofdiversevariablesanddisciplines,likegeomechanics,geologyandgeophysics.Theseaspectscontemplatefromdesigntogroundcontrolandinstrumentationmonitoring.

InDivisiónAndina,amethodologythatconsidersallthepreviousaspectsrelatedtoasuccessfulcavingoperationhasbeenimplemented,assuringtheviabilityofproductionandreducingassociatedrisks.

Acknowledgements

TheauthorswouldliketothankCodelcoChileDivisiónAndinaforallowingthepublicationofthispaper.

References

Brady,BHG&Brown,ET2004,RockMechanicsforUndergroundMining,3ndedition,Chapman&Hall;London.

Brown,ET2003,BlockCavingGeomechanics.TheInternationalCavingStudyStageI1997-2000.JKMRCMonographSeriesinMiningandMineralProcessing3.

Hoek,E&Brown,ET1980,UndergroundExcavations inRock. InstitutionofMiningandMetallurgy,London.

Hoek, E 2006, Rock Engineering, Course notes.Available from <http://ww.rocscience.com/education/hoeks_corner>.

InstituteMineSeismology(IMS)–JMTS2012,Análisisdeinformaciónsísmica,Australia.

InstituteMineSeismology(IMS)–JDIv5.02012,Visualizador tridimensionalde informaciónsísmica,Australia.

LagosC,ToroA,Diario-SGEOM_MS_27Nov2012,‘Informegeomecánicodiario,Nivel16Hundimiento’.

MerinoA,QuiñonesL2009,‘PlandeInstrumentaciónGeotécnicaIIIPanelLHD.Años2009-2016’,NotaInternaGRMD-SGEOT-063-09.

Ortiz,F,Gallardo,G2012,CaracterizaciónGeotécnicaáreas18y19,SectorNorteIIIPanel.

Soto,C,Merino,A,Quiñones,L,OrtizF,2009,‘RevisiónGeomecánicaProgramaQuinquenaldeObras2009–2014,MinaSubterránea-IIIPanelMinaRíoBlanco’,NotaInternaGRMD-SGEOT-120–08.

Villegas,D2013,CartillaNo.1,PrimeraSemanadeFebrero2013.


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New growth strategy in Esmeralda Mine

N Jamett Codelco, ChileRQ Alegría Codelco, Chile

Abstract

Esmeralda mine is the third major panel caving fully developed in primary ore at Mina El Teniente (CODELCO). Conceptual engineering was performed during the years 1992 and 1993, mainly based on the experience gained from the exploitation of former sectors along with geomechanical and mine design knowledge of that time. Esmeralda is one of the main productive sectors of the Mina El Teniente, with reserves of 205 Mt @ 0.92% Cu located on a footprint of 629,000 m2. Design for production goal of 45,000 tpd and applying mining method Advanced Panel Caving, CODELCO began its undercut process in 1996 and then drawbells blasting a year later in September 1997.

At the beginning of 1999 with 12,000 m2 of open area, Esmeralda experienced the first connection process at higher levels, which added to the fulfillment of production targets for the year 2000 and which established a high quality in its operation and big expectations for the future. However, since 2001 an instability phenomena of collapse type began to manifest itself, which continued until late 2004 causing loss of galleries and infrastructure, totaling 26,600 m2 of collapsed area behind the undercut front.

Given this situation in terms of active area and its own production capacity, it was impossible to achieve the production goal and another, new growth strategy of undercut process was put in place in August 2008. However, in December of that year, the instability phenomena began that continued until 2010, causing a full stop of the process and a loss of galleries and infrastructure equalling 30,605 m2 of loss area but this time ahead of the undercut front.

Given this critical scenario, in 2010, a new task force generated a robust proposal allowing to resume and continue the growth of Esmeralda mine towards the production goal. As a result, a sequence of exploitation with smaller fronts (blocks) and a change in the mine design, mining method and growth macro-sequence commenced, that is, Conventional Panel Caving plus preconditioning (hydraulic fracturing) and an orientation of the undercut front as a function of relevant structures and lithology, including updates of the mine plan.

In July 2011, growth activities commence based on this new strategy, giving good results and also continuity in the growing process, achieving the milestones and targets established in the mining plan. Currently, two blocks are being excavated simultaneously reaching 30,000 m2 of open area.

1 Introduction

Thecollapseprocesseslastedfrom2004to2010,particularlythosegeneratedaheadoftheundercutfrontbetween 2009 and 2010,which compromised themine plan due to the impossibility ofmining in theaffectedsectors.ThisdamagecausedafullstopoftheblastingprocessofnewdrawbellsandstoppedalltheactivitiesrelatedtogrowthandsustainabilityofEsmeraldamine(openareaandavailablereserves).Inresponsetothiscriticalsituation,aprojectteamcomposedofexpertsoftheDivisionwasestablishedwiththeaimtodefineaplanofactiontoreturntothegrowthtrajectoryinitiallygenerated.Asaresult,anewoperatingstrategythatinvolvedradicalchangestotheoriginalprojectintermsoftheminigmethod,minedesign,macro-sequenceandoperationalpracticeswasgenerated.Thisisthe“EsmeraldaSurProject”,whichgenerallyconsistsofaseriesofmodules(blocks)thatareoperatedindependentlybyapplyingPanel

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CavingConventionalplushydraulicfracturing(FH)methodtoallrockmassbeingmined.ThefirstblocktobeincorporatecorrespondstoBlock1,whichhasanareaof43,[email protected]%Cut.

2 Objectives

Theobjectiveofthispaperistodescribethemostrelevantaspectsofdesigncriteria,scope,implementationandoperationoftheProject“EsmeraldaSur”toexcavateBlock1.

3 Background

3.1 Geology

EsmeraldaMine is located in a sector composedmainlyof andesite rock and a series of intrusive andgeological structureswith preferential trendNE and subordinatelyNS toNNW (Figure 1).Block 1 ismainlycomposedbyrocksof three lithologicalunits:MaficComplexElTeniente(CMET)andDioritePorphyry,andsmallbodiesofigneousandhydrothermalbreccias.TheCMETcorrespondstoavolcano-plutoniccomplexconsistingofbasalts,andesites,gabbrosandjoints.TheDioriteHworDioritePorphyrycorrespondstoafelsicintrusiveintermediatecomposition,whichhasbeenassociatedwiththedevelopmentofmagmaticbreccias (igneous) andhydrothermal.Related to the locationof felsic intrusiveHw, therearedifferentbodiesofigneousandhydrothermalbreccias,someofwhichareassociatedwithhighgradecoppermineralization.Twomainstructuralsystemsaredefined;thefirstcorrespondstoPFaultSystemandLamprophyreDikewiththemainbranchofthissystemcorrespondstothePFaultandhasapersistenceestimateof900minthehorizontalandfillingsoflowcohesion.ThesecondsystemisNS-NNWsystem(JFault andLatitaDike).The failuresof this systemhavecloaks that tend tobe subvertical andfilledofanhydrite,molybdenite,carbonates,borniteandplaster.Inthisdomain,thepresenceoftheJFaultisdominant,whichhasanestimatedextentabove300mhorizontallyand200mvertically.

3.2 Stress State

Regardingthestressstate,theinformationisintegratedfromin-situstressmeasurementsandtheuseofathree-dimensionalnumericalmodeltointerpolateandextrapolatethestresstensorinthoseareasofinterest,wherenoinformationistakenin-situ(Table1).

4 Strategy & Macro-sequence

Afteraseriesofanalyzesandstudiestheoptiontoresumegrowthundertheconceptofoperatingmodules(blocks)inordertodecouplefromalargesinglefronttosmallerfronts(Figure2)wasdetermined.TheminemethodestablishedwastheConventionalUndercutDesignwithHydraulicFracturing(FH)appliedtotheentirerockmasstobeincorporated.Inaddition,theorientationofgrowthintheseblockshadtotakeintoaccountthemaingeologicalstructurestoavoidorreducetheparallelismpreviouslyexperiencedonthesinglefront.Finally,itwasdecidedthatallfutureminingwillbe“undershadow”,i.e.underTeniente4Sur,theoldsectorlocatedaboveEsmeralda.

InthecaseofBlock1,thedirectionfrontwouldbeSouth-WestinordertofacedirectlythePfaultsystemthroughagrowthperpendiculartoit.Thisstrategyalsosoughttobetteraddressthedioritecomplex,togetherwiththeJandHfault,andthusensuretheconnectionofcavinginthemostcomplexareatowardhigherstabilitymorefavorablespread.Inaddition,forthefirstblock,negativeFHwasmadefromproductionlevelonfailuresandrelevantlithologiccontacts(Pfault,dioriteHw).


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Figure 1 Esmeralda Mine Geology and Location of Esmeralda Sur Block

Table 1 Tensional pre-mining field for the first blocks in Esmeralda

Block Principal Stresses Magnitude Trend Plunge

Block 1 (Hw)S1 40 353 ± 30 1 ± 30S2 36 257 ± 25 5 ± 25S3 21 123 ± 40 81 ± 20

Block 3 (Central)S1 43 202 ± 20 9 ± 23S2 34 112 ± 40 5 ± 10S3 20 353 ± 8 80 ± 20

Block 2 (Fw)S1 50 191 ± 13 10 ± 12S2 31 95 ± 16 16 ± 17S3 19 327 ± 23 65 ± 9

5 Mine Plan

ThemineplanforEsmeraldaSurwasdefinedforeachblock independently, integratingall informationobtainedfrombackanalysisofTeniente4Suraswellasgeologicalandgeomechanicalaspects,mining,mine design and planning. Each blockwas planned to use south and north access under both shadowandtwolinesoforepasssystems(crosscut)formaterialhandling(Figure3),withanominalcapacityofproductionof3,500tpd,eachdriftatproductionleveland15,000tpdforeachcrosscutathaulagelevel.Block1wasplannedtobeginproductioninJuly2011and,thefollowingyear,theexploitationofBlock2wasplannedtocommencereaching,intotal25,000tpd,inAugust2014(Figure4).

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Figure 3 Blocks Design Esmeralda Sur

Figure 2 Macro-sequence blocks Esmeralda Sur


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Figure 4 Five Year Plan Esmeralda Sur 2011 -2015

6 Unit Operation

6.1 Activities Sequence

ThedevelopedsequenceofactivitiesrelevanttotheunitoperationsthatwerepartoftheminingoperationandaConventionalPanelCavingpreparation,considereddevelopmentsoftheundercutfronttoavariabledistancebetween100to150mandasecurityzoneortransitionzoneof70mfromthefront,whereallthebuildingsandfortificationshadtobemade.ForBlock2,theareawasextendedto100m.Inparallel,whileadvancing theminingpreparation, drillingdrawbells in theproduction level and radial fan at undercutlevelbegan.Thiswasfollowedbythefirstblastofdrawbell(twostages),leavinga3mslabbetweentheroof.Subsequently,theundercutlevelandmaterialremovalwerecompleteduntilthevoidwasgeneratednecessaryforthefanblastingattheundercutleveltowardsthecompletionofthedrawbell(approximately10fans).

6.2 Drilling and Blasting Design

ThedesignusedattheTeniente4Surminewithsomeimprovementstothelengthoftheholeswasusedfor thisnewproduction levels.Thedrawbelldesign (Figure5) considered two stagescorresponding totheraiseanditsnorthandsouthsidesasthefirstblastphase.Thelateralparts(W-E)wereplannedforthesecondphase.Thedesignconsidered58holesof15mhighleavingapillarof3mbetweentheroofandtheundercutlevel.Totaldrillinglengthwas731m,areawas588m2andtotalvolumeremovedwas1.824m3.Attheundercutlevel,whichcorrespondedtotheradialfandrilling,20holeswith270mtotaldrillinglength,burdenandspacingof2mandundercutheight16mwerecompleted.Thisdesignalsohadnegativeholesforconnectingdrawbelltotheundercutlevelandeliminatingthepillarof3m(Figure6).

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Figure 5 Drilling and Blasting Design Standard Drawbell Blocks Esmeralda

Figure 6 Drilling and blasting design: Standard Fan Undercut Blocks Esmeralda

6.3 Blasting

InMarch2011,drillingonthenorth-eastsideofBlock1began,giventhelaunchscheduleblastinginJulyofthesameyear,whichwassuccessfullyachievedonthe27thofJulyandmakingitthefirstblastinginEsmeraldaSur.Subsequently, blasting along the entire area of this blockwas achieved (undercut rates1500m2/month) togetherwith the fulfillment of production goals. In June 2012, the first evidence ofcavingconnectionwasalreadyachievedathigherlevels,generatingdamagesattheuppergallerieslocatedatTeniente5 levelunderanactiveareaof16,000m2(5x5drawbell).Togetherwith thisprocess, there


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was a rise in seismic events, typically on every breakthrough process, reaching peaks of 300 events / day. Blasting activities and drawing continued normally until three months later (October 2012) with 19,000 m2

of incorporated area. The second process of connecting to the Teniente 4 level that caused the breakthrough process on Block 1 was then declared as officially achieved.

6.4 Drawing

The drawing strategy was influenced by the evolution of the connection process. The extraction rate applied to the incorporated drawbells in production from the beginning until critical area was reached was 0.2 ton/m2-day. After the first connection process was reached, the strategy changed in order to accelerate the spread of the caving. Two distinct areas were defined: the first was on the periphery of the extraction area that included a line of drawbells and the other was a central zone composed of drawbells “inside” the active area, where higher extraction rate was applied. The release of the first draw point was performed in December 2012 in which 2,700 m2 were released. In terms of production and new drawbells incorporated, continuity and growth of both processes was achieved (Figure 7).

Figure 7 Monthly Production and New Area Block 1

7 Conclusions

Based on the experience during the various stages of implementation, drawing and continued growth of active area and subsequent breakthrough process in Block 1, the new strategy and macro-sequence showed great promise towards achieving production goals.

This conditions created high expectations towards, firstly, achieving the goals established in the original plan of Esmeralda and, secondly, towards suggesting that the application of Conventional Panel Caving was a viable alternative to be applied to future projects at Teniente or another Mine. These high expectations were based on improvements, such as, hydraulic fracture of the rock mass, small undercut front and orientation of growth taking into account the main geological structures.

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Nevertheless,theoperationaldiscipline,qualityandbettercontroloftheactivitiesshouldalwaysbeusedasanunderlyingrobuststandardtowardssuccessfullyachievingthegoalsandmilestonesdefinedbythemineplan.

ReferencesBarraza,M,Quiroz,R,Vega,H2010,‘EsmeraldaSurDefinicionesdeCrecimiento’,DPL-I-009-2010.

Cifuentes,C,Díaz,J&Orellana,M2013,‘BackAnálisisConexióndeBloque1,MinaEsmeraldaSur’.

Cuello,D,Gallardo,M,Díaz,S&Cavieres,P2010,‘IngenieríaGeomecánicaProyectoEsmeraldaSur’,SGM-I-029/2010.

Rojas,E,Quiroz,R,Leiva,E,Gaete,S2005,‘DiagnósticoMinaEsmeralda’,SGM-I-024/2005.


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Caving Mechanics

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Caving Mechanics

Assessment of broken ore density variations in a block cave draw column as a function of fragment size distributions and fines migration

L Dorador University of British Columbia, Canada E Eberhardt University of British Columbia, Canada D Elmo University of British Columbia, Canada B Norman University of British Columbia, Canada A Aguayo Codelco, Chile

Abstract

The broken ore density (BOD), commonly related to the swell or bulking factor, is an important parameter for block caving design. It is well known that the ore column density decreases (and swell factor increases) at the drawpoint due to the development of a loosening zone generated by ore extraction. However, the broken ore in the draw column also potentially experiences stress and density heterogeneities throughout, depending on the block properties (e.g., shape, aspect ratio and size distribution). Other important factors include the air gap thickness, draw rate and draw sequence. In addition, the blocks undergo abrasion and breakage (i.e., secondary fragmentation) which increases with draw column height. This generates rounder block shapes and smaller particles, enabling different block shape configurations and finer broken ore size distributions. These smaller particles migrate downwards into the draw column increasing the BOD. Important advances with respect to the calculation of minimum and maximum packing of coarse granular soils and rockfill makes it possible to estimate the ranges of draw column densities using as input the block size distribution. This information is used in this work to obtain the ranges of broken ore density and its spatial and temporal distribution in an ideal draw column. From this analysis, several broken ore density distributions are proposed for an ideal draw column and initial block arrangement based on data taken from the literature, and conceptual models regarding fines migration and broken ore size distributions.

1 Introduction

Theswellofacavedrockmassplaysasignificantroleintheplanninganddesignofblockandpanelcavemines,especiallyintermsofcavepropagation,subsidenceextentandorerecovery(VanAs&VanHout2008).Keyparameterssuchasdrawheightanddrawrateareinfluencedbytheratioofcaved(Vcaved)andin-situvolume(Vinsitu).Hence,thebulkingfactorisdefinedasB=Vcaved/Vinsitu-1andtheswellfactoris(1+B)x100(Brown2007).Withrespecttotheblock/panelcavingmethod,thereisalackofdataregarding the swell factor.Laubscher (1994) recommends swell factorsof108%,112%, and116% forcoarse,mediumandfinefragmentation,respectively.Incomparison,Gonzalez&Duplancic(2012)havesuggestedvaluesof130-140%basedonexperiencesattheTenientemine,Chile,andAlcaldeetal.(2008)obtainedvaluesof115%and120%attheAndinamine,Chile.Regardingamaximumswellfactor,Lorig&Pierce(2000)andHancocketal.(2012)havesuggestedporosityvaluesof0.4and0.5,respectively,basedonnumericalstudies,whichcorrespondwithswellfactorsof166%and200%.

The swell of a caved rockmass depends largely on the rockmass characteristics and properties (e.g.strength).Importantfactorsincludethenumberofjoints,theirorientation,spacingandpersistence,whichcontrol the insitufragmentation.Otherkeyfactors include the insitustressconditionsandwhetheranair-gapispresent.These,respectively,influencetheprimaryfragmentationandfallheightoftheblocks


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fromthecaveback,andsubsequently,theinitialblocksizeandshapedistributions.Theresultinginitialconfigurationofblocks thenundergoeschanges (secondaryfragmentation)as thematerialmovesdownthroughthedrawcolumn,withfurtherfragmentationandswelldependingonthechangingcolumnheight(Ross&VanAs2012)andchanginggravityflowmechanismswithchangingcolumnheight(Castroetal.2014).Ross&VanAs(2012)introducedthefactthatthebrokenoreundergoesachangeinthebulkingfactoralongtheorecolumn,asalsosuggestedbySharroketal.(2012).Herein,thetermbrokenoredensity(BOD),whichistheratiobetweenthebulkweightandcavedvolume,willbeusedtogetherwithswellorbulkingfactor.

2 BOD distribution along a draw column

Thebrokenoreinadrawcolumncanbeexpectedtoundergochangesthatleadtoaheterogeneousdensitydistribution. Single blocks released from the cave back can experience different block arrangementsdependingontheinitialblocksizeandshapedistributions,aswellasairgapheight.Aswillbediscussedlaterinthispaper,theinitialbrokenoredensitywillsubsequentlyundergochangesinresponsetocompressionanddilatancy.

2.1 Initial block arrangement

Singleblocksreleasedfromthecavebackcanaligntoformnumerousblockarrangements.Theairgapheightisarelevantparameterinthisregard.Inthecaseofanegligibleairgap,theblocksreleasedfromthecavebackwillhavelesschancetorotateandthuswillretaintheircontactwithadjacentblocks.Thiswouldleadtoatighterpackingandsmallerinitialswellfactor.Incontrast,thepresenceofasizeableairgapwouldfacilitateamoredisorderedblockarrangement,increasingtheinitialswellfactor.Blockshapeandaspectratioarealso important in the initialblockarrangement; forexampleacubicblockshapewouldallowa tighterpackingcompared toblockswithhighaspect ratios.Researchon the influenceofblockshapedistribution,usingcharacterizationtechniquespresentedbyKalenchuketal.(2006),arecurrentlyongoing.

2.2 Broken ore compression - increasing BOD with depth

Aftertheirinitialarrangement,individualblockswillstarttomovedownthroughthedrawcolumn.Astheheightofthedrawcolumnincreasesaboveablockinresponsetocontinueddrawandcaving,thebrokenorewillbesubjectedtoincreasingverticalload.TheresultingstressesexperiencedwilldependonitslocationwithinthedrawcolumnasshowninFigure1.Experimentally,thedifferentstressregimespresentinanorecolumncanberepresentedbya triaxialcompression test incorporatingshear/compressionandone-dimensionalcompressionzones.

Usingtriaxialcompressiontesting,Valenzuelaetal.(2011)investigateddensityincreasesinwasterockunderarangeofhighconfiningpressures(Figure2).Thisdataisfromwasterockwithanaveragespecificgravity(Gs)of2.7andmaximumblocksizeof0.2m,whichis20timeslessthanatypicaloversizeof4m.However,thesizedistributionintermsofgradationandblockshapedistributionsaresimilar,bothofwhicharecriticalparameterscontrollingBOD.Thus,itisfeasibletoemploythischarttoestimateincreasesinBOD.Basedonthischart,adensityincreasefrom1.85t/m3to2.12t/m3isseenforanoverloadof200mheight(effectivemeanstressof2.25MPa).NotethatthischartissuitabletoassesstheBODfromtheupperpartofamuckpilemovingdownthroughthedrawcolumn,excludingthedilatancyzoneandanypotentialarcheffectclosetothedrawbells.

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Figure 1 Different stress regimes acting on broken ore in a draw column

Figure 2 Influence of confining stress on broken ore density (Modified from Valenzuela et al. 2011)


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2-3 Broken ore dilatancy - decreasing BOD near the drawpoint

Thebrokenoreclosetothedrawpointtendstodilateduetooredraw(Meloetal.2008),likelyasarchingdevelopsandorepassesintothedrawbell.Withoutconsideringsecondaryfragmentationandfinesmigrationduringoredraw,thebrokenorehasamaximumdilation,whichcanbeassociatedwithaminimumpackingof thebrokenore.Thisminimumpackingcanbeassociatedwithminimumdensity testsperformedforcohesionlesssoils(ASTMD4254–00),togetherwithgeotechnicalcharacterizationofgravelsandrockfillreportedintheliterature(DelaHoz2007,Dorador2013).Thesedemonstratethattheminimumpackingdependsdirectlyontheblocksizedistribution(BSD)andblockshape(assumingconstantspecificgravity).

3 Secondary fragmentation and fines migration

3.1 Impact of secondary fragmentation on ranges of BOD

Secondary fragmentation is commonly associated with comminution, point load fracturing (splitting),corner rounding andcrushingofblocksdue to shear and compressive stresses imposedduringverticalmovementofthebrokenore.ThisfragmentationprocessisimportantfortheBODbecausetheaveragesizedistributiondecreases,impactingtherangesofbrokenoredensities.AsshowninFigure1,abrokenorezonemovingdownthroughanorecolumncanundergoacombinationoftwomodesofstress.Thefirstcanbeassociatedwithcompressionwithin thecentralaxisof thecolumn,where thebrokenorewouldpredominantlyexperienceasignificantamountofsplitting.Thesecondinvolvesshear/compressionoutsidethecentralaxisofthedrawcolumn,wherethebrokenorewouldgeneratemorefinesduetoshearingandroundingalongtheblockedges.Hence,thesecondaryfragmentationofbrokenorewillresultinadual-modeweightedgradationcurvefromsplittingandfinesgenerationderivedfromtwomodesof inducedstresses.

3.2 Fines migration impacting ranges of BOD

FinesmigrationisanotherkeyprocessinfluencingtheBODdistributionwithinadrawcolumn.Finestraveldownthroughthecolumnandfillthevoidsinbetweenlargerblocks,increasinginconcentrationtowardsthebottomofthecolumn.Incaseswherethereisasignificantamountoffinesclosetothedrawpoint,largeblocksmaybefoundfloatinginafinematrixofsandandgravel.Inordertostudytheevolutionoftheblocksizedistributionatadrawpointintermsofaddingfinesintothebrokenore,fourfluvialmaterialgradationcurves(G1,G2,G3andG4)arepresentedinFigure3andFigure4.Inqualitativeterms,theG-1curvewouldcorrelatewiththeblocksizedistributionatadrawpointwhencavingstartsandG-2toG-4wouldrepresentthetransitiontoafinergradationduetofinesmigration.

Figure5showsthecorrespondingrangesofminimumandmaximumdensitiesforeachofthesegradations.Thisshowsthatthedensityincreasesfromtheuniformgradation(G-1)throughG-2andG-3.However,thisthenslightlydecreasesforG-4producingapeakdensitywithgradationG-3,consistentwiththeFuller&Thompson(1907)curveofmaximumdensityforaggregates.Thisanalysiswillbeemployedinalatersectionofthispapertostudythebrokenoredensitydistribution.

Itshouldbenotedthatthereisnostandardproceduretoevaluatefinesmigrationthroughadrawcolumn.Doradoretal.(2014)haveinvestigatedusingsegregationandinternalerosionrelationshipsderivedforearthdamstoapproximatefinesmigration throughadrawcolumn.However, this requiresfurthercalibrationusingdatafromactiveblockcaveoperations.Finally,asshowninFigure6,theblocksizedistributionalongtheorecolumnvariesdependingonthesecondaryfragmentationandfinesmigration,whichgeneratesabrokenoredensitydistributionalongthedrawcolumn.Asfollows,therangesofBODinaorecolumncan be discussed using the “void index” (e) parameter, typically used in geotechnical characterization,equivalenttothebulkfactorB.

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Figure 3 Gradation curves for start of caving (G1) and transition due to increasing fines migration (G2 to

G4) (Modified from Dorador 2010)

Figure 4 Gradation samples

Figure 5 Minimum and maximum densities for gradation curves (Modified from Dorador 2010)

Figure 6 Example of influence of fines migration and secondary fragmentation on the final BSD at

the drawpoint

4 Ranges of BOD in a draw column

Severalauthorshaveobservedthattherangesofminimumandmaximumdensitiesforgravelsandsands,orintermsofvoidratioemaxandemin,dependsprimarilyontheparticlesizeandshapedistributions.TheformerfactorhasbeenreportedbyBiarez(1994)andDorador(2013)andcorrelateswellwiththemaximumpacking(oremin)usingtheuniformityindexCu,definedastheratiobetweenD60/D10(Figure7).Thedensityrangesinsandsandgravelshavebeenfoundtobelinearwhenplottingemaxvsemin(Cubrinovski&Ishihara2002,De laHoz2007). Inorder to extend these correlations, data fromKezdi (1979),Gesche (2002),De laHoz (2007) andDorador (2010) have been reviewed.This data consists of laboratory testing ofminimumandmaximumvoidratiosonsubangulargravels(Figure8).TheminimumpackingistypicallydeterminedbypouringthematerialintoacylindricalmouldassuggestedbyASTMstandardD4254–00.Themaximumpackingiscarriedoutinthesamemouldbymeansofacompactionprocessusingavibratorytable(ASTMstandardD4253–00).Hence,thesetwocorrelations(Figure7andFigure8)canbeusedforestimatingtheminimumandmaximumdensitiesofthebrokenorewhenitundergoesaninitialloosepacking(seesection5.1)comparabletothepackingofrockfillandgravelmaterials.


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5 Estimation of BOD distribution into a draw column

TwoscenarioshavebeenproposedtoanalysetheBODdistributioninadrawcolumn.Thefirstreferstoalooseinitialpackingofbrokenorematerial,involvingblocksthatwerefreetorotateandfallthroughanairgapontothemuckpilesurface.Duetotheinitialloosestateofthematerial,correlationsfromFigure2,figure7andFigure8canbeapplied.Conversely,thesecondcaseiswhereanairgapisnotpresentandthebrokenorereleasedfromthecavebackremainsinadensepacking.Becausetheblocksretaintheircontactswithadjacentblocksinatightassemblage,thecomparisonbetweenbrokenoreandrockfillorgravelsisnotapplicable,andthecorrelationspreviouslymentioneddonotapply.

5.1 Loose packing of broken ore

BlockSizeDistribution(BSD)curvesapplicablefordrawpointsderivedbyDoradoretal.(2014)havebeenappliedheretostudytheloosepackingofthebrokenoreinadrawcolumn(Figure9).TheinitialBSDcorrespondstoanidealsizedistributionatthetopofthemuckpile(afterprimaryfragmentationandrockfallimpact).ThishasaCu=27,whichishigherthanseveralCuvaluesofprimaryfragmentationreportedintheliteratureforoperatingcavemines(e.g.,SalvadorandPalaboramines;seeInternationalCavingStudyII).Hence, thisBSDwillbeusedinaqualitativeestimationofthebrokenoredensitydistribution.Thescopeofthisworkwillbelimitedtotheuseofablocksizedistribution(Cu=87)foradrawpointundera220mhighdrawcolumn(Figure9).

TheproceduretoobtaintherangeofBODinadrawcolumnforbothinitialandfinalBSDisbasedonthemethodexplainedinsection4.The“average value”curveinFigure7isusedtoobtainthemaximumpacking,whichisemin=0.26orBOD=2.14t/m3fortheinitialBSD(consideringaspecificgravityof2.7),andemin=0.22orBOD=2.21t/m3forthefinalBSD.Toobtaintheminimumpacking,thecorrelationprovidedinFigure8isemployed.Thus,theinitialandfinalBSDproduceanemax=0.59(BOD=1.70t/m3)andemax=0.53(BOD=1.76t/m3),respectively.

Hence,theBODdistributionforthetwodifferentinitialdensitiesisanalysedinFigure10(alternativesAandB).“A”correspondstoaninitialBODof1.80t/m3,whichincreaseswithcolumndepthfollowingthedashedcurvesdepictedinFigure2,untilitreachesitsmaximumvalueof2.0t/m3.Atthispoint(50mabovethedrawpoint),dilatancyisassumedtobegin.Insidethedilatancyzone,thebrokenoreishypothesizedtodecreaseindensityuntilitreachesitsminimumpacking(1.76t/m3).Conversely,alternativeBstartswithaBODof1.92t/m3increasinginasimilarmanneruptoamaximumof2.12t/m3andthendecreasingwithincreasingdilatancyuntiltheminimumpackingof1.76t/m3isarrivedat.

Figure 7 Correlation between BSD gradation (Cu) and maximum packing (emin). Modified from

Dorador (2013)

Figure 8 Minimum and maximum packing in gravel

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5.2 Dense packing of broken ore

TwocaseshavebeenconsideredtostudytheevolutionoftheBODdistributionforaninitialdensepackingof brokenore (Figure 11), for examplewhere a negligible air gapmaybepresent.Thefirst is awell-arrangedpackingofblockswithoutanyswellfactor(Sf=100%).Inthiscase,thedensitydoesnotundergosignificantchangesuntilthedilatancyzonewherethebrokenoreexhibitsasignificantdecreaseindensitysplittingintominimumandmaximumdensityscenariosatthedrawpoints(Figure12).ThesecondisthecaseofasmallswellfactorofSf=113%.ItisassumedthataslightreductionoftheBODwithdepthoccursuntilthestartofthedilatancyzone,whereasignificantdecreaseindensitythenoccurs.

Hence,basedonthesequalitativeBODdistributions,itispossibletoobservetheimportanceandimpactof the initialblockarrangementonbrokenoredensity. It is believed that the rockmass characteristicsand subsequent secondary fragmentation, aswell as theairgap thicknessplayakey role in this initialarrangementoftheblocks.Continuedinvestigationsarebeingcarriedoutonthesetopics.

Figure 10 BOD distribution along a draw column of 200 m height, with loose packing

Figure 9 Block size distribution at drawpoint for a column height of 200 m. After Dorador et al. (2014)

Figure 11 Two examples of different initial block arrangements with dense packing Figure 12 BOD distribution along a draw column of 200

m height, with dense packing


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6 Conclusions

Thispreliminarystudyaddressesthedensitydistributionofthebrokenorealongadrawcolumn.TheBrokenOreDensity(BOD)undergoeschangeswithincreasingdepth,relativetothetopofthedrawcolumn,andisstronglycontrolledbytheinitialblockarrangement.Thisinturnisconditionedbytheorientation,spacingandpersistenceofthediscontinuitieswithintherockmass(i.e.,insitufragmentation),aswellastheairgapthickness,presenceofveining,rockmassstrength,andotherfactorsthat influencetheprimaryandsecondaryfragmentation.Preliminaryresearchhasshownthatthediscontinuitynetworkandcorrespondingblocksizeandshapedistributionsaredominant factors, forexample,cubicshapedblocks tend to formamoreorderedpackingwithrespecttootherblockshapes.Asnoted,airgapisalsoasignificantfactorbecausenegligibleairgapwillresultinatighterinitialarrangementofblocks;withincreasingairgap,theblocksreleasedfromthecavebackwillhaveenoughspacetorotatecausingamoredisorderedpacking,generatingahigherbulkingfactorandsmallerBOD.NumericalmodelsaswellasoperationalminedatacanhelptoimprovethisunderstandingoftheinfluenceoftheinitialblockarrangementonBODdistribution,andisthesubjectofongoingresearch.

The analysis presented includes the influence of secondary fragmentation andfinesmigration.Both ofthesefactorsreducetheaveragesizeoftheblocksizedistribution(BSD).Usingcorrelationsandchartsfromempiricalrelationshipsderivedforcoarsegranularmaterials,thebrokenoredensitywasshowntoincreaseuntilacriticalBSD.ForfinerBSD,thisproducedamoregradualdecreaseindensity.However,itisbelievedthattheimpactofsecondaryfragmentationandfinesmigrationonBODisnotassignificantastheinitialblockarrangement.

Thecompressionanddilatancyofthebrokenoreduringcavinganditsmovementthroughthedrawcolumnwas also studied. In the case of an initial loose packing of broken ore, theBOD increaseswith depth(i.e.,confiningstress)down through thedrawcolumnuntilapointwhere theBODexhibitsadecreasewhenitentersintothedilatancyzone.Conversely,theinitialdensepackinginthebrokenoreundergoesadifferentBODdistribution.TheBODisassumedtoexperienceminorchangesalongthedrawcolumnbeforeenteringintothedilatancyzone,butthenexhibitsagreaterreductionindensityatthedrawpoints.Theseresults reflectfindings fromthefirstphaseofadetailed investigationforCodelcoChuquicamata(PMCHS),withfurtherresearchplannedtoimproveunderstandingofBSDtendenciesandevolutiondownthroughthedrawcolumn.

ReferencesASTMD4254–00.StandardTestMethodsforMinimumIndexDensityandUnitWeightofSoilsand

CalculationofRelativeDensity.

ASTMD4253–00.StandardTestMethodsforMaximumIndexDensityandUnitWeightofSoilsUsingaVibratoryTable.

Alcalde, F, Bustamante, M &Aguayo,A 2008, ‘Estimation of remaining broken material at divisionAndina’,In5thInternationalConferenceandExhibitiononMassMining,Luleå,pp.179-189

Biarez,J&HicherPY1994,‘ElementaryMechanicsofSoilBehaviour:SaturatedRemouldedSoils’,A.A.Balkema,Rotterdam.

Cubrinovski,M&IshiharaK2002,‘Maximumandminimumvoidratiocharacteristicsofsands’,SoilsandFoundations,vol.42,pp.65-78.

Castro, RL, Fuenzalida, MA & Lund, F 2014, ‘Experimental study of gravity flow under confinedconditions’,IntJRockMechMinSc,vol67,pp.164-169.

DeLaHoz,K2007,‘Estimacióndelosparámetrosderesistenciaalcorteensuelosgranularesgruesos’,TesisdeMagisterenCienciasdelaIngeniería,UniversityofChile,Santiago(inSpanish).

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Dorador,L2010,‘Análisisexperimentaldelasmetodologíasdecurvashomotéticasycorteenlaevaluacióndepropiedadesgeotécnicasdesuelosgruesos’,TesisdeMagisterencienciasdelaingeniería.UniversityofChile,Santiago(InSpanish).

Dorador, L&Besio,G 2013, ‘Some considerations about geotechnical characterization on soilswithoversize’.InFifthInternationalYoungGeotechnicalEngineeringConference-5iYGEC’13,Paris,pp.407-410.

Dorador,L,Eberhardt,E,Elmo,D,Aguayo,A,2014,‘Influenceofsecondaryfragmentationandcolumnheightonblocksizedistributionandfinesmigrationreachingdrawpoints’,InProceedingsofthe3rdInternationalSymposiumonBlockandSublevelCaving,Santiago.

Fuller,W&ThompsonSE1907,‘Thelawsofproportioningconcrete’.Trans.oftheAmericanSocietyofCivilEngineers;vol.59,pp.67-143.

Gesche,R2002,‘Metodologíadeevaluacióndeparámetrosderesistenciaalcortedesuelosgranularesgruesos’,ThesisofCivilEngineering,UniversityofChile,Santiago(InSpanish).

Gonzalez-Carbonell,P,Duplancic,P&Thin,I2012,‘Agenericoverviewoftheinteractionofablockcavedrawstrategyandcavemonitoring.In6thInternationalConferenceandExhibitiononMassMining,Sudbury.

Hancock,W,Weatherley,D&Chitombo,G2012,‘Modelingthegravityflowofrockusingthediscreteelementmethod’.In6thInternationalConferenceandExhibitiononMassMining,Sudbury.

Kalenchuk, KS, Diederichs, MS & McKinnon, S 2006, ‘Characterizing block geometry in jointedrockmasses’, International Journal of RockMechanics andMining Sciences, vol. 43, pp.1212–1225.

Kezdi,A1979,‘Soilphysics–selectedtopics’,ElsevierScientificPublishingCo.,Amsterdam.

Laubscher,D1994, ‘Cavemining– the stateof the art’. JournalofSouthAfrican Inst. ofMiningandMetallurgy,vol.94,pp.279-293.

Lorig,L&PierceM2000,‘Methodologyandguidelinesfornumericalmodellingofundercutandextractionlevelbehaviourincavingmines’,ItascaConsultingGroupInc,ReporttoInternationalCavingStudy.

Marsal,R1973,‘Mechanicalpropertiesofrockfill’,inEmbankment-damengineering:CasagrandeVolume,Wiley,NewYork.

Melo,F,Vivanco,F,Fuentes,C&ApablazaV2008,‘Kinematicmodelforquasistaticgranulardisplacementsinblockcaving:dilatancyeffectondrawbodyshapes’,IntJ.RockMech.Min.Sci.,vol.45,pp.248–59.

RossIT&VanAs,A2012,‘Majorhazardsassociatedwithblockcaving’,In6thInt.Conf.andExhibitiononMassMining,Sudbury.

Sainsbury,B,Pierce,ME&MasIvars,D2008,‘Analysisofcavingbehaviourusingasyntheticrockmass-ubiquitousjointrockmassmodellingtechnique’,inProceedings,SHIRMS,Perth,vol.1,pp.343-252.

Sharrock,GB,Beck,D,Capes,GW&Brunton,I2012,‘ApplyingcoupledNewtonianCellularAutomata-DiscontinuumFiniteElementmodels to simulate propagationofRidgewayDeepsBlockCave’,in6thInternationalConferenceandExhibitiononMassMining,Sudbury.

Valenzuela, L,Bard, E&Campaña, J 2011, ‘Seismic considerations in the design of highwaste rockdumps’,in5thInternationalConferenceonEarthquakeandGeologicalEngineeringICEGE,Santiago.

VanAs,A,VanHout,GJ2008,‘Implicationsofwidelyspaceddrawpoints’,in5thInternationalConferenceandExhibitiononMassMining,MassMin,Luleå,Sweden,pp.147-154.


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Assessing the state of the rock mass in operating block caving mines: A review

D Cumming-Potvin, University of Western Australia, AustraliaJ Wesseloo, University of Western Australia, Australia

AbstractThe block caving mining process limits access to the orebody, in turn limiting opportunities to gauge the state of the rock mass once the rock mass degradation and caving process has initiated. Poor knowledge of the evolving state of the rock mass inside the column and of the propagation of the cave can lead to some of the key geotechnical risks in block caving, namely uncontrolled, dynamic large scale caving events, caveback hang-ups and undesirable cave propagation outside of the orebody.

Due to this lack of access, geotechnical monitoring is often conducted to gain an understanding of the rock mass. Open hole and TDR monitoring give point measurements of the cave back which are usually reliable (Chen 2000). Extensometers give point measurements of displacement, however the results are not always entirely reliable (Brown 2003). These monitoring methods, while still important for gaining physical measurements of the cave propagation, are only point measurements. In order to monitor the entire cave volume, microseismic monitoring is commonly used (Lett and Capes 2012).

While some attempts have been made to quantify the rock mass degradation process in the cave column or to identify the location and profile of the cave back, there is no accepted and proven method for doing so based on monitoring data. Therefore, there is no accepted scheme for assessing the state of the rock mass in a block caving setting. This affects the knowledge that mines have of their cave as it propagates, but can also affect calibration of numerical models. If there is no reliable data on the state of the rock mass, then any calibration will subsequently be unreliable.

This paper reviews the existing literature on assessing the damage state of the rock mass in block caves, as well as other mining environments. The strengths and shortcomings of the different analysis methods are discussed. Based on this review it is proposed that proper systematic verification of the available suggested methods is lacking, as well as a systematic process of integrating the different methods into a single coherent system for the spatial and temporal evaluation of the damage state of the caving rock mass.

1 Introduction

Blockcavingisaminingmethodwhichhasbeenincreasinglyimplementedinrecenttimesduetoitscostefficiencyandhighproductionrates.Thelackofaccesstotheorebody,whichischaracteristicoftheblockcavingmethod,resultsinapoorknowledgeofthestateoftherockmassinsidetheorecolumn.Thisleadstosomeofthekeygeotechnicalrisksinblockcaving,namely,uncontrolled,dynamiclargescalecavingevents,cavebackhang-ups,poorfragmentationandundesirablecavepropagationoutsideoftheorebody(Hebblewhite2007;Westman,Luxbacher&Schafrik2012).

Inordertomitigatetheserisks,geotechnicalmonitoringiscarriedoutinordertogaininformationontheinitiationanddevelopmentofcaving.Brown(2003)definesfourgeneraltypesofmonitoringmethodsformeasuringthepropagationofacaveinblockcavingmines:manualmethods(suchasdepthmeasurementfrom open holes), Time Domain Reflectometry (TDR), microseismic methods and CavityMonitoringSystems(CMS).

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Whilstsomeanalysismethodsforassessingcavedevelopmenthavebeenappliedtocaving,noreviewofthedifferentmethodsusedhasbeenpublished.Thispaperwillreviewtheliteratureavailableondefiningthelocationofthecaveprofileandthedamageaheadofit.Wewillbeafocusontheuseofmicroseismicdataandanalysistechniques,asmicroseismictechniquesprovidetimecontinuousthree-dimensionaldataonthecave.

A number of analysis methods have been used to quantify rockmass damage in open stopingmines(Falmagne2001;Coulson&Bawden2008),inlaboratorytestedsamples(Eberhardtetal.1998;Chang&Lee2004)andinquantifyingtheeffectsofpre-conditioning(Reyes-Montes,Young&VanAs2012).Thesemethodscouldbeappliedtocavingminesforthepurposeofidentifyingthedamagestateoftherockmassandcaveprofile,howeverthescopeofthispaperwillbelimitedtoanalysisconductedonblockcavingmines.

2 Microseismic analysis methods

Duplancic(2001)dividedthecavingprofileintofivezones;thepseudo-continuousdomain,seismogeniczone, zoneof loosening, air gap and cavedzone (Figure1).Thismodel for the caving front iswidelyaccepted and has been adopted by the industry as the frameworkwithinwhichmonitoring results areinterpreted(Brown2003).

Figure 1 Zones of ground behaviour in a block caving mine (Duplancic 2001)

Anumberofanalysismethodshavebeenusedincavingminestoobtaininformationonthecavingprocessfrommicroseismicdata.Theseareperformedfortwomainobjectives,withdifferentanalysistechniquesbeingappliedtoeachofthemandcanbesummarisedasfollows:

• Findingcave/seismogeniczonegeometry

o Eventlocation

o Apparentstress/energyindex

o Passiveseismictomography


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• Identifyingrockmassdamageinthecavecolumn

o Passiveseismictomography

o Shearwavesplitting

3 Finding cave/seismogenic zone geometry

3.1 Event location

Anumberofauthorshaveusedtheverticallocationofseismicityincavingminestoestimatethelocationoftheseismogeniczone(Duplancic2001;Trifuetal.2002;Glazer2007;Hudymaetal.2007a;Hudyma,Potvin&Allison2007b;Hudyma&Potvin2008;Dixonetal.2010;Abolfazlzadeh2013).Thecaveisoftendividedintodifferentcross-sectionstodeterminetheseismogeniczone.Thecriterionfordeterminingthelimitsoftheseismogeniczoneisusuallyacontouralongthecross-sectionaboveorbelowwhichacertainproportionoftheeventslie(asseeninFigure2).

Someofthesestudieshavebeencomparedtoopenholedippingresultsforvalidation.Itshouldbenotedthatthisapproachimplicitlyassumesazoneoflooseningwithaconstantthicknessoverspaceandtime.Asthezoneoflooseningdoesnothaveaconstantthicknessthroughspaceandtime(Hudymaetal.2007b),thetwomethodscannotbeusedtovalidateeachother.

Abolfazlzadeh(2013)createdadetailedcasestudyoftrackingtheseismogeniczoneatTelfermine.Hesuggestedusingatwo-dimensionalgrid(inplanview)thateachcolumn’seventverticallocationdistributionisusedtodefinethelimits,with10%and90%verticallocationssuggestedastheeffectivelimitsoftheseismogeniczone.Thisapproachimplicitlyassumesthat thecavebackmigratesonlyvertically.Thisisgenerally not the case.Abolfazlzadeh acknowledged that ‘ultimately, there is no idealmethodology todefinetheseismogeniczone’.Theresultsofthemethodaredependentonthesubjectivitelychosentocut-offvaluesanddirectionandspacingofthecross-sectionsused.AlthoughthismethodgenerallycorrespondswiththeframeworkpresentedbyDuplancic(2001),thecleardelineationofdifferentzoneremainuncertain.

Figure 2 Seismogenic zone for Northparkes lift 2; May-August 2003 (Hudyma el al. 2007b)

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3.2 Apparent stress/energy index

Apparentstressisaseismicsourceparameteroftenusedasanindicatorofthestresslevelintherockmasswheretheseismiceventoriginated.EnergyIndex(EI)isconceptuallysimilarbut,incontrasttoapparentstress,isamodeldependentandscaleindependentmeasureofthestresslevels(VanAswegen&Butler1993).

Hudymaetal.(2007b)usedtheapparentstressofmicroseismiceventstoidentifytheseismogeniczoneatNorthparkesE26lift2.Thehypothesiswasthathighapparentstressevents(inthiscaseapparentstress>10kPa)weremorelikelytoappearontheedgeoftheseismogeniczone,whichisexpectedtobeanareaofhighstress.Thehighapparentstresseventsoccurredinatightspatialdistributionintheupperboundoftheseismogeniczone.Abolfazlzadeh(2013)cametosimilarconclusions.Althoughthepremiseoftheapproachisreasonableneitherofthesestudiesincludedanyverification.

Chen(1998)usedEItoindicatethelocationofade-stressedzoneimmediatelyabovethecavebackandahighlystressedzoneabovethisde-stressedzone(i.e.aseismogeniczone).WhilethisisbroadlyconsistentwiththecavingzonesofDuplancic(2001),theChennotedthatEIcouldnotbeusedtoinferfracturing.Healsonotedthatadelineatiationofthecaveprofileusingtheeventlocations,couldnotbeachieved.

3.3 Passive seismic tomography

Passiveseismictomographyestimatesthevelocitystructureofanareaofinterestusingmicroseismicevents.

Althoughitshowsgreatpotential,passiveseismictomographyisnotoftenusedtodefinethecavebackor seismogenic zone. Its use seems to be limited to two separate studies performed atRidgewaymine(Pfitzneretal.2010;Westmanetal.2012).SomeconfidenceinthepotentialofthemethodisprovidedbyatheoreticalstudyperformedbyLynchandLötter(2007).

LynchandLötter(2007)usedsyntheticdatatotestthepassiveseismictomographytechniqueinfindingthevelocitystructureandgeometryoftheoreticalblockcavingmines.Byusingagivenvelocitystructureandrandomlyplacedeventsandsensors,theyconvergedtovelocityresultsbyminimisingtheresidualsofthetraveltimes.Theytestedthreedifferentgeometries;asimplehomogeneousmodel,asinglecaveandamodelwithtwocavesofdifferentheights.Theywereabletofindthegeometricparametersofthemodelswithin5-7%ofthetruesyntheticvalues.Thestudyshowsthatitistechnicallyfeasibletousepassiveseismictomographytofindthegeometryofthecaveback.Thegeometriestestedwere,however,limitedtosimpleparabolicshapeswhichdonotaccuratelyreflecttheshapesofcavebacksinreality.Thesyntheticseismiceventsarealsoasimplificationofreality.Whilstthestudyincludedthebendingofraytravelpathsaroundthecave,itdidnottakeintoaccountthereflectionandrefractionoftenseenintrueseismicevents(Daehnke1997).

One of the studies that used double difference passive seismic tomography in order to identify theseismogeniczoneatRidgewaywasperformedbyPfitzneretal.(2010).Inthisstudythevelocityincreasewasusedasanindicatorofhigherstress,whichwasinterpretedasthelocationoftheseismogeniczone(Figure4).Thiszonewasnotcomparedtoseismogeniczonelimitsdefinedbytheeventlocations,suchasthosefoundinHudymaetal.(2007b).

ThesecondstudyatRidgewayusingdoubledifferencepassivetomographywasperformedbyWestman,etal.(2012).Theyusedpassiveseismictomographyoveran18monthperiodtoinvestigatethechangingrockmassandstressconditions.Thelocationoftheseismogeniczonewasalsoidentifiedaspartofthestudy.


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Figure5showsexamplesoftheresultsfromthestudy.Theeventlocationswerefoundtomostlyliewithinthehighervelocityzoneabovethecave.Thestudywaslimitedtoqualitativeassessmentsandnoattemptwasmadetocorrelatetheseismogeniczonewiththeeventlocationsortoquantifytheboundaryoftheseismogeniczone.

Figure 5 Contour plot of velocity for March 2010 with both the block and sub-level caves. The low velocity isosurface represents 5400 m/s (Westman et al. 2012)

4 Identifying rock mass damage in the cave column

4.1 Passive seismic tomography

Passive seismic tomography, despite showing strong potential, has not been extensively used in blockcavingminestodeterminerockmassdamage.GlazerandLurka(2007),Pfitzneretal.(2010)andMercieretal.(2012)haveusedthetechniquetoinferbothrockmassdamageandstressstatefromseismicvelocityinferredthroughtomography.

Figure 4 Seismogenic zone inferred by velocity increase (Pfitzner et al. 2010)

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GlazerandLurka(2007)usedpassiveseismictomographytoquantifyvelocitychangeatPalaborablockcaveminewhich theyrelated tostresschange.Theresults indicatedanareaofhighervelocity towardstheeastof theminewhichwas interpretedaseitherbeinganareaofhigherstress,orbeinganareaofcompactedcoarsercavematerial.Theuncertaintyofwhetheritindicatesanareaofsolidrockunderhighstressorcompactedcavematerialhighlightsthesubjectivityoftheinterpretationinvolvedwiththepassiveseismictomographymethod,asdescribedby(Glazer&Lurka2007).

GlazerandLurka (2007)performedaverificationexercisebycomparing thehighervelocityareas toacrosscutwith largeconvergenceandareaswhere the seismicityhadahigher energy index.Whilst thisverificationusedindependentmethodsforcomparison,itwaslimitedtoabroadqualitativecomparison.

Pfitzneretal.(2010)usedthedropinvelocitytoinferzonesofdamageatRidgewaymine.Theinferredlimitofdamage(Figure6)roughlyconcurswithDuplancic’s(2001)cavingmodel,howevertherewasnoreconciliationoftheareawithindependentmeasurementtechniques.Conventionalcross-holetomographicsurveyswerecarriedoutwiththeaimofquantifyingthechangeinrockmassmodulus,howeverthesurveyresultswereinconclusive(Morgan2009,inPfitzneretal.2010).Whilsttheparameterusedtoassessrockmassdamage (velocitydrop)wasquantitative, no cut-off valuesor guidelines for thequantificationofdamagewerepresented.The interpretationof themeaningof thedifferentvelocityvalues seems tobeuncertainandtheapplicationofthemethodseemstobesomewhatsubjectivewithoutameanstovalidatetheresults.

Figure 6 Rock mass damage inferred by velocity decrease (Pfitzner et al. 2010)

Mercier et al. (2012) used double difference passive seismic tomography to investigate the changes invelocityat theNorthparkesE48blockcavingoperationand the relationshipof thevelocitychanges tostresschangesintherockmass.Theybuiltp-waveands-wavevelocitymodelsfordifferenttimeperiods.Anexampleofs-wavevelocitytomographyresultsforfourdifferentperiodsisshowninFigure7.Thefourmodelscorrespondtotheprogressiveundercutting(January-July2010)andself-propagating(October2010)periodsinthecaveprogression.


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Figure 7 S-wave velocity models for Northparkes E48, 2010 (modified after Mercier et al. 2012). The approximate position of the undercut is given by the red line. The isosurface respresents a velocity of 3000 m/s.

Theresultingvelocitymodelshowstheprogressionofthehighervelocityzoneacrosstheorebody.Theyassociated thishighvelocity zonewith stress increase from theundercuttingactivity.For example, theoveralldecreaseinvelocityseenbetweenJulyandOctober2010wasassociatedwithcompletionoftheundercutalongwiththeupwardsmigrationofthehighstresszonewiththepropagationofthecavecolumn.

Theonlyvalidationconductedforthestudywastonotethat“The results are internally consistent and in accordance with accepted views of the caving sequence”.Theanalysisconductedwaslargelyqualitativeandbasedoninterpretationofbroadtrendsinthevelocitymodel.Whilethismayimproveoverallknowledgeofthecavebehaviour,itdoesnotaidintheidentificationandquantificationofrockmassdamagethroughspaceandtime.

4.2 Shear wave splitting

Shearwavesplittingisaseismicanalysistechniqueformicroseismiceventswhichtravelthroughisotropicmedia.Astheshearwaveentersthemedium,itissplitintotwoorthogonallypolarizedwaves.Thewavearrivaltimesareseparatedbyadelaywhichisproportionaltothedegreeofanisotropyandthetravelpathlength(Wuestefeldetal.2011).

Wuestefeldetal.(2011)usedshearwavesplittingtoidentifyfractureevolutionatNorthparkesE26blockcave.Theshearwaveanisotropywascalculatedforover13000events.Theyfoundvariationsinanisotropyovertime,whichwasattributedtothegenerationofnewfractures,howevertheanalysiswasneverconductedfordifferentareasofthemine(onlythewholecavecolumn),andspatialchangesinfracturingwerenotconsidered.Therewasnovalidationdonefortheanalysis,howeveritisdifficulttoindependentlyvalidatemeasuresoffracturingwithoutdirectvisualobservation.

4.3 Discussion

Aspreviouslymentioned,Brown (2003) described four differentmonitoringmethods for block cavingmines:manualmethods,TDR,microseismicmethodsandCMS.ConductingaCMSinablockcaveisusuallyimpractical,duetothelimitedaccessinherenttotheminingmethod.ManualmethodsandTDRmonitoringgive useful information on the location of the cave back, however they only give pointmeasurementsinspacewhichhavetobetakenincrementally(i.e.theyarenotcontinuousmonitoringmethods).Thesemethodsonlygivean indicationof the locationof thecaveback,withoutany informationon thestateof the rockmass.Microseismicmethods have the advantage of being a continuous three-dimensional

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representationofthecavedevelopmentinrealtime,however,theydonotgivedirectinformationonthecaveprofileandrockmassdamage,whichnecessitatesfurtherinterpretationofthemicroseismicdata.Inordertogetacompletepictureofcavedevelopmentwithmonitoring,microseismicmethodsneedtobeproperlyvalidatedsothatconfidencecanbeplacedintheresults.

In block caving,monitoring results and subsequent analyses aremost commonly interpreted based ontheframeworkprovidedbyDuplancic(2001).Complicatedanalysis techniquessuchaspassiveseismictomographyhaveemergedtointerpretstresschangefromvelocityinordertofindtheseismogeniczone.Theinterpretationofthese,however,isbasedontheframeworkofamodelderivedfromeventlocationandsotheresultsmaynotbeanimprovementuponsimplyusingthelocationofeventstodeterminetheseismogeniczone.

Thereisadiscordintheinterpretationofvelocitychangeinthepassiveseismictomographytechnique.Someauthors(Pfitzneretal.2010)haveinterpretedvelocitychangeasrockmassdamage,whereasothers(Glazer&Lurka2007;Westman,Luxbacher&Schafrik2012;Mercieretal.2012)haveuseditpurelyasanindicatorofstress.Itisreasonabletoassumethereductioninvelocitytobearesultofbothreductioninstressandanincreaseindamage.Thetwophenomenahappenconcurrently.Itseemsthatfurtherunderstandingofthecavingprocessisrequiredbeforemeaningfulseparationofthesetwoeffectscanbeachieved.

Whilst some showpromising results, none of the analysis techniques discussed have shown an abilitytoadequatelydescribe thecavingprofileanddamagezoneacrossspaceand time.Asystemcombiningthesetechniquescouldgiveimprovedperformanceoveranyindividualtechnique.Inordertoquantifyanyimprovement, averificationmethodwhichcangivean independentmeasureof rockmassdamageandthethree-dimensionallocationofthecavebackisneeded.Noneoftheverificationmethodsinthestudiespresentedcanproducethissortofindependentmeasure.

Thereisasystematiclackofqualityvalidationthroughallstudiesandsotherearenoobviouscriteriabywhichtojudgewhichofthetechniquesismostsuccessfulindescribingthecavingprofile.Anindependentmethodofverificationwhichcanidentifythelocationofthecaveprofile(includingdamageabovethecaveback)acrossspaceandtimeisnecessarytoevaluatecavinganalysistechniques.Thiscouldpotentiallybeachievedthroughtheuseofaphysicalmodel,wheredirectvisualobservationofthescaledrepresentationofthecavecanbetiedwithacousticemissionmonitoring.

5 Conclusion

Table1summarisestheprosandconsoftheanalysistechniqueswhichhavebeenappliedtoblockcavingoperations.

Followingareviewofthedifferentanalysistechniqueswhichhavebeenusedtoquantifycavedevelopment,it isunclearwhichof thesemethodsgives thebest results.Eachhasstrengthsandweaknesses,andwesuggestthatbetterresultcouldbeachievedbycombiningthesetechniquesintoasingle(calibrated)analysissystem.

Physicalmodellingmayprovideameansoffurtheringourunderstandingofthecavingprocessandprovideastrongempiricalbasisforverifyingandimprovingcurrenttechniquesforassessingtherockmassstatethroughoutthecavingprocess.


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Table 1 Summary of pros and cons of analysis techniques

Analysis technique Pros Cons

Finding Cave/seismogenic zone geometry

Passive seismic tomography

-Potentialforhighqualitydefinitionofallzonesinthecaveprofile

-Interpretationofvelocitylimitsforseismogeniczonearesubjective-Notroutinelyused

Apparent stress /energy index -Conceptuallysimple

-Unlessfurthertemporalinformationisused,canonlygiveinformationoncurrentseismicallyactiveareas

Eventlocation -Conceptuallyverysimple

-Unlessfurthertemporalinformationisused,canonlygiveinformationoncurrentseismicallyactiveareas-Definitionofsectionsandproportionofeventstouseissubjective-Gridmethodologyassumescavepropagatesvertically

Identifying rock mass damage in the cave column

Passive seismic tomography

-Abilitytogetquantitativeinformationonvelocitywhichcanberelatedtofracturing-Cangetinformationonaseismiczonesofrockmass

-Geologyor‘virginstate’mustbeknownprecisely,elseonlyrelativechangescanbeobserved-Interpretationofdamagefromvelocityissomewhatsubjective-Notroutinelyused

Shear wave splitting

-Possibilitytofindorientationoffracturing

-Hasnotyetbeenusedtodefinespatialchangesinfracturinginblockcaves-Notroutinelyused

References

Abolfazlzadeh,Y2013,ApplicationofSeismicMonitoringinCavingMines-CaseStudyofTelferGoldMine,Thesis,LaurentianUniversity.

Brown, ET 2003, Block Caving Geomechanics, Julius Kruttschmitt Mineral Research Centre, TheUniversityofQueensland,Indooroopilly,QLD.

Chang, SH & Lee, CI 2004, ‘Estimation of cracking and damage mechanisms in rock under triaxialcompressionbymomenttensoranalysisofacousticemission’,InternationalJournalofRockMechanicsandMiningSciences,vol.41,pp.1069-1086.

Chen,D1998,‘ApplicationofamicroseismicsysteminmonitoringE26BlockCaveatNorthparkesMines’,InternationalConferenceonGeomechanicsandGroundControlinMiningandUndergroundConstruction.

Coulson,A&Bawden,W2008,‘ObservationoftheSpatialandTemporalChangesofMicroseismicSourceParametersandLocations,UsedtoIdentifytheStateoftheRockMassinrelationtothePeakandPost-PeakStrengthConditions.’,42ndUSRockMechanicsSymposium.

Daehnke,A1997,StressWaveandFracturePropagationinRock,PhDThesis,TechnischenUniversitiitWien.

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Dixon,RA,Singh,U&McArthur,C2010,‘InteractionbetweenapropagatingcaveandanactivepitatTelferMine -Part II:monitoring interaction’, 2nd InternationalSymposiumonBlock andSublevelCaving.

Duplancic,P2001,Characterisationofcavingmechanismsthroughanalysisofstressandseismicity,PhDThesis,UniversityofWesternAustralia.

Eberhardt, E, Stead, D, Stimpson, B & Read, RS 1998, ‘Identifying crack initiation and propagationthresholdsinbrittlerock’,CanadianGeotechnicalJournal,vol.35,pp.222-233.

Falmagne,V2001,Quantificationofrockmassdegradationusingmicro-seismicmonitoringandapplicationsforminedesign,PhDThesis,Queen’sUniversity.

Glazer, S 2007, ‘Applications of mine seismology methods in block cave mining’, 1st InternationalSymposiumonBlockandSublevelCaving.

Glazer,S&Lurka,A2007,‘ApplicationofpassiveseismictomographytocaveminingoperationsbasedonexperienceatPalaboraMiningCompany,SouthAfrica’,1stInternationalSymposiumonBlockandSublevelCaving.

Hebblewhite, BK 2007, Management of geotechnical risks in mining projects, School of MiningEngineering,TheUniversityofNewSouthWales,Sydney,NSW.

Hudyma, M & Potvin, Y 2008, ‘Characterizing caving induced seismicity at Ridgeway gold mine’,MassMin2008.

Hudyma,M,Potvin,Y&Allison,D2007a,‘SeismicmonitoringoftheNorthparkeslift2blockcave—Part2productioncaving’,1stInternationalSymposiumonBlockandSublevelCaving.

Hudyma,M,Potvin,Y&Allison,D2007b,‘SeismicmonitoringoftheNorthparkeslift2blockcave—PartIundercutting’,1stInternationalSymposiumonBlockandSublevelCaving.

Lynch,R&Lötter,E2007,‘Estimationofcavegeometryusingacontrainedvelocitymodelinversionwithpassiveseismicdata’,1stInternationalSymposiumonBlockandSublevelCaving.

Mercier,J,Mercier,J,DeBeer,W&Morris,S2012,‘BeyondColouredBalls:PassiveSourceTomographyofMicroseismicDataforBlockCaving’,MassMin2012.

Pfitzner,M,Westman,E,Morgan,M,Finn,D&Beck,D2010,‘Estimationofrockmasschangesinducedby hydraulic fracturing and cave mining by double difference passive tomography’, 2ndInternationalSymposiumonBlockandSublevelCaving.

Reyes-Montes, J,Young, R &VanAs,A 2012, ‘Quantification of preconditioning efficiency in cavemining’,MassMin2012.

Trifu,C,Shumila,V&Burgio,N2002,‘CharacterizationofthecavingfrontatRidgewaymine,NewSouthWales, based on geomechanical data anddetailedmicroseismic analysis’, 1st InternationalSeminaronDeepandHighStressMining.

VanAswegen,G&Butler,A1993,‘ApplicationsofquantitativeseismologyinSouthAfricangoldmines’,3rdInternationalSymposiumonRockburstandSeismicityinMines.

Westman,E,Luxbacher,K&Schafrik,S2012,‘Passiveseismictomographyforthree-dimensionaltime-lapseimagingofmining-inducedrockmasschanges’,TheLeadingEdge,vol.31,no.3,pp.338-345.

Wuestefeld,A,Kendall,J,Verdon,J&VanAs,A2011,‘Insitumonitoringofrockfracturingusingshearwavesplittinganalysis:anexamplefromaminingsetting’,GeophysicalJournalInternational,vol.187,pp.848-860.


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Influence of secondary fragmentation and column height on block size distribution and fines migration reaching drawpoints

L Dorador University of British Columbia, CanadaE Eberhardt University of British Columbia, CanadaD Elmo University of British Columbia, CanadaB Norman B. University of British Columbia, CanadaA Aguayo Codelco, Chile

Abstract

In block and panel caving projects, the secondary fragmentation and its effect on the final block size distribution (BSD) reaching the drawpoints are key considerations in the design and success of a caving operation. Although there are existing empirical methods to predict these (e.g., Laubscher’s size distribution chart, Esterhuizen’s ‘BCF’, etc.), these incorporate several rules of thumb that can be improved upon through a more mechanistic understanding of the complex processes involved. This paper first explores the techniques commonly used in practice to assess secondary fragmentation as well as the key influencing mechanisms: comminution, fines migration and BSD into a drawbell. Comminution originates from point load breakage, shearing, crushing, and abrasion between rock blocks as they migrate downward into a drawbell, increasing the finer broken ore size distribution with depth. A simple methodology is proposed to estimate an approximate range of fines migration for different draw column heights, based on the technical literature published on internal erosion and fines segregation in earth dams. In addition, the shape of the BSD curve into a drawbell as a function of column height and undercut depth will be examined. The latter will account for the influence of the in situ stresses on the primary fragmentation and initial BSD below the cave back as the cave propagates and the column height grows. Experimental data from the literature examining particle breakage under compression/shear will be considered in order to characterize the BSD curve as a function of column height and depth.

1 Introduction

Rockfragmentationisoneofthemostimportantfactorsintheperformanceofablockcavingoperation(VanAs&VanHout2008;Moss2012).Inaddition,itiswellacceptedthatcavingfragmentationincorporatesthreecomponents:thein-situfragmentation,representingthenaturaldiscretefracturenetworkdistributedthroughout the rockmass; theprimary fragmentation,arising fromstress-induced fracturespropagatinginthecaveback;andthesecondaryfragmentation,resultingfromblockimpact,comminutionandotherfragmentationprocessesoccurringwithinthedrawcolumn(Laubsher1994;Eadie2003).Inthecontextofsecondaryfragmentation,thisinvolvesavarietyofmechanismsnotallofwhicharewellunderstood(Brown2007).Todate,empiricaldesignchartsproducedbyLaubscher(1994)aswellasseveralnumericalapproachesdescribedbelowaregenerallyusedtoassesssecondaryfragmentation.

TheBlockCavingFragmentation(BCF)modeldevisedbyEsterhuizenetal. (1996)employsempiricalrelationshipstoassesstheprimaryandsecondaryfragmentationaswellashang-uppotential.Althoughthisapproachisabletoquantifysecondaryfragmentationandhasbeencalibratedusingminedata,itsreliabilityhasbeenquestioned(Butcher2007).ExperiencesatPalaborafoundtheBCFoverpredictedthepercentageofoversizedblocks(>2m3)andunderpredictedthenumberofhang-ups(Ngidi&Pretorius2011).Pierce

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(2009)proposedanalternativemethodologybuiltonaparticleflowmodel(REBOP)andlaboratorytestingwithanannularshearcell.

In addition, a hybrid approach was developed during MMT1, which includes empirical rules basedon comminution theory (Kojovic 2010). Weatherley & Pierce (2011) compared the performance ofthesemethods, but the predictions did not fullymatch the data collected at RidgewayDeeps (RWD),underpredicting thefinesproduction. Instead, theyconcludedthatPierce’smethod(REBOP)performedbetter. Finally, Rogers et al. (2010) has proposed anothermethodology based on a stochasticDiscreteFractureNetwork(DFN)approach,whichcapturesthebreakageofblocksastheymovethroughthecave,butlackscomputingthefinesproduction.

Althoughthereissignificantinterestindevelopingthebasisandfundamentalsofsecondaryfragmentation,sometopicshavereceivedlessattention,suchastheinfluenceof:fallheight,blockrotationandrockfallimpactonthemuckpilesurfacewhereanairgapispresent;vein,rockstrengthandnon-persistentjointdistributionsintheblocks;theinitialarrangementofcavedblocksandsubsequentblockinteractions;thebrokenoredensityand itsdistributionwithinadrawcolumn;and the roleoffines incushioningblockinteractions.Several of these are influencedbyBSDand its evolutiondown through the draw columnheightovertime.

2 Secondary fragmentation assessment by means of large compression tests

Secondaryfragmentationiscommonlyattributedtoacombinationofblocksplittingandrounding,withblockmovementbeingcontrolledthroughacombinationofshearandcompressivestressesoccurringinthedrawcolumnzones(Pierce2009).Replicatingtheseconditionsthroughlaboratorytestingprovidesausefulmeanstodevelopempiricalrulesofthumbornumericalmodelcalibration.Accordingly,publishedresultsinvolvinglargetriaxialcompressiontestsCID(ConsolidatedIsotropicallyDrained)areavaluablesourceofdatatoevaluatesecondaryfragmentationofbrokenorewithinadrawcolumn.Thesearediscussedbelow.

2.1 Large compression tests to evaluate secondary fragmentation

AsshowninFigure1,secondaryfragmentationcanbelinkedtotwomodesofstressesactingwithinadrawcolumn.Inthecenter,thebrokenoreundergoesanisotropiccompression.Thisissimilartotheloadpathconditionsappliedinanoedometertest.Adjacenttothis,towardstheouterperipheryofthecolumn,thebrokenoreexperiencesshearstresses.Thisissimilartotheloadpathconditionsappliedindirectsimplesheartests.

Ofinterestarelaboratoryresultsinvolvingaunique,largetriaxialdevicecapableoftestingsampleswith1mdiameter,previouslyappliedtorockfillcharacterizationstudies(Marsal1973;Verdugoetal.2007).Theseserveasaproxyfortheloadpathexperiencedinthedrawcolumn(Figure2),whichincludesthedevelopmentofbothcompressionandshearzones(Figure3).Maximumparticlesizesoftherockfillandwasterocktestedinthisfacilityhavereached15-20cm.Theserepresentavaluabledatasourcethatcanbeextendedtosecondaryfragmentationstudies,givensimilaritiesintheintrinsicrockproperties(blockstrength,angularity,aspectratio),aswellasblocksizedistribution,initialmaterialdensity,andconfiningpressure(i.e.,columnheight).Twomaincontributionsof theselargescale tests is theshearingstrengthchartforrockfillbyLeps(1970)andgeotechnicalcharacteristicsoflargewasterockdumps(Valenzuelaetal.2007).Largediametertriaxialtestsofferausefulalternativetoestimatethecomminutionofbrokenoreinadrawcolumnundershearandcompressionstressesbasedonsampleswithamaximumparticlesizeof15cmandloadpathssimulatingadrawcolumnof200moverloadheight.Resultsusingthisdeviceforspecifictestingofsecondaryfragmentationarereportedinalatersection.


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2.2 Size scaling from large compression samples to broken ore sizes

Blocks in a draw column aremuch larger than themaximum particle size tested in the large triaxialcompressiontestsdiscussedabove;thusascalingrelationshipisneededtoapplytestresultstoblocksizedistributionrelationships.ThemostpopulartechniquetoscalegeotechnicalpropertiesfromsmallgranularsamplestolargebrokenrockisbasedontheparallelgradationmethodorparallelsizedistributionmethodfirstproposedbyLowe(1964).Thistechniqueinvolvesshiftingthesizedistributioncurve(onasemi-logplot)byafactor“S”toscalethesmallersizedtriaxialcompressionteststothelargerscaleinsitumaterial(Figure4).DelaHoz(2007)demonstratesthatthistechniqueissuitableinsandsandgravels,butforlargersamplesizes,thestrengthandstiffnesstendstodecrease(Frossard2013).

It isalsowellreportedthat thestrengthof individualblocksdecreasesasblocksizeincreases(Hoek&Brown1980;Santamarina&Cho2004).Ontheotherhand,theblockcoordinationnumber(i.e.,numberofcontactpoints)resultingfromtheparticlepackingalsoinfluencesblockfragmentation.Particleswithmorecontactpointsaregenerallysubjectedtoalowerprobabilityofsecondaryfragmentationduetotheloadsbeingmoredistributed(McDowelletal.1996).Thus,alargeblockadjacenttoanumberofsmallerblocksislesssusceptibletofragmentationduetoitshighercoordinationnumberbutmoresusceptibletocontainingstrengthreducingdefects(e.g.,veining,non-persistentjoints,etc.).Someauthorsagreethatthecoordinationnumber ismoresignificant thanstrengthreductionduetoblocksize;however there isnotenoughexperimentaldatatoconfirmthisassumption.

Moreover,anothereffectrelatedtothecoordinationnumberisthecontactnatureamongadjacentblocks.Largeblockaresusceptible tobreakagedependingonhowitsflawsarealignedrelative to thecontactsacting on it (e.g., corner-side or side-side). Research applying empirical and numerical techniques iscurrentlyunderwaytoquantifytheinfluenceoftheparticlearrangementaroundlargeblocks.

Figure 1 Stresses within a broken ore zone assuming narrow flow width (Laubscher 1994) and

interactive flow (Susaeta 2004)

Figure 2 Representation of draw column stress modes to triaxial compression loading

conditions

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3 Methodology to assess fines migration in a draw column

3.1 Background

Finesmigration(Figure5)hasbeeninvestigatedbyseveralauthorsconductingempiricalstudies(Hashim&Sharrock2012;Chenetal.2009;Castro2006)andnumericalmodelling(Leonardietal.2008;Pierce2009).Thissegregationprocesshasbeenidentifiedasakeyelementindrawcontrolandcushioningoflargeblocks(Laubscher1994),andmudrushriskduetothepresenseofwater(Jacubeketal.2012).Althoughsignificantadvanceshavebeenreportedonthis theme, thereisa lackofamethodologytoquantifythefinesmigrationfordifferentdrawcolumnheightsincavingoperations.Incontrast,numerousstudiesexistinvestigatingfinessegregationingranularmaterialsrelatedtointernalerosion,piping,suffusionandfilterdesigninearthdams.ThesearereportedinstandarddesignmanualssuchastheEarth&Rock-FillDamsGeneralDesign&ConstructionConsiderations(2004)andDesignandConstructionofLevees(2000).

MajoradvancesinDamEngineeringbyKezdi(1979),Sherard(1979)andKenney&Lau(1985)makepossibletheassessmentofsegregationpotentialoffines(<4.75mmsize)fromlargerparticles(>4.75mmupto1,000mm).Thesemethodsfocusonfinessegregationduetoseepagethroughanearthfilldam,whichisnotfullycomparabletothefinesmigrationinablockcavedrawcolumn.However,thefinessegregationfromabrokenorezoneisadynamicprocessinvolvingthecontinuousdownwardprogressionofblocks,includinginternalmovementsamongblocks,facilitatingthemigrationoffinesfromthebrokenore.Hence,thefinesmigrationincavingissomewhatcomparabletotheinternalerosionindams.

ApplyingKezdi’s(1979)methodtoanorecolumn,theinitialgradationcanbedividedintoacoarseandfinegradationasshowninFigure6.ThekeyhypothesisofthismethodisthatthesegregationofthefinesgradationwilloccuriftheratioD15/d85ishigherthan4,whereD15istheparticlediameterforthe15%ofmasspassingofthecoarsegradationandd85istheparticlediameterforthe85%ofmasspassingofthefinegradation.Hereitisnecessarytoestablishtheinitialgradation,whichthenallowsthecalculationofthefineandcoarsegradationsrelativetoaspecificblocksize(blackdashedlineinFigure6).Thesegregationpotentialcanthenbecheckedbyapplyingthisproceduretoseveralblocksizes.AcompleteexplanationofthismethodcanbefoundinKezdi(1979),Chapuis(1992)andLi&Fannin(2008).

3.2 Fines migration and broken ore size distribution

Itiswellacceptedthatfinesmovemorerapidlythancoarserparticlesthroughthedrawcolumn(Laubscher2000).Thiscanbeusedtodevelopaconceptualfinesmigrationsequenceoccurringdownthroughanore

Figure 3 Stresses developed in a triaxial compression test

Figure 4 Gradation parallel method


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column,guidedbyKezdi’smethodtogetherwithresultsfromlargediameter triaxial tests(Figure7–9).Thebrokenorezonesinthecolumnaredefinedwithromannumeralsadjacenttothecolumn,witheachrepresentingeithertheadditionofbrokenoretothetopofthemuckpile(throughcavingfromthecaveback)orsubtractionrepresentingremovalofthezonethroughextractionatthedrawpoint.Othernomenclaturein thesefigures includes thenumbers0,20,40…until200,whichare related to theoverloadheightofbrokenore.Includedwiththeoverloadnotationaretheletters“Cand“F”,whichrefertothe“coarse”and“fine”gradationspresentedinFigure6.Thus,aC40isacoarsegradationunder40moverloadheightandF120isafinegradationunder120moverloadheight.Furthermore, thesequencetakesintoaccount11stages,whichinturnincludes3sub-stages(lettersa,b,c).Letter“a”correspondstotheinitialsecondaryfragmentationoftheoreafteraverticalmovementof20m;“b”isrelatedtothecorrespondingmigrationoffines(blackenedgradations);and“c”correspondstothebrokenore’sresponsetotheoverloadpressures.

Thesequencestartsatstage0intheundercutlevel(Figure7).Theblastedrockisassumedtobemined,sothatthecaveinitiatesandbrokenorefallsintotheundercut.Next,theprocedureassumesthattwonewportionsofbrokenorearereleasedfromthecaveback(Stage1a),comprisingacoarseandfinegradation(C0+F0).Atthisearlystage,nofinesmigrationisassumedforthe“b”sub-stagebecausethedrawcolumnisnotdevelopedenoughtopermitsignificantinternalmovementofthebrokenore.Thusstage1bremainsthesameasstage1a(thisisthesameforstages2aand2b).

Atstage1c,anoverloadof20m isappliedand thestressesaredisproportionatelyconcentratedon thecoarsegradation.Inresponse,C0changestoC20andsomefinesaregenerated(equaltoF20minusF0).Thus,thefinesproductionisincreasedwitheverysub-stage“c”(i.e.,F40–F0,F60–F0,throughF200–F0)duetooverloadingthroughtheprogressionofthedrawcolumn(romannumerals“i”through“xx”).Asimilarsequencerepeatsforstage2.Instage3,theorecolumnismatureenoughtoconsiderthemigrationoffines.Thus,forstages3and4,itisassumedthathalfofthefineswillmigratedownwardtothebrokenorezonebelowand,forstages5to7,allofthefinesareassumedtomigrateintotheunderlyingbrokenorezone.Forstages8to10,itisassumedthatthefineswillmigratetwobrokenorezonesdown(or40mdownward).Finally,thissequenceisdescribeduntilstage10,whichcorrespondstoadrawcolumnheightof220m.

Figure 5 Segregation process along a draw column

Figure 6 Kezdi’s method to divide an initial gradation into a fine and coarse gradation. Thus, the initial gradation is the mixing product of both fine and coarse gradations. Fine and coarse particles are defined as particles smaller and larger

than 20 mm, respectively

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Figure 7 Fines migration sequence (Stages 0 - 2)

Figure 8 Fines migration sequence (Stages 3 - 4)

Figure 9 Fines migration sequence (Stages 5 – 10)

4 Evolution of BSD at different ore column heights

In order to study the evolution of theBSDwithin an ore column, a large triaxial compression test onsaturatedwasterockmaterialwascarriedout.ThematerialtestedcorrespondstoagranodioritewithaUCSof140-150MPaandspecificgravity(Gs)of2.77.Thetriaxialtestwasperformedapplyingaconfiningpressureof2.5MPa,resultinginadeviatoricstressatfailureofDsf=(s1-s3)of9.5MPaandverticaldeformationatfailureof18%.Thespecimendimensionwas100cmdiameterand180cmheight,withamaximumparticlesizeof15cmandspecimendensityof19KN/m3.Thus,thistriaxialtestapproximatesabrokenorezonewithanoverloadof200m.

Using the parallel gradationmethod, the initial gradation and gradation after testing were scaled to amaximumblock sizeof4m, imitatingblock sizeswithinadrawcolumn (Figure10).Thus, the initialscaledgradationrepresentstheprimaryfragmentationcurve.Finally,it ispossibletointerpolateseveral


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gradation curves (nine in total) representing different confining pressures (or overload heights) between the initial gradation (no stress) and gradation after testing (s3 = 2.5 MPa), as shown in Figure 10. Note that these interpolated curves can be used to characterize the coarse gradations (C0, C20…C200) as well as the fine gradations (F0, F20…F200) in the fines migration sequence (Figures 7-9).

After scaling the gradations to the in situ block sizes, the fines migration sequence is applied. Figure 11 presents the evolution of the scaled gradation (or BSD) for three different column heights. Based on these results, it is possible to observe how the initial BSD curve transitions at stage 10 to a more linear trend than an “S” shaped curve.

The magnitude of the fines migration on the final BSD at the drawpoint depicts a reduction in the average size of the material. For example, in Figure 11, the 50% of mass passing due to loading only (dashed curve) drops from 0.6 m to 0.3 m (two times less). However, this reduction considering both loading and fines migration (stage10 curve) drops from 0.6 m to 0.04 m (15 times less). Thus, the combination of large triaxial tests and the fines migration analysis is able to capture the evolution of the BSD from its initial gradation (primary fragmentation) to that mined at the drawpoints. Moreover, the impact of the fines migration on the BSD can be determined by comparing the initial scale gradation after testing curve (black dashed curve in Figure 10) and the “stage 10” curve (grey color). The former is representative of the fragmented ore close to the drawpoint with a muckpile overburden of 200 m but no fines migration, and the latter represents the same overburden but including the fines migration process.

5 Discussion

The shape of the block size distribution has been addressed in terms of applying relationships drawn from large compression tests (triaxial CID test) and a fines migration sequence analysis based on techniques developed to study internal erosion in earth dams. Some assumptions included in this work are discussed as follows:

• Triaxial tests used as a proxy for simulating the shear and compression zones within a broken ore column: As explained in section 2.1, a triaxial test combines elements of both simple shear and oedometer tests but this hypothesis needs to be corroborated with a conventional laboratory testing program involving simple shear, oedometer and triaxial CID testing, with special focus on particle breakage. For example, simple shear tests could involve significant splitting but much

Figure 10 Initial gradation (BSD) and gradation after testing, including scaled curves (parallel

gradations)

Figure 11 Evolution of Initial gradation for three different stages. Stages 3, 6 and 10 represent a

column height of 80, 140 and 220m, respectively

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lessfinesgenerationcomparedtoanoedometertest,whichinturncouldexperiencemuchmorefinesgenerationduetostressconcentrationsamongparticlesbutlesssplittingthansimpleshear.

• Finesmigrationsequence:Thissequenceappliessegregationandpipingcriteriausedforearthdams.However,duetotheinfluenceofwaterflowonfinesmigration,thismethodologycouldoverestimatethefinesarrivingatthedrawpoints.Inaddition,thissequenceassumesdifferentsegregationrates,whichstillrequirevalidationfromfielddataandpercolationratestudies(e.g.,Bridgwateretal.1978;Cardew1981;Pierce2009).

• Blocksizescalingeffectsonbrokenorefragmentation:Thisissuehasnotbeenaddressedinthisstudyandrequiresmoreexperimentalandnumericalstudiestobetterunderstandtheinfluenceofveinsandsmallerdiscontinuitiespresentinlargerblocksontheiroverallfragmentationwithinadrawcolumn.

6 Conclusions

Aconceptualmodelhasbeendevelopedaccountingfortheinfluenceofsecondaryfragmentationandfinesmigrationontheblocksizedistributionofbrokenoreencounteredatadrawpoint.Firstly,thebrokenorefragmentationbyshearandcompressionhasbeeninvestigatedusingalargediametercompressiontestonwasterock(TriaxialCID).Secondly,theparallelgradationmethodisusedtoscalethestandardparticlesizefromthetriaxialtestsampletothatrepresentingtheinsitublocksizes.Thirdly,asimplemethodisproposedtoestimateanapproximaterangeoffinesmigrationfordifferentorecolumnheights.ThissuggestsamorelinearBSDthanan“S”orexponentialshape.Finally,moreeffortsarerequiredtounderstandthefundamentalsofsecondaryfragmentationandthepredictionofthedrawpointBSDwithahigheraccuracy.

References

Bridgwater, J,Cooke,MH&Scott,AM1978, ‘Inter-particle percolation:Equipment development andmeanpercolationvelocities’,TransactionsoftheInstitutionofChemicalEngineers,vol.56,pp.157-167.

Brown,ET2007,BlockCavingGeomechanics,Indooroopilly:JuliusKruttschnittMineralResearchCentre.ISBN978-0-98003622-0-6,Queensland.

Butcher,RJ&Thin,IGT2007,‘Theinputsandchoicesforpredictingfragmentationinblockcaveprojects’,in Proceedings First International Symposium on Block and Sub-level Caving, SouthernAfricanInstituteofMiningandMetallurgy,Johannesburg,pp.35–49.

Cardew,PT1981,‘Percolationandmixinginfailurezones’,PowderTechnology,vol.28,no.1,pp.119-128.

Castro,R2006,‘Studyofthemechanismsofgranularflowforblockcaving‘,PhDThesis,UniversityofQueensland.

Chapuis, RP 1992, ‘Similarity of internal stability criteria for granular soils’, Canadian GeotechnicalJournal,vol.29,no.4,pp.711–713.

Cheng,YM,Liu,ZN,Song,WD&Au,SK2009,‘LaboratoryTestandParticleFlowSimulationofSilosProblemwithNonhomogeneousMaterials’,JournalofGeotechnicalandGeoenvironmentalEngineering,vol.135,no.11,pp.1754-1761.

Eadie,B2003,‘AFrameworkformodelingfragmentationinblockcaving’,PhDThesis.TheUniversityofQueensland.Australia.


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Esterhuizen,GS,Laubscher,DH,Bartlett,PJ&Kear,RM1996,‘AnExpertSystemApproachtoPredictingFragmentationinBlockCaving’,ProceedingMassminMiningMethods,SAIMM.

Frossard,E,Ovalle,C,DanoC,Hicher,PY,Maiolino,S&Hu,W2013,‘Sizeeffectsduetograincrushinginrockfillsshearstrength’,Proceedingsofthe18thInternationalConferenceonSoilMechanicsandGeotechnicalEngineering,Paris.

Hashim,MHM,&Sharrock,GB2012,‘Dimensionlesspercolationrateofparticlesinblockcavingmines’,In:MassMIN2012ConferenceProceedings.MassMin2012:6thInternationalConferenceandExhibitiononMassMining,Sudbury,Ontario,Canada.

Hoek,E,&Brown,ET1980,‘Undergroundexcavationsinrock’,InstnMin.Metall,London.

Kenney,T,&Lau,D,1985,‘Internalstabilityofgranularfilters’,CanadianGeotechnicalJournal,vol.22pp.215–225.

Kezdi,A1979‘Soilphysics–selectedtopics’.ElsevierScientificPublishingCo.,Amsterdam.

Kojovic,T 2010, ‘Application of theHybridModel toRWD’, SubprojectReport submitted toMMT2SecondaryFragmentationProject.

Laubscher,D1994, ‘Cavemining– thestateof theart’,TheJournalofTheSouthAfrican InstituteofMiningandMetallurgy,pp.279-293.

LeonardiCR,OwenDRJ,Feng,YT&FergusonWJ2008,‘Computationalmodellingfinesmigrationinblockcavingoperations’,Proceedingsofthe5thinternationalconferenceandexhibitiononmassmining,Lulea,Sweden.

Li,M&Fannin,RJ2008,‘Comparisonoftwocriteriaforinternalstabilityofgranularsoil’,Can.Geotech.Journal,vol.45,pp.1303-1309.

Lowe,J1964,‘ShearStrengthofCoarseEmbankmentDamMaterials’,Proc.8thInternationalCongressonLargeDams,vol.3,pp.745-761.

Marsal,RJ1973,‘MechanicalPropertiesofRockFillEmbankment-DamEngineering’,(ed.)HirschfeltandPoulos,JohnWiley.NewYork.

Mcdowell,GR,Bolton,MD,&Robertson,D1996,‘Thefractalcrushingofgranularmaterials’,JournaloftheMechanicsandPhysicsofSolids,vol.44,no.12,2079-2102.

Ngidi,SN&Pretorius,DD2011,‘Impactofpoorfragmentationoncavemanagement’,In6thSouthernAfricanBaseMetalsConference.TheSouthernAfricanInstituteofMiningandMetallurgy,pp.111-122.

Pierce,M2009‘AModelforGravityFlowoffragmentedrockinBlockCavingMines’,PhDThesis,TheUniversityofQueensland.

RogersS,ElmoD,Webb,G,&Catalan,A2010,‘Adiscretefracturenetworkbasedapproachtodefininginsitu,primaryandsecondaryfragmentationdistributionsfortheCadiaEastpanelcave’,InCaving2010,Proceedingsofthe2ndInternationalSymposiumonBlockandSublevelCaving,Perth,(EditedbyY.Potvin),AustralianCentreforGeomechanics.

Santamarina,JC&Cho,GC2004,‘SoilBehaviour:Theroleofparticleshape’,Proc.SkemptonConference,London.

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Sherard, JL1979, ‘Sinkholes indamsof coarse,broadlygraded soils’,Transactions,13th InternationalCongressonLargeDams,NewDelhi,India,vol.2,pp.25-35.

Susaeta,A2004,‘Theoryofgravityflow(part1)’,InProceedingsofthe5thinternationalconferenceandexhibitiononmassmining,Santiago,Chile,pp.167-178.

U.S.ArmyEngineerManual2004,‘Earth&Rock-FillDamsGeneralDesign&ConstructionConsiderations,EM 1110-2-2300’, available from: <http://www.publications.usace.army.mil/Portals/76/Publications/EngineerManuals/EM_1110-2-2300.pdf>.[1April2014].

U.S.Army EngineerManual 2000, ‘Design and Construction of Levees’, EM 1110-2-1913, availablefrom: <http://www.publications.usace.army.mil/Portals/76/Publications/EngineerManuals/EM_1110-2-1913.pdf>.[1April2014].

Valenzuela, L, Bard, E, Campana, J & Anabalon, ME 2008, ‘High waste dumps - challenges anddevelopments’,In:RockDumps2008,Fourie,A.(Ed.),AustralianCentreforGeomechanics,Perth,pp.65-78.

Verdugo,R,Peters,G,&Bejarano,I2007,‘Evaluacióndeparámetrosgeomecánicosdesuelosgruesos’,VIChileanGeotechnicalconference,Valparaíso.

Weatherley,D,&Pierce,M2011,‘ProgressReport-FundamentalsofCavingFragmentation’,ReporttotheMassMiningTechnology2Project,February.


138

Analysis of hangup frequency in Bloque 1-2, Esmeralda Sur Mine

E Viera Codelco, ChileE Diez Codelco, Chile

Abstract

Monitoring and analysis of secondary breakage is relevant in productivity of Block- Panel Caving mines, especially in mines where production area with low percentage of draw column (<30% of primary column) is majority in comparison to an area without geomechanical constraints. A smaller capacity of secondary breakage can generate deviations in draw strategy, which may compromise production plans in the short and the long term.

Bloque-1 of Esmeralda mine (located in the Sub -5 level El Teniente mine ) reflects the condition of extraction described, because 35% of production area has draw rate constraints (percentage of primary draw column <30%) and 42% of the open area has no draw rate constraints. In this particular sector, a low capacity of secondary breakage (S.B) can generate wrong practices of draw rate strategy that may impair considered planning strategies (dilution control, angle control and regularization of draw heights), due to the reduced availability of area in state of caving propagation process (which has higher hanging frequencies) and increase of extraction in area without draw rate constraints (lower hanging frequency).

The main goal to this work is to perform a quantitative analysis of the hanging frequency to Bloque-1, correlating this variable with the extraction percentage of primary column and granulometry curves.

For Bloque -1 a record of hangings and activities related with secondary breakage of draw points for the year 2013 has been made, which we have calculated the rate of extraction between hanging (REH) of production drifts throughout the detailed period. This index has been related to granulometry curves and the average percentage of primary column in each production drift.

It is aimed to establish a pattern that allows including the frequency of hanging by draw point in short-term plans, considering the predominant granulometry curves and the percentage of average primary column for future projects to be undertaken in Esmeralda Mine.

1. Introduction

AgreaterunderstandingofrockfragmentationisvitalforBlock/PanelCavingminesdesignandplanningstages, aswell as the variables that dependon the rock fragmentation.Theygo fromdesign of layout(spacingofdrawpoints),tothedailysecondarybreakagerequirements.Becauseofitsimportance,itisalsonecessary toperformbackanalysis studies that allow toestimate fragmentationpredictivemodels, andmanagementtools,inordertobeabletomonitorthepredominantgranulometrybehavior.

ThefollowingstudyperformsabackanalysiswiththedatarecordedinEsmeraldaSur,whichhavetheobservedgranulometryreportsinranks,andthesecondarybreakage(SB)madein2013.

Currently,EsmeraldaSurisformedbyBloques1and2,which,untilMarch2014,showedanincorporatedareaof30,589m2and13,163m2ofproduction,respectively.

Esmeralda’sMineSectorSurhasavolumeofmaficrocks,knownasElTenienteMaficComplex(Figure1),intrusionedbysubverticalmattersoffelsicrocks.FelsicrockspresentinBloque1arelocatedbetween

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productiondrifts23and33,andtrenches30to35tothemine,wheredioriticporphyrycanbefoundincontacttohydrothermalbrecciasofdioriticporphyryintheNE.

On the other hand, Bloque 2 had one intruded of tonalite that occupied half of the surface, cutting itdiagonallyfromNWtoSE,whichsufferedlaterintrusionsfrommattersofanhydritehydrothermalbreccias,biotitehydrothermalbreccias,andfinallyfromamicrodioriteporphyry:

Figure 1 Esmeralda Sur Lithology

2 Objectives

ThefollowingstudyaimstoanalyzetheEsmeraldaSurhangingdrawpointsfrecuency,byconsideringthesecondarybreakagesmade,andtherecordedgranulometryduring2012and2013.

3 Methodology

3.1 Information record

Thehangdrawpointsconditioninformationisrecordedin2databases:

• Control ProductionMine database: thementioneddatabase is hold by analyst’s routes that recordrelevant information about the drawpoints condition, such as humidity, hangs and observedgranulometryconditions.Ideally,themostrelevantproductiveareasmustbecheckeddaily.

• EsmeraldaMineOperationaldatabase: thedrawarea recordseverysecondarybreakageperformedin thedrawpoints.This databaseprovides the secondarybreakagedate (day and shift), amountofexplosivesused,driftandtrenchesinwhichthebreakagelaborsaremade.

When comparing both databases, which shows a greater reliability analyzing the hanging drawpointsfrequencyisthesecondone,sinceitcoversagreatertemporality(thefirstonefairlyshows1dailyshiftwithinformation,thesecondoneshows3dailyshiftswithrecords).


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Inordertoquantifytheeffectgeneratedinproductionbythesecondarybreakages,madeatdriftlevel,theSecondaryBreakageIndexisdefinedasitfollows:

Thisindexmustbedefinedonamineunit(fromminescalesizetodrawpointslevel)andatacertaintimeinterval.

3.2 Granulometry information record.

Thisinformationiscompiledbyateamofeightproductioncontrolanalysts,atleastonceadayinmajorareas.Duringtheirroutes,theypickupinformationrelatedtodrawpointcondition,granulometry,humidity,lithologyanddilution.

Theinformationusedforthisworkwasthedrawpointsgranulometry,accordingtothefollowingoverlaps:fragmentslowerthan5cm(A),between5and25cm(B),from25to50cm(C),from50to100cm(D),and rocksgreater than100 (E).Whenclassifying rocks intooneof theoverlaps, the three right-angledsizesarithmeticmeantothevolumeitshouldholditisobtained.Thegranulometryinformationisavisualappreciationtothepercentagesof5granulometryoverlapsontheslopesurfacemadeinthedrawpointsanditiscalibratedbypointmeasuresmadeongroundwithatapemeasure.TheinformationobtainedongroundandstoredintoadatabasehascontinuityfromthebeginningofMinaEsmeralda.Notethat,additionallythedrawpointscanbehang,whichmakesthedatacollectnottobesafeforthefieldpersonnel,thusnotcollectinggranulometricinformation.Thislackofinformationcreatesabiaswithinthedatabase,whichaffects the thickergranulometry representativeness.For this conditionwithnogranulometry surveys, a100%ofthegranulometrygreaterthan1misassigned.

The analyzed data for this article were the thicker granulometry information (>50cm), obtained fromJanuary2012untilMarch2014,whichcoversalmosttheentirestudyareaproductivelifesofar.

Finally, theschemeofstages toconsiderfor thehangdrawpointsandgranulometryanalysis isdetailedbellow:

AllofthesecondarybreakageinformationrecordedbetweenJanuary-November2013willbeconsidered.During2012,thedatabaseinformationwasnot100%completed,thereforeitisnotconsideredwithintheanalysis.

Notethatthesecondarybreakageanalysisonlyconsidersthedrawbellsinwhichthecavingheadinghascompletelypassed(productiondrawbells).Thepreviouscriterionleavesoutoftheanalysisthedrawbellswithswellingcondition,whichdonotshowhangingconditions.

As for a greater understanding concern, the analysis will be developed at production drift level. Thisconsiderationallowsaddingthesecondarybreakagevariablewithintheshorttermplansinaneasyway.

4 Results

When considering the secondary breakages laborsmade for drift and the draw during 2013, theREHindexiscalculatedforeverymonth,whichiscomparedtotheaverageprimarycolumndraw,obtainingthefollowing:

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Figure 3 REH as a function of extraction

Astheprimarycolumndrawrateincreases,theREHindexincreasesproportionally,thusshowingagreaterdispersionofamountsabovethe30%oftheaverageprimarycolumndraw.Thepreviousbehaviorcanbeexplainedbythefollowingpoints:

• Increasinggeomechanicaldrawrates:thecontinuousgeomechanicalincreasingdrawratesoccursascertainlimits,previouslydefined,areexceeded.Therefore,thegreaterextractionofprimarycolumn,thegreaterproductivecapacityperarea(nooperationalrestrictions).

Figure 2 Information process for analysis scheme


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• Variationsinthematerialhandlingsystem:asfortheEsmeraldaSurcase,thisvariationoccurswhenorepassesareincorporated,whichbecomesintoamoreefficientmaterialhandlingsystem,sinceitreducestheorehaulingmeandistance.

• Reductionofhangingdrawpointsand/orrocksizes:whenreducingthehangingsand/orrocksizes,it reduces theoperational interferences,which increases thematerialhandling systemproductivity(moreoperationalcontinuity).

Inthefollowingsection,thereductionofhangingdrawpointand/orrocksizeswillbeexplainedindetail,byanalyzingifthereisagratergranulometryrecordreduction(typeE),astheextractionofpercentageofprimarycolumnincreases.TheFigure4showsthegranulometryevolutionaccordingtotheprimarydrawpercentage,byconsideringadatarecordfrom2012and2013inEsmeraldaSur:

Figure 4 Fragmentation observed at drawpoints as a function of extracted column

AccordingtotheGraphic2information,itisevidentthatthecoarsefragmentsremainsbetween50%and60%fromthebeginningofthedrawuptothe80%oftheprimaryrockcolumn,gettingtoitsmaximuminthispoint,anddecreasingtothe30%indrawpointswithhigherextractionpercentageofprimarycolumn(120%).Regardingthetwogranulometryoverlapsbehavior,itcanbeensuredthatitisdespair,asthe“D”granulometryremainsrelativelystable,ontheotherhand,coarsefragmentsvariesaccordingtothedraw.Theaimedgranulometriccurveforthisminingmethodforeseesahighraiseinfinegraining,afteritexceedsthecolumn’s100%,theprimaryrockisreplacedforfragmentedmaterial.SuchdecreaseismitigatedatBloque-1,where the points having 120%of draw remain an average of 20%of sizeE rockswith thecapabilityofblockingtheoreflow.

Startingat80%ofprimarycolumnextraction,adecreaseinperformedsecondarybreakageshouldbeseen,bywhichitshouldraisetheREHindexdefinedabove.Thisresultisimportantfromtheshorttermplanningpointofview,sinceit ispossible to takethisvalueasareferenceto increasetheproductivityperdrift,withintheshortterm.

LinkingtheFigures1and2,wecanseethat theREHrateincreasesproportionallywiththeextraction,howeverreportsoffragmentationtype“E”noevidenceofvariationupto80%extractionoftheprimarycolumn,whereby the increasingofgeomechanicdraw rates and improvements in themineralhandlingsystemaremorerelevantinthatstretch.

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Whenreducingtheplottedvalues(inFigure2),calculatingtheREHindexwithintheentireperiod(January-November2013periodandnotmonthlyasperformedinthefirstcase),theobtainedresultsareasshowninFigure5.

Figure 5 Average extraction of primary column vs REH

Figure5shows thesamebehaviorseenwhenplottingallof themonthlydata.However, thedifferenceisthatinthisgraphsomeregressionfunctioncanbefound.Thelogarithmicregression(explainedinthegraph)showsanacceptablecorrelation,andthenit ispossibletousetheregressionequationwithinthemonthlyplanningexercises.

Inordertoexemplifythisanalysisusageintheshorttermplanning,thedefinedparametersforproductionmethodduringMarchatBloque-1areconsideredaslistedinTable1.

Table 1 Parameters Bloque-1 March 2014.

Production Drift Productivity (tpd) Amount of drawpoints Primary column average extraction (%)

21 301 3 1423 810 7 1825 1.244 10 1927 2.153 13 4629 2.579 15 5831 2.778 16 7333 2.817 16 7635 2.879 16 5737 1.379 8 3853 348 5 655 544 8 1357 1.095 10 1559 1.280 11 11


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WiththeaveragepercentageswecanobtaintheREHindexforeachoneoftheproductiondriftsdetailedabove(intheregressionformula).Consideringthatwehaveaverageplannedproductivity,itispossibletoestimatetheentriesamountinproductiondrifts,toperformdailysecondarybreakagelabors(accordingtoREHformula,Table2):

Table 2 Amount of overlaps per secondary breakage labors

Production Drift REH Production drift entries to S.B labors per day

Amount of points in S.B per day (*)

21 826 0,36 123 959 0,84 325 1.244 1,26 427 2.153 1,55 529 2.579 1,73 531 2.778 1,74 533 2.817 1,74 535 2.879 1,94 637 1.379 1,06 353 412 0,84 355 789 0,69 257 867 1,26 459 725 1,77 5

(*)inbaseto3pointsinS.Bperproductiondriftentry.

ForthedemandedrateduringtheMarchprogramandfortheaverageprimarycolumndrawlevelperdrift,itisnecessarytoperformsecondarybreakagelaborsin50distributedpoints,asshownintable2.Whenconsidering thefullmonthand the time this laborsperdrift take, it ispossible toestimate themonthlyrequiredtimeforsecondarybreakagelabors,whichcanbeincludedinthemonthlyplan.

Notethattosatisfythesehighproductivityrates,isnecessarythedetailedquantityofsecondarybreakageperproductiondrift.Significantdeviationsinthedistributionofsecondarybreakageimpactindrawstrategiesintheshorttermplanning(forexamplehumiditycontrol,dilutioncontrol,extractionanglecontrol,etc.).

ThisisexemplifiedinproductiondriftC-25andC-27ofBloque-1,whichhascompletelydifferentconditionsofgranulometry (Figure6). InproductiondriftC-25, thehangings frequency ishigher as compared toC-27,wherebytherequirementofsecondarybreakageinC-25isincreased.Howevermoreresourcesareallocated to reduce hanging drawpoints on production driftC-27,which helped to create a differencebetweenthetwoextractionareas(duetoincreasedavailabilityofareainproductiondriftC-27),causingadifferenceinextractionheightinbothareas:

3 Discussion and conclusions

AcorrelationisseenbetweentheprimarydrawpercentageandtheREHindex,whichisdirectlyproportionaltotheprimarycolumndrawpercentage.

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It is possible to state that starting at 80% of primary column extraction, a change was seen in thegranulometricdistributionreportsas the recordedranks,decreasing inaprogressiveway the fragmentspercentagereportsabove1m,startingataverageprimarycolumndrawlevel.ThistopicdevelopmentwillbeusefulforplanningandcontrolofthefollowingmineEsmeraldaSurproductiveblock.

Itispossibletoestimatethesecondarybreakageleveltobemadeconsideringtheprimarycolumndrawpercentageandtheplannedproductivityintheshorttermprograms(atproductivedriftslevel).Differencesin thesecondarybreakage requirementsmakedeviations in theshort termprogram,whichcouldaffectdrawstrategies(drawangleincrease,dilutioncontrol,humiditycontrol,etc.).

Acknowledgement

Contributions fromGabrielTapia (Universidad de Chile) andBoris Leal (Universidad de Santiago deChile)aregratefullyacknowledged.

References

Montecino,N,Castro,R2013,‘Modelodemezcladefragmentaciónsecundariaenmineríadeblock/panelcaving’,LaboratoriodeBlockCaving,Santiago,Chile.

Moss,A,Russell,F&Jones,C2004,‘CavingandfragmentationatPalabora:predictiontoproduction’,ProceedingsMassMin2004,Santiago,Chile.

Figure 6 Extraction height between production drift C-25 and C-27


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A 3DEC-FLAC3D models to predict primary fragmentation distribution in Cave Mines

T V Garza-Cruz Itasca Consulting Group, Inc., USA M Fuenzalida Itasca Consulting Group, Inc., USAM Pierce Itasca Consulting Group, Inc., USA

P Andrieux Itasca Consulting Group, Inc., USA

Abstract

Prediction of primary fragmentation is of great importance in cave mining. Numerical models provide an opportunity to link caving induced stresses at failure with associated fragmentation of rock masses. In this paper, the results of a set of 3DEC simulations, based on a methodology developed by Itasca to model the fragmentation of massive veined rock masses, are used to relate induced stresses at failure to percentage of large fragments produced. A new approach is described in which such fragmentation relationship is applied to predict the primary fragmentation distribution in a FLAC3D cave model based on computed stresses at failure.

1 Introduction

Thespatialdistributionofprimaryfragmentationasaresultofminingoperationsbecomescriticalinthecontextofcavemining.Theabilitytopredictanapproximatesizedistributionandlikelihoodofgeneratinglargefragmentsasafunctionofrockmassstrengthandcavinginduced-stressescanprovidesignificantinsightintotheminedesignandriskassessment.

Discontinuum approaches, such as DEM, can realistically simulate mechanisms such as spalling andbulking,butaretoocomputationallyexpensivetobeimplementedinalarge-scalecavingmodel.Forthisreasoncontinuumapproachesarerequiredtoensurereasonablecomputationaltimes.

Amethodology is presented here that combines the characterization of a heavily veinedmassive rockmass(byconstructionandtestingofasyntheticrockmass),withresultsofacontinuumcave-scalemodel(informedbytheSRM-derivedrockmassstrength)topredictthedistributionoffragmentsizesasafunctionofrockmassstrengthandcavebackstressasillustratedinFigure1.

Figure 1 Diagram illustrating the proposed methodology for primary fragmentation prediction

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2 Synthetic rock mass (SRM) modeling

AsyntheticrockmasswasdevelopedfollowingthemethodologydescribedbyGarza-Cruz&Pierce(2014)using3DECtomodelaheavilyveinedmassiverockmass.Therockmasswasrepresentedbyacollectionofinterlockedtetrahedralblocksbondedattheircontacts.Theblockcontactsrepresentanetworkoflowpersistenceveinsaswellasunveinedintactrock.Theadvantageof3DEC(Itasca,2013)isthatitallowsfortheconstructionofsampleswithzeroinitialporosityandhighlyinterlockedirregularshapesthatprovideresistance tomoment after contact breakage.This allows one tomimic the high uniaxial compressivestrength to tensilestrengthratiosandfrictionangles typicallyexhibitedbyhardrock.Virtual testingofSRMsamplespopulatedwithrealfielddataallowsustocharacterizethemechanicalbehaviorofarockmassand,whensubject tocavingstresspaths,provides informationabout theassociatedfragmentsizedistribution,bothofwhichareemergentresults.

2.1 Sample construction

Twodifferentsamples(a8x8x8-mand18x18x18-m)wereconstructedbyassemblingacollectionofhighlyinterlockedtetrahedralblockswithapproximateedgelengthof0.5musingKUBRIX-Geoandimportingtheminto3DECtobepopulatedwithpertinentmaterialproperties(Garza-CruzandPierce2014).

Blockcontactswerepopulatedwiththreedifferenttensilestrengthdistributions(accountingforintactrockand veins) to examine their impact on rockmass strength.The three distributions correspond to threedifferentgeologicaldomainsandincludeacertainpercentageofzerostrengthveinsthatconstitute12%,13%and22%ofallstrengthmeasurementsforthe“strong,”“medium”and“weak”domains,respectively(Figure2).Inordertopopulateasample,eachblockcontactwasassignedatensilestrengthvaluerandomlyselectedfromthecumulativedistributionofrocktensilestrengthofinterest(Figure2),anditslocalcohesionwassettobe2.5timessuchtensilestrength.Theusedcohesion-to-tensile-strengthratiowasbasedonasensitivitystudyinwhichsuchratioproducedmacroUCS/tensilestrengthratiointheorderof10-20,whichisconsistentwithtypicalobservations.Inallmodels,theblocksweredefinedaselasticandzonedwithanapproximateedgelengthof0.5m.Theblocksandblock-contactsmicro-mechanicalpropertiesusedaresummarizedinTable1.

Figure 2 Synthetic rock mass sample generation procedure. 3DEC 8x8x8-m sample after trimming 1 m off all sides (left). Tensile-strength cumulative distribution for the “strong,” “medium” and “weak” categories of rock used in the 3DEC models (center). Vertical cross-section of the sample showing the block contact tensile-

strength distribution based on the “medium” strength category (right). Block contacts in black have zero tensile strength and cohesion value, and account for 13% of contacts.


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Table 1 Elastic block and contact mechanical properties used in the model

Block Properties Young’sModulus 51GPaPoisson’sRatio 0.25

Density 2650kg/m3Contact Properties

Normalstiffness 163.2GN/mShearstiffness 81.6GN/m

Peakfrictionangle 21°Residualfrictionangle 42°

Dilationangle 10°Peaktensilestrength Variable(seeFigure3)

Residualtensilestrength 0Peakcohesivestrength 2.5*tensilestrength

Residualcohesivestrength 0

Anadditional8-mcubicsamplewithmorepersistentveinswasconstructedfollowingthesameprocedureoutlinedandsuperposingadiscretefracturenetwork(DFN)inthesamplebycuttingtheblocksintersectedbytheDFN.ThecontactsformedbytheintroductionoftheDFNwererandomlypopulatedwithastrengthdistributionfromsamplesthatfailedonastructureforthe“weak”,“medium”and“strong”subdomains(Figure3).Suchsamplewasusedtoaddresstheimpactmorepersistentjointshaveinprimaryfragmentation.

Figure 3 DFN with fractures colored by area used to cut the SRM sample (left). Tensile strength distribution of vein strength for the “strong”, “medium” and “weak” subdomains (center). Vertical cross-section of the

SRM sample with DFN showing the block contact tensile-strength distribution based on the “medium” strength category and DFN populated with the “medium” vein strength distribution (right)

2.2 Emergent rock mass strength

Failureenvelopeswereestimatedfortheweak,mediumandstrongsubdomainsbysubjectingsamplestoasuiteofvirtualnumerical triaxial testsunderdifferentconfinement levels,aswellas todirect tensionanduniaxialcompression.Theuniaxialcompressiontestwasperformedbycompressingthesampleataconstantstrainrateuntilitfailed.Themodelrepresentsthecompressionofarockmasssamplebetweenfrictionless rollerboundaryconditionswhich is relevant to spallingconditions. In ananalogousway, adirecttensiletestwasperformedbypullingeachsampleapartataconstantrateinaquasi-staticfashionuntilitfailed.TheresultsoftheUCSanddirecttensiletestsaswellastheapproximatefailureenvelopesfor

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thedifferentsyntheticveinedrockmasssamplesanalyzedareshowninTable2andFigure4respectively.Corresponding Hoek-Brown failure envelopes were estimated, resulting in an average mi = 13. Thisinformationwasusedasinputtomodelthedifferentrockmassesinacave-scaleFLAC3Dmodel.

Table 2 Summary of UCS and Tensile Strength Results for the Different Veined Rock Mass Samples Tested

Strength Distribution SRM Sample“Strong”

SRM Sample“Medium”

SRM Sample“Weak”

TensileStrength[MPa] 1.45 1.15 0.45

UCS[MPa] 20.5 16 8.7

Figure 4 Approximate failure envelopes derived from strength testing of veined rock mass samples with different vein strength distributions

2.3 Primary fragmentation under caving-induced stress path

AseriesofSRMsamplesweretestedundercaving-inducedstresspathstopredictprimaryfragmentation.The3DECmodelallowstheblocksformingthesampletobreakattheirsubcontactsasaresultofstressconcentrations,mimickingtheinitiationofcracksthatcancoalesceand/orpropagatetofracturetherockmass.Thisresultsinanemergentfragmentsizedistribution.

Thetestingenvironmentforfragmentationstudiesconsistedofeitheran8-mor18-mveinedrockmasscubeembedded inanelasticboundaryasshown inFigure5 (Garza-Cruz&Pierce2014).The topandsidesofthesyntheticrockmasscubewerebondedtotheelasticboundary.Theelasticboundaryfacilitatedtheapplicationofstressboundaryconditionstorepresenttheinducedstressesinthecavebackandwouldprovide a way for stresses to shed upwards during failure (as would be expected in situ). Boundaryconditionswereapplied to themodelas shown inFigure5.Themodelwascycled toequilibrium,andtheverticalstressonthesamplebottom-facerelaxedintenincrements,whileallowingthemodeltoreachequilibriumeachtime,simulatingtheupwardadvanceofthecavebackfrombelow.


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Figure 5 8-m edge-length cubic sample embedded in an elastic boundary for fragmentation testing. a) and c) show the boundary conditions applied to the model. b) Elastic boundary made transparent to aid visualization

Astheverticalstressesatthestopebackdecrease,tensilefracturesdevelopsub-paralleltotheface.AwiderangeofSRMtestswereconducted toexamine the impactofcavebackstress,blocksize,samplesizeandveinpersistenceonpredictedfragmentation.Figure6showsselectedresultsforthecaseof8-mcubicsamplesofthe“weak”,“medium”and“strong”domain(thedifferentcolorsrepresentfragments,definedasanycollectionofblocksthatwerestillbondedtogetheratoneormoresubcontacts).Figure7andFigure8showexampleresultsforthecaseof18x18x18m“strong”sampleand8x8x8m“medium”samplewithasuperposedDFN,respectively.Themodelsrevealedthatforagivenstressstate,thevolumeofstopebackthatundergoesspallingdecreasesastherockmassstrengthincreases.

Inordertocharacterizethespallingbehaviorinaquantitativeway,thecumulativepercentagebyvolumeasafunctionofapproximatefragmentvolumeforallthe“strong,”“medium”and“weak”sampleswerecalculated(resultscorrespondingtothethreecasespresentedinFigure6areshowninFigure9andFigure10).Thefragmentationtestindicatedthatasthestrengthoftherockmassincreases,sodoesthesizeoffragmentsthatcanbeexpectedtoresultfromtherockmassfailureandredistributionofstresses,whichinturnresultsinalargersizedistribution.Itisimportanttomentionthatthesizeofthesmallestfragmentthatcanbecreatedislimitedbythesizeoftheelementaltetrahedralformingthemodeledrockmass.

Figure9alsosummarizes the25th,50thand75thpercentilefragmentsizesbyvolumefor thedifferentstrengthsamplessubjecttoamaximumcavebackstressof70MPa.Figure10liststhepercentageofthemodeledsyntheticrockmassthatunderwentfragmentation,aswellasthepercentageoffragmentswithvolume inexcessof1.3m³. It is interesting tonote thatweaker rockwould fragmentfiner,and in thisspecificexamplewouldproducenolargefragments,whilestrongerrockmasseswouldtendtoproducemore large fragments thanmediumstrength rockmasses.Therefore, theprobabilityofgenerating largefragmentsincreasesasthequalityoftherockmassincreases.

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Vertical plane through center of themodel

Fragmentationatstopeback

“Weak”SRM

“Medium”SRM

“Strong”SRM

Figure 6 Fragmentation of a “weak” (top), “medium” (center) and “strong” (bottom) SRM samples after the vertical stress was fully relaxed from below. SRM sample dimensions: 8x8x8m. Views along a vertical section

(left) and looking up from below (right). Stress state tested: σv = 23.4 MPa, σH = 3*σv = 70.2 MPa, σh = 1.6*σv = 37.44 MPa


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“Strong”SRM

Figure 7 Fragmentation of a “strong” SRM sample after the vertical stress was fully relaxed from below. SRM sample dimensions: 18x18x18m. Views along a vertical section (left) and looking up from below (right). Stress

state tested: σv = 15.6 MPa, σH = 3*σv = 46.8 MPa, σh = 1.6*σv = 25 MPa.



“Medium”SRMwithDFN

Figure 8 Fragmentation of a “medium” SRM samples with superposed DFN (see Figure 3) after the vertical stress was fully relaxed from below. SRM sample dimensions: 8x8x8m. Views along a vertical section (left) and

looking up from below (right). Stress state tested: σv = 15.6 MPa, σH = 3*σv = 46.8 MPa, σh = 1.6*σv = 25 MPa

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Edge Length Strong Medium Weak

25thpercentile[m3] 0.04 0.03 0.02

50thpercentile[m3] 0.12 0.07 0.03

75thpercentile[m3] 0.27 0.17 0.07

FragmentedVolume 10.2% 13.7% 23.4%

Fragments>1.3m3 5.37% 0.0% 0.0%

Figure 9 SRM-derived primary fragmentation distribution for cave back stress of 70 MPa.

FragmentDiameterDistributionAssumingDiskShape(CaveBackStress=70MPa)

Strong

Medium

Weak

Figure 10 SRM-derived primary fragmentation distribution for cave back stress of 70 MPa, assuming disk shape for fragments


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AwiderangeofSRMtestswereconductedtoexaminetheimpactofcavebackstress,blocksize,samplesizeandveinpersistenceonpredictedfragmentation.TheresultsofalltestsaresummarizedinFigure11,whichrelatesthefragmentationtothestrengthofthedomainandtheinducedstressatfailureinthecaveback.Ingeneral,itcanbeseenthattheweakandmoderatedomainsgenerallyproducedfragments<1.3m3underbothlowandhighstress,whereasthestrongdomainhasthepotentialtoproducelargeslabs(severalmetersindiameter)whenfailingunderlowstress.

TheSRM-derivedfragmentationpredictionchartwascombinedwiththeresultsofcave-scalemodelingtoestimatehowprimaryfragmentationmightvarythroughthecolumnasaresultofdifferencesincavebackstressesaloneforagivenrockmassstrength.

Figure 11 Primary fragmentation prediction chart the “strong”, “medium” and “weak” domains generated from SRM sample testing

3 Numerical approach to analysis of caving

Fivekeygeomechanicalzonesareassociatedwithblockandpanelcaving,as shown in theconceptualmodel sketched inFigure12.Thisbuildson the conceptualmodeldevelopedbyDuplancic andBrady(1999).Thefollowingaredefiningcharacteristicsofeachofthefivezones.

• Elastic zone — Induced stresses may be high here, but are insufficient to induce measurablemicroseismicity.

• Seismogeniczone—Wheremicroseismicityoccurswithinthejointedrockviajointslipandfractureextension.ThisiscommonlydefinedviaanempiricaldamagethresholdcriterionthatisafunctionofthedeviatoricstressandintactUCS[0.3<(σ1–σ3)/(UCSintact)<0.5].

• Yielded zone—Where the rockmass has disintegrated and lost all of its cohesive and/or tensilestrength,buthasnotmovedasignificantdistanceyet.Theouterlimitofthiszonegenerallycoincides

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withthefracturelimit,wherevisiblefracturesareevidentinintersectedopeningsorongroundsurface,significantoffsetoccursinopenboreholesandTDRcablesbreak.

• Airgap—Anairgapcanexistiftheoverlyingrockmassretainssomelevelofcohesiveand/ortensilestrength.Asanairgapexpandsinsize,theoverlyingrockmassmayweakenfurther,causingadvanceoftheyieldedzoneandcollapseintotheairgap.

• Mobilizedzone—Thisiswherethedisintegratedrockmasshasmovedasignificantdistanceandisstartingtodilateandbulkasaresult.Thecriteriondependsonthescaleofthecaveandthemodulusoftherockmass;atotalorverticaldisplacementof1-3misgenerallyemployed.

The caving and stress-redistribution process inherently involves large deformations, shear along pre-existingjointsandbeddingsurfaces,fracturingofintactrockblocksandfragmentationoftherockmassabove the undercut level. Ideally, onewouldmodel this process using a discontinuum (e.g., 3DEC orPFC3D)approach inwhichpre-existingfracturesareexplicitly represented in themodel.However, thecomputationalsizeandtimerequirementstosolvemine-scaleproblemscurrentlystillmakeitimpossibletoattackaproblemcompletelywiththediscontinuumapproach.

Instead,analgorithmtosimulatecavingwithintheconceptofacontinuum-basedmodelhasbeendevelopedoverthepast15yearsduringtheindustry-fundedInternationalCavingStudy(ICSI&II)andMassMiningTechnology(MMTI&II)projects.Theconstitutiverockmassresponserequiredtorepresentcaving(i.e.,the rockmass yield, dilation and bulking)was developed using strain-softeningmaterialmodels,withstrain-dependentpropertiesthatareadjustedtoreflectthedilationandbulkingthataccompanycaving.ThecavingalgorithmasimplementedinFLAC3D(Itasca,2012)attemptstopredictthelimitsofthesezonesasafunctionofproductionfromthecave.Inadditiontothesecavelimits,theresultsofcave-scalemodelingareusedtoderiveestimatesofthefollowing:

• Caveability;

• Abutmentandcavestresses;

• Bulkingfactors,cavingrateandbreakthroughtiming;and

• Subsidence.

Successfulcomparisonsbetweenpredicted(viaFLAC3D)andactualcavebehaviorhavebeenachievedatanumberofoperationsworldwide(e.g.,NorthparkesE26,RidgewayDeeps,Palabora,GraceMine).

Figure 12 Conceptual model of caving


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Within the caving model, a rigorous mass-balance routine is implemented to ensure that the tonnes-based production schedule is represented accuratelywithin the numericalmodel.Although the routineiscomputationally intensive, andcan lead to relatively longmodel run-times (two to threeweeks), thenumericalapproachisrequiredtoaccuratelycapturethemechanismsofdamage,yield,dilationandbulkingnecessarytocorrectlyreproducetheevolvingcaveshapeandpropagationratesinresponsetoaspecificproductionschedule.

3.1 Numerical modelling of caving and primary fragmentation predictions based on SRM

AseriesofheterogeneousmodelswereconstructedinFLAC3DusingthestrengthofdomainsderivedfromSRMtesting(UCS=8.7,16and20.5MPaforweak,mediumandstrongdomains,respectivelyandmi=13)alongwithbrittlenessderivedfromback-analysisofstopeoverbreak(notpresentedhere).Themodelledundercutlayoutconsistsof350mN-Sand140mE-W,withahydraulicradius=50m.Thein-situstressstateusedisshowninFigure13(σHappliedat058°).

Figure 13 In-situ stress regime used in the FLAC3D cave model

Theproductionprocesswassimulatedassuming“weak”,“medium”and“strong”rockeverywhere.Themodelresultssuggestedthatitwouldbepossibletosustaincavingallthewaytogroundsurface.Figure14showsthestressredistributionaroundthemobilizedandyieldedzonesaswellasthepredictedshapeoftheinclinedcaveaftertwoyearsoffullproduction(“strong”case).

Figure 14 Predicted shape of the inclined cave two years after start of full production. Contours show the major principal stress redistributing around the mobilized and yielded zones

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Asthecaveispropagated,themodelrecordstheinducedstressesatfailure.Suchstressesatfailureforthecaseof“strong”domainaftercavingtogroundsurfacetookplaceareshownontheleftsideofFigure15.TheSRM-derivedfragmentationpredictionchart(Figure11)wascombinedwiththeresultsofcave-scalemodelingtorelateinducedstressesatfailuretopercentageoflargefragmentsproducedandestimatehowprimaryfragmentationmightvarythroughthecolumnasaresultofdifferencesincavebackstressesalone(Figure15).

Figure 15 Caving-induced stresses at failure assuming “strong” rock everywhere (left). Associated primary fragmentation as a percentage of fragments larger than 1.3 m3 for given stresses at failure (right)

At the beginning of cave life, brittle spalling is promoted by the large hydraulic radius, which limitsarchingandtheassociatedbuildupofconfinementinthecaveback,leadingtoahigherpercentageoflargefragments asderived from theSRMfragmentationanalysis (~50%of fragments couldbe>1.3m3).Ascavingprogresses,finerfragmentationshouldbeexpectedatthecolumnmid-height,wherethecavebackstressesarehighest(~30%offragmentscouldbe>1.3m3).Asthecavereachesgroundsurface,stressesarelowandfragmentationreliesmoreongravitationalpull,leadingtoahigherpercentageoflargefragments(~60%offragmentscouldbe>1.3m3).

4 Conclusions

Amethodology has been developed using 3DEC and FLAC3D to predict primary fragmentation as afunctionof rockmassstrengthandstresses inducedat failure inacavemine.SRMsamplesofheavilyveinedmassive rockmasses can be constructed using 3DECby assembling a collection of tetrahedralblocksbondedat their contacts;while contact strengthheterogeneity is introducedbasedonfielddata.EmergentSRMstrengthisusedtoinformaFLAC3Dcavingmodel.SRMsampleswerealsotestedundercave-likestresspathstopredictprimaryfragmentation.TheSRM-derivedfragmentationpredictionchartwas combinedwith the resultsof cave-scalemodeling inFLAC3D to relate induced stresses at failuretopercentageoflargefragmentsproducedandestimatehowprimaryfragmentationmightspatiallyvarythroughthecolumn.Theresultssuggestthatprimaryfragmentationcouldbequitecoarsewheremediumandstrongdomainscaveunderlowstresses,whichisexpectedatthebottomandtopoftheorecolumn.

Thismethodologycanbeapplied tomodelswhere the spatialvariationofgeologicdomains isknown,to predict primary fragmentation as a function of changes in rockmass strength and induced stresses.


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Furthermore,theestimatedprimaryfragmentationsizedistributioncanbeusedasaninputtopredictcavedrawdownandsecondaryfragmentationusingREBOP(Fuenzalidaetal,2014).

FurtherstudiesarewarrantedtorefinethestrengthcharacterizationofrockmassesusingSRMaswellastheirassociatedfragmentationundercavingstresspaths.

References

Duplancic,P,&Brady,BH1999,Characterizationofcavingmechanismsbyanalysisofseismicityandrockstress,Proceedings9thInternationalCongressonRockMechanics(Paris),vol.2,pp.1049-1053.Balkema,Rotterdam.

Fuenzalida, M, Garza-Cruz, TV, Pierce, M &Andrieux, P 2014, ‘Application of a methodology forsecondaryfragmentationpredictionincavemines’,Proceedings3rdInternationalSymposiumonBlockandSublevelCaving,Santiago,Chile.

Garza-Cruz,TV,&Pierce,M2014,‘A3DECModelforHeavilyVeinedMassiveRockMasses’,Proceedings48thUSRockMechanics/GeomechanicsSymposium.Minneapolis,USA.

ItascaConsultingGroup,Inc.2013,3DEC–Three-DimensionalDistinctElementCode,Ver.5.0User’sManual,Minneapolis:Itasca.

ItascaConsultingGroup,Inc.2012,FLAC3D–FastLagrangianAnalysisofContinuain3Dimensions,Ver.5.0User’sManual,Minneapolis:Itasca.

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Caving Mechanics

ALCODER, challeges of paradigms in caving methods

Gl Krstulovic Geomecánica Ltda., Chile

GA Bagioli Tetra Tech Metálica, Chile

Abstract

More than 90% of the Chilean metal production from underground mining sites is extracted through collapse mining methods. The experience gained in these mining sites has originated a series of assumptions, which largely respond to popular belief and have no sufficient analytical support to be considered as true to the behavior of in-situ rock.

The reformulation of old concepts in classic rock mechanics has allowed establishing new criteria in order to explain the behavior of rocks excavated thus, which include the “deterioration criterion” as an alternative to the “rupture criterion”; this allows reviewing the most frequent paradigms in mining operations by caving.

In our case, the Janbú-Kulhawi-Krstulovic concepts, which are alternative to the traditional Mohr-Coulomb-Hoek, allow anticipating the orientation that the collapsing rock will adopt, including the resulting seismicity, among other things. It may be assumed from the foregoing that this review concludes on geometric configurations that favor collapse, including the geometry of the anomaly that is currently known as pre-conditioning of rock, in order to also favor collapse.

The analytic formulation of this deterioration criterion has been incorporated in the ALCODER computer simulator. The necessary input data for these processes require identifying the behavior of the deformation module of the rock, and the variation with their surrounding tectonic confinement. Complementing the foregoing, the maximum deformation energy (DE) tolerated by such rock based on lab tests, constitutes a comparative pattern for establishing seismicity and potential rockburst in-situ.

1 Brief introduction to ALCODER

Morethan90%oftheChileanmetalproductionfromundergroundminingsitesisextractedbycollapseminingmethods.ALCODERisaFortransimulatorbyfinitestate-of-the-artelements,expresslydesignedtoadequatelyrespondtominingproblemsinrelationwithcollapsingrock.

TheALCODERalgorithmisbasedonoriginalcomputerprogramsfromUtku(1968)andKulhawy(1972).Bothalgorithmwellvalidatedasperreferences:

• ELAS-A.,SenolUtkuetal,1968.

• Kulhawy,FredH.,1972.

Theseoriginalconceptsaremodifiedaccordingtothereferencesindicatedbelow:

• KrstulovicG.,2004

• BagioliG.,KrstulovicG.,2008.

TherecentusesofALCODERinCavingandSLSMassBlastarethefollowing:


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• CODELCOSalvador,GeomecánicaLtda./MetálicaIng.,2000,PanelCaving.BasicEng.forIncaOeste.

• VasanteMetalsInc.GeomecánicaLtda./MetálicaIng.,2004,SLSMassBlast(Brasil).

• FreePortMcMoranSantos,AlcaparrosaandCandelariaOreDeposits,GeomecánicaLtda.,2000-2009,InternalprojectreportsanddesignresultsforMassBlastinSLSstoping.

• CAPRomeral,GeomecánicaLtda./MetálicaIng.,2010,SLCProjectUnderCurrentOpenPit.

• YamanaGoldInc.,GeomecánicaLtda./MetálicaIng.,2010,MiningProjectsforQDDL(Argentina),Jerónimo(Chile)OreDepositsforMassBlastinSLSStoping.

• IM2,GeomecánicaLtda.,2010-2011,InternalConsultingReports(InHouseConsultant)forRockPreConditioninginCaving.

• CODELCOChuquicamata,GeomecánicaLtda.,2011-2012,CavingPropagationEstimation(PMCHS).

• CODELCOAndina,GeomecánicaLtda./SKMIng,2012-2013,SupportAnalysisforHaulageIIIExcavationAlternative.

• CODELCOElTeniente,GeomecánicaLtda.2013-2014.ALCODERValidationof7RockBurstatPilarNorteOrebody

MoreinformationonALCODERresultsforfullscaletrialcanbeobtainedfromFreeportMcMoranforMassBlastinSLSstoping,andfromCODELCOElTenienteforRockBurst.

2 The “deterioration in rock” criterion that rules the ALCODER algorithm

Sinceitsbeginnings,RockMechanicsforGeomechanicsinMininghasinherited,fromitscivil“pair,”andaccordingtoclassicmechanics,theMohr-Coulomb-NavierRockRuptureCriterionconcept.Thisconceptwasmodifiedsomeyearsagobyanempiricalequivalent:theHoekRuptureCriterion.Inbothcases,theCriterionaimstoexplaintheconditionsthatruletherupturephenomenoninrock.Asaresult,thesoftwarethatarecommercializedforrockstabilityevaluationsduringminingexcavationsinvariablycontaintheserupturecriteriatoexplainthebehaviorofsuchrock.TheOutputofthesesoftwareinvariablyprovides:

• SafetyconditionsoftheremainingrockafterminingexcavationsthroughaSafetyFactor.

• Deformationconditionsofthissameremainingrock.

Both outputs require interpretation and validation, which are not always sufficiently achieved for thepurposesofthestudyunderevaluation.

Alternatively,inminingexcavations(unlikecivilexcavations),onemustcoexistwithdeterioratedrockthatarestillcapableofsupportingthe“miningbuilding.”Consequently,theexpecteddeteriorationdegreeinthisminingexcavationbecomesarelevantfactorforthedesigninCaving.

Hereweformulatethat“deteriorationinrock”isdirectlyassociatedtothedeformationthatthesematerialscouldsufferinaconfinement/deconfinementprocessresultingfromminingexcavations.Rocktakenfromvirgin (raw) conditions todeconfined conditions (near an excavatedwall) suffermicro fractureswhichcompensate(involume) thedeformationofsuchwalls towards theexcavatedspace.Theoccurrenceofthesemicrofracturesimpliesareductionofthecompetencecapacitiesofsuchrock,i.e.,areductionthatrespondstoa“deterioration”ofthecompetenceindexesthatdefinethequalityofthisrock.

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Thecompetenceindexthatiswelldetectedbythis“deterioration,”istheDeformationModule(E).Itisempiricallyproventhatrockwithlower(E)havelowercompetencequalities,i.e.,therearewidelyacceptedempiricalformulasthatrelate(E)/RMR,(E)/GSI,(E)/RQD,where:RMR,GSI,RQDarequalityindexescollectedthroughgeotechnicsin-situ.

Inotherwords,(E)isagoodindicatorofin-siturockqualityi.e.,(E)cansuffermodificationsaccordingto the confinement / deconfinement process resulting frommining excavations in-situ according to thetectonicsofthearea.Thelawthatrulesthisvariationof(E)inrockwasinitiallyformulatedby(Kulhawi1972).

TheALCODERincorporatesthisLawofVariationsof(E)underconfinement,thusconcludingtheresultingnew(E)intheremainingrockattheexcavation.Thevariationsof(E)aretransferredtovariationsofRMR,GSI,RQD,asappropriate,accordingtotheaforementionedempiricalformulas.

Therefore,theALCODEROutputisuser-friendlyinRMR,GSI,orRQDindexes,whichallowconfiguringthe“deterioration”experiencedbyrockundergoinganexcavationprocess.

3 Collective imagination myths regarding the caving process

Miningbycavinghasthefollowingunchallengedparadigms:

1. AftercausingcollapseovertheUndercut,Cavingprogressesaccordingtodomegeometry.

2. Theadvanceperimeteror“cavingface”inPanelCavingmustbeconcavetowardsthecollapse.

3. ForCavingsimulatoreffects,Modules(E)inrockcanbeassumedasinvariable.

4. Pre-Conditioning(PA)ofin-siturockalwayshelpstoacceleratecollapse.

5. Thecavingpolicyfrom“extractionpoints”canbemadeindependentlyfromthecollapseprocess.

6. Theresultinggranulometryin“extractionpoints”isindependentfromthecollapseprocess.

7. Computersimulatorscannotdetectfaultsthathavenotbeenpre-established.

8. RockBurstcanonlybeanticipatedwithseismicrecords.

InthefollowingSections,wepresentresultsoftheALCODERsimulatorwiththedeteriorationcriterioninrock,whichchallengetheaccuracyofthesemyths.

3.1 Myth 1

After causing collapse over theUndercut,Cavingprogresses according to the domegeometry.This asschematicallydescribedinFigure1.

INCORRECT.Thegeometricconfigurationofcollapseinprogressdependsonthequalityofin-siturockand the tectonicsof thearea.Undernormalconditions,collapse tends toadvance towardsageologicaldiscontinuityorslopesinmountaintopography.

Figure2showsthedeviationoftheadvanceincollapse.ExampleofanALCODERsimulatorOutputafter7iterationsofcollapseinCavingunderanOpenPittopographyandwithoutanexpressextractionpolicy.Inotherwords,retractionaccordingtospontaneouscollapse.Theblacklineinfigure2,isthemainfault.CollapseCriterion:SpontaneousbyHydraulicRadius(HR)inrockwithMRMRindexlowerthan40.


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Figure 2 Deviation of the Advance in Collapse

3.2 Myth 2

The“cavingface”perimeterinPanelCavingmustbeconcavetowardsthecollapse.

INCORRECT.Thegeometricconfigurationofthecollapsecavityinprogress/in-siturockoutlinedinplantprojectionandinverticalprojectionmakesitlooklikeavaultwhichauto-supportsitselfwiththeconcavityofitswalls.Tofacilitatecollapse,bothverticalandhorizontalprojectionsmustbeasstraightaspossible,sothattheabutmenteffortisminimizedwiththestraightfaces.

Figure3showsanisometricviewofALCODERsimulatorforCaving.

Figure4showsALCODERresultofthevariationintheabutmentStressaccordingtothegeometryofthefaceincollapse.Inotherwords,thecloserthefacegetstothevertical,theabutmentStresswilldiminish.

Figure 1 Collective Imagination Regarding Caving Process

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Figure 4 ALCODER Result of the Variation in the Abutment Stress

3.3 Myth 3

ForCavingsimulation,Modules(E)inrockcanbeassumedasinvariable.

INCORRECT.Module(E)variesaccordingtotheconfinement/deconfinementofthelocation.Figure5showsthevariationof(E)accordingtolabtestsforthecaseofPorphyryinChuquicamata.

TheLawthatrulesthevariationof(E)accordingto(Kulhawi1972)isfunctionoftheexperimentalconstants(K)and(n),wherePaistheatmosphericpressureandSigma3,thelowerconfinementin-situ.

Figure 3 Isometric View of ALCODER Simulator Devoted to Results on Figure 4.


164

𝐸𝐸 = 𝐾𝐾 ∙ 𝑃𝑃𝑃𝑃 ∙ �𝜎𝜎3𝑃𝑃𝑃𝑃�𝑛𝑛

(1)

Where:

E=Young`sElasticModulusPa=AtmosphericpressureexpressedinthesameunitsasEσ3=Minimumprincipalstressn=ModulusExponent,K=ModulusNumber

K,n=arepurenumbers

Figure6shows,accordingto(Barragan&Krstulovic2013),arecentcompilationwiththeempiricalrelation(K)/(n).ItisestimatedthatinrockwithRMRunder60,themistakeofnotconsideringthevariationof(E)canseriouslyaffecttheSimulatorresults.

Figure 6 Empirical Relation (K) / (n)

Figure 5 Variation of (E) According to Lab Test on Porphyry Rock

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Caving Mechanics

3.4 Myth 4

Pre-Conditioning(PA)ofin-siturockalwayshelpstoacceleratecollapse.

INCORRECT.The(PA)aimstocausea“quality”anomalyinthein-siturock.Insuchcondition,inordertoachievetheaccelerationofcollapse,the(PA)mustsatisfyarequirementofadequategeometricshapeand“intensity”inthereductionofthequalityofin-siturock.

Figure 7 shows anALCODER example of a (PA) geometry 20% - 40% deterioration in (E), whichsuccessfullyaccelerates(comparedwithFigure2)Cavingundermountaintopography.Theblacklineisthemainfault.

Figure 7 Example of (Pa) Geometry Deterioration in (E)

Figure8showsanALCODERexampleofaninsufficientapplicationof(PA)withdeteriorationin20%in (E) to accelerate the collapse process in a tectonic environment.This Figure 8 includes (%) of thecollapsedmaterialaccordingtoRMRindex.Inthiscase,thequadrantwith20%deteriorationin(E)doesnotacceleraterockcollapseintheobjectsectorof(PA).

Figure 8. ALCODER Example of Insufficient (PA)


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3.5 Myth 5

Theminingproductionpolicyfrom“extractionpoints”canbemadeindependentlyfromthecavingprocess.

INCORRECT.Ideally,miningfromtheextractionpointsshouldbemadewithpreviousknowledgeofthewayinwhichCavingcollapsesspontaneously.

Ifaminingpolicy thatcoincideswith thespontaneouscollapseofCaving isnotmaintained, thiscouldcauseextremeconditionsattheproductionlevel:

• IfmininghappensfasterthanthecollapsedmaterialinCaving,thiscouldcauseaspacebetweenthegroundin-situandthealreadycollapsedmaterial,originatinganabutmentcondition,orelse,thedetachmentofwedgesasin-siturock.

• If mining happens slower than the collapsed material in Caving, the non extracted columnis compacted and serves as temporal support forCaving and causes a collapseoption at theproductionlevel.

Figures 9 and 10 are records of collapse due to inconsistency between mining / Caving accordingto theALCODEROutput.The dark bodies located in the collapsedmaterial, arewedges incorporatedspontaneouslyintheCaving.Inotherwords,theALCODERallowsforecastingthespontaneouscollapseprocess,andthusallowsadjustingtheminingpolicyin-situ.

Figure 9 Collapse at the Production Level Figure 10 Wedges Incorporated in the Caving

3.6 Myth 6Theresultinggranulometryatthe“extractionpoints”isindependentfromthecollapseprocess.

INCORRECT.Duringthecollapseprocess,theconfinement/deconfinementconditionsinthein-siturockcausespontaneouscollapseofvariousgranulometries.Thus,itisincorrecttoassumethatinitiallythereisacoarsegranulometrythatsubsequentlyisreducedalongitstransittowardstheextractionpoint.

ThespontaneousgranulometryproducedbyCavingdependsontherockquality,tectonicstressesandthegeometryofthespontaneouscollapse,whichcouldincludeextremecases:

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• Abundantspontaneouscoarsegranulometryatthebeginningthatsubsequentlylimitsitselftospontaneouscollapseoffinegranulometry.

• Alternatively,onlyfinegranulometryatthebeginning,and/orsubsequently,largebouldersduetocollapseinhigherelevations.

Figure11describesanALCODEROutputwithgranulometriesdifferentiatedbycolorsascavingoutcropsunderanopenpitslope.).NotethatfragmentationisresultofdifferentRMR/Evalues.ValidationbycorrelationRQD/E/RMR/BlokSizeisinprogress.

Figure 11 ALCODER Output with Differentiated Granulometries

Figure12describestheconfigurationofthecollapsedmaterialbyCavingstagesaccordingtoALCODEROutputin14extractioncolumnsovertheundercut.

Figure 12 Extraction Columns Over the Undercut After 6 Caving Stages

3.7 Myth 7

Simulatorscannotdetectfaultsthathavenotbeenpre-establishedinthesimulationmodel.

INCORRECT.-ALCODERdetectsthespontaneousdisplacementinrockbodiesasCavingprogresses,andshedslightonsubsidenceFaults/CracksorFaults/Cracksinthemininginfrastructurenearthecollapse.


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Figure13schematicallyshowsanSLCprojectunderanexhaustedOpenPit.InFigure14,theALCODERsimulatoridentifiescracksintheSLCminingsequence.Thesecracksarefromsubsidenceinoverturn,andcracksinrampinfrastructureoftheSLCasminingoperationsprogress.

Figura 13 SLC Project Under Exhausted Open Pit

Figure 14 Cracks in the SLC Sequence After ALCODER Out Put

3.8 Myth 8

RockBurstcanonlybeanticipatedwithseismicrecords.

INCORRECT.TherearenobibliographicrecordsregardingRockBurstforecastsinminingsites.Althoughmore than 40 years have passed since micro seismic hearing systems were implemented in miningoperations,theRockBurstissuehasstillnotbeensolved.

AccordingtoClassicMechanics,RockBurstoccursinrockwhentheDeformationEnergy(DE)perunitvolumeexceedsthetensionresistanceintherock.Todeterminethis(DE)perunitvolume,rockisassumedasablockwithdifferentialdimensions,whichundergoestheactionofthemainnormalstresses(S).(ED)istheworkexecutedbythesestresseswhendeformingthecubeonemagnitude(dl)(ObertL.&DuvallW.1967).

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Inotherwords,Magnitude(ED)isequaltotheworknecessarytodeformtherockcubein(dl).RockBurstoccurswhentheworkexceeds the tensionresistanceof therock(Example:Resistance atAtmosphericConfinement).The(ED)canbeestimatedaccordingtoformulas(1)and(2).

(2)

Where:

E=Young`sElasticModulus

υ=Poisson`sRatio

σ=1,2,3PrincipalConfiningStresses

Figure 15 shows anALCODER simulatorwithOutput in collapse after 10 iterations. For each of theiterations, theALCODER identifies the (ED) that exceeds the maximum value accepted by this rockaccordingto(ED)verificationsinlab.Inthiscase,points1to5presentnumbersfor(ED)RockBurst.

Figure 15 ALCODER Out-Put with Maximum (ED)

Inotherwords,theALCODERcananticipatetheopportunityandtheplacewhereRockBurstwouldoccurduringthecavingprocess.MagnitudeofRockBurstanticipatedbyALCODERcanbeestimatedfromKrstulovic(1977).

4 Conclusions

TheRockDeteriorationCriterionbasedonthe(E)Moduledeformationindexsuggestedherehasanalyticalgroundsinclassicmechanics,andempiricalverificationsinFigure6.Takentoapplicationsofthe“miningbusiness”throughtheALCODERalgorithm,thiscriterionisadequateforaddressingaseriesof typicalcavingissues,i.e.,problemsthatrangefromabutmentstress,collapseinproductionlevels,toRockBurst.


170

Acknowledgements

TheauthorsthankTetraTechMetálicaandGeomecánicaLtda.fortheconsiderationsgrantedtosupportthisdocument.

References

Bagioli,G,KrstulovicG2008,‘AnALCODERforComputerMonitoringofSlopesStabilityDuringWTIPrograminOpenPitMining’,ISRMCongress,Lima,Perú.

Barragan,JL,KrstulovicG2013,Lab.datacompilationfromdifferentauthors.

Duvall,W&Obert,L1967,RockMechanicsandtheDesignofStructuresinRock,JohnWiley&Sons,Inc.

Fred,H.Kulhawy,1972,FiniteElementModelingTechniquesforUndergroundOpeninginRock.ContractNºH0210023.AdvancedResearchProjectsAgency,WashingtonUSA.

Krstulovic,G1977,MétodosyTécnicasMicroSísmicasen laEvaluacióndeEstabilidadDinámicadeMacizosRocosos.RI-77-1CentrodeInvestigacionesMineroyMatalurgicaCIMM-Chile

Krstulovic,G2004,ALCODERANewMethodforEvaluatingStabilityofRockExcavations,MassMineChile.

Senol Utku et al. 1968, General Purpose Computer Program for the Equilibrium Problems of LinearStructures.TR32-1240.JetPropulsionLab.CALTECPasadena,California.

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Characterization and synthetic simulations to determine rock mass behaviour at the El Teniente Mine, Chile. Part I

A Brzovic Codelco, ChileP Schachter Codelco, ChileC de los Santos Codelco, ChileJA Vallejos, University of Chile, ChileD Mas Ivars Itasca Consultans AB, Sweden

Abstract

A comprehensive geotechnical characterization has been undertaken at the El Teniente mine to describe and determine the rock mass behaviour strength properties of the primary copper ore. This type of rock can be considered as a heavily veined massive and unfractured rock mass. This compressive work is focused sequentially on; 1) an intensive structural data collection campaign from several oriented core placed within main geotechnical units, 2) discrete fracture network modelling, 3) laboratory testing of the intact rock and veins materials combined with scaling procedures, and 4) application of the Synthetic Rock Mass (SRM) approach to study the strength and deformation behaviour of the main geotechnical units.

The Synthetic Rock Mass (SRM) modelling approach, based on particle mechanics, has been developed to simulate the mechanical behavior of jointed rock mass. This technique uses the bonded particle model for rock to represent intact material and the smooth-joint contact model (SJM) to represent the in situ joint network. The macroscopic behavior of an SRM sample depends on both the creation of new fractures through intact material and slip/opening of pre-existing joints. SRM samples containing thousands of non-persistent joints can be submitted to standard laboratory tests (UCS, triaxial loading, and direct tension tests) or tested under a non-trivial stress path representative of the stresses induced during the engineering activity under study.

This paper describes the first part of the study, with focus on structural data collection campaign (points 1) and laboratory testing (point 3).

1 Introduction

TheprimarycopperoreattheElTenientemineisdescribedasverycompetentandmassive,duetoitexhibitsabrittlebehavior,oftenviolentfailureunderhighstressconditions(Rojasetal2001).Thisdescriptioniscoherentwiththegeologicaldescriptionoftherockmass,whichdoesnothavediscontinuitiesmatchasthedefinitionprovidedbyInternationalSocietyofRockMechanics(ISRM,1981).Onlyfaultscanbeclassifiedasdiscontinuities,buttheyarewidelyspaced.Theprimarycopperorehasahighfrequencyofveins,wherethecoopermineralizationishosted,theseveinnetworkstructuresareknownasstockwork(Figure1).Softveinscontainingweakmineralsasinfill(chalcopyriteandanhydrite)controlthedisassemblingoftherockmassduringcaving(Brzovic&Villaescusa2007;Brzovic2011).

Nowadays,therearetwotraditionalmethodsusedtoestimatethestrengthoftherockmass:1)determinationofthestrengthenvelopeoftherockmassusingscalingparametersfromlaboratorytests,forexampleHoek-Brown’sfailurecriterionand2)usingnumericalmodelingbasedonbackanalysisofpreviousexperiencesoffailureobservedandmeasuredinminingorcivilexcavations.


172

Figure 1 Panel caving method currently used at the El Teniente Mine (a), Intense vein network stockwork at a development ahead of the cave front (b), and Weak Veins as faces of caved rock blocks (c) (modified from

Brzovic & Villaescusa 1997)

TheprimarycopperoreofElTenienteminepracticallyhasnofracturesorjoints;thereforeitisdifficulttodeterminearatingabletoscalelaboratorydata,suchasRMR(Laubscher1977)orGSI(Hoek1994).Therefore, thewaynumber1cannotbeusedproperlyunless theRMR´sorGSI´s inputparametersaremanipulatedoradjustedinordertoobtainreasonableresults.Thesecondoptionrequires,ingeneral,agoodcharacterizationofapreviousfailureeventintherockmass,notalwaysavailable.

Inrecentyears,newtechniquesofnumericalmodelingassociatedwiththeconceptcalled“SyntheticRockMass”havebeendeveloped(PFC3D,ELFEN,Abaqus)aimingtocapturetherealbehaviouroftherockmass.Thosemethodologiesarethethirdwaytoestimatethestrengthoftherockmass,buttheyarestillindevelopment.

This paper is composed by two parts. It is aimed to implement the concept of SRM developed byItasca(Pierceetal.2007;MasIvarsetal.2011).Themethodologyisdividedinmakingageotechnicalcharacterization(mappingandlaboratorytests),developingscalinglawsandapplyingtheSRMapproach.ThispartdescribestheresultsofcomprehensiveefforttocharacterizetheElTenienterockmasses,whichinclude;corelogging,fieldwork,structuraldataanalysis,DiscreteFractureNetwork(DFN)modeling,andlaboratorytesting.Inafollowingpaper(Vallejosetal.2014),thestrengthanddeformationbehaviouroffourrockmassdomainsfromtheElTenienteminearestudied.

2 Intensive structural data collection

Anintensivestructuraldatacollectioncampaignfromseveralorientedcoreplacedwithinmaingeotechnicalunitswereundertakenaspartofthisstudy.Inordertoavoidorientationbiasduringdatacollectionateachlocation or geotechnical unit, three oriented coreswere drilled in three almost orthogonal orientationstoeachother.Thosegroupsofcores,9intotal,arecalled“triada”andrepresentmorethan2000metersof structural mapping (between 240 and 300 meters each triada). Geological and full structural corelogging (similar to scanlinemapping)wasundertaken todetermine the intensityofweakveinsat each

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location.Quantitativemineralogicalassemblage,orientationandgeometricfeaturesofveinswerethemaincharacteristicsobtainedduringcorelogging.

Weakveins and fault lineal intensity thenwere determined for eachgeotechnical unit, themeanvaluemeasuredofthelinealfrequency(P10accordingtoDershowitz&Einstein1988)ateachtriadalocation,rangefrom1m-1to12m-1(excludingthelowgradecentralbrechabraden).Ontheotherhand,faultlinealintensitymeasuredonthesamecores,rangefromP100.05m-1toP100.4m-1.ThoseintensityvaluesagreewiththegeologicaldescriptionofprimaryrockmassbyBrzovic&Villescusa(2007).

Additionalcorelogging(morethan10,000mfromun-orientedcores)andhistoricalstructuraldrivemappingwereusedtogenerateanewgeotechnicalzoningofprimaryrockmass,whichagreewiththealterationandgeneticgeologicalmodeloftheElTenienteporphyrycopper.ThenewgeotechnicalzoneareshowninFigure2,detailedsequenceofzoningbuildingwaspresentedbyBrzovic&Schachter(2013).

Figure 2 Plan view of the geotechnical model at the El Teniente mine (level 2121 and 2210) based on weak veins intensity measured in oriented cores

3 Discrete fracture network modelling

StructuraldataanalysiswerealsoundertakentobuildDiscreteFractureNetwork(DFN)asthebestwaytorepresenttherockstructureofthestockworkveinsnature(Figure3).ThemethodologyfollowedtobuildaDFNfromeachgeotechnicalunitisfullydescribedinBrzovic&Herrera(2011).Discontinuitysize,forsmallscalegeologicalstructureswereobtainedfromscanlinedatacollectedinminedrives,andforfaults,fromgeneralplanviewoffault interpretations.Basedon thestructuraldataanalysis, itwaspossible toobtaintheweakveinsvolumetricintensityP32(Dershowitz&Einstein,1988)ofeachgeotechnicalunit.DFNinFigure3,werebuiltusingcommercialFracMansoftware,whichallowedtoreadilydetermininginSituFragmentationofprimaryrockmass.TheP32ofweakveinsdeterminedattheElTenienteminerangefrom2m2/m3to15m2/m3,whichrepresentasmallpercentageofthestockworkveinsoftheprimaryrockmass.Ontheotherhand,faultintensityP32weredeterminedfrom0.15m2/m3to0.40m2/m3.


174

Figure 3 DFN model as the way to better represent the nature of the stockwork veins at the El Teniente mine (Adapted from Brzovic & Herrera 2011)

4 Laboratory testing results

Rockandveinssamplesofprimaryoreweretestedinseverallaboratoriestodeterminestrengthpropertiesofintactrockandgeologicalstructures.Laboratorytestingincluded;UCSandtriaxialtestofthestandardrocksamplesize,UCSof largerockspecimentodevelopscaling lawrelationshipformainrock types,directtensileandsheartestofallveintypes.Thatinformationwascomplementedwithdataanalysisofthehistoricallabinformationfromtheminesite.

4.1 Intact rock properties

Ingeneral,at theElTenientemine, thereare twomain factors thatcontrol thestrengthofa laboratoryspecimenthatrepresenttheintactrockmaterialofprimaryrockmass:theproperintactrockmaterialandtheveinsfeaturescontainedonthesmallrocksample.Thisaspectdescribeafundamentalcharacteristicoftheprimaryore,stockworkveinsintensityaresohighthatevenasmallcoresamplecontainseveralveinswithin.Basedon that fact,Marambioetal. (2000)suggestedaclassificationof rocksample failure (orfailuremode)duringtesting,whichissummarizedinFigure4.ThesamefigurealsopresentsthestandardvaluesofUCSandtriaxialtestingfrommaingeologicalunitsoftheElTenientemine.Inaddition,Figure5presentshistoricalvaluesofYoungModulus.

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Figure 4 Historical values of Uniaxial and Triaxial tests undertaken for main rock types at the El Teniente mine, which were classified according the failure mode suggested by Marambio et al (2000)

Figure 5 Historical values of Young Modulus and UCS to different rock sample sizes undertaken to main rock types at the El Teniente mine


176

Figure 5 also presents UCS values for different rock sample sizes undertaken to find out scaling lawrelationship. In this figure only failuremodeA,B andCwere included.Aswould be expected, largevariabilityof“intact rock”strengthpropertiesare foundoutdue thevein influenceduring rocksamplefailure.

4.2 Vein strength properties

Standard direct shear tests non-conventional direct tensile testswere undertaken for several vein typesinorder toobtain strengthpropertiesof theElTenientegeological structures.Those tests included fullgeologicalandgeometricaldescriptionofeachveinstypedtested.Morethan40sampleswereundertakenfordirectshearstest,mostofthemattheSPTechnicalResearchInstituteofSweden,andmorethan50sampleswereundertakenfordirecttensiletestatboththeSPTechnicalResearchInstituteofSwedenandtheIDIEMLaboratoryofUniversityofChile.Historicalinformationfromtheminesitewasalsoincludedduringdataanalysis.Descriptionof themethodologiesusedcanbeseen in:De losSantosandBrzovic(2013),Baraona(2012),andDelosSantos(2011).Figure6presentsomeshearandtensilestrengthvaluesoftheElTenienteveinscorrelatedtothemainmineralogicalassemblageasinfill.

Figure 6 Shear and tensile strength values of veins at the El Teniente mine (adapted from: De los Santos & Brzovic 2013; Baraona 2012; De los Santos 2011)

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5 Conclusions

From the comprehensive geotechnical characterization undertaken at the El Tenientemine in order todescribeanddeterminetherockmassstrengthpropertiesoftheprimarycopperore,itwasfoundthat:

• Anewgeotechnicalzonationwasachievedbasedontheconceptofweakveinsoccurrence.Averagelinealveinintensity(P10)ofmaingeotechnicalunitofprimaryrockmassrangefrom1m-1to12m-1.Average fault lineal intensity also range from P10 0.05m-1 to P10 0.4m-1. These new concept/zonationareinmoreagreementwithrockmassbehaviorattheminesitethanprevious,forinstanceFragmentationandSeismicity.

• StructuraldataanalysisallowedbuildingDiscreteFractureNetworkmodelthathonoredcoreloggingofmain geotechnical units at themine site.DFNoutput provide veins volumetric intensity of theprimaryrockmass(P32),whichrangefrom2m2/m3to15m2/m3.FaultvolumetricintensityP32range0.15m2/m3to0.40m2/m3.

• IntensivelaboratorytestingandhistoricalminesitedatawereusedtodeterminestrengthpropertiesofbothintactrockandgeologicalstructuresoftheElTenientemine.Dataanalysisalsoincludedscalinglawrelationship.All thosebasicinformationgatheredwereusedtoapplytheSyntheticRockMassapproachtostudythestrengthanddeformationbehaviourofprimaryrockmass.

Acknowledgement

Theauthorsacknowledge toTheElTenienteDivisionofCodelco-Chile for theirpermission topublishthedataand for supporting thiswork.This studywascommandedbyAPIT10E202ofCodelco-Chile.FONDECYTInitiationGrant#11110187alsofinancedthisstudy.

References

Baraona,K2014,‘ComportamientodeVetillassometidasaEnsayosdeTracciónDirecta,minaElTeniente’,InternalreportoftheSuperintendenceGeology,CODELCO-ChileElTenienteDivision,APIT10E202.[inSpanish].

Brzovic, A & Villaescusa, E 2007, ‘Rock mass characterization and assessment of block-forminggeologicaldiscontinuitiesduringcavingofprimarycopperoreattheElTenientemine,Chile’,InternationalJournalofRockMechanicsandMiningSciences’,vol.44,pp.565-583.

Brzovic,A2009,‘RockmassStrengthandSeismicityduringCavingPropagationattheElTenienteMine,Chile‘,InProceedingsof7thInternationalSymposiumonRockburstandSeismicityinMines(RaSiM07).Tang,C.A.editor.DalianUniversity.(2)838-52.

Brzovic,A&Herrera, S 2011, ‘AssessingGeologicalVein Size and Intensity usingDiscrete FractureNetwork Modeling at the El Teniente Mine, Chile’, InProceedings of the 45th US RockMechanics/Geomechanics,ARMASymposium,SanFrancisco,EEUU.11-252.

Brzovic,A&Schachter,P2013,‘RockMassGeotechnicalCharacterizationbasedontheWeakStockworkVeinsattheElTenienteMine,Chile’,InProceedingsof3thInternationalSeminaryofGeologyfortheMiningIndustry,GEOMIN.Santiago,Chile.

DelosSantos,C2011,‘EfectodelaMineralogía,Alteración,yGeometríaenlaResistenciaMecánicadelasVetillas,MinaElTeniente’,RegióndelLibertadorBernardoO’Higgins,Chile.MemoriaParaOptarAlTítuloDeGeólogo.UniversidaddeConcepcion.[inSpanish]


178

DeLosSantos,C&Brzovic,A2013,‘GeotechnicalPropertiesonCementedandHealedStockworkVeinsattheElTenientemine,Chile’,InProceedingsof3thInternationalSeminaryofGeologyfortheMiningIndustry,GEOMIN.Santiago,Chile.

Dershowitz,W&Einstein,H1988,‘Characterizingrockjointgeometrywithjointsystemmodels’,RockMechanicsandRockEngineering,vol.21,pp.21-51.

Hoek,E1994,‘Strengthofrockandrockmasses’,ISRMNewsJournal2,pp.4–16.

Hoek,E&Brown,E1988,‘TheHoek–Brownfailurecriterion–a1988update’,in:Proceedingsofthe15thCanadianRockMechanicsSymposium,pp.31–38.

ISRM1981,‘Suggestedmethodsforthequantitativedescriptionofdiscontinuitiesinrockmasses’inRockcharacterization, testingandmonitoring, ISRMSuggestedmethods, (editedbyETBrown),PergamonPress,pp.3-52.

Laubscher,D1977, ‘Geomechanicsclassificationof jointed rockmasses–miningapplications’,Trans.Inst.Min.Metall.,86,A1-A8.

Mas Ivars,D,Pierce,M,Darcel,C,Reyes-Montes, J,Potyondy,D,Young,P&Cundall,P2011, ‘TheSyntheticRockMassapproachforjointedrockmassmodeling’,InternationalJournalofRockMechanicsandMiningSciences,vol.48,pp.219–244.

Marambio,F,Pereira,J&Russo,A1999,‘ComportamientoEstudioPropiedadesGeotécnicasProyectoPipa Norte’, Internal report SGL-280/1999 of the Superintendence Geology, CODELCO-ChileElTenienteDivision[inSpanish].

Pierce,M,MasIvars,D,Cundall,P&Potyondy,D2007,‘Asyntheticrockmassmodelforjointedrock’,InProceedingsofthe1stCanada-USRockMechanicsSymposium,Vancouver,Canada,vol.1,pp.341-349.

Rojas,E,Cavieres,P,Dunlop,R,&Gaete,S2000,‘ControlofInducedSeismicityattheElTenienteMine,CodelcoChile’,InProceedingMassmin,Chitombo,G,editor,Brisbane,Australia,AusIMM,777-781.

Vallejos,J,Brzovic,A,Lopez,C,Bouzeran,L&MasIvars,D2013,‘ApplicationoftheSyntheticRockMassapproachtocharacterizerockmassbehaviorattheElTenienteMine,Chile’,ContinuumandDistinctElementNumericalModelinginGeomechanics:Proceedingsofthe3rdInternationalFLAC/DEMSymposium,Hangzhou,China,paper:07-02.

VallejosJ,Suzuki,K,Brzovic,A&MasIvars,D2014,‘CharacterizationandSyntheticSimulations toDetermineRockMassBehaviourattheElTenienteMine,Chile.PartII’,In:Proceedingsofthe3rdInternationalSymposiumonBlockandSublevelCaving,Santiago,Chile.

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Characterization and synthetic simulations to determine rock mass behaviour at the El Teniente mine, Chile. Part II

JA Vallejos University of Chile, ChileK Suzuki University of Chile, ChileA Brzovic Codelco Chile, Chile

D Mas Ivars Itasca Consultans AB, Sweden

Abstract

A comprehensive geotechnical characterization has been undertaken at the El Teniente mine to describe and determine the rock mass behaviour strength properties of the primary copper ore. This type of rock can be considered as a heavily veined massive and unfractured rock mass. This comprehensive work is focused sequentially on; 1) an intensive structural data collection campaign from several oriented core placed within main geotechnical units, 2) discrete fracture network modelling, 3) laboratory testing of the intact rock and veins materials combined with scaling procedures, and 4) application of the Synthetic Rock Mass (SRM) approach to study the strength and deformation behaviour of the main geotechnical units.

This paper describes the second part of the study, with focus on point 4.

The Synthetic Rock Mass (SRM) modelling approach, based on particle mechanics, has been developed to simulate the mechanical behaviour of jointed rock mass. This technique uses the bonded particle model for rock to represent intact material and the smooth-joint contact model to represent the in-situ joint network. The macroscopic behaviour of an SRM sample depends on both the creation of new fractures through intact material and slip/opening of pre-existing joints. SRM samples containing thousands of non-persistent joints can be submitted to standard laboratory tests (UCS, triaxial loading, and direct tension tests) or tested under a non-trivial stress path representative of the stresses induced during the engineering activity under study.

The micro-parameters of the bonds and the smooth-joint contacts between the particles have been calibrated against the mechanical properties and scaling laws for intact rock and veins, so that representative virtual SRM samples of the four different geotechnical units could be generated and tested.

Results from the SRM simulations include pre-peak properties (modulus, damage threshold, peak strength, etc.) and post-peak properties (brittleness, dilation angle, residual strength, fragmentation, etc.). Of particular interest is the ability to obtain predictions of rock mass scale effects, anisotropy, and brittleness, properties that cannot be obtained using empirical methods of property estimation.

1 Introduction

Presently, there aremainly two traditionalmethods used to estimate the strength of the rockmass: 1)determinationof thestrengthenvelopeof therockmassusingscalingparametersfromlaboratorytests,forexampleHoek-Brown’sfailurecriterion,and2)usingnumericalmodellingbasedonbackanalysisofpreviousexperiencesoffailureobservedandmeasuredinminingorcivilexcavations.


180

TheprimarycopperoreofElTenienteminehasnofractures;therefore,itisdifficulttodeterminearatingthatwouldenabletoscalelaboratorydata,suchas,RMR(Laubscher1977)orGSI(Hoek1994).Thisisthereasonwhythefirstoptionisnotvalidunlesstheentrydataismanipulatedoradjustedinordertoobtainreasonable results.Thesecondoptionrequires, ingeneral,agoodcharacterizationofaprevious failureeventintherockmass,whichisnotalwaysavailable.

Inrecentyears,newtechniquesofnumericalmodellinghavebeendeveloped(PFC3D,ELFEN,Abaqus)aimingtocapturetherealbehaviouroftherockmass.Thosemethodologiesarethethirdwaytoestimatethestrengthoftherockmass,buttheyarestillindevelopment.

Thispaperiscomposedoftwoparts.ThefirstoneisincludedinBrzovicetal.(2014).Thesecondpartincludes results from numericalmodelling in the primary copper ore in ElTenientemine, particularlyit hasbeen implemented theconceptofSRMdevelopedby Itasca (Pierceet al. 2007;Mas Ivars et al.2011).Themethodologyisdividedintomakingageotechnicalcharacterization(mappingandlaboratorytests),developingscalinglawsandapplyingtheSRMapproach.ThispaperaimstostudythestrengthanddeformationbehaviouroffourrockmassdomainsfromtheElTenientemine(Dacite,DioriteandCMET)andcomparestheseresultswiththeestimationsbasedonclassificationsystemsandothernumericalmodels.ThisstudyendeavoursincreasedknowledgebasedonapreviousworkwiththistechniqueinElTenienteveinedrockmass(Vallejosetal.2013).

2 Synthetic rock mass components

TheSRMmethodisbasedonthegenerationandtestingofthree-dimensionalsyntheticrockmasssamplesinordertosimulatethemechanicalbehaviourofjointedorveinedrockmasses.SRMisimplementedinPFC3D4.0software(Itasca2008)andusestheinterfaceSRMLab1.7(Itasca2012).Figure1summarizesthemaincomponentsof themodel,which represents the intact rockasanassemblyofbondedparticle(Figure 1a), using the Enhanced Bonded Particle Model (BPM), and an embedded Discrete FractureNetwork(DFN)torepresentjoints(Figure1b).Eachjointisrepresentedexplicitlyusingthesmooth-jointcontactmodel(SJCM).

Figure 1 Sample constructed with PFC3D particles (a), DFN superimposing onto the previous sample (b) and Synthetic Rock mass sample (c) (Board & Pierce 2009)

TwomodelscomposetheEnhancedBondedParticleModel(BPM),whichrepresentsintactrock;theparticlecontactandtheparallelbondmodel.AmoredetailedexplanationofthestandardandenhancedBPMforrockcanbefoundinPotyondy&Cundall(2004)andPotyondy(2011).Thesmooth-jointcontactmodel(SJCM)representsjointsintheSRMsamplessimulatingthebehaviourofasmoothinterface,regardlessofthelocalparticlecontactorientationsalongtheinterface.Thismodelmakespossiblethecreationoflarge

(a) (b) (c)

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volumesofsyntheticrockcontainingthousandsofnon-persistentjoints.AmoredetailedexplanationofSJCMcanbefoundinMasIvarsetal.(2008b).

ThemostlogicalchoicetorepresentexplicitlyaveinnetworkisbyusingaDiscreteFractureNetwork(DFN)(Dershowitz&Einstein1988).EachDFNrepresentsdifferentrockmassdomains,andarecharacterizedbyfractureintensityandorientationofeachsetofveins.ThedataandtheprocedureusedtodevelopeachDFNmodelisdescribedinBrzovicetal.(2014).

3 Calibration of the SRM model

InordertocalibrateaSRMmodelandtocreatealarge-scalemodel,laboratorytestsandfieldsobservationsareneeded.AsummaryofdataforintactrockandveinspropertiesandscalingproceduresaredescribedinBrzovicetal.(2014).

3.1 Intact rock

LaboratorydataforUCStestsisadjustedusingtherelationproposedbyYoshinakaetal.(2008),definingascalingpowerlawforeachlithology:

(1)

Where:

σc:istheuniaxialcompressivestrengthofacylindricalspecimenwithadiameter,

k:isamaterialconstant.

Eventhoughexistsacalibrationprocedure,thebasicwaytodefineasetofmicro-parametersisbyatrialanderrorapproach(Itasca,2008).ThesizeofcalibrationisdefinedbytheintactblockwithineachDFN.Thesizeofparticleisselectedequaltofourparticlesalongtheaverageintactblocksize,andtheaspectratioofthecalibrationsampleis2.1:1.TheassumptionstakenfortheintactrockcalibrationaredetailedinVallejosetal.(2013).Tosumup,thereareassumptionstoreproducebetterthebrittlebehaviourofElTenienterockmassesandotheronessuggestedonpreviousstudiesforreproducinghardrockbehaviour(Potyondy&Cundall2004;Potyondy2011).

ThemodelcannotreproducePoisson’sratioslargerthanapproximately0.10ifareasonablybrittleresponseisdesired.Duetotherockmassresponsebeingmoreinfluencedbytheveinsbehaviour,Poisson’sratioisnotconsideredinthecalibration.Therestofmacro-parameterswerematchedwithlessthan1%oferror.

3.2 Veins

Brzovic&Villaescusa(2007)suggest thatveinswiththicknessesgreater than2mmandwithless than1/3ofhardmineralsplaya relevant role controlling fragmentationand in the seismicityduringcavingpropagation.Thepresentstudyincludesonlysoftveinswiththicknessesgreaterthanorequalto1mm,assumingthatveinswiththicknessbetween1and2mmaffecttherockmassbehaviour.Macro-parametershavetobescaledtorepresenttheaveragein-situconditionsofeachrockmassunit.Inthiscasetheaveragelengthofveinsis1m,thereforeallmacro-parametersarescaledtothislength.Itisconsideredthatfrictionangleisnotinfluencedbyscaleeffect.Theproceduretocalibratethemodelconsidersestimatingmicro-parametersbasedonresultsofprevioussimulations.ThecalibrationprocedureisdetailedinVallejosetal.(2013).Theassumptionstakenforveinscalibrationconsiderpeakandresidualfrictionanglesofveinstobe40°anddilationangletobe0°.Itisassumedthatmostoftherockmassdilationcomesfromblockrotationandtherelativelargesizeoftheparticles.


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4 Characterization of rock mass units and behaviour

4.1 Geometry and test configurations

Thetestedgeometryisacylinderofwidthequaltotentimestheintactblocksizewithanaspectratioheighttowidthof2.1:1.EachDFNisastatisticalrepresentationofthenetworkofveinsformingthestockworkina30mx30mx30mvolume,buttheSRMspecimenwidthsaresettoonly10DFNaveragespacingforeachlithologyandassociatedDFNinordertominimizesimulationstime.Table1summarizethegeometryusedinthemodelling.IthastobetakenintoaccountthattheSRMsamplesizesusedinthisstudyarenotlargeenoughtoreachtheREV(Esmaielietal.2010).Consideringalllithologicalunits,P32infunctionofacubicsamplewidthconvergestoameanvalueinsampleswithover10mwidth.

Table 1 DFNs and SRMs dimensions for the block geometrical analysis

Lithology DFN Average spacing (m)

SRM Width (m)

SRM Particle diameter (m)

SRM Number of particles

Dacite 0.70 7.0 0.174 169,738Diorite 0.16 1.6 0.040 166,845

CMETHW 0.22 2.1 0.052 171,704CMETFW 0.13 1.3 0.033 159,375

4.2 Characterization of rock mass behaviour

Foreachtest,thepre-peakrockmassparametersandstress-strainbehaviourareregistered.Directtensiontestsand triaxial testsareperformed tocharacterize theSRMresponseof the rockmasses.The testingdirectionsincludetwoorthogonalhorizontaldirections(direction1referstotheE-Wdirection,direction2referstotheN-Sdirection)andtheverticaldirection(direction3)foreachlithology.

Figure2presents thestress-strainbehaviour for triaxial tests in the three testingdirections for the fourlithologies. It is observed that Dacite presents a degree of anisotropy.As expected, the peak strengthincreaseswith confinement.However, thepost-peakbehaviour tends tobemorebrittle as confinementincreasesinDacite,whileintheotherslithologiespost-peakbehaviourisbrittleonlyinlowconfinements.

4.3 Characterization of rock mass parameters and comparison with classification systems

Table2summarizethemaingeotechnicalparametersofeachlithologicalunit.ThesedataisusedtocompareSRMresultswithestimationsbasedontheclassificationsystems.

Table 2 Geotechnical parameters for each lithology (Brzovic 2001)

Lithology Ei (GPa) UCS (MPa) mi GSI D RMRDacite 43 167 10.6 75-90 0 72-77Diorite 45 140 9.2 70-90 0 68-72

CMETFW 55 97 12.1 70-85 0 66-72CMETHW 55 121 12.1 70-90 0 66-74

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SRMenvelopesarecomparedwiththeresultingenvelopesobtainedfromapreviousinvestigationinElTeniente,where a numericalmodel based in back analysis of documented collapses at Esmeraldawasdeveloped(Pardoetal.2012).Also,thepeakstrengthenvelopeoftherockmassiscomparedwiththeoneproposedbyHoeketal.(2002):

(2)

Where

(3)

GSI:GeologicalStrengthIndex,D:factorofdisturbance(blastdamageandstressrelaxation),σcuniaxialcompressivestrengthoftheintactrockmaterialand,mimaterialconstant.

Figure3presentsacomparisonofpeakstrengthenvelopesestimatedwithdifferentmethodologies.

ThestrengthenvelopesestimatedwiththeSRMtechniquearenon-linearandtheyarenotconsistentwiththeenvelopesestimatedwiththeHoek-BrowncriterionbasedontheparametersofTable2.NoneoftheenvelopesareclosetotheenvelopeestimatedwiththeminimumGSI.However,SRMenvelopesaresimilartotheenvelopesestimatedfortheminescaleelastic-plasticnumericalmodellingstudy(Pardoetal.2012).Table3showsasummaryincludingHoek-BrownandMohr-CoulombparametersadjustedinRocData.Therangeofvaluesincludesresultsinthethreetesteddirections.ThesevaluesindicatethattheGSIresultingfrom SRMmodelling is between 41 and 60; therefore all rockmasses have a fair quality.GeologicalinformationofElTenientemineindicatesthatElTenienterockmasseshaveagoodtoverygoodquality.

Figure 2 Axial stress–strain and volumetric–axial strain curves for triaxial tests in three directions


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Table 3 Geotechnical parameters adjusted from SRM modelling for each lithological unit

Lithology GSI c (MPa) φ (°)Dacite 19.4–22.9 55.5–57.7 3.7–4.1 50.0–53.2Diorite 11.3–13.1 45.3–47.3 2.6–2.7 42.6–44.7

CMETFW 12.8–15.7 51.8–54.5 2.6–2.9 42.6–45.0CMETHW 11.0–13.7 48.6–52.2 2.7–2.8 43.2–44.5

Uniaxial compressive strength andYong’s modulus of the rocks masses are compared with empiricalformulasbasedonclassificationsystems:

1. Uniaxialcompressivestrengthoftherockmass(Table4).EmpiricalformulasproposedbyHoeketal.(2002)andHoek&Brown(1988)areusedtocomparemodellingresults.Theseformulasarebasedonsandadefinedinequation(4),andRMR,therockmassratingofBieniawski(1974).

2. Young’sModulusoftherockmass(Table5).EmpiricalformulasproposedbySerafim&Pereira(1983)andHoek&Diederichs(2006)areusedtocomparemodellingresults.Theseformulasareestimatedusingtheintactrockmodulus(Ei).TheYoung’smodulusestimatedwithSRMapproachhasaslightdependenceontheminorprincipalstressandnosignificantevidenceofanisotropy.

Figure 3 Comparison of peak strength envelopes for each lithological unit

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Table 4 Comparison of uniaxial compressive strength of the rock mass using different methods

Table 5 Comparison of Young’s Modulus of the rock mass using different methods

4.4 Effect of preconditioning by hydraulic fracturing

Tostudytheeffectofthehydraulicfracturingontherockmassbehaviour,sub-horizontalfractureplaneswereincludedexplicitlyintheSRMsample.Figure4presentsthestress-straincurvesresultingfromtriaxialtestsindirections1(E-W),2(N-S)and3(verticaldirection).Comparingtheseresultswithstress-straincurvesinFigure2,itiscleartheimpactonelasticparametersofthesampletestedintheverticaldirection.Theseresultscanbecomplementedwithotherstudiesthathaveshowntherelevanceofpreconditioningbyhydraulicfracturingincavepropagationandprimaryfragmentation(SánchezJuncaletal.2014).

Figure 4 Stress-strain curves resulting from simulations with fractures due to preconditioning

5 Conclusions

TheSRMapproachhasbeenusedtocharacterizethebehaviouroffourlithologicalunitsfromElTenientemine.The resultsarepromisingandshowan improvementcompared to those reported in thepreviouspapers(Vallejosetal.2013;MasIvarsetal.2013).

ThemainadvantageoftheSRMapproachisthatitallowsestimatingthebehaviourofasyntheticsampleas a result of a geotechnical and geological characterization, and not as a result of a back analysis or


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empiricalformula.Resultsareconsistentwiththefundamentalprinciplesofrockmechanics.Nevertheless,somelimitationshavetobeovercome,suchasthefactthatitisnotpossibletofitthePoissonratiokeepingabrittlepost-peakbehaviour.Furthermore,theparticleassemblyiscalibratedtothestrengthandelasticbehaviouroftheaveragerockblocksintherockmass;thereforetherockblockscaleeffectsisnotcapturedexplicitly.

SRMstandardtestsshowacceptableresultsscalingveinsmacro-parameterstotheaveragelengthwithintheDFN.Duetoallveinshavingthesamemacro-parameters,theonlydifferencesbetweenmodelsforeachlithologyareintactrockmicro-parametersandtheinfluenceoftheveinnetworkgeometry(DFN).Theseassumptionsresultsinenvelopescomparablewithanothernumericalmodellingestimation.

Theeffectofthehydraulicfracturingontherockmassbehaviour,resultingfromSRMmodelling,showahighpotentialfortheSRMapproachtoevaluatetheeffectofincludingnewfracturesduetopre-conditioninginthefield.

It is recommended to further investigate the effect of increasing the SRM sample size and alsomakeadditionaleffortincomplementinglaboratoryandfielddatatosupportthenumericalmodellingresults.

Acknowledgement

TheauthorsacknowledgeTheElTenienteDivisionofCodelco-Chilefortheirpermissiontopublishthedataandforsupportingthiswork.ThisstudywascommandedbyAPIT10E202ofCodelco-Chile(contracts4501127645and4501142662)andbyFONDECYTInitiationGrant#11110187.CarolineDarcel,RomainLeGocandLaurianeBouzeranfromItascaConsultantsSASarealsoacknowledgedfortheircontributiontothiswork.

References

Board,M& Pierce,M 2009, ‘AReview of Recent Experience inModeling of Caving’, InternationalWorkshoponNumericalModelingforUndergroundMineExcavationDesign:Proceedingsofthe43rdUSRockMechanicsSymposium,Asheville,UnitedStates.

Brzovic,A,Schachter,P,delosSantos,C,Vallejos,J&MasIvars,D2014,‘CharacterizationandSyntheticSimulations to Determine Rock Mass Behaviour at the El Teniente Mine, Chile. Part I’,Proceedingsof the3rd InternationalSymposiumonBlockandSublevelCaving,Santiago,Chile.


Brzovic,A2001,‘Fundamentosgeológicosparaunsistemadeclasificacióngeotécnicodelmacizorocosoprimario,minaElTeniente’,InternalreportSGL-187/2001oftheSuperintendenceGeology,CODELCO-ChileElTenienteDivision[inSpanish].

Brzovic,A2009,‘RockmassStrengthandSeismicityduringCavingPropagationattheElTenienteMine,Chile‘,In:Proceedingsof7thInternationalSymposiumonRockburstandSeismicityinMines(RaSiM07).Tang,C.A.editor.DalianUniversity.(2)838-52.


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Hoek,E&Diederichs,M2006, ‘Empiricalestimationof rockmassmodulus’, International JournalofRockMechanicsandMiningSciences,vol.43,pp.203-215.

Hoek,E,Carranza-Torres,C&Corkum,B2002,‘Hoek-Brownfailurecriterion-2002edition’,ProceedingsofthefifthNorthAmericanRockMechanicsSymposium,Toronto,Canada,vol.1,pp.267–273.

Hoek,E1994,‘Strengthofrockandrockmasses’,ISRMNewsJournal2,pp.4–16.

Hoek,E&Brown,E1988,‘TheHoek–Brownfailurecriterion–a1988update’,Proceedingsofthe15thCanadianRockMechanicsSymposium,pp.31–38.

ISRM1981,‘Suggestedmethodsforthequantitativedescriptionofdiscontinuitiesinrockmasses’inRockcharacterization, testingandmonitoring, ISRMSuggestedmethods, (editedbyETBrown),PergamonPress,pp.3-52.

ItascaConsultingGroup,Inc.2012,‘SRMLabversion1.7’,Minneapolis,UnitedStates.

ItascaConsultingGroup,Inc.2008,‘PFC3D–Particleflowcodein3dimensions,Version4.0’,Minneapolis,UnitedStates.

Laubscher,D1977, ‘Geomechanicsclassificationof jointed rockmasses–miningapplications’,Trans.Inst.Min.Metall.,86,A1-A8.

Machuca,L&Villaescusa,E2011,‘SummaryofintactrockpropertyvaluesforCodelcoChile–DivisiónElTeniente’,WesternAustralianSchoolofMines-GeomechanicslaboratoryreporttoDivisiónElTeniente,CodelcoChile,APIT10E202.

MasIvars,D,Bouzeran,L,LeGoc,R&Darcel,C2013,‘FinalreportonSyntheticRockMass(SRM)Fragmentation Analysis – El Teniente March, 2013’, Itasca Consulting Group report toDivisiónElTeniente,CodelcoChile,APIT10E202.

Mas Ivars,D,Pierce,M,Darcel,C,Reyes-Montes, J,Potyondy,D,Young,P&Cundall,P2011, ‘TheSyntheticRockMassapproachforjointedrockmassmodeling’,InternationalJournalofRockMechanicsandMiningSciences,vol.48,pp.219–244.

MasIvars,D,Pierce,M,DeGagne,D&Darcel,C2008a,‘Anisotropyandscaledependencyinjointedrock-mass strength—Asynthetic rockmass study’,ContinuumandDistinctElementNumericalModelinginGeomechanics:Proceedingsofthe1stInternationalFLAC/DEMSymposium,Minneapolis,UnitedStates,paper06-01,pp.231-239.

MasIvars,D,Potyondy,D,Pierce,M&Cundall,P2008b,‘Thesmooth-jointcontactmodel’,ProceedingsoftheEighthWorldCongressonComputationalMechanicsandFifthEuropeanCongressonComputationalMethodsinAppliedSciencesandEngineering,Venice,Italy,papera2735.

Pardo,C,Villaescusa,E,Beck,D&Brzovic,A2012,‘BackAnalysisofintensiverockmassdamageattheElTenienteMine’,CRC-MiningConference,Brisbane,QueenslandUniversity.

Pierce,M,MasIvars,D,Cundall,P&Potyondy,D2007,‘Asyntheticrockmassmodelforjointedrock’,Proceedingsofthe1stCanada-USRockMechanicsSymposium,Vancouver,Canada,vol.1,pp.341-349.

Potyondy,D2012,‘Thebonded-particlemodelasatoolforrockmechanicsresearchandapplication:Currenttrendsandfuturedirections’,ThePresentandFutureofRockEngineering,Proceedings,ofthe7thAsianRockMechanicsSymposium,Seoul,Korea,pp.73-105.


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Potyondy,D2011, ‘Parallel-bond refinements tomatchmacroproperties of hard rock’,ContinuumandDistinctElementNumericalModelinginGeomechanics:Proceedingsofthe2ndInternationalFLAC/DEMSymposium,Melbourne,Australia,Paper08-04,pp.459-465.

Potyondy, D & Cundall, P 2004, ‘A bonded-particle model for rock’, International Journal of RockMechanicsandMiningScience,vol.41,pp.1329-1364.

Rojas,E,Cavieres,P,Dunlop,R,&Gaete,S,2000,‘ControlofInducedSeismicityattheElTenienteMine,CodelcoChile’, ProceedingMassmin, Chitombo,G, editor, Brisbane,Australia,AusIMM,777-781.

SánchezJuncal,A,MasIvars,D,Brzovic,A&Vallejos,J2014,‘Simulatingtheeffectofpreconditioninginprimaryfragmentation’,tobepublishedinProceedings:Eurorock2014,Vigo,Spain.

Serafim, J & Pereira, J 1983, ‘Considerations on the geomechanical classification of Bieniawski’,Proceedings of the International Symposium on Engineering Geology and UndergroundConstruction,Lisbon,Portugal,vol.1,pp.33-44.

Vallejos,J,Brzovic,A,Lopez,C,Bouzeran,L&MasIvars,D2013,‘ApplicationoftheSyntheticRockMassapproachtocharacterizerockmassbehaviorattheElTenienteMine,Chile’,ContinuumandDistinctElementNumericalModelinginGeomechanics:Proceedingsofthe3rdInternationalFLAC/DEMSymposium,Hangzhou,China,paper:07-02.

Yoshinaka,R,Osada,M,Park,H,Sasaki,T&Sasaki,K2008, ‘Practicaldeterminationofmechanicaldesignparametersofintactrockconsideringscaleeffect’,EngineeringGeology,vol.96,pp.173-186.


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Fragmentation estimates using BCF software – Experiences and pitfallsJ Jakubec, SRK Consulting Ltd., Canada

Abstract

Fragmentation estimates are one of the key inputs for cave mine design. Block cave fragmentation (BCF) software has been the industry standard for decades. However, experience from several mines has shown poor correlation between the initial fragmentation estimates and reality. Realistic input parameters in the BCF software are key to realistic fragmentation estimates. Often, such parameters are based on drill core data, but correct assessment of rock mass parameters based on such data could be a challenging task. The author of this paper discusses some of the reasons for poor reconciliation and shares his experience and methodology with using BCF software. Experience has shown that BCF software may overestimate fragmentation because of conservatism during the feasibility stage, drill core bias, ignoring fines and weathering, and inadequate accounting of rock block defects. The quality of fragmentation predictions using BCF software can be improved significantly through careful evaluation of these factors.

1 Introduction

TheBCFsoftwarewasdevelopedandintroducedtotheminingindustryinthe1990s(Esterhuizen1994;Esterhuizenetal.1996).TherehavebeenseveralchangestothesoftwarecodesincethenandcurrentlythemostuptodateversionisBCFV305.

Althoughthereareothertechniquestoassessblockcavingfragmentation,BCFsoftwareremainsaprovenandpracticalmethodthatenablestherapidevaluationofdifferentscenarios.

However, the current general experience in themining industry is that BCF software predicts coarserfragmentation than theactual fragmentationpresent.Theauthorof thispaperuseshis experience fromanumberofoperatingcaveminestoanalyzeanddiscusspotentialreasonsforsuchadiscrepancyandtosuggestsolutions.

2 Fragmentation in block caves

Duringtherockmasscavingprocess,therockblocksareformedbyfourmechanisms:

• Gravityliberationofexistingblocksboundedbyopenorweaklyhealedjoints.

• Stressfracturingviaintactrockorviarockblockdefects.

• Dynamicimpactbreakageduetorockfalls.

• Breakageduringthecommunitionprocessesinthecave.

Whendescribingtherockmassfragmentationincavingmines,threetypesoffragmentationsarerecognized:

• Insitufragmentation.

• Primaryfragmentation.

• Secondaryfragmentation.


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2.1 In situ fragmentation

Insitufragmentationistheblocksizedistributionofnaturallyformedblocksthatareboundedbyopenorweaklyhealedjoints.Thisblocksizedistributionwouldbeformedbygravityasiftherockmasssimplyfellapart.Nonewstressfractureswouldbeinducedorconsidered.

2.2 Primary fragmentation

Beside gravity, the rock mass caving processes typically involve induced stresses acting in the caveenvelope.Suchstressescauserockfracturingthroughintactrockoralongthepre-existingdefects,furtherreducingtheinsitufragmentation.Laubscher(2000)definesprimaryfragmentationasthefragmentationoftherockasitpartsfromthesurroundingrockmass.

2.3 Secondary fragmentation

Secondaryfragmentationisthefurtherbreakdownoftherockasitmovesdownthedrawcolumn(Laubscher2000).Theprimaryblocksaresubjectedtocommunitionprocesseswithinthecave,furtherreducingtheprimaryblocksandgeneratingfines.Inacavewhereanairgapispresent,theprimaryblockscouldalsobereducedbydynamicbreakageduringrockfallanddrilling.

3 Rock mass characterization

Tworockmassparameters thatarecritical todefinefor realistic fragmentationestimatesarerockmassdefectsandrockstrength.

3.1 Rock mass defects

Geologicprocessespriortominingsuchasbrittledeformationofand/orsedimentationcanintroducedefectsthat have variable geometry, continuity, shear strength, and cohesion. Such defects could significantlyreducetherockblockorrockmassstrength,especiallyinanunconfinedsituation(Jakubec2013).Rockmassdefectcharacterizationshouldincludearangeofdifferentscalestructures,fromlarge-scalestructuresthroughjointstorockblockdefects.

3.1.1 Large-scale structures

Large-scalestructuresinthecontextofthispaperincludealltypesoflargerockmassstructuressuchasfaults,shearzones,orcloselyspacedjointclusters.

Althoughlarge-scalestructuresdonottypicallyinfluencerockfragmentationprocesses,theyaresourcesoffines(typically,fragmentsaresmaller than0.001m3)andhenceshouldbedefinedforfragmentationanalysis.Typicalrockmassincavingoperationsincludes3–15%ofinsitufinescontainedwithinthelarge-scalestructures(Figure1).

3.1.2 Open and cemented joints

Itistypicalindustrypractice,whendescribingadrillcoreorduringthemappingoftunnelwalls,todescribedefectsthatdonothavebondingcementasopenjoints(Figure1).Inthecontextoffragmentationanalysis,theopenjointsshouldhavesufficientcontinuitythattheyformaninsitublock—inotherwords,theyshouldbeblock-bounding joints.Often, the jointsarecementedwithmineral infillofvariable strengthrangingfromveryweaktostrongwithstrengthsimilaroroccasionallyexceedingtheintactrockstrength

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(Figure2).FragmentationanalysisusingBCFsoftwareforrockmassesthatdonothaveopenorweaklycemented block-bounding joints is not reliable. In such cases, amore sophisticated analysis such as asyntheticrockmass(SRM)approachshouldbeused(Jakubecetal.2012).

Figure 1 Example of fines generating fault (left) and open joints (right) in the drill core

3.1.3 Rock block defects

Aspecialcategoryofdefectsaresmalldiscontinuousfracturesandveinsormicro-fractures(Figure2).Suchdefectshavelimitedcontinuityandarecontainedwithintheinsitublocks(Laubscher&Jakubec2001).

Figure 2 Example of cemented joint (left) and rock micro-fractures (right)

Thedescriptionofsuchdefectsandthechallengesintheircharacterizationhavebeendiscussedinseveralpapers (Jakubec2013).To avoid “double dipping” and incorrectmaterial strength characterization, themicro-defects contained within the hand specimen and affecting laboratory unconfined compressivestrength(UCS)testsshouldnotbetakenintothesubsequentIRSstrengthreductionprocess.ThedefectsthatdonotaffectUCStestsmustbeincludedinrockblockstrengthreduction.

3.2 Rock strength

Both primary and secondary fragmentation are influenced also by rock strength, stresses acting on therockinthecaveback,andpointloadinginthecavecolumn.Thefollowingcategoriesofrockstrengtharerecognized:

• Intactrockstrength.

• Rockblockstrength.

• Rockdeformation.


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TheMRMRclassificationsystemdevelopedbyLaubscher&Jakubec(2001)recognizesandaddressestheneedtoincludeallimportantdefectsintherockmassclassificationandrockmassstrength.

3.2.1 Intact rock strength (IRS)

IntactrockstrengthintermsofBCFanalysisisdefinedastheunconfinedcompressivestrengthoftherockspecimenthatcanbedirectlytested,includingallinternalmicro-defectscontainedinthespecimen.

3.2.2 Rock block strength (RBS)

Rockblocksboundedbyopenorcementedjointswillhavealowerstrengththantheintactrockstrengthiftheirdimensionsexceedapproximately50mm.Discontinuousjoints,fracturesandveinsthatterminatewithin rock blocks and do not take part in the formation of blockswill further reduce the rock blockstrength.TheintactblockstrengthintheBCFsoftwareisequivalenttorockblockstrengthintermsoftheLaubscher-JakubecMRMRclassification.Theconceptofstrengthadjustmenttorockblockandrockmass,andstrengthreductionofrockblockstrengthisillustratedinFigure3.

Figure 3 Example of RBS concept (left) and RBS reduction from the Chuquicamata mine in Chile (after Jakubec et al. 2012)

3.2.3 Rock failure criterion

RockmassfailurecharacteristicsarealsoincludedintheBCFsoftwareapproachviaHoek&Brownmb-valueforrockmass.TheHoek&Browncriterionisusedtodeterminethetriaxialstrengthoftherockandthevaluemaybeestimatedusingpublishedtablesfortypicalmaterialsordeterminedbylaboratorytests.

4 Block caving fragmentation

TheBCFsoftwareisacommerciallyavailablecomputerprogramauthoredbyDr.G.Esterhuizenandhasbeenusedbythecaveminingindustryforthepasttwodecades.

“The program is based on analytical and empirical rules describing the fragmentation processes and factors that play a role in block cave fragmentation. “ (Esterhuizen2005)

Theprogramconsistsofthreemainmodules:

• Primaryfragmentationmodule.

• Secondaryfragmentationmodule.

• Hang-upsmodule.

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ThelatestavailableversionisBCFV3.05from2005.

ItisnotanobjectiveofthispapertodescribeindetailtheBCFengineoralgorithmsandtheauthorofthispaper recommends that the reader refers to theBCFTechnicalReferenceandUserGuide (Esterhuizen2005).

4.1 BCF software input data

AcomprehensivedatasetisrequiredforallthreeBCFsoftwaremodulesandtheinputdataareorganizedinthreemaingroups:

• Geology(orrockmass)input.

• Caveinput.

• Drawinput.

Thefirst two inputs are used to calculateprimary fragmentation and the last input is used to calculatesecondaryfragmentationandconductthehangupanalysis.

4.1.1 Geology input

The primary fragmentationmodule requires input from two areas: geology and cave information.Thegeologyinputconsistsof:

Rockmassinformation

• Rocktype–simplerocktypenameorabbreviation.

• RockmassratingMRMR–(Laubscher1990).

• Hoek&Brownmvalueforrockmass(mb)–frompublishedtablesorcalculatedfromlaboratorytests.

• Intactrockstrength(UCSinMPa).

• Fracture/veinletfrequency/m,ff/m,(rockblockdefects).

• Fracture/veinletconditions(Laubscher1990jointconditionsequivalent).

• Intactblockstrength(sameasrockblockstrength)–thisvalueiscalculatedorcanbemanuallyinputted.

Insitublockboundingjointinformation

• Jointsetnumber–threesetsdefinetheblock,anadditionalsetwillshapeit;thereistypicallynomaterialbenefittoinputtingmorethanfivesets.

• Jointdip–averageandrange.

• Jointdipdirection–averageandrange.

• Jointspacing–averageandrange(minimumandmaximum).

• Jointcondition–averageandscatter(Laubscher1990jointconditions,1-40).

Jointorientationistypicallyobtainedfromstereonetanalysesofjointdata.


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4.1.2 Cave input

Thecaveinputtableisthesecondinputgroupofinformationthathastobecompletedinordertocalculateprimaryfragmentation.Thecaveinputdatainclude:

• Caveorientation–dipanddipdirectionofthecavingface.

• Stresses–dipandstrikedirection,andnormalstressoftheinducedstressesinthecaveback(valuescouldbeestimatedusingtheBCFsoftwaremanualguidelinesorobtainedfromnumericalmodellinganalyses).

• Stressspallingandfracturing–stressfracturesmayforminthecavefaceifthestressesarehighenoughtocausecompressivefailureoftherock.Spallingprovidesasecondoptiontomodeltheeffectsofstressfracturing.Theamountofspallingisenteredasafixedpercentageofthevolumeofrock.

4.1.3 Draw input

Drawinputdataarerequiredtomodelsecondaryfragmentationandhangupanalysis.Thedataincludethefollowing:

• Primaryfragmentationfile.

• Drawdata:

o Drawheight.

o Maximumcavingheight.

o Drawwidth.

o Swellfactor.

o Rockdensity.

o Additionalfines.

o Rateofdraw.

• Drawbellsize.

5 Experiences of using BCF software

Asmentioned before, the general experience in themining industry is that theBCF software predictscoarserfragmentationthantheactualfragmentationpresent.Thisviewissupportedbyobservationsfromseveralcavingoperationsthattheauthorhasbeeninvolvedwith.

Experienceshowsthatthereareseveralpotentialreasonsforthisdiscrepancy:

• Focusonanoversizeportion(+2m3)oftheblockdistributioncurveduringthefeasibilitystudy,henceacoarser,moreconservativecurveisselectedasthebasecase.

• Thequalityoftheinputdata,whichareoftenbasedondrillcorewithorientationbiasandundersampledjointspopulations.

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• Ignoringfinessourcedfromfaults,shearzones,andjointclusters.

• Notconsideringtheweatheringeffectonfragmentsinthecavezone.

• Ignoringportionofthejointpopulationswhenselectingmajorjointsetsandnotreconcilingwiththeff/mvaluemeasuredinthecore.

• Ignoringrockblockdefects.

5.1 Focus on course blocks

Therangeofinputdataresultsinarangeoffragmentationcurvesforthesamerockmass.Itisoftenthetendencytoselectthemoreconservativeanalysistoacknowledgeuncertaintyduringthefeasibilitystudystagebecausecoarseblocksduringthecaveminingoperationcancauseoperationaldifficultiesthatresultincostlydelays,duetohangupsandsecondaryblasting.Althoughthisisavalidconcern,inevitablycoarsefragmentationdistributioncurvesareselectedasthebasecasescenarioandthiscanpotentiallyresultinadiscrepancywiththeactualexperienceduringproduction.Typically,finerfragmentationrealizationisnotconsideredas important.However, it is important toconsiderbothsidesof thepossiblerange:acoarsecurveformaterialhandlingandproductionratesandafinecurvemainlyfordrawpointspacing.

5.2 Orientation bias of the drill core

Preferreddrillholeorientationcanundersamplejointspopulationsresultingintheunderestimationoftheff/mvalueand/orthejointsetnumber,seeFigure4.Itisimportanttoorientdrillholesindifferentdirectionstocaptureacompletefamilyofjoints.ItisalsoimportanttoapplyaTerghazicorrectioninthestereonedanalysistominimizeorientationbias.Acousticoropticaltelevierdatacouldalsoimproveunderstandingofthejointspopulations.

Figure 4 Example of drill hole orientation bias

5.3 In situ fines

Insitufinesaresourcedfromlarge-scalestructuressuchasfaults,shearzones,andjointclusters.Generally,fragmentssmallerthan10cm3aretobeconsideredasfines,whichmeansthatrockwithaff/mvalueof10canproducesignificantamountoffines.Itisrelativelyeasytoestimateamountofinsitufinesifcoreloggingdidnot ignore large-scale structures.Experienceshows that typicalcaveable rockmasseshave3–15%insitufines.


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5.4 Weathering of the caved rock

Weatheringsusceptibilityandoxidizationoftherockhastobeconsidered.Brokenrockinthecavezoneusually resides in the cave for several years with constant attrition and often water migration. Somerocks,suchaskimberlitesandserpentinite,aresusceptibletoweathering,whileoxidizationistypicalforsulphidicore.Bothprocessescouldgenerateconsiderableamountoffinesthatisnotaccountedforinthefragmentationanalysis.

5.5 Under sampling joints populations

Theundersamplingofjointspopulationsisconsideredasoneoftheleadingcausesofdiscrepancybetweenanalysisandreality.Jointsetsareoftenselectedgraphicallyonstereonetsusing“fences”aroundthejointpoleconcentrations(Figure5a).Plottingpolesrevealedwastenumberofjointsoutsidefences.

Figure 5 Selected joint sets based on stereonet without poles (a) and with poles (b)

Theproblemisthatalargeamountofmorerandomlydistributedandorientedjointsisnotaccountedfor.Ifafracturefrequencycheckasdiscussedaboveisnotundertaken,thefragmentationcurvescouldbeverycoarseandfarfromreality.Dependingonthestructuralcharacteroftherockstherearetwomainwaystorectifythis.Onewayistodecreasethejointspacingforoneormorejointfamiliestomatchtheexpectedff/mvalue.Theotherwayistocreateanewjointsetandassignthespacingtomatchtheff/mvalue.TheBCFsoftwaretoolforverifyingtheff/mvalueispowerfulandshouldbeusedtomakesurethatalljointsareaccountedfor.

5.6 Ignoring rock block defects

Another common reason for underestimating fragmentation distribution curves is ignoring rock blockdefects.Currently,theonlyclassificationsystemthatincludesrockblockdefectsisLaubscher&JakubecMRMR(2001).IfloggingisdoneforexampleusingBeniawskiRMRorHoek’sGSI,rockblockdefectsareignoredandrockblockstrengthwillbeoverestimated.AcomparisonofprimaryfragmentationresultswithandwithoutdefectsisillustratedinFigure6.

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Figure 6 Comparison of fragmentation curves with and without defects

6 Conclusions

TheBCFsoftwareremainsoneoftheleadingpracticaltoolstoassessblockcavingfragmentation.Despiteits limitations, it enables the rapid assessment of different scenarios and investigation of sensitivity toindividualparameters.Becauseofthecomplexityoffragmentationanalyses,itisverydifficulttoguesstheoutcomebychangingvariousparameters.Forexample,forastrongrockmassinalowstressenvironmentthestresslevelvariationwillprobablynotproducerealizationsthataremateriallydifferent.Ontheotherhand,forrockmasseswherethestressandrockblockstrengthvaluesareclose,asmallchangeinstresslevel could produce significantly different outcome. It is difficult to comment on the accuracy of thefragmentationanalysis.Theaccuracyisdifferentforeachcaseanddependentoninputdataquality,stressstrengthrelationship,etc.InthecaseoftheChuquicamataundergroundstudy,twoapproaches,BCFandSRMcombinedwithnumericalanalysiswereusedandyieldedsimilarresults(Figure6).

Oneofthebiggestchallengesforrealisticfragmentationestimatesistocorrectlydefineblockboundingjoints. In caseswhere such joints are cementedwith strong infill, amore sophisticated approach suchasSRMshouldbeused.Forexamples,theSRMapproachinfragmentationwasdiscussedinpapersbyJakubecetal(2012)andbyJakubec(2013).

Asinanyanalysis,itisimportanttohaverealisticandcompletedataforinputinordertoproducerealisticfragmentationestimates.Thispaperillustratessomeofthereasonsforthediscrepancyexperiencedbythemining industrybetween theestimatedandactual fragmentation.However, cautionhas tobeexercisedalwaystomanageexpectations.Noneofthetechniquescanandmostlikewillnotpredictfragmentationwiththeoftenexpectedhighdegreeofaccuracy.Thegeologicalnatureoftheorebodiesistoocomplexanditisnotrealistictocapturesuchcomplexityintheanalysiswithoutcalibrationtotherealdata.However,byconsideringalltheavailableinformationwecanproduceestimatesthatareroughlyrightandnotpreciselywrong.

Acknowledgements

TheauthorwouldliketothankDr.GabrielEsterhuizenforhisvaluablecomments,andMs.VanNgoandMs.SophiaKaradovfortheirassistanceinpreparingthedocumentforpublication.


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References

EsterhuizenGS1994, ‘Amodel forpredictingblockcave fragmentation’, inApplicationofNumericalModellinginGeotechnicalEngineering,SouthAfricanNationalGroupoftheISRM,Pretoria,SouthAfrica,September1994,pp147-151.

Esterhuizen,GS,Laubscher,DH,Bartlett,PJ&Kear,RM1996,‘Anexpertsystemapproachtopredictfragmentationinblockcaving’,MassiveMiningMethods,SouthAfricanInstituteofMiningandMetallurgy,Colloquium,pp.2-11.

Esteruhisen,GS2005,Aprogram topredictblockcave fragmentation -Technical referenceanduser’sguide.

Hoek,EandBrown,ET1997,‘Practicalestimatesorrockmassstrength’,Int.J.RockMech.Min.gSci.&Geomech.Abstr.,vol34,Nº8,pp.1165-1186.

Jakubec,J,Board,M,Campbell,R,Pierce,M,Zaro,D2012,Rockmassstrengthestimate—Chuquicamatacase study, in Proceedings MassMin 2012, June 10-14, Canadian Institute of Mining,MetallurgyandPetroleum(CIM),Sudbury,Canada,CD-romonly.

Jakubec,J2013,‘RoleofDefectsinRockMassClassification’,GroundSupport2013Conference,ACG,Perth,Australia.

Laubsher,DH,‘Ageomechanicsclassificationsystemfortheratingofrockmassinminedesign’,J.S.Afr.Inst.Min.Metall.,vol.90,no.10.Oct.1900.pp,257-273.

Laubscher, DH 2000, ‘A practical manual on block caving’, International Caving Study (1997-2000),UniversityofQueensland,Brisbane,Australia.

Laubscher,DHandJakubec,J2001,‘TheMRMRRockMassClassificationforJointedRockMasses’,inUndergroundMiningMethods: Engineering Fundamentals and International Case Studies,eds.W.A.HustrulidandR.L.Bullock,SocietyofMiningMetallurgyandExploration,SMME,pp.475–481.

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An alternative approach to verifying predicted fragmentation in weak rock

RN Greenwood SRK Consulting Inc., CanadaBN Viljoen SRK Consulting (Canada) Inc., Canada

Abstract

Fragmentation of ore and waste rock in block cave operations influence several aspects of mine design including draw-point spacing, dilution entry, secondary blasting, and material handling systems. Current accepted practice to estimate fragmentation and expected block size distribution includes empirical and numerical analyses, such as Block Cave Fragmentation software (BCF), discrete fracture network, and particle flow code. BCF is the most widely used and generally accepted numerical method to determine potential fragmentation within hard rock environments with high stress levels. However, the considered greenfields caving project, which is in a highly variable and relatively weak rock mass, has required an alternative approach to be compared with fragmentation estimates from the BCF program. A 5 x 5 observational matrix combines observations from drill core photographs of brokenness/breakability with weakening alteration/rock strength. The result is a fragmentation point estimate o for the percentage of rock not passing 0.3 and 1.0 m3 (mine-specific requirements for the materials handling system). The evaluation results show the spatial distribution of the fragmentation estimates across the project area as compared to the fragmentation estimates based on the more conventional geotechnical inputs in the BCF analyses.

1 Introduction

Predictionof rockmass fragmentation isused,amongother things, to selectmobileequipment,designmaterialhandlingsystemsandtoestimatedraw-pointspacing,attrition,anddrawcontrol.Itisalsousedtobudgetforsecondaryrockbreaking.Primaryfragmentationisrelatedtotherockfabric,conditionofinsiturockblocks,andinducedstresses,whilesecondaryfragmentationconsiderscomminutionoftheprimaryblocksastheyaredrawndownthroughthecaveandfinallyreporttotheproductionleveldraw-point.Arangeofindustryacceptedmethodsareavailabletopredictprimaryandsecondaryfragmentation.Thesetypicallyincludesoftwarepackagesdevelopedfromanalyticalandempiricalrockengineeringprinciplesandrefinedthroughbenchmarkingstudiesandvisualassessments.

As with many early stage projects, data sources are typically limited to drill core logging, downholegeophysics and laboratory testingof core samples.Mapping in early stageor explorationdrives is notalwaysavailable.

This paper reviews an alternative fragmentation assessment conducted for a greenfields caving projectinahighlyvariableandrelativelyweakrockmass,wheretheBCFfragmentationpredictionwascoarserthanexpectedbasedontheobserveddrillcorecondition.Predictedoversizeorenegativelyimpactedonthematerialhandlingsystemdesignandanalternativeassessmentwasdevelopedtopredicttheexpectedthesizedistribution.Thisapproachisconsideredsuitableasanalternateestimationmethodologyindata-limitedprojectswherecorephotographsaretheprimarydatasourceused.

2 Numerical fragmentation prediction

Inmost establishedmethodsof fragmentationprediction, generalized rockmass properties are used asinputstopredicttheprimaryandsecondaryfragmentation.Datafromcoreloggingormappingisprocessed


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to identify the typeandorientationofdiscontinuitiespresent(e.g., joints,cementedfeatures,veins,andmicro-defects),jointproperties(e.g.,frequencyorspacing,roughness,infill,andpersistence)andtheintactrockstrength.Dataisconsolidatedtoproduceasetofinputsthatisreasonablyrepresentativeoftheentirecavingarea(atthecoarsestlevel)orindividualgeotechnicaldomains.

Drill core logging data is typically themost common source of information and can be collected to ahighlevelofdetailincludingmanyparametersusedasinputtovariousclassificationschemes,suchasinLaubscherRMR(1990),JakubecIRMR(2000),CallNicholasCore2Frag(2004),andBieniawksiRMR(1989).Earlystageprojectstypicallydonothavethisluxury,anddespitethelargevolumeofdatapointspotentiallyavailable,coreloggingdataislimitedinvalueprovidingsmall,three-dimensionalsamplesoftherockalongmanyone-dimensionallinesthroughtherockmass.Thisdataisusefultoestablishdominantjoint orientations and spacing; however, properties such as joint persistence, primary block size, andblockaspectratioscanonlybeinferredbutnotdirectlymeasured.Thepresenceofmicro-defectsisoftenmisinterpretedespeciallyifdrillcoredoesnotseparateduringthedrillingandhandlingprocess.Specifictestshavebeendevelopedtoestimatetheintensityandstrengthofmicro-defectsincore.

Stresseswithinandaroundthecavecanalsoinfluencethecavingprocessandfragmentation.Duringtheearlystagesofaprojectthevirginstressstateisoftenuncertainandassumptionsarebasedonglobalstressdatabases(e.g.,WorldStressMap,Heidbachetal,2008)orestimatedbasedontheregionaltectonicandstructuralsetting.Inducedstressesarethenderivedfortheperceivedcurrentstressstateandtheplannedcavegeometry.

2.1 Block cave fragmentation

BCFsoftware(Esterhuizen,2005)isawidelyusedfragmentationpredictiontoolwithgenerallyacceptedresults. Primary fragmentation is derived from joint spacing, orientation, and joint conditions. Intactrock strength, primary block dimensions,micro defects, and induced stresses determine the secondaryfragmentation.BCFwasdevelopedandcalibratedforcavingminesinhardrockenvironmentswherejoints,stress,androckstrengtharethemaincontributorstofragmentation.Thealgorithmswerenotcalibratedforweakerrockcaveswherealteredweakrocktendstobreakupwithminordisturbance.Thesoftwareismostusefulforanalysisincaseswherepoorfragmentationisexpected.Thisis,whenrocksarehighlyjointed,fragmentationisunlikelytobeanissue,andBCFwillsimplyconfirmthatfinefragmentationcanbeexpected(Esterhuizen,2013).

BCFrequiresexplicitinputswithlimitedallowancetoaccountforvariability.Inputstothefragmentationpredictionarenormallyageneralizationoftherockmassandgeotechnicalpropertieswithasetdeviation.Minewidedataisappliedandlocalizedvariabilityisoftenoverlooked.Theuniquepropertiesassociatedwithcertainrocktypesorgeotechnicalconditionsarenotconsideredintheevaluation.

AnadditionalreviewoftheBCFfragmentationresultswasrequestedtore-assessfragmentationpredictionsandoversize.

3 Alternative fragmentation assessment

Attheprojectsite,a largenumberofdiamonddrillholeswerecompletedduring theexplorationphaseoftheprojecttodefinetheextentofthedepositanddeveloparesourcemodel.Therecoveredcorewasloggedgeologicallyandgeotechnicallybyon-sitestaffaccordingtothevariousowners’procedures.Thisprocedurehasresultedindataofvariablequality,withgeotechnicalparametersrelatedtomultiplerockmassclassificationschemes.Highqualitycoreboxphotographs,andmorerecentlyinsplit-tubephotographs,havealsobeencollectedforthemajorityoftheprojectdrilling.

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Figures 2.1 and 2.2 provide a comparison of core degradation through separation along pre-existingcementedfeaturesordiscontinuities.Thesamplesalsoshowformationofnewmechanicalbreaks.ThecoreinFigure2.1wasconsideredrepresentativeoftheinsiturock,whilethecoreinFigure2.2wasconsideredmorerepresentativeoffragmentedoreinanestablishedcaveafterbeingsubjectedonlytostandardtripletubedrillingandhandlingpractices.

Figure 2.1: Photo of core in split tubes during geotechnical logging

Figure 2.2: Photo of core in the core box after geotechnical logging

3.1 Core photograph re-logging

ThedifferenceincoreconditionhighlightedinFigures2.1and2.2presentedanopportunitytore-evaluatetheexpectedfragmentationandsizedistribution.Aconceptwasdevelopedinwhichthecorephotoswereindividuallyreviewedandusedtopredicttheperceivedconditionoftheoreatthedrawpointbasedonthedevelopedparameterdescriptions.Re-loggingwasbasedontheassumptionthatallcorewashandledina similarmanner andpotential inconsistencieshadno insignificant influenceon the interpretation.Thefollowingconditionswereappliedforthere-logging:

• Brokenness/breakability is the degree towhich the rockwas broken, irrespective ofwhether thediscontinuities were open joints, drilling-induced fractures, or mechanical breaks. Brokenness/breakabilitywasdefinedbytheintensityandspacingofdiscontinuitiesandthelengthofintactcore.

• Alteration/hardnessisthedegreeofcoreweatheringordeteriorationandtheperceivedhardness.Theratingwasquantifiedbasedonthevisualweatheringorweakeningoftherock,theamountoffines,andtherockhardness,basedonexperiencewiththecoreandsimilarmaterial.

• Fragmentation is the extent to which caved rock was expected to fragment, based on the corebrokenness,degreeofalteration,andfinescontent.


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Thephotore-loggingwasnotbasedonestimatingmeasurablenumbers;ratherthanattemptingtoderiveanaccuratefragmentationanalysis,theprocesswasusedtoqualifythefragmentationdistributionthroughoutthedeposit.Thesizedistributionwasconsideredintermsofthepercentageofcorethatwouldnotpassspecificdimensionsof300or1,000mm.Thesedimensionswereguidedbytheinfrastructuredesignofgrizzlies,conveyors,andcrushers.

Thecorefrom10selectedholeswasevaluatedandthebrokenness/breakability,alteration/hardness,andfragmentationwereratedindividually.Theresultswithcoreboxphotographswereusedascaseexamplestoestablishtherelationshipbetweentheinputfields(brokenness/breakabilityandalteration/hardness)andfragmentationina5×5matrix(Figure2.3).Thisillustratestheexpectedcoreconditionforthevariousratings.Thematrixwasusedasatemplatetoguidefurtherre-loggingandtomaintainstandardizedratingsusingthebrokenness/breakabilityandalteration/hardnessratingsastheinputfieldstopredicttheexpectedfragmentationandsizedistribution.

Figure 2.3 Core photographs representing brokenness/breakability, alteration/hardness, and fragmentation ratings

3.2 Fragmentation assessment

There-loggingprocedurewasthenappliedtoassessfragmentationinandaroundthedeposit.Theselectionofdrillholestobereviewedwasguidedbythepositionofthecavefootprintsandthecavinginfluencezone.Thecorephotographsfrom75drillholes(8,727coreboxphotographs)werere-logged.Eachcoreboxwasconsideredasalogginginterval(asingleratingrepresentstheentirebox)andtheintervalwasspatiallyreferencedbydepthanddrillhole.Thisallowedanyfurtherassessment tobespatiallyconstrainedtoahorizonofinterest(e.g.,aproductionlevel)ortoaparticulardomainorlithologywithinthecavefootprint.Thedistributionofratings(Table3.1andFigure3.1)showsthenumberofboxesforeachratingandthepercentageofalltheloggedboxes.

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Table 3.1 Summary of photo re-logging results

Rating Brokenness/Breakability Alteration/Hardness FragmentationNumber of

Boxes Percentage Number of Boxes Percentage Number of

Boxes Percentage

1 106 1% 784 9% 200 2%

2 365 4% 4469 51% 367 4%

3 2155 25% 2526 29% 1589 18%

4 4640 53% 842 10% 4986 58%

5 1461 17% 106 1% 1585 18%

The re-logging provided an overview of the core condition and individual rating distribution, but nomeasurable output in terms of size distribution or fines content.The experience gained during the re-loggingmadeitpossibletorelatetheassignedratingstoamodifiedsizedistributionandapercentageofcontainedfines.

3.3 Size distribution

Thesizedistributionanalysisproducedfor thisprojectwasnot the typicalS-curvebut rather indicatedthepercentageoforeexpected toexceedspecificdimensions.Theassignedpercentages(Table3.2)aresubjectiveestimatesbasedon thepercentageofcoreexpected toexceed the300and1,000mmlimits.Theevaluationwasbasedontheactualcorelength.Blockaspectratiowasnotconsideredasitcannotbereliablyestimatedfromdrillcore.

Table 3.2 Core size distribution for the range of brokenness ratings

Brokenness/ Breakability Rating

Percentage Not Passing 300 mm

Percentage Not Passing 1000 mm

1 90% 30%

2 70% 10%

3 40% 0%

4 0% 0%

5 0% 0%

Themodifiedsizedistribution for thedepositwasestimatedbasedon thedistributionof fragmentation(Table3.1)andtheestimatedpercentageofcorethatwouldnotpass300mmand1,000mm(Table3.2).Aweightedaveragemethodwasusedtocalculatethetotalpercentagecorethatwasexpectednottopassthe300or1,000mmlimits.Thevalueswerecalculatedforeachfragmentationratingandexpressedasapercentageofthetotalcorevolume(Figure3.2).Thelinesshowthecumulativepercentageofcorethatwouldnotpass300and1,000mm,respectively.


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Figure 3.2 Percentage of rock not passing 300 and 1,000 mm

Theresultsfromthecorephotoreviewwerethenusedtoindicatetheexpectedfractionofcorenotpassingthetwospecifieddimensionsof300mmand1,000mm.Theseresultswererelatedbacktosizedistributioncurvesbyassuminganaspectratioofone,andapplyingthecalculatedpercentagepassingtotherespectiveblockvolume.Figure3.3presentstheBCFdistributioncurvesresultingfromvaryingtheinputparameterswithintheidentifiedlimitsoftherockmass(i.e.,fine,average,andcoarse)andthetwopointsderivedfromthecorephotoreview.ThepointsestimatedfromthecorephotoreviewpredictmuchfinerfragmentationwhencomparedtotheBCFsizedistributioncurve.

3.3 Fines estimation

Forthepurposeofthisstudy,finesweredefinedasanymaterialconsistingofsmallpiecesinwhichthevolumeoftheindividualfragmentsdidnotexceed1cm3.Thepercentageoffinescontentwasestimatedbasedonthealteration/hardnessratings,wheremoreintenseweakeningalterationtypicallyresultsinanincreaseinfinescontent.Thealteration/hardnessratingsdonotspecificallyconsiderthefinescontent,butratherontheoverallconditionandappearanceofthecore.Thisresultedinvaryingpercentagefinesforcorewiththesamealteration/hardnessrating.Asensitivityanalysisapproachwasadoptedtotestthepotentialrangeofcontainedfines,andtheupperandlowerlimits(Table3.3)werederivedfromvisualassessmentofthecorephotographs.Theserepresentthelikelymaximumandminimumpercentagefinesmaterialforeachalteration/hardnessrating.

Table 3.3 Estimated fine material associated with alteration/hardness rating

Alteration/Hardness Rating

Lower Fine Material Limit

Upper Fine Material Limit

1 0% 0%2 0% 5%3 5% 10%4 10% 50%5 50% 90%

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Thecalculationof theexpectedfineswassimilar to thecalculationofcoresizedistribution.Theupperandlowerfinemateriallimitswerecombinedwiththealteration/hardnessratingsdistributiontocalculatethefinematerial volume.The expected amount offines for eachof the alteration/hardness ratings is apercentageofthetotalcorevolume(Figure3.3).

Figure 3.3 Fine material distribution based on alteration/hardness ratings

Figure 3.3 Comparison between BCF and core photo review predicted size distribution


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3.4 Further evaluation

Incorporationofthefragmentationratingsintothepreviouslyestablishedgeologicalmodelpreventedtheevaluationfrombeinganisolatedproduct.Eachdrillingintervalcontaineddataontherockandalterationtype,alterationintensity,fragmentationratings,andspatiallocations.Reviewofthisinformationhighlightedtherelationshipbetweenthedifferentrockpropertiesandtheexpectedfragmentation.Blockmodellingthefragmentationdatacreatesathree-dimensionalrepresentationoftheexpectedsizedistributionandfinescontent throughout the cave footprint,which canbettermanagepotential bias resulting fromdrill holespacing,orientation,anddepth.Improvedfragmentationdistributionscanbepredictedfortheentiremine,individualhorizons,orspecificlocationswithincaveareas.

4 Conclusions

Avisualevaluationofcorephotographswasusedtore-logdrillcoretodetermineboththebrokenness/breakabilityandthealteration/hardnessoftherock.Theobservationswerethenappliedtoaratingsystemtodeterminetheexpectedfragmentation.Thepercentageofmaterialexceedingthespecifiedsizelimitswasbasedontheratingoftheindividualcoreboxesandthegeneralizedphysicalcharacteristicsofthespecificbrokenness/breakabilityclassification.Themethodestimatesfragmentationfromtheconditionofactualcoreratherthananempiricalestimatebasedongeneralizedpropertiesoftherockmass.

There-evaluationoutcomeprovidedsizedistributionpointestimatesforcomparisonwiththeBCFanalysis.ThedetailedreviewofthecoreafterdrillingandhandlingindicatedthefragmentationpredictionsmadebytheBCFsoftwaremaybebiasedtowards“coarsefragmentation”.Uponcomparisonofthetwoanalyses(photore-loggingandBCF),thefragmentationresultsdeterminedfromthephotore-loggingareexpectedtobeamorereliablepredictorofsecondaryfragmentationinawell-establishedcavewithinasimilarweakrockmass.

Acknowledgements

The authors would like to thank Messrs. Chris Page and Jarek Jakubec for their input during thedevelopmentandreviewof thealternativeapproach to fragmentationestimation.Aswell, thankyou toGabrielEsterhuizenforhisavailabilitytodiscusstheBCFprogram.

References

Bieniawski,ZT1989,Engineeringrockmassclassifications,NewYork:Wiley.

Esterhuizen,GS,BCFVersion3.04–AProgramtoPredictBlockCaveFragmentation-TechnicalReferenceandUser’sGuide,2005.

Esterhuizen,GS,PersonalCommunication,April,2013.

Heidbach,O,Tingay,M,Barth,A,Reinecker,J,Kurfeß,D,andMüller,B,2008,TheWorldStressMapdatabaserelease2008doi:10.1594/GFZ.WSM.Rel2008.

Laubscher,DH1990,‘Ageomechanicsclassificationsystemfortheratingofrockmassinminedesign’,JournaloftheSouthAfricanInstituteofMiningandMetallurgy,90,pp.257-273.

Laubscher,DH&Jakubec,J2000,‘TheIRMR/MRMRRockMassClassificationSystemforJointedRockMasses’,SME2000.

Nicholas, DE, & Srikant, A 2004, ‘Assessment of primary fragmentation from drill core data’, InProceedingsofMassMin2004,A.Karzulovic&M.A.Alfaro(Eds.),Santiago,Chile:InstitutodeIngenierosdeChile,pp.55-58.


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Block Caving using Macro Blocks

S Fuentes Codelco, Chile

F Villegas Codelco, Chile

Abstract

This paper presents the Block Caving Macro Blocks concepts and genesis. Chuquicamata Underground Project has considered using this configuration as an underground exploitation method in order to improve the management of mining operations and control of geomechanical problems.

1 Introduction

Aftermorethan100yearsofoperations, theChuquicamataopenpitwillreachtheendofitseconomiclifeattheendofthisdecade.However,thegeologicalexplorationprogramthatCodelcoundertooksomeyearsagoshowedalargeamountofremainingresourcesbeneaththefinalshellpit.Duetothegreatdepth,experiencesuggestedthattheonlyfeasibleexploitationmethodcouldbeanundergroundoperation.

The Chuquicamata Division commenced studies in early 2000 to assess the technical feasibility andeconomic potential of a massive underground mining operation, which could maintain the historicalproductionlevel.Asaresult,theChuquicamataUndergroundMineProjecthasbeendesignedtorecoverapproximately1,760milliontonnesofore,withanaverageoregradeof0.71%Cu,512ppmofMoand492ppmofAs,overa39-yeartimehorizon,precededbyaperiodof8yearsofconstructionandcommissioning.Today,thefeasibilitystudyiscompleted.Theconstructionofthepermanentinfrastructure(mainaccesstunnels,intaketunnels,exhaustandshafts)commencedin2012.Theproject´smasterscheduleconsidersintensivedriftingandconstructionuntil2018,followedbya7-yearramp-upperiodtoachievethe140,000tonnes/daydesigncapacity.

1.1 Project location

TheChuquicamataMineislocated1,240kilometersnorthofSantiago,theChileancapital,at2,900metersabovesealevel.ThesiteisveryclosetothecityofCalama;itcanbereachedbyhighwayandthenearestairport,AeropuertoElLoa,isonly20kilometers(Figure1).

Themineislocatedintheheartofoneofthemostimportantcopper-producingdistrictsintheworld.Itstarteditscurrentoperationsin1910,althoughthehighqualityoredeposithasbeenwell-knownsincepre-Hispanictimes.

2 Block caving macro blocks origin

TheconceptofMacroBlockswasanalyzedanddevelopedasanextensionoftheclassicalBlockCavingmethod, incorporating the latest of theCodelco’s experiences inBlock andPanelCaving exploitation,especially,intheminingoperationsmanagement,geomechanicsandorebodygeometryrelatedtopics.

2.1 Necessity of new production area

TheChuquicamataUndergroundMineplanningconsidersahighproductionrate,140,000tonnes/day.Toachievethistarget,itisnecessarytoprepareaverylargeareaof102,000m2(400draw-points)forthefirst


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productiveyearandanaverageof70,000m2/year(270draw-points/year)fortherestoftheproductionhorizon(Figure2).

ConsideringthesehugepreparationrequirementsandtheexperienceatElTenienteMineonhowtodealwiththeinterferencesproducedbysimultaneouspreparationandexploitationactivities(Araneda&Sougarret2007),theprojectteamanalyzedanddefinedtheimplementationofanexploitationconfigurationmethod,whichfacilitatesthemanagementofinterferencesduringtheminingcycle.Thismethoddividesorseparatestheareaunderdevelopmentfromtheareabeingundercut/blastedandfromtheareaunderexploitation.Eachareaisindependentandiscalleda“MacroBlock”(Figure3).Additionally,thisproductiveconfigurationisperfectlysuitableforthegeometryoftheChuquicamataoredeposit(longandthin).

Figure 1 Project location – Chuquicamata Underground

Figure 2 New production and drawpoints number

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Figure 3 Macro block method (Fuentes 2009)

Figure4showsthesequencingoftheminingactivities,asconsideredbytheChuquicamataUndergroundMineProjects,usingtheMacroBlocks(MB)configuration.ThecentralMBisinitsproductivestage,whilethesurroundingMacroBlockshaveinitiatedtheundercuttingprocess.

Itappearstobeaneasydefinition,butactuallythediscussionwithinternalandexternalexpertswashardand tookavery long time to reachanagreement.Today,Codelco is starting tominemanyotherareaswiththismodularconfigurationapproach,followingthesameprinciplesconsideredfortheMacroBlocksdevelopment.

Figure 4 Macro block method in Chuquicamata underground

2.2 Management of geomechanical problems

ThemaingeomechanicalprobleminBlockandPanelCavingistheproductionlevelcollapse.Themostusedstrategytoresolvethistypeofissuehasbeentoabandonthecollapsedareaandre-initiatethecaving


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(Pardo&Villascusa2012),whichmeanstoleaveaseparation“pillar”betweentheaffectedareaand“fresh”productionarea(Figure5).

ThisconcepthasbeenincorporatedintotheMBdesign.A30mpillarhasbeenleftbetweeneachMBinordertopreventanygeomechanicalproblemand,also,tohavetheoptiontoisolatethissectortocontinuewith the exploitation. Pillar dimensioning criteria consider the estimation of the local abutment stress,whichmeans that the pillar is big enough to avoid the effects of the abutment stress generated by thepreviousMacroBlockontheproductiondriftslocatedinthefollowingMBintheminingsequence.

Figure 5 Boundaries of production drifts collapsed 2001 – 2010

3 Macro Block, an exploitation unit

InordertomaximizetheproductivityofeachMB,eachMBwasdefinedasanindependentexploitationunitwithitsownorepasses,crushingandaccessingsystem.Inotherwords,eachMBisconsideredas“anindependentmine”thatproducesmineralanddeliversittothemaintransportationsystem.

Inaddition,theChuquicamataUndergroundMineProjecthasconsideredthepre-conditioningoftherockcolumnineachMBtoimprovethecavingpropagationandextraction,maximizingtheproductioncapacityofeachMBsystem.

Figure6showsanMBunit.EachMBshouldhaveenoughareatoinitiateanewcaving,andalso,duetothepossibledilutionoftheneighboringblocks,eachnewMBhasconsideredaspecialdrawcontrolpolicy,basedonastrictplanningandproductionoperation,toavoidorcontrolthepossiblelateraldilution.

4 Conclusions

Theoutcomesof theanalysisofusingMacroBlock inBlockCavingoperationscanbesummarizedasfollows:

• BlockCavingusingMacroBlockconfigurationasminingmethodgivesflexibilityinproductionplanning,developmentofmininginfrastructureandotheroperations,improvingthelikelihoodofsuccess.

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• MacroBlocksaresuitableforthegeometryoftheChuquicamata’sorebody(longandthin).

• Themodulardesignallowstheincorporationoftechnologicalchangesmoreeasily.

• Majorcollapsesintheproductionareascouldbeeasilyisolated.

• Theactivitiesofdevelopment,construction,andundercuttingareseparatedfromtheproductionprocesseswiththepossibilityofachievinganimprovedproductivitycomparedtoallothercavingconfiguration.

Acknowledgement

TheauthorswouldliketothankCodelcoChileforsponsoringthepresentationofthispaperandallthosewhohelpedusinsomewaytoproperlywritethispaper.

References

Araneda,O&Sougarret,A2007,‘Lessonslearnedincavemining:1997-2007’,InternationalSymposiumonBlockandSub-LevelCavingCaveMingKeynoteaddress.TheSouthernAfricanInstituteofMiningandMetallurgy,SouthAfrica,pp.57-71.

Chitombo,GT2010,‘Cavemining-16yearsafterLaubscher’s1994paper‘Caveminingstateoftheart’”,SustainableMineralsInstituteTheUniversityofQueensland,Australia,Perth,pp.45-61.

Fuentes, S 2009, Key Decisions Document. Pre-feasibility study,Vice-presidency of project Codelco-Chile.(InternalReport,CodelcoChile).

Jofre,J.Yanez,P&Ferguson,G2000,‘EvolutioninpanelcavingundergroundanddrawbellexcavationElTenienteMine’,MassMin2000Brisbane,Australia,pp.249–260.

Figure 6 Macro Block exploitation unit, Chuquicamata Underground Project


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Laubscher,DH1994,‘Cavemining-Thestateofart’,TheJournaloftheSouthAfricanInstituteofMiningandMetallurgy.

Moss,A,Diachenko,S&Townsend,P2006, ‘Interactionbetween theblockcaveand thepitslopesatPalaboraMineInStabilityofRockSlopesinOpenPitMiningandCivilEngineeringSituations,Johannesburg,SAIMM,SymposiumSeriesS44,pp.399–410.

Pardo,C&Villascusa,H2012, ‘Methodology forbackanalysisof intensive rockmassdamageat theTenienteMine’,6thInternationalConference&ExhibitiononMassMining,Sudbury,Canada.

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La Encantada: An inclined cave design

J Valencia NCL Ingeniería y Construcción, ChileP Paredes NCL Ingeniería y Construcción, ChileF Macías First Majestic Silver Corporation, Mexico

Abstract

Massive caving methods offer low cost and high productivity alternatives when they are able to fit the orebody’s geometry and geomechanical characteristics. Nevertheless, when the orebody does not fulfil the typical caveable characteristics and stopping methods are not applicable, difficulties appear in finding a caving method that suits the orebody’s geometry and competence. An alternative to this problem is the use of inclined caving methods.

This paper presents the methodology and main results of the preliminary study for the exploitation of the Breccias sector in First Majestic’s La Encantada mine. Some parts of this sector have been previously mined with Cut & Fill methods, leaving several excavations in the orebody. Difficulties related to the low competence of the rockmass and lower grades have led to the need of exploring alternatives to the traditional Cut & Fill mining methods. Attending the orebody’s geometry in the sector, the use of low cost and non selective mining methods, such as caving methods, has been considered.

The use of sublevel caving implicates the construction of several excavations in the orebody, attending to the orebody’s conditions related to its low competence and representing the higher costs within the caving methods. Two horizontal Block Caving layouts were proposed: (1) a 3.5 yd3 LHD operated offset herringbone layout and (2) a scrapper operated regular layout. Attending to recovery considerations, both layouts consider a small drawpoint spacing, which results in small pillars that would cause stability issues and high excavation density. Thus, an inclined caving layout is proposed, solving recovery, costs, productivity and stability problems.

1 Introduction

La Encantada SilverMine, from FirstMajestic Silver Corporation (FMS), consists of silver/lead/zincoxidizedmineraldepositslocatedintheStateofCoahuila,México,708kmsnortheastofTorreon(Figure1). The mine comprises numerous mineral concentrations within the underground development area,includingsomeexhausteddepositsandadditionalgeologicpotentialinotherareas.

TheBrecciasSectorconsistsoftwobreccias(MilagrosandSanJavier)andapartlymineralizedmagmaticintrusive.ThissectorhasbeenminedbyFMSandpreviousownersusingselectiveminingmethodsandcontains,therefore,severalremainingtunnelsandmineopenings(Figure2).DifficultiesrelatedtothelowrockmasscompetencehaveledFMStoseekanalternativeforthecurrentcut&fillmethodsappliedinthemine.Attendingthemassiveshapeoftheorebodyinthesectorandlowergrades,FMShasconsideredthe use of non-selectivemassiveminingmethods, such as, cavingmethods.The following paragraphsdescribe the caving alternatives and thefinal inclined cavedesignproposedbyNCL forLaEncantadamine’sBrecciasSector.


218

Figure 1 La Encantada mine site location

2 Background and considerations

TheBrecciassectorhasthefollowingcharacteristics(Table1):

• Massiveshapedandlowsilvergradedorebody.

• LowrockmasscompetenceintheBrecciasunits(MRMRrangingfrom21to34).

• Regulartofairrockmasscompetenceinthefootandhangingwalls.

• Orebodylimitedbytopography,nowasteoverload.

Table 1 Geotechnical parameters for the geological units

RMR Bieniawski MRMR Laubscher

MilagrosBreccia 39 21

SanJavierBreccia 49 23

MilagrosIntrusive 60 34

Limestone 61 35

Figure 2 a) Isometric view of remaining mine openings and orebodies (in red San Javier Breccia, in blue Milagros Intrusive and in green Milagros Breccia); b) schematic plan view of the geological units

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Future Projects

Thesecharacteristicsallowtheuseofcavingmethodsgiventhat:

• Self-supportedmethodsrequirehighrockmasscompetenceintheorebody,inordertoobtainfairrecovery.

• Cut& Fill methods are oriented to high graded orebodies, due to their high costs and lowproductivities.

Attendingtheabovementioned,sublevelandblockcavingdesignswereproposedforthesectorinordertodefinewhichmethodbettercompliedthefollowing:

• Representalowcostandhighproductivityalternative.

• Minimizethevolumeofmineopeningsinthelowrockmasscompetence.

3 Method selection

3.1 Sublevel caving alternative

TheSublevelcavingmethod(SLC)consistsofdrillingandblastingtheorebodyinseveralsuperimposedlevelsandofcaving.Asaconsequence,thewasteoverloadfromtheroofandhangingwalloccurs.Thismethodhas, therefore, an intensive excavationdensity in theorebodyand, in theparticular caseofLaEncantadamine,wouldimplicatesafetyhazardsforbothpeopleandequipment.Ontheotherhand,thecostofaSLCalternativeishigherthanaBlockCaving(BC)operation.Thus,duetosafetyhazards,relatedtotheexcavationdensityintheorebody,thefactthattheoperationalcostofaSLCisthehighestamongcavingmethodsandhigherdilutionpotential;theSLCalternativewasdiscardedfortheBrecciassector.

3.2 Block caving alternatives

Blockcavingand itsvariants consistofgeneratingadrawpointbase at thebottomof theorebodyandundercuttingitsbaseinordertoallownaturalcavingoftherockmass.Consideringthepreviouslyexposedcharacteristicsofthedeposit,thismethodwasselectedduetothefollowingreasons:

• Thegeneralshape,gradedistributionandstructuralcharacteristicsof thedepositfit themaincharacteristicsofthecaving(undercutting)variants.

• Minimizesthevolumeofexcavationsinsidetheorebody,reducingthemtoonlytheextractionandundercutlevels.

• Representsthelowestcostandhighestproductivityoption.

Consideringtherockmassclassification(Table1),andLaubscher’sabacusfordrawpointspacing(Figure3), thesuggestedspacingforahorizontalBClayoutshouldbebetween7and13m,consideringa3mdrawpointwidth.TakingintoaccountthefactthattheMRMRoftheBrecciasorebodies(SanJavierandMilagros)areinthelowerlimitoftherockmassclass(4),ahorizontal10mx10mlayoutwasproposed,consideringtwotrammingoptions:

1. Using3.5yd3LHD,whichisthelargestloaderthatfitsthegeometry

2. Usingscrappers


220

Figure 3 Laubscher’s abacus for drawpoint spacing (Laubscher 1994)

Figure 4 Horizontal layout using LHD

Figure 5 Horizontal layout using scrappers

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Figure4and5showthehorizontal layoutdesignforLHDandscrappers, respectively. It ispossible toappreciatethathighexcavationdensityisneededtosatisfya10mx10mspacingdrawpattern.Previouslymentionedstabilityproblems in theexcavationsof theBreccias sectormake thehorizontalBC layoutsa high risk alternative in terms of the stability, due to the high excavation density that they implicate.Therefore,thehorizontallayoutBCalternativeswerealsodiscardedfortheBrecciasSector.

Oncethetypicalcavingvariantshadbeendiscarded,theevaluationofaninclinedcavelayoutwasconsidered,duetothehigherrobustnessthatitrepresents,byreducingtheexcavationdensityatthesameelevation,usingseverallevelstobuilddrawpoints.Despitethefactthisisnotacommonarrangement,inclinedcavealternativeshavebeenproposedandimplementedinseveralminingcountries,suchas,Australia,Canada,SouthAfrica andZimbabwe (Jakubec 1992;Carew1992;Hangweg et al. 2004,Laubscher& Jakubec2000;Jakubec&Laubscher2012)resultinginsuccessfulexperiencesinsomecases.

Figure6showstheinclinedcavelayoutproposedforLaEncantadamine.Thislayoutwasselectedduetothelowerexcavationdensityperleveland,therefore,thehigherstabilitythatitrepresents.Itisworthnotingthat,incontrasttotheconventionalBC,thisdesigndoesnotconsideranundercutlevel.Thisreliesonthefactthatthepoorrockmassqualitywouldenabletheorebodytocavewithalongdrawbellblastingconfiguration.

Figure 6 Inclined cave layout

4 Mine Design

Asingleliftinclinedcavedesignwasproposedforthedepositusingtheinternaltool“BlockCave”forfootprintelevationdefinition,basedonLaubscher’sverticalmixingalgorithm(Laubscher1994).Figure6showsthegeneralminedesign,whichconsidersfourdrawpoints’liftsseparated10metersinverticalandmaindriftsateverydrawpointelevationthatconnectthesedrawpointswithproductiondrifts.Thehigherlevelislocated300mbelowsurface.Drawbellsof17mheightconnectthedifferentproductionlevels.Productiondriftshaveatotallengthof26m,14mofwhicharedrilledtogeneratethedrawbell.Oreisdumpedinanore-pass,transferringittoexistinghaulagelevels.Trammingisperformedby3.5yd3LHD’sandhaulage is divided between a conventional trucking system (800 tpd capacity) and a railway-shaftsystem(1,200tpdcapacity).Ventilationshaftslocatedatthenortheastsideinjectfreshairfromsurfaceattheendofeverymaindrift.


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Figure 6 Breccias sector general mine design

Figure 7 Breccias sector general mine design isometric views

5 Mining Sequence

Aninitialareaof4,800m2(withinatotalof11,200m2)allowsa17mHydraulicRadius(HR)footprint,whichisenoughtoinitiatecavingatthemine,consideringthecaveabilityassumedfortheBrecciassector.Ontheotherhand,a2drawbellspermonthincorporationrateisproposed.ThiswouldallowtheBrecciassectortoachievefullproductionat2,000tpdduringthesecondyearofminelife.This2,000tpdproductionrateislimitedbytheprocessingplantcapacity,furtherproductionrateincreasecouldbestudiedforthesamedesignifaplantcapacityexpansionisevaluated.Finally,theminingsequenceproposedgoesfromthetopnorth-easttothebottomsouth-westdrawpoints,obeyingthegeometricalrestrictionsinordertoavoidundercuttinganon-openeddrawpoint.

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6 Industrial scale test

Inordertoidentifypossiblerisksandfurtherissueswithoutsignificantlycompromisingcapitalandmineproduction,anindustrialscaletestisproposedfortheinclinedcavedesign.Thistestconsistsofbuilding2drawpointsintheupperlevelandoperatethemforagivenperiodoftimeinordertocaptureasmuchexperienceaspossibletobeusedintheengineeringprojectfortherestofthearea.Thistestisnotintendedtoproduceanycavingatall,butitwillgeneratevaluableinformationrelatedwiththeconstructionofthedrawpoints, support requirementsanddrillingandblastingprocedures forundercutting,providingFMSwithsomeexperienceinthesubject.

Figure 8 Breccias sector industrial scale test proposal plan view

7 Conclusions

Inclinedcavingmethodsrepresentaviablesolutiontolowgradeddepositswithweakrockmasseslastheyareaflexiblealternativewhentraditionalcavingmethods’implementationpresentstechnicaldifficulties.Theuseofaninclinedschemeallowsdefininganadequatedrawpoint’spatternandatthesametimeaccountforareducedexcavation’sdensityinasinglelevel,improvingstabilityconditions.Inparticular,Brecciassector’s technical challengecanbeovercomebya simple solutionusing inclinedcavingmethods.Theimplementationofanindustrialscaletestwouldbeofgreatutilitytoimprovebothtechnicalandeconomicalinformationinordertodeveloptheproject.Finally,theuseofaninclinedcavinglayoutrepresentsavalidalternativefortheapplicationofcavingmethodsinmediumscalemining.

References

Carew,TJ1992,FootwalldrawpointcavingatCassiarMine:InproceedingsMASSMIN92,Johannesburg,SouthAfrica,pp.295-301.

Hannweg,Letal.2004,Koffiefonteinminefrontcave–CaseHistory:InproceedingsMASSMIN2004,Santiago.Chile,pp393-396.

Jakubec, J 1992, Support at Cassiar undergroundmine: In proceedingsMASSMIN 92, Johannesburg,SouthAfrica,pp.111-123.

Janelid, I 1978,Method formining of rock or ore according to the block caving principle inmassiveformations.U.S.Patent4,072,352.


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Laubscher,DH1994,“Cavemining-thestateoftheart”,TheJournaloftheSouthAfricanInstituteofMiningandMetallurgy,vol94,Nº10,pp.279-293.

Laubscher,DH2012,InclineCaveMining–AViableAlternativetoHorizontalLayout:InproceedingsMASSMIN2012,Sudbury,Canada.

Laubscher,DH&Jakubec,J2000,BlockCavingManual–InclineCave,ICS2000internaldocument.

NCLSpA2013,“Proyectodeexplotaciónsectorbrechas,MinaLaEncantada.Estudiopreliminar”,StudyreportforFirstMajesticSilverCorporation(InSpanish).


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Geomechanic Design

Considerations for designing a geomechanics monitoring plan for each engineering stage

AE Espinosa Codelco, ChileP Jorquiera, Codelco, ChileJ Glötzl, Glötzl GmbH, Germany

Abstract

Frequently, plans for monitoring mining geomechanics are designed with the aim of measuring deformations or displacements that allow early identification of the onset of potential instability. This approach is suitable for the control of risks pertaining to the field of geomechanics. For this purpose a methodological support is necessary to link with a systemic functional approach, the process from the definition of purpose to the performance evaluation and compliance targets.

From the experience of the last ten years in the development of implementation plans and monitoring geomechanics in the El Teniente Division of CODELCO - Chile, this paper proposes a methodology that guides the development of a plan for implementation and monitoring geomechanics. The methodology applies particularly for each project with a clear focus on the applicability of the records obtained in stages of conceptual design, functional implementation, procurement start to evaluation of results and fulfillment of objectives. All this will be done to finally close the loop with a stage design which fits oriented to the utility for operation over the life of the mine.

The methodology proposed here uses, as a structure for defining purposes and objectives, the different stages of the mining project engineering, ranging from all engineering stages and then binding steps with the start of production operations. Finally the result is a map that identifies the processes required to develop an implementation plan and monitoring geomechanics to be considered as a necessary requirement and functional utility for the safe performance of mining operations activities.

1 Introduction

Normally,inrelationtothegeomechanicalmonitoringinundergroundmines,theplans,insuchtechnicalapplication,areindicatedastoolstocontrollossescausedbygeomechanicalinstabilitieswhichoccuralongtheminingworking.Fortheappropriatedesign,theobjectivehasbeenmainlycenteredintheacquisitionofproperdata.Themainfocusshouldbeontheuseandtheevaluationinsteadofjustcollectingdata.

Oneparadoxicalaspectisthatthedesignisdirectedformamodelofbehaviorpreviouslyestimatedandthe expectation resides in the confirmation of the assumptions of the original design. If this occurs, itnecessarilywouldbeaninvitationtomodifythemodelinwhichsuchdesignwaselaborated.However,understandingthatmonitoring(asacontrol)isonlymadeoncetheprocessesareinitiated,thiscouldmeantoimplementmodifications.

Inthatway,thedesign/setupofageotechnicalinstrumentationandmonitoringplanisaconstantprocess,in which the main requirement is to define the main purpose for each engineering stage, consideringthegeotechnical,geomechanicalminingdesignsandmostofall the tolerancerecords,according to theassociatedgeomechanicalrisks.


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Thedevelopmentofthisworkingactionplanrespectivelyprojecthasbeenmotivatedbythefinalresultsobtainedbyevaluationofmonitoringofgeomechanicalplans,implementedinthelasttenyearsinDivisiónElTenienteCODELCO–CHILE.

Inthatperioditwaspossibletoidentifythatthefocusonthedesignisveryhelpful,gettingverydetailedrecordswhichcanbeusedandappliedin theminingprocess.Thepreviousconsiderationhasdriventocheck thepurposeof the implementationof thesemonitoringandgeomechanicalsystemsaswellas itscontributiontotheminingdevelopment.

Thisprojectproposestheconsiderationstoelaborateamethodologythatcanguideproperlytowardthedevelopmentof ageotechnical instrumentation,monitoringand in the end takegeomechanical actions.Thiscanbedoneforeachindividualcase,withaclearguidancetotheuseandapplicationofthedifferentregistratedfilesobtained.Forthecontrolofgeomechanicalrisksorlosses(alsodevelopedamongthestagesofconceptualanddetailingengineering)properlyevaluatedafterthedifferentminingjobs.

ThegeomechanicalinstrumentationinundergroundminesandparticularlytheavailableexperienceinElTenientedeCodelcoChile,hasplayedamajorroleasasupportingtoolfortheunderstandingofcavingprocessesingeneral,withregardstothecomprehensionofmechanicalbehaviorofthesurroundingmassifandexploitation.

However,applicationsondecisionsinanemergencyweremoreaninstanceinspiredbypreviousexperiencesthanusingactualmeasurements.Inprincipalthissituationarosefromhistoricalcasesandhadanunknownconsequence.Theresultssimplyneedtobelinkedtotherecordswiththefinalconclusionsandalsowithunknownconditionsalongthedifferentstagesoftheprojectandmoreimportantatthestartofproduction.

Forthisdiagnosisitappearsasarelevantfact,tobecomeproperlyacquaintedwithastructure,allowingthedefinitionofobjectiveswhichdirecttothefulfillmentoftheexpectations.Accordingtothestageofengineering that will be developed during a determinate instance, making geomechanical monitoringcoherentwithmining design.Also taking into account the geomechanical vulnerability implied to theexploitationandtheplansfortheminingdevelopment.

AverygoodexamplefortheapplicationofasystemforgeomechanicalmonitoringistheseismicsystemavailableinElTeniente(ISS–Mina).ActuallyhereAnalysisoftherecordallowstakingconcretedecisionswithregardstoblastingandtheisolationofsectors(forinstance,personnelenteringandleaving).Besidesthecontributionofdataformakingprogressevaluatingseismicmenacingordangers,amongotherrelevanteffectsitisimportantforlongtermplanningfortheminingdevelopment.

Atpresentweareworkingonasummaryofamethodologicalproposal,toapproachfunctionalconceptualaspects aswell as an instrumentation andgeomechanicalmonitoringplan,with thegeneral purposeofincludingitindifferentstagesofengineering,accordingtotheprojectexpectationsandthecontributiontocontrolgeomechanicalrisks.

Atleastfourrelevantstagesareconsideredintheproject:

• IP&GMexpectations

• Methodtofocusonpointsofeachengineeringstage

• Designsforengineeringandgroundimplementations

• Evaluationoftheresultsandfulfillmentoftheexpectations

From this perspective design and evaluation stageswill occur, but previously expectations need to bedefinedthat allowadecisioncarryoutaIP&GM.All this impliesthatgeomechanicalmonitoringisa

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meansbutnotthepurpose.Intheenditdependsontheprojectowner,hispersonalattitudetofacetheriskandtheavailablealternativesandmeasurementsintheparticularcase.

1.1 Background consideration

Checkingthebackgroundthatcontainthedifferentplansofgeomechanicalinstrumentationandmonitoring,developedbythemineofELTENIENTEdeCODELCO–CHILEinthelasttwelveyears,itshowsanemphasis orientated togetmeasurementsmostlywith regard to the application.This is evident in thelimiteddocumentswhichleadtothepostevaluationandduetothisactionswereimposedbytheresultsoftheapplicationofgeomechanicalmonitoringplans.

2 Methodological development

Following this exposition themethodological focus including each stagewill be described, in order toelaborateanInstrumentationPlanandGeomechanicalMonitoring(IP&GM),thatillustratesitscontributiontovaluetheminingprocessandthatinsertsitselfineachoneofthefourgeomechanicalengineeringstages.

2.1 Expectations of IP&GM for each engineering stage

Themethodologyconsidersonefirststepwheretheexpectationsforeachengineeringstagewillbeindicated.Theseexpectationsarereducedtothefollowingdefinitions:Purposes,Objectives,GoalsandProducts.

Table 1 Expectations on the IP&GM

Profile Pre-feasibility Feasibility Details

PurposeIdentify

geomechanicalpotentialrisks

Definemonitoringrequirements

accordingtothegeomechanical

model

EstimatecostsofIP&GM

Insertintotheminingplan

objectiveDevelopworkplan(timeandcosts)for

thenextsteps

Sizerequirementsandevaluatetechnologies

Determinetype,amountanduseof

instruments(CAPEXandOPEX)

Developlocationplansandmonitoring

plans

GOAL

Internalbenchmarkingandbackground

available

Consideravailabletechnologiesandshorterrangeofinnovation

Designwithtechnologiescommerciallyavailableandaccessibleintermsdefinedby

engineering

Product

Reportofdescriptivescenariosandpotentialrisks

Reporttosizerequirementsintimeandcostsastechnologies

Designandimplementaplanformonitoring

geomechanics.

Planeswithalllocationsandmonitoringfrequency


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2.2 Methodological design for each engineering stage

The fundamentalobjectiveof thiswork is tomaterialize tasks, activitiesand/orconcreteproducts.Theaspectsthataregoingtobedevelopedineachengineeringstageandthegoaltoimplementaninstrumentationandgeotechnicalmonitoringplanhaveavaluablecontributiontotheminingprocess.

2.2.1 Profileofengineeringstage

Thisstagedefinesthebusinesspotential,itdescribestheprincipalrisksinqualitativetermsandusesbenchmarkingrecordsfortheIP&GM.Thisisnecessarytogetanoverviewofthelevelofcostsandthemagnitudeofworks,usingsimilarexperiencesandpresentidentifiedrisksofmayorrelevance.

Table 2 Key contents for IP&GM in the Profile engineering stage

Process IP&GM Engineering stage

Del

iver

y de

tails

1.- review of measurements available:Itconsistsofacompilationofinstrumentationrecordsmadearoundtheareaofinterestorcomparablegeomechanicalconditions(geotechnical,mining,environmentstress).Thisbackgroundisusefultosupportnewrequirementsifnecessary.2.- Budget and business plan:Mustbeconsideredcostsassociatedwiththeconstructionofconceptualgeomechanicalmodelandplanthattheproposedimplementationisaproductthatismadeaftertheavailabilityofthemodel

Offer process:

Startaplanfordevelopingasystemtomonitortheimpactofgeomechanicalinstabilities,addingvaluetodefinethetruedimensionoftherequirementsthatmeetthestatedobjective.

Background required

1.-Geographicallocationandtimingoftheoperation.2.-SecurityPolicy,Standardfatalitiescontrolandriskclassification

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2.2.2 Pre-feasibility engineering stage

In thisstagedifferentexploitationalternatives,miningdesignsandstagesofexploitationareevaluated.Theworkendswiththeselectionofthebestalternativethatwillbefocusedinthefollowingstage.Theinstrumentation and geomechanical monitoring plan has been defined conceptually according to thetechnologyandthetypeofmeasurementnormallyusedforthatkindofactivity.

• Extensometersandperiodicalmeasurementsinamanualway.

• Localdeformationmeasurementsaccordingtonon-systematicrequirements.

• Seismicactivityrecords,remoteandcontinuousautomaticmonitoring

2.2.3 Feasibility engineering stage

Foreachusefulstageadefinitionaboutthemethodandthedimensionoftheexploitablesectorisavailable.Itcorrespondstodevelopmentofparametersthatallowselaboratingtheminingplan.Forthisstage,wherethetemporalityisdefinedandwheretheconstructionsarerealized,resultsareveryimportanttoincorporatetheinstallationworksthatdevelopminingactivity.

Table 3 Key contents for IP&GM in prefeasibility engineering stage

Process IP&GM to prefeasibility engineeringD

eliv

ery

deta

ils

1. - Report with Geomechanical instrumentation plan that incorporates definitions:thekindofdeviceandtheamountestimates(intherangeof25%)2.- Report with the qualitative assessment of the expected risk control:isdefinedthescopeandrangeoffunctionalityfordesign,shouldbeclearlyestablished“forwhat”istheIP&GM3.- Report with the evaluation of the expectation value contribution of IP&GMinminingdevelopmentindicatorsaredefinitetoassesstheeffectivenessofthedesign

Offer process:

IdentifyrelevantaspectsoftheexpectedgeomechanicalbehaviorandincorporatethemintoarationaldesignofIP&GM

Background required

1.-Miningmethod.2.-Sizingofmining


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2.2.4 Detailed engineering stage

Inthisstageplansforinstallationareelaboratedaswellastechnicalbasesforallowances(permissions).Practical groundplans aredesigned and the strategy touse the available resources in construction anddifferentdevelopments(availabilityforperforations,constructions,electricinstallations).

3 Performance Indicators

As a manner to identify the necessity of modifications and corrections of the parameters that defineinstrumentation,monitoringandrecordanalysisitisproposedtouseindicatorsthatinformaboutefficacyandtheuseofthesystem.Likeanyevaluatortogivethefinalvaluetothefinalproductofthesystem.Thepremisesfortheworksonthecomportmentsofthegeomechanicalmonitoringsystemsare:

• Matrixresults:theoptionsofthefinalresultsarereducedtofourstagesthatdependonwhetherthealertwasrightornot.

• Modificationsoftheinstrumentalmonitoringparametersamendtheoriginalcostsofthesystem.Quantificationsofthesemodificationsareanavailableeconomicindicatorofthesystem.

Table 4 Possibilities on the final results of the geomechanical system

Consequences No consequences

Alert Success Minorfail(cost)

NoAlert MajorFail(Safety) Monitoring

Modificationsofthesystemarerealizedfundamentallyonthebaseofacceptablecriteriaofthenegative/positivedecisionsregistered.Inthissensethecostindicatordependsonthesystemwork.Regardingtheoperativeimplementation,eachcasewillbespeciallyanalyzedinordertotakedecisionsonthemodificationoftheavailablesystem.

4 Conclusions

• Thedescribedmethodologyallowsastructuredfocusheadedtoidentifytheexactvaluescontributedbyasystemforageomechanicalmonitoringminingproject.

• Theinclusionofappropriateindicators,givesgreatreliabilityofthepresentinitiative.

• Theadvantagesofidentifyingtheroleofageomechanicalmonitoringsystemforminingprocessesallowstoplandeliverableproducts,valueengineeringateachstage,satisfyingexpectationsfromtechnicalandalsoeconomicperspectives.

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Table 5 Key contents of IP&MG in feasible engineering stage.

Process PI&GM to engineering feasibility

Prod

uct d

etai

ls

Report(conceptualPlan)descriptionthekindofthegeomechanicalinstrumentationformonitoringexcavationandforcontroloftheprocessofminingontherockmass.Report(Instrumentation)technicalspecificationsandcostestimateforthepurchaseandinstallationoftherequiredinstruments.

Report(Monitoring)definitionsofthefrequencyofmeasurements,dataanalysis,thresholdvaluesandactionsincaseofdeviationsintheexpectedresponse.

Offer process:

Designgeomechanicalmonitoringsystemforthecontrolofmajorexcavations(caves)andforcontrolovertheresponseoftherockmassagainsttheadvanceofmining

Background required

1.-GeomechanicalConceptualmodel2.-Plandevelopmentsinmining

References

Espinosa,Cornejo,Fuentes2012,‘GeomecánicaproyectoDacita–Enlaceingenieríasbásicaydetalles’,SGM-I-052.

Morrison, RGK 1976, ‘A philosophy of ground control’, Department of mining and metallurgicalengineeringMcGillUniversity,Montreal.


234

Integrated support quality system at El Teniente Mine

MS Celis, Codelco, ChileRA Parraguez, Codelco, ChileE Rojas, Codelco Chile, Chile

Abstract

The improvement of the design and installation of underground support requires an integrated quality control system. It rises from the relevance of the support quality in its response to loadings associated to mining (especially rock burst and collapses), as support quality can achieve a better damage control, protecting personnel and mining infrastructure.

The integrated quality support system has been implemented during year 2013 and it is based on the legal framework. It considers 3 main aspects: design - monitoring - post evaluation.

Ground control engineers check the quality of the installed support and compare it to accepted standards. Considering the limited resources, monitoring is focused in some critical areas defined according to seismic activity, mining and stress field. Technical reports have been prepared including the main aspects surveyed in the field for each support system. Post-evaluation reports are generated including lessons that should feedback designs, establishing a support improvement cycle. The resulting information is monthly sent to the areas of interest (Critical Risks, Mine Operations, construction companies). Depending on the critical level of findings causality analysis or corrective actions were undertaken.

1 Introduction

InTeniente´sminetheGeomechanicsSuperintendencehasthefollowingmission:

“To contribute to maximization of economics value in long term of El Teniente Division and Corporation, support mining explotation with Geomechanical application, with emphasis in rock burst risk control”.

Tocontrolrockburstrisk,3pointsareinvolved:

• Source:miningcontrolorrockmasspreconditioningcanreduceseismiceventmagnitude.

• Damagecontrol:installationofsupportwithbetterresponsetodynamicsloadingscancontrolthedamage´slevelinabetterway.

• Personnel exposition: definition of exclusion criteria, abutment stress zone and use of remote-controlledequipmentslookingforreducingexpositionofpersonalandequipmentinhigherriskzones.

Inaccordancewiththat,wededucetheimportanceofanIntegratedSupportQualitySystem,consideringtherelevanceofthesupportqualityinitsresponsetoloadingsassociatedtominingandbecauseit’snecessarytoimproveourmanagementoffindingsandinteractionswithothersGeomechanicsareas.

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2 Component of System

TheintegratedSupportQualityIndex(ISQS)includesthemaincomponentsasindicatedinFigure1:

DESIGN considersasinputstheminingdesignvariables:gallerysizes,lifespan,use,dynamicloadings,corrosion, lithologicalandstructuralconditions, stressfieldamongothers.Thisdata isused toproducedrawings,calculation logs, technical specifications,qualitystandards.Thoseproductsare the inputs formonitoringsupportbehavior.

MONITORING: inadditiontothesurveyoftheinstalledsupportelements,itincludesalsothetestingofotherinnovativesupportsystemsthatcouldbeusedinsomespecificundergroundconditionsanddailysolutionstooperationalrequirementsforundergroundsingularities.

POST EVALUATION. Itallowstoputtogetherinformationnotes,loadingcharacteristics(rockbursts,collapses,abutmentsstresslevels,fallingwedges)andtheexpectedsupportbehavior.Postevaluationnotesincludesthelessonsthatshouldfeedbackdesigns,establishingasupportimprovementcycle.

Figure 1 Components of the Integrated Support Quality Index (ISQS)

The3components,design,monitoringandpostevaluation,defineaworkingcycle.Itincludescontinuousimprovementofdesigns.Butbesides,itproducesaninteractionwiththeCriticalRiskarearesponsibleforthemanagementofareasconsideredcriticalduetotheirimpactinthepersonnel´ssafetyandfortheminingbusiness.Thisallowimplementingcorrectiveactionsinafasterandeffectiveway.

3 Monitoring

3.1 Definition of attention focus

Groundcontrolengineerscheckthequalityoftheinstalledsupportandcompareittoacceptedstandards.Consideringthelimitedresources,monitoringisfocusedincriticalareasthathavebeendefinedasseismic


236

activity,miningandstressfield.Notesarepreparedincludingthemainaspectssurveyedinthefieldforeachsupportsystem.

Figure 2 Parameters to define attention focus

Abutment Zone: Zoneinthevicinityofcavingfront,whererockmassshowevidencesoftheconcentration,variationandrotationofstresses.Thewidthofthiszoneisdefinedforeachsectordependingontheappliedcavingmethodandgeomechanicalandgeotechnicalconditions.

Table 1 Analysis criteria for Abutment Zone

Variable Evaluation

ABUTMENT ZONE

Criteria:toincludeZTaheadof

extractionlimit

Energy Index:ratiobetweeneventradiatedenergyv/sexpectedradiatedenergy.EIiscalculatedforeachevent(Mendecki,1997).Besidesforseismicactivitythecriteriainvolveclusterseismicmagnitude(Table2and3).

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Geomechanic Design

Table 2. Analysis for Energy Index

Variable Evaluation

ENERGY INDEX

Criteria:toincludezoneswith

IE>0,8

Table 3 Analysis criteria for Seismic Activity

Variable EvaluationSEISMIC

ACTIVITY

Criteria:toincludeeventcluster

magnitude≥0,5inlastyear

Fractures Pressures:characterizationwithgeo–statisticalmodelsofthespatialdistributionofpropagationfracturepressuresinducedbyhydraulicfractures.Inthiscasethecriticalaredefinedasthesethatreachesmorethan30MPaofpressuretobreakafracture(Table4).


238

Table 4 Analysis criteria for Fractures Pressures

Variable Evaluation

FRACTURES PRESSURES

Criteria:toincludefracturespressuresgreaterthan30MPa

Peak Particle Velocity: maximumvibrationvelocityestimatedfromseismicsensorrecords.Inthiscaseacriticalzoneisdefinedasthesewheretheppvreachesxxmm/sasshowninTable5.

Table 5 Analysis criteria for Peak Particles Velocity

Variable Evaluation

PEAK PARTICLE VELOCITY

Criteria:toincludethe

greatestvaluesofPPV

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Geomechanic Design

Theaboveindexesareconsideredtodefineacriticalzonewhenallcriteriaaremeet.ThatiswhenconditionsdescribedfromTable1to5arereached.

Figure 3 Identification of Attention Focus

3.2 Survey Information

Technicalreportsarepreparedincludingthemainaspectssurveyedinthefieldforeachsupportsystem(survey date, name of geomechanical engineer, identification of evaluation site, contractor companyresponsibleofthesiteandevaluateditems).

4 Management of Critical Findings

Geomechanicsgroupsendmonthlyallthesurveyedinformation.Dependingonthecriticalleveloffindings,CriticalRiskarearequestcausalityanalysisorcorrectiveactionsthathavetobesenttoMineOperationsorConstructionCompanies.

Reports include criticalfindingswith their impact in theproductiveprocess, verificationof correctivesactionsobtainedfromthesefindingsand informtheunfulfilmentofsupportsystemqualityofsurveyedgalleries(summaryoftechnicalreports).


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Thisistheevaluationscale:

:Agreewiththedesign:Possibilityofimprovement:Majorflaw

Table 6. Example of Technical report. Bolt – plate – nut and mesh system

Technical Report Evaluation Point % complianceBoltlengthagreewithdesign 100 98-99 0-97Spacing between bolts agree withdesing 100 86-99 0-85

Allboltsinstalledwithplateandnut 100 98-99 0-97Plate in correct position (relative todome) 100 <100

Goodconnectionbetweenplate,nut,boltandmesh 100 <100

30cmmaximumheightfromfloortofirstbolt 100 <100

Sawgroutingoutsidedrilling 100 90-99 0-89

Meshwithoutcorrosion 100 <100

Overlapmeshwithoneboltline 100 <100

Meshfromfloortofloor 100 90-99 0-89

Intactmeshwithoutrockslabs 100 <100

Figure 4 Actual reduction of major flaw

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Figure 5 2013 Evaluation sites in Production Level (Esmeralda, Reno – Dacita and Diablo Regimiento sectors)

5 Conclusions

Asaconclusion,wehaveidentifiedsomecontributionsofthisIntegratedSupportQualitySystem:

• It improves themanagement offindings in areas considered critical due to their impact in thepersonnelsafetyfortheminingbusiness,executingcorrectiveactionsinamorerapidandeffectiveway.

• It gives simple solution to some operational problems and includes learned lessons of postevaluationofthesupportdesigns.

• ItallowsthesamestandardforevaluatingtheinstallationqualityofundergroundsupportinalltheMinesectors.

• Adatabasecanbecreatedtofeedpostevaluations.

• Itisanindicatorformanagementregardingtheinstalledsupportquality.Itisawarninginrelationto practices that impact negatively the quality of installation and motivates the searching ofsolutionsforthem.


242

Forthisyear,weexpecttoextenddesignandpostevaluationaspects,toimplementthisSysteminthenewprojectsoftheDivisionandtostructureacomputationalplatformwithallthesysteminformation.

References

Cornejo, J,Muñoz,A&Rojas, E 2012, ‘Estimación de presiones de propagación de hidrofractura envolúmenespreacondicionadosPQ2013,MinaElTeniente’,InternalreportSGM-I-056/2012.

Dunlop,R,Gaete,S&Rojas,E1999, ‘Sismicidad inducidayestallidosderocaenMinaElTeniente’,InternalreportPL-I-099/99.

Juran,M,GrynaFM1995,AnálisisyPlaneacióndelaCalidad,ed.McGraw-Hill.

Mendecki,A1997,SeismicMonitoringinMines.Chapman&Hall.

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Management indicators for the cave geometry control, El Teniente mine

J Cornejo Codelco, Chile

C Pardo Codelco, Chile

Abstract

The fulfillment of production targets for massive cave mining is achieved by a successful application of cave back management. In this context, the planning tools used to identify the quality of extraction process are essential. For panel caving operations, the cave back could be managed through the extraction process and the incorporation of drawbells into production. He main geomechanical hazards, such as, rock bursts, collapses and early dilution could be related to unfavourable cave geometry. Therefore, the constructions and follow up of cave back management indicators are essential for an effective planning of caving operations. In this article, the authors present the results from the application of the angle of draw as an indicator of the performance of the cave back at Reservas Norte sector of El Teniente. The results indicate that there is need to improve the draw control over the sector as the indicator is underperforming. This would help to identify and to reduce the probability of occurrence of major geomechanical hazards.

1 Introduction

Manygeotechnicalriskshavebeenassociatedwithunfavorableconditionsofcavebackgeometry.Somerock bursts and collapses experienced at different operations have been facilitated by deviations fromplanneddrawprocess.Normally, thegeotechnicalguidelinessetouttherequirementsthatmustbemet;however,itiscrucialtolookforsomegeometryindicatorsforevaluatingtheconceptualmodelsusedas“cavingrules”.

Inthepresentstudy,managementindicatorsweredevelopedaskeyprocessindicators(KPIs),reflectingthegeneralstateofthecavityregardingthegeotechnicalguidelines.Basedonthekeyprocessindicators,itispossibletoanticipategeometriesissuesthatincreasetheplanvulnerabilitiesreducingtheoccurrenceofmajordetentionsandriskofpersonnelexposure.

Thecavebackgeometrywouldbestronglyassociatedthestressvariationsaroundthecavefrontduringcompleteextractionstepsinanyblockcavingmine.Itiscrucialtokeepageometrycontrolatanypanelcaving,becauseanydeviationcouldleadtounfavorableconditions.

Figure1shows(Floresetal.2004)representativephasesforconnectingprocessbyblockcaving.Duringthefirsts stages theflatundercut induceanactivezone locatedupper this cavity.Thisaffectedvolumeofrockmasscorrespondstoacombinationofseismogeniczoneandlooseningareawhichinducestresschangestowardsedgesofthecavitymeancavityprogress(Duplancic&Brady1999).

Although one of themain geotechnical aspects of caving process is the crown pillar breaktrough andconnectingtouppercavities,thereareotherpartsoftheprocessthatwouldbealmostasstrategicastheprevious ones. The exploitationmethod considers incorporating area after connections, increasing theextensionoftheundercut,advancingwiththecavefrontandincreasingtherockmassaffectedbyminingadvance.Therefore,forwiderundercuttingfronts,suchas,atReservasNortecase,theprogressiveprocessofthebreakingtheorecolumnproperlybecomesaveryimportantissue(Landerosetal.2012).


244

(a)Initialundercutting,themainmechanismsforpropagationistheunconfinementofrockmass.

(b)Cavepropagationinducesacurveshapeincreasingtheeffectofstresscaving.

(c)Additionalupwardcavingpropagationincreasesthecurvatureofthecaveback,andmakesthestresscavingmechanismpredominant

Figure 1 Evolution of the cave back and caving mechanisms through time due to the upward propagation of caving (Flores 2005)

ExperienceatElTenienteMineallowsconcludethatunfavorablecavebackgeometriesincreasepotentialgeotechnical hazards, such as rockbursts, collapses, hang-ups and airblasts and dilution entry. In thiscontext,thereisevidenceofseismicactivitywhichhasinducedrock-burstsandalsocollapsesexperiencedinseverallevels,affectingrecoveryofreservesandstaffsafetyworkingindifferentoperations(Landerosetal.2012).

In order to identify unfavourable geometry condition of cave back, a number of direct and indirectmethodologiesformeasuringand/orestimatingthecavebackshapehavebeenimplemented.Oneofthemostwidelyusedindirectrelationsistheratioofextractionsurface(angle)withthecaveback.Thisconceptisasimplificationdefinedastheaverageanglebetweentheeffectiveextractionheightanddistancetoapointreferencetoaspecificdirectionofadvance(Araneda&Gaete2004);inFigure2aschemeofthisconceptisshown.

Fromtheaboveconcept,itisassumedthattheextractionrateislinkedtothedrawbellincorporationratebytheextractionsurface(simplifiedasanangle).Thisgeometricalrelationshipbetweentheextractionrate(Pi+1,Pi,Pi-1)andcavingrateisdefinedbythefollowingequation:

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Figure 2 Simplified caving model

(1)

Where:

ev :Extractionrate[t*m2/month].

nv :Incorporationrate[m2/month].

x :extractionangleforadrawpoint

Thus,thereisalinealrelationshipbetweenthedrawbellincorporationrateandtheextractionrate.Therefore,inordertogettoaconstantdrawangle,anyincreaseoftheextractionratewillhavetobeaccompaniedbyanincreaseonthedrawbellincorporationrate.

Oneoftheresearchconsiderationisthatanacceleratedincorporationraterelativetotheeffectiveextractionratewouldmeana lowextractionangleandapotentialunfavorable stressconditionaround thecavity.Ontheotherhand,ahigherextractionratewithrespect to theincorporationratecouldinducepotentialordilution(mudordilution)resultinginlossofreserves.Furthermore,theactivationofgeologicalfaultscouldbefacilitatedbyahigherangleofextraction.

4 Cavity control indicators for Reservas Norte Sector

An indicator could be defined as a number that describes the performance of a specific activity. Keyperformance indicators (KPI’s) are defined as indicators of strategic significance,which are perceivedascriticalundercurrentbusinesscircumstances(Tomkins1988).Inthisstudy,amethodologybasedonthecurrentpracticeofcalculatingprofilesextractionwasused.Thismethodologyconsistsofcalculatingtheheightdrawatspecificreference linesalong themine. In thisstudy, referenceplaneswereused tocalculate the indicatorsand the stateofeachpoint ismeasuredagainst theseplanesof reference, thesepointsaregroupedintozonesaccordingtothepreferentialgrowthofthecavity.InFigure3,anoutlineofthemethodologyandthedescriptionofhowtocalculatetheindicatorareshown.

)tan(xvv

n

e =


246

Figure 3 Plan view of zones defined to calculate the angle of draw (zones 1 to 6)

The criterion to define the categories took into account the influence of the distance from the height of draw of a given drawpoint to the edge of the cavity, i.e., it was considered that the closer a point on the edge of the cavity, the greater its influence on stability condition in the surrounding infrastructure, while its influence decreases as the distance increases. In Figure 4 the categories associated with different conditions of points with respect to the reference planes, where the critical conditions are given by the category “red” and “magenta”. In this four categories are defined:

1. Low angle of draw, that is when the calculated angle is smaller than α° (magenta).

2. Good angle of draw, when the draw angle is within α-β° range (green).

3. Intermediate angle of draw (yellow), when the angle od draw is larger than β and smaller that a critical angle (CA).

4. Large angle of draw, this is when angle of draw is larger than the CA (red).

From this classification, sectors under critical conditions were 1) and 4). In these cases, actions are conducted:

1) In the case of areas reaching 1), they are considered as “priority extraction areas”. In this case, the continuity of the operation suggests to increase the extraction and/or decelerates the incorporation of new drawbells.

2) In the case of areas reaching 4), these were termed “Areas of over extraction”. In this case, there is a need to accelerate the incorporation of new drawbells and not to over-extract drawpoints from what has been planned.

As example, the methodology was applied in Reservas Norte sector at the El Teniente mine (Figure 5), where the following features were considered:

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• Theindicatorwascalculatedonlyforactivefronts.

• Basedontheempiricalrelationthatindicatesconnectiontouppercavitieswhen33%ofcolumnheightofprimaryrockhasbeenextracted,adistanceof60mforevaluationhasbeencalculated.Thisevaluationdistanceisfixedbetweentheincorporationfront(firstdrawbellincorporated)andthelastpointusedtocalculatetheangleofthecaveback.

• Thesectorhasbeendividedinzonestoimprovetheinterpretation(Table1andFigure5):

a. Panelcavingvariants.

b. Lithologicalandstructuralcondition.

c. Columnheightofprimaryrock.

It shouldalsobe considered that inReservasNorte there aredifferentminingmethods takingplaceasindicatedinTable1.

TheresultsoftheanalysesareindicatedinFigure6whichshowsthatZone1hasthelargestnumbersofdrawpointswithoverdrawn.Zonetwoandfourhavethelargestnumberofdrawpointswithunderdrawn.

Finally, through the use of the proposed tools, it is expected to maintain control of geometry cavity,whichallowsimprovingconsistentlytheresultsoftheindicators.Inparticular,expectedtotheestimatedcompliance from thefive-year plan for 2014 throughmanagement in the short andmedium term, it ispossibletoreachlevelsthatallownon-stopoperation.

Figure 4 Angle of cave back and categories used in this study


248

Table 1 Zoning used mina Reservas norte

CATEGORY ZONE 1 ZONE 2 ZONE 3 ZONE 4

Variationofpanelcaving

-Advancedpanelcaving

-Conventionalpanelcaving

-Advancedpanelcaving


fromXC5NhaciaelNorte


Lithologicalandstructuralcondition

-FaultsGandEast-WestSystem

-CMET

-FaultsGyF.

-Dacita

-BrecciaAnhidrita

-FalladeAguayAnd1.

-CMET

-FaultsN1AND2,AND3yAND4.

-CMET

-BrechaAnhidrita

-PórfidoDioríticPorphiric

Columnheightofprimaryrock

160to180meters.

160to180meters. 160to180meters. 180to360meters.

Results

Figure 5 Plan view of zoning used to cavity control at Reservas Norte Sector

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Figure 6 Results of the application of performance indicators, November 2013

5 Conclusions

Surface extraction (draw angle) is one of the key indicators of effective cave back control at El Teniente mine. The results so far have identified some specific spots, where the extraction does not follow expectations on the draw angle. Based on that finding, this tool has allowed improving the operational control over the draw strategy, reducing the possibility of early mud entry and instability problems at the mine. In the future, to achieve continuous improvement goals, an attempt will be made to use this criterion selectively.

Acknowledgement

The authors would like to thank Codelco Chile, El Teniente, to authorize the publication of this document and, in particular, to all the people who make the Superintendency of Geomechanics GRMD.

References

Araneda, O & Gaete, S 2004a, ‘Continuous modelling for caving exploitation’, MassMin 2004, A Karzulovic & M Alfaro eds., Chilean Engineering Institute, Santiago, Chile.

Duplancic, P and Brady, BH 1999, ‘Characterisation of caving mechanisms by analysis of seismicity and rock stress’, Proc. 9th Congr., Int. Soc. Rock Mech., Paris (eds G. Vouille and P. Berest), vol. 2, pp. 1049–53, A. A. Balkema: Rotterdam.

Flores, G, Karzulovic, A and Brown, ET 2004, ‘Current practices and trends in cave mining’, MassMin 2004, A Karzulovic and M Alfaro (eds), Chilean Engineering Institute, Santiago, Chile,.

Landeros, P, Cuello, D & Rojas, E 2012, ‘Caveback management at Reservas Norte Mine, Codelco Chile, El Teniente Division’, 6th Conference and Exhibition on Mass Mining, Massmin 2012, Canada.

Tomkins, J 1988, The warehouse management handbook, Mc Graw Hills eds, pp 513-559.


250

Geomechanical issues and concepts associated with scoping study and prefeasibility stage of a Block/Panel Caving Project

J Díaz DERK Ltda., Chile P Lledó DERK Ltda., Chile F Villegas Codelco, Chile

Abstract

Considering the amount of underground mining projects in development that have studied the application of the caving methods together with other ore bodies that will also evaluate the feasibility of a future underground caving, this document summarizes the main geomechanical issues associated with the stages of a scoping study and a prefeasibility study for the Block/Panel Caving operation that must be considered. In addition, this technical paper will present the usual practices adopted and other considerations during the development of this type of studies. Equally, this material could be used as a general reference for engineering professionals in technical offices and/or mining companies interested in caving issues as well as in education and professional training.

1 Introduction

ThedevelopmentofthefirstengineeringstagesinaBlockorPanelCavingundergroundproject,asanyotherproject,requirethedefinitionofvariouskeygeomechanicalaspects,whichscopeanddepthmustbeinaccordancewiththestageofthedevelopmentoftheproject.ThetermsandtargetsofeachengineeringstageplayarelevantroleintheadequatesupplyofresourcesinGeomechanicsduringtheprojectdesignandplanning,avoidingexcessivelyadvancedorunnecessarydefinitions,whichwillprobablybereviewedandmodifiedagaininthefollowingstagesoftheproject.

Thiswork is focused on the Scoping and Prefeasibility stages ofBlock or PanelCaving undergroundminingprojectsbecause,accordingtotheauthors,occasionally,thescopeanddepthofthegeomechanicalissuestendtobeconfusedbetweentheabovementionedengineeringstages.

2 Stages of a mining project

Ingeneralterms,aminingprojecthasfivemajorstages:

1. Planning Stage: It considers the period since the conception of the project, at the level of“idea”or“profile”,uptothedemonstrationofitseconomicfeasibilityatthelevelofscopingengineering.

The Scoping Engineering identifies the project’s business potential, key factors, fatal risks,investmentorderofmagnitudeandoperatingcoststogetherwiththerelevanttechnicalissues.

The Prefeasibility (Conceptual) Engineering studies the possible alternatives of the projectto establish themost favourable case.The technical and economic feasibility of theminingconfigurations,technologies,capacities,andothersaredetermined.Inthisstage,theinvestmentsums(CAPEX)andoperatingcosts(OPEX)areestablished.

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Geomechanic Design

2. Design Stage:Thisisthestagewheretheprojectis“DESIGNED”,includingtheactivitiesthatbelongtotheBasicandDetailedEngineeringstagesorcontexts.Togetherwiththeformerstage,thisonecorrespondstotheprojectcategory.

ThealternativeselectedinthePrefeasibilitystageisdevelopedinthisstagetodemonstrateitstechnicalandeconomicviability.

3. Development Stage:Thisstagecorrespondstotheconstructionordevelopmentoftheproject,accordingtothedesignspecifiedintheformerstage.However,therecanbedesignmodificationsduringthisstagewithoutchangingoralteringthecoreconcepts.

4. Operational Stage: Thisstagecorrespondstotheprojectoperationduringitslife,includingthepre-productionorramp-upstage.

5. Closure Stage:Thisstagecorrespondstotheprojectclosureaftertheendofitslife.

Toframethesegeneralstageswiththeproject’sengineeringphase,Figure1presentsaschemewiththeserelationships.

Figure 1 Relationship between the general stages of a mining project with the engineering stages and the category or stat

Asexplainedpreviously,thisworkisframedwithintheplanningstageandaddressesthegeomechanicalissuestobedefinedintheScopingandPrefeasibilityEngineeringstagesofaBlockand/orPanelCavingundergroundproject.

Ageomechanicalproblemcanhaveasignificantimpactonanundergroundproject;thus,itisnecessarytoreduceorminimizetheriskofmakingerrorsintheproject’searlystages(ScopingandPrefeasibilityEngineering).


252

3 Geomechanical issues of a Block/Panel Caving mining project

ThemajorgeomechanicalissuesinaBlock/PanelCavingprojecttobeaddressedarethefollowing:

• Geological,Structural,HydrogeologicalandGeotechnicalcharacterization.

• Intactrock,geologicalstructuresandrockmassproperties.

• Stressfieldcharacterization.

• Caveabilityassessment.

• Fragmentation(PrimaryandSecondary).

• InducedSeismicity.

• Geotechnicalassessmentfordesign.

• Geotechnicalassessmentforplanning.

• Subsidenceanalysis.

• Geotechnicalhazards.

Althoughtheseissuescanvarydependingonthecharacteristicsofeachprojectandtypeofdeposit,it’spossibletoclustertheseissuesinthefollowingcategories:

1. Geometry:Geometryestablishes thefactors thatdefinetheshapes,sizesanddistributions inspace.Itcanbedividedintwosub-groups:NaturalandUnnatural.Thenaturalgeometryconsidersthegeologic,structural,hydrogeologicalandgeotechnicalmodelsinadditiontothefracturingleveloftheenvironment(primaryfragmentation)andthesurfacetopography(geomorphology).TheUnnaturalgeometryinvolvestheminingmethodscenarioorcontext,greatlymeasuredbythegeomechanicalissuesfordesignandplanning.

2. Geomechanical Context:Thegeomechanicalcontextisspecificforeachdepositandproject;it canbedivided in two largegroups:Pre-miningproperties and InducedLoads.Pre-miningpropertiesconsiderthematerialcharacterizationandcontactzonesinthegeological,structural,hydrogeologicalandgeotechnicalmodels,inparticular,theparameterstoevaluatethestrengthanddeformabilityoftherockmassanditscomponents(intactrockandgeologicalstructures).Thisgroupalsocharacterizestheinsituorpre-miningstressstate.Ontheotherhand,InducedLoads correspond to the effects that will be produced by caving on the environment, withthe reviewof aspects associated to caveability, cavepropagation, abutment stress, secondaryfragmentationandinducedseismicity,amongotherissues.

3. Interaction: Interactionconsiderstheimpactsthattheprojectcancreateontheenvironment,withitssubsidencephenomenaandtheoccurrenceofgeotechnicalevents.

Figure2showsadiagramthatsummarizestheissuesinvolvedineachoneofthemajorgroupsorcategoriesthattheGeomechanicalDisciplinemustmanageinaBlock/PanelCavingproject.

4 Geomechanical concepts and parameters in the scoping and prefeasibility engineering stages

In practice, during the development of engineering projects, it has been possible to observe that thereare requirements and contributionsmade toGeomechanics thatmostly don’t have enough and reliableinformation to respondaccording to theproject’sengineeringstages. Inaccordance to this, it isalwayshighlynecessarytodefinewhichsubjectsandparametersmustbeaddressed,bothintermsoftheirscope

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anddepthfromthegeomechanicalcavingmethodsviewpointintheScopingandPrefeasibility(Conceptual)Engineeringstages.

Figure 2 Geomechanical Issues and Parameters considered in a Block / Panel Caving Mining Project

Table1providesasummaryof the issuesorsubjectsof interestand thegeomechanicalparameters theauthorssuggesttobeaddressedduringtheScopingandPrefeasibilityEngineeringstagesinaBlock/PanelCavingMiningProject.

Table 1 Geomechanical Issues and Parameters in the Scoping and Prefeasibility Engineering stages

Issues of interest / Parameter Scoping Engineering Prefeasibility EngineeringCharacterization Geological Planviewsandsectionswith

Lithology,AlterationandMineralization,preferablysystematicallyspaced.Planviewsmustconsidernearnesstoundercuttingandproductionlevels.SectionsmustbeorientedinthegeographicNSandEWand/ormina(local);spacedbetween100and200m.

Geologicalmodel(3D)inacomputerplatformthatincludesatleastthelithologies,alterationsandmineralizationattributes.

Thefirstgeomechanicalmodel(orgeotechnicalmodel)correspondstothegeologicalmodel;that’swhyit’simportanttohaveitasearlyaspossible.

Structural Planviewsandsectionswithmajorand/orprincipalstructurestracesandprojections.

Planviewsneartheundercuttingandproductionlevels.Theplanviewsandsectionsmustspatiallycoincidewiththegeologicalinformation.

Structuralmodel(3D),inacomputerplatformwiththemajorstructuresidentified,includingstructuraldomainswiththeinterpretationoftheinformationabouttheminorstructuressurveyedfromdrillholesand/oroutcroppingorundergroundexplorationworks.


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Table 1 Geomechanical Issues and Parameters in the Scoping and Prefeasibility Engineering stages (Continued)

Issues of interest / Parameter Scoping Engineering Prefeasibility EngineeringCharacterization Hydrogeological Recordingofwaterlevelsinprobe

holesand/ordrillholes.Watertableandrechargeresultingfromprecipitation(waterand/orsnow).Knowledgeofsurfacerun-offsorpresenceoflakedeposits.

ConceptualHydrogeologicalModel,identificationofpermeableandimpermeableunits.

Geotechnical Geotechnicalcharacterization,atleastonerockmassqualitymethod(i.e.,RMRLaubscher)inthegeotechnicaldatabase.Planviewsneartheundercutting/productionlevelsandsectionswiththegeotechnicalqualitydistribution.

Incasethereisnotgeotechnicalcharacterization,thereatleastmustbethespatialdistributionsand/orRQDvaluesand/orFractureFrequency,FF.

UniaxialCompressiveStrength(UCS)estimationsandPointLoadTests(PLT)inmajorunits.

Geotechnicalmodel(3D)abletodisplayatleasttworockmassqualitymethods(forexampleRMRLaubscher,GSIf(RMRBieniawski)orQ).

Developmentofintactrocksamples’labtestcampaignsfortheprincipalunits;uniaxialtestsmustbeincludeddeterminingtheelasticconstants(15testsperunit),triaxialtests(5testsforeachconfinementlevel,preferably5levels),indirecttensiletests(15testsperunit);inadditiontotheindexproperties(unitweight,porosity,PandSwavepropagationvelocity).

StressState Preliminaryestimationofinsitustressesaccordingtotopographyandlithostaticcolumn,benchmarkingoftheregionand/orminingdistrict,useoftheworldstressmap,etc.

Itissuggestedtoreviewthetechnicalliterature(Hoek&Brown(1980);Amadei&Stephansson(1997);Díaz&Lledó(2005);Lledó&Díaz(2006)).

Carryoutpreliminarystressmeasurements,preferablythroughhydraulicfracturingtoobtainthedeepstressdistributionandusingsomemine-scalenumericalmodels(3D).

Itmustbeconsideredthatthesenumericalmodelscouldreacherrorlevelsbetween25%and50%.

PrimaryFragmentation

FracturefrequencyFFandRQDanalysisPrimaryfragmentationestimationfrombenchmarkingandpreliminarysimulationsfordifferentrockqualities,structuraldomainsandstressstates.

PrimaryfragmentationsimulationforthegeotechnicalunitsofinterestforeachstructuraldomainundertheexpectedstressstatesandapplyingtoolssuchasBCF,Size,etc.(Díaz,Lledó,Aguilar&Sepúlveda(2013)).

SurfaceTopography Updatedsurfacetopography. Updatedsurfacetopography.MineDesign MiningMethod Analyzingthepossiblemining

alternative(s),developmentofdecisionmatricesfortheundercuttingvariantscomparativelyevaluatinggeomechanicalconcepts.

Itmustbepossibletodetectpossiblefatalflawsasmajorfocusofattention.

Analyzingthepossibleminingalternative(s),developmentofdecisionmatricesfortheundercuttingvariantscomparativelyevaluatinggeomechanicalconceptswiththehighestdetailandinformationpossible.

Levelselevation Knowingpreliminaryelevationsofthemainlevels(undercuttingandproduction)oftheminingalternative(s).

Itisnecessarytoknowtheelevationsoftheundercutting,production,ventilationandhaulagelevelsoftheminingalternative(s).

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Geomechanic Design

Issues of interest / Parameter Scoping Engineering Prefeasibility EngineeringMineDesign Levels

LayoutExtractiongrid Carryingoutacomparative

analysisofthedifferenttypesofextractiongrids,benchmarkingsupportandexpertjudgement.

Thealternative(s)forselecteddrawpointspacingmustbeanalysed,hopefullywith3Dnumericalmodels.ThismodelmustincludeexcavationsandpillarsfromtheProductionlevel,orepasssystem,drawbellsandundercuttingintheundercuttinglevel.

CrownPillar Thenominalthicknessofthecrownpillarcanbedefinedthesupportofbenchmarkingandexpertjudgement.Inaddition,itmustbeinagreementwiththeundercuttingvariantandgeotechnicalscenario.

Thethicknessofthecrownpillarproposedcanbeanalysedthroughavailable3Dnumericalmodels.

Drawpoint Thedrawpointdesignmustconsidertheexpectedgeotechnicalscenario.

Thethicknessofthecrownpillarproposedcanbeanalysedthroughavailable3Dnumericalmodelsandmustcomplywiththegeomechanicaldesigncriteria.

Undercuttypeandheight Carryingoutacomparativeanalysisofundercutheight(low,mediumorhigh),benchmarkingandexpertjudgementareused.

Definingtheundercuttypeandheightalternative,itmustconsidertheexpectedgeotechnicalscenario,theuseofcavingassistancemethods(preconditioning),geomechanicaldesigncriteriaanddrillingandblastingcriteria.

LevelsGroundSupport

Undercutting

Definitionofgeneralgroundsupportcriteria,usuallyfromgeomechanicalclassifications,experienceobtainedatthesiteand/orothercompanies,inadditiontothespecialist’sexpertjudgement.

Generationofmajordrawingswithsupportrecommendations,supportsystemsandelementsarrangementinadditiontotechnicalspecifications.

Itispossibletomakeasupportzoningfromtheadditionalinformationavailable.

Verificationofthesupportrecommendationsthrough2Dnumericalmodellingfordifferentgeotechnicalscenarios.

ProductionVentilationReductionTransportationTemporaryInfrastructure



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Issues of interest / Parameter Scoping Engineering Prefeasibility EngineeringMinePlanning BlockHeight Theinsituandextractable

blockheightproposedmustbeanalysedfromempiricalrelations,benchmarkingandexpertjudgementtomakecavingpropagationanalyses,toestimatetheinsituandinducedstressstateandthesubsidence.Parametric2Dnumericalmodelscanbedeveloped.(Karzulovicetal.2004)).

AdeeperanalysisismadetotheassessmentofblockheightimpactinasimilarmannerastheScopingstage,includingthedrawpointsupportdesignanddrawbelldesignintheanalysis.

Itispossibletodevelop2Dand3Dnumericalmodelsdependingonthecomplexityoftheproject(Díaz&Lledó2008).

AdvanceSequence Theadvancesequencemustconsidertheprincipalstressorientationandtheorientationofmajorgeologicalstructures.

Thedefiniteadvancesequencemustconsidertheorientationofmajorstresses,theorientationofmajorgeologicalstructures,theundercuttingfrontgeometry,theundercuttingandproductionlevelslayoutandthedistancebetweenlevels.

Possibleitwillbenecessarytomake3Dmodellinginthedefinitesequence.

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Geomechanic Design

Issues of interest / Parameter Scoping Engineering Prefeasibility EngineeringMinePlanning CavingRate Thedefinitionoftheaverage

cavingratesmustbemadethroughbenchmarkingand/orexpertjudgement.

Itisnecessarytodefinetheoperationallyreasonablecavingraterangesconsideringdifferentiatedratesfordesign,planningandgeomechanicalconditions.Thistriestomitigatetheeventualoccurrenceofgeotechnicalhazards.

Itisrecommendedtouseexpertjudgementandtheexperienceobtainedinsiteswithsimilarscaleandgeomechanicalcontext.

DrawRates Thedefinitionoftheaveragedrawratesmustbemadethroughbenchmarkingand/orexpertjudgement.

Itisnecessarytodefinetheoperationallyreasonabledrawraterangesconsideringdifferentiatedratesfordifferentdesign,planningandgeomechanicalconditions.

Itisrecommendedtouseexpertjudgementandtheexperienceobtainedinsiteswithsimilarscaleandgeomechanicalcontext.

SecondaryFragmentation Normallybasedonbenchmarkingandprojectrequirementsfortheexpectedproductivityofthedrawpointandproductionrate.

Asecondaryfragmentationanalysisisrequiredtoestablishtheexpectedproductivityinthedrawpointandproductionrate.

Sensitivityanalysisismadeforthedifferentgeomechanicalunits,structuraldomainsandstressstate.

CaveabilityAssessment

Caveability Useofempiricalmethods(Laubscher’scaveabilitychart(1990).

Itisrecommendedtouseempiricalmethods(Laubscher’scaveabilitychart(1990)orLaubscher&Jakubec(2001)includingthedataofsitesusingcavingwithageotechnicalscenariosimilartotheproject’sone.

CavingPropagation Useofempiricalrelationsandbenchmarking(Karzulovicetal.2004).

Inadditiontoempiricalrelations,makeuseofnumericalmodellingifanunfavourableconditionisdetected.



258

Issues of interest / Parameter Scoping Engineering Prefeasibility EngineeringGeotechnicalHazards

Collapses Collapseoccurrencepotentialanalysistoidentifyifitcorrespondstoafatalflaworaseriousconditionfortheproject.

Collapseriskanalysis.Identificationofpotentialcollapseoccurrencezones.Recommendationsondesignandplanningmeasuresformitigationpurposes.

Rockbursts Rockburstoccurrencepotentialanalysisbasedontherock’smechanicalpropertiesandstressstateinordertoidentifyifitcorrespondstoafatalflaworaseriousconditionfortheproject.

Rockburstriskanalysis;seismicriskanalysis.

Recommendationsondesignandplanningmeasuresformitigationpurposes.

Hang-ups(AirBlast) Hang-ups(airblast)occurrencepotentialanalysisinordertoidentifyifitcorrespondstoafatalflaworaseriousconditionfortheproject.

Hang-ups(airblast)riskanalysis.Identificationofmitigationmeasures.


Water/MudRushes Water/mudrushesoccurrencepotentialanalysisbasedonhydrogeologicalinformationinordertoidentifyifitcorrespondstoafatalflaworaseriousconditionfortheproject.

Water/mudrushesriskanalysis.


Geotechnicalevents

Subsidence SubsidenceCrater

Preliminaryestimationofthefinalextentofthesubsidencecrateronthesurface,useofempiricalmethodsand/orbenchmarking.Generalsubsidenceanglesaredefined.

Preliminaryestimationofthefinalextentofthesubsidencecrateronthesurface,useofempiricalmethodsand3Dnumericalmodelling.Anglesofsubsidencearedefinedbyperiodandbycraterwall,consideringthegeotechnicalscenario.

ZoneofInfluence

Preliminaryestimationofthezoneofinfluencearoundthefinalsubsidencecrater,useofempiricalmethodsandbenchmarking.

Estimationofthezoneofinfluencearoundthesubsidencecraterbyperiod,useof3Dnumericalmodelling.

MajorInfrastructure Orientationandlocationofexcavationswithrespecttotheprincipalstructures,stressstate(insituandinduced),geotechnicalqualityoftherockmassandsubsidencedevelopment.

InadditiontodeepentheworkmadeintheScopingEngineering,thegeometries,sizesandlevelofstabilityoftheexcavationsmustbeanalysed.

Preparingtheconstructionsequenceoftheexcavationsanddefiningsystematicgroundsupportrecommendations.


259

Geomechanic Design

5 Conclusions

Theinputinformationineachoneoftheengineeringstagesmustbeconsideredasstrategicinformation.Consequently, thequalityand reliabilityof this informationdefine thequalityand the reliabilityof theanalysesmadetosupporttheproject’sgeomechanicalrecommendations.

Itisrelevantfortheclienttohaveateamofatleastonegeotechnicalgeologistandageomechanicalengineerwithexperienceinundergroundcaving.Thiswillsubstantiallyimprovethequalityofthegeomechanicaloutputs associatedwith the scoping and prefeasibility engineering.This becomes increasingly relevantwhenthereisatransitionfromopenpitminingtoundergroundmining.

References

Hoek,E&BrownET1980,UndergroundExcavations inRock, InstitutionofMining andMetallurgy,ISBN0419160302,E&FNSPON.

Amadei,B&Stephansson,O1997,RockStressanditsMeasurement,ISBN:0412447002,Chapman&Hall,Londres-Inglaterra.

Díaz,J&Lledó,P2005,EstadoTensionalInSituy/oPremineríaenMinaChuquicamata-DivisiónCodelcoNorte, InformeTécnicoIT-DCN-E01-01-05,DivisiónCodelcoNortedeCodelcoChileporDerkIngenieríayGeologíaLtda.

Lledó,P&Díaz,J2006,‘MedicionesdeEsfuerzoInSituMedianteTécnicasdeHidrofracturamiento’,inProceedingsofMining2006IIInternationalConferenceonMiningInnovation,May23to26,SantiagoChile.

Lledó, P & Díaz, J 2008, ‘Modelos Numéricos 2D para Diseño de Soporte, Ingeniería ConceptualChuquicamataSubterráneoCodelcoChile–VCP’,InformeTécnicoIT-VCP_CHS-E09-01-08emitidoporDerkIngenieríaLimitadaalProyectoChuquicamataSubterráneo.

Laubscher,DH1990,‘AGeomechanicsClassificationSystemfortheRatingofRockMassinMineDesign’,SouthAfricanJournalofMiningandMetallurgy,vol.90,no.10,Oct.1990,pp.257-273.

Díaz, J, Lledó, P, Aguilar, J& Sepúlveda, J 2013, ‘Geotechnical Pre-Feasibility StudyCarrrapateenaProject’,TechnicalReportIT-NCL-E01-02-2013,DerkIngenieríayGeologíaLtda.ToNCL.

Karzulovic,A,Flores,G&Brown,T2004,‘CurrentPracticesandTrendsincavemining’,Massmin2004,CodelcoNorteDivision,CodelcoChile.

Laubscher,DH&Jakubec, J2001,TheMRMRRockMassClassification for JointedRockMass,eds.Hustrulid,W.A.&Bulock,R.L.UndergroundMiningMethods.EngineeringFundamentalsandInternationalCaseStudies,718p.SME:Littleton,Colorado.

Codelco2014,Availableathttp://www.codelco.com/el-abc-de-un-proyecto/prontus_codelco/2013-10-24/114451.html.


261

Geotechnical Characterization

263


Ciresata geotechnical evaluation and caving study, Romania

N Burgio Stratavision Pty Ltd, Australia

Abstract

The Ciresata deposit is located in the Golden Quadrilateral Mining District of the South Apuseni Mountains in west-central Romania. Carpathian Gold Inc. commissioned a prefeasibility study to determine a suitable bulk scale underground mining method to achieve a production capacity of 30,000 t/day. Geotechnical investigations focused on the sublevel and block cave mining options.

Material property testing of diamond drill core was undertaken to provide essential information for caveability, fragmentation and stress modelling analysis. Anomalous rock strength information required a second testing laboratory to be utilised for data verification. Initial predictions of fragmentation were highly sensitive to rock strength and local stress field information which impacted undercutting strategies for the block cave option. This paper outlines how engineering judgements were applied to cover data uncertainty in the early stages of the prefeasibility study and how the ongoing geotechnical evaluation was managed as new information became available.

1 Introduction

TheCiresatadepositislocatedintheGoldenQuadrilateralMiningDistrictoftheSouthApuseniMountainsinwest-centralRomania(Figure1).HistoricgoldproductionfromtheGoldenQuadrilateralexceeds55MMOz.CiresataisoneofthethreemaindepositsoccurringwithintheRovinaExplorationlicense,managedbyCarpathianGoldInc.TheRovinaandColnicdepositsareplannedasopenpitoperationswhilstCiresataisundergoingstudiesforabulkundergroundminingtechnique.Thelicenseislocatedapproximately20kmssouthwestofRosiaMontana.AprefeasibilitystudyconcludedthatCiresatawasamenabletothesub-levelcavingorblockcavingbasedonaproductioncapacityof30,000t/day.

Figure 1 Ciresata Location Map


264

2 Ciresata geology & geotechnical domains

Gold-coppermineralizationishostedbysubvolcanicintrusionsofNeogeneageandadjacenthornfelsedCretaceoussediments(Nebaueretal.2005).Theporphyriescompriseofsmall,verticallyattenuated,coarse-grained hornblende-plagioclase intrusives. Mineralisation occurs as pyrite-chalcopyrite disseminationsassociatedwithsheetedandstockworkquartzveining.Alterationtypesrangefrommagnetiteprogressingoutwardstopotassic,phyllic,andpropyliticassemblages.Mineralisationcommencesbetween50mand150mbelowthepresentdaysurfacedue toahornblendeporphyrywhichcapsmineralisationnear thesurface.Thehostsedimentsdipatmoderateanglestothenortheast.

Geotechnical domains were defined based on lithology, structure, alteration and rock mass properties(Figure 2). Several intrusive phaseswere grouped into a single domain referred to as the IntermineralPorphyry.Weakergeotechnicalconditionsoccurclosertosurfaceduetoincreasesinargillicalterationandelevatedfracturing.TherockmasswascharacterisedbasedonLaubscher’sRMRsystemofclassification(Laubscher1990)andsummarisedinTable1.

Preliminaryfaultinterpretationswerepreparedtoidentifystructuralfeaturesthatcouldassistcaveabilityandcavepropagationbehaviour.Sub-verticalfaultsstrikealonganorthtonorth-westerlydirection.Haloesof carbonatewall rock alteration are oftenobservednext to faults zones.Milledbreccias, representingthrustfaults,dipsub-paralleltobeddingatmoderateanglestothenorthandnortheast.Thethrustfaultsappeartobeoffsetbyearlierverticalfaults.

ThefracturefrequencybelowtheArgillicCapisgenerallylowandtypicallyrangesfrom1.5to2fracturespermeter.Thereappearstobenopreferentialdevelopmentofjointingalongthebeddingplanes,despitesedimentsoccasionallydisplayinglaminatedtextures.

Figure 2 Ciresata Geotechnical Domains

265


Table 1 Average Rock Mass Ratings for Geotechnical Domains

Domains Description RQD RMRL90

SurfacefractureZone Oxidation(1-5m)andelevatedfracturingto50mdepth 38 43ArgillicCap Argillic-carbonatealterationextendingto150mdepth 75 55Sediments Sedimentsdippingmoderatelytowardsthenortheast 83 60IntermineralPorphyry Sub-verticalintrusivepipesanddykes 85 60WesternPorphyry Northweststrikingintrusivelocatedwestoftheorezone 73 57EasternCappingPorphyry Intrusivesillwhichcapsmineralisation 82 58LowerBeddedZone Sedimentaryzonewithoccasionallaminatedbedding 71 55LowerFractureZone Zoneofincreasedfracturingduetofaulting 67 51

3 Caveability

Aconceptualblockcave layoutwaspositioned650mbelowsurface.Thehydraulic radius (HR)of theproduction area (HR=92) iswell in excessof that required to initiate caving (HR=42). By contrast, aminimum span of 120m (HR~28-30) was established for the primary SLC level within the narrowercarapiceof themineralisedzoneapproximately120mbelowsurface (Figure3).The rockmassabovetheSLClevelhasanMRMRrangefrom42to60,hence,theminimumspanoffersareasonablebalancebetweenproximitytosurfacewhilstenablingaprogressivestepoutfortheexpansionofsubsequentSLCproductionlevelsatdepth.

Figure 3 Ciresata SLC and Block Cave Caveability Chart


266

4 Stress field estimation

The likely stress field conditions were estimated based on a review of local and regional tectonicinformation. Several geologically recent extensional basins and over-thrusting combinedwith ‘current’earthquakes,associatedwithsubductingremnanttectonicplates,hasgeneratedatectonicallycomplexarea.ThegeneralisedmapofEuropeanstresstrendsimpliesthereislittlestressdataneartheCiresatadepositanditisnotcertainifthelocalstressesareEuropean(NW-SE)orrelatedtotheAnatolianProvincetothesoutheast.InadditiontheEuropeanstresstrendsarebasedondeepearthquakeandhydraulicfracturingdataratherthanshallowin-siturockstressmeasurementsinminesorcivilconstructions(Lee2012).

Depositscalestructures(i.e.dykes,contacts,faultsshears,beddingetc)arethoughttoinfluencelocalstressorientationsandprinciplestressratios,ratherthanregionaltectonicconsiderations.AsthelithologiesatCiresataaregeologicallyrecentitcanbearguedthatthestyles,andoffsetsofthemainfaults,arelikelytobegoodindicatorsofthecurrentregionalstressfield.Thesurroundingover-thrustingimpliesthathighhorizontalstressconditionscannotbesustainedwithintheringingCarpathians.

TheinterpretedsteeplydippingNNW-SSEfaultsandhaveasimilarorientationtotheregionallydevelopedstructuresandmayimplyN-Sorientationfor the localmaximumhorizontalstress.Therehavebeennoobservationsofdrillingdifficultiessuchassqueezingorcorediscing.Twoacousticteleviewersurveyswerecompleted,howeverthesewereshallowandboreholebreakoutsordrillinginducedfracturingwouldnotbeexpectedatthesedepths.Usingcurrentinterpretations,itwasconcludedthatthemostlikelyorientationforthemaximumhorizontalstressatCiresataisNNW-SSEi.ethebisectoroftheacuteanglebetweentheinterpretedfaultstructures.Estimatedrockstressrelationshipsweredevelopedforthemostlikelyscenarioaswellasmoreandlessdeviatoricoptions(Table2).

Table 2 Estimated Rock Stresses for Ciresata

267


5 Rock strength testing

Field observations and point load testing indicated hard rock strengths, yet initial UCS tests wereconspicouslylowatalmosthalftheexpectedvalue.Adecisionwasmadetoapplythehigherrockstrengthparameters inferred fromfieldobservations, forpreliminarygeotechnical analysis.FurtherUCS testingfromanalternatelaboratory(LaboratoryB)generatedmuchhigherresultswhichmorecloselymatchedfieldobservationsandverifiedengineeringassumptions(Table3).

Table 3 Initial UCS Test Results

RockType LaboratoryA(MPa) LaboratoryB(MPa)Min Average Max Min Average Max

Porphyry 30 56 118 28 116 180

Sediments 22 42 69 40 76 120

AnindependentsuiteofsamplesweresourcedandsuppliedcourtesyofNewcrest’sCadiaValleyoperationinAustraliatochecktheperformanceofbothlaboratories.Sampleduplicateswerepreparedincludinga‘standard’comprisingofdentalplaster.TheresultsconfirmedearlierobservationsthatLaboratoryAwasalmostconsistentlygeneratinglowerthanexpectedresults.Thereremainssomeuncertaintyifthelowerresultswereduetosamplepreparation,equipment,orsomeothersystematicerror.

Table 4 Comparative UCS Testing using Cadia Valley Drill Core

CadiaValleySampleDescriptions

LaboratoryA

(MPa)

LaboratoryB

(MPa)

Volcanoclasticrock 116 171

Volcanoclasticrockwithepidote 90 68

Pyroxenephyricvolcanoclasticrock 64 102

Pyroxenephyricvolcanoclasticrock 61 184

Dentalplastermould(15MPastandard) 8 12

6 Fragmentation

Fragmentationpredictionsarehighlysusceptibletothedegreeofnaturalfracturingandalsotheratiobetweeninducedstressandrockstrength.TheCiresatarockmasshasawidelyspacedjointingsystem,hence,thedegreeofstressinducedfracturingbecomesanimportantfactorinreducingfragmentationprovidedthestress/strengthratiosarefavourable. TheanalysiswasundertakenusingtheBlockCaveFragmentationprogram(BCFv3.05)developedbyEsterhuizen(2005).Fragmentationprofilesweregeneratedforlowandhighstrengthscenariosforthesedimentaryandintrusivehostrocks(Figures4and5).


268

Figure 4 Fragmentation Domains

Figure 5 Primary Fragmentation for Sediments and Intrusives

269


There isa substantialdifference inprimary fragmentation foreachscenariobasedon the rockstrengthinputs.The intrusives are expected togenerateverycoarse fragmentationalbeitover a relatively smallproductionarea.Secondaryfragmentationanalysissuggestedthat50mto75mofdrawwouldberequiredbeforemoremanageablelevelsofoversizewereencounteredfromthesedimentsfortheblockcaveoption.Thedesignofahighundercutwouldmitigatetheinitialcoarsefragmentationtomoremanageablelevels(Figure6).

Figure 6 Undercut Design and Secondary Fragmentation

7 Conclusions

Comparative laboratory testingbecamenecessary toaddressandconfirmdiscrepancies inUCS results.SamplestandardsandduplicatesofferedanopportunitytotestthequalityandreliabilityoflaboratoryresultsandsupportstheengineeringjudgmentsappliedtothebasecasescenariosforCiresata.Thedecisiontouseanalternatematerialtestinglaboratorywassignificantaslowerrockstrengthswouldhaveunderestimatedfragmentationandoverestimatedtherockmassdeformationresponse.

TheCiresata rockmass and geometry is amenable to block cave and sub-level caveminingmethods.Minimum dimensions were established for the primary SLC level to initiate caving. The block cavefootprint far exceeds thehydraulic radius required for caveability, however, cavepropagationcouldbeslowduetothecompetentrockmassandlimitedfaulting.Ahighundercutdesignwasincorporatedintothe block cave option to reduce secondary breakage requirements and improve early productivity.Animprovedunderstandingoftherockmasswillbeachievedonceundergroundexposuresbecomeavailableandsitestressmeasurementsareundertaken.Opportunitiesalsoexisttoconsiderpreconditioningtoreducefragmentationandassistcavepropagation.

Acknowledgement

IwishtoacknowledgeMrRandyRuffofCarpathianGoldInc.forpermittingthepublicationofthispaper.


270

References

Esterhuizen,G2005,BCFV3.05Aprogramtopredictblockcavefragmentation,TechnicalReferenceandUser´sGuide.

Laubscher,DH1990,‘Ageomechanicsclassificationsystemfortheratingofrockmassinminedesign’,TransactionsA.Afr.Inst.Min.Metall,vol.90,Nº10.

Lee,M2012,CiresataRockStressEstimate,InternalMemo,CarpathianGoldInc.

Neubauer, F, Lips,A,Kauzmanov,K, Lexa, J& Ivascanu, P 2005, ‘Subduction, slab detachment andmineralization: The Neogene in the Apuseni Mountains and Carpathians’, Ore GeologyReviews,Elsevier,pp.13-44.

271


Identification of different geomechanics zones in panel caving- application to Reservas Norte El Teniente

P Landeros Codelco, ChileJ Cornejo Codelco, ChileJ Alegría Codelco, ChileE Rojas Codelco, Chile

Abstract

The identification of different geomechanics behavior has been a constant focus of interest for the rock mechanics engineering applied to planning and projects at El Teniente Mine, considering that most commonly used rock mass classification systems (i.e.: RMR, RQD and Q), represent similar values where most important exploitation sectors are taken place up to now. From that point of view, geologists have reached a good advance for establishing differences in terms of geotechnical aspects, mainly associated to vein infilling and in situ fragmentation.

Then, for the planning process, it is very important to be able to have a geomechanics model which interprets geotechnical characterization against mining exploitation on different scales, such as local scale (excavations) or global behavior (cavities).

In this study, a methodology for the analysis and evaluation of geomechanics behavior is presented, considering a case study with panel caving exploitation, emphasizing aspects like induced stresses, seismicity induced by mining, hydraulic fracturing and damages ahead the undercutting front.

Finally, discussion of results is focused on its relationships with geotechnical characterization and geomechanics guidelines for mine planning.

1 Introduction

ElTenienteMineincludesdifferentproductionsectors(seeFigure1),allofthemlocatedaroundachimneyof sub-volcanic brecciaswith an inverted cone shape, known as ‘BradenPipe’.ReservasNorte – alsoknownasSub6Sectoratthebeginning–islocatedonthenorth-easternside,anditsexploitationstartedin 1989 using conventional panel caving. Several rockbursts occurred during the 1990s and differentexploitationsequencesweretestedtoensurecontinuousoperation.Uptodate,ReservasNorteisminedbyadvancepanelcaving,consideringaproductionplancloseto40,000tons/day.Uptodate,thecaveatReservasNortemineisinasteady-statecondition,anditsgeometryconsistsofanactive700meterswideundercuttingfront.

OneofthemainconstraintotheexplotaitionatElTenienteisrelatedtothegeomechanicalhazards.Thishazardsalsomeanplanningconstrains(rateofdraw,….etc)thatdependsonthesectortobeanalised.InthisarticlewepresentthemethodologyusedatElTenientetodefinethegeomechanicalareasforplanningpurposes.These are based on an understanding of the geotechnical environment, understanding of theinducedstressesonmineinfrastructure,seismicpotentialandblastinginducedseismicity.Inthefollowingsections,eachcriteriaisdescribedfinalizingwiththedefinitionofzonesforReservasNorte´ssector.


272

Figure 1 Productive sectors and projects, El Teniente Mine (Business Plan 2013)

2 Geological and geotechnical data

PredominantlithologycorrespondstoAndesiteintrudedbythreemajororebodies:DacitePorphyryandAnhydriteBrecciaonthewesternsideandDioritePorphyry,onthecentralandeasternsides.Intermsofintact rockproperties, allof themareverystiffwithaverageYoung’sModulusapproximately60GPa.Ontheotherhand,intermsofrockmassqualityindexes,mostoftheseorebodiesareverysimilarandcompetentwithGSIintherangeof75to90andIRMRintherangeof55to62.Mostimportantgeologicalfaultsareclassifiedas“masterfaults”(faultsG,C,N1andN2)and“majorfaults”(faultsF,AND-1,AND-2,AND-3,AND-4andAND-5),seeFigure2.

3 Induced stress state

2.1 Numerical modelling

Thegeometryofthebrokenmaterialcavitycontrolsmajordifferencesintermsofinducedstressconditions.For the evaluationof this impact into theminingplan, comparative analysis aredevelopedbasedon atri-dimensionalnumericalanalysiswithalinearelasticboundaryelementsoftware,calibratedwithfieldinformationsuchasstressmeasurements,damageaheadtheundercuttingfrontandpropagationpressuresofhydraulicfracturing(Cuelloetal2010).ReservasNorte’snumericalmodelispartofabiggerminescalemodel,consideringcavitiesofdifferentproductivesectorsandlithologicalaspectssuchas‘BradenPipe’.Thismodeliscalibratedtogetagoodapproachofpreminingstressstatesindifferentzonesofthemine.Then,togetalocalstressconditionatReservasNortemine,improvedgeometriesareincorporatedsuchasspecificcavebackandundercuttingsurfaces,asshowninFigure3.

273


Figure 3 Mine scale numerical model (left) and local conditions for Reservas Norte sector (right), map3d code

Inputparametersaredefined,mainlyconsideringthreepredominantmaterials:BradenPipe,Andesitesandbrokenmaterial.Numericalmodellingstrategyconsiderstheutilisationoffieldinformationtocalibrateandvalidatethemodel.CalibrationprocessisdescribedbyCuelloetal2010,anditconsidershistoricalrecordofdamageaheadtheundercuttingfrontandpropagationpressuresobtainedduringthehydraulicfracturingprocess.DifferencesbetweenmodelestimationandfieldinformationareshowninFigure4.

2.2 Propagation pressures of Hydraulic Fracturing (HF)

Averagemagnitudeofthepropagationpressureobtainedfromthepre-conditioningprocessbyhydraulicfracturingallowsobtainingaverygoodapproximationofconfinementmagnitudesinsidetherockmass.Thisvaluableinformationisalsousedforcalibratingnumericalmodelling.

Geostatistical analysis have been developed using this information and a block modelling by krigingtechniques(CornejoandLanderos2013).ResultsforthezoneofinterestareshowninFigure5.

Figure 2 Plan view of major geological aspects, Reservas Norte mine, undercutting level (modified from Gonzalez 2013 and Gallardo 2013)


274

Figure 5 Propagation pressures of HF (left) and kriging results (right), from CornejoandLanderos2013

4 Analysis of seismicity induced by mining

3.1 Evaluation of seismic hazard

Fortheevaluationoftheseismichazard,theselectionofthevolumeofanalysiscovered375*10^6m3,includingbothReservasNorteandPilarNortesectors.AlltheeventsrecordedbytheseismicmonitoringsystemfromDecember2011toApril2013wereconsidered.

Figure 4 Severe damage criterion applied to undercutting layout, Reservas Norte (modified from Cuello et al 2010)

275


Initialfiltering is related to spatial locationof eachevent and thegeneraluncertaintyof its estimation.Then,adistanceisestimatedwiththe95%ofthedatalowererror.Bytheotherhand,modalmagnitudeisusedforestimatingminimalsensibilityoftheseismicsystemforthezoneinanalysis,inthiscase,thislocalmagnitude corresponds toMw=-0.7.Theobjectiveof the applicationof a spatial-timefiltering isto eliminate all the events that do not interactwith principal clusters, because their location is furtheraccordingtothedatabasecharacteristicsorbecausetheiroccurrenceintimeisnotrepresentativeofthereallatencyofthezoneofanalysis.Then,onlyeventswithaneffectiveinteractionareincludedontheanalysis,asshowninFigure6.

Figure 6 Seismic events considered for the analysis (left) and the spatial-time filtering applied (right)

Alltheselectedeventsareclusteredinalaterstageofanalysis,usingagglomerativehierarchicaltechniques,maximizingdistancebetweenclusters.Atfirst,adendogramisbuilttocalculatethedistancematrixbetweeneventsandthen,anoptimalnumberofgroupsaredeterminedwithat least250eventseach.ThewholeprocessisdescribedinFigure7.

Figure 7 Identified groups according to agglomerative hierarchical techniques


276

Rockburstdatabasewasusedasbaseinformationtocategorizetheseismichazard,where95%ofrockburstsarerelatedtolocalmagnitudeshigherthanMw=1.5andenergyreleasehigherthan10^6J.Thereisalsoahighcorrelationbetweenmagnitudes,energyreleasesandlocationofdamage.Then,adispersiongraphwasusedtoexaminetendencies,estimating50metersoflineardamage.

Finally,differenthazardlevelsweredefined,consideringtheoccurrenceofthoseconditions.Higherhazardlevelare related toalldefinedconditions;by theotherhand, lowerhazard levelsare related toclusterswherethereisnooccurrenceofmagnitudesandenergyreleaseshigherthanpreviouslyestimated,asshowninFigure8.

Figure 8 Zones identified with different seismic hazard

5 Seismic events after blasting

ThisstageoftheanalysisconsideredallseismiceventswithmagnitudehigherthanMw>0recordedinoneachsingleblasting.Severalassumptionsweredefinedasitfollows:

• Theradiusofinfluenceofeachblastingcorrespondsto100meterssphericallymeasured.

• Timeofinfluenceofeachblastingcorrespondsto24hoursafter.

• Spatialcoordinatesarereferredtothecentreforeachblasting.

The analysis was developed per year, and considering geotechnical zones as shown in Figure 9. Thegeotechbicaldomainsarebasedondifferentkindofveininfilling,establishinglimitsbetweenrockmasscharacteristics.InthisFigurealsothjemagnitudeandlocationofeventsareplotted.Thisshowsthatthe

277


blastingrelatedseismiceventsarelocatedtotheWestofthesector.Asthefrontmovestothewestsodidtheevents.

Ingeneralterms,itispossibletoidentifythatseismiceventsrelatedtoblastingeventswithhighermagnitudeshaveaverysimilarspatialdistributionduring2008and2009,whentheundercuttingfrontwerepositionedingeotechnicalzoneswithsimilarcharacteristics.Oncetheundercuttingfrontwasmovingaheadtothewest(years2010and2011),thefrequencyofthesekindsofeventsincreased.Asminingconditionscouldbeconsideredconstantsimilarly,theincreasecouldberelatedtoadifferentrockmass.

Figure 9 Seismicity recorded after blastings, including undercutting front at the end of each year (modified from Riquelme 2012 and Benado 2008)

6 Delimitation of different geomechanics zones

Theprocessofidentifyingareaswithdifferentgeomechanicalbehaviorincludesalltheparametersdescribedintheprecedingparagraphs,addingsomeoperationalaspectssuchasorientationofundercuttingfront,theextensionofthetransitionzoneandorientationofthedrifts.

Resultsarefocusonthedefinitionofpolygons,usedforthedailyoperationalactivitiesandtheshorttermanalysisdevelopedbygeomechanicsengineers,supportingtheoperationalprocess.AplanviewwiththezonesisshowninFigure10,wherexpolygonsweredefinedforgeotechnicalcontrol.


278

Figure 10 Plan view with the current operational polygons for geomechanics ground control (modified from Gallardo 2013)

7 Conclusions

Theanalysisandevaluationofgeomechanicsaspectsisarelevantfocusforminedesignandplanning.Theidentificationofdifferentgeomechanicsbehaviourisbasedonacomplementaryanalysisbetweeninducedstresses, seismicity inducedbymining,hydraulic fracturing,damages ahead theundercutting front andsomeoperationalaspects.

Thisprocessisiterativepermitstodefinepolygonswhichrepresentthebasefordailyevaluationofseismicactivity and for further analysis.These polygons are updated periodically by geomechanics staff ofElTeniente.

Acknowledgement

TheauthorswishtothankCodelcoChile,ElTenienteDivisionforallowingthepublicationofthispaperandtheGeomechanicsStaffthatsupplieddataandinformation.

References

BenadoD2008,‘Geotechnicalzonesbasedonveininfillingstockwork’,InternalReport,CodelcoChileElTeniente.

Cornejo, J, Landeros, P 2013, ‘Estimation of propagation pressures associated to hydraulic fracturingprocess, using geostatistics techniques, El Teniente Mine’, XVIII Symposium of miningengineering,Simin2013,UniversidaddeSantiago,Chile.

279


Cornejo, J 2013, ‘Identification of hazard zones, using seismic events clustering’,Msc thesis, miningengineeringdepartment,UniversidaddeChile.

Cuello,D,Landeros,P&Cavieres,P2010,‘Theuseofa3Delasticmodeltoidentifyrockmassdamagedareas in the undercut level at Reservas Norte sector’, Proceedings of 5th internationalconferenceondeepandhighstressmining,Santiago,Chile.

Gallardo,M2013,‘Polygonsforseismiccontrolandfrequencyeventscriterion’,InternalReport,CodelcoChileElTeniente.

Riquelme,O 2012, ‘Analysis and evaluation of the geomechanics behavior of high drawbell’,MiningEngineerThesis,MiningEngineeringDepartment,UniversidaddeSantiagodeChile.


280

Geostatistical evaluation of fracture frequency and crushing

SA Séguret MINES ParisTech, FranceC Guajardo Codelco, ChileR Freire Rivera Codelco, Chile

Abstract

This work details how to estimate the Fracture Frequency (FF) ratio of a number of fractures divided by a sample length. The difficulty is that, often, a part of the sample cannot be analysed by the geologist because it is crushed, a characteristic of the rock strength that must also be considered for the Rock Mass Rating. After analysing the usual practices, the paper describes the (geo)-statistical link between fracturing and crushing and the resulting method to obtain an unbiased estimate of FF at a block or point support scale. Some concepts are introduced: “True” FF, “Crushed” FF, crushing probability and crushing proportion. The study is based on a real data set containing more than 13,000 samples. An appendix gives a very general formal demonstration on how to obtain unbiased ratio estimation.

1 Introduction

One of themost important attributes used in theRockMassRating (RMR) is the Fracture Frequency(FF);aratioofanumberoffracturescountedbythegeologistdividedbythesamplelength.However,thecalculationisnotthatsimplebecauseitoftenhappensthatasignificantpartofthesampleiscrushed,making the fracturescounting impossible,andFFbecomes the ratioof twoquantities thatbothchangefromonelocationtoanotheroneinthedeposit,makingtheevaluationdifficult,whetheratsampleorblockscales-inotherwords,thisratioisnotadditive(Carrascoetal.2008).Togetaroundthisdifficulty,theusualpracticeconsistsofusinganadditiveformulathatcombinesfracturesnumberandcrushlength.Theaimofthispaperis:

• Analyzingthegeostatisticallinkbetweenfracturingandcrushing.

• ProposinganunbiasedwaytoestimateFF.

• Introducingtheconceptofcrushingprobability.

2 Formalization

Figure1showsasamplewithfracturesanddefinesthevocabulary.

Inthefollowing,allthesamplesaresupposedtohavethesamelength(1.5m).Forsimplification,onewillconsiderjustonelocation“x”(centerofgravityofthesample)forLNC,LCandNfract.ThequantitiesLNC,LCandNfract,countedby1.5mlength,areadditiveandcanbeestimatedbythebasicgeostatisticalmethodcalled“kriging”(Matheron1963).Nfractplaystheroleofa“fracturesaccumulation”,theequivalentofthe“metalaccumulation”inconventionalmining,i.e.theproductofthegradebythethicknessofthevein.

281


Figure 1 Scheme presenting the useful variables, Crush Length and Fractures Number

Thequantity:

fracttrue

NC

N ( )FF ( ) L ( )

xxx

= (1)

isthekeyfrequencyasitrepresentsthetruefracturesfrequencyinthenon-crushedpartofthematerial.

However,itisnotadditive:whenxmovesinthespace, fractN ( )x and NCL ( )x changeandtheaveragefrequencybetweentwomeasurementslocatedatx1andx2is:

fract 1 fract 2true 1 2

NC 1 NC 2

N ( ) N ( )FF ( )

L ( ) L ( )x x

x xx x

+=

+U

This latter ratio is equal to the averageof true 1FF ( )x and true 2FF ( )x only if NC 1 NC 2L ( ) L ( )x x= .

Therefore,adirect“kriging”of true 0FF ( )x foranyx0,usingsurroundingmeasurements trueFF ( )ix ,isnotpossible.

Thisisthereasonwhypracticesusetheformula:

fract Ccorrected

N ( ) aL ( )FF ( )1.5

x xx += (2)

InEquation(2),thecoefficient“a”representsanarbitraryquantitysupposedtogivemoreorlessimportancetocrushingincomparisonwithfracturing(a=40inourcase).Bythisway,thegeotechnicianincorporatestheinformationgivenbycrushing.Equation(2)hasalsotheadvantageofcombiningadditivequantitiesthatcanbeestimatedseparatelyandthencombined:

(3)fract C

* *

corrected

N ( ) .L ( )F̂F ( )

1.5x a x

x+

=


282

InEquation(3),theexponent“*”denotesvariousestimates.

Tounderstandwhatthecoefficient“a”represents,letusdevelopEquation(2):

NC C NC Ccorrected

NC C NC C

L ( )FF ( ) L ( ) L ( )FF ( ) L ( )FF ( )FF ( )L ( ) L ( ) L ( ) L ( )

true true crushedx x x a x x x xxx x x x

+ += =

+ +(2’)

Presentedinthisway,Equation(2’)appearsasanadditiveformulacombiningtwofrequencies:“a”being

theoneassociatedwithcrushing(nowwritten crushedFF ).Thislatterquantitymustbeatleastgreaterthan

anyobservableFFtrue andwewilldetailthispointinthefollowing.

First,letusanalysethelinkbetweenfracturingandcrushing.

2 Observation of a natural phenomenon

Westartbytheexaminationoftwosamples:

Figure 2 Two samples: Few crushing and fractures (a) and important crushing, numerous fractures (b)

Figure2apresentsadrillcorewherethecrushlengthisonly11cmwithjustonefractureinthenon-crushedpart;Figure2bpresentsthecontrary:crushlengthisimportant(74cmover1.5m)and16fracturesintheremainingpart. Is it aparticularexampleor is therea statistical linkbetweenNfractandLc?Wehaveanalysed13,000samples(1.5mlength)comingfromanundergroundmineina1000x2300x1000m3boxalongx,y,z.(Figure3).

283


Figure 3 Planes presenting projections of the data

ThescatterdiagrambetweenNfractandLC(Figure4a)leadstomixedconclusions:

• Thecorrelationcoefficientisimportant(0.75).

• 70%ofthepopulationliesinsidetheconfidenceintervaldefinedbytheconditionalexpectationcurve,theremainingpartdoesnotpresentsignificantcorrelation.

4 True frequency estimation

Figures4b,4c and4dpresent, respectively, thedirectNfractvariogram (Matheron1962,or apossiblealternativecalculationgivenbyEmery2007),Lcvariogramandtheircrossvariogram.Allthesevariogramscanbemodelledbyauniquemodel,up toamultiplicativefactor, inotherwords,NfractandLcare inintrinsiccorrelation(Wackernagel1995).

Twoimportantconsequencesresultfromthisexperimentalproperty:

• Itisnotusefultousecokriging(Wackernagel1995)forestimatingNfractorLc.

• Theratioofbothestimatesobtainedbykrigingisnonbiased(seeAppendix).

ThislatterpropertyleadsimmediatelytothemethodforestimatingthenonadditivequantityFFtrueatablockscaleVlocatedatcoordinatesx:

(4)fract

NC

*true

N ( )FF ( )

L ( )

Kx

x Kx

VV

V=


284

Figure 4 Scatter diagram between crush length (Lc, horizontal axis) (a) and Fractures number (Nfract). Line represents the linear regression of Nfract against LC, and the conditional expectation curve. Red

dotted lines represent the standard deviation around the conditional curve. Resp. Nfract, Lc, and Nfract cross Lc variograms. Points are experimental, continuous curves the intrinsic model (all the variograms are

proportional) (b-c-d)

InEquation (4), exponentK denotes the estimate of the variable by kriging, using a set of around 50surroundingsamplesthatchangewhenthelocationxchanges(”movingneighbourhood”,Chilès&Delfiner1999).Thesamplesusedfornumeratoranddenominatormustbethesametopreservethenonbiasoftheratio.

Figure5apresentsamapof*true

1FF ( )xV ,whenVx issized10x10x9m3.Geotechniciansprefer the

reverseofthefrequencybecauseitrepresentstheaveragesizeofnon-fracturedcore.Whenthisquantityissmall,thestrengthoftherockisbadandalowRMRisassociatedwiththeblock.Anotherconsequenceofintrinsiccorrelationbetweenbothtermsoftheratioisthatestimatingtheratiooritsreverseisthesameproblem.Generally,thisisnotthecase.Forexample,thereverseofanadditivegradeisnotadditive.

5 Crushing percentage or probability

Equation(4)isaratiooftwoestimationsthatcanbeusedseparately.Whenwedividethedenominatorbythesamplelength,wecanobtainanunbiasedandoptimalestimateofthecrushingproportion:

285


(5)

Figure 5 Map of inverse True Fracture Frequency using block kriging (a). Map of inverse Usual Fracture Frequency that incorporates crushing estimate and arbitrary frequency for crushing equal to 40 (b). Same as (b) but with crushing frequency inferred from statistics and set to 80 (c). Crushing proportions at block scale

estimated by kriging (d)

Figure5dshowsacrosssectionoftheresultwithimportantcrushingproportionsattheWestofthedomain,thatcorrespondtoawellknowndamagezoneduetoamajorfault.

6 Usual formula improvement

The intrinsiccorrelationbetweencrushingandfracturing leads to theoptimalandunbiasedestimateofformula(2)atblockscale,forexample:

(6)

Figure5bshowsacrosssectionof ,acombinationofFigure5aandFigure5d,withtheresultthattheWestdamagedzoneisreinforcedbyaccountingforcrushingproportions.


286

7 Crushing frequency inference

Equation(2’)showsthatthecoefficient“a”usedinEquations(2)and(6)playstheroleofafracturefrequencyassociatedwithcrushingandnamedFFcrushed.Inourcase,forsomereasonsunknownwhenwritingthispaper, thisquantitywasset to40andthequestionis:couldthisparameterbeobtainedexperimentally?Letusconsider the scatterdiagrambetweenLcandFFtruecalculatedusing the13,000 samplesatourdisposal(Figure6).

Figure 6 Scatter diagram between crush length (Lc, horizontal axis) and FFtrue as defined by (1). Solid line represents the conditional expectation curve; dotted segment represents a conservative extrapolation

When Lc increases, FFtrue increases, this is a consequence of the correlation between crushing andfracturing(thenumberoffracturesareinaveragemorenumerouswhencrushinglengthisimportant).TheincreasingrateisnotlinearbuthyperbolicbecausewedivideNfractbyaquantitythattendstozerowhenLcincrease.

Ifwesupposethat:

ThecrushingphenomenonappearswhenFFTrueishigh,FFcrushed>FFTrue.

Onaverage,FFcrushedisindependentfromLC,thenFFcrushedcanbecharacterisedbyitsaverage(referencetotheconditionalexpectationcurve)andmustbeatleastequaltothelimitofFFTruewhenLCtendsto1.5m.Figure6showsthatFFTrue=40forLCaround1m.Thereisstillapartofthesamplethatisnotcrushed,incontrarytotheprevioushypothesisandFFcrushedmustbeatleastgreaterthanthemaximumofE[FFtrue|LC]wecancalculate,here50atLc=1.14m.Ifwemakeacrudelinearextrapolationofthecurveweobtain,forLc=1.5m:

FFcrushed>FFTrue=85(7)

Aseveryextrapolation, this result is extremely sensitive to thehypothesison thenon linear regressionmodeling.ThemappingoftheFractureFrequencyobtainedwhenwereplace45by85inEquation(2)ispresentedinFigure5c.Comparedtothemapusingthetraditionalformula(Figure5b),theWestdamagezoneisreinforcedbecausetheinfluenceofcrushingismultipliedbymorethantwo.

8 Conclusions

Analysisofusualpracticesandpropertiesof the twovariables involved in theFractureFrequency: theCrushLengthandtheFractureNumber,doesnotrequireinclusionofbothquantitiesinasinglearbitrary

287


formula.Analysis of adata set showed that bothvariables are statisticallyhighly correlated aswell asspatiallyandtheysharethesamevariogram.Thiscircumstancemakespossibletoestimatedirectlytherealinterestingquantitythatistheratiooffracturesnumberdividedbythesamplelengthtoshortcutthelackofadditivityofthisratio.Theresultingestimateisunbiased,abasicrequirementwhenevaluatingaquantity.

Ontheotherhand,thecrushingphenomenamustbeestimatedseparately,givingacrushingproportion(atblockscale)oracrushingprobability(atpointsupportscale)thatmustbeincorporatedinRMRinthesamewayasFFandothergeotechnicalattributes.

AllthesepossibilitiesdependdirectlyonthemutualbehaviourofFracturesNumberandCrushLengthandanystudyonthesubjectshouldstartbythegeostatisticalanalysisofthesetwovariables.Amoredetailedanalysisof their link,andanothercase study thatwillbepublished in thenext future, showed that thepresentobservedcorrelationisnotduetohazard:fracturingsometimecontributestocrushing,sometimenot, dependingon themutual organizationof the fractures. Finally,with such studies,we evaluate themechanicalpropertiesoftherock.

Acknowledgement

TheauthorswouldliketoacknowledgeSergioFuentesSepulveda,VicePresidentoftheProjectsDivisionofCODELCO,Chile,andhiscompany,fortheirstrongsupportintheimplementationofgoodgeostatisticalpracticesalongthecopperbusinessvaluechain,aswellasanonymousreviewerswhogreatlycontributedtoimprovingthequalityofthemanuscript.

Appendix: Unbiased ratio estimation

ConsiderZ1(x)andZ2(x), twounknownvaluestobeestimatedusingasetof2nmeasurements{Z1(xi),Z2(xi),i:1,n}.Let“*”denoteanyestimateandwianyscalars.If:

(8)

thentheratioisunbiasedonaverageifweassumeitsorderonestationarityattheneighbourhoodscale.

Proof:

(9)

If“*”isKriging(whetherOrdinaryorSimple,Rivoirard1984),withthesamevariogramforZ1andZ2andsamesamplelocationsforbothvariables(isotopy),thentheratioisunbiased.

Proof:

Asthekrigingweightsλiareidenticalforbothtermsoftheratio,wehave

(10)


288

with

(11)

Equation(8)isverified,theratioisunbiased.

References

TheauthorsdidnotfindanyreferenceconcerningFractureFrequencyestimationinGeostatistics,whichessentially focus on fracture network characterization and simulation (Chiles 1999).They notice somepapersmentioningtheuseofArtificialNeuralNetwork(Fitgerald&Al1999)andcrushingphenomenaisneverstudiedasaRegionalizedVariable.

Carrasco,P,Chilès,JP&Séguret,SA2008,‘Additivity,MetallurgicalRecovery,andGrade’,inProceedingsofGeostatistics-theEighthInternationalGeostatisticsCongress,ed.Emery,X.,vol.1,pp.237-246.

Chilès,JP&Delfiner,P1999,‘Geostatistics.ModelingSpatialUncertainty’,Wiley,703p.

Chilès,JP&deMarsily,G1999,‘Stochasticmodelsoffracturesystemsandtheiruseinflowandtransportmodeling’,inFlowandContaminantTransportinFracturedRock,eds.AcademicPress,SanDiego,Ca,Chapter4,pp.169-236.

Emery,X2007,‘Reducingfluctuationsinthesamplevariogram,StochasticEnvironmentalResearchandRiskAssessment,vol.21(4),pp.391-403.

Fitzgerald,E.M.,Bean,C.J.,Reilly,R.,‘Fracture-frequencypredictionfromboreholewirelinelogsusingartificialneuralnetworks’,GeophysicalProspectingJournal,vol.47,Nº6,pp.1031-1044.

Matheron, G 1962, ‘Traité de GéostatistiqueAppliquée’, Tome I, Mémoire du Bureau de RechercheGéologiqueetMinières,No14,EditionsTechnip,Paris,France.

Matheron,G1963,’PrinciplesofGeostatistics’,EconomicGeology,vol.58,pp.1246-1266.

Rivoirard,J1984,‘Lecomportementdespoidsdekrigeage’,DoctoralThesis,E.N.S.desMinesdeParis.

Wackernagel,H1995,‘MultivariateGeostatistics’,Springer,Berlin.

289


Geomechanical ground control in block/panel caving

J Díaz DERK Ltda., Chile Y Sepúlveda DERK Ltda., Chile P Lledó DERK Ltda., Chile

Abstract

Numerous underground mining projects that use caving methods will be in operation in the next 10 years. For example, we have the case of Chuquicamata Underground Mine (2018) and El Teniente New Mine Level (2017) in Chile, Oyu Tolgoi in Mongolia (2015), and Grasberg Block Cave (2017) in Indonesia, among other smaller projects. Basis for their success is both the constructive capacity of their mining and major infrastructure works in the initial-phase and in the projects’ operation. On the other hand, currently, there are many mining sites operating that apply caving methods, which have faced different geotechnical contexts and various operational challenges, where the ground geomechanical control has been fundamental in the mine development and a complement to operational decisions. This geomechanical control directly receives the acquired experience, both bad and good, of what we are doing as a mining activity.

A field geomechanical engineer has the main goal of controlling the potential deviations from the various operational activities that directly influence the geotechnical design and planning parameters defined in the previous project study stages and, in turn, facing the short-term geomechanical problems that typically arise from the daily field activities.

From the geomechanical viewpoint, three general lines associated with the field control work can be established; namely, (1) Control line associated with Mine Preparation (developments – construction); (2) Control line associated with Mine Production activities (short-term planning and mining), both in a short-term horizon, and finally, (3) Control line associated with the Principal and Permanent Infrastructure in a short and medium term horizon.

This work consolidates and summarizes the major activities to be executed by a field geomechanical professional during a mine shift, the types of outputs or deliverables of his technical work to be normally used during the caving of a sector and the resources that must be available for its adequate field geotechnical or geomechanical execution.

Finally, this work includes a general description of the most adequate technical profile for the professional that will be in charge of the field geotechnical or geomechanical control in a short and medium term horizon.

1 Introduction

Asshown inFigure1,Geomechanics identifies threemajor focusesor“clients”:Planning,DesignandMineOperations,thelatteronebeingthecontextofinterestforthiswork.


290

Figure 1 General diagram with main focus on the Geomechanics discipline in caving mining

Ingeneral,thesuccessfulpreparationandexploitationofaminingprojectaccordingtoqualitystandardsandingeomechanicaltermsisbasedontheadequateandpermanentcontrol,recordingandanalysismadeonthemine’sshorttermactivities.

Theobjectiveistoknowandcontroltherockmassandcavebehavioursincetheirbeginningandtherefore,proactivelydetecting,evaluatingandcorrectinganysignofdeviationthatcouldcauseanytypeofpotentialgeotechnicalhazard thataffects thenormaloperation, safetyandcontinuityof themethod’sproductiveprocessanditsassociatedmajorinfrastructure.

Hence,theroutinevisualinspectionisvitalintheworkofafieldgeomechanicalengineer,whichmustbecarriedoutonadailybasis,supportedbyinstrumentsandgeological–geotechnicalcharacterizationforanadequaterecordingoftheinformation.

This visual inspection is developed in all the mine levels and on the surface with the frequency andperiodicity associated to the importanceand timingof the facilities.That iswhy the inspections at thestrategic levels and at the highly geotechnically exposed levels (i.e., ventilation levels, size reduction,mainaccesses,permanenttransportationandinfrastructure,amongothers)areusuallyscheduledinmoreextendedperiods,accordingtothegeomechanicalcontextofeachsiteandthatusuallycanbe:onceaweek,onceamonth,onceeverysemesterandevenonceayear.

2 Operational geomechanical control lines

BasedontheschematicinFigure1,wecanestablishthreegenerallinesrelatedtofieldcontrol,inparticular:

1. ControllineassociatedtoMinePreparation(developments–construction).

2. ControllineassociatedtoMineProductionactivities(Shorttermplanninganddrawrate),bothinashorttermhorizon,andfinally

3. ControllineassociatedtoPrincipalandPermanentInfrastructureinashortandmediumtermhorizon.

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Thefirstgeomechanicalcontrol lineconsidersactivities related to thefieldofconstructionqualityofadesign,suchas:

• Marking,drilling,blastingandnumberofdrawbellcommissioningstagestocontroldamageandachievedesigns(i.e.,rockbrowwidthatdrawpoint,drawpointsupportdamage,etc.)

• Marking,drilling,blastingandamountofcavingrunstocontroldamageandachievedesigns(i.e., prevent remnant pillars and support damage at the undercutting and production level,preventgeometricalsingularitiesintheundercuttinglevel,etc.).

• Reviewof the topographical controlof theadvance in theproductionandundercutting levelinfrastructure(miningpattern),specifically,pillaroverbreak.

• Systematic and definite advance support installed: pre-mining zone, and transition zoneestablishedinanas-builtgroundsupportdrawing.

Thesecondgeomechanicalcontrollineconsidersactivitiesrelatedtothefieldofplanning,suchas:

• Advance sequence management, mainly to allow the evaluation of the undercutting frontgeometryandorientation.

• Strategytominethemineralizedandinsitucolumntoassessthecavebackgeometryandcavingpropagation.

• Subsidencepropagationandbehaviour;craterandinfluencezones.

The thirdcontrol linespecifically refers to theperiodicalcontrolof thestatusof theserviceandmajorinfrastructureworkthatisoperationaland/orinapreparationstageinashortandmediumtermhorizon.

Awelldocumentedand timelyfieldgeomechanical controlwill favourcontinuous improvement in themedium–longtermmining,especiallygivingafeedbacktothedesignandmineplanningareas(Figure2).

Figure 2 Feedback from operational planning to the short, medium and long term planning and mine design

3 Deliverables and activities

Thefieldgeomechanicalcontrolconsiderstheobservationandrecordingofvariouspiecesofinformation.If the information is timely analysed and evaluated, it allows improvingmining and foresee importantgeotechnicalhazards.Inthefollowing,wedescribethemainactivitiesanddeliverablesassociatedtotheoperationalgeomechanicalwork:


292

1. GeomechanicalBehaviourAs-BuiltDrawing:Thisdrawingcorrespondstotherecordingoftheexcavationbehaviourintermsofassociateddamageandpresenceofabnormalconditions,suchasdrawpointhang-ups,browdamage,notoriousoverbreak,loadedpillars,etc.Damagecanbeobservedbothintheinstalledgroundsupportandintherockmass.Theobjectiveistoqualifyand evaluate the typeof damageor condition and their evolution through time to determinetheirpotentialcausesandtoadoptremedial,controlorrestrictionmeasuresintheoperationalactivities.Itisimportanttoverifyifthereisanyrelationbetweenthedocumentedbehaviour,themininginthesectorandthepresenceofanygeotechnicalhazardthatistypicalofthecavingmethod.Monthlydelivery.

2. Geomechanical Hazards As-Built Drawing: This drawing maintains the records of all thegeomechanicaleventsorhazardsoccurredduring themine’s life.Thisallowsthe traceabilityof theinformationtomakeevaluationsatdifferentperiods(retrospective,presentandfuture)thatcancontributetotheidentificationofanycharacteristicphenomenon,theirconditionsandtherefore their potential solutions.These drawingsmust include the potential geomechanicalhazards(i.e.,collapses,airblasts,rockbursts,etc.)identifiedbytheProjectandthatneedtobevalidatedperiodbyperiodwithupdatedgeological–geotechnicalinformation.Monthlydelivery.

3. GroundSupportAs-BuiltDrawing: Thisdrawingcorresponds toadetailed recordofall thegroundsupportinstalledbothintheadvanceanddefiniteexcavationsandpillars.Thisdrawingspecifies the type of support system and element, amount, distribution and their relevantcharacteristics.Basedontheserecords,anevaluationismadetoknowifwhathasbeeninstalledactuallycorrespondstowhatisestablishedinthedesignguidelinesornot.Ifthereisanydeviation,itmustbereportedandcorrected.Astheformerone,thisrecordordeliverablecanalsobeusedforlaterstudiesandretrospectivegeomechanicalhazardanalyses.Monthlydelivery.

4. DrawbellCommissioningControl:Thiscontrolconsistsofrecordingthedrawbelldevelopmentstatus, its characteristics (commissioning in 1, 2 or 3 stages), location and amount. Thisinformationallowscontrollingthecommissioneddrawpoints,theachievementofthedefineddesign(i.e.,browwidth)andthedrawbellcommissioningrateinagreementwiththeestablishedminingplans.Weeklyorasrequireddelivery.

5. UndercuttingControl:This control consists of recording the undercutting front advance, thecharacteristicsandconditionsofthecommissionedarea.Thisimpliescontrollingtheamountofblastedrounds,amountofassociatedexplosives,roundcondition(chargedandcutblastholes),type of undercutting (low or high), undercutting design, and blastholes firing diagram. Inaddition,thisinformationallowscontrollingtheundercuttingfrontgeometricalcharacteristics,presenceofremnantpillarsandassociationofpotentialdamageduetotheexecution,andtheareacommissioningrate,characteristicsandamount,inagreementwiththeestablishedminingplans.Dailydelivery.

6. Review of Development Advances: This implies having available a development progressdrawing with the condition of the daily advance fronts’ condition to detect any instabilitycondition or failuremechanism that recommends applying preventative or controlmeasures.Inaddition,thisreviewincludesthetopographicalactivityrecordsinordertocharacterizetheexcavationsdeveloped,whichallowsdetectingpotentialoverbreaks,underbreaksand/ortheirdeviations.Thiscouldinfluencetheinfrastructureconstructionqualityinthelevels,namely,theconditionof thedrawratemodule (excavationsandpillars) in theproduction level.Monthlydelivery.

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7. DrawControl:Thiscontrolconsistsofrecordingthedrawatthedrawpointstocreatedrawiso-curves,toverifythelowandhighdraw,executedratesinthefrontperimeterandinthecavingregimezones.Thedrawrateisaparameterthatisrelatedtoseismicactivityandthegeotechnicalhazardpotentialincaving.Weeklydeliveryandmonthlysummary.

8. FragmentationControl:Thiscontrolconsistsofrecordingtheparticlesizebymeansofdigitalphotographyatthedrawpointsinasystematicmanner,correlatingitwiththedrawpointlifestage, associated column height and draw rate.Occasionally, it’s necessary to create controlsheetsforeachdrawpointandmonthlycontrol.Weeklydeliveryandinaccordancetothedrawrate.

9. SubsidenceAdvanceControl:Thiscontrolconsistsofrecordingthedifferenttypesofdamageassociatedtosubsidencezones.Ideally,thisrecordingmustbemadeinoldworkingslocatedinthesub-surfaceintermediatezoneandonthegroundsurface.Thisinformationallowscontrollingthecratergeometry, itsbehaviour, influencezoneandcorrelatingitsgrowthwiththeexistingmining activity. If possible, this work must be supported by semi-annual or annual ortho-referencedand/orsatelliteaerialphotography.Semestralandannualdelivery.

10. SeismicityReview:Thisreviewimpliesupdatingandreviewingthedaily,weeklyandmonthlyseismic records statistics and trends. This type of instrumentation and information allowsanalysing the behaviour in zones that can’t be observed by the “human eye”, namely, thebehaviourofthecavebackzoneintheareasandtheirrelationshipwithdrawratesandcavingpropagation.Daily,weeklyandmonthlydelivery.

11. BlastingDamageCharacterizationandEvaluation:Inspecificsituations,mainlywhenthereisnodedicatedservicetocontroltheblasting-inducedvibrations(developmentandproduction),itisnecessarytohavethefieldGeomechanicalEngineercarryingoutsomemeasurementsandnearand/orfarfieldvibrationanalysis,asrequired,toevaluatetheeffectsofthedevelopmentandproductionblastingintherockmassandsurroundingexcavations,inadditiontotheconstructionand/orcalibrationof thedamagemodelsavailablefor thegeotechnicalunits.Thiscommonlyhappenswiththedrawbellcommissioningblasting(phases)orcaving(undercutting)blastingthatcreatespotentialdamagesintheproductionandundercuttinglevelpillarsandexcavations.Deliveryfrequencyaccordingtostudies.

12. ReviewofGeotechnicalInstrumentation:Thisimpliesbeingupdatedandreviewingthestatisticsand trends of the installed instruments’ monitoring on a daily, weekly and monthly basis(productionlevelpillars,undercutting,pre-miningzone,transition,de-stressing),basedonthemonitoredparameters:deformation,displacement,andstressstate,amongothers.

Figure3showsthedocuments’schemeforthedeliverablesdevelopedbytheoperationalgeomechanicalcontrolandtheirdeliveryfrequency.Thisexampleunderlinestheimportanceofincludingtheoperationalgeomechanical works in the mine integral management system through each one of the updated andavailableworkingprocedures(DERK2013).


294

Figure 3 Document management examples for the operational geomechanical control in caving (DERK 2009; Díaz 2010; DERK 2011)

3 Resources required

Thissectionprovidesasummaryofequipment,instruments,toolsandaccessoriesthatmustbeincludedinthebasicresourcesthatmustbeavailableforthefieldgeomechanicalcontrolpersonnel:

• Personal computer with Windows platform, with Office, Grapher and Autocad softwareapplications.

• Softwareforfragmentationestimationfromdigitalphotographyprocessing.

• Tabletfordamagedigitalmappingandfieldgeotechnicalinformation.

• State-of-the-artpersonalprotectionequipmentand/orelements.

• Compassand50metermeasuringtape.

• Digitaldistancemeter.

• Photocameraandhigh-resolutiondigitalfilmcamera.

• Longrangeflashlights.

• Tripodhalogenfloodlights.

• Geologyhammer.

• Triaxialgeophonesand/oraccelerographsandcables.

• Seismograph(forexample,MinimatePlusEquipment).

• BoreholeCameraequipment.

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• Adhesiveagentasrequiredbyhumidityandpollution(forexample,Poxipol).

• LaserScannerequipment(forexample,MineScanGeosite).

• Logbookinthreecopies.

• 4-wheeldrivediesellighttruckequippedwithradiobroadcasting.

• 1fieldtechnicalassistant.

• Drillholeliftingequipment(drillholedeviation).

4 Technical profile

There are several technical aspects thatmustbeknownby theprofessional thatwillworkon thefieldgeomechanicalcontrolorintheoperationalGeomechanics.Inparticular,he/shemustknowandmanageconceptsrelatedtothefollowing:

• Operationalprocessesinaminethatusescavingmethods.

• MineCavingplanninganddesign.

• Geologyandgeotechnicalaspectsassociatedtocaving.

• Inducedseismicity.

• Civilworksconstruction.

• Management of support computer tools for geomechanical analysis and evaluation; i.e.,Rocscience,Autocad,Grapher,etc.

• Geotechnical instrumentation; i.e., convergence stations, extensometers, stressmeasurement,TDRs,amongothertypicalinstrumentsapplied.

• Vibrationcontrolandmodellingofblasting-induceddamage.

The professional shall preferably be a Mining Civil Engineer, with minimum general experience inundergroundminingfrom2to5years,andwithspecificexpertiseinGeomechanicsforcavingminingofatleast1to3years.

5 Conclusions

Theshort-termgeomechanicalknowledgemanagementanddocumentationinaminesiteistheresponsibilityoftheoperationalGeomechanicsanditisofvitalimportanceforitsmediumtolong-termsustainability.

An efficient and constant communication among the mine operations areas, planning, geology andGeomechanicsisfundamentaltosuccessfullymineaproductivesectorandtocontrolitslosses.

The on-going training of the mine personnel on operational Geomechanics increasingly reduces thedeviations that can take to geotechnical hazards that affect safety and the continuity of the productiveprocess.

Theoperationalgeomechanicalmanagementmustbeincludedinthemine’sintegralmanagementsystemsthroughitsupdatedandavailableworkingprocedures.


296

Acknowledgement

DERK Ltda. gives thanks to its geomechanical teamwhoworked with a high quality in El SalvadorDivision’contracts(CodelcoChile)since2009to2013.

References

DERK2013,SistemadeGestiónIntegralDERKIngenieríayGeologíaLtda.

DERK2011,Serviciodeasesoríaintegralengeología,geotecnia,geomecánicayplanificaciónminera–DivisiónSalvador,GerenciadeOperacionesMinas-Plantas,SuperintendenciadeGeologíayPlanificaciónMinera-Metalúrgica,DivisiónSalvador–CodelcoChile.

Díaz,J2010,Productostécnicosserviciogeomecánicaoperacionalminasubterránea(SGOMS),DERKLtda. PR-DSAL_SAEG_MS-P02-01-2011, Servicio de Asesoría Integral en Geología,Geotecnia,GeomecánicayPlanificaciónMinera–DivisiónSalvadordeCODELCOCHILE.

DERK 2009, Servicio de asesoría integral en geotécnia y geomecánica –División Salvador,Gerenciade Operaciones Minas-Plantas, Superintendencia de Geología y Planificación Minera-Metalúrgica,DivisiónSalvador–CodelcoChile.


297

Gravity Flow

299

Gravity Flow

Use of experiments to quantify the flow-ability of caved rock for block caving

RE Gómez, University of Chile, ChileR Castro, University of ChileD Olivares, University of Chile, Chile

Abstract

Block/panel caving mining is a massive underground method, in which an ore column of broken rock is generate above the production level, as the cave propagates upwards through the ore body. ,As reserves deplete from near surface, the next generation of block caves will be carried out in deeper condition than currently known, with large column heights and therefore higher vertical stresses. There are unknowns related to the flow characteristics that deeper caves would face. The aim of this study is to quantify the impact of large vertical pressure on the flow-ability of fragmented rock. For this reason, experiments representing the stress and geometry conditions of deep caves were conducted under a range of vertical pressures, materials and humidity conditions. The results indicate that the flow-ability of caved rock depends on the vertical stresses, fines content and humidity conditions.

Keyword: caving mining, flow-ability, hang up, vertical stresses, fines and humidity content.

1 Introduction

In block/panel caving, ore production is affected by interferences associated with the caving process,especially,thoserelatedtothegravityflow,suchas,hang-upsandoversizerocks.Theminedesigncapabilitytoprovideagivenproductionrateisaffected,amongotherfactors,bytheoreflow.

Flow-abilityisdefinedas theflowconditionorabilityofagranularmaterial toflowunderagivensetofmaterial properties, infrastructure geometry and stress conditions.The flow-ability can be classifiedinto free flow, intermittent flow, assisted flow and no-flow (Castro 2014).Kvapil (2008) indicates thatflow-abilitydependsonmanyparametersincludingparticlesize,extractionrate,particlesshape,surfaceroughness between particles, friction between particles,moisture content, compressibility, compaction,particleresistance,andmagnitude,distributionanddirectionofexternalloadsandforces.However,despitebeinglisted,theflow-abilityunderallthosesetsofparametershasnotbeenquantified.

Flow-ability could be characterised both qualitatively and quantitatively. In terms of qualitativecharacterization,theflowcouldbequalifiedasfreeflow,intermittentflowandnoflow,dependingontheratiobetweenparticlesizeandopening(Laubscher2006).Studiesongravelhaveshownalsothattheflow-abilityofgranularmaterialisinfluencedalsobytheverticalload(Fuenzalida2012).Castroetal.(2014)haveproposedaflow-abilitychartforcoarseanddryrock,whichispresentedintermsofverticalstressanddrawpointwidth/d50.


300

Figure 1 Influence of vertical stress in flow-ability

Inquantitativeterms,flow-abilitycanbecharacterisedin termsof thenumberofhang-upsevery1,000tonsorbrokenrockdrawn.Hangupsareoneoftheflowinterferencesthataffectproductivity.Moreover,hangupscanbeusedtomeasuretheflow-abilityofmaterialbecausewhenahangupoccursitmeanstheflowofmaterialhasbeeninterruptedandthebrokenrockcannotbeextracted(Troncoso2006).Twokindsofhang-upscanbeformedincoarsematerial:cohesiveandmechanical(Kvapil,2008.Beusetal.,2001;Hadjigeorgiou&Lessard2007).Theformationofarchesonaroughwallisgeneratedbytherotationoftheprincipalstressesonthewallandbyinducedwallpressures(Handy1985).Thedimensionsofthearchdependsonthefrictionangleofthematerial,depthorverticalstress,inclinationofthewallsinadrawpoint,drawrate,shapeandstrengthoftheparticles,andhumidity(Kvapil2008).Atthemineithasbeenobservedthatasmorematerialisextractedfromadrawpointthefrequencyofthehangupsdecreases(Maass2013).Thisphenomenonisprobablyrelatedtothedecreaseoftheparticlesizeduringtheextractionofanorecolumn(Montecino2001).

Therearemanyunknownsrelatedtotheflowofmaterialsespeciallyunderconfinedconditions.Forexample,whatistheroleofthefines,waterandstressesontheflow-abilityofthebrokenrock.Inthisarticle,wepresenttheexperimentsconductedtoevaluatetheflow-abilityofcavedrockunderhighverticalloadfordifferentfines andhumidity conditions.Extraction is carriedout by a scaledLHDsystem to representcurrentcavingcharacteristics.

2 Laboratory scaled model and material characterization

2.1 Experimental set up and materials

Theexperimentswereconductedinasetuptostudyconfinedflow.Thisconsistofasteelcylinder,whichisfilledwithbrokenrock(70-80kgofcrushedore)underahydraulicpressmachinewithacapacityof1,800kN.Thesteelcylinderhasdiameterof340mm,asshowninFigure2.Acylindricalshapewaschosentoavoidtheconcentrationofstressesatsingularities.Theheightofthedesignedcylinderis700mminordertoholdthedesiredvolumeofgravelandtosuittheemulatedAndinaminedrawbellwithascaleof1:75(Figure2b),witharectangularopeningof53x96mm2.Sincethedrawbellislocatedinthecenterofthecylinder,flowzoneswillnotintersectthewallsofthemodel.AsteelextractionsystemwasbuilttoreplicatetheextractionthesameasLHDdoesfromanextractionpoint(Figure2a).

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Gravity Flow

Figure 2 Experimental equipment: (a) cylindrical model in a press machine which changes the vertical load, σV, and (b) extraction system, located in the bottom, center of the model

Thematerialusedintheexperimentaltestswascrushedsulphideorewithahighaspectratiotorepresentthegeometryofcavedrock(sphericityof0.58andaroundnessof0.25).Twodifferentparticlesizedistributionsofthismaterialwerepreparedandtested:onewithapassingsized80of11.8mmandtheotheronewithad80of15.6mm.Bothsampleshavethesameuniformitycoefficient(Cu=d60/d10)of2.Thoseparticlesizedistributionswerescaledfromthesizedistributionoftheprimaryfragmentationcurveoftheunderground´sChuquicamataproject(Figure3).Table1summarizesthecharacteristicsofthesamples.

Figure 3 Particle size distribution of samples used in the experiments


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Table 1 Samples characteristics

*Drawpointwidthfornon-squaregeometrycanberepresentedbyhydraulicdiameter(Jennings&Parslow1988).

2.2 Experiments

A total of 18 experiments have been carried out to date described inTable 2.Ten experimentswereperformedwithoutfinesorhumidityinordertodefineabasecaseconsideringdifferentverticalloads.Thenhumidity andfineswere added to the samples tomeasure their impact on theflow-ability. Finematerialusedinthisstudyhadauniformdistributionwithd100equals1mm.Humidityusedinthisstudywas1.5litersper10kgoffinematerialthatis15%ofwater.

Table 2 Summary of experimental conditions

Test Material Vertical load σv [MPa]

Humidity [%] Fines [%] Size d80

[mm]1

A1

0 0 0 11.82 1.5 0 0 11.83 3 0 0 11.84 6 0 0 11.85 10 0 0 11.86

A2

0 0 0 15.67 1.5 0 0 15.68 3 0 0 15.69 6 0 0 15.610 10 0 0 15.611

B0 0 20 15.6

12 6 0 20 15.613

C0 0 40 15.6

14 6 0 40 15.615

D0 15 20 15.6

16 6 15 20 15.617

E0 15 40 15.6

18 6 15 40 15.6

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3 Results

Theexperimentswereperformedtwiceforcoarseoreandonceforfinematerial(thelatteronlyforoneparticlesizedistribution).Duringthetests,theflow-ability,thehangupfrequencyandthehangupheightwererecorded.

3.1 Flow-ability

Flow-abilityisclassifiedintofreeflow,intermittentflow,assistedflowandno-flow(Figure1).Intermsofflow-ability,theresults(Table3)indicatethatformaterialsA1andA2flow-abilitydecreasedfromfreeflowtonoflowwhenverticalloadincreasedfrom0to10MPa.Whenfineswereaddedwithouthumidity,theflowconditionwasintermittentorassisted.Whenwaterwasadded,theflowwasassistedflowand,whentherewerea40%offines,theflowconditionwasnoflowatall.

Table 3 Summary of experimental results

Test Material Vertical load σv [MPa] Flow condition Interferences

[g/hang up]Standard dev.

[g/hang up]1

A1

0 FreeFlow 1246 6402 1.5 AssistedFlow 928 3713 3 Intermittentflow 1068 2564 6 AssistedFlow 368 2465 10 NoFlow 0 -6

A2

0 FreeFlow 1177 4717 1.5 Intermittentflow 1036 4718 3 Intermittentflow 761 3569 6 AssistedFlow 599 27610 10 NoFlow 0 -11

B0 Intermittentflow 1014 248

12 6 AssistedFlow 586 31213

C0 AssistedFlow 501 153


D0 AssistedFlow 352 97


E0 Noflow 0 -

18 6 Noflow 0 -

3.2 Hang up frequency

Duringtheexperiments,itwaspossibletodetectflowinterruptionsorhang-ups,whichwererecordedintermsofmassandheight.Thehangupfrequency(Hg)isdefinedastheamountofmaterialthatcanbedrawnbeforeaninterruptionhappens.TheexperimentalresultsofHgasafunctionoftheverticalstressforeachlaboratorytestarepresentedinFigure4.


304

Figure 4 Hang up result in laboratory test. A: coarse material test (duplicates included), B: 20% of fine material, C: 40% of fine material and D: 20% of fine material with humidity

Theexperimentalresultsincoarsematerial(A)showthatincreasingtheverticalstressdecreasestheflow-abilityofmaterial.ForthemediaB,thatistheonewith20%ofdriedfines,thehang-upfrequencynumberdecreased.FormaterialsCandD,theverticalloadhadnosignificantinfluenceonthefrequencyofhang-upsastheywerenotabletoflow.

Fieldmeasuredhang-upsarequantifiedbytheirhang-upsindex(numberofhang-upsper1,000tonofore).Themeasuredhang-upsindexintheexperimentsissimilartotheobservedindexofprimarysulphidesinmines.Theindexvariesfrom1.6to3.6inminesand,ascanbeseeninTable4,thescaledexperimentalindexvariesfrom0.75to3.95.

Table 4 Scaled hang ups index

Verticalload[MPa]

Hangupsindex[#hangup/1000ton]

A1 A2

0 1.05 2.051.5 1.26 2.293 1.09 3.116 3.17 3.9510 0.75 3.32

Fortheexperimentswithverticalloadof10MPathematerialgotstronglycompactedoverthedrawbellgeneratingagreathang-upabovethedrawbellandalmostnohang-upsoflowerheight.

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Gravity Flow

3.3 Height of hang-ups

Aclassificationwasmadeaccordingtotheheightofthehang-up:

• Low:inextractionpoint.

• Medium:indrawbell(0-13.5mfromtheroofoftheproductionlevel)

• High:Abovedrawbell(over13.5m).

ThegeometryofthedrawbellfromwhichthedimensionswerescaledisrepresentedinFigure5.

Figure 5 Drawbell geometry in real size

Averagehang-upheightofeachexperimentbasedontheverticalstressisshowninFigure6.Itcanbeseenthatastheverticalstressincreases,theheightofthehang-upsincreasessimultaneously.Thedimensionsofeachobservedhang-upwerescaledinordertoquantifytheirheightinthemine.

Figure 6 Hang-up heights in laboratory test. On coarse material A1: d80=15.6 [mm], A2: d80=11.8 [mm], B: 20% of fine material, C: 40% of fine material and D: 20% of fine material with humidity

Ingeneral,theheightofthehang-upsincreaseswiththeverticalpressureforthecoarsematerial(A1andA2).Inthecaseoftheadditionoffine(materialsBandC)theresultsindicatethatverticalloadhasasmallimpactintheincreaseoftheheightofthehang-ups.Whenfinesandwaterwereadded(materialD)thereisnoeffectoftheverticalloadontheheightofthehang-ups.


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4 Conclusions and discussion

Basedonexperimentaltests,thispapershowsthatparticlesizeaswellasthemoisturecontentandverticalstress have a noticeable impact on theflow-ability of caved rock.Thenumber andheight of hang-upsincreasesastheverticalloadincreases.Finesandhumidityincreasethenumberofhangups.Also,highhang-upsoccurswhenaverticalpressurewasappliedorwhenhumidityandfineswereactingtogether.

Thescaledmodelwassuccessfulinunderstandinghowconfinement,particlesizedistribution,humidityandfines’presenceaffecttheflow-abilityofmaterial.Itisexpectedthatasblockcavesgetdeeperthenumberofhangupswouldincreaseiffragmentationkeepsconstant.Theresultsoftheaboveexperimentsshowsthat thehang-upnumberforminedesignapplicationscouldbeobtainedfromthiskindofexperiments.Thiswouldrequirefurtherresearchandanalysis.Itisexpectedthatthiskindofexperimentswouldbewillbecomestandardtothecavingindustry,especiallyforfutureandunknownconditions.

Acknowledgement

WewanttothankPatricioÁvilaandAlonsoVivesfortheirhelpintheseexperiments,thestaffofBlockCavingLaboratoryfortheirsupportandencouragementandAsiehHekmatforheradvices.ThisprojectwasconductedunderthepartialfundingofthebasalprojectforBasalExcellences,whichisaninitiativeoftheChileangovernment.

References

Beus,M,Pariseau,W,Stewart,B& Iverson,S2001, ‘DesignofOrePasses’, inUndergroundMiningMethod,pp.627-634.

Castro,R,Fuenzalida,M&Lund,F2014,‘Experimentalstudyofgravityflowunderconfinedcondition’,InternationalJournalofRockMechanicsandMiningSciences,vol67,pp.164-169.

Fuenzalida,MA2012,Studyoftheconfinedgravityflowanditsapplicationtocaving,Master’sThesis,Santiago,Chile,UniversidaddeChile.(inSpanish).

Jennings, BR & Parslow, K 1988, Particle size measurement: the equivalent spherical diameter, inProceedings of the Royal Society of London. A. Mathematical and Physical Sciences,419(1856),pp.137-149.

Hadjigeorgiou, J & Lessard, JF 2007, ‘Numerical investigations of ore pass hang-up phenomena’,InternationalJournalofRockMechanicsandMiningSciences,vol.44Nº6,pp.820-834.

Handy,RL1985,‘Thearchinsoilarching’,JournalofGeotechnicalEngineering,vol.111Nº3,pp.302-318.

Kvapil,DR2008,Gravityflowinsublevelandpanelcaving–Acommonsenseapproach.

Laubscher2006,CaveMiningHandbook.

Maass, S 2013, Technological alternatives for hang ups removal, Master’s Thesis, Santiago, Chile,UniversidaddeChile.(inSpanish).

Montecino,N2011,Secondaryfragmentationmixmodelinblock/panelcavingmining.Master’sThesis.Santiago,UniversidaddeChile.(inSpanish).

Troncoso,S2006,Simulationofoperationalinterferencesimpactonproductionplanning,Thesis,Santiago,Chile,UniversidaddeChile.(inSpanish).

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Gravity Flow

An analysis of the lateral dilution entry mechanisms in Panel Caving

PS Paredes University of Chile, Chile MF Pineda University of Chile, Chile

Abstract

Dilution is an integral part of a cave mining operation and its behaviour affects different dimensions of a caving project from the economic results to safety inside the mine.

One of the objectives of mine planning and draw control in panel caving is to maximize ore recovery by minimizing the overall dilution content extracted. The existing gravity flow models that aim to predict dilution entry and its behaviour do not consider the possibility of dilution particles to move laterally and affect the content of drawpoints that are contiguous to a previously exhausted area.

This paper contributes to the understanding of this important variable in cave mining methods by presenting an analysis of the lateral dilution entry mechanism hypothesis presented by Castro & Paredes (2014). The objectives of this analysis were to determine the processes through which this mechanism enables dilution particles to move laterally from a previously exhausted panel to a new panel in production and to set the basis for further development of guidelines for mine design and planning of new panels contiguous to previously exhausted panels. The analysis was performed with two approaches: a mechanical analysis supported with mine data and experimental observations at a laboratory scale. The authors postulate that there are two processes through which dilution can migrate lateral distances: lateral displacement over the broken ore pile and lateral preferential flow from the dilution source. A mechanical analysis supported with mine data shows that lateral displacement of dilution over the broken ore pile is feasible under cave mining conditions. Consequently, experimental observations suggest that lateral preferential flow of dilution is possible under certain conditions. Additionally, vertical fines migration through shear zones was observed. Finally, for mine design and planning of a new panel contiguous to a previously exhausted panel, an expression for the maximum caving face width as a function of several mining parameters is proposed.

Keywords: panel caving, dilution entry; gravity flow mechanics; draw control.

1 Introduction

Dilution control is crucial in managing a block/panel caving operation. This variable affects differentdimensionsofacavingprojectfromtheeconomicstosafetyinthemine.Severalauthorshavestateddifferentparametersaffectingdilutionentry(Table1)andtwomaindilutionbehaviormodelshavebeendevelopedinordertoprovidetoolsfordilutionentrypredictionandcontrol:Laubscher´s(1994)andSusaeta´s(2004).Laubscher´sformulaefordilutionentrypredictionarebasedontheestimationoftheheightofinteraction,theswellfactorofcavedmaterialandameasureoftheuniformityofextraction.Thislastquantityisbasedonthestandarddeviationofthetonnagesextractedfromtheworkingdrawpoints.Hepostulatesthatasthedrawuniformityincreases,dilutionentrywillbedelayed.SusaetausedminedatatoexpandLaubscher’smodelandpostulatedthatdilutionentrydependsonthewaydrawisconducted,definingthreebehavioralmodels:IsolatedFlow,Isolated-InteractiveFlowandInteractiveFlow.Flowbehaviorcanpassfromonemodeltoanotherbyincreasingordecreasinguniformityofextraction.Asthedrawismoreuniform,theflowwillbemoreinteractiveanddilutionwillbelower.Bothapproachesassumethatthedilutionsourceis


308

locatedabovetheareaunderanalysis.Finally,theyassumethatthedilutionparticleswillflowdownwardtothedrawpointswithoutbeingaffectedbythecavebackpropagationprofileorrillingpotential.

Table 1 Parameters affecting dilution (Castro & Paredes 2014)

Parameter Effect on dilution AuthorOre/waste

contactsurfaceinclination

Tominimizedilution,ore/wastecontactinterfacemustbekeptat45to50°movingawayfromtheuncavedorethroughdraw

control.

DeWolfe(1981),Julin

(1992)Orevolumetoore/wasteinterfacearea

Thehighertheratiooforevolumetothesurfaceareaoftheore/wasteinterface,thelowertheoverallpercentageofdilution.

Laubscher(2000)

Attitudeofore/wasteinterface

areaIrregularore/wasteinterfaceareaswillcauseseveredilutionentry. Laubscher

(2000)Fragmentationrangefromoreandwaste

Finelyfragmentedwasteandcoarseoremeansearlyandextensivedilution,whilstcoarsewasteandfineoremeansalowoverall

extracteddilutionpercentage.

Laubscher(2000)

Heightoftheinteractionzone

Gooddrawzoneinteractionandparallelflowwillrepresenttheoptimumconditions.Poordrawpointinteractionanddrawzonesangledaccordingtolocalvariationswillleadtohighdilution.

Laubscher(2000)

Differencesindensityoforeand

waste

High-densityoreandlow-densitywasteleadtolowdilutionandviceversa.

Laubscher(2000)

Blockorpanelcavingstrategy

Ablockcavestrategywillleadtomorelateraldilutionmixingthanpanelcaving.

Laubscher(2000)

Uniformityofdraw

Highuniformityofthetonnagedrawnfromtheneighbourdrawpointswillleadtohighinteractionandlatedilutionentry.

Julin(1992),Susaeta(2004)

Despitethefactthatthelatestfieldstudiesusingmarkershaveshownhowchaoticoreflowbehaviorisinthenearfield(Bruntonet.al.2012;Castro&Armijo2012),thereisstillsignificantpotentialtoconductfarfieldexperimentstobetterunderstanddilutionbehaviourinthefar-field.

EmpiricalobservationsofthehistoricaldilutionbehavioratCODELCO’sElSalvadorandAndinaminesconductedbyCastro&Paredes(2014)showedthat51%ofthedrawpointswithdilution(overatotalof1674drawpointswithdilutionanalyzed)hadapulsed-shapedbehaviourinwhichthedilutiongoesbacktozeroafteritsfirstappearance.Thisnon-continuousdilutionentrybehaviorledtothedefinitionofadilutionentry criterion based on the cumulative dilution curves of the drawpoints.Thus,when the cumulativedilutioncurveovercameacertainthreshold(3%forCastro&Paredes’analysis)significantdilutionentrywasdeclaredforthedrawpointatitscorrespondingextractionpercentage.Usingthiscriterion,Castro&Paredesanalyzedthedilutionentrybehavioratadrawpointscalefor6differentsectorsconsideringtheinitialin-situcolumnheights,extractionsequenceanduniformityofextractionusingSusaetas’sUniformityIndex.Thisanalysisledtothedevelopmentofahypothesisforthemechanismsbywhichdilutionenteredthedrawpoints:(1)verticalentrybydescendinggravitationallyfromthesourcelocatedabovethesector,(2)dilutionentryafteranairblasteventduetosuddenpropagationof thecavebackwhennewarea isincorporated, and (3) lateraldilutiondue tohorizontaldisplacementofdilutionparticles froma sourcelocatednexttothepanelunderanalysis.

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Gravity Flow

ThefollowingsectionscorrespondtoananalysisofthelateraldilutionentrymechanismhypothesizedbyCastro&Paredes,incorporatingexperimentalresultsderivedbyPineda(2012),inordertocontributetotheunderstandingofdilutionbehaviorinPanelCavingmethods.

2 Lateral dilution entry mechanism hypothesis

AccordingtoCastro&Paredes(2014),whenthedilutionsourceislocatednexttothepanelunderanalysis,thecavebackcouldpreferentiallypropagate, in termsof rate, to the lateral interfaceof the in-situandcavedwasterock.Thispreferentialpropagationofthecavebackcouldenabledilutionparticlestoenterthedrawcolumnsandflowalong thecaveboundary (asobserved in sandflowexperimentsbyKvapil(2004)), travellinghorizontaldistancesdependingonthepile’sslopeandproducingearlydilutionentryindrawpointslocatedfarfromthedilutionsource.ThisphenomenawasobservedattheIncaCentralEast(ICE)andtheIncaNorth(IN)sectorsinElSalvadormineandattheGrizzlyClusterofAndinamine’sPanelIIIsector.

Figure 1 Schematic sequential drawbell sections, showing lateral dilution mechanism (Castro & Paredes 2014)

Figure2illustratesICE’sdrawpointsdilutionentrybehavior:asthesequenceprogressesgoingfromthedilutionsource(ICpreviouslyexhaustedpanel),allocatedtothenorthofICE,towardsthesouth;dilutionparticles enter the drawpoints earlier (DEP decreases as drawpoints aremore distant from the dilutionsource).

According toCastro&Paredes, this behaviour suggests that dilutionparticles coming from the lateraldilution source travelled large lateral distances rilling over the broken ore pile, following the processdescribedinthepreviousparagraphs.

The authors identified a secondmechanism governing lateral dilution entry, that is, lateral percolationpreferentialflowofparticlesfromthedilutionsourceintothedrawpointsofarecentlyincorporatedarea.Undercertainconditions,whenthenewbrokenoreis incontactwithwaste, thereisapotentialfor themovementzonetodeveloptowardsthehigherporosityzone,sinceitrepresentslowerstrengthpathforthemovementtodevelop.This,inthecaseofanewpanelcontiguoustoapreviouslyexhaustedpanel,could


310

promotelateralpreferentialflowofdilutionparticlesintothenewdrawpoints.ThereisalaboratoryscaleevidenceofthisphenomenonasobservedintheexperimentalresultsderivedfromtheuseofaphysicalmodelbyPineda(2012).

Figure 2 Plan view and Schematic cross-section of an ICE sector´s drift showing decrease in DEP as the sequence progresses from the lateral dilution source

3 Lateral displacement mechanical feasibility analysis

Inordertoprobethemechanicalfeasibilityofthishypothesis,alimitingequilibriumanalysisofthebrokenmaterialwedge, formedby theexistenceofanairgap,wasperformed.Thisconsistedofanalysing thestabilityoftheparameterizedproblemundertheconditionsinapanelcaveoperation(Figure3).

α:Failuresurfaceangle.

β:Granularmaterialpileslope.W:Verticalforceduetothewedge’sweight.

σz:Forceappliedbytheoverload.

, φ, c:Bulkdensity,frictionangleandcohesionofthebrokenmaterial.

h:Airgapheight.

b:Lengthofthehorizontalprojectionofthefailuresurface.

z:Overloadheight.

L:Failuresurfacelength.

Figure 3 Limiting equilibrium analysis theoretical approach

Theequilibriumanalysiscanbereducedtothedeterminationofthesafetyfactor(SF)definedas:

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[1]

whereFEandFDarethesummationofstabilizinganddestabilizingforcesactingoverthefailuresurface,respectively.IftheSFvalueislessthan1,thebrokenmaterialwedgeisunstableandthematerialwillslideoverthefailuresurfacetoenterthegranularmaterialpile.Consideringgastheaccelerationofgravity,andtheJanssen(1985)approachfortheforceduetotheoverload,thedestabilizingforcescanbeexpressedasfollows:

[2]

Where:

[3]

Forthebrokenmaterial,thestabilizingforcecorrespondstotheshearstrengthofthefailuresurface.UsingtheMohr-Coulombcriterion,thestabilizingforcescanbeexpressedasfollows:

[4]

Thus,anexpressionforthesafetyfactorasafunctionoftheproblem’sparametersis:

[5]

FromtheevaluationofthesafetyfactorusingthegeotechnicalcharacterizationofthebrokenmaterialfromthePanelIIIsector,itispossibletoobservethatforoverloadheightsover8m,theformationofafailureplanewithaninclineofover35°willcauseinstabilityinthewedgeevenfora10cmairgapheight.

Thepreviouslydescribedconditionissufficienttoallowthedetachmentofagroupofdilutionparticles.Nevertheless,toallowasignificantdilutionentryonthegranularmaterialpile,theairgapheightmustbeenoughtoallowdilutionparticlestoovercomethearchingeffectdescribedbyHoek(2004).Hoek(2004)statesthattheopeningthroughwhichagroupofparticlescanflowmustbegreaterthan3timestheaverageparticlesizeinordertoovercomethearchingeffect.Thus,consideringPanelIII’sconditions,anairgapheightover60cmcouldallowasignificantnumberofdilutionparticlestoenterthegranularmaterialpile(Paredes2012).

Oncedilutionparticleshavebeendetachedfromthelateraldilutionsourceandenteredthegranularmaterialpile,lateraldisplacementoverthepilewillbecontrolledbythepile’sslope(β)andtheangleofreposeofthesedilutionparticles(Ød).IfβisgreaterthanØd,dilutionparticleswillbeabletorilloverthegranularmaterialpile.InaPanelCavingoperation,βwilltakedifferentvaluesdependingontheextractionrates,andexceedingØdinsomecases.Ifweconsiderthelongtermcase,wherethegranularmaterialpilefindsitsangleofrepose(Øp),lateralmigrationofdilutionparticleswilloccurifthisangle(Øp)isgreaterthanØd.ConsideringPanelIII’sgeotechnicalcharacterizationofdilutionandore,theangleofrepose(32°and53°,respectively)wouldallowtheoccurrenceoflateralmigrationofdilutionoverthegranularmaterialpile.

4 Lateral preferential flow observations at lab scale

AttherequestofAgnico-Eagle,aphysicalmodelwasbuiltandtestedbytheBlockCavingLaboratoryattheUniversityofChileinordertostudythegravityflowmechanismsofalargestope(320mheightand45.000m2ofproductionarea)extractedusingablockcavelayoutforblastedmaterial.Thisfreeflowmaterialconditionemulatesapanelcavingoperationoncethewholeorecolumnhasbeencaved.

SF = FE

FD


312

4.1 Physical model experimental set-up and plan

Forexperimentalpurposesa1:200scalemodelwasbuilt.Duringthedesignstageoftheexperiments,allthelawsofkinematicsimilitudeforgranularmaterials(fullgeometricalsimilitudeandextractionrate)weretakenintoconsiderationasdetailedbyCastroetal.(2012).

Table2describestheexperimentalset-upandFigure4presentstheexperimentaldesign.

Figure 4 Experimental design: Physical Model (right) and drawpoints and apex (left)

Table 2 Experimental set-up

Main Assembly4dismountableplexiglasswallsdelineatingthefinalgeometryofthestopePlexiglassassemblysupportedbyironverticalcolumnsandrowsDimensionsofthemodel1.6mheightx1mlengthx0.25mwidthatlabscale

Loading System Materialandlabelledmarkersloadedmanually

Extraction Material System

11drawpoints,eachoneincludesshovellinkedtoaservomechanismdevicethatsimulatestheLHDextraction

Servomechanismallowsvaryingtheextractionrate

Model Media

Coarsegravelwithad100of4.75mmandad50of2.2mmallowingfreeflowaccordingtoHustrulidandSun(2004)

Materialdriedtoeliminatethehumidityeffectandcapillaritybetweengrains

Forsimulatingdilutionentrythemodelmediaisdyedusingreddust

TheexperimentalplananditsmainobjectivesareoutlinedinTable3.

Table 3 Experimental Plan

Experiment Draw strategy Objective

1 Isolateddraw Todetermineisolatedflowzonediametergeometryandtotesttheproperoperationofthemodel.

2 Uniformdraw Thisexperimentsimulatescontinuousdilutionentryatthetopofthestope.Theaimistoquantifypotentialdilutionentrymechanism

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Gravity Flow

4.2 Experimental Results

4.4.1 Experiment 1: Isolated draw

Inafirststage,anIsolatedDrawZoneexperimentwasperformedtocalibratethecorrectoperationoftheextractionsystemforthephysicalmodel.Forthisexperience,theextractionwasconductedonlyinonedrawpoint.OncetheIsolatedMovementZone(IMZ)reachedthesurfaceofthestopetheextractionwascompletedandtheIMZwasmeasured.ThemainresultsofthisexperimentcanbeobservedinFigure5.

Figure 5 Progress of the Isolated Draw Zone

4.4.2 Experiment 2: Uniform draw with continuous dilution entry from the top

Anexperimentsimulatingdilutionentryat the topof thestopewasperformed.Dilutionwassimulatedbyaddingagranularmaterialontopofthestopedyedinredcolour.Thisexperimentwasperformedbyextracting from twoextraction levels; themainextraction level locatedat thebaseof themodel andasecondaryextractionlevellocatedinthestopefootwall,30m(15cmatmodelscale)aboveand60m(30cmatmodelscale)awayfromthemainextractionlevel;thesedistancesareprovidedatprototypescale.ThishorizontaldistancewasdeterminedbasedontheIMZexperimentconductedpreviouslyinordertoavoidanearlyconnectionbetweenflowzones.

Theextractionforeachdrawpoint,inthemainlevel,wascontinueduntildilutionwasreported,atwhichpointdrawfromthatindividualdrawpointwasstoppedimmediately.Afterwards,extractionwascontinuedfor the rest of thedrawpoints until all of themexhibited a significant content of dilution.Once all thedrawpointsofthemainextractionlevelwereclosed,becauseofthedilutioncontent,thedrawpointsfromthesecondarylevelwerestartedintoproductionfollowingthesameconsiderationsstatedabove.

Asaresultfromthisexperiment,Pineda(2012)observedhowtheflowzonedevelopedfasteratthelowercolumnheight,generatinganextractionprofile;thisprofileiskeyinordertodefinetheore/wasteinterfaceduring thewholeextractionprocess.Dilutionmovesdownwardsaccording to theflowvelocityprofile,beingfaster in theproximityof thehangingwallasobservedinsandexperimentsbyKvapil(2004).Asextractionprogressed,itwasnotedthatthefinescontainedintheredmaterial(tintingreddust)migratedtothedrawpoints(Figure6d).

Oncethearearelativetotheextractionfromthemainlevelwasexhausted(Figure6g),theextractionfromthesecondary levelbegan.Thisgeneratedadifferentextractionprofileandpromoted lateralmovementforthedilutionsource.Duetothelateralmovementofthematerial,dilutionparticlesmovetowardsthe


314

recentlyopeneddrawpointsdilutingtheminanearlystageashypothesizedformerly(Figure6h).Therefore,intermsofminedesign,alargerspacingwouldberecommendedtoavoidtheflowzonestoconnecttothedilutinglowerdensityzones.

Figure 6 Progress of the extraction under uniform draw

5 Avoiding lateral displacement of dilution particles over the ore pile

Considering the lateral dilution entrymechanisms described previously, in this section, an analysis ofthemainvariables inminedesignandplanningofanewpanel isperformed.Theanalysisproposesanexpressionofthemaximumcavingfacewidththatwouldpreventlateraldisplacement(rilling)ofdilutionparticlesovertheorepile.

BlockCavingmethodsneedtomaintainequilibriumbetweenthematerialextractedandthematerialcavedtopropagatecavingduringtheearlystagesofablockorpanelproduction.Therefore,itisnotpossibletoavoidtheformationofshortairgaps.Thus,onepossiblestrategytoavoidlateraldilutionmigrationovertheorepileistocontrolthegranularmaterialpile’sslopeduringcavingpropagation.

Once the air gap is formed, if the local slopebetween thepileheightsof twocontiguousdrawbells inthesequencedirectionβ(thelocalpileslope)isgreaterthantheangleofreposeofdilutionparticlesØd,dilutionwillbeabletorilloverthepile(Figure7a).Usingasimpleapproach,thetimedelayindays,definedasthedifferencebetweenthebeginningofproductionfortwocontiguousdrawbells,canbeexpressedasafunctionofthenewareaincorporationrate(VD,inm2/day),thewidthofthecavingface(W,inm),thedistancebetweenthecontiguousdrawbells(d,inm)andthestrikeangleofthefootprintδ(seeFigure7b):

[6]

Thus, for a given period during the caving propagation phase, the pile height difference between twocontiguousdrawbellsinthedirectionofthesequence,thathavethesamecavingverticalpropagationrate,willbe:

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Gravity Flow

[7]

Where:VEX,int/m2/day,istheextractionrateofthedrawpoints;VCP,inm/day,isthecavingverticalpropagationrate;ρS,int/m3,istheoresoliddensity;andeisthevoidratioinsidethepile.

Then,thelocalpileslope(β)willbegivenby:

[8]

Anddilutionwillbeabletomigratelaterallyoverthepileif:

[9]

Fromtheparametersshownabove,therearetwomainminingvariablesthatcanbecontrolledfordesignandplanningpurposes:(1)thenewareaincorporationrate,and(2)thecavingfacewidth.Thenewareaincorporationrateisgenerallylimitedbyseveraltechnicalconditions,sothemostflexiblevariableisthecavingfacewidth.Thus,anexpressionforthecavingfacewidththatwouldensurethepreventionofearlydilutionentrycausedbyrillingparticlescomingfromalateraldilutionsourceispresented:

[10]

Figure 7 a) Schematic section view showing pile heights difference (Δh) and spacing (d) for two contiguous drawbells; b) Schematic plan view of drawpoints showing caving width (W), sequence direction, contiguous

drawbell spacing (d) and footprint strike (δ)

The maximum caving face width for different vertical caving propagation rates Vcp is calculated,consideringthevaluesinTable4,asanacademicexample(Figure8).Fromthefigure,itisworthnotingthatthemaximumresultingcavingfacewidthvariessignificantlywithsmallchangesinVcp.


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Table 4 Variable values for maximum caving face width example

Variable Unit Valuee % 40Vex t/m2/d 0.3

S t/m3 3δ ° 30

φd ° 32VD m2/d 30*

*equivalenttoarateof2drawbellspermonthina30mx15mdrawpattern

Figure 8 Example of maximum caving face with as a function of the vertical caving propagation rate considering Table 4 values

6 Conclusions

Twohypothesesforthelateraldilutionentrymechanisminpanelcavingwerepresentedanddescribed:(1)displacementofdilutionparticlesfromalateraldilutionsourceoverthebrokenorepile(viarilling)and(2)preferentialflowofdilutionparticlesfromalateraldilutionsource(viainclinationoftheIMZstowardsmore free-flowingmaterial). Additionally, theobservationoffinesverticalmigration suggests that therelationbetweenoreanddilutionparticle’ssizeaffectstheverticalentryofdilution,asthetintingreddust’sparticlesizewassignificantlysmallerthanthemodelmediausedtorepresentore.

Throughalimitingequilibriumanalysis,thefirstmechanismwasproventobefeasibleunderpanelcavingoperationconditions.Ontheotherhand,experimentalevidencesuggeststhat,underfreeflowconditions,thesecondmechanismoccurrenceispossible.

Finally, an expression for themaximum caving facewidth that avoids dilution entry through the firstmechanismdescribedisproposedforminedesignandplanningpurposes.

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7 Acknowledgments

TheauthorswouldliketoacknowledgeCODELCO-Chile,Agnico-EagleandtheBlockCavingLaboratoryoftheUniversityofChileforprovidingfundinganddatafortheresearch.WewouldalsoliketothankMrRaulHottforhishelpfulsupportduringthewritingofthisarticle.

8 References

Brunton,I,Sharrock,G&Lett,J2012,‘FullScaleNearFieldFlowBehaviourat theRidgewayDeepsBlockCaveMine’,ProceedingsofMassMin2012,Sudbury,Canada.

Castro,R,Pineda,M2012,‘DrawcontrolatGoldexmine.InternalreporttoAgnico-Eagle’,LaboratoriodeBlockCaving,UniversidaddeChile.

Castro,R,Armijo,F2012,‘ExperimentaldesignforthefullscaleflowtestmarkeratElTeniente’,InternalReporttoCodelcoChile,LaboratoriodeBlockCaving,UniversityofChile.

Castro,R,Pablo,P2014,‘Empiricalobservationsofdilutioninpanelcaving’,Acceptedforpublication.Paper12/110.JournalofSouthAfricanInstitutionofMiningandMetallurgy.

DeWolfe,V1981,‘DrawcontrolinprincipleandpracticeatHendersonMine’,DesingandOperationofCavingandSublevelStoppingMines,(Ed:D.R.Stewart),SocietyofMiningEngineers,USA.

Hoek,E2004,Modeltodemonstratehowrockboltswork.[online]Hoek’scorner<http://www.rocscience.com/hoek/corner/15_Model_to_demonstrate_how_rockbolts_work.pdf>

Hustrulid,W&Sun,C2004, ‘Some remarksonorepassdesignguidelines’,ProceedingsofMassMin2004,Santiago,Chile.(Ed(s):A.KarzulovicandM.Alfaro),ChileanEngineersInstitute,pp.301-308.

Janssen,H2004,‘Experimentsregardinggrainpressureinsiloswrittenin1895’,ProceedingsofMassMin2004,InstitutodeIngenierosdeChile,Chile,pp.293-300.

Julin,D 1992, ‘BlockCaving’, SMEMiningEngineeringHandbook, 2nd edition. Society forMining,MetallurgyandExploration.

Kvapil,R2004,Gravityflowinsublevelandpanelcaving-acommonsenseapproach,Luleå,Sweden:LuleåUniversityofTechnology.

Laubscher,D1994,‘CaveMining–thestateoftheart’,JournalofSouthAfricanInstituteofMiningandMetallurgy.

Laubscher,D2000,BlockCaveManual.Prepare for the InternationalCavingStudy1997-2000. JuliusKruttschnittMineralResearchCentre,TheUniversityofQueensland.pp.111-118.

Paredes, P 2012, Dilution Entry Mechanisms in Block and Panel Caving Operations, Master Thesis.UniversityofChile,Chile,(inSpanish).

Pineda,M2012,StudyofthegravityflowmechanismsatGoldexbymeansofaphysicalmodel,MasterThesis,UniversityofChile,Chile.

Susaeta,A 2004, ‘Theory of gravity flow (Part 1)’, ProceedingsMassMin 2004, Santiago, Instituto deIngenierosdeChile,Chile.

Susaeta,A 2004, ‘Theory of gravity flow (Part 2)’, ProceedingsMassMin 2004, Santiago. Instituto deIngenierosdeChile,Chile.


318

Application of a methodology for secondary fragmentation prediction in cave mines

MA Fuenzalida Itasca Consulting Group, Inc., USAT Garza-Cruz Itasca Consulting Group, Inc., USAM Pierce Itasca Consulting Group, Inc., USAP Andrieux Itasca Consulting Group, Inc., USA

Abstract

This paper describes how primary fragmentation estimates can be combined with REBOP simulations to predict the secondary fragmentation of caved rock. Primary fragmentation estimates are obtained from separate numerical models that link caving induced stresses at failure (estimated with FLAC3D cave-scale models) with associated fragmentation of rock masses (estimated through systematic Synthetic Rock Mass testing with 3DEC). REBOP takes the predicted spatial distribution of primary fragmentation (along with intact rock strength, drawpoint layout and draw schedule) as input and provides estimates of the evolving secondary fragmentation in the column and at the drawpoints. The logic is based on laboratory studies that link attrition (via block splitting and rounding) to the product of shear strain and normal stress (essentially the work done on the fragments).

1 Introduction

Fragmentationofanorebodyplaysanimportantroleincavemines,dictatingwhethertheoperationwillbesuccessful.AccordingtoLaubscher(1994,2000),fragmentationwillinfluencedrawpointsizeandspacing,equipmentselection,drawcontrolprocedures,productionrates,dilutionentryontothedrawcolumn,hang-upsandtheneedforsecondarybreakage.

Ascavedrockisextracted,thecaveprogressesupwardsformingrockfragmentsinthecavebackvicinityduetotheeffectofgravityorinducedstressactingontheinherentdiscontinuitiesintherockmass,whichisaprocessknownasprimaryfragmentation.Correspondingly,zonesofmovingmaterialknownasIsolatedMovementZones (IMZ)develop in theorecolumnabove thedrawpoints.Within the IMZs, fragmentsofrockaresubjectedtorelativelylownormalstresses(duetoarching)buthighshearstrains(duetothenon-uniformnatureofthevelocityprofile,whichischaracterizedbyacentralplugflowregionsurroundedbyashearannulusroughly10-15fragmentdiameterswide).InthesurroundingstagnantzoneoutsidetheIMZs,normalstressesarehighbutshearstrainsareverylow(LorigandCundall2000;Pierceetal.2010).Fragmentattritionisbelievedtooccurinbothofthesezones,aprocessknownassecondaryfragmentation(Brown2007).

Effortshavebeenmadetoprovidemeanstopredictsecondaryfragmentationbasedonrulesestablishedthroughexperienceandengineeringjudgement(Esterhuizen1998),andalsothroughexperimentalwork(Castro et al. 2014). In the following sections, amethodology for secondary fragmentation estimationispresented that isbasedon the resultsof experimental studiesofattritionunder shearcombinedwithREBOPflowsimulation.Theapproachconsidersshearing-dominatedattritionwithin theIMZs,butnotcompaction-dominatedattritionwithinthestagnantzones.Applicationtoacasestudyisalsopresented.

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2 Methodology to assess fragmentation in cave mines

Anewmethodologyfordrawpointfragmentsizedistributionpredictionhasbeenformulated(Figure1).Thefirst step is to calculate the primary fragmentation through the use of a combinationof numericalmodelstakingintoaccountthediscontinuitiesintherockmassandtheinducedstressesthatarelikelytodevelopintheorecolumnasthecavingprogresses.Usingtheresultingprimaryfragmentationestimatesasinput,REBOP(Pierce2010)isabletoestimatethesecondaryfragmentationthroughtheuseofanattritionmodel.

Figure 1 Diagram illustrating the methodology to predict secondary fragmentation

2.1 Assessment of primary fragmentation

Syntheticrockmass(SRM)sampleswereconstructedbyassemblingacollectionoftetrahedralblocksandpopulatingtheircontactswithstrengthvaluesrandomlyselectedfromacumulativestrengthdistribution(Garza-Cruz&Pierce2014).AvarietyofSRMsamplesweretestedunderasuiteoftriaxialtestsaswellasuniaxialcompressionandtensileteststocharacterizetheirstrength.Themodelallowstheblocksformingthesample tobreakat theirsubcontactsasa resultofstressconcentrations,mimicking the initiationofcracksthatcancoalesceand/orpropagatetofracturetherockmass.

TherockmassstrengthderivedfromtestingtheSRMsampleswasusedtoinformacave-scaleFLAC3D(Itasca 2013)model.As the cave is propagated, themodel records the induced stresses at failure.ThestressesatfailurethenarecombinedwiththeSRM-derivedassociatedfragmentationdistributiontopredictspatialprimaryfragmentationthroughthecolumn(Garza-Cruzetal.2014).

BecauseREBOPconsidersprimaryfragmentationasaGaussiandistribution,aleastsquarestechniquewasusedtobestfitthedifferentprimaryfragmentationcurvesforthedifferentgeologicaldomains.

2.2 REBOP as a tool to estimate secondary fragmentation

REBOPwasdevelopedbyItascafortheindustry-fundedInternationalCavingStudy(ICSIandICSII)andMassMiningTechnology (MMT)projectsasa tool for rapidsimulationofmaterialflowwithinblock,panelandsublevelcaves.REBOPmodelsflowbytrackingthegrowthofIMZsassociatedwithdraw.An


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IMZinREBOPiscomprisedofanumberofdiscretedisk-shapedlayersstackedabovethedrawpoint.Thevolumeofan IMZ is tracked inREBOPbybalancing the incrementalmassdrawn from thedrawpointwiththemassproducedbyabulkingofmaterialattheperipheryoftheIMZ.TheamountofbulkingiscontrolledatthelocallevelbytheaverageinitialandmaximumporositiesofmaterialattheIMZperipheryasspecifiedintheblockmodel.Therelativerateofupwardandlateralgrowth(whichcontrolsIMZshape)iscontrolledbythefragmentationandfrictionangleofmaterialattheIMZperipheryasspecifiedintheblockmodel.Experiments (e.g.,Castro2006)andsimulations (e.g.,Lorig&Cundall2000) reveal thatanIMZtendstodevelopanapproximatelyellipsoidalorcylindricalshapewithanevolvingwidththatismoststronglycontrolledbymeanfragmentsize.Asaresult,IMZstendtobenarrowinfinelyfragmentedrockandwideincoarselyfragmentedrock.Thisbehavior,whichcanbesimulatedwithinREBOP,directlyaffectsthedrawpointspacingthatisrequiredtomaximizemobilizationofmaterialthroughoutthevolumeof interest (Pierce 2010).Materialmovementswithin the IMZs are tracked via a field ofmarkers, thepositionsofwhichareupdateddailyaccordingtothelocalvelocityprofilewithintheIMZ.Markersareassignedatensilestrengthandfragmentsizerandomlyfromtheinputdistributionsoftheirparentlithologywhentheyarefirstcreated.AsmarkersmovedowntheIMZsandmix,theirdiametersareaveragedwithineachdisk-shapedlayertocontrolthelocalIMZexpansionrateandinternalvelocityprofile.

Secondaryfragmentation inREBOPishandledbysystematicallyreducing thefragmentsizeassociatedwithmarkersbasedontheirtensilestrengthandthestressandstrainexperiencedastheytransitthroughtheIMZtowardthedrawpoint.Morespecifically,thedegreeofbreakageexperiencedbyarockfragmentmovinginsidetheIMZisafunctionoftheaveragestressinsidetheIMZ(estimatedviabintheory)relativeto thestrengthof the fragmentsand the incremental shear strain.Atpresent,REBOPmakesuseof theshearingattritionmodeldevelopedbyBridgwateretal.(2003):

(1)

Where:

=themassfractionattritedfromthemono-sizedinitialassemblies(percent);

=thenormalstressappliedtotheassembly(MPa);

=thetotalshearstrainappliedtotheassembly(dimensionless);

=thefractureortensilestrengthoftheconstituentparticlesintheassembly(MPa);and

=empiricalconstants(dimensionless).

2.2.1 Strength Scale Effect

REBOP’ssecondaryfragmentationmethodologytakesintoaccounttherockblockstrengthscaleeffect.It has been established that the intact strength of rock decreaseswith increasing scale due to a higherprobabilityofthepresenceofdiscontinuities.Hoek&Brown(1980)developedanempiricalscaleeffectrelationforintactstrengthonthebasisoflaboratorytestingconductedbyanumberofdifferentresearchersonhomogenoushardrocksamples(i.e.,sampleslackingsignificantmicrofracturingoralteration).

(2)

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Where:

σc =uniaxialcompressivestrength;

σc50 =uniaxialcompressivestrengthofacylindricalspecimenwitha2:1height-to-widthratioandabasefragmentdiameterof50mm;

de =arbitrarydiameterinmm;and

k =constant,scaleeffectexponent.

WhileHoekandBrown(1980)estimatedkbeingequalto0.18forrelativelyhomogenoushardrock,workbyYoshinakaetal.(2008)demonstratedthattherangeinscaleeffectcanbemuchwiderwhenmoreflawedintactrocksareconsidered,withkrangingfrom0.1to0.9(Figure2).Adjustmentstothestrengthscaleeffectexponent,k,allowREBOPmodelstoaccountforpotentialeffectsofdefectsthatmayexistintheintactrockblocks,andthereforereducerockblockstrength.

Figure 2 Scale effect relations for intact rock UCS proposed by Yoshinaka et al. (2008). The relation of Hoek & Brown (1980) is shown for comparison (Pierce et al. 2009)

2.2.2 Modes of secondary fragmentation

REBOPaccountsforthefollowingthreemaineffectsthatlinkattritiontotheproductofshearstrainandnormalstresswithrespecttosecondaryfragmentation:

1. BlocksplittingoccurswhentheaveragenormalstressesinsidetheIMZarehighrelativetotheblockstrength.Splittingresultsfromtensioninducedinafragmentviacompression.Iftheinducedtensilestressexceedsthetensilestrengthofthegrain,itwillsplit.

2. Roundingof block corners andgenerationoffines refer to the removal of asperities from thesurfaceoftheblockasthefragmentsmovedowninthedrawcolumn.

3. Fines cushioning takes placewhen large blocks are surrounded by a large number of smallerfragments,providingconfinementandincreasingstrengthandsurvivabilityasaresult.


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Allthreemodesareresponsibleforthereductionofblocksize.TheresultingcharacteristicevolutioninfragmentsizedistributionisillustratedinFigure3.

Figure 3 Main effects of secondary fragmentation results observed using REBOP: (1) corner rounding leading to fines production, (2) block splitting leading to a breakdown in blocks of intermediate dimension and (3) fines

cushioning leading to survivability of large blocks

3 Application of the methodology in cave mines

3.1 Case study

Thecaseofstudyconsistedofanoredepositwiththreemaingeologicaldomains:“weak,”“moderate”and“strong”representingdifferentrockmassstrengths.

The resultsofSRMprimary fragmentationpredictionswerecombinedwith thedistributionof strengthdomainsandthepredictionsofcavebackstressesatfailuretoderiveadistributionofprimaryfragmentationforinputtoREBOP.AsillustratedinFigure4,arelationshipbetweeninducedstressandmeanfragmentvolumecouldbeestablishedassumingalinearbehavior.

Basedontheassumptionthatfragmentsarelikelyshapedascubes,thefollowingequationscanbefittedtothemeanandstandarddeviationofthefragmentdiameters.Table1showsthefittedparametersforeachgeologicaldomaincontainedintheblockmodel.

(3)

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Table 1 Parameters for the Mean and Standard Deviation Fragment Size

Type of Rock Parameter

Stronga=-0.015,b=1.9,

c=-0.014,d=1.7

Moderatea=-0.011,b=1.3,

c=-0.011,d=1.2

Weaka=-0.006,b=0.8,

c=-0.007,d=0.8

TakingintoaccounttheinducedstressesatfailurefromtheFLAC3DsimulationsandusingtherelationshipsinEquation2withtheparametersestablishedinTable1,ablockmodelcanbepopulatedwithaprimaryfragmentationcurvefittedasGaussiandistributions.Asaresult,Figure4illustratesthepopulatedblockmodelshowingthemeanfragmentsizefordifferentgeologicalrockdomains.

3.2 Results

ThesecondaryfragmentationlogicinREBOPissensitivetoseveralinputs.SinceIMZshapeiscontrolledbytheprimaryfragmentationandfrictionangle,achangeintheseinputswillhaveanimpactonhowthesizeoffragmentsevolveastheygodownthroughthecolumn.Furthermore,thestrengthscaleeffectalsoplaysamajorroleinsecondaryfragmentation,asaweakerintactrock(lowtensilestrength)willproducedafinerfragmentation.Thiseffectcanbeachievedbyincreasingtheinputexponentfactork.Hence,havingacoarserinitialfragmentation,blockswillbreakmoreeasily.

Figure 4 Mean fragment volume with respect to the induced stress at failure


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Additionally,drawpointspacinganddrawschedulearealsoessentialinputsforsecondaryfragmentation.REBOPwillpredictoverlapbetweenIMZsandassociateduniformdrawdownifcloselyspaceddrawpointsandcoarsefragmentationareconsidered.Thiswillachieveamoreuniformdrawdown,lowershearstrainsinthecolumnandcorrespondinglylowersecondaryfragmentation.Ontheotherhand,morebreakageislikelytooccurwithwidelyspaceddrawpoints(isolateddrawscenario).

Itisanticipatedthatastheproductionprogresses,thesizeofrockfragmentswilltendtodecrease.Havingadifferentialdrawwillincreasetheeffectofsecondaryfragmentation.Onthecontrary,havingauniformextraction rate among drawpoints will result in more uniform drawdown, resulting in less secondaryfragmentation.

Employingauser-defineddrawpointlayoutanddrawschedule,andusingtheparametersdetailedinTable2asinputsforREBOP,adrawpointfragmentsizedistributioncanbeestimated.

In order to capture the effect of having different lithologieswith different fragment sizes in the drawcolumn,afirstsimulationforeachzonewasrunwiththemechanismofsecondaryfragmentationbeinginactive(primaryfragmentationcurve).TheeffectofthesecondaryfragmentationwithrespecttotheinitialfragmentationovertheyearsofproductionisillustratedinFigure6.Ingeneral,thesizeofrockfragmentstendstodecreaseovertimeduetotheeffectsofblocksplitting,finescushioningandroundingofblockcornersandgenerationoffinesastheblocksmovedownthroughthecolumnasdescribedinSection2.2.However,someofthechangesinfragmentationcanalsobeattributedtothespatialdistributionofprimaryfragmentation,whichtendstobecoarseatthetopandbottomofthecolumnandfineratthecolumnmid-height,andalsovarieslaterallywithlithology.

Figure 5 Cross-section of the block model mean fragment size for different geological rock domains, blue being fine fragmentation and purple coarse fragmentation

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Table 2 Inputs parameters for REBOP model

Input Parameters

Initial/Maximum Porosity [%] Clay:45/65,Rock:0/45

Solid Density [tonnes/m3] 2.8

Angle of Repose [°] 50

Friction angle[°] Rock:42,Clay:20

Tensile Strength [MPa] Mean:8.23Stdv:2.18

Fragment Size [m] Clay:0.1,Rock:Mean[0.6-1]

Block Model Cell Size [m] 12

Base fragment Size for Scale effect relation [m] 0.05

Exponent in Scale Effect relation k=0.18

Figure 6 Evolution of the global fragmentation over years of production

4 Conclusions

Anewmethodology for prediction of secondary fragmentation in cavemines has been presented.Themethodology relies on the use of flow simulation aswell as experimental studies on shearing-inducedattrition.Theprimaryfragmentationinput toREBOPhasbeenderivedfromseparatenumericalmodelsthatlinkcavinginducedstressesatfailureinthecavebackvicinitywithassociatedfragmentationofrockmasses. REBOP simulations of flow (employing a user-defined drawpoint layout and draw schedule)estimatesecondaryfragmentationinthecolumnandatthedrawpointsasafunctionofshearstrain,normalstressandtensilestrength.


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Ithasbeenconfirmedthatthemethodologyisreliable;usingthemechanismsfoundintheliteratureatthesametimeservethepurposeofbeingpracticalandapplicabletoperformforwardanalysesincavemines.

Adirectiontoimprovethismethodologywouldbetoincludeasecondaryfragmentationmechanismviaimpactloading,whichcanoccurwhenrockfragmentsfallthroughanairgap.Byincludingthisapproachand performing further enhancements, thismethodology could be used routinely as a practical tool toestimatecavedrocksecondaryfragmentationforthedesignofcavingoperations.

References

Bridgwater,J,Utsumi,R,Zhang,Z&Tuladhar,T2003,‘Particleattritionduetoshearing–theeffectofstress,strainandparticleshape’,ChemicalEngineeringScience,vol.58,pp.4649-4665.

Brown, ET 2007, Block Caving Geomechanics, The International Caving Study 1997-2004, 2nd ed.,JKRMC:Brisbane.

Castro,R,Fuenzalida,M&Lund,F2014,‘Experimentalstudyofgravityflowunderconfinedconditions’,IntJRockMechandMinSc,vol.67,pp.164-169.

Castro,R2006,Studyof themechanismsofgranularflowforblockcaving,PhDThesis,UniversityofQueensland,Australia.

Esterhuizen,GS1998,ICSMeetingMinutes,BCFReview,Brisbane,Australia.

Garza-Cruz,TV&Pierce,M2014,‘A3DECModelforHeavilyVeinedMassiveRockMasses’,Proceedings48thUSRockMechanics/GeomechanicsSymposium,Minneapolis,USA.

Garza-Cruz,TV2014,‘A3DEC-FLAC3DModeltoPredictPrimaryFragmentationDistributioninCaveMines’,Proceedingsofthe3rdInternationalSymposiumonBlockandSublevelCaving,(ed.R.Castro),Santiago,Chile.

Hoek,E&Brown,ET1980,UndergroundExcavationsinRock,London,Instn.Min.Metall.

Itasca Consulting Group, Inc. 2013, 3DEC – Three-Dimensional Distinct Element Code, Ver. 5.0.Minneapolis:Itasca

ItascaConsultingGroup,Inc.2013,FLAC3D–FastLagrangianAnalysisofContinuain3Dimensions,Ver.5.0.Minneapolis:Itasca

Lausbcher,D2000,BlockCavingManual.PreparedforInternationalCavingStudy.JKMRCandItascaConsultingGroup,Inc.:Brisbane.

Laubscher,D1994,‘CaveMining-TheStateoftheArt’,J.SouthAfricanInst.Min.Metall.,vol.94,pp.279-293.

Lorig,LJ&CundallPA2000,‘Arapidgravityflowsimulator,FinalReport’,InternationalCavingStudy,E.T.Brown(Ed.),JKMRCandItascaConsultingGroupInc.,Brisbane,Australia.

Pierce,M2010,AModelforGravityFlowofFragmentedRockinBlockCavingMines,Ph.D.Thesis,UniversityofQueensland.

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Pierce, M,Weatherley, EK & Kojovic, T 2010, ‘A hybrid methodology for secondary fragmentationpredictionincavemines’,inProceedingsofthe2ndInternationalSymposiumonBlockandSublevelCaving,(ed.Y.Potvin),pp.567-581.

PierceM,Gaida,M&DeGagne,D2009, ‘Estimationof rockblock strength’,Proceedingsof the3rdCANUSRockMechanicsSymposium,ed.M.DiederichsandG.Graselli,Toronto,Canada.

Yoshinaka,R,OsadaM,ParkH,SasakiT&Sasaki,K2008,‘Practicaldeterminationofmechanicaldesignparametersofintactrockconsideringscaleeffect’,EngineeringGeology,vol.96,pp.173-186.


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Case Study: Improving SLC recovery by measuring ore flow with electronic markers

S Steffen Elexon Mining, AustraliaP Kuiper Elexon Mining, Australia

Abstract

Many industries have embraced the concept of improving an operation’s performance by measuring a process, analysing the results, implementing an improvement, and then measuring the effect of the improvement. Sublevel caving lends itself well to such an approach, because the mining method involves a process that is continually repeated. Changes can be implemented commencing from any blast ring, which means each blast ring is an opportunity to improve the process, and that improvement can be extended to all subsequent rings. Sublevel caving (SLC) is prone to dilution, which has a significant effect on a mine’s profitability, so keeping dilution low or reducing dilution by process improvement is attractive. However, most of the factors relating to dilution ultimately relate to ore flow and until recently, there were limited means to monitor ore flow. Before Smart Markers, steel markers were used to measure ore flow; however, their use was time consuming and interfered with production and they were therefore not systematically used. Thus, SLC mining remained a “black box” mining method with limited control over production.

An SLC gold mine in Australia, managed by a dynamic and innovative team, has succeeded in closing the process improvement loop: The mine used Smart Markers to measure ore flow and analysed the data. Based on the data, it then reassessed its drill and blast processes and implemented changes. It then measured the effect of the changes. The new data indicates a 4% improvement in primary recovery. An analysis of the effect of this improvement indicates that, if the improvement in primary recovery is sustained, the mine’s overall profit should increase by between 4% and 14%.

1 Introduction

Withopenstoping,minesassesstheperformanceof theirrecoverybymonitoringthemined-outcavity.Under-andover-breakareeasilyidentifiableandcanbetargetedforimprovement.However,withSublevelCaving, voids are automatically filled by gravity with broken rock. Thismechanism does not usuallyallowavisibleassessmentoforerecoveryandflow,inparticularwherethedesiredoreandwastematerial(dilution) is coming from, andwhichmaterial has been left behind (ore loss).Understanding oreflowis crucial to be able to systematically improvemining practices. For example, SmartMarker data hasshownthatpartsoftargetedSLCringsareconsistentlynotrecoveredandresultinunderperformingmineralrecovery.Knowingwhichpartsoftheringarenotrecoveredcanenableminestosystematicallytargetthecausesoftheoreloss.

Sincethe1960’s,SLCresearchhasconcentratedonunderstandingandmodellingmaterialflow.Themainthrusthasbeentoidentifyagenerallyvalidoreflowmechanismthatcouldthenbeusedtooptimiserecoveryinsublevelcavemines. Investigations involvedsmall-scalephysicalmodels, full scaleflowmonitoringmarkertrialsandcomputermodelling.Todate,anumberoftheorieshavebeenproposedtoexplaintheoreflowmechanisminsublevelcaving.Theseinvestigationshaveprimarilybeenscientificinnatureandtheresultshavenotusuallybeenintegratedintominingproductionorusedtoimproveminingprocedures.Thishasbeenpartlyduetotheimpracticalityofusingsteelmarkersinproductionenvironments.Theavailabilityof electronicSmartMarkershasnowmade it possible tomonitorflowandore recoverywithminimalinterferencetoproduction.

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Overrecentdecades,theunderstandingofmodernsublevelcavinghasadvancedsignificantlymainlyduetoacademicresearchinconjunctionwithsomelargerminingoperations.Newcrest’sRidgewaySLCmineisregularlycitedasanexampleofwhatcanbeachievedbyawellorganisedproductionteamfocusedonoptimisingmining.Theirachievementsincluded:

• Miningproductionabove6milliontonsperyearwhichwaswellabovetheplanned4milliontonperyearnameplateproduction(Popaetal.2012),

• Minedtonneswere10%morethanreservetonnes(Manca2012),

• Augradewasdownby6%butAuquantitywas3%higherthanreserve(Manca2012),and

• Cugradewasdownby2.5%butCuquantitywas7%higherthanreserve(Manca2012).

Ridgeway’s SLC mining success can be attributed to a combination of disciplined mining and theimplementationoffeedbackloopstosystematicallyimproveminingprocedures.TheRidgewayteamusedsteelmarkerstoinvestigatedilutionentrypointsandunderstandmultilevelrecovery(Power,2004).Initially,animprovementinprimaryrecoverywasnotachieved(Power,2004),althougha20%costreductionwasachieved.LaterworkbyLucaPopashowedthatimproveddrillandblasttechniquesdidincreaseprimaryrecovery(Popaetal,2012).

2 The importance of controlling dilution

Uncontrolledoreflowis likely tohappen ifminingpracticesarenotoptimisedfor the localmine.Theconsequence of uncontrolled ore flow from a recovery perspective is excessive grade dilution and orelosses.(Uncontrolledoreflowcanhaveotherconsequences,includingsafetyissues,butthatisnotcoveredhere).DilutionandorelossbothhaveadetrimentaleffectontheeconomicperformanceofSLCmines;howeverdilutionhasagreatereconomicimpactthanoreloss.Supposeatonneoforeislost.Anothertonnewillbeminedinsteadofit,andeventually,themineclosealittlebitearlier.Thatmeanstheprofitonthattonneislost,andthatlossisrealisedattheendofthemine’slife.However,ifatonneofwastematerialisminedinsteadofore,thattonnewillstillbehauledandprocessed.Thecostsinvolvedinprocessingatonneofwastematerialarethesameasprocessinghighgradeore;howeverthattonnegenerateszerorevenue.Thatmeansmineprofitsarereducedbytherevenuefromthemineralinatonneofore,nottheprofitmadeon a tonne of ore.That causes an amplified effect on profit. For example,with amine likeRidgewaySLC,animprovementof0.1g/tinminedgradethroughreduceddilutionwouldhavegeneratedadditionalrevenueofUSD25,200,000peryear(6Mt/y*0.1g/t*42USD/g).Conversely,anincreaseindilutionof0.1g/tgradewouldreducerevenuebyUS25,200,000peryear.Higherthanplanneddilutionratesquicklyerodemineprofits.

Projectswithmarginaleconomicratesofreturnareparticularlysensitive tochanges indilutionrates:asmallchangecanmeanthedifferencebetweenaprofitablemineandaloss-makingone.Insublevelcaving,dilutionandorelossgousuallyhandinhandbecauseacertainnumberoftonnesareextractedfromeachdrawpoint.If,forwhateverreason,acertainnumberoftonnesoforeareleftbehind,materialwillflowfromsomewhereelsetomakeupthetonnesextracted.Thismaterial’sgradeismorelikelytobediluted.

3 The importance of drill and blast to control ore flow

Amentionedabove,exertingoptimisedcontroloveroreflowiscriticalforachievinggoodmineperformance.Drillandblastproceduresarethemaininstrumenttoinfluenceoreflowandengineerorerecovery.Thepurposeofdrillandblastistofragmentthetargetedrockandtomobiliseandinducetheflowoftheblastedoretodrawpoints.Appropriatedrillandblastproceduresarecriticaltothesuccessofsublevelcaving(Popa2012).Productionblastinginsublevelcavingisconfinedblasting,meaningthatthemateriallyingflush


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with the rock tobeblasted iseitherpreviouslyblastedmaterial,cavedmaterialorfill (Brunton,2009).Confinedblastingisdifferenttounconfinedblastingasisgenerallyusedinopenstoping.Theeffectsofsuboptimaldrillandblastinsublevelcavingcaninclude:Sub-optimaloreflow,Oversizedrocks,hangups,frozenrings;holelosses,browdamageandbackbreak.

Theconsequencesofthisaresafetyissues(e.g.workingnearunstablebrows),increasedproductioncostsduetoproductioninterruptionandreworks,andgradeandproductionvariability.Assessingtheperformanceofaconfinedblastischallengingduetothefactthatthephysicalimpactofdrillandblastonthein-siturocksisnoteasilyassessable.Confinedblastperformanceishighlydependentonanadequatedistributionofexplosiveenergyoptimisedforthelocalmineconditions,suchasthemine’sdesign,rockproperties,stresses,mining sequence etc.The delivery of explosive energy depends on amultistep process.Blastringsneedtobedesignedwiththeoptimalexplosiveenergydistributioninmind.Theblastringsneedtobedrilledaccordingtotheplan.Theblastholesneedtobepreparedandchargedproperly.Theholeshavetobedrilledaccurately,asanydeviationmaychangethegeometryoftheexplosiveenergydistribution.Holesfoundtobeblockedduringpreppingmayrequirecleaningorredrillinginordertochargethemwithexplosive.Boostersmustbepositionedappropriatelyandthedetonatorsequencingtimingmustbeproperlyimplemented.Eachsteppriortotheinitiationoftheblastisimportanttosuccess.Thekeytoimprovingdrill and blast results lies inmeasuring the performanceof each step, includingore recovery and thenimplementingchangestodrillandblastdesignandpracticetoachieveimprovedresults.

Optimised drill and blast can improve primary recovery, which has been achieved at Ridgeway SLC(Popa,2012).Primaryrecoveryisthebestopportunitytorecoverundilutedoregrade(Brunton2009).Thepotentialconsequenceofnon-primaryrecoveryisthathighgradeoremaymixwithlowgradeorwastematerialwhileflowingoverseveralsublevelstotheextractionpoint.Theresultcanbesignificantdilutionofminedoregrade.

4 Deviating flow

TherearethreemainrecoverytargetzonesinanSLCring:thecore,shoulderandapexzone(Figure1).

Figure 1 SLC ring recovery zones (Steffen & Clark 2013)

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Gravity Flow

Markerdatahasshownthat:

• Mostof theprimaryrecovery tends tobeextractedfromthecorezone,which isverticallyabovetheextractiondrive.However,onlyacertainfractionofthismaterialisrecoveredduringprimaryrecovery.

• Lessmaterialtendstoberecoveredfromtheshoulderzone,butasitisliesabovetheapexofthelevelbelowit,itshouldbeaccessibleassecondaryrecoveryfromthedrivebelow.

• Recoveringtheapexzoneisimportantforoverallrecoveryasitisasignificantcomponentofthering’stonnageanditisalsothekeytorecoveringtheshoulderzonematerialinthelevelaboveit.

Incaseswherethereisa12.5mspacingbetweendrivecentres,thecorezonerepresentsapproximately50%ofthering’stonnage,whiletheapexandshoulderzoneeachrepresentaround25%ofthering’smaterial.

GavinPower (Power2004), described the effect of deviatingflow,whichoccurswhen the apex is notsufficientlyfragmentedandmobilised.Thispreventsoreflowfrompropagatingthroughtheapexandintotheshoulderzonestargetedbysecondaryrecovery.Instead,theoreflowdeviatesintothedepleteddrivesofthelevelabove(Figure2).Thiswillintroducematerialofpotentiallydilutedgrade.Theapexandshoulderzone,whichaccountforaround50%oftheringtonnage,arenotbeingwellrecoveredduethedeviatingfloweffectdescribed.Withtonnage-baseddrawcontrol,apredeterminedamountoforeisbeingrecoveredfromtheproductionring.Iftheamountofdrawnmaterialislargerthanthematerialrecoveredfromthetargetedringorfromtheshoulderzoneofthelevelabove,ithastobecomingfromotherplaceswhichareofunknownandpotentiallydilutedgrade.Ifdeviatingflowoccurssystematically,possiblytoduetoinadequatedrillandblastprocesses,asignificantamountofhighgradeorecanbeleftbehindintheapexesandtheshoulderzone.

Figure 2 Deviating flow illustration (Steffen & Clark 2013)

5 Case study

Thiscase study showsanexampleof amine thathasactivelyusedSmartMarkers tounderstand theirrecoveryandmadechangestominingprocedurestoimprovetheirperformance.ThiscasestudyisofanAustraliangoldmineemployingsublevelcavingproductionprincipleswithzerogradewastebackfillfrom


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thetoptoreducethelikelihoodofcavingofthecrownpillar.Themineisanonymousinthispaper,becausetherewasinsufficienttimetohavetheminemanagementreviewthispaperbeforethesubmissiondeadline.Themineagreedtohavetheirdatausedwithouttheirreview,iftheirdetailswerekeptanonymous.Theminehasanannualproductionrateofaround1.5Mt.Themineisinfullproductionandto-datehasmetitseconomicperformanceexpectations.Thiscanbeattributedmainlytotheeffortsofthedynamictechnicalteamattheminesite.Theminehasgonethroughseveralstagesofoptimisingtheirminingprocedures,including drill and blast, using a SmartMarker system.This paper shows the actual ore flow patternsrecordedby theSmartMarkerSystem in thismine.Todate,SmartMarkershavebeen installedonsixsublevels.On thefirst two levels,SmartMarkerswere installed inupholes.Fromthe third leveldown,SmartMarkerswereinstalledindown-holesfromthelevelabovethetargetedsublevel.ThisapproachwaschosentosimplifyinstallationbyloweringchainsofMarkersdowntheholesinsteadofhavingtopushthemupindividually.Aftermininghadprogressedthroughtheproductionringsonlevelstwoandthree,theSmartMarkerdatawasanalysedtoevaluateareasofpoorrecoveryandadversefloweffect.Thedatacollected indicated that deviatingoreflowhadoccurred.Secondquartile primarymarker recoveryhaddroppedwitheverylevelandreached30.3%.Theprimaryrecoveryfigurequotedhereissomewhatlow.Theactualfigureisprobablyhigher,becausehighlyfragmentedmaterialclosetotheblastholesismorelikelytoflowtothedrawpoint;Markerscanonlybeplaced65cmfromtheblastring.TheSecondquartilemarkerrecoveryfromtheapexzoneswas20.6%.Figures3and4showSmartMarkerdatasignatures;theseshowdeviatingflowandpartialrecoveryoftheapexandtheshoulderzonesonthelevelabove.

Figure 3 Deviating Flow Pattern measured with Smart Markers (L) and Example of partial apex and shoulder zone recovery (R)

Anoutcomeofthemarkerdataanalysiswasarecommendationtoreassessthedrillandblastprocesses,whichwassubsequentlydone.Thereassessmentindicatedpotentialareasforimprovementsindrillandblastdesignandprocedures.Anumberofchangeswereimplementedandtheirresultsassessed.MoreSmartMarkerswereinstalledtoanalysetheeffectofthechangesonprimaryrecovery.Thechangesinvolved:

• Increasingthedistancebetweenthetoesofblastholesandanypreconditionedground(suchasdrivesandblastedoreathigherlevels),inordertopreventexplosiveenergyfromventingintopreconditionedground,toreduceholeblockageduetoloosematerialandtodecreasedamagetoringholesinneighbouringproductiondrives.

• reducingthepowderfactor.

• changingtheunchargedcollarlengths.

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• improvingdrillingaccuracy.

• triallingdifferentringdesignsincludinganinnovativeasymmetricalringdesign.

Reducingthenumberofblastholeswouldreduceproductioncostsaccordingly.Theresultsfromthefirstroundofprocesschanges,whichincludedaringdesignwithalowernumberofholes,resultedinanotablereductionofflowdeviatingthroughthedepleteddriveofthelevelaboveandimprovedrecoveryoftheapexzones.Secondquartileprimarymarkerrecoveryincreasedto31.6%andapexrecoveryof45%.TheeffectonsecondaryrecoverywasunfortunatelynotobservablebecauseSmartMarkerswerenotinstalledintheringsonthesublevelabove.

Figure 4 Improved Apex and primary recovery

Figure 5 Further improvements to apex and primary recovery


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Inasecondsetoftrials,aninnovativeasymmetricalblast-ringdesignwastrialled.Theaimofthisdesignwas to reduce thedamage to shoulderblastholes in theneighbouringproductiondriveand to increaseprimaryorerecovery.Theresultsareshowninthegraphicbelow.Afurtherincreaseintheapexrecoveryandoverallprimaryrecoverywasobserved.Secondquartileprimaryrecoveryincreasedto34.4%andapexrecoveryof50%.Again,secondaryrecoverywasunfortunatelynotassessedbecauseSmartMarkerswerenotinstalleddirectlyabovetherelevantSmartMarkerinstallation.Thesetrialshaveshownsomepromisinginitial results.The authorbelieves that evenmoredrill andblast improvements are achievable throughrefiningthecurrentapproach.

7 Financial effect of improving primary recovery

This section attempts to estimate the financial benefit of improving primary recovery, assuming theimprovementisimplementedinallfuturerings.SLCmineshaveanoverallrateofdilution.Forthesubjectmine,thisisassumedtobe20%.Primaryrecoveryextractsvirginore,withzerodilution.Thatmeansthedilutionisalwaysinthenon-primaryrecovery.Thesubjectminedrawsapproximately1450tonnesoforefromeachring.Theearlydataindicatesthattheimplementedchangesimprovedprimaryrecoveryby4%.Thismeansthat4%x1450=58tonnesoforecamefromthetargetringinsteadoffromsomewhereelse.(Asmentionedpreviously, theoverallprimaryrecoveryfigureissomewhat low;howevera lower-than-actualprimaryrecoveryfigureunderstates,ratherthanoverstatesthebenefitcalculations).

Herearefiguresfromthesubjectmine’s2013financialreports:

141846ouncesproduced(4,411kgat31.1gramspertroyounce)

AUD$1122perounceallincosts($36pergram)

AUD$1562perounce($50pergram)

Oregrade:2.8gramspertonne

High estimate: Let us assume that the improvement in primary recovery also results in an equivalentimprovementtosecondary,tertiary,etcrecovery.Inthatcase,revenuewillincreaseby4%.

Usingtheabovefigures,revenuewas4,411x103gx50dollars/g=$220Million.

4%xrevenue=$8.8Million

The2013profitwas4,411x103gx(50-36dollars/g)=$62Million

Therefore,thehighestimateofimprovementtoprofit,expressedasapercentageis$8.8m/$62m=14%.

Low estimate:Letusassumethatthechangesonlyimproveprimaryrecovery,butdonotimprovesecondary,tertiaryetcrecoveryatall.Ifthatwasthecase,the58tonnesperringofimprovementinprimaryrecoverywouldbereplacing58tonnesofdilutedore.Toworkouttheimprovedoregrade,weneedtoestimatethedilutioninthenon-primaryrecovery.Ifoveralldilution(includingprimaryrecovery)is20%,andprimaryrecoveryis30%,thenallofthedilutionisinthe70%non-primaryrecovery.Therefore,thenon-primaryrecoveryhas20%/70%=29%dilution.

Ifprimaryrecoveryimprovesfrom30%to34%,thenprimaryrecoverywouldhaveincreasedannuallyby

(0.34–0.3)x4,411,000g/2.8g/t=64kt

This tonnagewouldhavehad an improvement in gradeof 29% (the differencebetweenundiluted anddilutedore).Thatworksoutto0.29x64x103x2.8grams=52,000gofadditionalgold.

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Thisequatesto52000x$50=$2.6millioninadditionalprofit.

Fromabove,themine’soverallprofitwas$62Million.Thereforethelowestimateofpercentageimprovedprofitis4%.

Overall estimate: The actual improvement will lie somewhere between the low and high estimate.Secondary,tertiaryetcrecoveryshouldimprove,butwedonotknowbyhowmuch.Fornow,let’sassumehalfwayinbetween.SeeTable1.

Table 1 Estimate of improved profit from implemented changes

Estimate Dollars As % of profit

High $8.8Million 14%

Low $2.6Million 4%

Probable $5.7Million 9%

6 Conclusion

Nowthattherearesuitabletoolstomeasureoreflow,SLCminesareidealtoimplementprocessimprovementbymeasuringperformance,analysing thedata, implementingan improvementandmeasuring theeffectof the improvement.Thispapershowsearlyresultsfromasuccessful implementationof thisapproach.Preliminaryanalysisshowsthattheimprovements,seeTable1,areverypromising,butthedatawasquite“fresh”attimeofwriting.Asmoredataisgathered,thepicturewillbecomeclearer.Also,thisisonlythebeginning-furthergainsareverylikelytobefoundbycontinuingthisprocessofimprovement.

Acknowledgments

Theauthorwouldliketothankthefollowingpeopleforsharingtheirknowledgeandfortakingthetimetoengageinmanydebatesonhowtoimprovecavemines(innoparticularorder):StuartLong,LucaPopa,NigelClark,IanBrunton,GideonChitombo,AlanGuestandOttoRichter.Thispaperdoesnotnecessarilyrepresenttheiropinions.

References

Brunton, I2009,The impactofblastingonsublevelcavingmaterialflowbehaviourandrecovery,PhDthesis,WHBryanMiningandGeologyResearchCentre,UniversityofQueensland,StLucia,Australia

Jamieson,M2012,‘DevelopmentofSubLevelCaveDrawOptimisationatNewcrestMining’,MassMIN2012ConferenceProceedings.

Manca,L&Malone,E2012,‘CadiaValleyMinespast,presentandfuture’,AUSIMM,Availablefrom:http://www.ausimm.com.au/content/docs/branch/sydney_2012_11_02_presentation.pdf [28April2014].

Power,GR2014,Modellinggranularflowincavingmines:largescalephysicalmodellingandfullscaleexperiments,PhDThesis,TheUniversityofQueensland,Brisbane.


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Power,G 2004, ‘Full scale SLCdraw trials atRidgewayGoldMine’, Proceedings ofMassMin 2004,(KarzulovicA.andAlfaroM.eds),22–25August,Santiago,Chile:InstitutodeIngenierosdeChile,pp.225–230.

Popa,L,Trout,P&Jones,C2012,‘TheevolutionandoptimizationofsublevelcavedrillandblastpracticeatRidgewayGoldMine-productionrings’,inProceedingsMassmin2012.

Vila,D2012,Calibrationofamixingmodel for sublevelcaving,PhDThesis, theFacultyofGraduateStudies,TheUniversityofBritishColumbia,Vancouver,Canada.

Steffen,S&Clark,N2013,‘Sub-levelcaving:engineeredtoperform’,TheMiningMagazine,SeptemberEdition.

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Gravity Flow

Stochastic models for gravity flow: numerical considerations

WH Gibson SRK Consulting (Australasia) Pty Ltd, Australia

Abstract

Material flow analysis is commonly required in mining methods where the rock has to move from its initial position to the extraction point, commonly known as gravity flow. This happens in mining methods, such as, sublevel caving, panel caving, block caving. During this process, the ore is diluted with waste from the walls of the cave column or waste sitting on top of the ore. It is essential for the mining design to assess the degree of dilution for different draw strategies to minimise the waste extraction and optimise the ore recovery. Stochastic methods have proved to be good options for this type of assessment.

1 Introduction

Rulesandprobabilitiesarethebasesofstochasticmethodsfordescribingphenomena,theyarenotfoundedonaphysicalprinciplethat“forces”themathtoproducetherightresults(forexample,theFiniteElementMethodminimisingpotentialenergytoderivetheequationsusedtosolvetheproblem).Stochasticmodelsforgravityflowusejustconservationofmass.

Despite the weak formulation, stochastic methods can be very powerful tools to solve material flowproblems,andsomeofthemerits,limitationsandpitfallsofthesemethodsareexploredinthispaper.

2 Description of the program MFlow

Stochasticmethodsaremodellingtoolsforestimatingoutcomesbyallowingforrandomvariationsinoneormoreinputsovertime.Whenmaterialisremovedfromadrawpoint,avoidiscreatedthatisfilledwithmaterialfromabovethevoid.Theexactsourceofthatmaterialisunknown;therefore,arandomlocationisassigned.

Figure 1 shows a plan view of a grid describing this concept. When part of thematerial is removedfromacellbelowthecentreofthegrid,thevoidisfilledfromanyoftheninecellsabove.Thisprocessis random,making stochasticmodels ideal for this type of problem solving. In this particular case, aprobabilityisassignedtoeachcellindicatingthechancesthatthevoidwillbefilledwithmaterialfromthecellimmediatelyabove(60%)oranyofthesurroundingcells(8%chancecellswithadjacentsidesand2%forcellswithadjacentcorners;percentagesgivenasanexampleonly).

ThegridshowninFigure1doesnotmatchtheaxisymmetricnatureoftheproblem;abettergridisshowninFigure2.Thistypeofgridisbetterforanalysingmaterialflowbecausethegridcanmodelnaturallyanaxisymmetricproblem.Thedisadvantageofthistypeofgridistheshapeofthecells,whichcomplicatesthecalculation.


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Figure 1 Plan view of basic cube grid

Figure 2 Plan view of hexagonal prisms grid

Figure3showsagridthatcombinesthesimplegeometryofcubesandthecircularlocationofhexagonalprisms.MFlowusesthisconfigurationofcells;prepresentstheprobabilitythatmaterialwillbetransferredfromthecellabovewhile(1-p)/6istheprobabilitythatoneofthecellsaroundthecentralcellcantransfermaterialtothevoidbelow.

Figure 3 Plan view of cube shifted grid

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3 Pascal “cone” and correlation between probabilities and ellipsoid width

ThePascaltrianglecanbeusedtounderstandhowprobabilitiescanbeusedtoassessmaterialflowin2D.Theconceptisextendedtoa3D(PascalCone)tomodelthegravityflowinthreedimensions.

3.1 Model in 2 Dimensions

ThePascaltrianglecanbeusedtoassesstheprobabilitythatadrunkenmancanreachhome(depictedbytheshadedboxinFigure4),startinginaparticularstreetandwalkingrandomly(Harr1987).Ateverycorner,thereisa50%chancethatthemanwillturneitherleftorright.FromFigure5,itispossibletoseetheprobabilityofreachinghomeis5/16.

Ingeneral,theprobabilityofreachingcornerratstreetncanbecalculatedasfollows:

(1)

Figure 4 2D random walk (after Harr 1987)

Theprobabilisticanalysis indicates thechancesof themanendingsomeblocksawayfromhome. Theactualdistancebetweenthemanandhishomewilldependonthesizeofthecityblocks.Thesameproblemoccursinmaterialflowasisexplainedinthefollowingparagraphs.

Figure5presentstheprobabilitiesassociatedwiththis2Drandomwalk.IfweimagineFigure5upsidedown,wecanseethatthevalueindicatedinanycellrepresentstheprobabilityofacellbeingaffectedbyextractionofthecellwithprobability1(nowatthebottom).Thisisaverysimplistic2Dmodel,whereeachcellhasonlytwocellsabovewithequalprobabilitytofillthevoidgeneratedinthecellbelow,inthiscase,thechancethatthecellhighlightedinFigure5isaffectedbytheextractionis5/16.Wecansay,afterthisanalysis,thatthecellaffectedisone(1)celltotheleftofthecellofextractionbuttheactualdistanceofthiscelltotheextractionpointwillbeafunctionofthecellsize.

A2Dmodelcanbebuiltwithonlythree(3)cellsthatcantransfermaterialtocellsbelow.Withthissimplemodel,itispossibletostudytheimpactoftheparameterpontheellipsoidofextraction.Figure6presentsaverticalsectionshowingprobabilitiesthateachcellwilltransfermaterialtothecellbelow.


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Figure 5 Probabilities associated with 2D random walk

Figure 6 Probabilities within a simple 2D model

Whenthisconceptisappliedina2Dproblem,itispossibletoassessthechancesthataparticularcellwillbeaffectedbytheextraction.Figure7presentsacross-sectionwithasingledrawlocatedatx=7m.Itshowstheprobabilitythatmateriallocatedabovethedrawpointismobilisedduringextraction.Theresultsarepresentedfortwovaluesofp=0.40and0.70.Inbothcases,thesameamountofmaterialisremovedfromeachdrawpoint.

Itispossibletoobservethattheparameterpcontrolsthewidthandheightoftheellipsoidofextraction.Ap-valuecloser to1producesanarrowerellipsoidofextraction thanasmallerp-value. Acorrelationbetweenpandtheactualwidthoftheellipsoidofextractionisdiscussedlater.

Therealchallengeisin3D,wherethecalculationsoftheprobabilitiesaremorecomplicated.IfFigure3isusedasareference,thenthereareseven(7)cellsthatcanfillthevoidinthecellbelowandtheprobabilitiesforeachofthecellsarenotthesame.

3.2 Model in 3 dimensions

The3DproblemcanbeviewednotasaPascaltriangle,butmoreasaninverted“Pascalcone”.Itstartswithacellwithprobabilityof1(certainlywewillremovematerialfromthere)andthenextlayeraboverepresenttheprobabilitythatonecellwillfillthevoidcreatedbelow.Aswemoveupalayerandmorecellsareinvolvedinthecalculation,thewidthofinteractionbetweencellsiscontrolledbythevalueoftheprobabilitypindicatedinFigure3.

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Gravity Flow

Asimilarcalculationcanbecarriedoutforthe3Dcase.Withthiscalculation,itispossibletoevaluatethediameteroftheellipsoidofextractionfordifferentvaluesofp,asshowninFigure8.Notethatthewidthisexpressedinnumberofcellsaffectedbytheextraction,andnotinactualdistancemeasuredinmetres.

Figure 8 Width of ellipsoid of material mobilised as a function of parameter p

Figure 7 Probability of material being mobilised for parameter p=0.7 and p=0.4


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Thiseffectcanbevisualisedona3Dmodeloftwoindependentdrawpointsinuniformmaterial.Figure9showsthemobilisedellipsoids,clearlyillustratingtheeffectofparameterponthewidthoftheellipsoidofextraction.Thisisanobservedbehaviourthat,dependingonphysicalpropertiesofthebrokenrock,iscontrolledbythewidthoftheellipsoidofextraction.

Figure 9 Extraction column width for different p parameters

3.3 Calibration of parameter p with observed material behaviour

Itwasshownthatinthesetypesofstochasticmodels,thecellsizeplaysaroleintheresults;therefore,theselectionofthecellsizehastobemadeconsideringotherparametersinthemodeltobeabletoreproducethephenomenathathaveactuallybeenobserved.

Theparameterpcontrolsthewidthordiameterofthedrawcolumninthemodel(nottheactuallengthinmetres,butthenumberofcellsthatwillbeaffectedbythedraw).Ifthewidthoftheellipsoidofextractionisknown,itispossibletoselectacellsizeandaprobabilityptobeusedinthemodel.

Thequestionthatremainsishowtorelatetherockmassconditionorotherobservedbehaviourtoparameterpinordertobuildmodelsthatrepresenttheactualmaterialflow.

Therehavebeensomeattempts to relate rockmasscondition toellipsoidwidth. Laubscher relates thewidth of the ellipsoidwith rockmass classification: the better the rock quality, thewider the ellipsoid(Laubscher1994).Kvapilrecognisedthesamefact–thatanincreaseintherockmassqualityincreasestheellipsoidwidth(Kvapil2008).

Sharrock (2008)made a reviewof several aspects of isolated draw and discusses the results of scaledmodels.Unfortunately,thescaledmodelsdidnotcapturechangesinellipsoidwidthwithmaterialpropertiesorparticlesize.Susaeta(2004)presentedacorrelationbetweenfrictionangle,particlesizeandellipsoidwidth.

Forminesinoperation,itispossibletouseinformationcollectedfromthepreviousextractiontocalibratep.Figure10showstheouncesextractedinaperiodoftimecomparedwiththevaluespredictedusingan

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Gravity Flow

MFlowmodel. Theparameterpwasmodified tominimise thedifferencebetween thecurvesand thencalibratedvalueswereusedinthefutureforecastingmodels.

Figure 10 Ore extracted used to calibrate parameter p

Therearesomeguidelinestocorrelateobservedmaterialbehaviourwithellipsoidwidthandthiscanbeusedtodefineparameterpforagivencellsize(usingFigure8).Theprocessofassessingellipsoidwidthisbasedheavilyonexperienceandobservations,andlessonastrongformulationincludingthecharacteristicsofthematerial.Nevertheless,therearesomeguidelinesthatcanbeusedtobuildthestochasticmodel.

4 Stochastic modelling capabilities

Despite the simplicityof the formulationof stochasticmodels, theycanaddress complexbehaviourofmaterials.Someoftheseareshownanddiscussedinthefollowingparagraphs.

4.1 Variable flow of different materials (w Factor)

Severalfactorsallowforsomematerialstotravelfasterthanothersinthedrawcolumn(Hashim2009).Thistypeofphenomenoncanbeincludedinthemodelbyintroducingaweightingfactor,w,thatmodifiestheprobabilityofmaterialmovingfromonecelltoanother.

Avalueofw=0rendersthematerialunaffectedbydrawanditdoesnotflow.ThiscanbeusedtodefinethelimitsofthedrawringsinSLC(SubLevelCaving)ifitisassumedthattheoutsideoftheblastedringwillnotmove.Avalueofw=1allowsthematerialtomovefreely.

Valuesbetween0and1can,therefore,beusedtocontrolthespeedofmaterialflowinthemodel.Toshowtheeffectoftheparameterwontheresults,themodelshowninFigure11wasbuilt.Abovetheextraction


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pointAthereisfineoreundercoarsewaste,abovetheextractionpointBthefinematerialisontop.

TheresultsareshowninFigure12.Duetofinewastematerial,thereisareductioninoreextractionindrawpointBduetoahigherdilutionoffinematerialtravellingfasterthantheore.

Figure 11 Section of simple model with different materials

Figure 12 Effect of fine material on extraction

4.2 Markers

The formulation of stochastic models is easy and that simplicity allows us to incorporate additionalcalculationintotheanalysiswithoutaddingcomplexitytotheoverallanalysis.Inthiscase,markersareaddedinthemodeltotrackthemovementofthematerial.

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Gravity Flow

Figure13showsthetrajectoryofmarkerslocatedabovedrawpointA.ThesequenceofextractionisAthenBthenC.Itispossibletoseethechangeintrajectoryofsomeofthemarkersandtocomparethenatureofmaterialthatisextractedatthedrawpoints.

Figure 13 Effect of fine material on extraction

4.3 Finger path (void diffusion)

Ithasbeenobservedthatanellipsoidshapedextractioncolumnisnotalwaysgenerated,andthatmaterialcanflowfollowinga“fingerpath”- reachingsurfaceearlier thanexpected(Brown2003). Thiscanbemodelledbymodifyingtheweightingfactorwandgivingahigherprobabilityofmovementtomaterialalreadymobilised.ThisisshowninFigure14forasingledrawpoint.

4.4 Modelling Surface Flow

Surfacefloworunconfinedflowiscontrolledbythereposeangle;thematerialwillflowuntilthereposeangleisreached.Thisintroducesanotherconstraintforthegeometry,drivenbymaterialproperties.Castro(2009)mentionedthatinordertomodelthesurfaceflow,theheight(h)andthesidelengthofthecells(L)shouldfollowtherelationshiph/L=tanf,wherefisthereposeangle.

Figure15illustratesafailureonabenchandflowoftheloosematerial.


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Figure 15 Surface Flow

5 Conclusions

Stochasticmodelshaveamucheasierformulationthanothermethods,suchas,theFiniteElementMethod.However,thelackofformulationbasedonaphysicalprinciplemakesthemmoredifficulttosetupunlessinformationabouttherockmasstobemodelledisavailable,thusenablingthemodellertocalibratethemodel.

The results are cell size dependent; therefore, cell size has to be selected alongwith other parameters(probabilities)toensurethemodelbestrepresentsthematerialbehaviourobserved.

Figure 14 Finger path for a single draw point

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It is suggested to calibrate a model using the ellipsoid of extraction width estimated from rockmasscharacterisation or assessment of eccentricity of the ellipsoid, and a correlation presented betweenprobability p and number cells. Past draw performance at themine can be used for calibrationwheninformationaboutthetypeofmaterialextracted,suchasore,wasteandgrades,isavailable.

Despitethelimitationsandtheweakformulation,thispapershowsthatstochasticmodelscanbeusedtodescribecomplexbehaviour,suchas,acceleratedflowoffinematerial,fingerpathflowanddilution.

References

Brown, ET 2003, Block Caving Geomechanics, The International Caving Study Stage I, 1997-2000,UniversityofQuuensland,JuliusKruttschnittMineralResearchCentre,Brisbane.

Castro,R,Gonzalez&Arancibia2009,‘DevelopmentofaGravityFlowNumericalModelfortheEvaluationofDrawpointSpacingforBlock/PanelCaving’,JSouthAfricanInstMinandMetall.vol119.

Harr,ME2005,Reliability-baseddesigninGeotechnicalEngineering,DoverPublicationsInc.,Mineola,NewYork.

Hashim, MHM & Sharrock, GB 2009, ‘Numerical investigation of the effect of particle shape onpercolation’,Proceedings43rdU.S.RockMechanicsSymposium&4thU.S.-CanadaRockMechanicsSymposium,AmericanRockMechanicsAssociation,28 June-1 July,Asheville,NorthCarolina,8pages.

Kvapil,R2004,GravityFlowinSublevelandPanelCaving–ACommonSenseApproach,LuleaUniversityofTechnologyPress,Lulea,Sweden.

Laubscher,DH1994,‘Cavemining–thestateofart’,TheJournalofTheSouthAfricanInstituteofMiningandMetallurgy,October1994,pp.278-293.

Sharrock,GB2008,‘TheIsolatedExtractionZoneinBlockCaving–AReview’,inProceedingsSHIRMS2008,Perth,pp.255-272.

TheInternationalCavingStudyStageI,1997-2000,UniversityofQuensland,JuliusKruttschnittMineralResearchCentre,Brisbane.


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First steps in monitoring gravity flow at El Teniente Mine: installation stage in Block-2, Esmeralda Mine

E Viera Codelco, Chile

M Montecino Codelco, Chile

M Meléndez Codelco, Chile

Abstract

The future scenarios under which panel caving is developing will be even more adverse, mainly because of the complex geomechanical conditions and greater demands for production plans. The quantity and quality of the required information for monitoring and controlling these variables fulfill a main role, thus technology incorporation constitutes a key step in this topic.

From the variables that rule the mining method, cavity control and gravitational flow mechanisms are of vital importance. Technology development has allowed smart electronic devices (Smart Markers) usage, which are installed on higher levels with respect to the production level (undercut level, haulage levels, special drillholes, etc.) Once undercutting and the later caving process are started, the smart markers are part of fragmented material which, once the mining process starts are subject to induced movements, dependent on the draw strategies to be performed. The emergence of these markers in drawpoints and their later transportation through LHD equipment, allow the installed readers at production level (or main haulage level) to record the markers passing, under a certain detection radius.

The aim of the following work is to detail the Smart Markers installation stage, and to show that preliminary results obtained at Esmeralda Mine. In this, near field flow tests (31 m maximum length drillholes between ring blast hole) and far field test (vertical drillholes with a maximum length of 100 m), are being performed on 3 undercut level drift (55, 57 and 59). The installation of 305 markers in the near field test is highlighted (in 16 ring drillholes) from which 96 markers have been registered so far. In the far field tests, 92 markers were installed in 3 vertical drillholes, from which 4 markers have been recorded.

1 Introduction

Inthecomingyears,ElTenientewillbefacingevenmorechallengingscenarios,characterizedbycomplexgeomechanicalviewsalongwithgreaterproductivedemands.Understandingthefragmentedmaterialflowinside the ore columnhas been a verydebated and investigated topic for several years.Differentflowmechanismshavebeenproposed,mostofthembasedonscalesizemodels.However,uptodatetrialstomonitorthematerialflowinsidethecaveatfullscalehavebeenfew(Brunton2012).Mostofthedesignandplanningtoolsthathavebeendevelopedthroughouttheyearsforthematerialflowpredictingarebasedonempiricalrelationshipsmainly.Inordertoimprovethisunderstanding,SmartMarkerswereinstalledatEsmeraldaMine’sBloque2aimingtounderstandandknow,inabetterway,thefragmentedmaterialflowinsideathroughBlockCavingmethod.

2 Objectives

ThemaingoalofthisworkistodetailtheinstallationprocessandmarkersregistryperformedatBloque2EsmeraldaMine,bydistinguishingbetweennearandfarfieldtests.

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Gravity Flow

3 Methodology

Forgravitationalflowmarkerssystemdeployment,stagesformarkersandreadersinstallationareplanned.Asforthemarkersinstallation,stagesaredefinedforfarandnearfieldtests.Bothofthemdetailedbelow.

3.1 Markers installation

3.1.1 Nearfieldtest

Thenearfieldarethosemeasurementofflowunder30fromtheundercuttinglevelasshowninFigure1.ThenearfieldtestwerecarriedoutbetweenundercutsdriftsC-57andC-59whicharespacedat30mapart,withamaximuminstallationheightof31m.Themaingoalofthetestsistostudytheinteractionsofflowatthedrawbellandthroughtheminorandmajorapex.Themarkersareinstalledonradialdrillholesring(3”diameter)interspacedwithblastholeringsusedduringtheundercuttingprocess.

Figure 1 Near field test design (isometric view and HW-FW section)

Theinstallationinnearfieldtestconsideredtheuseofaspiderwithmarkers,whichprovidesadherenceinsidethedrillhole.Oncethefirstmarkerisinstalled,a5mlongPVCbarisintroduced,whichallowstokeepthedistancebetweeneachmarker(avoidingverticaldriftsfromsurroundingblasting).Oncealloftheestimatedmarkersareinstalled,eachoneofthedrillholesiscoveredwithwoodencones,whichavoidsthewholemarkerscompletecolumnverticaldescent.

Figure 2 Installation process in near field


350

TheparametersusedformarkersinstallationinthistestarelistedinTable1.

Table 1 Design parameters in short reach test

Near field design Number /Quantity

Numberofstandsofpipe 10

Numberofdrillholesperstandofpipe 9

Markersperstandofpipe 39

Drilledmetersperstandofpipe(m) 217

Verticalspacingbetweenmarkers(m) 5

3.1.2 Farfieldtest

Far field flowmeasurements are carried out by installingmarkers above the 30m, up to 70 – 100m(accordingtoavailabledrillholelength).Themarkersareinstalledinverticaldrillholeswithamaximumlengthof100m,whichareusedinthepre-conditioningprocess.Thegoalistoobtainrelevantinformation(markersdrifts,mainly),whichallowsverticalandlateralmovementofthemarkerstobequantifiedformedium/longtermproductionplanning.TheusedparametersformarkersinstallationinthistestareareshowninTable2.

Table 2 Design parameters in long reach test

Far field design Number /Quantity

Numberofdrillholes 6

Numberofmarkersperdrillhole 35

Drillholeslength(m) 70-100

Spacingbetweenmarkers(m) 2

Totalnumberofmarkers 210

ThedesignformarkersinstallationinlongdrillholesisshowninFigure3.

Thestepsformarkersinstallationsinverticaldrillholesarethefollowing:

1. Anchorinstallation:ananchorisinstalledinthedrillholebottom(6”diameter)withtheropelinkedtothepulley.ThisprocessusesWassaraorCubexdrillingequipmenttoraisetheanchor.Oneendisfixedandtheotheroneremainsfree.Aslowadvancemustbeconsidered,sinceanyanchortiltingmightresultinitsloss.

2. Freeendfixationtohoistingwinch:oncetheanchorisinstalled,thefixedendmustbeadheredtoahoistingwinchstronglyenoughtorollbackmorethan100mropeandrisingtheplannedload(40kgapproximatelyofrope).

3. Onropemarkersinstallation:markersareinstalledonropewithhighresistanceadhesivetape.Theyarenotinstalledwithspiders,sinceitispossibletheygetstuckwiththeotherrope’send.

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Gravity Flow

4. Rope liftingwithmarkers: lifting isperformeduntil thefirst installedmarkergets to thedrillholebottomor,untiltherope’saddedloadovercomesthewinchhoistingcapacity.

5. Freeendfixationandsecurityplug:thehangedloadissecuredandtheverticaldrillholeisblockedonitsbase.

6. Groutedstage:atthisstageaconcretebombwasused(PutzmeisterequipmentTK–40)giventhatthegroutedlengthisconsiderable.Todothisa2”shut-offvalvewasusedatdrillholetop,underwhichthepipelineisconnectedtotheequipmentexit.A1/2”diameterhoseintothedrillholebottomallowstoknowwheneveritiscompletelygrouted.ThisisshowninFigure4.

Figure 4 Markers installation stages in vertical drillholes

Figure 3 Far field design


352

3.1.3 Installation of markers from higher levels

AboveEsmeralda’sBloque2islocatedthemainhaulagelevel;therailroadTeniente5.TheSuperintendenceofGeologydrilledaseriesofdescendingholes,aimingtoperformauscultationoftherockmass,inordertodeterminethecavebackgrowing.Oncetheselaborsareperformed,thedrillholesarereadyformarkersinstallation.ThedrillholeslocationandthemarkersinstallationcanbeseeninFigure5.

Figure 5 Markers installation in XC-40 FFCC Teniente 5

ThetotalinstallationindrillholeP-3(seeFigure5)were10markers.Inthisareamarkerswillcontinuetobeinstalledasauscultationlaborsarecarriedout.Notethat10markersweinstalledintheXC-40boxinarowof10shortdrillholesof60cmeach.

Consideringtheproposeddesignfortheinstallationandtheoperationalrestrictionsunderwhichtheprocesswasperformed,therehavebeen397markersasinstallationresult(92installedforfarfieldtestand305fornearfieldtest):

Figure 6 Markers installation in near and far field tests

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Gravity Flow

Thenearfieldinstallationcoversa3.192m2surface,whichcorrespondsto5drawbellsbetweenproductiondriftsC-57andC-59(fromdrawbellsZ-30toZ-34).

Thefirstverticaldrillhole(P1T-C59)wasinstalledintheareawhereBloque2(drawbellsZ-30betweenproductiondriftsC-57andC-59)miningstarted.Theapparitionofmarkersinstalledatheightsof30mwasimportantforproductioncontrolpurposes,sinceitallowstoidentifythroughitsextractionthatthecavingwasprogressingastheextractionandareaoftheblockincreased.

3.2 Readers installation

Inordertorecordmostoftheinstalledmarkers,itisnecessarytoplacethereaders,whichcaptureaseriesofinformationwheneverthemarkerspassunderthedeantenna-readerset.Thedatathatiscollectedincludesthedateand timeregistration, IDnumberandmarker type.Figure7shows the readers’ location in theproductionlevelofBloque2.

Figure 7 Readers installation (1, 2 and 3) in Bloque 2 production level

Theselocationofthereadersnearavailableinorepasses,ensuresthatalloftheLHDequipmentworkingattheareahadtopassthroughtheantenna-readerset.Notethatoutofservicereaderswerenotdetectedduringtheestimatedtime.

InMarch2014,areaderwasinstalledatTeniente’s8mainrailroadhaulagelevel,whichislocatedatthemainhaulagedrift,onwhichTeniente’s8mainrailwaypassesby.This readerwillalloweverymarkerinstalledinprojectBloque-2(andalsointhefutureones)toberecorded,whichturnsintoabettermarkerscontrol.

4 Results

4.1 Markers registry

UptoMarch2014atotalof335markershavebeenrecovered.Thisequaltoa30%ofrecovery.

Forthenearfieldtestcase,96markersfromatotalof305havebeenrecorded,whichcanbeseeninFigure8.Anotherfourmarkershavebeenrecoveredfromthefarfield.Therecoveryofthemarkersdoesnotallowtoperformtoomanyconclusionsandthiswouldrequiremoreinformationtobecollectedandanalyzed.


354

Figure 8 Markers recovery in near field test markers installed under drawbells

Inthenearfieldtest,2installationareashavebeenidentified,inwhichtherecoverypercentageisdifferent:

a. Markersrecoveryinstalledindrawbells:asforthemarkersinstalledunderadrawbellminingdirecteffectcase,agreatermarkersrecovercanbeseen,whichgetsto58markers(60%ofthetotalmarkersrecoveredinthenearfieldtest).

b. Markersrecoveryinstalledinminorapex:themarkersrecoveryinstalledintheminorapexhasbeenlowerincomparisontothemarkersinstalledinthedrawbell(40%ofthetotalrecoveredmarkersinthenearfieldtest).

c. Markersrecoveryinfarfieldtest:asforthemarkersinstalledinverticaldrillholescase,4markershavebeenrecordeduptodate.Thesemarkersrecoveryandtheheightsevolutionminedfromthenearingdrawbellsclustercanbeobservedbelow:

Figure 9 Markers recovery in far field test

InFigure9, redcolorunderlines theextractionheight from thedrawpoint,bywhich themarkerswererecorded in thedifferent indicateddates.Note that all of the entriesmatch the area that showsgreaterheightsminedintheindicateddate.Inthenearfuturefurtheranalysiswillbecarriedouttodescribetheflowfromtheseresults.

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Gravity Flow

5 Discussion and conclusions

ThesmartmarkerstestperformedinBloque2EsmeraldaMinevalidatestheusageofthistechnologyforthefutureprojectsoftheElTeniente´sDivision.Thisisbasedonthesuccessfulinstallationof397markersinstalled,withnonomajorincidentsandreachingahighlevelofreliability.Up-to-date,100markershasbeenrecoveredatthemine.

Regardingtheinstallationprocess,themainworkinthefuturewillbetoimprovetheprocessofgroutingverticaldrillholesabove70m.Thisiskeyforthisprojectasmarkersneedstobefixedtorepresenttheflowoftherock.Oneofthelearningsoftheprocesswastheinstallationofareaderinthemainhaulagelevels,whichallowsincreasepossibilitiesrecordpossibilities(andsogettingagreateramountofinformation).

Themarkers’ record installed inverticaldrillholes, that isaboveheightsof50m, it is important, sinceit helped to correlate to the information by the Operational Geomechanical area, which indicated theconnectionofthecavebackofBloque2withTeniente4-Sur,whichislocatedinanupperlevel.Fromaextractionpointofview,thismeantthattherateofdrawwasincreasedto1tpd/m2onanareaof4.700m2.

References

Brunton,I,Sharrock,G&Lett,J2012,‘FullScaleNearFieldFlowBehaviourat theRidgewayDeepsBlockCaveMine’,inProceedingsofMassMin2012,Sudbury,Canada.


356

Experimental study of fines migration for caving mines

F Armijo BCTEC Engineering and Technology, ChileS Irribarra Universidad de Chile, ChileR Castro Universidad de Chile, Chile

Abstract

Fine material has the potential to flow and migrate through the caved zone during its gravity flow. This may have a large impact on the dilution content depending on the grades that fine rock could have. To date there have been some theory and experiments which have tried to quantify the fines migration potential. The objective of this paper is to present results of fine migration using experiments to quantify for a given particle size and draw strategy to the fines migration. For this purpose, a pilot test was constructed to study fines migration on block caving mine using a production level that is geometrical similar to a block cave. In this case, fines and coarse particles were dried while the size of the coarse particles was thirty times the size of the fine particles. The results show that no shear strain occurred when the draw was uniform from drawpoints and then no migration occurs. On the other hand, shear strain occurred under isolated draw and, therefore, fines migration was observed. The results shows that more research needs to be done in terms of fine migration quantification.

1 Introduction

Gravityflowinblockcavingisoneofthekeymechanismsoforedrawing.Underthisconcept,gravityisoneofthemostimportantparameterstoallowthepercolationoffinesthroughcoarseparticles.Iffinesfragmentsofcavedrockcanmigratemorerapidlythancoarsefragments,itmayhavesignificantimpactonorerecoveryanddilution,particularlyifthefinesdoesnothavemineralwitheconomicinterest(Pierce2009).Thereisevidencetosuggestthatfinescanaccumulateinstagnantportionsofthecave(Pierce2009),inthiscase,withthepresenceofwater,highlyunstablemudmaybehappened(Guest2008,inPierce2009).Then,finesmigrationisan important issue inminingbusiness, therefore,understandandmitigatefinesmigrationisfundamentaltogeneratevaluefortheminingbusiness.

Accordingto(Laubscher1994)thefinesmigrationisrelatedtothedifferencethatexistsbetweentherockmassratingRMRoftherocks.Thus,anin-situcolumnwithalargedifferenceofRMRbetweentheoreanddilutionhasahigherheightofinteractionthananin-situcolumnwithasmalldifferenceofRMR.Ahigherheightofinteractionmeansalsoasmallerpointofentrydilution(Figure1).

Figure 1 Fines migration quantification through the HIZ (Laubscher 1994)

357

Gravity Flow

AsnotedbyPierce(2009)experimentalstudiesoffinesmigrationwereperformedby(Bridgwateretal.1978,9)whousedasimpleshearcelltodeformarectangularbedofspheres.Thefinesparticleswiththediameterofdpwereplacedonthetopofthebedofcoarseparticleswiththediameterofdb,thentherateofpercolationisgivenby:

(1)

Where:

-γisshearstrain;

- L(m)isdefinedasthemeanpercolationdistance;

-k1andk2aretwoarbitraryconstantsequalto20and8respectively(Bridgwateretal.1978,inPierce2009).

Notethatitisnecessarytoapplyashearstrain()tomeasurepercolation(),shearstrainandpercolationare directly proportional. Recently, the Equation 1 has been tested in PFC3D (Pierce 2009), (Hashim&Sharrock2009;Hashim2011;Hashim&Sharrock2012).This formulationhas been included in thesoftwareREBOPtocalculatethepercolationoffineparticles(Pierce2009).

Fromanexperimentalpointofview,experiments large3Dphysicalmodelhavebeenalso includedthequantificationoffinesmigration(Power2004andCastro2007).Inlarge3Dexperiments,flowcannotbeobserved,soresearchershaveconsideredtheuseof2Dexperiments(Pineda2012&Orellana2012).Inthisarticle,itshowstheresultsofanexperimentalprogramaimtoquantifyfinesmigration.Theresultsoftheexperimentsarepresented,sotheaudiencecouldmaketheirowninterpretationofoneoftheparadigmsofblockcavingpractice.

3 Methodology

3.1 Physical model design

Inordertoperformexperimentssomerulesneedsfirsttobecomplied,sotheresultsofthemodeldoessomethingtodowithreality.Therearethreesimilaritytypesbetweenaprototype(minefull-scalesituationunderstudyandwhichincludeallthefeaturesofinterest)andamodel(simplifiedphysicalrepresentationoftheprototype,inwhichtheessentialfeaturesareincluded),whicharegeometric,kinematicanddynamicsimilarity.

• TwosystemssatisfythegeometricsimilaritywhenthedistancebetweentwohomologouspointsdependsonascalefactorλL.

• TwosystemssatisfythekinematicsimilaritywhentwoeventshomologousoccuratatimescalefactorλTand

• TwosystemssatisfythedynamicsimilaritywhentherelationbetweentheinertiaandanyexternalforceintwohomologouspointsdependsontheforcescalefactorλF.

Inthistest,onlygeometryandkinematicssimilaritywillbeconsidered.Compactionandbreakagecouldonlybeobservedifverticalloadcouldbeapplied.InTable1,thegeometryscalefactorsareconsideredtowhathavetermedunconfinedflowcondition.


358

Table 1 Scale factor used in pilot model

Fromthemandthegeometry,experimentswereconductedusingapilotmodelof250[cm]height,70[cm]widthand23.3[cm]length,whichinascale1:200represents500[m]ofin-situcolumnand140[m]ofgallery.Therearethirty-sixdrawpoints,whichindicatesthreegalleries;Figure3showsthepilotmodel.Thispilotmodelhasbeenconstructedconsideringgeometrysimilitudeconditionswitharealminingoperation.

(A) (B)

Figure 3 Front view of the physical model (A) and isometric view of draw system (B)

3.2 Physical model experiments

Twoexperimentswererealizedwithdifferentdraw,uniformandisolateddraw.Physicalmodelisloadedwithcoarseparticleupto240[cm]andfineparticlesareloadedovercoarseparticle.ThesizedistributionoffineandcoarseparticlesisshowedinFigure4.

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Gravity Flow

Figure 4 Particle size distributions of fine and coarse materials

Theaveragesizeoffineandcoarsematerialsare0.14[mm]and4.45[mm]respectively,whichcorrespondtoaratioof30.Eachdrawpointinthemodelhasaseparatebin,soitispossibletodeterminetheextractedweightofeachone.Thereisanelectronicdrawsystemwhichsimulatestheextractionatdrawpoints.Thissystemiscontrolledbyacomputerandenablestomovetheparticlesineachdrawpointindependently.Eachdrawpointhasasensorthatidentifieswhenmaterialflowsorproducesahang-up.

During theexperiments, thepointofdilutionentry (PDE)wasobtained,which is thedrawnmass inadrawpointuntilthefirstfineparticleisdrawn,dividedbytheassignedmassforthepoint.Forisolateddrawexperimenttheassignedmasscorrespondstothetotalmassintothepilotmodel.

4 Results

4.1 Results of uniform draw

Figure5showstheflowpatternasafunctionofthedrawstagewhichobservedinexperiments.Inthiscase,thedilutionisrepresentedbyredcolor.Thesequenceisshownintermsofmassdrawn.AsillustratedinFigure5,finemigrationisnothappenedinanypercentageofdrawncolumn.Thisistothefactthatthereisnoshearstrainwhendrawpointsaredrawconcurrently.Attheapproximateheightof20centimeterstheirregularblueandgreenlayersofmaterialshownouniformmovementsothatwecouldinferthatshearstrainisoccurring(Figure5at20%ofmassdrawn).ThisiswhatLaubscher(1994)havetermedheightofinteractionwhichisaheightatwhichfinesmigrateswhen95%ofthemassdrawnhavebeendrawn.


360

Figure 5 Images of uniform draw experiment.

4.2 Results of isolated draw

Because for this experiment fines migration not was observed, an experiment was set when onlyone drawpointwas drawn. Figure 6 shows the flowpattern as a function of the stage of draw. In thiscase, the dilution is represented by red color.The sequence is shown in terms ofmass drawn. In thiscasetheflowzonewasnotuniformanddeviatedtotheleftsideof thesetup.Clearlyalsotheisolateddraw created condition for shear strain.After drawing 60% of the totalmass, finemigration occurredduetotheshearstrain.Differenceinmovementspeedinfineandcoarsematerialwereclearlyidentified.

Figure 6 Images of isolated draw experiment

StagnantZone

Migration

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Gravity Flow

Table 2 shows the mean PDE for thirty-six draw points for uniform and isolated draw experiment(consideringdilutionasfineredparticles).

Table 2 Point of dilution of uniform and isolated draw experiment.

Uniform draw Isolated drawPDE [% of material] 95% 60%PDE [kg of material] 507 323

5 Conclusions

Inthisarticlewepresentsomeexperimentsaimedtoquantifyfinesmigrationpotentialforgravityflow.Foruniformdrawexperimenttherewasnomigrationbecausetherewasnoshearstrain,whichkeepsstraightthemostpartof the timeuntil theyareclose to thedrawpoint so themigrationoccurs in theend.Theisolateddrawexperimentsallowfinesmigrationduetoshearstrainbecausethemovementspeeddependsontheheight.Itisexpectedthatanisolateddrawinablockcavingminesprecipitatetheentrydilutionwhenitislocatedattheback;however,auniformdrawretardstheentrydilution.Theseresultsshouldnotbetakingasconclusiveandmanyothersexperimentsshouldbeproposedandcarriedouttofurtherquantifyfinesmigration.Forexampleexperimentsshouldbeconductedtofindatwhichratioofsizes(finesandcourse)finesmigrationoccurs.Inaddition,therearesomecomplexphenomenathatcouldaffectmigrationand are notmodeled at the physicalmodel specially the influenceofwaterwhich could increasefinesmigrationforcavingmines.

Acknowledgment

WewouldliketothanktheChuquicamataUndergroundProjectfordiscussionoftheaboveresultsandtheBlockCavingLaboratoryatUniversityofChilefortheexperimentsbeingconducted.

References

Castro,R2001,Escalamientoparamodelofísicodeflujogravitacional,MemoriaparaoptaraltítulosdeIngenieroCivildeMinas,UniversidaddeChile,Santiago,Chile.

Castro,R 2007, Study of themechanics of granular flow for block caving, PhDThesis,University ofQueensland,Brisbane,Australia.

Laubscher,D1994,‘Cavemining–Stateoftheart’,JournaloftheSouthAfricanInstituteofMiningandMetallurgy,vol.94Nº10,pp.279–293.

Hashim,M& Sharrock,G 2012, ‘Dimensionless percolation rate of particles in block cavingmines’,MassMin 2012, 6th International Conference and Exhibition on Mass Mining, CanadianInstituteofMining,MetallurgyandPetroleum.

Hashim,M2011,ParticlePercolationinblockcavingmines,PhDThesis,UniversityofNewSouthWalesAustralia.

Hashim,M&Sharrock,G2009,‘NumericalInvestigationoftheEffectofParticleShapeonPercolation’,43rdUSRockMechanicsSymposium&4thUS-CanadaRockMechanicsSymposium,AmericanRockMechanicsAssociation.


362

Orellana,L2012,Estudiodevariablesdediseñodelsistemademineríacontinuaapartirdeexperimentaciónenlaboratorio,TesisdeMagisterenMinería,UniversidaddeChile,Santiago.

Pierce,M2009,Amodelforgravityflowoffragmentedrockinblockcavingmines,PhDThesis,UniversityofQueensland,Australia.

Pineda,M2012,StudyofthegravityflowmechanismsatGoldexbymeansofaphysicalmodel,TesisdeMagisterenMinería,UniversidaddeChile,Santiago.

Power,G2004,Modellinggranularflowincavingmines: largescalephysicalmodellingandfull-scaleexperiments,PhDThesis,UniversityofQueensland.Brisbane,Australia.

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Towards an understanding of mud rush behaviour in block-panel caving mines

ME Valencia University of Chile, ChileK Basaure University of Chile, ChileR Castro University of Chile, ChileJ Vallejos University of Chile, Chile

Abstract

One of the most serious caving mine geotechnical risks are mud rushes. Mud rushes in block caving could be defined as sudden inflows of saturated material from drawpoints. In the last years literature has been written that tries to propose the causes for mud rushes. In general is accepted that mud rushes are due to the presence of water, fines and extraction through drawpoints. This paper describes a framework to develop a model to predict mud rush potential and to gain fundamental understanding from a geotechnical point of view. In order to do that, the authors establish a limit equilibrium model and also carried out a geotechnical characterization of mud obtained at El Teniente´s mine at the lab. From this the main geotechnical indexes are establish and a model to establish the shear strength of the mud as a function of density and water content.

1 Introduction

Block/panelcavingoperationscaninvolvenumeroushazards,oneofthosearemudrushes.Mudrushesaresuddeninflowsofsaturatedfinesfromdrawpointsorotherundergroundopenings(Butcheretal2000).Thequickresponseofthisphenomenonhasterribleconsequencesforsafety.Mudrushesareresponsibleofnumerousfatalitiesandseveredamagetoinfrastructure.

Cavingoperationsareinherentlysusceptibletomudrushes(Jakubec2012).Duetothenatureofcavingithasthepotentialofaccumulatingwaterfromsubsidencefieldaswellasgeneratingfines(comminutionprocess)duringtheextractionprocess.Persistenceofbothwaterandfinematerialcouldcauseamudrush.ElTenientemine(CODELCOChile)isnotimmunetotheproblemofmudrushes.AccordingtoBecerra(2011),ElTenientehistoryhasplentyofexamples.OneofthelastmudlargerushesoccurredinOctoberof2007.Theincidentinflictedanextensiverestructureofthecontrolandextractioninsaturateddrawpoints.After thisevent, theoperationspolicieshavebeenset to limit theextraction rateandcloseareaswhendrawpointshaspresenceofmud(Ferrada2011).Thestrategyofrestrictextractionhasnotonlyhadasevereimpactonorereservesbutitisalsounabletoresolvetheprogressiveappearanceofmudindrawpoints.

There are four conditionsnecessary for amud rush;water,mud formingmaterial, a disturbance and adischargepoint.Operationalexperienceshowsthesefouraremandatoryelementsfortheoccurrenceofamudrush(Butcheretal.2000).Accordingtopublishedliterature,thereareseveraltriggeringmechanismsofmudrusheswhichareclassifiedbasedonthesourceofmudformingmaterialandwater.Table1resumesthemudrushesclassificationproposedbyButcheretal.(2005).


364

Table 1 Mudrushes classification and proposed mechanism (Butcher et al. 2005)

Classification Mechanism

External Inflowoftailings.Thematerialflowsthroughashaft,aditoropenbench.

Failureofbackfillinstopes.Materialcanflowthroughabarricadeduetothepoorqualitybackfill.Openpitslopefailures.Mudflowsduetothefailureofaopencutslope.

Internal/Mix Formationofmudpocketsinorecolumnwithcomminutedshaleandrainwater.Rapidmuckpilecompactation.Responsibleofmudpocketdischarge.

Todaymineoperationswithmudrushriskdealswiththeproblemofmitigationpracticesthrough:

• Drawpointcategorizationaccordingtothepercentageoffinesandmoist(CallandNicholasetal.,1998).

• Drawcontroltoensureuniformdraw(Butcheretal.2000).

• Limitationoforereservesbyheightforspecificdrawpoints(Butcheretal.2000).

• Drainagetoreducethepotentialformudrushes(CallandNicholasetal.1998).

• Limitationofextractionrateandclosureofareaswithdrawpointscontainingmud(Ferrada2011).

• Mudrushscoresystem(Holder2013).

GeotechnicalcharacterizationofthemudwascarriedoutbyCall&Nicholas(1998)forIOZmine(FreeportMcMoran Indonesia).On the other hand, Jakubec (2012) fulfil experiments aboutmudflowbehaviour.Bothwere conducted to know thematerial properties and size theflowpotential. Likewise, they haveestablishedfailuremechanismforfinegranularmaterials:mudflowandliquefaction.Nevertheless, theyhavenotsuggestedamodelthatexplainsamechanismformudrushes.

Themainobjectiveof thisarticle is toprovideageotechnicalmodelframeworkformudrushpotentialforadrawpoint.Then,resultsfromtheageotechnicalcharacterizationofmudtoElTeniente´sminearedescribedandteststodefinetheshearstrengthofthemud.

2 Framework for a geotechnical model of mud rush

Mudrushesareessentiallyastabilityproblem.Variationsinwatercontentandstressconditionscanincreasetheporepressureandthereforethepotentialforsuddenfailure.Inthiscasethegranularmateriallossesitsstrengthandbehaveasafluid.Besides, theyspecify threekindsofmechanismsthatmakethegranularmaterialfluid:

• Staticmechanisms:Relatedwiththeextractionofmud.

• Dynamicmechanisms:Relatedwithperturbations likeblastingvibrations.Thesecause inducedliquefactionbyseismicmovement.

• Waterasamovementforce:Relatedwiththeincreaseofwatercontent.Thiscanchangethemudproperties,makingthematerialfluidordragalongduetheexcessofpressure.

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Gravity Flow

The mudrush issue in block caving could be considered similar to the case of mining with backfill,particularlystopeswithhydraulicandpastebackfill.Hydraulicbackfillarecharacterizedbyincludefinegranularmaterialandhighwatercontentfromtails.

Ananalogycanbedrawnbetweenthegeotechnicalanalysisoffilledstopesandanisolateddrawpoint.Botharemade in rock(drawbellandstope)filledwithsaturatedfinematerial.Stopestabilityanalysesallowcalculatingtheresistanceofabulkheadtosealofftheextractionpoint.Forblockcavingapplicationstressanalysisisalike,exceptforthebulkhead.Inblockcavingthereisnotapermanentbulkheadandonlyreliesonpartofthebrokenoreatthedrawpoint(seeFigure1).

AsnotedinFigure1,theactingforcesarefromtheweightofthematerialthatfillthedrawpoint(Fm+Fc)andwater(Fw).Fillingmaterialhastwoparts:brokenoreandmud(HeightsofthismaterialareHbandHmrespectively).Thehorizontalcomponentofthisforces(FH)istryingtogettheoreoutofthedrawpoint.Theshearresistanceofthismaterial(FB)isactingagainstthehorizontalforce.

Figure 1 Analytical model of a single extraction point

Tobeinequilibrium:

FB=Kh (Fm+Fc)+Fw (1)

Alimitequilibriumanalysiscanbedevelopedaccordingmethodstodesignbarricadesforbackfilledstopesundersubmergedconditions(SmithandRoettger,1984).Tounderstandhowthemudpressureactsit isnecessary to acknowledge two components of total pressure: effectivefill pressure andwater pressure.AccordingtoSmithandMitchell(1982)thetotalbulkheadpressureforafullysaturatedfillcanbeestimatedasequation(2).Thefirstcomponentrepresentsthehorizontaleffectivefillpressureonthepileofmaterialindrawpoint.Ontheotherhand,thesecondcomponentrepresentsthewaterpressureonthedrawpoint.

(2)

Risreferredasadrainageratioandiscalculatedaccordingequation(3).

(3)


366

Where:

H =Fillheightofmudincolumn(m)

γm =Unitweightofthemud(kN/m3)

l =Pileofmateriallength(m)

w =Pileofmaterialwidth(m)

γw =Unitweightofwater(kN/m3)

P =Percolationrateinthedrawpoint(cm/s)

P1 =Percolationrateinorecolumn(cm/s)

A =Totalareaofdrawpoint(m2)

A1 =Orecolumncross-sectionalarea(m2)

Thismodelisusefulforthefirstpartoforecolumn,filledwithmud.Accordingtotheincidentsreportedtodate,thisheight(H)shouldnotexceedtheheightofthedrawbell.Theoverloadisconsidered.usingtheJanssen(2004):

(4)

Where:

γcr =Unitweightofcavedrock(kN/m3)

φ =Internalfrictionangle(º)

Rh =Hydraulicradius

μ =Frictioncoefficient(tan(φ))

k =1–sen2(φ)/1+sen2(φ) z =Depth(m)

Then,thetotalstressonthepileofmaterialindrawpointitcanbeestimatedas:

(5)

3 Case study

Theuseofthestressequationisillustratedwithasampleapplication.TheparametersusedfortheexampleareshowedinTable2.

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Table 2 Parameters for illustrative case

Parameter Unit Value

Depth (z) m 100

Unit weight of caved rock (γcr ) kN/m3 19

Pile of material length (l) m 2

Pile of material width (w) m 4

Hydraulic radious (Rh) - 2.25

Internal friction angle (ϕ) º 45

Unit weight of the mud (γm) kN/m3 27

Figure2showsthevariationofthetotalstressonthepileofmaterialasafunctionofdrainageratiothroughtheorecolumn.Itiscalculatedforthreeheightsofmudinadrawbell.Itcanbeseenthatstressesincreaseswhenthereisnodrainagethroughtheorecolumn.Thestressofwateronlymatterswhenitisaccumulatinginthedrawbell.Whentherearenotdrainageconditions(R=0.1)morethanahalfofthetotalhorizontalstressactingatthedrawpointisduetotheheightofwater.

Figure 2 Total stress on the pile of material as a function of drainage ratio (R) and mud height (H)

Once, stresses on drawpoint have been estimated, it is necessary to know the resistance of the pile ofmaterial.Thisorecouldbeunderdifferentconditionsofmoistureandgranulometry.Inordertoget thecorrectparameterstoestimatethisresistance,ageotechnicalcharacterizationneedstobeperformed.Thiswasachievedthroughanunderstandingofthestrengthofthemudwhichisdiscussedinthenextsections.

4 Geotechnical characterization

Samplesofsaturatedfinematerialwerecollectedfromdrawpointsclassifiedas“Critical”inElTenientemine.Theriskclassificationwasimplementedinthemineaccordingtothefinematerialandwatercontent


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(Becerra,2011).FollowingthestandardprocedureforrepresentativesamplinginElTeniente,threesampleswerecollected.This samples represent threedifferentmudsdifferentiated through theobservedcolour:Grey(Sample1),Yellow(Sample2)andMixture(Sample3).

Twodifferenttestareimplementedinthisstudy:

1. Teststodefinetheindexpropertiesofmaterial:GrainSizeDistribution,AtterbergLimits,SpecificGravity,MaximumandMinimumVoidRatio.

2. Tests to evaluate the relations between water content and compaction: Set of unconfinedcompressionandsetofslumptests.

Theoutcomesofthefirsttestareuseddefinetheconditionsforthesecondtests.Fullsizedistributioncurveswereusedtoperformslumptestandunconfinedcompressiontest.Thesetestsarebothcarriedoutwithdifferentsaturationandrelativedensityvalues.

Saturation,VoidRatioandRelativeDensityaredefinedusing(6),(7)and(8)equationsrespectively.

(6)

(7)

(8)

Where:

Vw= Volumeofwater

Vv= Volumeofvoids

Vs= Volumeofsolids

Watercontentisarelationbetweenmassofwaterandmassofsolids.Thesemeasureareusedtodeterminethedegreeofsaturationandmobilityofthematerial.TheAtterberglimitsareameasureofcriticalwatercontentoffinesoil.Thoselimitsclassifybehavioroffinegrainsoilfromsolidtoplastic(LP)andplastictoliquid(LL).Densityofthemudmaterialisimportantindetermininghowmuchwaterandaircanfillthevoidsbetweenparticles,whicheffectstheflowpotentialofthematerial.Thevoidratioisanimportantmaterialpropertyinassessingstrengthpermeability,andcollapsepotential.TestsareaccomplishedaccordASTMstandardandaredescribedinTable3.

Table 3 Performed tests for geotechnical characterization

Parameter MethodWater Content OvenDry(ASTM)Grain size distribution SieveAnalysis(ASTM)Atterberg limits MethodofCasagrande(ASTM)Specific Gravity Picnometer,SubmergedMass(ASTM)Void Ratio Maximum MinimumDensity(ASTM)Void Ratio Minimum ModifiedProctor(ASTM)

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Gravity Flow

5 Geotechnical Characterization results

Geotechnicalindexesareusedtounderstandthebehaviourofsaturatedfinematerial.Table4synthetisethemainresults.Accordingtotheresultsoflaboratorytest,thematerialisawellgradedgranularaggregate.Itcontainsgravel,sand,limeandclayindifferentproportions.Sample2seemstobetheonewiththemostportionoffines(22%limesandclays)andlessgravel.While,sample1hasthefewerportionsoffines(11%limeandclay)andmostgravel(60%).Moisturecontentsrangefrom9.5to13.4percent.Atterberglimitsproducedaliquidlimitrangebetween21.7and26.1percent.Theplasticlimitsarebetween16.9and21percent.Thesevaluesindicatethatthefinesareclassifiedaslowplasticitysiltandclay(ASTMD2487-00).

Table 4 Outcomes from geotechnical characterization

Sample 1 Sample 2 Sample 3Water Content 9.50% 13.40% 12.50%Grain size distribution D60=11.35mm D60=3.907mm D60=12.288mm

D30=2.385mm D30=2.43mm D30=1.045mmD10=0.043mm D10=0.006mm D10=0.018mm

Atterberg limits LL=21.7% LL=25.7% LL=26.1%LP=16.9% LP=21% LP=19.1%

Specific Gravity (Gs) 2.76 2.68 2.72Maximum Void Ratio (emax) 0.9 1.00 0.92Minimum Void Ratio (emin) 0.26 0.28 0.22

Uptodateteststodeterminethestrengthforthemudhaveconsideredunconfinedcompressiontest.Figure3showstheunconfinedstrengthforthethreesamplesatdifferentsaturationsandrelativedensities.RelativedensitiesarerelatedtothemaximumdensityachievedbythemudusingaProctortest.Theresultsindicatethatresistanceincreaseswithadecreaseontherelativedensity.Atlowrelativedensityandhighsaturationthemudshowsnoshear resistance.Thewatercontent for thiscase isover theplastic limitso themudbehaveslikealiquid.

Slumptestindicatethatinconditionsofhighrelativedensity,thereisnosettling(nodeformation).Forarelativedensitylowerthan65%,thepossibilityoffloworplasticbehaviorsisdependingonsaturation.Inparticular,whenfluidbehaviorappears,watercontentisoverorneartheplasticlimit.Sample1,includingthemostcasesonfluidstate,hasthelowestlimit.

Theoutcomesofthegeotechnicalcharacterizationsuggestthattheresistanceofthepileofmaterialcouldbeovercomedependingontherelativedensity.Relativedensitydependsontheextractionofthemudatdrawpointsanditisnotusuallymeasured.

6 Conclusions

Thispaperoutlinesageotechnicalmodelframeworkformudrushprediction.Thenewmodelrepresentsaninstantbeforeamudrush.Thismodeltakesaccountthreecomponents:theweightofthemudindrawbell,thepressureofwaterthatisaccumulatedinthecolumnandtheoverloadofthebrokenmaterial.

Thesimplifiedmodelisconsistentwithsomemitigationpracticeslikedrainage.Drainageratioisoneofthemostimportantvariableaswellasrelativedensity.Whentherearehardlydrainageconditions(R=0.1)morethanahalfofthestressbelongstowaterpressure.


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Once the amount of loadwas estimated, a geotechnical characterizationwas performed regarding theresistanceofthepileofmaterial.Saturatedfinematerialwastestedtoknowthebehaviourofmudunderdifferentconditions.Resultsindicatethatplasticdeformationscouldexplainmudrushesforabruptchangesofloadormoist.Furthermore,relativedensityhasanimportantroleintheresistanceofmaterial.

Incorporate theextractionofmaterial is thenext step tounderstand thebehaviourofmudunderblockcavingmining.Thiswillbedevelopinganumericalmodelandexperimentsatlabscale.

Acknowledgement

ThispaperhasbeenpreparedasanoutputoftheInnovaCORFOProject12IDL2-15145.TheauthorswishtothankMauricioMelendez(CODELCOChile)forprovidethematerialoftheexperimentalstudy,aswellastoCORFOthatallowedthedevelopmentofthisresearch.

References

Becerra,C,2011,‘Controllingdrawpointspronetopumping,ElTenienteMine’,ProceedingsofSecondinternationalSeminaronGeologyfortheMiningIndustry,Antofagasta.

Butcher,R,Joughin,W&Stacey,TR,2000,‘ABookletonmethodsofcombatingmudrushesindiamondandbasemetalmines’.Simrac.

Butcher,R,Stacey,T&Joughin,W,2005,‘Mudrushesandmethodsofcombatingthem’,TheJournalofTheSouthAfricanInstituteofMiningandMetallurgy,vol105,no11,pp.807-824.

Call&Nicholas,1998,‘IOZWetMuckStudy’,FreeportMcMoRanCopperandGold,C.&HydrologicConsultants.

Figure 3 Unconfined compression test results (average between three samples)

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Gravity Flow

Ferrada, M, 2011, ‘Gravity flow under moisture conditions – Control and management of drawpointmudflow’,In35thAPCOMSymposium,Wollongong.

Jakubec, J, Clayton, R&Guest,A, 2012, ‘Mudush Risk Evaluation’, Proceedings ofMassmin 2012,Subdury.

Jansen,HA,2004,‘Experimentsregardinggrainpressureinsilos(TranslatedfromgermanbyW.HustrulidandNorbertKrauland)’,ProceedingsofMassmin2004,Santiago.

Holder,A,Rogers,AJ,Bartlett,PJ&Keyter,GJ,2013,‘ReviewofmudrushmitigationonKimberley’sold scraper drift block caves’, The Journal of The SouthAfrican Institute ofMining andMetallurgy,vol113,no7,pp.529-537.

Smith,JD&Mitchell,RJ,1982,‘Designandcontroloflargehydraulicbackfillpours’,CIMBulletin,vol.75,no838,pp.102-111.


372

Statistical analyses of mud entry at Diablo Regimiento sector - El Teniente’s Mine

IM Navia Universidad de Chile, ChileRL Castro Universidad de Chile, ChileMA Valencia, Universidad de Chile, Chile

Abstract

Mudrushes have plagued block and panel caving operators with many fatalities and can have posed a major hazard to safety in block and panel caving mining. Closing drawpoints with a high mudrush potential can be introduced as an effective way of preventing mudrush hazards. Since draw point closure is due to mudrush potential, not dilution, different amount of remnant saturated ore (RSO) would be remaining in the block column. Tonnage and grade of RSO in this group were calculated based on the actual situation of closed drawpoints. The second group contains drawpoints located in a zone with a high potential mud entrance. In this group, the RSO that could potentially be removed once mud enters in drawpoints was predicted based on the historical extraction data. The results indicated that RSO is itself an interesting quantity in terms of tonnage and average grade. Respecting to occurrence of mud, the initial inflow of mud was associated to drawn heights and draw uniformity that are similar to in situ height of the initial entrance area. It is proposed that the subsequent entry of mud resulted not only in relation to the connection with higher mined levels, but other mechanisms, such as the entry of water directly from surface.

1 Introduction

Aflowofmudinblock/panelcaving,called“mudflow”,“mudpush”o“mudrush”,isdefinedasasuddenandviolentinflowofamixtureofwaterandfinestomineopenings,withahighinjury,deathanddamagepotential.Amudflowcandamageequipment,causeoperatinglossesandeven,cancausefatalities(Butcheretal.2005).

The existence ofmud at broken columns can cause two effects:mudflows, either violentmudflowsorlessviolentspillsofmud;andtheredefinitionofreservesduetothecuttingofthedrawableheights.Thelastwiththepurposeofnotincludingmuddrawinginmineplanningduetosafetyactionsandtechnicalcapability(Barahona2014,pers.comm.,03February).Thismeantthatthereisorethatcannotbeextractedwhichhasbeentermedremnantsaturatedore(RSO).ThereisaneedtoquantifytheeconomicpotentialofRSOindrawpointswhichhavebeenclosedtopreventthehazardsofmud-water.Nowadays,threestatuseshavebeendefinedtofacethemudrushhazardinElTeniente’ssectors:

1. Mud-waterstatus,orcriticalzone,hasthemostprobabilityofmudrushoccurrenceand,thushavebeenclosedforever toprevent theentranceofmud.Awetmuckclassificationmatrixhasbeendeveloped,todefinethemudrushriskconsideringfinematerialandmoisturepercentage(Becerra2011).

2. Limited status,which is happened in the drawpoints surrounding themud-water status.Thesedrawpoints have the hazard of lateral immigration ofmud from the critical zone. Therefore,the extraction rate from these drawpoints is limited.The result of extracting the limited statusdrawpointsismudimmigrationtooperativedrawpoints.

373

Gravity Flow

3. Barrierstatus,inwhichsomedrawpoints’sorecolumnsareusedasabarriertocontroltheentryofmud.Thecontentofmoistureandfinematerialinthesedrawpointsarenotnecessarilycriticalbuttheyareestimatedashighriskpointsbasedontheflowdirectionofmud.Duetoanullextractionin this statue, the lateral advance ofmud is paused or delayed (Vargas 2013, pers. comm., 30September).

Inordertominimizetheproblemofdealingwithmudrushinproductionareas,anumberofresearchershave suggested to drawuniformly (Widijanto et al. 2012;Laubscher 2000).This strategywould allowthe extractionofmud inmanydrawpoints, as a result,wouldpreventmud concentration in just a fewdrawpoints.Anextensivedewateringprogramcanalso reducewaterwhich leads to thewetmuckruns(Barberetal.2000).Drainagestrategycanbeimplementedinbothsurfacewater,whichentersthecavethroughrainfallingontothesubsidence,orundergroundwater.(Samosiretal.2008;Barberetal.2000).

InthisarticletheeconomicpotentialofRSOinthecloseddrawpointsaswellasthosethatcouldbeclosedin the future due to the ingress ofwater-mudwas calculated. Furthermore, the historical databases ofresources,reservesandextractionconditionsatamineofElTenienteknownasDiabloRegimientowasusedtodefinetherelationshipbetweentheappearanceofmudandthedrawnstrategybasedonthebackanalysisstatisticalmethod.ItshouldbenotedthatthisdatabaseincludesallresourceandproductionhistoryofDiabloRegimientofromtheinitialdateofextractiontoNovember2013.

2 Economic potential of RSO

TheeconomicpotentialoftheRSOatDiabloRegimientowascalculatedbasedonthecolumnmodelandproductiondatahistoryofeachdrawpoint.Throughthisdatabaseitispossibletoidentifygrade,tonnageanddensityofeachbenchineverydrawcolumn.Duetomudrushhazards,somedrawpointshavebeenclosedbeforereachingtheeconomicdrawableheights;therefore,twogroupsofdrawpointsareintroducedtodeterminetheaccurateRSOas:

• Drawpoints affected by mud-water: This group includes drawpoints with mud rush hazardswhich are categorized in three statuses (Mud/Water, Limited and Barrier). To evaluate RSO,botheconomicalandmarginaldrawableheightsareconsidered,underwhichtheminimumandmaximumRSOdefinedrespectively.Table1showstheresultsineachdrawableheight.Asitisindicatedintable1,inthecaseofmarginaldrawableheight,twodifferentcutoffgradewastakenintoconsider.

• Drawpointsnotaffectedbymud-water:Thisgroupincludesdrawpointslocatedinthezoneunderupperminedlevelswhicharealreadydrawn(fromEasttoWest,theyareRegimiento,PuenteandFortuna).Thedrawpointsareconsideredwithahighmudentrancepotential.Itshouldbenotedthatthedrawpointsconsideredintheprevioussectionwereexcluded.

Table1showstheresultsofRSOcalculationbasedonmarginaldrawableheightfortwovariouscutoffgrades.Asillustratedintable1theminimumRSOis11.8Mtorematerialwiththeaveragegradeof0.63%.

3 General analysis of mud occurrence at Diablo Regimiento

According to criticalmatrix used inElTeniente coppermine (Becerra 2011), if the percentageoffinematerialandmoisturecontentinadrawpointreachesthecriticalvaluethestatusofdrawpointwillchangestoMud/Waterstatus.Inthissituationthedrawpointwillbeclosedtopreventthehazardofmudrush.Inthispaperthehistoricaldatabaseofcloseddrawpointsisconsideredasasituationthatmudoccurrence.


374

Closure grades

Closuregradeisdefinedasthefinalextractedgradeofadrawpointwhichisclosedduetothemudrushhazards.Infigure1theclosuregradeofdifferentdrawpointsatDiabloRegimientoisillustrated.Itcanbeconcludedfromthisfigurethatmostdrawpointsareclosedwithhighcoppergrades.

Table 1 RSO at Diablo Regimiento

Drawpoints considered Considerations RSO (Mt)

Average grade

(%CuT)

Total of RSO (Mt)

Average grade (%CuT)

Affectedbymud-water

Initial reserves atDiabloRegimiento 4.4 0.72% 4.4 0.72%

Affectedbymud-water Considering marginal

heights; cutoff gradeequalto0.4%CuT

14.9 0.62%26.6 0.57%

Notaffectedbymud-water 11.7 0.51%

Affectedbymud-water Considering marginal

heights; cutoff gradeequalto0.5%CuT

11.4 0.67%18.8 0.63%

Notaffectedbymud-water 7.4 0.57%

Figure 1 The frequency of closure grades at Diablo Regimiento

3.2 Drawn heights

Thedrawnheightswerecalculatedusingdailydrawndatabasesaswellasresourcesmodelperbenchanddrawpoint.ThehistoricaldatabaseofDiabloRegimientoisusedtoanalysescloseddrawpointsinthecaseofclosuresequenceanddrawnheights.

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Gravity Flow

3.2.1 Closure Sequence

AplanviewofcloseddrawpointsatDiabloRegimientoisillustratedinfigure2.Itisobservedinfigure2(a)thattheinitialmudentrydrawpointisatthecenteroftheDiabloRegimientosector,anditcoincideswiththedrawpointswhereextractionbeganinordertogeneratethedome.Subsequently,themudwasalwaysappearinginneighboringpoints,andthenappearedintheeastsector.Basedonthedrawnheightinfigure2(b)itispossibletocomparetheextractionheightatdifferentpartofDiabloRegimientosector.

Figure 2 (a) Closure sequence and (b) drawn height of closed drawpoints

3.2.2 Drawn heights at closed drawpoints

Figure3 shows the frequencyofdrawnheights incloseddrawpoints atDiabloRegimiento sector.Thehighvariabilityisillustratedfordifferentdrawpoints.Basedonfigure3and2(b),itappearsthatthelowerelevationscorrespondtothedrawpointsthatbeginconnectingwithminedandcavedupperlevels;thatisthecenterofthesectorandtheeastside.Subsequently,theneighboringpointstotheaforementionedareassociatedtoagreaterdrawnheightbeforetheapparitionofmud.

Figure 3 Drawn height frequency of closed drawpoints

Itisobservedthat30%ofthedrawpointsinMud/Waterstatus,wereatalowerorequalto170mheightdrawn,correspondingtotheapproximatedistancebetweenDiabloRegimientoandminedupperlevels.Inaddition,atthatheightiswherethehighestfrequencyofclosuredrawpointsoccurred.OccurrenceofmudinthesedrawpointsmaybethroughtotheaccumulationofwaterandmudintheoverlyingRegimientosector(DiabloRegimientoisbelowRegimiento).However,theentryofmudatagreaterdrawnheightmay


376

beattributable to thedrawmaintaininganirregulardrawingprofilecombinedwith theaccumulationofmud-wateronthesurface.Mudentrancebelow170mmaybeduetoverticalorlateralmigrationofmudalreadywithinbrokencolumns.

Analyzingthedrawnheightofcloseddrawpoints(figure4)observedthatasminingproductionprogresses,therangeofpossibleheightscloseddrawpointsisincreased.Itcanbeconcludedfromfigure4thatthefirstmudentranceisduetoconnectionwithuppersectors;butinotherdrawpointsthatwereextractedlatterinthesequence,itmayhavebeenotherreasonsformudentrance(waterfromothersources).

Figure 4 Evolution of accumulated drawn height at Mud/Water status declaration

Accordingtotheanalysisofthedatathefollowingcouldbeconcluded:

• Rockcavingcommencementinvirginareashasagreatinfluenceonthepotentialofmudentrancetothesector.Forexample,thedomeshapeofcavebackinthecenterofaminesectorcausesanearly interaction to thesurfaceor tominedlevels locatedabove.Thiscreateschannels throughwhichfinematerialandwaterwouldentrancetoproductionlevel.

• Ingeneralaneffectivewayofavoidingmudingressisthroughuniformdrawsoastobringtheore/mudinterfaceashorizontalaspossible.

• Itisimportanttodetectthesourcesofwaterandmudandtodefineastrategyfordewateringandforadrawstrategytofacehighpotentialareasformudingress.

4 Determining the probability of mud entrance

Asit is illustrated, theRSOcouldhavean important roleon thereservesevaluation; therefore themidand long termproduction planningwould be changed based onRSO.SinceRSO is the result ofmudoccurrenceindrawcolumns,itisessentialtodetermineinadvancetheentranceofmudindrawpoints.Asaresult,amodelisproposedtopredicttheprobabilityofmudoccurrenceemployingalogisticregression.Thismodelcanbeusedasamineplanningtool.Themaindatawhichareconsideredinthismodelaretemporalevolutionofdrawrate,finematerialcontent,drawnheightandseasonoftheyear.Thelastisduetoacorrelationthatcouldexistbetweenwaterseasonsanddrawpointsclosedduetomud.Someaspectsrelatedtomudflowsaredescribedbelow.

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Gravity Flow

AlogisticregressionmodelisproposedinthisstudytopredicttheMud/WaterstatusbasedonhistoricaldatagatheratDiabloRegimientosector.Logisticregressionisatechniqueformakingpredictionswhenthedependentvariable isadichotomy,and the independentvariablesarecontinuousand/ordiscrete. Inordertopredicttheriskofmudrushhazard,alogisticregressionusedwithdependantvariable(p)astheprobabilityofpersistenceofmud.Foreachdrawpointtheobtainedvalueofpshowsifthereismudinthecolumn(p=1)ornot(p=0).

Formally,logisticregressionmodelisdefinedasequation(1).

(1)

Solvingforp,thisgivesequation(2).

(2)

Whereβi(i=0,…,n)aretheestimatorsandXi(i=1,…,n)theindependentvariablesincluding:

X1:Drawrate

X2:Finematerialcontent

X3:Drawnheigth

X4:Season

Thevariableseasonaddedtothismodelbecauseintheprobabilityofmudoccurrenceinspringismorethanotherseasonsoftheyear.

Theestimatorsobtainedforequation(1)areshownonequation(3).

(3)

Basedonlogisticregressionmodel,thepersistenceofmudinvariousdrawpointsinDiabloRegimientosectorevaluated.Figure5showstheresults.Itisillustratedinfigure5thatthismethodenablestopredictmudoccurrenceindifferentpartofthesectors.Moreover,theprecisionofmodelis74%.

5 Conclusions

In this paper, statistical analyses of database atDiabloRegimiento sectorwas used to study the effectof differentparametersonmudoccurrence indrawpoints.Basedon this study, it is concluded that theaccumulateddrawnheightcouldbethemostinfluenceparameterincontrollingmudentrance.

Accordingtodataanalysisinthisresearch,it isconcludedthatinthecaseofirregularprofileofdrawnheights,uniformityandcontinuously strategycannot solve themudrushproblem. In this situation,firstobjectiveofshorttermproductionshouldbeobtainingauniformdrawnheightprofile.Afterreachingthisobjective,uniformityandcontinuouslystrategyseemstoreducetheabovementionedproblems.

Moreover,inthisstudytheeconomicpotentialofRSOisevaluated.Eventhoughclosingdrawpointsisthebestwaytoensuresafetyinproductionlevel,theresultsofeconomicevaluationshowsthatRSOarepotentialtoprovideatleastonehalfyearproductionofthissector.


378

Finally,itisillustratedthatlogicalregressionmethodcanbeusedtopredictthemudrushhazardsindifferentpartofsector.Furtherresearchneedstobeconductedinordertoevaluatethemudpotentialingressformineplanningpurposes.

Acknowledgement

TheauthorsacknowledgetheassistanceofRodrigoBarrera,MaxBarahona,RicardoVargasandAntonioPinochet fromCodelco´sElTenienteMine,andAsiehHekmatfromBCLAB,for theirhelpfulsupport.ThisresearchhasbeenfundedthroughCorfoandbytheConicytfundsfortheAdvanceTechnologyCenter(AMTC)oftheUniversityofChile.

References

Barber, J, Thomas, L, Casten, T 2000, ‘Freeport Indonesia’s Deep Ore Zone Mine’, in Proceedingof Massmin 2000, Brisbane, Queensland, 29-October-2-November 2000, ed. G.C., TheAustralasianInstituteofMiningandMetallurgy,pp289-294.

Becerra,C2011,‘ControllingDrawpointsPronetoPumping-ElTenienteMine’,inInternationalSeminaronGeologyfortheMiningIndustry,8-10June2011,Antofagasta,Chile.

Butcher,R,Joughin,W&Stacey,T2000,MethodsofCombatingMudrushesinDiamondandBaseMetalMines, SRK Consulting, The Safety inMines ResearchAdvisory Committee (SIMRAC),Braamfontein.

Butcher,R,Stacey,TR&Joughin,WC2005,‘Mudrushesandmethodsofcombatingthem’,TheJournalofTheSouthAfricanInstituteofMiningandMetallurgy,SAIMM,Volume105,pp.817-824.

Laubscher,D2000,APracticalManualonBlockCaving,InternationalCavingStudy.

Samosir,E,Basuni,J,Widijanto,E&Syaifullah,T2008,‘TheManagementofWetMuckatPTFreeportIndonesia’sDeepOreZoneMine’,inProceedingofMassmin2008,Luleå,Sweden,9-11June2008,eds.H.S.&E.N.,LuleåUniversityofTechnology,Luleå,pp.323-332.

Widijanto, E, Sunyoto,WS,Wilson,AD,Yudanto,W & Soebari, L, ‘Lessons Learned inWet MuckManagementinErtsbergEastSkarnSystemofPTFreeportIndonesia’,ProceedingsofMass2012,Sudbury,Canada.

Figure 5 Status of drawpoints on Diablo Regimiento sector; (a) real status of drawpoints and (b) predicted status of drawpoints (W: wet drawpoints; D: dry drawpoints)


379

Innovation

381

Innovation

Hybrid composite, a way to enhance the mechanical properties of breakable ground support

V Barrera Mining and Metallurgy Innovation Institute IM2 – Codelco, ChileP Lara Mining and Metallurgy Innovation Institute IM2 – Codelco, ChileG Pinilla Codelco, ChileF Báez Codelco, Chile

Abstract

When old mining workings levels are caved, the traditional ground support that has offered a safe place for mining could become a problem for the continuity of the production process. This problem is related to the flow of unbreakable traditional support elements (tramp iron) in the ore conveyance lines and comminution circuits in underground mining. For this reason, the need to find breakable systems for ground reinforcement is an open problem. This issue is partially addressed by market alternatives of fibre-reinforced polymers. However, these products that warrant a good tensile strength show poor deformability due to their brittle nature. IM2’s Geological Mining Area and GT&I Codelco Chile are carrying out a research study to develop breakable ground support elements based in the hybridization effect applied to fibre-reinforced polymer to obtain systems with enhanced mechanical properties.

1 Introduction

CaveMining,usedtominelargeorebodiessuchasthetasksofCodelco,usesgravityandconsiderstheexistenceofdifferent levels in theirdesign.These levels, oncemined, are successively abandonedandtheminingisdeepened.Eachoftheselevelsrequirestheuseofsupportsystemsinordertoensureasafeworkingenvironmentforbothoperatorsandmachinery.Thebasicfunctionofthesupportandcontainmentsystemsintherockmassistohelpself-supportingbecauseeverytimeanundergroundexcavationismade,thenaturaltendencyistooccupytheemptyvolumeandreturntoitsundisturbedcondition.Thisreturntobalanceisdonebystressredistributionaroundtheexcavation,resultinginagradualdeformationoftheexcavatedcavity.However,whentheseprocessesexceedthemechanicalstrengthoftherocksurroundingtheexcavation,thismaycausethebreakageandsheddingofblocksand,inextremecases,causeviolentrockburststhatoccurwhentheexistenceofbrittlerockiscombinedwithahighstressconcentration.Itisundertheseconditionsthatthesupportsystemscontributetocreateasafeworkingenvironmentduringtheminingworks.

Thetraditionalsupportsystemsaremadeofferrouselementsduetotheirhighplasticityandtheybecomeunbreakable tramp ironwhen they are installed into new production caving levels.Table 1 provides adescriptionofthemechanicalpropertiesofthesteelusedinrockboltmaking(Carvajal2008).

Table 1 Minimum mechanical properties of steel used for rock bolts

TensileStrength440MPa

Elongation16.0%

ShearStrength251MPa

Density7850kg/m3


382

For thisreason, thepassageof theseunbreakablematerials inconveyanceandcrushinglines inminingoperationsintroducesdisturbancesinthecontinuityof theproductiveprocess.Sometimes, thisscenariowillcreateunscheduledshutdowns,increasingtheriskofnotfulfillingthecommitmentsstipulatedintheproductionplanning.

Awaytosolvetheproblemofunbreakableundergroundsupport,chosenbyIM2Geomining-Codelco,istodesignnewelementsusingmaterialsthathavethecapabilitytowithstandstressesinherenttotheminingworksandthatbecomebreakableoncetheyareabsorbedintothecaving.Modifiedfibreglasscompositehasbeenchosenforthispurposebecauseitmeetsbothrequirements.Toincreaseitsabilitytodeform(tryingtoreachthepropertiesofferrouselements),themodifiedfibreglasscompositehasbeenprovidedwithacoreofductilematerial(minimumvolumesofsteel),usingthehybridizationeffect(Marometal.1978)appliedtofibre-reinforcedpolymer.Tensileandsheartestsinauniversaltestingmachinehavebeenmadetoobtainmeasurableparametersunderstaticloads.

2 Methodology

2.1 Fabrication

TwotypesofHybridSpunBars(HSB-X1,withSAE1045steelcore,andHSB-X2,withA630-420Hsteelcore)weremanufacturedbypultrusion.Thisproductiontechniqueisalow-cost,high-volumemanufacturingprocessinwhichresin-impregnatedfibresarepulledthroughadietomakethepart.Theprocessissimilartometalextrusion,withthedifferencebeingthatinsteadofmaterialbeingpushedthroughthedieinitially,it is pulled through the die in a second process. Pultrusion creates parts of constant cross-section andcontinuouslength(Mazumdar2002).Figure1showsthisformingtechnique.

Figure 1 Schematic pultrusion process

2.2 Test

AsurveyconductedintheChileansuppliermarket,carriedoutbyIM2-CODELCO,determinedthestrengthofGlassFibreReinforcedPolymer(GFRP)bars(Barreraetal.2013).ThisinformationissummarizedinTable2.

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Innovation

Table 2 Properties for GFRP bars supplied in the Chilean market

TensileStrength506MPa

Elongation7.31%

ShearStrength158MPa

Density709kg/m3

Todeterminethepropertiesoftheseelements,twotypesofstatictestswereconsidered:tensileandsheartests.Thebreakabilitypropertywillberesearchinfurtherstepofthisinvestigation.

2.2.1 Tensile Test

Thestandardtensiletestistheuniaxialtensiletest.Inthiscase,asthetestsamplewasmadeofpolymeric-naturematerials,theASTMD7205standard(ASTM2006)wasapplied.

Figure 2 Tensile test and HSB specimen

2.2.2 Shear Test

TheshearstrengthofthehybridprototypebarswasdeterminedusingadoublesheartestbasedonASTMD7617(ASTM2011).Toexecutethistest,apiecespeciallydesignedforthispurpose,whichwasassembledintheuniversaltestingmachine,wasused.

Figure 3 Parts and assembly of HSB specimen for double shear test


384

3 Results

3.1 Fabrication Results

Figure4showstherawmaterials thatwereusedandtheresultof thepultrusionprocess,whicharethehybridspunbars,HSB.

Figure 4 Raw materials and HSB (right bottom corner picture)

3.2 Test Results

Thetestsperformedwasmadetoevaluatethemechanicalstrengthcharacteristicsofthiselements.Asitisindicated,Figure5showsthefragilefracturemodeinboth,tensileandsheartests.

Figure 5 HSB specimens after tensile (left) and shear (right) tests

Table3summarizestheresultsoftensileandsheartests.

Table 3 Properties for HSB prototypes

HSB-X1 TensileStrength445MPa;Elongation10.8%;ShearStrength191MPa;Density1,473kg/m3

HSB-X2 TensileStrength392MPa;Elongation14.4%;ShearStrength190MPa;Density1,592kg/m3

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Inthelightoftheseresults,acomparisoncanbemadewithbothferrouselementsaswellasGFRPbarsprovidedinthemarket.ThiscomparisonisprovidedinTable4,wheretheloss(gain)isshowninpercentagewithrespecttothebasestateforthisresearch(rockboltsteelandGFRPbar).

Table 4 Comparison among prototypes and existing technology (rock bolt steel and GFRP bar)

Prototype WithRespectto TensileStrength Elongation ShearStrength Density

HSB-X1RockboltSteel

-1.10% 32.5% 23.9% 81.2%

HSB-X2 10.9% 10.0% 24.3% 79.7%

HSB-X1GFRPBar

12.1% -47.7% -20.9% -108%

HSB-X2 22.5% -97.0% -20.3% -125%

-:Percentagegainwithrespectto;+:percentagelostwithrespectto

4 Conclusions

ThetensilestrengthoftheHSB-X1prototypeshowsthebestbehaviourwithrespecttobothbasestates:rockboltsteelandGFRPbar.Furthermore,italsoshowsanincreaseofabout50%elongationand21%intheshearstrengthoftheGFRPbar.Thelossofabout32%intheshearstrengthwithrespecttothesteelrockboltleavesamarginforfurtherresearchtoimprovethispropertywithnewmaterialcores.AsforthepropertiesshownbytheHSB-X2prototypewithrespecttotheGFRPbar,theyarelowerthanthepreviousprototype,exceptforelongation,whereitshowsaresponseclosetotheminimumacceptedforsteel.Bothprototypesshowalowerdensitythanrockboltsteel,however,thispropertyisenhancedwithrespecttotheGFRPbar.

Acknowledgement

The authors acknowledge the sponsorship ofCodelco in the context of the completion of ProjectAPIM11DE12“ConceptualizationandExperimentationofBreakableGroundSupportElements”.Inaddition,PatricioLaraisgratefulforthevaluableassistanceofCristianWelsch,whomadeitpossibletofabricatethebreakableprototypesatPERNOMINLtda.

References

ASTMInternational2006,‘ASTMD7205StandardTestMethodforTensilePropertiesofFibreReinforcedPolymerMatrixCompositeBars’,ASTMInternational,Pennsylvania.

ASTMInternational2011,‘ASTMD7617StandardTestMethodforTransverseShearStrengthofFibrereinforcedPolymerMatrixCompositeBars’,ASTMInternational,Pennsylvania.

Barrera,V,Lara,P,Pinilla,G,Arancibia,E2013,‘BreakableGroundSupportaverificationofmechanicalpropertiestodiminishferrousSolidWasteinUndergroundMining’,ProceedingsofCopper2013,ed.IIMCH,Santiago.

Carvajal,A2008,ManualSistemadeRefuerzodeRocasconPernosSaferock,GerdauAza,Santiago.

Marom,G,Fischer,S,Tuler,FR,Wagner,HD1978,‘Hybrideffectsincomposites’,JournalofMaterialScience,vol.13,pp.1419–1426.

Mazumdar,S2002,CompositeManufacturingMaterials:product,andprocessengineering,CRCPress,BocaRaton.


386

Pilot tests as a tool for the design of autonomous mining systems

J Riquelme University of Chile, ChileR Castro University of Chile, ChileS Valerio University of Chile, ChileJ Baraqui Codelco Chile, Chile

Abstract

For many years mining innovation has relied on full scale tests to verify the implementation of new technologies. At the University of Chile, a methodology has been developed for using pilot tests for mining engineering with the aim of speeding the process of design and implementation of novel technologies. In this article, we present the potential of this approach for the conceptual design of an autonomous and continuum mining system using scaled models. Autonomous means that the system could operate without people taking decisions on when each dozer would draw. This is achieved using sensors (lasers and cameras) and a control system for the dozers. This research also assists in the understanding of the behaviour of interactions between the components of the Continuous Mining System. The results indicate the extraction sequence and the use or not of controlling systems could increase the draw rate for this type of material handling systems.

1 Introduction

ContinuousMining is a newmaterial handling systemdesigned to increase the rate of draw for blockcavingmines (Encina et al. 2008).This technologyhas been tested atCodelco´sElSalvadorDivisionbetween 2006 and 2007 and is currently under construction atAndina´sDivision.At the same time anumber of experiments using scaled and numericalmodelling have been carried out at theUniversityofChile (Alvarez2010;Orellana2011;Orellana2012).TheContinuousMining systemconsist on theuseofnovel-to-blockcaving´smaterialhandlingequipment:(1)stationaryfeederslocatedindrawpoints(dozers);(2)acontinuousconveyor(panzer)whichreceivesthematerialfromdozers;and(3)acrusherinordertoreducethesizeofmaterial(Encinaetal.2008)(Figure1-Left).ThesystemwhichiscurrentlybeingtestedatAndinaMinecouldoperate8dozersperpanzerasshowninFigure1-Right.

Figure 1 (Left) General scheme of extraction level in a Continuous Mining module (Encina et al. 2008); (Right) Plan view scheme of extraction level used in the current industrial test. The arrows indicate the direction of

movement of the material

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One of the key factors of successful continuous mining systems is achieving higher extraction ratescompared toLHDs.Therefore, it is necessary to simulate the resultsof interactionbetweenequipmentandmaterials.InthisarticletheresultsofapilottestprogramarepresentedtoillustratetheinteractionsbetweenthecomponentsoftheContinuousMiningSystem.TheresultsalsoincludetheimplementationofAutonomousandContinuousMiningSystemforfutureapplicationsinmining.

2 Pilot tests

2.1 Objectives

TheaimofthepilottestingwastosimulatetheextractionofaproductiondriftinaContinuousMiningmodule(Figure1).Thisresearchfocusedontheproductionrateandthepotentialinteractionofthedozer-panzersystem.Thestepsperformedinthisresearchwereasfollows:

1. Conductingascalingstudyandconstructionofa1:50scalepilottestwitheightdozersandonepanzer as considered on the detailed engineering (JRI Ingeniería 2010). In this case both thegeometry andmodelmedia were scaled accordingly. Thematerial with which the tests wereperformedcorrespondstoapproximately1.2tonnesofgravel,withasizerangebetween6.35mmand40mmwithameandiameterof16mm(Figure2).

2. Commissioning:detectionandresolutionofoperatingsystemproblems.

3. ContinuousMiningExperimentation:testingstagewithoutsensingorcontrolsystems.

4. AutonomousContinuousMiningExperimentation:developmentoftestswithsensingandcontrolsystemsforautonomousoperationofthedozers.

5. Analysisofexperimentalresults:comparisonandanalysisofresultsobtainedafterthecompletionofthetests,generatingconclusionsandrecommendationsforfuturestudies.

Figure 2 (Left) Material’s sample used in experiments; (Right) Particle size distribution of the sample

3 Laboratory equipment

Thelaboratoryequipmentconsistedofamodelframe,extractionequipment,sensingsystemandcontrolsystem(Figure3).Themodelframeworkconsistedofaplexiglassframeworkof1.58mwidthx0.57m


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depthx0.82mheight.Inthismodel,eightdrawbellswerebuiltalsoinplexiglasstoobservethehangupsandgravityflowduringthetests.

Figure 3 Physical model used for experiments: (Left) Physical model with the electrical and actuation panel, and a computer desk; (Right) Perspective of filled physical model

Figure4showsdifferentviewsofthescaleddozerwhichislocatedunderthedrawbellwithawidthof40mm(Figure4-Center).Theextractionsystemoperatesbycompressedair,pushestheupperpartandmobilepartthroughacylinder.Inthisscaledmodelitispossibletomeasurethepressureforeachmovementofthedozer.

Figure 4 Dozer system in the scaled model: (Left) Frontal view of dozer; (Center) Side view of dozer; (Right) Plan view of dozer gallery

ThesecondequipmentoftheContinuousMiningSystemisaPanzer.Basedonexperiments,thispartofthesystemwasrepresentedbyabeltconveyorwithgapsasinthechainsystem(Figure5).Thebeltoperatesbytwoelectricalengineslocatedattheendofit.

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Figure 5 Panzer construction in the scaled model: (Left) Perspective view of panzer gallery; (Right) Plan view of panzer

Thesensingsystemconsistsoffourvolumesensorprototypesdesignedandimplementedforthestudiedphysicalmodel.Thesystemisbasedontheprincipleofstructured-lightphotogrammetry,whichisacombinationofimageprocessingandstructuredlight.Avideocamera,whichobtainsastaticimageofthematerialonthepanzer,isinstalledontheroofofthepanzergalleryandisorientedataspecificinclinationangletothehorizontalaxisofthegallery(Figure6).Theimageprocessingalgorithmoutlinestheedgesorthecontoursofthetransportedmaterialbasedontheobtainimage.

Figure 6 Sensing system in the scaled model: (Left) Side view of the sensing system; (Right) Perspective view of panzer gallery with sensing system


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Thelastcomponentisthecontroldrawsystem(hardwareandsoftware).Theaimofthecontrolsystemistomaximizetheamountofmaterialaswellastheuniformloadinthepanzer.Todothatthesystemstartswithanassumptionoftheamountofmaterialdrawperdozerwhichisthencomparedtothemeasurementsatthepanzerbythesensingsystem.Thesystemthencorrectstheamountofmaterialinthepanzerandactivesifrequiredotherdozersinthepanzer.Theimplementationofthecontrolsystemismulti-platform:ithasbeentestedinWindows,LinuxandOS-Xsystems.Ithasacontrolinterfaceinwhichitispossibletosetextractionrates,dozer’sstate(activeorinactive),thespeedofthepanzer,andcycletimesofthedozer.Italsoshowsamodelrepresentationoftheloaddistributioninpanzer.

4 Experiments and results

4.1 Experiments

Untilnowfiveexperimentshavebeenrunatthepilottestsinfrastructure.Theexperimentswereruntosetupthepilottests,toquantifytheextractionofthedozerandpanzersystem,totestdifferentdrawsequencesandtotesttheautonomoussystem.TheaimofeachexperimentispresentedinTable1.Themainvariablesareasfollow:

• Dozer dump length (Dl) (mm): isthepanzerlengthwhichisusedbythedrawnrockfromadozer.

• Dozer productivity (Dp) (g/cycle):istheamountofrockpercycleofthedozer.

• Panzer utilization (Pu) (%):isthepercentageofthetotallengthofthepanzerusedbyrocks.ThepanzerutilizationiscalculatedasPu=Lg/Lt;InwhichLgistheportionofpanzerwithvisiblematerial (discounting panzer gaps) andLt is theoretical length estimated considering the firstandlastparticleoutofthepanzer,independentoftheverticalextensionofmaterial.Utilizationwasestimatedusingtenconsecutivecyclesofthesystem(casewithoutautomation)andfulltestduration(casewithautomation).

• System production rate (Sp) (g/min; t/h):isthetotalbrokenrockextractedbythecontinuoussystem(panzer).

• Dozer sequence: experiments considered three operational dozer’s configurations (Figure 7).TypeI:Twoalternatingdozersoperatingfromthesamepanzergalleryside;TypeII:Twoextremeandtwocentraldozersoperate;TypeIII:Onlythefourcenterdozersoperate.

Themainparametersusedintestsarethefollowing:

• Panzer speed (Ps) (cm/s):fixedparameterscaledfromthefullscalespeedofthepanzer.

• Dozer cycle time (Dt) (s/cycle): isthetimetakestomakeadozer’sdump.

• Distance between operational dozers (Dd) (mm): is the distance between the centers of theoperationaldozer’sgalleries.

• Maximum number of simultaneously running dozers (Nd):isthemaximumnumberofpermittedoperationaldozersduetothefullscaleconstraints.

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Table 1 Experiments conducted in this research

Exp. Objectives0 Checksystemfunction

1 Characterizetheinteractionofdozerpanzersystem

2 Determinemainoperationalconfigurationsandgeneratedozer’ssequencesDeterminenumberofcyclesrequiredtoreachasteadystate

Estimatepanzerutilization

3 DetermineproductivityoftheContinuousMiningforagivensequence(basecase)

4 DetermineproductivityandpanzerutilizationoftheAutonomousContinuousMiningsystemwithsensingandcontrolcomponents

Figure 7 Operational dozer’s configurations: (Left) Type I configuration; (Right) Type II configuration; (Bottom) Type III configuration. Active dozers are the marked by grey colour

4.2 Results

TheresultsofthedozerandpanzersystemsareshowninTable2.Valueswerescaledusingthe1:50scalefactor.Theresultsindicatethatwhenadozerisactivatedanaverageof19cm(9.6mscaled)isusedbythedrawnrock.Thishasavariabilityof9cm(4.8mscaled)witheachdischargefromthedozers.Thustheresultsindicatethatwhenadozerdischargestheusedlengthisvariableaswellastheamountofdrawnrock.

Asecondseriesofexperimentsconsistedintestingthecontinuousmining(experimentalbasecase)andtheautonomoussystemfordifferentdrawstrategies.Thetimetoactivateadozerinthesequencewascalculatedusing theaveragedozer’sdump length, thedistancebetween thedozers and thepanzer speed (Barriga


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2012).ForbothstudiedcasestypeIIIconfigurationistheonethatproducesmore.Thisispossiblyduetothegreaterproximityoftheactiveextractionpoints,resultingintheinteractionoftherespectivemovementellipsoids.Table3indicatesthepercentageincreaseofproductivityandpanzerutilizationassociatedbyusinganautonomoussystem,comparedtothebasecase.

Table 2 Statistic results of dozer’s dump length and dozer’s productivity

Laboratory scale Scaled results Dump length

[cm]Productivity

[g/cycle]Dump length

[m]Productivity

[t/cycle]Average 19.18 76.35 9.59 9.54

Standarddeviation 9.76 74.38 4.88 9.30Minimum 0.50 0.25 0.25 0.03Maximum 50.50 423.72 25.25 52.97

Cyclesnumber 400 400 400 400

Comparingthesamedrawsequence,theautonomouscontinuousminingproductivityis,onaverage,58%higherthantheuncontrolledsystem.Regardingpanzerutilization,theaverageforautomationcaseis7%higherthannonautomationcase.Thus,itcanbeconcludedthatAutonomousContinuousMininghasalsoahigherpanzerutilization.

Table 3 Percentage increase of productivity and panzer utilization for studied cases

Configuration Type I Type II Type III AverageProductivity 50.2 60.6 62.0 57.6

Panzerutilization 1.0 21.4 -2.1 6.8

Ingeneral,productivitiesandutilizationofthesystemincreasethroughtheimplementationofthesensingandcontrollingsystems.

5 Conclusions

Thisresearchworkconfirmedtheusefulnessofpilottestingtooltowardstheunderstandingofbehaviorofinnovativeminingsystems.Itwasverifiedthat,underexperimentsandparticlesizedistributionconditionspresented,thedozer-panzersystemisahighproductionsystemandthatthedrawperdozerishighandvariable.

The results of testing theContinuousMiningSystem subjected to experiments conditions showed thatconfigurationsusingcentralextractionpointsallowreachinghigherextractionrates.

TheresultsfromtheAutonomousContinuousMiningtestingsubjectedtoexperimentsconditionsshowthattheimplementationofanautonomouscontrolsystemallowstheachievementofhigherproductivity,extractionratesandpanzerutilization.

Basedontheresultsobtained,itisrecommendedtoimplementtheautonomoussysteminthemineinordertoverify the increaseofproductivityof thesystem.Itwouldbebeneficial toperformnewexperimentsunderamoredemandingscenario.Forexample,incrementingtheparticlesizedistributionwiththeaimofquantifythereductioninsystemproductivity.Itwillberelevanttoidentifythepotentialproblems,which

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this technologycould face in the future incaseofnotachieving theexpectedparticlesizedistribution.Finally,itisrecommendedtointegrateuniformityindicatorstowardsmaximizingtheproductionratesandtheextractionofreservesinlongtermproductionplanning.

Acknowledgement

TheauthorswouldliketoacknowledgethefinancialandtechnicalsupportfromCodelcoChile.Theauthorswould also like to acknowledge the contributionofErnestoArancibia fromCodelcoChile and to IM2(InstituteforInnovationinMiningandMetallurgy)researchersduringthisproject.

References

Alvarez,P2010,Modelamiento físicode laMineríaContinua,Memoriade Ingeniería,UniversidaddeChile,Santiago,Chile.(inspanish)

Barriga, J 2012, SecuenciaAccionamiento Dozer, Nota Técnica Nº IA-004, IM2, Santiago, Chile. (inspanish)

Encina,V,Baez,F,Geister,F,&Steinberg,J2008,Mechanizedcontinuousdrawingsystem:Atechnicalanswertoincreaseproductioncapacityforlargeblockcavingmines,ProceedingsofMassMining2008Conference,Lulea,Suecia,pp.553-562.

JRIIngeniería2010,InformedelaIngenieríaConceptualyBásicadelaValidaciónIndustrialTecnológicadelaMineríaContinua,Santiago,Chile.(inspanish)

Orellana,L2012,EstudiodevariablesdediseñodelsistemadeMineríaContinuaapartirdeexperimentaciónenlaboratorio,TesisdeMagister,UniversidaddeChile,Santiago,Chile.(inspanish)

Orellana,M2011,ModelamientoNuméricodelaMineríaContinua,TesisdeMagister,UniversidaddeChile,Santiago,Chile.(inspanish)


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Implementation of LiDAR technology to evaluate deformation field induced by panel caving exploitation, Codelco Chile El Teniente Division

AE Espinosa Codelco, ChileP Landeros Codelco, Chile

Abstract

Empirical experience and theoretical developments in rock mechanics have demonstrated that cavities generated by extraction in cave mining induce changes in deformation fields, reaching different extensions over time while their location varies according to the mine growth. Considering changes in deformation field as a result of induced stress field on a rock material by caving, extensive recording of this information is an important data that permits to evaluate the performance of mine design and allowing short term engineers to have the chance to take appropriate actions, if deviations are identified.

LiDAR technology is based on the principal of calculating laser pulse time of flight (TOF). Therefore, if the information, such as, laser pulse velocity, angular reference used to measure and the difference of time between emitted and reflected ray is known, it is possible to determinate the relative distance of an obstacle or object.

This work explains in detail the implementation of these concepts to geomechanical monitoring at Dacita Project at El Teniente mine, showing results obtained as a the baseline measurement and during a comparative analysis while considering mining activity and ground control information.

1 Introduction

1.1 El Teniente Mine overview

ElTenienteMine is aCodelcoChileundergroundcoppermine. It is located in theAndes range in thecentralzoneofChile,approximately70kmSSEfromthecapitalcity,Santiago.ElTenienteisthelargestknowncopper–molybdenumdepositintheworld.Itishostedinacopperporphyrysystem.ThemainrocktypesincludeAndesites,DioritesandHydrothermalBrecciasoftheMioceneera.Since1906,morethan1,100milliontonsoforehavebeenmined.Themineiscurrentlyextractingapproximately140,000tons/dayusingmechanizedcavingmethods.Panelandpost-undercutcavingmethods,variationsofthestandardblockcaving,wereintroducedin1982and1994,respectivelytoexploitprimarycopperore.

1.2 Dacita Project overview

ElTenienteMine includes different productive sectors, all of them located around a chimney of sub-volcanicbrecciaswithaninvertedconeshape,knownas“BradenPipe”.

DacitaProjectislocatedonthewesternsideofReservasNorteMineanditcorrespondsgeometricallytoanextensionofthatproductivesector(Figure1).ItsexploitationstartedinNovember2013anditconsidersaproductionplancloseto17,000tons/dayfortheyear2019,usingaconventionalpanelcaving(post-cutundercutting)andanintegratedminingsequenceofbothmines.

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Averageprimarycolumns’heightsare180meters,varyingfrom90to250meters,findinglowervaluesbeneathSurAndesPipaMineandPipaNorteMine.Thosevaluescouldbeassociatedwithlowstressstateregime,however, thesizeofall thecavitiesaroundDacitaProject inducesahigher levelofpre-miningstressverysimilartoReservasNorteMine.

Figure 1 Schematic view from the north of Dacita Project footprint. It is possible to identify its location between cavities (Espinosa et al. 2012)

1.3 Geological and geotechnical data

According toBrzovic (2012), predominant lithologycorresponds toDacitePorphyry,which in termsofintactrockpropertiesisverystiffwithaverageYoung’sModulusofapproximately60GPa.Intermsofrockmassqualityindexes,DacitePorphyryisverycompetentwithGSIintherangeof75to90.Mostimportantgeologicalfaultsareclassifiedas“masterfaults”(faultsG,C,N1andN2)and“majorfaults”(faultsF,K,L).

2 Geomechanics monitoring plan concepts

Consideringchangesindeformationfieldasaresultofinducedstressfieldonarockmaterialbycaving,extensiverecordingofthisinformationprovidesimportantdatathatpermitstoevaluatetheperformanceofminedesignandallowingtheshorttermengineerstotakeappropriateactions,ifdeviationsareidentified.

Therefore,someofthemainobjectivesofthegeomechanicsmonitoringplanarebasedonthefollowingconsiderations:

• To be able to identify changes, in terms of induced stresses and deformations, affectingexcavationsintheproductivelevels.

• Toprovidenewfieldinformationfornumericalmodellingcalibration.

• Toestimatezonesaffectedbytheminingadvance.


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Rockburstisoneofthemostrelevantgeomechanicsrisksidentifiedinthedevelopmentofthegeomechanicsengineering ofDacita Project.To complement its evaluation, vulnerable zones are identified, using anintegrated methodology, considering geological aspects, size of the excavations and stress conditions,selectingtheplacestobemeasuredandthen,comparingresultswithfieldinformationobtainedbygroundcontrolengineers.Inspecificcases,identificationiscomplementedbynumericalmodellinganalysis(Cuelloetal.2010).

Majorgeomechanicalhazards,suchas,rockburstandcollapsesthataffectlargepanelcavingoperations,arehighlydependentonacavebackgeometry(Landerosetal.2012).Basedontheassumptionthatanygeometricalchangeisrelatedtoachangeofthestressfield,thegeomechanicsplanconsideredtheconceptshowninFigure2.

Figure 2 Interpretation process of monitoring results (Espinosa 2012)

According to all the aspectsmentionedpreviously, oneof the areas ofgeomechanicsmonitoringplanwas focusedon the implementationofLightDetectionandRanging technology (LiDAR)as a tool formeasuringthegeometriesofexcavationsandtheirchangesintime.

Development of LiDAR technology started in the 1970’s inUSA andCanada, usedwith satellites fortopographyscannerwithhighcostandmanylimitations.Withhigherdevelopmentofinformatictechnology,it is currently used inmany different fields. The device basically works emitting laser light pulses todeterminatethedistancebetweensurfacesanditsposition,generatingcloudswithmillionsofpoints.Thistechnologyishighlyaccurateandpreciseanditiswidelyusedforsurveyingmeasurementsbothopenpitminingandundergroundmining.

TherearedifferenttypesofLiDARbutitisnotamatterofthisstudytodescribealltypes.Theoneusedinthiscasecorrespondstoalaser-basedrangingandimagingsystem,terrestrialandstatic(mountedonatripod),capabletocapturedatainmediumandlongdistanceinsidethegalleries.

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Principal advantages of this technology, as compared with traditional methods, such as, convergencestationsor extensometers, are related to lessoperational interference and the capability to capturenewinformationinawiderextensionandnotonlyatoneselectedpoint.

3 Preliminary results and data processing

3.1 Preliminary results

First stage of measurements started in July 2012, in both the undercut and the production levels; itrepresentedthebaselineforfuturecomparisons.DuringJune2013,newmeasurementswerecompletedatspecificlocations,basedonan“excavationvulnerabilitycriterion”;approximately45%ofthesurfacewascomparedineachlevel.Figure3showsanexampleofcaptureddatainaproductiondrift.

Figure 3 Example of production level drifts LiDAR measurements at Dacita Project (Espinosa & Landeros 2012)

ThedecisiontoincludeLiDARtechnologyinthedevelopmentofDacitaProjectinvolvedanewchallenge.Thereareat leastfourdifferentdocumentedalgorithmsfor thecalculationsof themeasurementresults,eachoneofthosebasedoncertainassumptionsandlimitations.

3.2 Existing distance measurement methods

The approaches described byGirardeau (2006) and Lague et al. (2013), used tomeasure the distancebetweentwopointcloudsinthecontextofgeomorphologicapplications,areshowninFigure4andaredescribedinthefollowingparagraphs:

• Digitalelevationmodel(DEM)ofdifference:

○ DEMofdifferenceisthemostcommonmethodofpointcloudcomparisoninearthscienceswhenthelargescalegeometryofthesceneisplanar.


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○ ThetwopointcloudsaregriddedtogenerateDEMseitherdirectlyifthelargescalesurfaceisnearhorizontal.ThetwoDEMsarethendifferentiatedonapixel-by-pixelbasiswhichamountsatmeasuringaverticaldistance.

○ Thistechniqueisveryfastbutitsuffersfromamajordrawback:itcannotoperateproperlyon3Denvironmentsorroughsurfaces.

Figure 4 Existing 3D comparison methods between two point clouds PC1 and PC2 (modified from Lague et al. 2013)

• Directcloud-to-cloudcomparisonwithclosestpointtechnique(C2C):

○ Thismethodisthesimplestandfastestdirect3Dcomparisonmethodofpointcloudsasitdoesnotrequiregriddingormeshingofthedata,norcalculationofsurfacenormal.

○ Foreachpointofthesecondpointcloud,aclosestpointcanbedefinedinthefirstpointcloud.Initssimplestversion,thesurfacechangeisestimatedasthedistancebetweenthetwopoints.

○ Improvementscanbeobtainedbyalocalmodelofthereferencesurfaceeitherbyaheightfunctionorbyaleastsquarefitoftheclosestpointneighbours.

○ Themeasureddistanceissensitivetothecloudsroughness,outliersandpointspacing.

• Cloud-to-meshdistanceorcloud-to-modeldistance(C2M):

○ Thisapproachisthemostcommontechniqueininspectionsoftware.Surfacechangeiscalculatedbythedistancebetweenapointcloudandareference3Dmeshortheoreticalmodel.

○ Thisapproachworkswellonflatsurfacesasameshcorrespondingtotheaveragereferencepointcloudpositioncanbeconstructed.However,creatingasurfacemeshiscomplexforpointcloudswithsignificantroughnessatallscalesormissingdataduetoocclusion.

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○ Interpolationovermissingdataintroducesuncertaintiesthataredifficulttoquantify.Meshconstructionalsosmoothoutsomedetailsthatmaybeimportanttoassesslocalroughnessproperties.

• MultiscaleModeltoModelCloudComparison(M3C2):

○ Thisapproachoperatesdirectlyonpointcloudswithoutmeshingorgridding.

○ Itcomputesthelocaldistancebetweentwopointcloudsalongthenormalsurfacedirectionwhichtracks3Dvariationsinsurfaceorientation.

○ Itestimatesforeachdistancemeasurementaconfidenceintervaldependingonpointcloudroughnessandregistrationerror.

3.3 Comparing preliminary results and field information

As mentioned previously, there are different numerical approaches available to calculate differencesbetweenpointclouds.Theanalysisofresultswasdividedintoseveralstages,asfollows:

3.3.1 Stage 1

AllavailablealgorithmsdescribedinSection3.2wereusedtocalculatedifferencesbetweenpointclouds.Theseresultsrepresentthebaselineforfurthercomparativeanalysis.

3.3.2 Stage 2

Consideringreinforcementandsupportinstalledatproductionlevel(Figure5),severalzoneswerechosenforcomparativeanalysis.

Figure 5 Description of typical reinforcement systems used for the analysis.


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Reinforcementsystemsusedintheproductionlevelarecomposedbysystematicinstallationofbolt-mesh-shotcrete system during the development of the galleries. In a later stage, a cable-wiremesh-concreteconfinementwall isbuilt, thiskindof structure ismore rigidand ithasadifferent expectedbehaviouragainstloadings.

Theevaluationofinduceddamagedisdonebyshorttermgeomechanicsengineersandtheinformationishostedinaninternaldatabaseavailableforfurtheranalysis(Cifuentesetal.2012).Inthiscaseofstudy,observedchangesonroofsandshouldersareconsideredfortheanalysis.

3.3.3 Stage 3

Placeswithnoobserveddamagewereusedtocomparedifferentapproaches.Differencesshouldtendtominimalvalues.AnexampleisshowninFigure6;itispossibletoobservethatM3C2algorithmestimatelowervaluedinthiscase,comparedtoC2CandC2C_HF.

Figure 6 Comparison of results between different approaches and field information at C21/Z9N, production level of Dacita Project (modified from Cortes 2014)

In the sameexample, if90%of reliability isconsidered forfiltering thedata,minimumandmaximumdifferentialvaluesarebetween-0.01and0.03meters.AplanviewwiththefiltereddataanditsrespectivehistogramisshowninFigure7.

Figure 7 Differential analyses at C21/Z9N with M3C2 algorithm, production level of Dacita Project

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Ontheotherhand,thesameprocedurewasappliedtozoneswithobserveddamageasshowninFigure8.Itispossibletoobservethatresultsshowtwodifferentpopulationsofdata,consideringdeformationofthegalleryandtheloosenessofsomeshotcretelayer.

Figure 8 Differential analyses at C21/Z5N with M3C2 algorithm, production level of Dacita Project

4 Conclusions

Implementation of LiDAR technology for geomechanics monitoring is based on the concept that anychangeindeformationfieldisdirectlyrelatedtochangesofinducedstressfieldonarockmaterialbycavingandextensiverecordeddatawillassistshorttermengineerstoidentifythedeviationstotheminingplan.

Comparative analysis between field information and available algorithms for calculation indicates thatispossibletoprocesslargeamountofdataandbuilddifferentialmaps.Multiscalemodeltomodelcloudcomparison(M3C2)fitswellforcomplextopographies.

Theresultsarepromisinginordertoevaluatetheperformanceoftheminedesignduringmineexploitationlifetime.

Acknowledgement

TheauthorswishtothankCodelcoChile,ElTenienteDivisionforallowingthepublicationofthispaperandtheGeomechanicsstaffthatsupplieddataandinformation.

References

Brzovic,A2012,‘Geologyandmineralresources,DacitaProject’,InternalReportforfeasibilitystudy.

Cifuentes,C,Zepeda,R,Parraguez,R&Gaete,S2012,‘Implementationofsystematicdamagemappingforgeotechnicalevaluation,ElTenienteMine’,Proceedingsof6thInternationalConferenceonMassMining,Massmin2012,Sudbury,Canada.

Cortes,O2014,‘Geomechanicsmonitoringusing3Dscanner,DacitaProjectCodelcoElTeniente’,ThesisworkdevelopattheGeomechanicsSuperintendentofCodelcoElTeniente.

Cuello,D,Landeros,P&Cavieres,P2010,‘Theuseofa3Delasticmodeltoidentifyrockmassdamagedareas in the undercut level at Reservas Norte sector’, Proceedings of 5th InternationalConferenceonDeepandHighStressMining,Santiago,Chile.


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Lague,D,Brodu,N&Leroux,J2013,‘Accurate3Dcomparisonofcomplextopographywithterrestriallaserscanner:ApplicationtotheRangitikeicanyon(N-Z)’,ISPRSJournalofPhotogrammetryandRemoteSensing,vol.82,pp.10-26.

Landeros,P,Cuello,D&Rojas,E2012,‘CavebackmanagementatReservasNorteMine,CodelcoChileElTenienteDivision’,Proceedingsof6thInternationalConferenceonMassMining,Massmin2012,Sudbury,Canada.

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Innovation

Semi-autonomous mining model

M Fishwick Codelco, ChileM Telias IM2-Codelco, Chile

Abstract

A conceptualization of semi-autonomous nining has been prepared by CODELCO in a “Semi-autonomous Mining Program”. In this conceptualization each of the constituent features of the model were defined, generating a version that is ready for industrial validation.

A diagnosis and a comprehensive assessment of the applications and trials of SA LHD technology in CODELCO were performed, taking into account, as main parameters, the following:

• The industrial application in Pipa Norte Mine (El Teniente Division) with Sandvik as provider (current).

• The trials in Andina Division with Caterpillar (2011) and Atlas Copco (2012) as providers.

This paper presents the SA Mining Model developed in order to satisfy the requirements of structural projects.

1 Introduction

TheSemi-autonomousUndergroundMiningisatechnologicalbreakthroughforundergroundminingusingcavingmethodology,basedonsemi-autonomousLHDtechnology.ItsobjectiveistoprovidehigherlevelsofsecurityandsustainabilityalignedwiththeproductivityrequiredforCODELCO’sstructuralprojects.

The experience acquired in industrial applications indicates that in order to obtain the same results ofmanualoperations,itisnotenoughreplacingmannedLHDunitsbysemi-autonomousunits(aswasusedinKirunavaara(IM22013;FredrikKangaset.al.2004)).Acompleteredesignoftheextractionprocessisrequired,inparticular,schedulingandredefiningtheso-calledinterferences,whichareaninherentpartoftheextractionprocess

Hence,aSAMiningModelmustbebuiltinordertofullyleveragethistechnology.Thenewmodelhasfeatureswhichdrasticallychangesthepresentoperationmodelanditsparts.Thesepartsare:minelayout,operational strategy, technology, safety and health, a new business model (technological developmentmodelbetweenclientsandproviders),humanresourcesandmanagement,andmaintainability.

It is clear that each of these aspects requires a specific treatment, based on the new technology. It isnecessarytomodifytheminelayoutsoastobecompatiblewiththecapabilitiesandconstraintstheSALHDtechnology,e.g.,wearingcoursequality,sizeandlayoutofdrawpoints,numberofoperatingunitspermodule,etc.Furthermore,operationstrategyiscompletelydifferentdependingonhowthesemachinesoperateinrelationtomannedunits,especiallyifcontroliscentralized.Itisnecessarytodefinerequirementson the technology depending on layouts and strategies. For this, a different model of technologicaldevelopment is needed - since, due to the reduced amount of clients, companies donot spontaneouslydeveloptherequiredimprovements.Besidesall,operatorprofileforthiskindofmachines(andtechnology)differscompletelyfrommanualoperations,aswellasmorespecializedmaintainersarerequired.Inadditiontotheabove,theculturalchangeassociatedwiththistechnologicalbreakthroughneedstobeaddressed.Regardingtheaspectsofsafetyandhealth,itisimportanttonotethattherearenecessarylegalchangeswhichmustbesupported.


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AconceptualizationofSemi-autonomousMininghasbeenpreparedbyCODELCOinaSemi-autonomousMiningProgram. In this conceptualization, eachof the constituent featuresof themodelweredefined,generatingaversionwhichisreadyforindustrialvalidation.

In order tomodel these components, a diagnosis and a comprehensive assessment of the applicationsandtrialsofSALHDtechnologyinCODELCOwereperformed,takingintoaccountthefollowingdata(Schweikart,V.&soikkeli,T.2004):

•TheindustrialapplicationinPipaNorteMine(ElTenienteDivision)withSandvikasprovider(current).

•ThetrialsinAndinaDivisionwithCaterpillar(2011)andAtlasCopco(2012)asproviders.

ThispaperpresentstheSAMiningModeldevelopedtosatisfytherequirementsofstructuralprojects.

2 Semiautonomous Mining (Semi-autonomous mineral extraction solution)

Thissolutionisbasedontheconjunctionoftwomainactionscopes:

2.1 Operational model for semi-autonomous underground mining

Thismodelisspeciallydevelopedforintroducingthistechnology,itgeneratestherequirementsandallowsestablishing the parameters that ensure expectedKPIs from the application (in this case,CODELCO’sstructuralprojects),ifindustriallyvalidated(TheexpectedKPIsareatlessthesameobtainedusingmanualtechnology).

2.2 Technological development model

Automationtechnologydevelopmentprogramorientedtodevelopthetechnologythatallowsthesystemtowork,insuccessivestages.Ontheonehand,itenablesacontinuousandsustainabledevelopment;ontheotherhand,itallowsestablishingacommonbaselineforfuturedevelopmentinthedifferentstageswithproviderswhichacommercialagreementfortechnologicaldevelopmentisestablishedwith,followingapre-establishedmodel.

3 Operational model for semi-autonomous underground mining

3.1 Key components

Aspartof theAutomationProgram forUndergroundmining,Technologyand InnovationManagement(TIM) developed an OPERATIONAL MODEL FOR AUTONOMOUS/SEMI-AUTONOMOUS UNDERGROUND MINING,focusedonSALHDthatwilloperateinundergroundstructuralprojects.

AutonomousminingwithSALHDisperformedwithpiecesofequipmentthatdonotrequireanoperatoronboard;theyarecontrolledfromanoperationandmanagementcenter,usuallyoffsite,withremoteoperationlimitedtoafewoftheirfunctions.Thisimpliesadeepchangeinthewayminingisconceivedandrequiresanoperationalmodelensuringtheproductivecapabilitiesandproductivity,controllingcostsandvariabilityofprocesses.Introducingthistechnologywithoutatailormadevalidatedoperationalmodelleadstotheriskofhavingalossofvaluewithautomationtechnology,asobservedinrecentapplications.

Experiencesofapplicationofautomationtechnologiesinextractionprocesses—e.g.autonomoustrucksforCODELCO’sGabrielaMistralmine— indicate that inorder to successfully introduceautonomous

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equipment,ahugeamountofthescopeswhicharepartoftheprocessarerequiredtobemodified,suchus:minelayout,operationandmaintenancestrategies,planningandcontrol,safety,humanresources,etc.Ontopofall,adeepculturalchangeregardingtheconceptionandoperationofthemineisrequired—particularlyinpeople,andinvolvingthetopmanagement.Inthiscontext,anAutonomousOperationModelforUndergroundMiningwasdesigned, thatwascompletely introducedbyCODELCO’sChuquicamataUndergroundProject(PMCHS)andpartiallyintroducedbyCODELCO’sNewMineLevelProject(PNNM).

Basesofthemodelarecharacterizedas:

• SAModule:Productiveunitthatconsistsonasemi-autonomousLHDoperatingonasegmentoftheproductiondriftconfinedinbothends,withaorepassandseveraldrawpoints.

• Several SA Modules per street. Two adjacent modules could be simultaneously involved onthe extraction processwith SALHD, allowing the increase of the extraction speed per street,counteractingthelowerperformanceoftheSALHDcomparedtotheManualLHDunits.

• TheSAmodules are always performing a task: production, secondary reduction,maintenance,sampling,etc.Ifthisisended,theLHDunitshiftsModule.TherewillbenoSAModulesonhold,withoutactivity.

• CentralizedControl froma roomoffsitewithstrategic, tacticalandcontrol levels;withexpertsystemsforcontrolandmanagementofproduction,controlofvitalsignsforpredictivemaintenance,interactionwithotherminecontrolsystems,etc.

• 24/7ContinuousOperations fromcontroldeskwith ‘callcenter’system:Operatorshiftchangewithminimumrelieftime(manillaamanilla).Theequipmentarestoppedonlyforneedsinherenttothem.

• Rigorousmaintainability, since theautonomoussystemrequires lower tolerance thresholdsandvarianceoftheelectrical-hydraulicsystemsoftheequipment.

• Ongoingtechnologicaldevelopmentmodelthatrequiresmid-termagreementswithmanufacturers.

3.2 Expected impact on the production process

• Higher use of the active area

This is feasiblebecause it is a simultaneousprocess inmore thanonemoduleperdrift, increasing theutilizationofproductivearea.

• Simultaneity and operational continuity

Operationalcontinuity is increased,eliminating theconceptofproductionshift.There isanoperationalstrategymakingmodulestobeunderproductionuntiltheyarestoppedbecauseofprocessconsiderations—moving,then,ontoanoptimizedsupportactivity,soasnottohavedowntimesinmodules.Amaintenanceandreliabilitysystemisalsoconsidered,supportingtheavailabilityofthesemi-autonomousoperationinmodules,inordertohaveasmuchasavailableproductivetimeaspossible.Inaddition,Semi-autonomousLHDandcentralizedoperationandcontrolfeaturesfromanoffsiteoperationroomareleveraged,inordertohaveaminimumrelieftimeoperation.

• Increase in productivity

Tailormademiningdesignandplanningsoastosystematicallyachieveabetterproductivityoftheopenarea.Alongwiththat,newautomationfunctionalitiesgeneratingbetteroperationalperformancesoriented


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to:fleetmanagementandcontrol,decreaseincycletimesinordertoincreaseperformance,andanincreaseinutilizationandavailability.

Productivityparametersandlearningcurvesforoperatorsinshorttrammingdistances(below40meters)with a 1:1 and1:2 equipment operator ratiowill be determined along the shift. Distribution and timerangeforequipmentqueuingwillbedeterminedinordertoassessproductivityandrequiredrostersforthescalabilityoftheoperationmodelinStructuralProjectsandDivisions.

• Operational Variability

Havingaloweroperationalvariability,deviationsbetweenwhatisplannedversuswhatisproduceddecrease,having amore reliable production plan. This is feasible since technology in automated tramming anddumpingactivitieshasproventohavelowvariability(around5%to10%).Thetechnologyisalsorequiredtobehighlyreliable,withhighavailabilityandoperationalcontinuity,havinganextractionprocesseswithproductivemoduleswith low interferences and operational losses. Noteworthy that the fragmentationhandledwithSALHDisthesameasthemanualLHD.Duringtherangeof0%to30%oftheorecolumnextractedamanualtechnologyisrecommendedoverthesemi-automation.

3.3 Construction of the operational model for semi-autonomous underground mining

Themodelisbuiltfrom:

1. Actionsforthedevelopmentofaspecificoperationmodel

ResultsfromtrialsandapplicationsperformedwithintheCorporation:OperationofthreeSandvikSALHDinPipaNortemine—ElTenienteDivision2004-2013—andpilottrialsof1SALHDunitfromCaterpillarandAtlasCopco(IIIPanel,AndinaDivision,2011-2012)showedtheneedofestablishingadifferentoperationmodeladaptedtotheconditionsoftheSALHDsystem,soastoobtainthehighestreturnfromthistechnology.

Itwas evidenced theneedof a strategyandoperation tactics; andautonomousequipmentorientedmaintenance;asuitableminelayout;differentskillsandabilitiesfromthestaff;acriticalinvolvementofthetopmanageriallevel;hardware,softwareandtechnologicalplatformwithredundancyneedsforautomation,givingroomtoatechnologydevelopmentplancarriedouttogetherwithproviders,soastofullysatisfytheminingoperationdemands.

2. StructuralProject’srequirements

Inadditiontoperformancerequirements(tonsperhour,effectivehourspershift,availabilitypercentage),structuralprojectsneedanumberofmanagementtoolsandfunctionalitiesthathavetobeincludedinautomationsystemsinordertoincreaseefficiencyinproductionmanagementandmaintenance,hugefleetmanagementandotherfeaturesorientedtoincreaseautomationlevelsoftheprocess,suchason-linedatacollectionrelatedtoinfrastructurestatusandactiveitemsoftheproductiveprocess.(E.g.:Drawpointstatus,shaft).

3. Stateofthearttechnology

Through a specialized survey, a detailed technical analysis on the state of the art regarding LHDautomationandanassessmentofstrengthsandweaknessesofautomationtechnologiesfromthefouravailableLHDproviders;theirfunctioningprinciples;componentsandkeymechanisms;aswellasaprojectionofthetechnologywereperformed,confirmingtheexistenceofroomforimprovement.Thisway,developmentstobecarryoutbyproviderswerestated.

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3.4 Justification for introducing this technology

3.4.1 Strategic Alignment

• Taking into account the firstVALUEOFCODELCO:RESPECTFORLIFEANDDIGNITYOFPEOPLE,takingpersonneloutofhighriskyworkingenvironments.

• It takesoperatorsoutof theLineoffire,beingalignedwith thegoalofCODELCOoferadicatingsilicosis.Inaddition,thereisnocompliancewiththeregulationofexposuretovibrations.

• Fulfillingcorporatestrategicgoalsofhavingallhighlyriskyprocessesautomatedby2015.

• TakingintoaccountcompliancewithFATALITYCONTROLSTANDARDS:

• ECF1and3:Havingablockagesystemallowingequipmentisolation

• ECF3:Anti-collidesystem,heavyequipmenthandling

• Itsignificantlyimprovesconditionstowardsthecomplianceoffewofthestandards.E.g.:Mudrushes(ECF-15),Rockburst(ECF-16),Oxygenandgascontrolinundergroundmining(ECF-17).Inadditionto

• EST#4:Ergonomics.TrialsandfutureoperationswilltakecareofergonomicdesignoftheLHDOperationRoom(UserInterfaceDesign)

• EST#5:Compatiblehealth.Pre-laborandlaborexaminations

ο EST#6:Fatigueandsomnolence

3.4.2 Contributing to the results of PMCHS and PNNM Structural Projects.

3.4.2.1 Result improvement

Performance,usageandavailabilityresultsobtainedduringtrialsandapplicationsperformedinCODELCOshowthroughsimulationsthattheoperationmodeldevelopedisabletocontributeinimprovingthemostrelevantKPIs(Ton/hr,Hr/Shift,Avail.)

3.4.2.2 Enabling technology and requirements

Thestateofthearttechnologydoesnotaccountforalltherequirementsofstructuralprojects,thatiswhythedevelopmentofnewprovenandvalidatedfunctionalitiesandtechnologicalimprovementsisrequired(hugefleetmanagement,connectivitywithothersystemsandequipmentinthemine,enhancingsecurityandsafetysystems,bettersupportforremoteoperation,environmentvisualization,dynamicallocationofdrawpointschedule,etc.).

3.4.2.3 Terms and opportunity

The schedules of structural projects require the implementation of theOPERATIONALMODELFORSEMI-AUTONOMOUSMININGvalidatedforimplementationandcommissioningin2017and2019inthePNNMandPMCHSprojects,accordingly.Takingintoaccountthattechnologicaldevelopmentsofthissizetakelongtime,newfunctionalitiesandvalidationwillstartimmediately,notlaterthan2014.


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3.4.2.4 Knowledge management

• Atechnological leadership fromCODELCOis required, inorder toboost themarketdevelopmentsoastosatisfyCODELCOneeds,whichareuniqueintheindustry.Inaddition,basesforamid-termdevelopmentplanorientedtoamulti-brandautomationsystemarerequired.

• Creating a critical mass of professionals, technicians and operators, both within CODELCO aswellaswithinserviceandsupportprovidercompanies,inordertoensureanefficientandeffectivecommissioningofstructuralprojects.

• Keepingtechnicalexperienceoftheautonomousoperation.In2014,PipaNorteoperationreachesitsend,theonlyonewithSALHD.

3.4.2.5 Validation trial for SA LHD operation model

IndustrialValidationtrialfortheSemi-autonomousminingoperationmodelwillbetakingplaceinBlock1ofEsmeralda,during2015.Inthisblock,thefullimplementationaftertheSALHDtrialwillbeassessedduringthetrial.Thissectorhave26Mtonorereserves,withamid-gradecopperorebodyof1.07%.Theareaencompasses45,200m2,withnine220-meter-longproductionstreets(approximatelength),withorepassevery100m,withsemi-steadyremotecontrolledhammersand40”*40”mesh.IthasaTeniente-style30*19.6mextractionlayoutwithPanelCavingextractionmethodwithtraditionalundercut.

Figure 1 SA LHD industrial validation trial layout

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Thetrialwilltakeplaceintwodrift,withfourmodulesfortheoperationof2SALHDunits,asshowninFigure1.

• ItconsidersmininglayoutsassimilaraspossibletotheonesdevelopedforPNMMandPMCHS.

• Ittakesintoaccountpiecesofequipmentwithspecificationsforautomationsystemsandscalableperformances,takingasabaselinetheperformancesconsideredfortheStructuralProjects.

• Useof technology fromSAsystemproviders,under specificationsof theestablishedoperationmodelandtechnologicalrequirements(alphalevel).

• Technicalparametersandcostsoftheoperationperpieceofequipment,module,streetandtrialsectoraredetermined,withtheircorrespondingvariabilityanalysis.

3.4.2.6 SA LHD industrial validation trial objectives and deliverables

IndustrialvalidationtrialaimstodevelopSALHDindustrialtrialsinCODELCO’sEsmeraldaDivision,ElTeniente,inordertoindustriallyvalidatethetechnology,andatthesametimevalidatingthetechnologicaldevelopmentmodelinitsfirststage.Asaresultofthetrial,thefollowingdeliverableswillbeprovided:

• Scalability model— i.e. design bases, calculation methodologies, and backup parameters forengineeringofsemi-autonomousminingforEsmeraldaDivision,ElTeniente.

• Scalabilitymodelforsemi-autonomousminingforPNNMandPMCHSstructuralprojects.

• Necessarytechnicalparametersforbidding(October2015forPNNM,2017forPCHMS)

• Baselineforthegenerationofamid-termdevelopmentplanforthetechnology.

• EstablishmentofoptimizationitemsfortheoperationmodelfortheNewMineLevelProject.

• Developmentofamarketofproviders,pointingtoamulti-brandsystem

• Generationandmaintenanceofcorporatetechnicalexpertise.

3.5 Key variables of the SA mining model

3.5.1 Mining layout and operational strategy:

• Blocksaredividedinsemi-autonomousmodules.

• Each module consists on one drift segment (i.e. one street contains several modules) withindependentaccessperdrift.

• Eachsemi-autonomousmodulehasthefollowingfeatures:

• Isolatedandconfinedbyphysicaland/orvirtualbarriers.

• Onemoduleshouldhaveasuitableamountofdrawpointsandorepasssoastoensurethemaximumproductivity(ton/day)oftheminingsystem.Basedontheanalysesperformeduptodate,eachmoduleconsiders16to22drawpointsandoneorepass.

• Operationof1SALHDpermodule.

• Severalmodulesperblockoperatingsimultaneously,eveninthesamestreet.


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• ThecontinuousextractionoftheSALHDissubjectto:fullstopduetohangups,humandecisions,oversizemuckindrawpoints,fullshaftoranyothereventimpedingtheextractioninthemodule.Ifrequired,thepieceofequipmentshouldbeabletoshiftmoduleinordertocontinueoperatinginotheravailablemodule.

• Eachmodulehasalwaysanactivityallocated.Thisway,ifitisnotinproduction,isperforminganactivitysupportingproduction:extraction,secondaryreduction,solvinghangups,sampling,repairing,etc.

• Operationallossesandinterferencesbetweenunitaryoperationsareminimized.Theworkingcycleoftheautonomousmoduleisindependentfromtheshiftscheduleoftheroster.

• Operationfromacentralizedcontrolroomwith1operatoravailabletooperate1,2ormoreSALHDunitsfortrammingdistancesbelow40meters.

3.5.2 Planning and production control

Focusedonahigherutilizationoftheopenarea,inordertomaximizetheminingsystemperformancewithsemi-autonomoustechnology.This involves theIntegrationof theMiningPlanandProductionControl,withon-line feedbackwith theproduction information fromorigin todestination (bucket tonnage fromdrawpoints–orepass).

Figure 2 Design of a Semi-autonomous mining operation model

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• Short term management:Assistance from the CIOG Room for compliance of the productionprogram,withproductiontools.

• Longtermplanning:Historicinformationforre-definingcriteriaandparameters.

Itrequiressignificantchangesin:

• Modular production, associated to the confined functioning of semi-autonomous LHD units:Thisistranslatedintothecreationofanewextractionunit, themodulethathastobemanagedtaking into account thatmaintenanceof thesemodules has to be coordinated so as to keep anavailabilityratesupportingtheproductionallthetime.

• Systemic management of productive and support resources:Alwaysinamodularitycondition,the use of different items and resources of the system (LHD, crushing hammers, auxiliaryequipment,crews,infrastructure,spaces,etc.)mustbeeffectivelycoordinatedsoastoreducebottlenecksandminimizeinterferences.Thiscoordinationhavetoaccountforboth,plannedavailability(e.g.maintenanceofapieceofequipment)aswellassuddenavailability(e.g.hangupinashaft).

• Operation and planning alignment: Ruleorstrategydefinitionwillbeorientedtothecomplianceinthelongrunwiththeproductionandbudgetarygoalsdefinedintheplanningprocess.

• Production management system with central control: Allowing integrated data collectionanddecisionmakinginordertosupportthebulletsabove.Thisimpliesafleetmanagementwithcentralized allocation, dispatch, drawpoint schedule control, supervision,monitoring andfleetcontrol.Apredictivemaintenancesystembasedonavitalhealthmanagementsystem.

• Extraction equipment:Semi-autonomousLHDwithincrementalinnovationofnewfunctionalities,aiming to improveperformanceforproductionstandards,performanceandavailabilityofhugefleesforfutureCODELCOprojects.

4 Technological development model

ItisunderstoodasaSALHDtechnologicaldevelopmentmodelthestrategythatCODELCOwillputinplacetoestablishitsrelationshipwithLHDproviders,inordertoobtainaSALHDsystemwithdesiredfunctionalitiesandperformances,alignedwiththeinterestofstrengtheningthetechnologyandmakingacontributiontotheStructuralProjects.

Themodelaimsfor:

• CreatingvalueforCODELCO,generatingstableinnovationsinSALHD,suitabletotheneedsofminingoperationsCODELCOisdeveloping.

• ChangingtherelationshipbetweenCODELCOanditsproviders,makingitanactivepurchaser,guidingSALHDdevelopments inorder to fulfillproductioncommitmentsof theseequipment,expressedinengineeringparameters.

ThetechnologicaldevelopmentmodelhasaseriesofitemsthatallowfluidinteractionofCODELCOwithitsproviders.Firstofall,CODELCOistheoneinchargeofdesigningtheextractionsystemitneeds,withdesiredproductivityandreliabilityparameters.

Fromthisstandpoint,aroadmapisdesigned,containingsuccessivetechnologicaldevelopmentsforLHDunits,alongwiththeircorrespondingoreextractionandtrammingsystems,takingthetechnologytothedesiredlevelintheshortesttermpossible.


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Theplanencompassesatleastthreestages:αconsiderstheshortterm,thatis,developmentsabletobeachievedwithinoneyear,βconsidersthemidterm,twotothreeyears,γandδlongtermdevelopments,thatis,fiveyears.Eachstagehasasetoffeatures(performanceandfunctionalities)thatCODELCOwilldemandasaminimumforSALHDsandotheroptionalfeaturesthataredesirable,notcompulsory.

Withthis,itisexpectedtomobilizeR&D&Ecapabilitiesoffactories,i.e.mobilizingdevelopmentengineersfromprovidersratherthanmarketingengineerswhodonothavethenecessarycapabilitiesnorthevision.

CODELCOwillbecommittednottoprocureSALHDequipmentthatnotcomplywiththecompulsoryfeaturesinthecorrespondingstage.ThiswillbedonethroughtheinclusionofthesefeaturesinthetechnicalbasesofALLLHDbidsofthecorporation.

CODELCOwillkeep,atleast,twoSALHDproviderswhoareuptodateinthecorrespondingtechnologicalstage.

Intime,forspecificfeaturesconsideredtobecritical,co-financingstructuresorCODELCOfundingwillbeimplemented.Inthislattercase,CODELCOwillbetheowneroftheintellectualpropertygeneratedandwillbeentitledtotransfersuchknowledgetootherproviders.

AlltheaforementionedrequiresarealignmentwithinCODELCO—sinceuptodate,eachDivisionandStructuralprojectactsindependently,dilutingthestrengthsoftheircorrespondingpointsofview.Fromthispointandon,thewayCODELCOwillbeoperatingwillbebrieflydescribed.

Technology and Innovation Management will act as a representative of clients (Divisions, StructuralProjects)againstproviders.

For this to happen, it is necessary to agree with the Divisions and Structural Projects the minimumtechnologicalthresholdstobestatedoneachofthedescribedstages(α,β,γ,δ).Inaddition,specificneedseachofthemmayhaveduetotheowncharacteristicsofaDivisionwillbestatedasdesirable.

TIMwillreviewvendorcapabilitiespertainingtheachievementoftheminimumtechnologicalstandardsstatedoneachstage(α,β,γ,δ),agreeingwiththemadevelopmentpathforthecorrespondingfunctionalities.

TIMwill participate, at least, as an observer and inspector in factory prototype trials (FAT), andwillparticipateboth,indesigningtrialprotocolsaswellasinmeasuringpilottrialsonminesite(SAT).

TIMwillprovidetechnicalsupportfortrialsandindustrialapplicationsDivisionsand/orstructuralprojectsperform. Itwill supervise trial realization andwill determinewhether they complywith protocols andproceduresforthemtobevalid.

From this trials,TIMwill commit to its clients the statistical certitudeof relevant parameters andwilldevelopwiththemminelayoutsandoperationstrategiessuitabletotheseparameters.

αdevelopmentisimmediatework(<2years),andsolvesshortrunproblemintrialsandapplications,andgeneratestheαsolutionfortheextractionsystem,alphaversion(TIM-CODELCOdesign).

βdevelpment isamid termwork(>2years) thataddressmorecomplex technologicalandoperationalneeds.Itcontainsatailor-madedevelopmentforanoperationmodel.Finally,γdevelopmentisalongrunwork(fullautomation)withthedevelopmentofrelevantinnovations.

ThisplanhasbeendesignedbyTIMtogetherwithstructuralprojects,actingTIMasthecoordinatorentity.

CODELCOwill develop trials of the different versions (α,β) with the different providers, generatingcertificationsforbiddingprocessesofstructuralprojects.

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For thedevelopmentof eachof the stages, a trial plan enablingCODELCO tokeep a suitable controlon resultswill be foreseen,minimizing costs.These are:Factory trials— trialsmade in factory, theremightormightnotbeaCODELCOobserverpresent,but theresultsaredelivered toCODELCO,whoissuesajudgment.Onsitetrials—trialswithinCODELCOfacilitieswithvendorsupport,buttheultimateresponsibleofdataisCODELCO.Trialscouldmeasureglobalindicators(e.g.performanceperhour)ormorespecificvariables(e.g.tonspercycle).Inthefirstcase,CODELCOwillestablishtheconditionsthemeasurementsshouldtakeplaceat.

5 Future development

In Figure 3, the future development plan being performed is shown. In this, operational losses andinterferencesbetweenunitaryoperationsareminimized.Theworkingcycleoftheautonomousmoduleisindependentfromtheshiftscheduleoftheroster.

This plan is being developed in alignmentwith the future and current requirements ofCODELCO, incooperationwithprovidersand technologicaldevelopmentcenters, inorder toachieve the introductionof significant improvements in technology, based on aCODELCO’s proprietary automation roadmap,developed by the Technology and Innovation Management. During the term of validity of structuralprojects,aftertherampup,it isexpectedtohavemulti-brand,fully-automatedtechnology,enablingthebestexpressionofthistechnology.

Figure 3 Over time technological solutions evolution

6 Main conclusions

Inordertointroduceautomation,itisnecessarytoredesigntheentireprocessinallofitsparts;toassesstheimpactofautomation;andthentodeterminestrategicandoperationalrequirementsandgoalsoftheprocess.Establishinganoperationmodelinordertoachievethegoalsgeneratesarequirementforenablertechnology,drivingtechnologicaldevelopment thatfinisheswith thedevelopmentof the technology, itsvalidation,andimplementationofthemodel.

Allthesestagescouldleadtoupstreamreviews,ifresultsarenotconsistent,butthemostimportantistoconsidertheprocessinallitsaspectsandnottrytoautomateasinglemachine.


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Theaforementionedshowsthatthecontributionoftheoperationmodelmakesthistechnologycompetitive,eventhoughafewparametersarefarbelowmannedsystems.Thealignmentofprovidersaswellasthemobilizationoftheircapabilitiestowardstherealrequirementswillleadtoasignificantaccelerationofasuccess.

7 References

Gustafson,A 2011,Automation of LoadHaulDumpMachines, ResearchReport, LuleåUniversity ofTechnology.

Schweikart, V, Soikkeli, T 2004, ‘Codelco El Teniente–Loading automation in panel caving usingAutoMine™’, Proceedings of the 4th Int. Conference and Exhibition on Mass Mining,MassMin2004.pp.686-689.

Kangas,F,EmmothCE,&Lindahl,P2004,‘CodelcoPCRB-Surfacecontrolcentreformineautomation’,Proceedingsofthe4thInt.ConferenceandExhibitiononMassMining,MassMin.2004,pp.690-691.

IM22013,InformeTécnico1ProyectoIM2-65-12,PruebaAtlasCopco,Marzo,2013.

Superintendencia Mina Sur, 2007, Post evaluación proceso de extracción con LHD semiautomático,sectoresPipaNorteyDiabloRegimiento,InternalReport.(inspanish)

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Future automated mine operation: Synergistic collaboration between humans and automated systems

J Ruiz-del-Solar EE Department, University of Chile-AMTC, ChileE Widzyk-Capehart University of Chile-AMTC, ChileP Vallejos University of Chile-AMTC, ChileR Asenjo University of Chile-AMTC, Chile

Abstract

The world resource industry is being transformed by its increasing use of automation technologies. At one end of the scale, this revolution is occurring through leveraging off-the-shelf technologies to incrementally improve the control of various mining processing lines with best industrial practice. At the other end are bold initiatives to implement fully autonomous mines, which, currently, are based on the use of automated systems that work in confined spaces; they are not allowed to interact with other systems or humans (isolated). However, between these two extremes, is a spectrum of innovations that stands to profoundly change the industry over the next 30 years by enabling future mining systems to be flexible with simultaneous operations of automated, tele- and manually-operated machines as well as humans.

In this paper, we present a new automation paradigm that considers the synergistic collaboration between humans and automated systems, underpinned by mine planning and design required for such operation within mining environment. In this paradigm, humans and machines would interact in a flexible, collaborative and synergistic way, without the need for isolation. The implementation of this concept within mining operations will have to be underpinned by science and technological developments or adaptation from many fields: robotics, automation, distributed systems, communication, human factors, pattern recognition, mine design, and others.

1 Introduction

In2011,atthe2ndInternationalFutureMiningConference(Bednarzetal.2011;Gippsetal.2001;Giurcoetal.2011;Kleinetal.2011),visionsofthe“futuremine”werepresentedfromvariousperspectives:miningcompanies, service industry, equipmentmanufacturers (OEMs) and research organisations. In general,thoughalongdifferentpaths,theywereallheadingtowardsminesoperatedusingautonomousrobotsandcomputerswithpeopleremovedtothesafetyofremoteoperatingcentres.Simultaneously,severalresearchcentersand think tankshavebeen focusingon imagining the futureofmining,with theSwedishRockTechCenter(Dozolme2014)releasingin2011itsvisionoftheminingindustryin2030.Amongstthetop3characteristicsoftheindustryafter2030,RTCbelievesin“fully automated mining operations without human interface”with“no human exposure at production faces, no accidents and employees satisfaction”.Hedlin(2013).

However,despite the tremendous technological advancesover thepast severalyears, thisvisionof thefutureminemight still be severalyears away. In themeantime, autonomousunits, tele- andmanually-operated equipment andhumans are continuously interactingwithin themine environment.Atpresent,theseinteractions,however,areachievedunderstrictconstraints:physicalbarriersareplacedbetweentheareaswhereautonomousequipmentoperatesandtherestoftheminingoperation,withstringentregulationsandcontrolwhenhumansmustentertheareaofautonomousoperation.Suchrestrictions,whileenablingautonomous operations and thus higher production and lower costs, naturally lead to the discontinuitywithin theminingoperationas strict safetyprotocols are followed to avoid fatalities and/ordamage to


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theequipmentand theenvironment.Thequestionariseswhetheran intermediatestep,onebetween thecurrentautonomous-humanarrangementand the fullyautonomousmine, ispossible to further increasethecontinuityofoperationwithhigherequipmentavailabilitybutwithoutcompromisingsafety,beforetheautomatedmine isachieved. In thispaper, thecurrent strives towards fullyautonomousminesarepresentedfollowedbytheintroductionofthehybridcollaborativesystemsformininginviewofplannedtechnologicalandoperationaldevelopments.

2 The Mine of the Future, without people?

Currently, for the mining enterprises, mining automation projects appear to be one of the availablecost-cutting strategies by driving human resources cost down, improving global safety and increasingproductivity.Themainemphasisisonthedevelopmentofunmannedhaulandtransportationtechnologies,bothinsurfaceandundergroundmining.

2.1 Surface mining

WorkingwithKomatsu,CodelcowasthefirsttotrialtheautonomoustrucksatitsRadimiroTomiccopperminein2007(Walker2014)followedbythecommissioningofthe11Komatsudriverlesstruckfleetin2008attheGabrielaMistral“Gaby”coppermine(Jordan2008).Inthesameyear,2008,RioTintolauncheditsMineoftheFuture™programme,withthemissionto“…finding advanced ways to extract minerals deep within the Earth while reducing environmental impacts and further improving safety”.RioTinto(2014).TheR&Deffortswithinthisprogramme,havebeenfocusingonthedevelopmentandimplementationofdriverlesstrucksandtrains.Fortheformer,RioTintoreliesonKomatsuautonomoustrucks,with53unitsinoperationatRioTinto´sPilbarasitesinearly2013,which,accordingtoRioTinto,weremovinghighgradeoreandwerecontrolledfromRioTinto´sOperationsCentreinPerthmorethan1,500kilometresaway(RioTinto2014).Byearly2014,RioTintowasplanningtofurtherincreasethenumberofdriverlessunitsthroughadditionsofKomatsutrucksatLilleymanmine(Duffy2013)andHopeDowns4(Validakis2013)withupto150truckstobecommissionedby2015intheirironoreoperationsinPilbara,WesternAustralia(Walker2014).Othercompaniesare, likewise,becomingpartof theautonomousworldwithFortescueMetalsGroup(FMG)commencingitsintroductionofautomatedtruckin2013atitsSolomonhub(King´sMine)witheightofCaterpillar´s793Fandplanningtohave37moredeliveredoverthenextfewyearstorun45autonomoustruckswhentheKing´sminereachesitsfulloperationalcapacity(Probert2013).Inaddition,BHPBiscommencingitsoperationalexperiencewithautonomoustrucksatitsJimblebarminewhileHitachiisconductingtrialsofautonomoustrucksattheMeanducoalmineinQueensland,Australia(Walker2014).Beyondthetransportationsector,automationR&Disbeingappliedtootherequipment,fromdrillrigstoloaders(Dozolme2014).

2.2 Underground mining

TheuseofautonomousLHD(Load–Haul–Dump)vehiclesandtrucksisveryrelevantforcurrentandfuturemassiveundergroundmineoperations.Thedevelopmentofthetechnologybehindtheseautonomousmachineshasbeendrivenbytheneedofincreasingthesafetyoftheoperationsandreducingthecosts.AnexampleoftheapplicationoftheseautonomousmachinestolargeundergroundminesisElTenientemine,CODELCO,wheresemi-autonomousLHDshavebeenoperatingforabout10years.

2.3 Automation implementation: where are the people?

Asmentionedpreviously,theautonomousandsemi-autonomousmachinesarepartofmanysurfaceandundergroundmineoperationsthroughouttheworld(Chile,Australia,Scandinavia),however,theiroperationisachievedthroughtheimplementationofphysicalorlogical/virtual(incaseofopenpits)barriersbetween

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theworkplacesofthosemachinesandothermanuallyoperatedequipmentand/orhumans(Figure1).Themaintenanceorotheractionsrequiringthepresentofpeopleispossiblewithinthe“automatedzones”onlywiththefullshutdownoftheequipment.

AsJorgeNillsonpointedoutinhispresentationduringtheExpomin2014(Nillson2014),thereisstillneedforpeoplewithinthemineseventhoughtheplannedmineoperationsareheadingtowardsthetele-remotelyorautonomouslyoperatedequipment; therewillbeneedforhumanstoinstallandmaintainthesensorsthatprovidethesituationawarenessforthetele-orautonomousoperationsand/ormonitorin-groundorsurfaceslopedisplacementinopenpitmines.Forundergroundmines,themaintenanceofequipmentneedstobeaddressedeitherin-situorinspecialmaintenancebayswhilethehang-upsindrawpointsincavingoperationsarestill,inmanycases,handledbyminepersonnel.

Figure 1 Current Mine Automation paradigm

3 Hybrid Collaborative Mining Systems

3.1 General concept

Whilsttechnologicaladvancesarecontinuallybeingmade,themineofthefuturewillrequiretheinteractionsbetweenhumansandequipment.Asthelevelofautonomyoftheequipmentincreases,itisclearthattheroleofhumanswillchange:thehumanswillactasco-pilotstoanautomatedmachineorremotelysupervisemachinery.However,therewillcontinuetobeaneedforhumanstophysicallyinspectthemineenvironmentormineequipment,andformachinerythathasnotbeenautomated(eitherbecauseitistoohardornotcosteffective)humanswillneedtophysicallyoperatethemachinery.Thus,miningequipmentmightstillrangefrommanual(requiringaphysicaloperator)tofullyautonomous(requiringnohumaninterventionatall).

In the futuremine, all of these various roles andmodes of operationwill need to operate in the sameworkspaceand,moreimportantly,someoftherolesmaychangedynamicallyastheenvironmentchanges.For example, a vehicle that is beingmanually driven, could be switched to an unmanned autonomousmodewhilst thepersonmaychange their role to supervisory.For thisworkspace tooperate safelyandproductively,weneedtodevelopsystemsthatcancopewiththe“mixedtraffic”and“changingroles”.Inthisdynamicenvironment,thereisaneedforsignificantimprovementsincommunicationsandsituationalawareness.


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Consider the scenario shown in Figure 2, wherewe have aminewith a number ofmachines. In thisworkspace,thereareanumberofhumansaswell:somemayactasobservers,someasoperatorsandsomemaybemaintainingequipmentorinfrastructure.Theremayalsobeanumberofhumansworkingbackatthemineofficeortheheadoffice–potentiallythousandsofkmaway.Theworkspaceitselfismonitoredbyanumberofsensors(i.e.cameras)inamixtureofwiredandwirelesscommunicationnetworks.If,inthisworkspace,weenvisionedaremotely-locatedhumanthatwishestotakecontrolofapieceofequipment,itisessentialforhim/herthattheyaremadeawareofthelocalenvironment.Likewise,humansandmachinesthat are inproximityof themachine that is about tobe takenover alsoneed tobemadeawareof theexpectedbehaviourofthatmachine.Inthesamescenario,thelocalhumanmayactasa“nanny”tothelocalmachine;havingcontroloftheE-stopwhilsttheremotehumancontrolsthemachine.

The joint andharmoniousoperationofpeopleandequipmentwithin theminingenvironmentmightbepossiblewiththeintroductionoftheso-calledHybridCollaborativeMiningSystems.Underthisparadigm,itwillnotbenecessarytoconfinetheautonomousteamstocertainareasoftheminenorstoptheoperationofasectionoftheminewhentasksrequirethepresenceofhumans,asinthecaseofclean-uporemergencymaintenance.Thejointoperationofmenandmachinescouldbeachievedifallmobiledevices,regardlessiftheyareautonomous,tele-ormanuallyoperatedaswellasallthepeopleworkinginsidethemine,areequippedwithsensors thatenablesafe interactionamongallproductionelements ina jointoperationalspace. The centralized control system would consider all teams and individuals as mobile users withperformancefeaturesandmovementpatternssharedamongtheindividualusers.Fromthepointofviewofeachuser,havingtherelevantinformationfromtheenvironmentinrealtime,thisworkingenvironmentprovidesthemwiththeabilitytomakedecisionslocallyandtohaveassistanceduringunexpectedsituations(i.e.,emergencyalarms,automatedbraking)whensuchconditionsarise(Figure2).

Figure 2 Future Mine Automation Paradigm

3.2 Requirements and challenges

Forhumansandmachinestointeractsafelyandproductivelyinthefuturemine,theyneedtobefullyawareoftheirlocalenvironment.Whilstthereareanumberoftechnicalchallengestoimplementsuchoperation(communication and automation), there are three areas that need to be developed for the synergisticcollaborationbetweenhumansandmachines:

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1. Data framework thatfacilitatescommunicationandtaskplanningbetweenhumansandmachineswithdifferentlevelsofsensingandautonomy.

2. Robust sensor-based surveillance systemtosupportfuturemineoperationsinwhichincreasedinteractionbetweenhumansandmachinesisenvisioned.

3. User interfaces that are capable of supervising a number of remotemachines and integratingcomplexgeologicaldatawithreal-timedatastreamsfrommultipleremotesensors.

3.2.1 Data framework

Forthesynergisticcollaborationbetweenhumansandmachines,adistributeddataframeworkinwhichmachinesandhumans(agents)caninterrogatetheirlocalandglobalenvironmentneedstobecreated.Thisframeworkwillneedto(Duff2007):

• Maintainaconsistentviewoftheworld(maintainasharedreality)

• Bedynamic:havetheabilitytobemodifiedwithadditionaldataasitisbeingcollected.Inmining,wheretheterrainoftheenvironmentisbeingmodifiedduetoexcavation,thisrequirementisvital

• Havetheabilitytoshareandsynchronizeregionsoftheworldwithbothhumansandmachines

• Handlethemovementofhumansandmachines

• Handleuncertaintiesorevendisagreementsaboutthestateoftheworld

• Havelowcommunicationbandwidthandlatencybetweenagents

• Berobusttolossofagents

3.2.2 Sensor-based surveillance system

Withinthefutureinteractivemineparadigm,inwhichhumanandmachineoccupiedspacesoverlap,minesecurityandsafetywillrequirerobustmonitoringsystemsforthecoordinationofactivities,avoidanceofaccidentsandeliminationofhazards/riskstopersonnel,equipmentandtheoperation.

Toaidfuturemineoperationsinwhichincreasedinteractionbetweenhumansandmachinesisenvisioned,robustsensor-basedsurveillancesystemswillbenecessary,whicharecapableofdetecting,trackingandclassifyingmovingobjectswithinbothundergroundandopenpitminingenvironments.Robustsolutionstothisproblemwouldrequirethesetechniquestofunctionunderavarietyofatmosphericconditionsincludingdustandhumidity.Effectively,avarietyofsensorsandsensingtechniqueshavetobecombinedtoprovideareliablemonitoringsystemwithadequateredundancyinthesystem.

Therefore, the feasibility of commercially available RFID technologies in terms of its multi-trackingabilities and time overhead for the scanning of RFID tags will need to be investigated together withautomated systems requiring no person/equipment identification tags, based on cameras, laser rangefinders,radarandthermalimaging.Thedifferencesandcomplementarynatureofthetwoapproacheswillneedtobeanalyzedwiththeaimofcombiningtheiradvantagesformaximumefficiencyandcostbenefit.Thefinalselectionofsensorswouldhavetoofferimproveddetection,trackingandidentificationofobjectsinvariousatmosphericconditions includinghighdust levels.Thesystemwouldhave tobedesignedtobe deployed in both stationarymode, atmultiple permanent locationswithin amine, and agilemode,on-boarda tele-operatedor autonomousvehicle,with theobjectivesof identifyingpeopleandvehicles


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andtheirtypeswithinthemines,analysetheinformationrequiredforthesafetyandefficientoperationofinteractiveminingenvironments,withaccompanyingsoftwareforprovidingthisinformationtotheoverallminemonitoringsystem.

3.2.3 User interface

Theuserinterfaceneedstobecreatedforboththelocalandremoteoperators.Thenatureoftheinterfaceisdictatedbythelevelofcontrolovereachmachine.Thisrelationshipisrelatedtothelevelofautonomyofthemachine(Figure3),whichcanbedividedintoanumberofareas:

• Themachine

• Theintelligenceofthemachine

• Theextentofknowledgeabouttheenvironment

• Thecommunication

• Theuserinterface

Theuserinterfacecanrangefromrealtovirtualwithmixedrealitiesinbetweenthesetwoextremes,whichfurtherrangedfrom:

• Augmentedreality,wheredataisoverlayedontoreality

• Augmentedvirtuality,whererealdataisusedtodriveavirtualreality.

Foraugmentedrealitytoworkitisimportanttobeableto:

• Putdisparateandnumerousdatasetsintoacoherentwhole

• Basethedisplayedinformationontheactualstateofthemachine/environmentinreal-time

Inaddition,theaugmentedrealityshouldprovideanimmersiveandinteractiveenvironmenttothehuman,including:

• Experienceofbeingfullypresent

• Appropriatepresentationofdatatotheuser

• Engagementoftheoperatorinanaturalandintuitivemanner

Druryetal.(2003),providefurtherguidelinesregardingthehuman-machineinterfaces,includingbutnotlimitedto:

• Provisionofuserinterfacethatsupportsmultiplerobots(machines)inasinglewindow,ifpossible.Ingeneral,minimizethenumberanduseofmultiplewindows

• Lower information overload: automatically present contextually appropriate information. Theinformationdisplayshouldinclude:aframeofreferenceandindicatorofrobothealth/state

• Consistencywithintheuserinterfacesforeachrobotiftheoperatorisoperatingmultiplerobotsandinthefunctionalityassignedtomanipulators(joysticks)formodesofoperation

• Groupingincontrolsanddisplay

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• Fusionofsensorsinformationtodecreasecognitiveload

• Increaseinuserefficiencythroughadditionalsensorsorstreamlineddesigntailoredtousers´needs.

Figure 3 Autonomy and virtuality spectrum (after Duff 2007)

3.3 Next Steps

Forthisnewparadigmtobefullyfunctionalinminingenvironmentinthefutureasasteppingstonetothefullyautomatedmine,differentand/oradditionaltechnologydevelopmentstepsneedtobeconsideredtothosealreadyadvocatedwithintheexistingtechnologymaps.Therequiredtechnologymustbeunderpinnedbyscienceanddevelopmentfrommanyfields:robotics,automation,distributedsystems,communications,humanfactors,patternrecognition,minedesign,andothers,andwillneedtobeeitherdevelopedoradapted.Therefore,itisproposedthatatechnologyroadmapisdevelopedasapracticalplanthatmatchesshort-termandlong-termgoalswithspecifictechnologicalsolutions.

Inthepast,technologyroadmapswerepreparedtoidentifythegapswithintheexistingminingoperationsthatpreventedthemfromreachingthefullyautonomousoperationwithhumansremovedfromminesitetoaremotelocation.Theseroadmapsdidnotconsidertheparadigmofsynergisticcollaborationbetweenhumans and automated equipmentwithout theneedof isolations.Thus, the roadmapdevelopmentwillprovidefullunderstandingofthenew,andperhapsyetunknownornotfullyunderstood,challengesfacingtheminingindustryinthefutureandidentifythetechnologygaps,whichwillhavetobefilledthroughtheimplementation/adaptationofexistingtechnologies,developmentofnewproductsand/orprocessesandtheassessmentofemergingtechnologies.

Thedevelopmentoftheroadmapforfuturemineoperationwillhavethreemajoruses:itwillguidetheminingcompaniesinspecifyingtechnologicalrequirementstosatisfyfutureoperationalneedsforhigh-levelcollaborativehuman-machinesystems,itwillprovideamechanismtoidentifytheexistingtechnologygaps,anditwillprovideaframeworktoplanandcoordinatetechnologydevelopments.

It is envisioned that the initial phaseof this activitywill define thevisionof the futuremine from theindustryperspectiveandconductasurveyoftheexistingtechnologies.Thisphasewillbeguidedbythe


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miningindustrytoensurefullalignmentwiththefutureoperationaldirectionswithintheminingindustryandidentificationoffuturechallengestheminingoperationsmightfacetoaccomplishanautomatedmineoperationwherehumansandmachinescaninteractsafelyandefficiently.Onthisbasis,the2ndphasewilldevelopaframeworktoplanandcoordinatetechnologydevelopments,implementationand/oradaptation.

4 Impact on the mining industry

We believe that the development of the FutureAutomatedMine Operation: Synergistic collaborationbetweenhumansandautomatedsystemswillhaveaprofoundimpactontheminingindustryvaluedrivers:economic,environmentalandsocialwiththebenefitsmanifestedthrough:

• Reduction in capital and operating costs through: elimination of the operational constraints ofisolation toenableaccess toequipmentandoperational siteonacontinuousbasis,preciseandreliableequipmentoperation,highutilizationofequipment (fewernumberofmachines for therequiredproductivity),reductioninenergyconsumptionandprocessoptimization.

• Increasedproductionthroughhighutilizationofequipmentandprocessoptimization.

• Improved security andqualityofworking life through: reductionof accidents involvingheavymachinery,safeinteractionsbetweenhumansandequipmentwithouttheneedforisolation,tele-operation and tele-presence, predictive maintenance and training for automation, and preciseequipmentoperation.

• Developmentofnewproductsthatwillstrengthentheminingprovidersincludingcommunicationinfrastructure,sensingtechnologiesandnewsoftwareintegrationpackages.

5 Conclusions

Theparadigmofsynergisticcollaborationbetweenhumansandautomatedequipmentwithouttheneedforisolationwaspresentedinthispaper.Thefeasibilityofitsimplementationwithintheexistingandnewmineswilldependonthetechnologicaldevelopmentsthatwillfillthecurrentgapsinsensing,communication,dataprocessinganduserinterfaceswiththebenefitsintheeconomic,environmentalandsocialareasfortheminingindustry.

References

Bednarz,T,James,C,Alem,L&Widzyk-Capehart,E2011,‘DistributedCollaborativeImmersiveVirtualRealityFrameworkforFutureMineScenarios’,Proceedingsofthe2ndInternationalFutureMineConference,pp.145–151.

DozolmeP2014,‘TheUnmannedMineHaulandTransport’,About.comMining-Maintenance-Equipment.Availablefrom:http://mining.about.com/od/MaintenanceEquipment/a/The-Unmanned-Mine-Haul-And-Transport.htm.<5May2014>.

Drury, JL, Scholtz, J&Yanco,HA2003, ‘Awareness in human-robot interactions’, Proceedings IEEEInternationalConferenceonSystems,ManandCybernetics,vol.1,pp.912–918.

Duff,E2007,Teleroboticrequirements,DraftInternalReport,CSIRO.

Duffy,A 2013, ‘Rio expanding driverless truck fleet’, MiningAustralia.Available from: http://www.miningaustralia.com.au/news/rio-expanding-autonomous-truck-fleet.<4May2014>.

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Gipps,I,Cunningham,J,Fraser,S&Widzyk-Capehart,E2011,‘NowtotheFuture–aPathTowardstheFutureMine’,inProceedingsofthe2ndInternationalFutureMineConference,pp.157–163

Giurco.D,Prior,T&Mason,L2011,‘Vision2040–MiningTechnology,PolicyandMarketInnovation’,inProceedingsofthe2ndInternationalFutureMineConference,pp.163–171.

Hedlin,J2011,‘SmartMineoftheFuture,RockTechCentre’.Availablefrom:http://mining.about.com/gi/o.htm?zi=1/XJ&zTi=1&sdn=mining&cdn=b2b&tm=7&f=11&tt=14&bt=0&bts=1&zu=http%3A//bergforsk.se/wp-content/uploads/2011/05/hedlin.pdf.<5May2014>.

Jordan, P 2008, ‘Chile’s new Gaby copper mine steps into the future’, MiningAbout.com.Availablefrom: http://mining.about.com/gi/o.htm?zi=1/XJ&zTi=1&sdn=mining&cdn=b2b&tm=123&gps=362_12_1242_585&f=11&tt=2&bt=7&bts=7&zu=http%3A//uk.reuters.com/article/2008/05/21/chile-codelco-gaby-idUKN2133325020080521.<3May2014>.

Klein,B,Bamber,A,AltunNE&Scoble,M2011,‘TowardsTomorrow’s‘SmartMine’–EmbeddedSensorTelemetryand

Sensor-BasedSorting’,inProceedingsofthe2ndInternationalFutureMineConference,pp.59-69.

Nillson, J 2014, ‘Tecnología & InnovaciónMinería a RajoAbierto Codelco’, Presentation at the 3rdInternationalWorkshopCodelco:InnovationinUndergroundandOpenPitMines,Expomin2014,Santiago,Chile.

Probert,O2013,‘Catdelivers8driverlesstruckstoFMG’,AustralianJournalofMining.Availablefrom:http://www.theajmonline.com.au/mining_news/news/2013/october/october-10-2013/supplier-news/cat-delivers-8-driverless-trucks-to-fmg.<3May2014>.

Rio Tinto 2014, Mine of the Future. Available from: http://www.riotinto.com/ironore/mine-of-the-future-9603.aspx.<4May2014>.

Validakis,V2013,‘Rio’sdriverlesstrucksmove100milliontonnes’,MiningAustralia.Availablefrom:http://www.miningaustralia.com.au/news/rio-s-driverless-trucks-move-100-million-tonnes.<3May2014>.

Walker,S2014,‘Autonomygraduallygainsmomentum’,Engineering&MiningJournal,January2014,pp.32-37.


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Mine sequence optimization for Block Caving using concept of ‘best and worst case’

D Villa, Dassault Systems Geovia, Canada

Abstract

The generation of a mine sequence for a block caving mine is always a challenge since it represents the direction for opening draw points, but it is controlled by several factors as caveability, orebody geometry, induced stress, primary fragmentation, grade distribution, production requirements, etc. and in most the cases the main objective is to combine all these factor to maximize the economic value of the project and this is more challenging.

Several complex theories and mathematical optimizations have been presented in the last decade, but most of them are too complex to provide a real solution for real Block Caving mines where the dimension exceeds the capacity of these models or it needs to work with super computers and the processing time is an issue.

This paper presents a new option to get an optimum mine sequence using the concept of ‘best and worst case’ adopted from open pit mines. The value of the sequence for the project can quickly be evaluated using a reasonable number of iterations to provide an optimum and realistic solution. Examples of this optimization are presented in this paper to demonstrate the concept and their implementation in practical cases.

1 Introduction

Thechoiceof initiationpoint for thesequenceand thepreferreddirectioncanbe influencedbyseveralfactors includingshapeof theorebody,access infrastructure,gradedistribution, insitustressdirectionsandmagnitudes,etc.butoneofthemainfactorsistooptimizethevalueofaprojectcreatingaproductionschedulemaximizingtheNetPresentvalue.

There are many studies and theories developed trying to solve this problem with very complexmathematicalapproachwheretheamountofvariables,constraintandformulationmakeofthesolutionadifficultimplementationandnotflexibleenoughtoaddnewconstraint,forexamplemixedintegerlinearprogramming(Y.Pourrahimianetal.2012)orintegerprogramming(T.Elkingtonetal.2012).

Thispaperwilldiscusstheoptiontogenerateanoptimumminesequenceusingtheapproachof‘BestandWorstCase’intensivelyusedinOpenPitoptimization(Smith2001)appliedinFootprintFinder.

2 Footprint Finder

FootprintFinderisamoduleofGEOVIAPCBC™.Itwasdevelopedprimarilytodoquickstudyworkingwithablockmodel trying tofind thebestelevationandorientation for locatinganextraction level forblockcavemining,butnowitisabletocreateasimpleproductionschedulewherethesequenceisaninputandthenitoffersaperfectopportunitytodoseveralrunsinashorttimetoevaluatedifferentoptionsfordirectionandshapeofthecavefronttogetanoptimumminesequence.

TypicalstepstorunFootprintFinderareillustratedinFigure1,whereallblocksinsidetheclippingpolygonsareevaluatedforeachlevelselected.Eachblockrepresentsonedrawpointandthenthesystemcreatesa

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drawcolumnwhereLaubscher’smixingmethod(Laubscher,1994)isapplied.Afterthatthebestheightofdrawiscalculatedbasedontheeconomicmodel.

Figure 1 Typical Footprint Finder’s evaluation steps

ThegraphattheleftinFigure2showsthetypicalresultsforanevaluationoftheentireblockmodel.Itispossibletoseethevalueforeachelevationandthetonnagereported.Thebestelevationislocatedat1200level.AlsotheeconomicvaluepercolumnisdisplayedattherightinFigure2,wherehighervaluesareshowninwarmercolors.Itisveryinterestingtoseetheirregularshapeoftheeconomicvalueperelevation(redline),itsuggeststhepossibilitytohavemorethanoneliftasoptimumsolution.

Figure 2 Results from Footprint Finder

Thecreationofaproductionschedulerequiresasequencewhichisdefinedbasedonashapeofthecavefront.ThisissimplycreatedusinganX-Ycurveandappliedwithcertaindirection(azimuth).AnexampleaproductionscheduleusingasequencegoingEastinV-shapeisshowninFigure3.

Havingtheoptiontocreateaminesequencedefiningashapeanddirectionprovidestheopportunitytorunmanyschedulesevaluatingdifferentscenarios.FinallytheoptiontostartinaspecificpointandmovinginacircletoemulateadiamondshapeisalsoavailableandthenallthesequenceoptionscanberapidlytestedusingFootprintFinder.


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Figure 3 Production schedule in Footprint Finder

3 Best and worst case concept

Theconceptof‘BestandWorstCase’hasbeenintensivelyusedinOpenPitoptimizationformorethan15years(Smith,2001)byGEOVIAWhittle™.Whittleisabletogenerateaseriesofnestedpitsbasedontheeconomicvalues(grades,revenuefactorandcosts)providinglimitsonwheretomineandwhen.Thebestcasescheduleisassociatedwithcompletelyminingapushbackbeforeproceedingtothefirstbenchinthesubsequentpit.InthismannerthehighestvaluedoreisminedasearlyaspossiblemaximisingNPV.Intheworstcasescheduletheentirebenchacrossallpushbacksisminedpriortoproceedingtothesecondbench.Thisamountstoprestrippingtheentiredeposit,defersoreproductionandtherebyminimisescashflowbyplacingstrippingcostupfrontwhiledelayingpositiverevenue.

Thesameconceptwasadaptedandimplementedinablockcavingminingenvironmenttoidentifythebestandworstsequence,basedontheresultsobtainedfromFootprintFinder.Whereeachblockcolumncanbetreatedindividuallyasadrawpointtocalculateitseconomicvaluebasedonthemetalprice,costandgradeanddilutionprofile,etc.

Thesequencedefinitionisbasicallyanordertoopeneachblockcolumnusingcertainshapeanddirectionandobviouslythisneedstobesuitableforblockcavingpurposeswheregeotechnical,designandoperationalconstraintsneedtobesatisfied.

Now theBest andWorst case concept canbe easily implemented inblockcavemine trying toget themaximumandminimumNPV.InthiscasetheBestsequencewillbetheextractionofthecolumnsortingfromthehighesttothelowesteconomicvalueandtheWorstsequencewillbetheopposite.Bothcasesarenon-operationalbutprovideavalidreferenceforplanningpurposesasmaximumandminimumvalueofanyoperationalsequence.Figure4describesanexampleoftheapplicationofthisconceptshowingaplanviewoftheblockmodelinExcelformat,whereitispossibletoseetheBestandWorstsequencegeneratedbasedontheeconomicvalueofeachblock.

Figure 4 Application of Best and Worst sequence

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Figure5showsanexampleoftheapplicationoftheBestandWorstsequenceandhowanyothersequencewillbelocatedinsideofthisrange.Ifasequencevalueisclosetothebestcase,thenthereislittlefurtheropportunity to improve the sequence. If thedifferencebetweenbest andworst cases is small, then theoverallprojectitselfisnotsensitivetosequencing.SoasimpleplotsuchasshowninFigure5isveryusefulinassessingtheeffectivenessofvarioussequences.

Figure 5 Example of sequence value for several options including Best and Worst case

4 Application of this concept in real footprint

TheRegaldeposit (Bui2014) is afictitiousorebodybut it ismodelledas amassiveporphyrycopperdepositsimilartomanyofthelargeblockcaveminescurrentlyinoperation.Overallviewofthecopperdistributionin3Dandplanviewat1,200levelisshowninFigure6.

Figure 6 Ore body displaying copper grade

AnexampleoftheapplicationofminesequenceoptimizationusingconceptofbestandworstcasewasdoneinRegaldepositusingtheinputforFootprintFindershowninTable1.

RangebetweenBestandWorst

sequence


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Table 1 Inputs for Footprint Finder (Regal deposit)Footprint Finder parameters Value DescriptionHIZ 100 HeightofInteractionzone(Laubscher)FIRST_DIL 60% Firstdilutionentry(Laubscher)DEV_COST 1,000 DevelopmentcostperunitareaDISCOUNT 10% Discountrate(Eg0.1or10%)HMAX 600 MaximumallowableHODTONS_MAX 4,000,000 Maxtons/yearinscheduleYEARS_BUILD 3 YearstobuildtomaximumNEW_PER_PERIOD 96 NewblocksperperiodRATE_MAX 50 Max vertial mining rate (m/yr)

4.1 Mine sequence applied for the entire footprint

Forthisexampleonlyonelevelwillbeevaluatedanditis1,200wheretheoptimumelevationwasfoundbasedonrundonewiththeentireblockmodel.Figure7showstheresultoftheFootprintFinderevaluationforeachblock,where theheightofdrawisdisplayedat the leftandtheeconomicvalueat theright.Ablacklinewasdigitizedtolimitthefootprinttohaveamorerealisticshapeforablockcavefootprint.Itisinterestingtoseethesizeofthefootprint(1,500mlongand400mwideonaverage)alsothedistributionoftheeconomicvaluesincetherearethreepotentialareaswithhighgradewherethesequencecanstart.

Figure 7 Results from Footprint Finder, height of draw (left) and economic value (right)

ThebestandworstsequencewascreatedforthefootprintdefinedbytheblacklineandthegraphicresultsareshowninFigure8,wherethestartofthesequenceisshowninwarmercolors.Itisclearthatthreepointsarehighlightedashigheconomicvaluesandpotentialinitialpointforthesequence.

Oncewehavethebestandworstsequencedonewecanevaluateanyotherpossiblesequencesandtypicallythemainoptionsevaluatedare:

• StartintheborderofthefootprintandadvancedwithaflatshapeorV-shapeforcavefront.Bothoptionsareverycommonsincetherearegeotechnicalconstraintstobeconsidered(e.g.lead/lag,abutmentstress,etc.)

• Startinginthecenterofthefootprintandmovinginadiamondshape.ThisoptionisveryattractivefromtheNPVperspective,sincethesequencecanstartwherethehighgradeislocated,butitalsogeneratesmanychallengesfromtheoperationalside,sinceitconcentrateslotofactivitiesinthesameareaandtheninoneproductiondriftwecanhaveproduction,constructionanddevelopmentatthesametime.

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Figure 8 Sequence created using best (left) and worst (right) option

UsingFootprintFinder,itispossibletoevaluateseveralscenariosinonestepandthenbeabletocompareandassesswhichoftheseoptionsisclosertothebestcase.Table2showsthelistofthe55runsdoneforthefootprintdescribedabove.

Table 2 Inputs for Footprint Finder (Regal deposit)Run Azimuth Shape Name Run Azimuth Shape Name Run Loaction Shape Name

1 BEST BEST 20 0 FLAT FLAT-0 38 1 CENTRE CENTRE-02 0 VSHAPE VSHAPE-0 21 20 FLAT FLAT-20 39 2 CENTRE CENTRE-203 20 VSHAPE VSHAPE-20 22 40 FLAT FLAT-40 40 3 CENTRE CENTRE-404 40 VSHAPE VSHAPE-40 23 60 FLAT FLAT-60 41 4 CENTRE CENTRE-605 60 VSHAPE VSHAPE-60 24 80 FLAT FLAT-80 42 5 CENTRE CENTRE-806 80 VSHAPE VSHAPE-80 25 100 FLAT FLAT-100 43 6 CENTRE CENTRE-1007 100 VSHAPE VSHAPE-100 26 120 FLAT FLAT-120 44 7 CENTRE CENTRE-1208 120 VSHAPE VSHAPE-120 27 140 FLAT FLAT-140 45 8 CENTRE CENTRE-1409 140 VSHAPE VSHAPE-140 28 160 FLAT FLAT-160 46 9 CENTRE CENTRE-160

10 160 VSHAPE VSHAPE-160 29 180 FLAT FLAT-180 47 10 CENTRE CENTRE-18011 180 VSHAPE VSHAPE-180 30 200 FLAT FLAT-200 48 11 CENTRE CENTRE-20012 200 VSHAPE VSHAPE-200 31 220 FLAT FLAT-220 49 12 CENTRE CENTRE-22013 220 VSHAPE VSHAPE-220 32 240 FLAT FLAT-240 50 13 CENTRE CENTRE-24014 240 VSHAPE VSHAPE-240 33 260 FLAT FLAT-260 51 14 CENTRE CENTRE-26015 260 VSHAPE VSHAPE-260 34 280 FLAT FLAT-280 52 15 CENTRE CENTRE-28016 280 VSHAPE VSHAPE-280 35 300 FLAT FLAT-300 53 16 CENTRE CENTRE-30017 300 VSHAPE VSHAPE-300 36 320 FLAT FLAT-320 54 17 CENTRE CENTRE-32018 320 VSHAPE VSHAPE-320 37 340 FLAT FLAT-340 55 18 CENTRE CENTRE-34019 340 VSHAPE VSHAPE-34020 WORST WORST

Foreachrunaproductionschedulewascreatedandthediscountedcashflowwascalculatedtocreateacomparisonbetweenallofthem.AnexampleofthescheduleisshowninFigure9.

Figure 9 Production schedule result from Footprint Finder


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ThesummaryresultsareshowninFigure10,whereitispossibletoseethelocationofthebestandworstsequence,twolinesofrunsforsequencescreatedusingaflatandV-shape(45deg)cavefrontshapeandoneadditionallinestartingatthecentreofthefootprintfrom18differentinitialpointslocatedinthehighesteconomicvalue.Thevaluesoftheoperationalsequencesareinarangeof75%to85%comparingwiththebestoption.IngeneralthecentreoptionsseemtobebetterwhencomparedwithflatandV-shapeoptions.

Figure 10 Schedule value for all 49 runs done

ThreesequencesareshowninFigure11,wherethestartofthesequenceisshowninwarmercolors.Duetotheshapeofthefootprintalltheseoptioncreateaverylongfaces(morethan500m)andthisisverydifficulttomaintaininpractice.Inaddition,comparingvaluesforeachoptionthedifferencesbetweenthemsuggestsinitiationinthecentreofthefootprintwherethisalternativegeneratesamorecomplexscenarioforoperation,constructionanddevelopment.Asaresultoftheconsiderationanewanalysiswasdonedividingthefootprintintwozones(EastandWest)whereeachzonewasevaluatedindependentlytoidentifynewoptionsandapossiblebetteroverallsequence.

Flatsequence(Az=60deg)V-shapesequence(Az=100

deg)Sequencestartingatthe

Centre

Figure 11 Four sequences used for the entire footprint

4.2 Mine sequence applied in two zones (East and West)

Becauseoforebodyshapeandgradedistribution the footprintwasdivided into twozonesasshown inFigure12.

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Figure 12 Footprint divisions into two zones

4.2.1 Sequence evaluation for West zone

UsingthesameproceduredescribedbeforeanewsetofsequencewasmodelledforWestzone.Duetothesizeofthiszoneonlyonecentresequencewascreatedstartingatthehighesteconomicvalue.Theresultof38sequencesevaluatedforthisportionofthefootprintisshowninFigure13.

Figure 13 Schedule value for West Zone

Figure14showsthreeofthebestalternativessequenceforthiszone.Inthiszone,itisclearthatthebestalternativeisstartinginthecentreorusingV-shapewith280degofazimuth.


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Flatseq.(Az=280deg) V-Shapeseq.(Az=280deg) Centresequence

Figure 14 Sequences shapes for West Zone

4.2.2 Sequence evaluation for East zone

ThesameprocesswasrepeatedfortheEastzone,butinthiscasemoresequencesweredonestartingatthecentreofthefootprint.Theresultof45sequencesforthiszoneisshowninFigure15.

Figure 15 Schedule value for East Zone

Figure16showsthreeofthebestalternativessequenceforthiszone.Inthiszone,theresultsusingflatorV-shapeareverysimilar.Surprisinglytheoptiontostart inthecentrewasnotbetterthantheothers.Thisisanindicationthatanyofthebestshapeoptionscouldbeused.Nowtakingintoconsiderationthecomplexityoftheoperationforcentreorthelargeofthefrontonflatshape,thebestsequenceisV-shapewithanazimuthof80deg.


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Flatsequence(Az=80deg)V-Shapesequence(Az=80

deg) Centresequence

Figure 16 Sequences shapes for East Zone

4.2.3 Final results for sequence selection

ComparingalltheoptionsevaluatedforRegaldeposit,thebestoptionwasdividingthefootprintintotwozones.ThesequencestartsinthemiddleofbothzonesmovingwithaV-shapeminingfirstEastthenWest.ThecomparisonbetweensequencesfortwozonevsentirefootprintisshowninFigure17.

Figure 17 Comparison between sequences for two zone vs entire footprint

Itisveryclearthatmoreworkneedstobedonetomodelthefinalsequenceindetail,sincethisisjustaquickanalysisofdifferentoptions,butthistoolprovidesstrongevidenceforwhichalternativescouldbemodeledinmoredetailtogetanoptimumresult.ThenextstepistotaketheseresultsandcreateamodelinGEOVIAPCBC™,wherethefootprintcanbemodeledexplicitlybydrawpointaddingmoreresolutionanddetailtotheanalysisincludingopeningsequencedetailsbymonthandbyzoneandmoresophisticateddilution/mixingmodelasa3DCellularAutomaton.

5 Conclusion

TheresultspresentedinthispaperdemonstratethattheminesequenceoptimizationforBlockCavingusingtheconceptof‘bestandworstcase’isaveryusefultoolforamineplannertogetamaximumandminimumvaluelimitforasequence,creatingavalidreferencetocompareanysequencecreatedusingallthetypicalconstraintsforablockcavemine.Also,itisveryhelpfultohavetheopportunitytogenerateandevaluateseveralrunsatonceandtobeabletoseetheimpactofcavefrontshape,orientation,startingpoint,etc.upontheproductionscheduleandfinalNPV.

This isnotacomplexmathematicaloptimizationsolutionfor thisproblemsince it isasimple iterativeprocess, but themain advantage is that it is a very easy and straightforwardmethod to evaluatemanyscenariosprovidingenoughinformationtotaketherightdecisioninshorttime.


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References

Bui,T2014,‘TacticalShut-offValueStrategiesforPanelCaveMines’,SME2014,SaltLakeCity.

Laubscher,D1994,‘CaveMining:StateoftheArt’,SAIMM,pp.279-293.

Smith,M2001,‘UsingMilawa/4Xasastartingsolutionformixedintegerprogrammingoptimizationoflargeopencutproductionschedule’,StrategicMinePlanningConference,Perth.

Elkington,T,Bates,L,Richter,O2012,‘BlockCavingOutlineOptimisation’,Massmin2012,Sudbury,Canada.

Pourrahimian,Y,Askari-Nasab,H,Tannant,D2012, ‘BlockCaveProductionSchedulingOptimizationUsingMathematicalProgramming’,Massmin2012,Sudbury,Canada.

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Fast-track detailed engineering for Panel Caving

JC Vienne AMEC Internacional, Chile

AbstractAll mining projects, including those in underground mining, require meeting a series of sequential steps to be materialized. The completion of the engineering enables the start of works associated with the project construction activities. Generally, detailed engineering is the longest stage in project design and defines the constructive aspects.

This paper shows how it is possible to implement “fast-track” detailed engineering for a typical panel caving project, with the use of LHD equipment, trucks or conveyors and in-mine crushing. It highlights the staffing requirements, the need for coordination between the client and the consulting firm, and the advantages and risks of performing “fast-track” detailed engineering.

1 IntroductionAtypicalundergroundminingprojectusingblock/panelcavingconsidersthedesignofundercut,production,ventilationandintermediatetransportlevels,inordertofeedcentralizedcrushers.Minedesignsalsoneedto consider subsequentorehandling to surface,which ismainlydone through shafts (S) andconveyorbelts(C),oreventrains(T).ThesetypesofdesignareusedforexampleinResolution(S),Argyle(C),OyuTolgoi(S),andGrasberg(C).Somedesignseliminatetheintermediatetransportlevelsbylocatingthecrushersintheperimeterofthefootprint,withdirecttransportoforetocrushersbyLHD.Examplesofthistypeofoperationinclude:Northparkes(S),Palabora(S),RidgewayDeeps(C),CadiaEast(C),DiabloRegimiento(T)andPipaNorte(T).Dependingontherockmasscharacteristics,someoftheseprojectsaddaspeciallevelforhydraulicorexplosivepreconditioning.

Everyprojectincludesasequenceofseveralengineeringphaseswhicharerequiredtobecompletedbeforethe commencement of detailed engineering. Detailed engineering needs timely completion of projectmilestones,inordertogeneratetherequireddesigns,complywithbiddingtimes,constructionandprojectstartupthatcomplieswiththeprojectproductionplan.

Thispaperfocusesonthedevelopmentofdetailedengineeringforundergroundfacilitiesformediumsizedprojects.Thiscouldcorrespondto,forexample,theinitialphaseofalargeprojectorthefulldesignofamediumsizeundergroundmine,withproductioncapacityofbetween10to20ktpd.

Therefore,theengineeringscopecouldinclude:

• Mineleveldesignofundercut,extraction,haulageandventilationlevels,includingtheorepassesandchutestotruckloading.

• Design of the crusher chamber with participation from all disciplines, including excavation,constructionandinstallationofallassociatedequipment.

• Infrastructure design including full multidisciplinary design for excavation, construction andassemblyoftheLHDworkshop,truckworkshop(ifrequired),offices,sewagetreatmentplantandmanagementplantofindustrialresidualwastewater.

• Designof ancillary facilities suchasventilation,drainage, surveying, accesses, compressedairsystem, industrial water, fire water, electrical power supply and electrical power distribution,refugechambers,automation,communicationsandcontrolsystems.


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• Economicalstudies,includingcapitalandoperationalexpenditureestimates.

• Procurement support including equipment definition and the preparation of the data-sheet andtechnicalspecificationsforpurchasing.

• Other studies likeconstructability,maintainability, energyefficiencyanalysis, riskanalysis andclosureplan.

• Preparationofmasterprogram,engineeringprogramandtechnicalbasisforbiddingconstruction,assemblyandstart-upofcrusherandotherfacilitieswithallassociatedequipment.

Detailedengineeringwiththisscopeshouldhaveanominalestimateddurationofaround12monthsandshouldgeneratebetween2,300to2,800deliverables,includingdrawingsanddocuments.

Thispaperanalyzestheconditionsthatmustbesatisfiedandactionstobedevelopedbyboththecompanyowningtheproject(theowner)andtheconsultingfirm(theconsultant)toreducethetimeusedinthestudyfromtheestimate12monthstoonly8months.

2 Factors affecting the timing of a successful engineeringThissectionshowssomefactors,whichareinfluentialtodevelopsuccessfulfasttrackdetailedengineering,intermsoftimeandquality.

2.1 Quality of previous information

Akeyrequirementforreducingprojectdurationatthedetailedengineeringlevelisthequalityofthepre-feasibilitystudy.Inthisrespect,itistheprojectowner´sresponsibilitytoensurethateachoftheaspects,inalldisciplines,havebeenadequatelycoveredinthepreviousengineeringstages.

Forfast-trackdetailedengineeringtobeperformed,theownermustconsolidatetheengineeringdesignseitheratthefeasibilitystageoratliaisonengineeringstages,performedduringthebiddingprocessofthedetailedengineering.

Failureofjustonedisciplinetoproperlydevelopthedesignsonpreviousengineeringphasescanproduceadelaytothewholeproject,duetothenecessaryadjustmentsrelatedtoalternativereviewsofdesigns,modificationsorevenrelocationoffacilities.

2.2 Client and consultant project and engineering managers

Theapproachandattitudeofeachprojectstakeholdertofasttrackdetailedengineeringiscriticaltothesuccessoftheproject.Collaborativeattitudeofeverystakeholderismandatory,especially,fromboththeowner´sandconsultant´sadministrationteam.

Inthisregard,itisimportantthattheowner´sengineeringmanagerhasacloseliaisonwiththeconsultantinorder toprovidehisglobalvisiontoeachdisciplineleaderaswellaseffectivelyinteractingwith theconsultant´scounterpart.Tofacilitatethisitisrecommendedthattheowner´sengineeringmanagerisbasedpermanentlyintheofficesoftheconsultant.

Theowner´sprojectmanagershouldmeetwiththeconsultant´sprojectmanageratleastonceaweekinordertosolveanytechnicaloradministrativeissues,minimizingprojectdelaysduetounsolvedorpendingissues.

Eithertheconsultant´sprojectmanagerorengineeringmanagerneedtofulfilltheroleofgivingcoherencetotheproject.Thismeansclosecoordinationbetweendisciplinesandstrictqualitycontrol.

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2.3 Owner and consultant discipline leaders

Experienced discipline leaders should form an essential part of the consultant teamwho should havesignificantundergroundminingexperience. Ideally, these leaders shouldhaveperformedotherdetailedengineeringundergroundprojectspreviously.

Thesamerequirementsapplytotheowner´sdisciplineleaders.Aninexperiencedcounterpartcanpotentiallygenerate delays to design approvals due to the need to consultwithmore experienced teammembers.Thissituationisexacerbatedwhenundergroundmineoperationalstaffareconsulted,asthesepersonsaregenerallynotinvolvedintheprojectandarefocusedondifferenttaskswithoperationalpriorities.

Theowner´scounterpartshouldhavedailycontactwiththeconsultant´sofficestoeffectivelyparticipateintheconsultant´sdesigns.Thisreducesthereviewandapprovalprocess.

Acharacteristicof fast-trackdetailedengineeringprojects is theamountofdeliverablesproducedeachweekbytheconsultantteamthatneedbereviewedbythetechnicalspecialistsfromtheowner.Duringpeak times,usuallyatmonths3 to6 fromcommencement, there isapossibility that thevolumeof theconsultant´s monthly deliverables exceeds the owner´s review team capacity, usually one person perdiscipline.Tocompletethereviewsinatimelyfashion,anadequatelystaffedcounterpartteamisrequired,ifnotsignificantprojectdelayscanbeexpected.

Theowner’sreviewteamsshouldbecarefullystaffedandmanagedtoavoidduplicatedreviews,andmoreimportant,applyingdifferentacceptabilitycriteria.Asaruleofthumb,thereviewofadeliverableshouldbeperformedbythesamepersononeachstageofthereview.Achangeofreviewermaydelaytheconsultantduetoadditionalpotentialchecksandcommentsthatwerepreviouslysolvedwiththeformerreviewer.

2.4 Quality and quantity of technical personnel

Suitabletechnicalstaffforbothownerandconsultantiscriticalforteamperformanceandtoallowweeklyprogressaccordingtoprojectmilestones.

Inthisregard,someconsultantcompaniesmaybechallenged,asitisnotalwayseasytohaveexperiencedtechnicalstaffinallengineeringdisciplinesinvolvedinundergroundmining.

TheapproachthatAMECusestotacklethisissueistoassembleatechnicalteamthatincludesoneortwoseniordesignersperdisciplinewhohavetherequiredexperience,andwhoalsoactasmentorsforthelessexperienceddesignersordrafters.

2.5 Project control

Akeyroleinordertoachieveprojectmilestonesisperformedbytheprojectcontrolperson,designatedbytheconsultingcompany.Thispersonmustgenerateglobalandbydisciplineprogressreports,alertingeachdisciplineleadertwoweeksinadvanceregardingdeliverablesassociatedtoforthcomingprojectmilestones.Theprojectcontrolroleisalsorequiredtosupporttheprojectmanagerwithproductivityestimatesofeachdiscipline,andtomonitorandcontrolcosts. Therefore,akeyrequirement isensuringthatexperiencedprojectcontrolpersonnelareassignedtotheproject.

2.6 Change orders management

In any engineering project, change orders are generated mainly associated to additional work, designchangesor additional studies.Themanner inwhich these changeorders are approached fromboth theownerandconsultingteamiscriticalforsuccessfulprojectprogression.Iftheowner´sattitudeistorefusedesignchanges,orbudgetmodifications,oriftheconsultantpresentschangenotesthatarenottechnicallysupported,amutualdistrustwillbegenerated.


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Acollaborativeandprofessionalapproachisrequired,suchthatchangeordersareapprovedorotherwisewithinapprovaltimelines,inordertominimizeoravoidprojectdelays.

2.7 Document control

Fast-trackdetailedengineeringgeneratesa substantial amountofdeliverables inavery shortperiodoftime.Aprocessofinternalinterdisciplinaryreviewmustbefollowedbytheconsultantforeachdeliverable.Afterthatprocess,theconsultant´sdisciplineleadincorporatescommentsandissues(inrevisionB)andthedeliverableissent,throughconsultant’sdocumentcontrol,totheowner’sdocumentcontrolarea,whichdistributesandcoordinatestheinternalmovementofthedeliverablesreceived,untilitisreturnedtotheconsultantwithcomments.Theprocessisrepeatedwithsuccessiverevisionsandapprovals.

Thismeansthatforaprojectwith2,500deliverables,atleast10,000submissionsaregeneratedbetweentheownerandtheconsultant,inadditiontoallinternalcoordinationofbothparties.

Allthisflowofinformationbothinternallyoneachside,asbetweentheparties,ismanagedbythedocumentcontrolarea,whichshouldknowexactly,atalltimes,whereeachdeliverableislocated.Therefore,it isrequiredthatboththeownerandtheconsultantassignpersonnelwithadequateexperienceindocumentcontrolprocedures,otherwisedelaysmaybegeneratedinundertakingreviews,riskoflowproductivityofdeliverablesorevenmisplacedorlostdocumentationondeliverables.

Animportanttooltoimprovecoordinationandtransferofinformationbetweenbothparties,istheconcurrentuseofdocumentcontrolanddeliverysoftware,usuallyprovidedbytheconsultant.Inthiswaytheclientcanusethesamemethodologytotransferinformation,facilitatingtrackingofdeliverablesbetweenboththeconsultantandtheclientuntilitsfinaldelivery.

2.8 Quality control

In fast-track detailed engineering there generally is no time to redo deliverables that may have notbeenproperlycompleted.Therefore, theconsultantmust implement, fromthebeginningof theproject,strictqualitycontrolwithclearallocationofresponsibilitiesofeachoftheprofessionalsinvolvedinthedevelopmentofdeliverables.

Acommonerror,forexample,isthedifferentdraftingstandardsusedbyeachdiscipline,potentiallycreatingdifficulties in the use byother participants.To avoid this, at the beginningof the project, theSystemsEngineeringconsultingfirmarea,mustgeneratetheformatsandstandardstobeusedontheproject,whichhavebeenpreviouslyagreedwiththecustomer,andensurethatallparticipantsutilizethem.

Theconsultant´sEngineeringManagermustrequesttoconductatleasttwotechnicalauditstorandomlyselectedprojectdeliverables,thefirstonenolaterthanthesecondmonth,inordertodetectdeviationsandgeneratecorrectiveactionsbeforethenumberofdeliverableswithproblemswillbegreater.

Additionally, each discipline on the consultant’s teammust implement a strict quality control system,generallybasedonastandardchecklist,andappliedtoeachdeliverablepriortodeliverthedocumenttothedocumentcontrolarea.

2.9 Site visits

Detailedengineeringmustacquireknowledgeofexistingsiteconditionsoftheproject.Thisusuallyrequiresconductingsitevisitsinordertocollectinformationandinspectsiteconditions.

Theconsultantgenerallyshoulddefine,withatleasttwoweeksnoticeinadvance,whattheywanttoseeandatwhat levelofdetail.Thisallowstheownerstoperformthenecessarysitevisitcoordination.For

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example;obtainrequestedinformation,authorizationandaccesstospecificareas,aswellascommitmentofoperationsstafftohostthevisit.

Apoorlycoordinatedsitevisitbybothparties,isawasteoftimeandresources,andmayresultintheneedtoundertakeadditionalsitevisits.

2.10 Risk assumptions by the owner

Toperformfasttrackdetailedengineering,theownermustbewillingtotakesomerisksassociatedwiththefinalselectionofequipmenttobeinstalled.Thedesignsmadebytheconsultantarebasedondimensionsandspecificationsofthe“mostprobableequipment”accordingtothedefinitionsofbasicengineering,andtheexperienceoftheconsultant.

These designs are generally valid for the bidding stage of the work, but should be validated for theconstructionphaseandfinalassembly.Therefore,theownermustacceptthatsomedrawingsaresubjectto change.These drawingsmust be clearly identified and generallywill have a stampwith the phrase“FinalDesignPendingofVendorInformation”.Thesedrawingsmustbereviewedagainoncethetechnicalspecificationsoftheselectedequipmenttobeinstalledbecomeavailable,beitbytheconsultantorbytheowner´steam.

2.11 Customer satisfaction surveys

Itisrecommendedthattheconsultingcompanyperformatleasttwosatisfactionsurveys,tobeansweredbytheProjectManageroftheowner.Thefirstisundertakenwhentheengineeringhasprogressedbetween30to40%inordertodetectwhethertheclientisinaccordancewiththeconsultant’sworkandtakecorrectiveactionsasappropriate.Thefirstsurvey,infasttrackdetailedengineering,mustincludecustomerperceptionabouttheprogressandcompliancewiththemilestonescriteria.

Thesecondsurveyshouldbeconductedattheendofengineering,aftertheclient’sfinalacceptance.Thesearedonetogeneratelessonslearnedandgenerallyimproveconsultant´sworkflowsforfutureprojects.

3 ConclusionsItispossibletoperformfast-trackdetailedengineeringthatallowstheownertocontinueontotimelytenderexcavation,construction,installationandcommissioningofalldifferentprojectinstallations,accordingtothemilestonesdefinedinthemasterprojectplan.However,thereareanumberofcriticalissuesthatneedtobeconsideredwhendecidingon fast-trackdetailedengineering.Firstly,anexperiencedundergroundminingteam,forboththeconsultantandtheclient,isabasicrequirementforthetimelyachievementofmilestonesandprojectsuccess.

Inadditionsomeadministrativetasks,suchasprojectanddocumentcontrol,playanimportantroleforboththeconsultant´sandowner’steamsinordertomanagetheprogressinformation,andtransferofdeliverablesthatsupportachievingprojectmilestones.

AcknowledgementTheauthorwouldliketothanktoAMECInternational,IngenieriayConstruccionforprovidingauthorizationtopublishthispaper.


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Optimizing Hill of Value for Block Caving

A Ovalle AMEC International, ChileM Vera AMEC International, Chile

Abstract

The intrinsic value of a mining project is realized with the selection of the optimal production capacity and cut-off grade. We describe how this can be achieved for a project planned to be mined using the block/panel caving methods. The “hill of value” tool is used as a starting point. After selecting the cut-off grade, the mineral resources and the corresponding throughput that offers the maximum NPV, these values are used to investigate how this optimum may vary by incorporating the influence of specific realistic characteristics for the selected mining method. The main characteristics influencing block caving NPV evaluation are: dilution, undercutting level elevation, height of the extraction columns, footprint of the production area, production capacity, production ramp-up and ramp-down profiles, sequence and undercutting rates, production program, development and construction schedule, capital investment schedule and operating costs. The methodology is illustrated with an example for optimizing a project, where the cut-off grade, the production capacity and a near-optimal NPV were determined.

1 Introduction

Thebulkoftheeconomicreturnofaprojectisdefinedintheprocessofidentifyingthemineralreserves.Itisacommonpracticeintheminingindustrytousethemarginalcut-offgrade(COG)todefinereserveswiththeaimofmaximizingtonnageandthroughput.Thishoweverdoesnotwarrantthebesteconomicreturn.

This paper describes a procedure to determine the planning parameters (COG, mineral reserves andproductionrate)byincorporatingrealcharacteristicsoftheblock/panelcavingmethodsandalsoincludinganestimationofthecapitalexpenditureforthewholeproject,whichwillprovideavaluethatapproachestheoptimalNetpresentvalueorNPV.

2 Methodology

2.1 Definitions

a. Marginal cut-off grade

ThemarginalCOGisthemetalgradeintheorethatmakesitsprofitofextractionequaltozero.Foritsapplicationtoblockcaving,dilutedcolumnsofablockmodelareassessed,calculatingtheprofitassociatedtotheexploitationofeachblockinthecolumn.Aslongastheprofitofthecolumnispositive,thecolumnisaddedtotheresourcestobemined(thecolumnheightisdeterminedbymaximizingthecolumnprofit).

b. Optimal cut-off grade

ThepreviouslydefinedCOGdoesnotnecessarilydefine thebest economic result for the company. Acriterionofselectinggreaterthanzeroprofitcolumnsyieldsbettereconomicresults.Thiscriterionisnotuniqueintheindustryanditvariesfrommarginaldeterminationstoverysophisticatedoptimizations.Here,wepresentamethodologythatidentifiestheCOGthatverynearlymaximizestheNPVofaproject.

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c. NPV

Companiesuseseveralmethodstoevaluatethemaximumvalueoftheirprojects.Somecompanieswillbefocusedinthereductionofproductioncostsbymaximizingreservesandthroughputsintheirprojects;otherswillsearchtooptimizespecificeconomicindicatorssuchasNPV,theinternalrateofreturn(IRR),thepaybackperiod(PB)ortheprofitinvestmentratio(PIR).ThisanalysisassumesNPVastheindicatortomaximize.

AsyntheticwaytoestimateNPVisusingthe“hillofvalue”tool,whichallowsagraphicalidentificationof the full rangeof theproject´sNPVvalue as a functionofCOGandproduction capacity.Normally,thereisasingleoptimalpoint,definedbyoneCOGandoneproductioncapacitycombination.However,thehillofvalueisbuiltonunrealisticassumptionsasindicatedinTable1.Thecontributionofthispaperistodefinemorerealisticassumptions.The“hillofvalue”isusedbasicallytoselecttheCOGrangethatprovidedanapparentoptimalNPV.Fromthisstartingpoint,“morerealisticNPVvalues”arecalculatedbyincorporatingthecharacteristicassumptionsforblock/panelcavingshowninTable1.

Table 1 Comparison of Assumptions between NPV´s from “Hill of Value” and “More realistic NPV” values

NPV´s from “Hill of Value” “More realistic NPV values”

Constantproductioncapacity Ramp-up,steadystateandramp-downproductioncapacity

Constantaveragegrade VariablegradefromproductionschedulesAllCAPEXinyear0Mathematicalfootprint

AllCAPEX:scheduledintimeSmoothedoutfootprint

100%miningrecovery Miningrecoverylessthan100%

2.2 Steps

Thefollowingstepswereappliedinatheoreticalexerciseforamassivecopper,goldandsilverdeposit.

a. Block model visualization

Thisstage is fundamental tounderstand thedepthandshapeof themineralizedbody, thespatialgradedistribution,thelocationofhighergradezones,andwhetherthemineralizedzoneiscomposedbyasingleormultiplebodies.Multipleorebodiesmayrequiredifferenttreatment,whereassessmentofindividualzonesmaybemoreappropriate.

b. Mining method selection

Theanalysisisfocusedontheblock/panelcavingundergroundmethod.Itisassumedthattheselectionoftheminingmethodisappropriateforthecharacteristicsoftheorebody.

c. Mineral selection by Cut-off grade

TheamountofmineralizedmaterialtobeminedisdeterminedfordifferentCOG’s.Firstly,themarginalCOGwhichyieldszeroprofitisselected.Theformulaisdefinedasfollows(theexamplepresentedusesCuequivalentgrade,whereAuandAggradesareconvertedtoCuEq):

Profit=Income-Cost=0


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(1)

Where:

Lcm = MarginalCOG(CuEq)

M= Miningandprocessingcostspertonofore

PCu= Copperprice

SCu= Sellingcostofcopper

RM= Metallurgicalrecoveryofcopper

Themarginalcriteriontoselectreservesignorestheopportunitycost.Thisconceptconsidersaprofitgreaterthanzero(COGgreaterthanmarginalCOG),resultinginareductionoftotaltonnagebutwillincreasetheproject´sNPV.

d. Generating the “Hill of Value”

The“hillofvalue”isdeterminedbycalculatinganNPVvalueforeachcombinationofCOGandproductioncapacity.ThisisdonebyutilizingRevenue,thecapitalexpenditure(Capex)andtheoperationalexpenditure(Opex)valuesasafunctionofproductioncapacities.

Intheexercisewhichispresented,atotalof15COG´swereused,startingwiththemarginalCOGandincrementingthevalues.

EachCOGgenerateditsowncavingfootprint,whichisinverselyrelatedtotheCOG.

InordertodeterminetheNPVforeachpairofpoints(COG,minedurationrelatedtoproductioncapacity);thefollowingformulationswereusedforRevenue,CapexandOpex:

• Revenue

EachCOGgeneratesitsownmineralresourcevaluewhichisusedtodeterminetheassociatedrevenuesassumingthatthetotalresourcesareminedoutin1,2,…nyears.Anestimateoftheyearlyrevenuesforeachproductivecapacityis:

(2)

Where:

INCOMESi = Yearlyincomeforduration“i”(USD/a)

TPYi = Yearlyproductioncapacityforduration“i”(t/a)

GCuEq = Averagegrade(%CuEq)

RCu = Metallurgicalrecovery(%)

PCu = Copperprice(USD/lbCu)

SCu = Coppersellingcost(USD/lbCu)

Conversionfactor= 2,204lb/t

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• Opex

Againtheproductivecapacitiespreviouslydeterminedareusedtocalculateyearlycosts,using:

(3)

Where:

OPEXi = Yearlyoperatingcostforduration“i”(USD/a)

TPYi = Yearlyproductioncapacityforduration“i”(t/a)

Cm = Mineextractioncost(USD/t)

Cd = MinePreparationCost(USD/m2)

At = Footprintarea(m2)

TON = Totalminableresouces(t)

Cp = Processingcost(USD/t)

• Capex

TheCapexestimateiscomposedoftwofigures,theMineCapexandthePlant&InfrastructureCapex,andisbasicallyafunctionoftheproductivecapacity,butitisalsoafunctionofthetotalminableresources.Theempiricalrelationsthatfollowcomefromtheexperienceinvariousprojects:

(4)

(5)

Where:

CAPEXM = MineCAPEX

CAPEXP&I = Processingplant,tailingsandinfrastructureCAPEX

TPDi = Dailyproductioncapacityforduration“i”(t/d)

TON = Totalminableresources(t)

• NPV´s for “Hill of Value”

TheNPVwasestimatedfor15COGsandfor40durationsoftheproject.TheexpressionusedtocalculatetheNPVforduration“i”oftheprojectisthefollowing:

(6)


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Where:

NPVi= NPVforduration“i”

rd =discountrate

e. Selecting an apparent optimal NPV from “Hill of Value”

EachCOGgeneratesNPVvaluesaccordingtothedurationsoftheproject.Themaximumvalueisselectedfromeachcurve,andthenthemaximumNPVvaluefromthe15COGcurvesisselected.ThisbecomestheapparentoptimalNPVfromthe“HillofValue”,whichdefinestheCOG,thequantityofminableresourcesandthedurationoftheprojectwithitscorrespondingproductioncapacity.

Thispointofthehillofvaluebecomesthestartingpointtocalculatemore“realistic”NPV´sbyincorporatingthespecificplanningparametersinfluencingblock/panelcavingperformanceasdescribedintable1.Theexerciseisrepeatedonemoretimenarrowingdownthecalculationsto4COG´s(theoptimalCOG,theneighboringlowervalueandthetwoneighboringhighervalues).IthasbeenourexperiencethattheoptimalCOGvalueshiftstohigherCOGsandlowerproductioncapacitiesthanthe“apparent”optimum,whentherealisticplanningparametersareincorporated.

f. Calculating more realistic NPV values

Mineable resources are determined for the fourCOG´s selected from the “hill of value” exercise.Thefootprintsoftheresourcesaretheninspectedandsmoothed.AproductionplanforeachCOGiscalculated,establishing the undercutting sequence and the undercutting rate, resulting in a grade profile for eachproduction capacity. Maximum production capacities for each COG are calculated using appropriateformula.Aproductionramp-upandproductionramp-downisthenincorporated.AtotalCapexscheduleisalsoformulated.TheendresultisthedeterminationofmorerealisticNPVvaluesfortheselectednarrowrangeofCOG´sandproductioncapacities.Thisisperformedforthecasesthatareclosetotheoptimumusingthesimplifiedcalculations.

3 Example

Atheoreticalexamplewasdoneusingablockmodelandthemethodologydescribedpreviously,withthefollowingplanningparameters:

• Longrangeplanningprices:3USD/lbCu,1,300USD/ozAuand23USD/ozAg.

• Metallurgicalrecoveries:85%Cu,75%Auand55%Ag.

• Sellingcosts:0.36USD/lbCu,5.00USD/ozAuand2.00USD/ozAg.

ο Blockmodel:

ο blockdimensionsare20mx20mx10m.

ο 1,500,000blockunits.

ο blockmodeldimensionis2,000mwide,3,000mlongand2,000mhigh.

ο themodelintersectsthesurfacetopography.

• 50%dilutionentrypointusingMeta-Laubschervolumetricmodel.

• Minimumcolumnheightof90mandnomaximumlimit.

drf

+=

11

4 Results

Figure1showsthe“hillofvalue”withapparentNPVvaluesontheverticalaxis,theCOGontheyaxisandtheminedurationsonthexaxis.OnlythepositiveNPVvaluesareshownandthenegativevaluesarenotshownforclarityofrepresentation.FivevaluerangesofNPV´sareshownwithdifferentcolors.TheyellowlinerepresentsthemaximumNPVvaluesfordifferentCOG’s.

ThemarginalCOGis0.29%CuEq,anditisclearlyseenthatthereisnopositiveNPVvalueforthisgrade.

Figure 1 “Hill of Value” for NPV´s > 0

Figure2showstheoptimumNPVvaluesasafunctionoftheCOGs,bothforthe“hillofvalue”exerciseandforthe“morerealisticNPVvalues”.

Figure 2 Maximum unrealistic NPV from Hill of Value and “more realistic NPV values”

Asmentionedpreviously,theapparentmaximumNPVofthe“hillofvalue”(1,301MUSD)andthethreeneighboringvalues(1,271;1,228&953MUSD)areselectedasthestartingpointstocalculatemorerealisticNPVvalues.Allthesevaluesarehighlightedingreen.Thecorresponding“morerealisticNPVvalues”are-480;93;241&248MUSD.Itisclearlyshownthatthatthe“morerealisticNPVvalues”aremuchlowerthantheNPVvaluesfromthe“hillofvalue”exercise,andthattheNPVoptimalvalueshiftsfrom0.42%CuEqto0.48%CuEq.

CuEqCOG(%)

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Mine Planning

4 Results

Figure1showsthe“hillofvalue”withapparentNPVvaluesontheverticalaxis,theCOGontheyaxisandtheminedurationsonthexaxis.OnlythepositiveNPVvaluesareshownandthenegativevaluesarenotshownforclarityofrepresentation.FivevaluerangesofNPV´sareshownwithdifferentcolors.TheyellowlinerepresentsthemaximumNPVvaluesfordifferentCOG’s.

ThemarginalCOGis0.29%CuEq,anditisclearlyseenthatthereisnopositiveNPVvalueforthisgrade.

Figure 1 “Hill of Value” for NPV´s > 0

Figure2showstheoptimumNPVvaluesasafunctionoftheCOGs,bothforthe“hillofvalue”exerciseandforthe“morerealisticNPVvalues”.

Figure 2 Maximum unrealistic NPV from Hill of Value and “more realistic NPV values”

Asmentionedpreviously,theapparentmaximumNPVofthe“hillofvalue”(1,301MUSD)andthethreeneighboringvalues(1,271;1,228&953MUSD)areselectedasthestartingpointstocalculatemorerealisticNPVvalues.Allthesevaluesarehighlightedingreen.Thecorresponding“morerealisticNPVvalues”are-480;93;241&248MUSD.Itisclearlyshownthatthatthe“morerealisticNPVvalues”aremuchlowerthantheNPVvaluesfromthe“hillofvalue”exercise,andthattheNPVoptimalvalueshiftsfrom0.42%CuEqto0.48%CuEq.

CuEqCOG(%)


448

Table2showsacomparisonbetweenthe“hillofvalue”NPV´sandthe“morerealisticNPVvalues”fortherangeofCOGsselectedasthestartingpoints:

Table 2 “Hill of Value” NPV´s and the “more realistic NPV values”

CUT-OFF

GRADE

NPV “Hill of Value” “More realistic NPV values”

NPV RESOURCES PROD. FOOTPRINT NPV RESOURCES PROD. FOOTPRINT% CuEq MUSD Mt % CuEq kt/d m2 MUSD Mt % CuEq kt/d m20.40 1,271 1,600 0.49 270 740,000 -480 1,650 0.49 100 800,0000.41 1,301 1,200 0.52 220 540,000 93 1,240 0.52 90 610,0000.44 1,228 1,000 0.54 190 450,000 241 1,030 0.53 80 510,0000.48 953 700 0.58 140 300,000 248 720 0.57 65 360,000

FromFigure2,itisnotclearifthemorerealisticNPVreachedthemaximumvalueat0.48%CuEqCOGandanobviousquestioniswhethertheNPVfor0.50%CuEqCOGishigher.AninspectionofthefootprintareaforthelatterCOGshowsthatitissmallerandwithanimportantdiscontinuity.ThisisthemainreasonfornotconsideringtheNPVatthisCOG,andtoconsidertheNPVat0.48%CuEqCOGasveryneartheoptimalsolution.

5 Conclusions

TraditionalNPVevaluationbasedsolelyon“hillofvalue”toolsmakemanyunrealisticassumptionssuchasproductionprofiles,grade,Capexexpenditureandrecoveries.

ThemethodologyoutlinedinthispaperincorporatesmorerealisticblockcavingcharacteristicsandselectsanoptimumNPVvaluethatresultsinhigherCOG;longerminelifeandlowerproductioncapacitiesthantraditional“hillofvalue”methods.

Inaddition,theoptimumNPVforaprojectisobtainedatCOG´shigherthanthemarginalCOG,asthemarginalCOGdoesnotconsidercapitalcostexpenditure.

Acknowledgements

TheauthorsthankAMECInternationalfortheendorsementtopublishthispaper.

References

Hall,BE2003,‘Howminingcompaniesimprovesharepricebydestroyingshareholdervalue’,CIMMiningConferenceandExhibition,Montreal.

Lane,KF1988,‘Theeconomicdefinitionofore,cut-offgradesintheoryandpractice’,MiningJournalBooks:London.

Lane,KF1964,‘Choosingtheoptimumcut-offgrade’,ColoradoSchoolofMinesQuarterly,vol.59,N°4.

449

Mine Planning

Footprint and economic envelope calculation for Block/Panel caving mines under geological uncertainty

E Vargas University of Chile, ChileN Morales University of Chile, ChileX Emery University of Chile, Chile

Abstract

Traditional long-term mine planning is based on deterministic ore body models, which ignore the uncertainty in the geological resources. Therefore, the mineable resources and mine plans are not robust and the actually obtained values may not meet the promised values at the beginning of the project. Geological uncertainty can result in important differences in the economic value of the plan and in the outline shape of the mine.

This paper deals with developing a tool that incorporates geological uncertainty in early stages of the planning process: defining the economic envelope in a massive underground mine. The rationale is to compute an economic outline of the mine that aims to maximise the contained value while limiting the difference of the height of adjacent columns, all this for each level. As a result, this tool gives an approximation of the shape and value of the economic envelope of a block cave mine, which can be used as an input to a post scheduling process.

The algorithm is tested on a real case study and validated against existing software alternatives. Afterwards, it is extended to work with geological uncertainty, which is modelled using a set of conditional simulations of the mineral grades. The results for this case study indicate that geological uncertainty can generate a gap greater than 100% in the economic value of the footprint and the total tonnage of the envelope, between the best and the worst grade scenarios. On the other hand, the shape of the envelope varies in each grade scenario, making it difficult to make an optimal decision on the placement of the developments for a posterior extraction sequence.

1 Introduction

Traditionallong-termmineplanningisbasedondeterministicinformation,therefore,plansanddecisionsmaynotberobustagainstuncertaintyandestimatedvalueandproductionpromisesmaynotbeachieved.Oneexampleofthisistheuncertaintyontheresourcemodel:whiletechniqueslikeconditionalsimulationsarewelldevelopedtomodel thespatialvariabilityofgrades,existingmineplanningtoolsdonotallowincorporating them into the planning process.They only allow integrating uncertainty a posteriori, bymeansofsensitivityanalyses,sothatvariabilityisestimatedbutnotcontrolled.

Manyauthorsanalysetheimpactofgeologicaluncertaintyinopenpitminesintermsofdifferencesbetweenpromisesandactualvalues(e.g.,Dimitrakopoulos2011),butthereisalackofreferencesaboutuncertaintyinundergroundmines.Ontheotherhand,approachesareusedtocalculateminereservesinblock/panelcavingmines.Thedrawpointorientedmethodology(Diering2000)hasbeenvalidatedandimprovedalongtheyearsandseemstobethemainstreammethodology;meanwhileanotherrecentmethodologybasedontheupsidedownpitalgorithm(Elkingtonetal.2012)generatemineoutlinesandfootprintsusingdifferentcut-offs,butnoneofthesemethodologiesconsidergeologicaluncertaintyintheiralgorithm.

Thiswork aimsat developinga tool suchcan incorporategeologicaluncertainty in early stagesof theplanningprocess:definingtheeconomicenvelopeinamassiveundergroundmine.Theresultsofthecasestudywillbespecifictotheorebodyandblock/panelcavingminingmethod.


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2 Methodology

Themainobjectiveistocalculatetheeconomicenvelopeofanorebodytobeminedusingtheblock/panelcavingmethod.Tothisend,twomethodologiesareusedtocalculatetheeconomicfootprintandenvelope,respectively.

2.1 Footprint calculation

Similartothefootprintfindermethodology(Dieringetal.2008),wecalculatetheeconomiclevelwheretheundercutlevelshouldbeplaced,itmeanstheeconomicboundaryandlayoutoftheundergroundmine.Itisbasedontheprofitoftheblocksdiscountedbywhentheywillbeextractedgiventhepositionoftheblockintheblockcolumn(equation1).

(1)

Where:

vandvi’=blockeconomicvalueanddiscountedvalueoftheblockassumingiastheundercutlevel[$/t].

dz=blockheight[m].

vmining=VerticalMiningRate[m/yr].

α=discountrate.

Tosimplifythedecisionwheretoputtheundercutlevel,thevalueofthefootprintwillbetheonlydecisionvariable.Thisimpliesfindingthemaximumfootprinteconomicvalue.

2.2 Economic envelope calculation

Given the resultsof the economic level, thenext step is to calculate the economicenvelope.Thiswillrepresentanapproximationtotheminingreservesintheorebody.Themethodologybehindthissectionisbasedontheultimatepitalgorithm,andisappliedwithsomemodificationsinordertoresemblethecavinggeometry,asfollows:

• Cuttheblockmodel:

ο RemovetheblockmodeldatabelowtheeconomicZlevel.

ο Setthemaximumheightofcolumn.

• InverttheZcoordinateintheblockmodel.

• Createasetofslopeprecedenceconstraintsinordertocontrolthemaximumadjacentheightofdraw(HOD).

• Calculatetheeconomicenvelopeusingtheconstraintsandmodifiedblockmodel,givenequation2:

(2)

Where:

B=totalnumberofblocks

v(x, y, z)vi (x, y, z) =

(1+α)

max∑B=1vi·xi

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Mine Planning

vi=economicvalueofblocki

xi=binarydecisionvariabletoextractblockiornot

• Postprocessingoftheenvelope:

ο Setminimumcolumnheight

ο Setminimumminingfootprintdimensions.

ThestepsdescribedabovearesolvedusingtheMineLinklibrarywhichispartof theBOS2Mopen-pitschedulerandsequencer(Rubioetal2011).Inaddition,theresultsoftheeconomiclevel(footprint)arevalidatedagainstPCBC(GEMS)softwarecommonlyusedincavingmines.

2.3 Extension to consider geological uncertainty

Oncewehavedevelopedatooltooptimisetheeconomicalenvelope,thegeologicaluncertaintyisintroducedbyusingconditionalsimulations togeneratedifferentresourcemodels.ThesimulationsareconstructedwiththeTBSIMprogram(Emery&Lantuéjoul2006).Foreachsimulation(blockmodelscenario), theoptimal footprintandeconomicoutlineof theminecanbecomputed.Subsequently,aquantificationofthe uncertainty is done, applying theValue atRisk (VaR) evaluationwhich has been used in previouspublicationstoassesstheimpactofgeologicaluncertaintyinopenpitprojects(Vielmaetal.2009).

3 Data

Thedataconsistof100simulationsofarealorebody.Eachoneofthesesimulationshasatotalof2.34million blocks of 10x10x10m3 and 149 levels (10meters per level).The copper gradewas the onlysimulatedvariable,andthetonnageanddensityforeachblockareassumedconstant.Itisalsosupposedthatallthecalculationsaredoneoverthesamemineralisedzone(samerocktype).TheeconomicdatausedintheblockevaluationisshowninTable1.

Table 1 Economic parameters

Parameter ValuePrice[US$/t] 2.5

SellingCost[US$/t] 0.35MineCost[US$/t] 10PlantCost[US$/t] 16.1

Recovery 87%Density[ton/m3] 2.7

MaximumColumnHeight[m] 500MinimumColumnHeight[m] 100

Productivity[tpd] 200Utilisation[days/year] 200

SlopeAngle 45°-60°-90°

Also,validationwasdoneoverblockmodelobtainedbykrigingand10differentsimulations,usingthesameeconomicparametersinthetwomethodologies.Nodevelopmentcostswhereusedintheeconomicevaluation.


452

4 Results

4.1 Validation

BetweenthemethodologyusedinthispaperandPCBCtherearesomedifferencesintermsofaccumulatedvalue,tonnageandareaofthefootprint,thustherearedifferencesintheeconomiclevelwheretheundercutlevelwillbeplaced.Table2summarises thesedifferences for the11blockmodelsevaluated (negativevaluesmeanPCBCvaluesaregreaterthanMineLinkvalues).

Table2 Differences between PCBC and MineLink methodologies

DifferencesBlock Model Level Economic Value Tonnage Area

Kriging -2 -5% 10% -33%1 -5 9% 17% -14%2 -1 4% 14% -6%3 -1 8% 15% -13%4 -1 3% 13% -7%5 -1 -2% 11% -18%6 -1 -4% 11% -17%7 1 2% 11% -14%8 -1 1% 13% -12%9 -4 0% 11% -8%10 0 6% 15% -1%

To illustrate the previous table, the results for the accumulated economic value and tonnage for onesimulatedblockmodelareshowninFigure1.

Figure 1 Footprint validation results over one simulation

Thedifferenceinvaluebetweenthesetwomethodologiesisupto10%neartheoptimumeconomiclevel,andgreaterdifferencescanbeobservedinthelastandlessdeeplevels.

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Mine Planning

4.2 Footprints results

Applingthemethodologydescribedinthispaper,differentcurvesofaccumulatedvalueandtonnagecanbegeneratedforeachscenario.Giventhatallthesecurvesweregeneratedover100simulationsoftheorebody,thedifferencesbetweenthecurvesdepictthegeologicalvariabilityoruncertainty(Figures2and3).

Figure 2 Footprint Results: value over 100 simulations (dashed curve is the kriging scenario)

Figure 3 Footprint Results, tonnage over 100 simulations (dashed curve is the kriging scenario)

Theaccumulatedvalueofthefootprintvariesforeverysimulation,thustheplacementoftheundercutlevelwillhavethesamebehaviour,resultinginadistributionofelevations(Figure4).

FromFigure4,classlevel1hasthegreatestaveragevalueandfrequencywhileclasslevel36(wherethekrigingscenarioisplaced)hasoneofthelowestaveragevalues.


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Mine Planning

Footprint value statistics [MUSD]Level Minimum Maximum Average

1 4,846 6,325 5,613

6 5,404 5,691 5,548

11 5,096 5,704 5,325

16 5,529 6,461 5,995

21 4,389 6,347 5,349

26 4,802 7,516 5,745

31 4,322 5,938 5,183

36 4,187 6,478 5,091

41 4,534 5,038 4,799

46 3,853 4,628 4,190

Figure 4 Undercut Level Placement Distribution (Kr indicates the place of the kriging scenario)

4.3 Economic Envelope Results

Giventheundercutelevationforeachblockmodel(economicfootprintresult),theenvelopeoroutlineofthemineiscalculatedoverthe100simulationsinordertogiveanideaofthereservesoneachblockmodel.ThedistributionsofthevalueandmeangradeareshowninFigure5.

Figure 5 Economic Envelope Value and Mean Grade Distribution

Theshapeoftheenvelopechangesbecauseofthegeologicaluncertaintyandthevariabilityintheplacementoftheeconomicfootprint.Toillustratethispoint,thekriging,bestandworsteconomicvaluesaredisplayedinFigure6.

Figure 6 Economic Envelope for Kriging, Best and Worst Values Scenarios


455

Giventhepreviousresults,ameasureoftheriskisreallyusefultosummarisethevariability.Inthiscasethevalueatrisk(VaR)evaluationisdone.TocalculatetheVaRoftheeconomicvalue,thedistributioncanbeestimatedasalognormaldistribution,whichallowscalculatingthevalueassociatedwithsomeriskslevels(Table3).Similarfittingsareconsideredforthetonnage,areaandmeangradedistributions.

Table 3 Value at Risk for Economic Envelope results compared against average and kriging values

Value at Risk1% 3% 5% Average Kriging

Value [MUSD] 4,030 4,400 4,605 6,477 6,207Tonnage [Mton] 321 348 363 494 576Area Footprint [m2] 271,000 294,000 306,500 420,084 550,000Mean Grade [%] 0.904 0.932 0.951 0.930 0.894

5 Conclusions

Geologicaluncertaintyisasubjectthatrecentlyhasbeenintegratedinopenpitminingtoknowtherisksandopportunitiespresent inminingprojects, but thisuncertaintyhasbeen less studied inundergroundmineprojects,specificallyinblock/panelcavingmineswhichrepresentmassiveoperationsand,oncetheystart thecave,greatmodifications to theminingmethodarenoteasy toperform.With thismotivation,a methodology able to calculate the footprint and economic envelope of an underground mine undergeologicaluncertaintyisproposed,inordertohaveawidevisionofthepossibilitiesbesidesdeterministicapproachesorkrigingestimates.

ThefootprinttoolwasvalidatedagainstcommonlyusedPCBCsoftware,resultingindifferencesaround10%nearthemaximumeconomiclevel,whichisagoodapproximationconsideringthatbothtoolsareanapproximationtoreality.

In terms of economic value, the kriging scenario is one of theworst along the levels in the ore body.Using the uncertainty approach, one generates possibilities to improve the profit, and in addition theplacementoftheeconomicfootprintvariesbecauseofthevariabilityintheaccumulatedvaluepercolumn,notingdifferencesinfootprintvalueupto8,000MUSD.Giventhe100simulationsshownhere,thereisaprobabilityofabout36%tofindtheeconomicfootprintinthedeepestelevation(level1)andonly14%probability tofind it in the level36 (where thekrigedmodel says it shouldbe).Agooddecisionmustconsiderthevaluesandtheseprobabilitiessothemaximumprofitcouldbegainedattheminimumrisk.

Oncetheplacementofthefootprintisdone,thenextstepistoestimatetheeconomicenvelopeoroutlineofthemine.Inthisaspectdifferencesintheshapeandvaluearenoted.Theenvelopeeconomicvalueobtainedbythekrigedblockmodelisbelowtheexpectedeconomicvalueobtainedwiththe100simulations,whichcouldbeattributedtothegradesmoothingmadebythekrigingmethod.Thevalueatriskanalysisinthiscasetellusthatwitha5%ofriskthevalueoftheeconomicenvelopecouldbe29%lessthantheexpectedvalue,whichmeansapproximately1,800MUSD.

Asageneralthought,geostatisticalsimulationsgiveusmanypossiblescenarios,whichcanbeassumedaliketherealorebody,thusariskanalysisfortheresultsofalargeamountofsimulationscouldhelpustotakethebestdecisionfortheprojectgiventhegeologicaluncertainty.


456

Acknowledgements

TheauthorsthanktheAdvancedMiningTechnologyCentre(AMTC),DelphosMinePlanningLaboratoryandtheMiningEngineeringDepartmentattheUniversityofChileforsupplyingtheresourcesneededtodevelopthisresearch.

References

Dimitrakopoulos, R 2011, ‘Stochastic Optimization For Strategic Mine Planning: A Decade ofDevelopments’,JournalofMiningScienceMarch2011,vol.47,Nº2,pp.138-150.

Diering,T2000, ‘PC-BC:ABlockCaveDesignandDrawControlSystem’,MassMin2000,Brisbane,Australia,pp.469-484.

Elkington,T,Bates,L&Richter,O2012,‘BlockCavingOutlineOptimisation’,MassMin2012,Sudbury,Ontario,Canada.

Diering,T,Richter,O&VillaD2008,‘BlockCaveProductionSchedulingUsingPCBC’,MassMin2008,Luleå,Sweden.

Vargas,M,Morales,N&Rubio,E2009, ‘A short termmineplanningmodel for open-pitmineswithblendingconstraints’,MinePlanning2009,Santiago,Chile.

Emery,X,Lantuéjoul,C2006,‘TBSIM:Acomputerprogramforconditionalsimulationofthree-dimensionalGaussianrandomfieldsviatheturningbandsmethod’,Computers&Geosciences,vol.32,Nº10,December2006,Pages1615–1628.

Vielma,J,Espinoza,D&MorenoE2009,‘Riskcontrolinultimatepitsusingconditionalsimulations’,ProceedingofAPCOM2009,Vancouver,Canada.

457

Mine Planning

Determination of the best height of draw in block cave sequence optimization

F Khodayari University of Alberta, Canada

Y Pourrahimian University of Alberta, Canada

Abstract

Production scheduling is one of the most important problems in mining operation which has a significant impact on the profitability of the mining project. Most of the common production-scheduling methods in the industry rely only on manual planning methods or computer software based on heuristic algorithms. These methods cannot guarantee the optimal solution. On the other hand, most of the software packages determine the height of draw (HOD) before production scheduling without considering the advancement direction. Improvements in computing power and scheduling algorithms over the past years have allowed planning engineers to develop models to schedule more complex mining systems. Applications of mathematical programming in mine planning have proven very effective in supporting decisions on sequencing the extraction of materials in mines. The objective of this paper is to develop a practical optimization framework to compute the best height of draw as result of the optimal production schedule for each advancement direction. This paper presents a model application of a production schedule for 102 drawpoints over 14 periods.

1 Introduction

Aproductionschedulemustprovideaminingsequencethattakesintoaccountthephysicalandtechnicalconstraintsand,totheextentpossible,meetsthedemandedquantitiesofeachraworetypeateachtimeperiod throughout the mine life. In block cave mining, production scheduling determines the amountofmaterialwhich shouldbemined fromeachdrawpoint in eachperiodofproduction,numberofnewdrawpointsthatneedtobeconstructed,andtheirsequenceduringthelifeofmine(Pourrahimian2013).

Mostofthecommonproduction-schedulingmethodsintheindustryrelyonlyonmanualplanningmethodsorcomputersoftwarebasedonheuristicalgorithms.Thesemethodscannotguaranteetheoptimalsolution.Theyleadtomineschedulesthatarenottheoptimalglobalsolution.Improvementsincomputingpowerandschedulingalgorithmsoverthepastyearshaveallowedplanningengineerstodevelopmodelstoschedulemorecomplexminingsystems(Alfordetal.2007).

For optimization of block-caving scheduling, most researchers have usedmathematical programming;LinearProgramming(LP),Mixed-IntegerLinearProgramming(MILP)andQuadraticprogramming(QP).LPisthesimplestoneinmodellingandsolving.SinceLPmodelscannotcapturethediscretedecisionsrequired for scheduling, mixed-integer programming (MIP) is generally the appropriate mathematicalprogramming approach to scheduling. Solving of a MILP problem can be difficult when the size ofproductionsystem is largebut it is ausefulmethodology forundergroundscheduling (Rahal2008). Inspiteof thedifficultiesassociatedwithapplyingmathematicalprogrammingtoblock-cavingproductionschedulinginundergroundmines,theauthorshaveattemptedtodevelopmethodologiestooptimizeblock-cavingproductionschedules.Theyhaveuseddifferentobjectivefunctionsandconstraints.Table1showssomeoftheappliedmathematicalmethodologiesinblock-cavingproductionscheduling.


458

Table 1 Summary of applied mathematical methodologies in block-caving production scheduling

Author Methodology Model’s objective(s) FeaturesSong(1989) MILP Minimizationoftotalmining

costLP:

Thismethodhasbeenusedmostextensivelyanditcanprovideamathematicallyprovableoptimumschedule.ButstraightLPlackstheflexibilitytodirectlymodelcomplexundergroundoperationswhichrequireintegerdecisionvariables.

MILP:

MILPcouldbeusedtoprovideaseriesofscheduleswhicharemarginallyinferiortoaprovableoptimum.Computationaleaseinsolvinganintegerprogrammingproblemisdependentupontheformulationstructure.Itcanprovideamathematicallyprovableoptimumschedule.TheadvantagethatMILPhasoversimulationwhenusedtogeneratesub-optimalschedulesisthatthegapbetweentheMILPfeasiblesolutionandtherelaxedLPsolutionprovidesameasureofsolutionquality.ThedrawbackinusingMILPisthatitisoftendifficulttooptimizelargeproductionsystemsbythebranch-and-boundsearchmethod.

QP:

Blockcavingprocessisnon-linear,soitwouldnotbeappropriatetouselinearprogrammingforproductionschedulinginblockcaving.ButsolvingofthiskindofproblemscouldbeachallengebecausewemustchangethemtoLPandthensolvethem,sowehaveconversionerrors.

Chanda(1990) SimulationandMIP

Minimizationofthedeviationintheaverageproductiongradebetweenoperatingshifts

Guestetal.(2000) LP MaximizationofNPVRubio(2002) MIP Twomodels(a)maximization

ofNPVand(b)optimizationoftheminelife

Diering(2004) NLP MaximizingNPVforMperiodsandminimizationofthedeviationbetweenacurrentdrawprofileandadefinedtarget

RubioandDiering(2004)

LP,IP,QP MaximizationofNPV,optimizationofdrawprofile,andminimizationofthegapbetweenlongandshorttermplanning

Rahaletal.(2008) MILGP Minimizingdeviationfromtheidealdrawprofilewhileachievingaproductiontarget

Weintraubetal.(2008) MIP MaximizationofprofitSmoljanovicetal.(2011)

MILP OptimizationofNPVandminingmaterialhandlingsystem

Parkinson(2012) IP Findinganoptimalopeningsequenceinanautomatedmanner

Epsteinetal.(2012) LP,IP MaximizationofNPVDiering(2012) QP Objectivetonnage(tooptimize

theshapeofthecave)Pourrahimianetal.(2013)

MILP MaximizationofNPV

Alonso-Ayusoetal.(2014)

MILP MaximizationofNPVwithconsideringuncertaintyincopperprice

Theinherentdifficultyinapplyingthesemodelstothelong-termproduction-planningproblemisthattheyresult in large-scaleoptimizationproblemscontainingmanybinaryandcontinuousvariables.Thesearedifficult tosolvewith thecurrentavailablecomputingsoftwareandhardware,andmayrequire lengthysolutiontimes.

ThispaperwillintroduceaMILPmine-schedulingframeworkforblock-cavinginwhichsolvingalarge-scaleprobleminareasonableCPUtimeandoptimalminingreservebasedonadvancementdirectionwillbeaddressedtogenerateanear-optimalproductionschedulewithhigherNPV.

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Mine Planning

2 Methodology, assumptions, and notation

Theproductionscheduleofablock-cavemineissubjecttoavarietyofphysicalandeconomicconstraints.Inthispaper,theobjectiveofthetheoreticalframeworkistomaximizethenetpresentvalue(NPV)oftheminingoperationanddeterminethebestheightofdraw(BHOD),whilethemineplannerhascontrolovertheplanningparameters.Theplanningparametersconsideredinthisstudyare:(i)miningcapacity,(ii)drawrate,(iii)miningprecedence,(iv)maximumnumberofactivedrawpoints,(v)numberofnewdrawpointstobeopenedineachperiod,(vi)continuousminingand(vii)reserve.Theproductionschedulerdefinestheopeningandclosingtimeofeachdrawpoint,thedrawratefromeachdrawpoint,thenumberofnewdrawpointsthatneedtobeconstructed,thesequenceofextractionfromthedrawpointsandtheBHODforeachdrawcolumn.

Several assumptions are used in the proposed MILP formulation. The ore-body is represented by ageologicalblockmodel.Thecolumnofrockaboveeachdrawpoint,whichisreferredasadrawcolumn,isvertical.Eachdrawcolumnisdividedintoslicesthatmatchtheverticalspacingofthegeologicalblockmodel.Numerical data are used to represent each slice’s ore-body attributes, such as tonnage, density,gradeofelements,elevation,percentageofdilution,andeconomicdata.ThedevelopedMILPmodelusesPCBC’s(GEOVIA-Dassault,2012)slicefileasinput.InordertomaximizetheNPV,allthematerialinthedrawcolumnorsomepartofthatcanbeextracted.Inotherwords,theminingreservewillbecomputedasaresultoftheoptimalproductionschedule.Extractionprecedencefordrawpointsandclustersisusedtocontrolthehorizontalandverticalminingadvancementdirection.Accordingtotheadvancementdirection,theprecedencebetweendrawpointsisdefinedusingthemethodpresentedbyPourrahimianetal.(2012;2013).

AftercreatingtheslicefileusingPCBC,thesliceswithineachdrawcolumnareaggregatedintoselectiveunitsusing amodifiedhierarchical clusteringalgorithmdevelopedbasedonan algorithmpresentedbyTabeshandAskari-Nasab(2011). Then,theoptimallife-of-minemulti-periodscheduleisgeneratedfortheclusteredslices.TheoptimizationformulationisimplementedintheTOMLAB/CPLEX(Holmstrom,2011)environment.

Anefficientwayofovercomingthelargenumberofdecisionvariablesandconstraintsistoapplyaclusteringtechnique.Variousmethodsofaggregationhavebeenusedtoreducethenumberofintegervariablesthatarerequiredtoformulatethemine-planningproblemwithmathematicalprogramming(Epsteinetal.2003;NewmanandKuchta,2007;Weintraubetal.2008;TabeshandAskari-Nasab2011;Pourrahimianetal.2012;Pourrahimianetal.2013).Inthemodifiedalgorithm,thesimilarityvalue(Sij)betweenslicesiandj,iscalculatedby

(1)

Where:

Lij=thenormalizeddistancevaluebetweenslicesiandj,

EVij=thenormalizedeconomicvaluedifferencebetweenslicesiandj,

Dij=thenormalizeddilutiondifferencebetweenslices iandj.

WL, WEV, andWD areweighting factors for distance, economicvalue, anddilution, respectively.Theweightsaredefinedbythemineplanner.

Thenotationusedtoformulatetheproblemisclassifiedasindices,parameters,sets,anddecisionvariables.


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2.1 Notation

2.1.1 Sets

dS Foreachdrawpointd, thereisaset Sddefiningthepredecessordrawpointsthatmustbestartedpriortoextractingdrawpoint d.

dclS Foreachdrawpointd,thereisasetSdcl definingtheclustersinthedrawcolumnassociatedwithdrawpointd.

dlclS Foreachdrawpointd,thereisasetSdlcldefiningthelowestclusterwithinthedrawcolumnassociatedwithdrawpointd.

clS Foreachclustercl,thereisasetScldefiningthepredecessorclustersthatmustbeextractedpriortoextractingclustercl.

2.1.2 Indices

{1,..., }cl CL� Indexforclusters.{1,..., }e E� Indexforelementsofinterestineachcluster.

l IndexforadrawpointbelongingtosetSd.n IndexforaclusterbelongingtosetSdcl.p IndexforaclusterbelongingtosetSdlcl.q IndexforaclusterbelongingtosetScl.

Indexforschedulingperiods.

2.1.3 Parameters

CL Maximumnumberofclustersinthemodel.

clCLSEV Economicvalueofclustercl.D Maximumnumberofdrawpointsinthemodel.

,d tDR Minimumpossibledrawrateofdrawpointdinperiodt.

,d tDR Maximumpossibledrawrateofdrawpointdinperiodt.i Discountrate.

eclG Averagegradeofelementeintheoreportionofclustercl.

,e tG Upperlimitoftheacceptableaverageheadgradeofelementeinperiodt.

,e tG Lowerlimitoftheacceptableaverageheadgradeofelemente inperiodt.

tM Lowerlimitofminingcapacityinperiodt.

tM Upperlimitofminingcapacityinperiodt.

,Ad tN Maximumallowablenumberofactivedrawpointsinperiodt.

dNcl Numberofclusterswithinthedrawcolumnassociatedwithdrawpointd.

,Nd tN Lowerlimitforthenumberofnewdrawpoints,theextractionfromwhichcanstartinperiodt.

,Nd tN Upperlimitforthenumberofnewdrawpoints,theextractionfromwhichcanstartinperiodt.

T Maximumnumberofschedulingperiods.

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clTon Totaltonnageofmaterialwithinclustercl.

dTon Totaltonnageofmaterialwithinthedrawcolumnassociatedwithdrawpointd.

hdTon Tonnage ofmaterial related to theminimum height of draw hwithin the drawcolumnassociatedwithdrawpointd.

2.1.4 Decision variables

Binaryvariablecontrollingtheprecedenceoftheextractionofclusters.Itisequalto1iftheextractionofclusterclhasstartedbyorinperiodt;otherwiseitis0.Binaryvariablecontrollingtheclosingperiodofdrawpoints.Itisequalto1iftheextractionofdrawpointdhasfinishedbyorinperiodt;otherwiseitis0.Binaryvariable controlling the startingperiodof drawpoints andprecedenceofextractionofdrawpoints.Itisequalto1iftheextractionofdrawpointdhasstartedbyorinperiodt;otherwiseitis0.Continuousdecisionvariablerepresentingtheportionofclustercltobeextractedinperiodt.

3 Mathematical model

Theobjectivefunction,equation(2),oftheMILPformulationistomaximizethenetpresentvalueoftheminingoperationwhichdependsonthevalueoftheclusteredslices.Theeconomicvalueofeachclusterisequaltothesummationoftheeconomicvalueofthesliceswithintheclusterandthecostsincurredinmining.TheCLSEVisaconstantvalueforeachcluster.Theconstraintsarepresentedbyequations(3)to(19).

(9)

(2)

(3)

(4)

(5)

(6)

(7)

(8)


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Equation(3)ensuresthatthetotaltonnageofmaterialextractedfromclustersineachperiodiswithintheacceptablerange.Equations(4)and(5)forcetheminingsystemtoachievethedesiredgrade.Equation(6)forcesvariableEd,ttochangeto1whenaportionofthelowestclusterofthedrawcolumnisextractedinperiodt,becauseThelowestclusterineachdrawcolumncontrolsthestartingperiodofextractionfromtheassociateddrawpoint.Equation (7)ensures thatwhenvariableEd,t changes to1, it remains1untiltheendoftheminelife.Equation(8)ensuresthatwhendrawpointdisopen,atleastaportionofoneoftheclusterswithinthedrawcolumnassociatedwithdrawpointdisextracted.Iftheextractionofaclusterisnotstartedafterfinishingtheextractionoftheclusterbelowinperiodtort+1,therelevantdrawpointmustbeclosed.Equation(9)ensuresthatwhenvariableCd,tchangesto1,itremains1untiltheendoftheminelife.Themaximumnumberofactivedrawpointsineachperiodiscontrolledusingequation(10).Theprecedencebetweendrawpointsiscontrolledinahorizontaldirectionwhiletheprecedencebetweenclustersiscontrolledinaverticaldirection.Equations(11)to(14)controlprecedencebetweendrawpointsandclusteredslices.Equation(15)guaranteesthatclusterclisextractedwhentherelevantdrawpointisactive.Thenumberofnewdrawpointsopenedinorafterperiodtwoiscontrolledbyequation(16).Atthebeginningandinperiodone,thenumberofnewdrawpointsisequaltothemaximumnumberofactivedrawpoints, equation (17).Equation (18) ensures that the draw rate fromeachdrawpoint iswithin thedesiredrangeineachperiod.Equation(19)ensuresthattheamountoftheextractedmaterialfromdrawcolumnassociatedwithdrawpointdisnotmorethanthetotaltonnageofthematerialwithintherelateddrawcolumn.Thelowerboundofthisconstraintisthetonnagerelatedtotheminimumheightofthedrawineachdrawcolumnwhichisdefinedbythemineplanner.

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(19)

(10)

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4 Results and discussion

We have developed, implemented, and tested the proposed MILP model in the TOMLAB/CPLEXenvironment(Holmstrom2011).Themodelisverifiedbynumericalexperimentsonadatasetcontaining102drawpointsand3457slicesover14periods.One-thousandclusterswerecreatedbasedonthepresentedalgorithm.Themaximumnumberofslicesineachclustercouldnotbemorethanfive.Theweightfactorsofthedistance,economicvalue,anddilutionweresetto5,3,and3,respectively.Theheightofdrawislimitedtonotlessthan50m.Thismeansatleast50mofthedrawpointsmustbeextracted.Theproblemwassolvedfortwodirections,westtoeast(WE)andsouthtonorth(SN).Table2presentstheschedulingparameters.Resultsshowthatallassumedconstraintsaresatisfiedintheconsidereddirections.Figure1illustratesthenumericalresultsfortheproposedformulation.TheresultingNPVsatEPGAPof3%are$135.13Mand$132.91MintheWEandSNdirections,respectively.Figure2illustratestheproductiontonnageandtheaveragegradeofproduction ineachperiod.The total tonnageofmaterial thatmustbeextracted in theWEandSNdirectionsis11.9Mt,whichislessthantheextractablematerialbasedontheslicefile.Figure3illustratesthenumberofactivedrawpointsandthenumberofdrawpointsthatmustbeopenedineachperiod.IntheWEdirection,themineworkswiththemaximumnumberofactivedrawpointsfromperiodtwo to ten. In theSNdirection, themineworkswith themaximumnumberof activedrawpoints fromperiodstwoto13exceptperiodnine.

Table 2 Scheduling parameters

,, / e te tG G(%)

/ ttM M(kt)

,, / d td tDR DR(kt/yr/perDP) ,Ad tN ,, / Nd tNd tN N

0.8/1.7 100/900 10/40 50 0/15

Figure 1 Obtained NPVs for different EPGAPs

Figure 2 Production tonnage and average grade of production in the WE and SN direction


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Figure 3 Number of active new drawpoints in the WE and SN direction

5 Conclusions

Mostofthecommonproductionschedulingsoftwarepackagesintheindustrydeterminetheheightofdrawbeforeproductionschedulingwithoutconsideringadvancementdirection.Thismethodcannotguaranteetheoptimalsolutionandwillleadtomineschedulesthatarenottheoptimalglobalsolutions.ThispaperinvestigatedthedevelopmentofaMILPformulationforblock-caveproductionschedulingoptimization.The proposed formulation can be used in different advancement directions which are selected basedongeotechnical considerations.Consequently, (i) themining reserve,which is a resultofoptimization,alsovariesfromonedirectiontoanother;(ii)planerisabletofindthebestsingleoperationdirectionorcombinationthereof,andthebeststartinglocationtoreachthemaximumNPV.

References

Alford,C,Brazil,M,&Lee,D2007,‘OptimisationinUndergroundMining’,inHandbookOfOperationsResearch In Natural Resources, vol. 99, International Series In Operations Research, (A.Weintraub,C.Romero,T.Bjørndal,R.Epstein,andJ.Miranda,Eds.),SpringerUS,pp.561-577.

Alonso-Ayuso,A,Carvallo,F,Escudero,LF,Guignard,M,Pi,J,Puranmalka,R,&Weintraub,A2014,‘Mediumrangeoptimizationofcopperextractionplanningunderuncertaintyinfuturecopperprices’,EuropeanJournalofOperationalResearch,vol.233,Nº3,pp.711-726.

Chanda,ECK1990,‘Anapplicationofintegerprogrammingandsimulationtoproductionplanningforastratiformorebody’,MiningScienceandTechnology,vol.11,Nº2,pp.165-172.

Diering,T2004,‘Computationalconsiderationsforproductionschedulingofblockcavemines’,ProceedingsofMassMin2004,Santiago,Chile,pp.135-140.

Diering,T2012,‘QuadraticProgrammingapplicationstoblockcaveschedulingandcavemanagement’,Massmin2012,Sudbury,Canada,pp.1-8.

Epstein,R,Gaete, S,Caro, F,Weintraub,A, Santibanez, P,&Catalan, J 2003, ‘Optimizing long-termplanning for underground copper mines’, Proceedings of Copper 2003, 5th InternationalConference,CIMandtheChileanInstituteofMining,Santiago,Chile,pp.265-279.

Epstein,R,Goic,M,Weintraub,A,Catalán,J,Santibáñez,P,Urrutia,R,Cancino,R,Gaete,S,Aguayo,A,&Caro,F2012,‘OptimizingLong-TermProductionPlansinUndergroundandOpen-PitCopperMines’,OperationsResearch,vol.60,Nº1,pp.4-17.

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GEOVIA-Dassault2012,Ver.6.2.4,Vancouver,BC,Canada.

Guest,A,VanHout,GJ,Von,JA,&Scheepers,LF2000,‘Anapplicationoflinearprogrammingforblockcavedrawcontrol’,ProceedingsofMassMin2000,TheAustralian InstituteofMiningandMetallurgy:Melbourne,Brisbane,Australia.

Holmstrom,K2011,TOMLAB/CPLEX,ver.11.2.Ver.Pullman,WA,USA:TomlabOptimization.

Newman,AM&Kuchta,M2007, ‘Usingaggregation tooptimize long-termproductionplanningatanundergroundmine,EuropeanJournalofOperationalResearch,vol.176,Nº2,pp.1205-1218.

Parkinson,A2012,EssaysonSequenceOptimizationinBlockCaveMiningandInventoryPolicieswithTwoDeliverySizes,Thesis,TheUniversityOfBritishColumbia,199p.

Pourrahimian,Y2013,Mathematicalprogramingforsequenceoptimization inblockcavemining.PhDThesis,TheUniversityofAlberta,Edmonton,Alberta,Canada,Pages238.

Pourrahimian, Y, Askari-Nasab, H, and Dwayne, DT 2013, ‘A multi-step approach for block-caveproductionschedulingoptimization’,InternationalJournalofMiningScienceandTechnology,vol23,Nº5,pp.739-750.

Pourrahimian,Y,Askari-Nasab,H,andTannant,D2012,‘Mixed-IntegerLinearProgrammingformulationforblock-cavesequenceoptimisation’,Int.J.MiningandMineralEngineering,vol.4,Nº1pp.26-49.

Rahal,D2008,DrawControlinBlockCavingUsingMixedIntegerLinearProgramming,PhDThesis,TheUniversityofQueensland,342p.

Rubio,E2002,Longtermplanningofblockcavingoperationsusingmathematicalprogrammingtools.MasterThesis,TheUniversityofBritishColumbia,126p.

Rubio,EandDiering,T2004,‘Blockcaveproductionplanningusingoperationresearchtool’,Massmin2004,Santiago,Chile,pp.141-149.

Smoljanovic,M,Rubio,E&Morales,N2011,‘PanelCavingSchedulingUnderPrecedenceConstraintsConsideringMiningSystem’,Proceedingsof35thAPCOMSymposium,Wollongong,NSW,Australia,pp.407-417.

Song,X1989,‘CavingprocesssimulationandoptimalminingsequenceatTongKuangYumine,China’,Proceedings of 21st Application of Computers and Operations Research in the MineralIndustry,SocietyofminingEngineeringoftheAmericanInstituteofMining,Metallurgical,andPetroleumEngineers,Inc.Littleton,Colorado,LasVegas,NV,USA,pp.386-392.

Tabesh,M&Askari-Nasab,H2011, ‘Two-stageclusteringalgorithmforblockaggregation inopenpitmines’,MiningTechnology,vol.120,Nº3,pp.158-169.

Weintraub,A,Pereira,M,&Schultz,X2008,‘APrioriandAPosterioriAggregationProcedurestoReduceModelSizeinMIPMinePlanningModels’,ElectronicNotesinDiscreteMathematics,vol.Nº30,pp.297–302.


466

Block Caving strategic mine planning using Risk-Return Portfolio Optimization

E Rubio REDCO Mining Consultants, Chile

Abstract

Cave mining is a complex mining system that relies entirely in the constitutive behavior of rock mass caving that leads to ultimate fragmentation at the draw point, gravity flow behavior, stress performance among others. All these aspects of cave mining have shown the industry that block caving is a highly uncertain mining method. In the last years the industry has been pushing the concept of super caves that supports the concept of large underground block cave operations with production in the excess of hundred thousands tons per day of run of mine ore, promoting the concept of large footprints and high draw columns. This tendency has been supported by the traditional approach of net present value optimization throughout a constant throughput optimization. Nowadays, there is evidence in several operations around the world that the approach of ignoring the actual intrinsic caving variability and uncertainty of its constitutive behavior may lead to jeopardize the project value and eventually have a mine design or mine planning fatal flaw. This paper introduces the concept of portfolio optimization in which every decision related to mine design and mine planning could be a component of a set that defines a feasible portfolio. Thus, this set is optimized for different production targets to maximize return subject to a given level of reliability, as a result of this optimization process a frontier efficient is proposed as a boundary to display different strategic designs and planning options for the set of variables under study. The efficient frontier shows graphically the maximum return that a mining system could deliver throughout a coherent production schedule under a given level of risk. Thus, it is for the decision makers to define the point along the frontier efficient where they want to place a given project. This tool has been used in the industry at a prototype level to justify equipment technology and its mining system as well as to define production targets of large block cave operations that are efficient for the level of return and risk that shareholders what to place the mine set. In the paper there will be theoretical and applied examples of this technique that is under development and application to mine design and mine planning of large block cave operations.

Key words: block caving, mine planning, strategic planning, sequence optimization, operational hedging, risk assessment, reliability production planning, portfolio optimization.

1 Introduction

Block caving is a complexmining system since its functionality depends upon caving process that isinducedatthebaseofablockanditpropagatestosurfaceasmaterialiswithdrawnfromasetofregulardrawpointsontheproductionlevel.Materialtakenfromthedrawpointsisdumpedintoorepassesthatconnecttothehaulagelevel.Fromthehaulageleveltheproductionistakentocrushersbytrucks,trainsorchainbeltsdependingontheminingsystem.Theprocessofundercuttingablockanditssequencerespecttothedrawbellblastinghasdemonstratedtobequitecriticaltoavoidearlyrockcollapsesorrockbursts.Sothedesignofthedrillingandblastingofdrawbellsandundercutintermsofgeometryandsequenceisextremelyrelevantforthesuccessofablockcaveoperation.Nevertheless,despitetheeffortsonthedesignandoperationaldisciplinethatcanbeappliedinaBlockCaveoperation,therearestillseveraluncertaintiesthat triggered risk that perhaps could lead to jeopardize the expected returnof oneof theseoperations(Summer2000).Someoftheuncertaintiesthatleadtoriskareasfollows:

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1. Cavepropagation.If therockmassishighstressandcompetent thecavepropagationcouldbeuncertainandtriggerederraticdilutionandnonuniformgradeextractionand/orairgapsthatattendtowardsthesafetyofthemethod.

2. Fragmentation.Sincerockmassisbrokenbycavingtheactualfragmentationexpectedatthedrawpointsisuncertain.Thus,drawpointproductivityisuncertainandtheamountofareathatneedstobedevelopedandundercuttedalsobecomesuncertain.

3. Grade.Once the rock is fragmented the particles of rockflow towards the production level indifferent ways depending on the fragmentation profile and fragmentation distribution. Thus,forecastgradebecomesquitedifficultduetothenumberofunderlyingflowingmechanismsthatcouldbeinteracting.

4. Stress distribution.Depending on the design of theCavingmethod one could obtain differentstressperformancesatthefrontcave.Typically,itiswellknowtheeffectofabutmentstressthatisproducedbyundercutting.Abutment stress implies that thereare three times thepreminingverticalstressandsometimesthereisrotationofthestresstensor.Thiseffectleadstounexpecteddamage,driftcollapsesandsometimesrockburst.

Alltheaboveaspectsleadtohaveaminingsystemthatisunreliableintermsofproductionoutcomes.Inordertocomputeminingreservesonceneedstosimulateseveralexcenariosintegratingrandomvariablesthatareconnectedconstitutivelytothesourcesofuncertaintymentionedabove.Thus,expectedproductionoutcomesalsobecometheresultofasimulatedstochasticprocessthatisoftenpresentedasanhistogramofpotentialproductionoutcomes.Figure1depictureshowsaproductionhistogramofablockcaveanditsevolutionathecavingpropagatesandthewholeminingsystemmatures.

Figure 1 Reliability evolution troughout an active production schedule due to draw point maturity and draw point opening sequence

Asaresultof therandomnessofproductionoutcomesareliabilityassessment isneeded inorder tofixtheamountofproductionthatisdesiredtostateunderagivenlevelofuncertainty.Reliabilityassessmentisa tool toassess the robustnessofaminingsystem. Ithelps toanalysedifferentproductionscenariosandalternativeminingsystems.Also,itallowsdecisionmakerstoevaluatedifferentlevelsofhedgingtoachieveagivenproductionoutcome.Inotherwords,flexibilityneedstobeaddedasaconsequenceofthecharacteristicofthiscomplexminingsystem.


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2 Operational hedging

Operationalhedginginthispaperneedstobeunderstoodasthekindandtheamountofundercuttingareathatneedstobedevelopedandundercuttedtosustainacertainproduction,thenumberofequipmentsthataregoingtobeneeded,thenumberofsecondaryblastingcrewsamongothers.

Operational hedging is not free and often involves a fair amount of capital expenditure that must beengineeredinaproperproductionschedulesarequantifiedandrationalizedinordertooptimizetherentandtheriskoftheproject.Thispapersdescribesamethodologycalledfrontierefficient,commonlyusedinportfoliooptimizationinfinanceapplications,thatallowsminingengineersandmanagerstobeconfidentregardingtheamountofdrawingthat isscheduledthenumberofsecondaryblastingcrews, theopenedareathatisscheduled.Also,insomeinstancesinwhichtherealhedgecanbefeasibletheoptimalitycouldbefoundatalowerproductionleveltoleveragethehighestminingreturn.Figure2depictureshowsbothoptionseitherreducingtheproductivityofanactivefootprintduetoitshighproductionvariabilityortheintroductionofarealhedgesuchthatwillraisethereliabilityoftheunderlyingproductionschedule.

Figure 2 Illustration of hedging options in a block cave scenario. For a given active layout production outcomes are reduced or for a fixed production target a larger amount of active area is undercutted

Asaresultofintroducingoperationalhedginginplanningandschedulingablockcavemine,theexerciseofjustoptimizingthenetpresentvaluebecomesmeaninglesssincethereisCaPexashedgesthatneedtobeoptimizedintegratedaspartoftheminingsystem.Thus,thedesignandplanningoftheseoperationsneedtobesystemicinasencethatcaving,undercutting,drawbelling,drawperformance,repairs,materialhandlingsystemneedtobeintegratedtovaluecorrectlythereliabilityofagivenproductionscheduledthatisplannedwithoperationalhedges.

3 Systemic approach to design and planning for Block Caving

BlockCaveistheminingmethodthatneedstobeengineeredasanintegratedminingsysteminasensethatcavingwillinfluencetheperformanceoftheproductionareaintermsofproductivityandregularityofproduction.Thematerialhandlingsystemcouldinfluencetheabilitytousedifferentdrawprofilesthatcouldinfluencethewayhowtherockmasscaves.Inordertointroduceoperationalhedginginablockcaveproductionscheduleamodeloftheminingsystemneedstobebuiltupinordertoreplicateandmimictheproductionperformanceinordertoquantifyasproductionmeansdifferentoptionsfortheminingsystemintermsofhedgingandproductionoutcomes.Figure3depictureshowstheinteractionbetweenmodellingtechniquesinordertoquantifythevalueofoperationalhedging.

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Figure 3 Illustration of different numerical techniques used to quantify operational hedging in a Blok Cave operation

PlanninganddesigningofaBlockCavefollowingaminingsystemapproachallowsminingengineerstocaptureandtomeasuretheeffectsoftheinteractionsamongthemainproductiondriversandthatcouldprovideinsidesregardingthecertaintyorreliabilityofachievingagivenproductiontarget.Severalauthorshaveprovidedwithmethodtoaccomplishthissuchas(Rubio2006;Troncoso2006),however,itisveryimportanttodeviceanengineeringtoolthatcouldbeusedatastrategicleveltosupportplanningdecisionssuchasminingmethod,sequence,productiontargets,drawstrategyamongothers.TheteamofREDCOminingconsultantshasdeviceamethodtomimicthemarketfinancialbehaviourtothecomplexitiesofablockcaving,usingthefrontierefficientmethoddevelopedbyMarkowitz(1959)tooptimizeportfolioforuncertaintyoutcomes.

3 Block Caving Frontier Efficient Method

EfficientportfoliohasbeendiscussedextensivelybySamisetal(2006),andDavisandNewman(2008)usingrealoptionsandquantifyingtheriskofdifferentminingstrategiesandalsoreviewingvalueatriskmethod. In this paper, the authorwanted to give a fresh review at theMarkowitzmethod (1959) andcomplementedbyHaugen(1990)andMerton(1990)inwhichhedefinesafrontierefficientoptimizationmethodtoallocateresources toaportfolioofassetswithdifferentreturnover investmentandrisk.Themethodology consists of computing the cross covarianceof all the possible combinationof assets in aportfoliotocomputethemedium-.variancespaceuponwhichagivenportfolioisefficienttobeinvestedin.Thusforinstance,inFigure4thehighlighteddotsrepresentaportfoliothatisinefficientsincetherearecombinationofassetsthatcouldprovideahigherreturnforthesamecomputedaveragerisk.

Notethattheriskinthiscontextisseeingastheaveragevolatilityoftheunderlyingassetportfolio,inblockcavingthiscouldwellbethevolatitiltyofmetalproductionduetouncertaintyorrunofmineproductionduetouncertainty.Subsequently,theminingapplicationwillbetomimicseveralminingdecisionssuchasminingmethods,productionrate,miningsequenceandproductionscheduleasifthesedecisionswherecomponentofaportfoliointhecaveminingsystemmodel.Thenthecovariancesofdifferentdecisionswilldefinethevarianceofagivendecisionsubjecttotheotherstatussuchasmine,productionrate,sequenceandothers.


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Figure 4 Frontier efficient optimization

Whentheminingsystemmodelisdeviced,avaluationmodelisintegratedinordertoquantifythereliabilityofdifferentminesettings(design,sequence,productioncapacities,materialhandling,etc)andthereturnofthesettingbasedontheamountofhedgingthatisinvolvedinthescenario.Forexample,foraverylargeproductioncapacityandminimumhedgingtheriskandthereturnwillbehigh.Forthesameproductionscenarioalargeamountofhedgingisadded,forexamplealargeproductionfootprintisdeveloped,thentheriskofthescenarioandthereturnwilldecrease.Figure6depictureshowsthescenariovaluationexercise.Anoptimizationmodelcanbeusedtofindthefrontieratthemaximumlevelofreturnforagivenamountofriskthatshareholdersandprojectstakeholdersarewillingtotake.Thistoolallowsengineeringgroupstomakeassessmentofhedgingandvalueoftheprojectfordifferentlevelsofriskandreturn.

Figure 5 Frontier efficient method used to value the risk and the return of different configurations of a mining system to support strategic decision making

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Themethodologyproposed to use the frontier efficientmethod to plan aBlockCave starts simulatingdifferentreplicationsoftheminingsystemfordifferentlevelsofdrawpointmaturityandmaterialhandlingavailability. Thus, the mining system is simulated using Arena software in which using volumetrictransporters one could replicate scenariosof productionmodelling every cycle of themuckingprocesswhileintroducingrandomvariablessuchasfragmentationsize,hangupoccurrence,equipmentavailability,materialhandlingavailability,drawpointstructuralfailure.Theresultofthissimulationprovidesproductionhistogramsfordifferentstagesofblockmaturityasshownbelow.Blockmaturityisunderstoodastheblockmaturesitscavingasafunctionofdrawingproduction.

Figure 6 Block production histogram for different levels of draw point maturity, cave 1, cave 2 steady production and closure

Clearly,theabilityofthesystemtohandledifferentlevelsofproductionchangesaccordinglyfordifferentstagesofcaving.Basedontheabovechartforacollectionofblocks(productionunitscomposedofmultipledrawpoints)onecansetuptheproductiondistributiontotakefromeveryblocktobeaportfoliodecisioninwhichtheproductiontobetakenfromeveryblockwouldbetheportfoliodecisionsubjecttotheconditionalprobabilitydistributionrepresentedintheproductionhistogramshownabove.Asaresultoftheportfoliooptimizationonecouldobtaintheamountoftonnagetobetakenfromeveryblock,thenumberofactiveblocks,thenumberofblocksthatneedtobeunderdevelopmentandthedrawingstrategythatneedstobetakenfordifferentlevelsofrisk.Foragivenportfolio,onecouldevaluatetheproductioncapacityofthisscenarioanditsreturn.

Givenablockiofasetofnactiveblocksthatcontainsqidrawpoints,thefollowingoptimizationmodelcanbeformulatedtomodeltheblockcaveproductionscheduleasaportfolioofmultiassetssubject tounderlyingproductionvolatility.


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Tt;maximumproductiontonnageforperiodt,thisistheoptimizationfunction

Yi;Binaryvariablethatis1iftheblockisopenedor0otherwise

Xi;Continuesvariablethatistheproportionoftonnagetobetakenfromblockioverthemaximumtonnagetobedrawnbasedontheoveralldrawingstrategy.

Tmi;Maximumtonnagethatcanbedrawnfromblockibasedontheoveralldrawingstrategy

Vmaxi;Maximumtonnagethatcanbetakenfromblockibasedondrawrateanddrawpointsmaturity

Thesimpleformulationallowstosetuptheproductionscheduleofablockcavemineasaportfoliomodel.Theaboveformulationisasimplificationduetoa lackofmultiperiodsetting,sequenceconstraintandexposureofmaterialhandlingalternativedesignsthatareagreatportionoftheoperationalhedgingexpectedtobeaddedinablockcaveminingsystem.Nevertheless,thisformulationallowstosetupacomprehensiveunderstandingofthemethodandleadstoamoresophisticatedmodelstobeconstructedastheresearchevolves.At themoment theREDCO´s teamissettingupdifferentexperiments tobetterunderstandtheconstitutiveofthecovariancematrixanditsrelationshipwithdifferentaspectsofcavingandflexibility.Forexamplesitcanbeshownthatthecovariancematrixamongactiveblockswoulddependupontheflexibilityoftheminingsystemwhichtranslatesintoalternativematerialhandlingsystemsandperhapsequipmenttechnology.For example automatedLHDequipmentwould translate into adifferent covariancematrixcomparedtomanualLHDbecausethetechnologyaddsdifferentlevelsofintrinsichedgingthatisnotoftenquantifiedasmoreCapExormoreinfrastructure.WhensolvingtheabovemodelonecoulddrawFigure7.

Figure 7 Frontier efficient chart resulting from solving a portfolio optimization model to plan a Blockcave production schedule subject to multiple blocks with intrinsic dynamic uncertainty.

Figure7showsthatoncetheoptimizationmodelissolved,therecouldbedelineatedafrontieratwhichforagivenlevelofvolatilityorriskonecouldfindthemaximumreward,valuingthecontainedandthedesignedhedging.Thus,forapre-plannedscenariothatislocatedbelowthefrontieronecouldactivatetheoverdesignedhedgingi.e.acceleratingproductionorreallocatethehedgingbydecreasingthelevelofriskorvolatilityofsomeblockscomposingtheproductionschedulethatmaybeoverstressed.

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Mine Planning

4 Example of application

Anindustrialexperimentwassetupconsidering20alternativeblockswhichcanbeopenedinanyordertomaximizeproductionforagivenlevelofrisk.

Figure 8 Block setting and material handling to be included in the portfolio optimization model

Everyblockiscomposedoutof:

• 4productiondrifts.

• 2orepassesperproductiondrift.

• 2crushers.

• 4orepassesdumpintothe2crushers.

• 2beltsoneforeachcrusher.

• 1belttocollectfinalproduction.

Everyblockwasmodelledusingconditionalprobabilitydistributionhistogram,withproductionrandomvariables,suchas,drawpointblockageasperoversizeandhang-ups,orepassinterruptions,productiondriftrepairs,crushersandLHDsavailability,secondarybreakageproductionperformance,undercutareaavailability.Figure9showstheproductionhistogramsconditionaltoblockmaturity.


474

Figure 9 Production histogram derived from the simulation of the mining system integrating intrinsic uncertainty for different levels of draw point maturity

Using theportfoliooptimizationapproach,asetof11scenarioswereoptimized, foreveryscenario theproductioncontributionofeachblocktotheoverallproductionwascomputed.Figure10showstheresultofapplyingtheoptimizationmodeloverthesetofblockssubjecttotheaboveshownblocksvolatility.

Figure 10 Production histogram derived from the simulation of the mining system integrating intrinsic uncertainty for different levels of draw point maturity

Figure11showsthat,fordifferentlevelsofriskacceptance,thereisadifferentcombinationofblocksthatneedtodrawnandtheamountofdrawingacrosstheactiveblockschangesaccordingly.Thisisanexpectedresultsincetheinternalbalancebetweenthelevelofvolatilityofeachblockandtheamountofdrawing

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Mine Planning

needtobeinbalancetoprovidethemaximumreturntoagivenlevelofrisk.Notethat theoperationalhedginghereisactivatedbasedonthenumberofblocksthatareopenedandtheamountofdrawingtakenfromeachactiveblock.

Figure 10 Production distribution per block as a function of the maximum tolerable production volatility

Atthemoment,thereisasecondmodelthatisunderconstructionthatismultiperiodandintegratessequenceanddevelopmentconstraintsthatshouldprovideasetofefficientfrontiersbaseonthemaximumnumberofactiveblocksthatareacceptablebasedonthedevelopmentratesandgeneralinfrastructureavailability.

5 Conclusions

Themain conclusion of thework to date is that the Frontier EfficientMethod could provide insightsregarding the block cavemine planning includingminingmethod, production rate, sequence, drawingstrategy,developmentrateandequipment/miningtechnologyamongothers.

Another observation is that the number of active resources involved, such as, general infrastructure ordevelopmentratecapacity,wouldprovideintrinsichedgingwhichistheabilitytomigratefromafrontierof risk return into amore aggressive return capture exposing the ability of the system to optimize itsproductioncapacity.Thus,arealoptionmodelcanbesetupontopofthefrontierefficientmodelinordertooptimizetheintrinsichedgingthatneedtobedevisedinagivenproject.

It is fully recommended that themining industry adopts thisway or a similar tool to set up strategicscenariosinwhichreturnandriskarebothsetupintothesamemaptoleavetheshareholdersordirectorstomakedecisionsregardingtheminingsystemproductionplanningvariablesthatpositionedtheprojectintothelevelofriskandreturnacceptableforthecompany.Thereareseveralexamplesinblockcavingandmassivemininginwhichbyjustconcentratingonnetpresentvaluehasledtounrealisticproductiontargetsandacompleteunbalancebetweenthecontainedoperationalhedgingandproductionperformance.Thisbehaviourleadstoinefficientscenariosthatquiteoftenwhenoperatingthemineitreflectsintohigheroperatingcoststhaninthescenariowhenoptimizingthecontainedhedging.Anotherobservedeffectofoperatingamine in the inefficientareaof the risk returnchart is the fact thatdevelopment,productionandoverdrawingareasbecomeunbalanced,i.e.therecouldbealargeundercutareawithoutconstantandsustainedproduction,therecouldbehighoverdrawndrawpoints,therecouldbeanarrowareaofsteadyproductionwhilelargezoneofoverdrawnandlittleundercutting,inallthesescenariosmostlikelytherewillbegeotechnicaldamageatthedrawbellingmanifestingasrockcollapsesandstrainbursts.Therefore,integratingoperationalhedginginanoptimalwayforagivenproductionandminesettingwouldleadtoamorecontrolledoperatingcostandlessgeotechnicalhazards.


476

Acknowledgement

TheauthorwouldliketothankthewholeREDCOMiningConsultantsteamforsupportingtheworkandeveryday contribute in a great deal to apply the risk return approach to the solutions recommended toourclients.SpeciallyIwouldliketothankJoaquinJimenezagraduatefromtheUniversityofChilewhodidmostof thenumericalapplications.Also, Iwould like to thanksmyclosedcolleaguesGabrielPaisandSebastianTroncoso forcontributing inmanydiscussions, theoretical frameworkandcomputationalanalysis.

References

Haugen,R&NardinB1990, ‘DedicatedStockPortfolios’, JournalofPortfolioManagement,Summer1990,pp.17-22.

Markowitz,H1959,PortfolioSelection:EfficientDiversificationofInvestments,JohnWiley&Sons,Inc.

Merton,RC1990,Continuous-TimeFinance,Blackwell.

Norstad, J 1999,An introduction to portfolio theory.Available at: http://homepage.mac.com/j.norstad/finance.

Samis,M,Davis,GA,Laughton,D&Poulin,R2006,‘Valuinguncertainassetcashflowswhentherearenooptions:arealoptionsapproach’,ResourcesPolicy,vol.30,pp.285-298.

Kazakidis,V&Scoble,M2002,‘Accountingforground-relatedProblemsinplanningmineproductionsystems’,MineralResourcesEngineering,ImperialCollegePress,London,England,vol.11,Nº1.

Rausand,MyHoyland,A2004,Systemreliability theory,models,statisticalmethodsandapplications,Secondedition,Canada,Whiley-Interscience,132p.

Rubio, E 2006,Block cavemine infrastructure reliability applied to production planning, PhDThesis,TheFacultyofGraduateStudies(MiningEngineering),TheUniversityofBritishColumbiaVancouver,Canada.

Summers,J2000,‘Analysisandmanagementofminingrisk’,MassMin2000,Brisbane,TheAustralasianInstituteofMiningandMetallurgy:Melbourne.

Troncoso, S 2006, Simulación del impacto de interferencias operacionales para la planificación deproducción,Memoria IngenieroCivildeMinas,UniversidaddeChile,Santiago,Chile. (inspanish)

Troncoso,S2009,ConfiabilidaddeProgramasdeProducciónenSistemasMinerosSubterráneosComplejos.TesisdeMagísterenMinería,UniversidaddeChile,Santiago,Chile.(inspanish)

Vesely,W 1991, ‘Incorporating aging effects into probabilistic risk analysis using a Taylor expansionapproach’,ReliabilityEngineeringandSystemSafety,pp.315-337.

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Mine Operations

Numerical Modelling

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Numerical Modelling

Numerical modelling of Pilar Norte Mine development using Abaqus

R Cabezas MVA Geoconsulta, ChileF García MVA Geoconsulta, ChileM Van Sint Jan MVA Geoconsulta, ChileR Zepeda CODELCO, Chile

Abstract

Modelling mining processes and their future extraction is an important tool for planning and design, especially in deep stress mining, that can be affected by collapse or rock burst and creates several risk conditions for workers as well for expected economical revenue. The main purpose of this research is to explain and to comment our current computational modeling state of art, using as example some of main aspects in modeling geomechanics in the development of Pilar Norte Mine, El Teniente Division, CODELCO. Numerical modeling was performed using software ABAQUS. Finally, the advantages of numerical modeling and some future research requirements are defined. and expected short-terms improvements, making numerical modeling as an important but not ultimate decision tool, but takes advantage in relating the most important characteristics of mining process: geomechanics, design and operation.

Keywords

Geomechanics, ABAQUS, Pilar Norte, El Teniente, Numerical Modelling

1 Introduction

ElTeniente is anundergroundmine located in theAndesCordilleraof centralChile, approximately at100kmsouthofSantiago,underoperationsincethebeginningsoftheXIXcentury.BasedinporphyrydevelopedintheearlyPliocene,itisformedbysecondaryandprimaryrock,weremineralizationisformedprincipallybystockwork,reachinggradeorenearlyto0.6%.Geologyhavebeenextensivellymapped.(Vryetal.2010).

PilarNorteMineislocatedatNortheastofBrechaBraden,themaingeologicalformationwithouteconomicalprofitandcenterofadministrativework.PilarNorte is locatedbetweenEsmeraldaandReservasNorteMine.Sincetheearlypreparationforminingandfirstoperations,PilarNortehavepresentedrockburstproblems.Also,Esmeraldaminewasaffectedbythecollapseofpartoftheirtunnels,thus,collapseandsqueezinghasbeenanissueatElTenienteandcannotbeunderestimated.

Duetorecenthistory,itisnecessarytoevaluatefutureminingriskyconditions.Thus,anumericalmodelwas developed in order to identify hazardous zones, quantify possible problems and evaluate differentexcavationssequencesthatminimizeriskorexpositiontimetohazardousconditions.Thispaperpresentssomeof themajor considerations of thatmodel, aswell as some conclusions about the validity of theresults,theircapacitytoevaluateanddesign,someoftheirlimitationsandfutureworkassociated.

2 Methodology

NumericalmodelwasdevelopedusingtheFiniteElementsoftwareABAQUS,whichcanconsiderssolidbodiesorplatesinabidimensionalenvironment.Nevertheless,interactionbetween2Delementsand3D


480

elementsarenotallowed.Interactionsbetweendifferenttypesofmaterialscanbemodeledasrestrainedorallowingrelativedisplacement.Someofthemainconsiderationsarelistedinthefollowingsections.

2.1 Geology

ThemainunityisElTenienteMaficComplex(CMET),placedaroundBrechaBradenunity,inawaythatlocalstructuresdecreaseaswellasthedistanceofBechaisincreased.Localalterationincludesveinswithlowerresistancethatmakesrockmassresistancedecrease.InsidetheCMETunit,severalminorbodiesarefound:inPilarNorteMine,thethreemainbodiesareAndesite,BrechaandDiorite.Interactionbetweenthisunitsaremodeledwithdisplacementrestraint,therefore,onlychangesinstressduetoelasticityareallowed.Thissimplificationimprovesthecalculationtime.

RegionalfaultsandtheirstressimplicationwerenoticedbyElTeniente(Karzulovicetal.2006;Windsoretal.2006).Localfaultsandmainstructuresareobtainedininternalreports,andrankedduetoimportancelevel, using 10 cases. Faultswere considered as thin solid bodies that cannot yield and allow relativedisplacement,whileintersectionsweretreatedbyarelativeimportancecriteriaandmostimportantfaultscontainingthelessimportantones.Yieldinghypothesiswasdismissedbecauseofconvergenceproblems,however,due to thiselementsbeingalmoststiffless, results tend tobesimilar to reported resultsbyElTeniente.

2.2 Geometry

Boundariesarelimitedtoaboxof6kmwideper7kmlengthand1.5kminheight,enoughtoensurethatscale effectswon’t affect the results.Geometryofmainbodieswasobtained through softwareVulcan,previousdesignofmininglayoutwasaninput.SomeoftheelementsarepresentedinFigure1.

(a) (b)

Figure 1 Modelling of main bodies in FEM software. (a) Equivalency of Vulcan Model output to Abaqus Solid sketch. (b) Main faults and structures applied over the production layout of Pilar Norte Mine, colored by

importance criteria, were red is more important than green and green more than blue

2.3 Geotechnical properties

Fourtypesofrocklithologyareconsidered,eachoneinapre-miningconditionandatbrokencondition,aftermininghavestarted.FailuremodelusedforlithologywasMohrCoulombinsteadofHoek&Brownbecauseofcurrentlimitationsofthesoftware.ParametersofsomeprincipalbodiesarepresentedinTable1.Inordertooptimizetimecalculations,anelasticdomainandanelastoplasticdomainweredefined,wereplasticpropertiesareavailableinnearminingvolume.

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Table 1 Parameters of main bodies in Pilar Norte Mine Model

Material Young Modulus

E (GPa)

Weight

(ton/m3)

Friction angle

(º)

Cohesive strength

C (MPa)PreminingCMET 38.0 2.7 36.0 6.5BrokenCMET 2.0 2.0 42.0 1.0

Diorite 46.0 2.7 37.0 8.0Andesite 50.0 2.7 37.0 7.0Brecha 36.0 2.7 38.0 8.0

BrechaBraden* 25.0 2.6 - -Faults 4.0 2.7 3.0 1.0

*denoteselasticmaterial

3 Field data and special considerations

SomespecificconsiderationsappliedtopanelCavingandPilarNortemineareexplainedinthefollowingsections.

3.1 Preconditioning by hydraulic fracturing

Preconditioningcausesamediaexchange,expandinginaradialwayallalongthelengthof theboring.Thisradialpropagationmobilizescohesivestrength,convertingoriginalrockinabigsizeblocksactingasfrictionalmaterial.Thepropagationoffracturesbyhydraulicpressurewasnotdirectlymodeled;insteadaspecificvolumeasacylinderwasdefined.Ontheotherhand,stiffnessinaxialaxisisnotwidelyaltered,whilestiffnessatsheardecreases,inducingmodelingofanorthotropicmedia.

RepresentativeideaoforthotropicpropertiesandcalibrationofthemodelusingminorstressobtainedbypreconditioningfielddataarepresentedinFigure2.Figure2ashowsatheoreticalpropagationofHF,whichwascalibratedconsideringisotropicmodelswithexplicitstructures.IdealizedmediaispresentedinFigure2b,withoutconsideringexplicitstructuresinrockmass,inordertoreachsimilarresults.Finally,Figure2cshowsthecomparisonbetweenHFassessofminorprincipalstressandmodeldataatasingleHFboring,obtainedbycalibratingmodelboundarystresscondition.

(a) (b) (c)

Figure 2 Hydraulic fracturing characterization used in the model. (a) Elemental idea of radial propagation in HF (b) Equivalent orthotropic properties of preconditioned volume. (c) Calibration of minor principal stress

with HF data, including their spatial location about the layouts


482

The orthotropic media assumption improves calculation time, but needs to be carefully calibrated ifadditional information of preconditioned volume is required. Otherwise, if preconditioned volume isconsideredjustasaoverloading,hypothesisisrecommended.

3.2 Progressive development of Cave

Fortheprogressionofthecaveanexternaliterationisapplied,relatedtoPlasticEquivalentdeformation(i.e.PEEQvariableatABAQUS).Thisconditionconsistofchangingmaterialpropertiesifathresholdvalueisreached,whichisdeterminedbytypicalvaluesofyieldingdeformationlike0.1%to0.4%.Physically,thehypothesisconsidersMohr-Coulombfailuremode,thatcanbereachedifmediaisunconfinedandhangingblocksfalldownbytraction,ifstressincreasesandductilefailuremodecanoccur.

For this iteration, time isnotdirectlyavariableand isonlycontrolledby thehypothesisofcontinuousminingprocess and extraction rate through theopeningof the following extractionpoints. InFigure3resultsoftheiterationtechniqueareshown.

(a) (b)

Figure 3 Progressive Development of Cave back. (a) Plastic equivalent yielding using different threshold limits (b) Shape of Cave using plastic equivalent strain

3.3 External loading and boundary conditions

Stressesinwholemodelareinitiallycontrolledbyoverloadingandtectonics.Previousstudiesrecommendedcoefficientsatrestof1.35inE-Waxisand1.14inN-S.Brokenmaterialduetominingprocessdecreasetheirdensitytovaluessimilartodensegravel,nearto2.2ton/m3.Thisreductionissimilartoalllithologies.Figure4presentsresultsofstressconditionindrawpoints.InFigure4aprincipalstressesarepresented.InFigure4baboreholecamerarecordisshown,evidencinganacceptablelevelofcorrespondencebetweenthem.

Interactionbetweengeologicalbodieswaspreviouslyexplained.Relativedisplacementallowedbetweenlithologiesandstructurescanhelptopredictpotentialfailuremovementsaswellasstrainenergyabletobedissipatedthroughseismicactivity.

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Numerical Modelling

(a) (b)

Figure 4 Stress flow due to caving process and over excavations given by tectonics

4 Results

Itwasnotedthatawaytotrytoreduceriskistoopenfollowingextractionpointstryingtoavoidstressoverconfiningduetocoupledeffectbyabutmentstress(miningcondition)andtectonicstress(pre-miningcondition).Infact,itisnotpossibletodismissbothstresses,butthedirectionoftheminingfrontcanreducetheflowingofthestress.Changingorientationofminingfrontcanbelimitedbythepresenceofmainfaults.

Thecurrentnumericalmodelcanbring,inabasicform,anestimationofpossiblefaultmovement,goingapproximatelyfrom0cmto2cminthemostloadedzones,conducingtoavailablerelativedisplacement.Figure5shows thechange instrainalloverseveralmainfaultsandstructuresconsidered inmodeling.Availableelasticenergytobedissipatedcanbecomputedwithmomentrecommendation(Bath1966):

(1)

Inothercase,energycanbeapproximatedasforceanddisplacement,makingmomentmagnitude:

(2)

Figure 5 Faults and joints stored energy obtained by numerical simulation


484

AsnotedinEquation1,themaximumseismicmomentiscalculatedtobeof2.2,thatisacceptablecomparedtogeophysicsestimationof2.1.Inotherwords,seismicityinferredbymodelingcannotbeeasilyseparatedfromseismicityduetocavingitself,whichmakeitdifficulttoreduceorcontrolfutureseismicevents.ItisimportanttosaythatelasticenergyestimatedbythesoftwarewascorrectedtoempiricaldataavailableatElTenienteDivision,wheretheenergyvaluehastodecreasedto5%.Thiscorrectionisduetodifferentcauses.Firstly,faultsarenonpersistentandthereforesomeportionofenergyisdissipatedasnoiseorheat.Secondly, stressmeasurementsdonotgive reliable results.Thirdly, the frequency range ingeophysicalequipmentislimitedanddonotcoverallrealratiofrequency.

ChanginginabutmentstresscanalsobeobtainedasshowninFigure6,showsincreaseofmajorprincipalstressduetominingadvanceaswellstressreducetozeroatcavezone.Inducedstressisnearto3timesthein-situmajorprincipalstress.

Figure 6 Development of Cave back and change in abutment stress, connection to Reservas Norte Mine

Pillarsconditioncanbeestimated,forexample,withprincipalstresses,SecurityFactororConvergencerate.Estimationsofchanging inpillar loadingareshown inFigure7. Itcanbenoticed thatpillarsandcrownpillarsaresubjectedtoareductionofconfinementstressprocessatboundaries,whichcanbeseeninpracticebeforesupportisapplied,despitecentralsectionofthepillarstillremainingathighconfinementstress.This estimation can be related to rock burst problems if high stress and low security factor arefoundedatthesametime,neverthelessitisnotpossibletocalculatethatprobabilityyet.

5 Conclusions

PilarNorteMine,partofElTenienteMineinChile,haveexperiencedseveralproblems,suchas,rockburstduringpreparationtominingprocess,makingnecessaryamethodtolocateandquantifyriskorhazardouszones.NumericalmodelofPilarNorteMinewasdevelopedwithsomerestrictions;limitedgeologicalentryandcalculation time,whichdonot allowanexhaustivemodelingprocess.However, currentnumericalmodelisanimprovementinordertoreachthatgoal.

Someofthehypothesisweretestedandcalibratedwithfielddata.Withinthosehypothesismodelingorhydraulicfracturesasanorthotropicmediaandcavingpropagationasanplasticstrainconditionsaresomeofthemostimportant.Theseareyettobeprovenhypothesis,thusthereisaneedoffurtherresearch.

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Numerical Modelling

Resultsareconsistentwiththeoryandareanadvancetodesignandidentifyhazardouszonesandconditionslikerockburstorseismicactivity.Nevertheless,isnotpossibleyettodetermineexactlyhoworwhenthosesituationswilloccur.Thus,itisnecessarytoimprovenumericalandrelateresultstofielddatainordertogetbetterestimationsoffailureinpillarsandcorrelationstopracticalproblems,suchas,seismicity.

Futureworkscanincludemodelingpropagationsoffracturehydraulicsandtheirinteractionsovertheentirenetwork.Anumericalmodelorcorrelationinordertodeterminewhereinducedseismiceventsarelocatedissuggestedtoo.Finally,itisnecessarytodevelopatoolorconstitutivemodelthatallowstorecognizeinanimprovedwaywhereandhowhazardoussituationscanoccur,becauseitisnotpossibleyettoquantifyrisk.

Acknowledgement

TheauthorswanttothanksElTenienteDivision,CODELCO,forthepermissiontopublishthisstudy,aswellfortheentireassistanceinthedevelopmentoftheresearch.

References

Bath, M 1966, ‘Earthquake energy and Magnitude’, Contributions in Geophysics: In honor of BenoGutenberg,(M.Benioff,&B.Howelleds),NewYork:PergamonPress.

Karzoluvic,A2006,Modelo conceptual de campode esfuerzos enMinaElTeniente.Reporte Interno,Santiago.(inspanish)

Vry,V,Wilkinson, J,Seguel, J&Millán, J2010, ‘Multistage Intrusion,Brecciation, andVeiningatElTeniente,Chile:EvolutionofaNestedPorphyrySystems’,EconomicGeology,pp.119-153.

Windsor,C,Cavieres,P,Villaescusa,E&Pereira,J2006,‘ReconciliationofstrainstructureandstressintheTenienteMine’,InternationalSymposiumonIn-SituRockStress,(L.Ming,L.Charlie,K.Halvor,&D.Halgeireds.),Trondheim,Norway:TaylorandFrancis,pp.533-540.

Figure 7 Pillars condition at Pilar Norte mining process


486

Geomechanical evaluation of large excavations at the New Level Mine - El Teniente

E Hormazabal SRK Consulting, ChileJ Pereira Codelco,ChileG Barindelli, Codelco, ChileR Alvarez SRK Consulting, Chile

Abstract

The New Level Mine is a 130.000 tpd panel caving project set to start in 2017 at the El Teniente mine.VP-NNM CODELCO (Vice-President Office of the New Level Mine) is currently finishing a detailed engineering design of the underground mine. The evaluation considers, the design of the crusher cavern Nº1 located in the Braden Pipe, which is a waste rock chimney located in the central part of the ore body. A geo-mechanical study has been carried out to evaluate the stability of the planned infrastructure and to provide recommendations about the design of underground caverns and galleries, including support. As part of this study, empirical methods, two-dimensional and three-dimensional continuum models have been developed and applied to evaluate the influence of the high stresses and different geotechnical units, on the mechanical response of the excavation. This paper introduces general aspects of the New Mine Level underground project and discusses in particular geo-mechanical analyses and design carried out to evaluate stability and support of some of the large excavations involved in the project.

1 Introduction

ElTenientecoppermineislocatedinthecentralpartofChile,CachapoalProvince,VIRegion,about50kmNEfromRancaguaCityandabout70kmS-SEfromSantiagoCity(Figure1).

At the ElTenientemine, the copper andmolybdenummineralization occurs in andesites, diorites andhydrothermalbrecciassurroundingapipeofhydrothermalbrecciascalledBradenPipeandlocatedinthecentralpartoftheorebody.TheBradenPipehastheshapeofaninvertedcone,withadiameterof1,200matsurfaceandaverticalextentofmorethan3000m.TheBradenbrecciasarewasterock.Therefore,thedifferentproductivesectorsofElTenienteminearesurroundstheBradenPipe,andthemaininfrastructureandaccessshaftsarelocatedinsidethepipe(Pereiraetal.2003).

TheNewMineLevelisa130,000tpdpanelcavingprojectsettostartin2017attheElTenientemine.Theminingprojectconsidersusing thepanelcavingmethod tominecopperore.TheVice-PresidentOfficeof theNewLevelMine(VPNNM)hasfinishedadetailedengineeringevaluationof theproject,whichconsiderstheconstructionandoperationofseveralminingunitstobeoperatedindependentlyfromeachother.

Amongthemostimportantelementsofthepermanentmininginfrastructuretobedesignedandconstructedfirstarelargecrushercaverns,designatedasSChNº1,SChNº2andSChNº3caverns.Thesecavernsarerequiredtoreducetheoresizefromtheoperationminingsectorsthatwillguaranteethecontinuedoperationforaperiodof50yearsormore.

The objective of this paper is to present general aspects of the design of one of the crusher chambers(SChNº1cavern),includingtheinterpretationofgeotechnicalsiteinvestigationdataanduseofempirical,analyticalandnumericalmethodstodeterminetheappropriatepermanentsupporttobeconsideredforthiscavern.

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Numerical Modelling

Figure 1 El Teniente mine location in relation to Santiago and Rancagua cities in the central part of Chile

2 Geotechnical characterization

Until the early 90’s the Braden Pipe was considered an almost homogeneous body, composed by aconcrete-likerockcalledBradenbrecciaand,initsperimeter,byabrecciacontainingcoarserrockblocks,calledMarginalBreccia(Pereiraetal.2003).However,thebehaviorobservedatdifferentsectorsoftheBradenPipeindicateddifferencesthatcouldonlybeexplainedbythepresenceofdifferentbrecciatypes.Therefore,acomprehensivegeologicalcharacterizationoftheBradenBrecciawasdevelopedinthepast,whichallowedamuchmoredetailedzonationoftheBradenPipeandthedefinitionofseveralbrecciatypes(Floody2000&Karzulovic2000).Themainbrecciatypesarethefollowing:

a) SericiteBreccia–thisbrecciaconstitutesamajorityofthepipe.

b) ChloriteBreccia–foundprimarilyinthesouthernportionofthepipe.

c) TourmalineBreccia–characterizedbylargeclastsandvein-likeoccurrence.

d) MarginalBreccia–hardbrecciaattheboundaryofthepipe.

For eachof thesebreccias, there isvariability in the sizeof the fragmentsor clasts and in themineralconstituentsandalterationof thematrixcement. In theBradenSericiteBreccia, thereappears tobeaneffectoftheratioofSericite/Quartzcontentinthecementtothecompressivestrengthofrocksamples.Figure2representsaplanviewcontainingthelocationofcrushercavernNº1andshowingthedifferentgeotechnicalunitsasinterpretedfromtheavailablegeologicalandgeotechnicalinformationfromthesite.Themain geotechnical units are the SericiteBradenBreccia unit (BBS),ChloriteBradenBreccia unit(BBC),TourmalineBradenBrecciaunit(BBT)andtheDaciticPorphyryunit(PDAC).


488

Figure 2 Plan view at mine level 1790 of the Crusher Chamber SCh Nº1 location, indicating the main geotechnical units as interpreted from available geotechnical information (taken from SRK, 2014)

Ingeneral,theBBS,BBCandBBTunitsarerockmassesofgoodqualitywithaBieniawski’sRMRvaluelargerthan70;fordetailsabouttheBieniaswki’sclassificationsystemseeBieniaswki(1989).Forexample,Figure3showsaphotographofsomerepresentativecoresofthemaingeotechnicalunitsatthesitelocationofSChNº1;solidandintactcores,fewjoints,lowfracturing,acommoncharacteristicoftheBBS,BBCandBBTunitswhichtranslatesintogoodqualityrockmass,canbeobservedinthephotograph.

Aspartofthegeotechnicalcharacterization,adatabasewithgeotechnicalinformationfromsiteinvestigations(geotechnicalboreholes)atElTenienteMinewasanalyzed;thisdatabasewascreatedandismaintainedbyVP-NNM(VCP2010aandVCP2010b).Inparticular,valuesofgeotechnicalparametersdescribingthequalityoftherockmass,includingFractureFrequency(FF),RockQualityDesignation(RQD),IntactRockStrength(IRS)andBieniawski’sRockMassRating(RMRB).

BasedongeotechnicalwindowmappingofdriftsandgalleriesclosetothesitelocationoftheSChNº1,acharacterizationoftherockmassqualityintermsoftheGeologicalStrengthIndex(GSI)andBarton’sQ-systemvalueswererevised(fordetailsaboutthesesystemssee,Hoek,1994,Hoek&Brown1997,Hoeketal.2002;Bartonetal.1974;GrimstanandBarton1993;Barton,2002).Theresultingrangeof thesevalues,expectedtobeencounteredduringexcavationoftheSChNº1,isshowninTable1.

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Numerical Modelling

a) b)

c) d) Figure 3 Cores of the main geotechnical units at the site location of the SCh Nº1.a) BBS. b) BBC. c) BBT and

d) PDAC

Froma structural geologypoint of view, the sitewhere the crusher cavernwill be emplaced has beenreferredtoas‘BrechaBradenMarginal’(or‘BradenBrecciaMarginalStructuralDomain’).Analysisoftheavailablegeologicalinformationhasrevealedtheexistenceofthreesystemsofminorfaultsandtwojointssets.Table2summarizestheorientationofthesestructuralsystems.

Thein-situstressstateconsideredforthedesignofthecrushercavernSChNº1wasobtainedfromover-coringtestsperformedatXC-01-ASsiteNº5(undercuttinglevel1880).Table3summarizesthein-situstressfieldatcrushercavernlocation.

Values of strength and deformability for all the geotechnical units were computed according to thegeneralizedHoek-Brownfailurecriterion(Hoeketal.2002;Hoek&Diederichs,2006)andfollowingsomespecificrecommendationstotheElTenienteminebyDiederichs(2013).Themechanicalparameterswerederivedfromlaboratoryunconfined,triaxialandtensiletestingofrocksamplesandestimationsofvaluesofGeologicalStrengthIndexfromgeotechnicalwindowmappinginthemainaccesstunnel(TAP),driftsandgalleriesnexttotheSChNº1location.


490

Table 1 Classification systems values of the rock mass at the SCh Nº1 location

UGTB RQD (%) RMRB89 Q’ GSI

BBS 70–100(80) 60–92(72) 1.2–250(14) 56–90(69)

BBC 94–100(98) 70–85(77) 40–100(70) 63–82(72)BBT 80–100(90) 72–82(75) 5–71(23) 61–80(73)

PDAC 79–100(89) N/I N/I 65–86(72)

():Meanvalues. RQD:RockQualityDesignation(Deere,1963).Q’:modifiedBarton’sQ-system(Jw/SRF=1). GSI:GeologicalStrengthIndex(Hoek,1994).RMRB89:RockMassClassificationsystem(Bieniawski,1989). N/I:Noavailableinformation.

Table 2 Structures at the site location of the SCh Nº1 (VCP, 2010b)

SETSMinor Faults Joints

Dip/DipDir Nºdata Dip/DipDir Nºdata

S1 84°/125° 12 75°/324° 34S2 83°/035° 7 35°/010° 21

S3 76°/172° 6

Table 3 In situ stress field representative of the site location of the SCh Nº1

Principal Stresses Magnitud (MPa) Bearing (°) Plunge (°)

σ1 50.73 344.0 -7.8σ2 33.11 75.5 -10.7σ3 26.50 218.6 -76.7

Table3summarizesthemechanicalparametersfortherockmass,forthethreegeotechnicalunitsanalyzedwiththeHoek-Brownmethod.[InTable4,miistheHoek-Brownintactrockparameter;σciisunconfinedcompressivestrengthoftheintactrock;γ is thespecificgravityoftheintactrock;Ei is themodulusofdeformationoftheintactrock;GSIistheGeologicalStrengthIndex;mb,sandaareHoek-Brownrockmassparameters;andERMandνarethedeformationmodulusandPoisson’sratiooftherockmass,respectively.

Tocalibrateandvalidatethestressfieldandrockmasspropertiessomeback-analysesweredonetocheckifthebehaviorpredictedusingthesepropertiesagreeswiththeobservedbehavior.Two-dimensionalplane-strainmodelswere constructed fordifferent sectionswithdifferentgeotechnicalunits andorientations,involvingsectionsforwhichoverbreakweremeasured.ThemodelsweredevelopedusingthefiniteelementsoftwarePhase2(Rocscience2009),whichallowsanalysisofexcavationsinplane-strainconditions.

Figure5showstheresultsfromafiniteelementback-analysisofoneofthesectorsconsideredfortheTAPtunnelinChloriteBradenBrecciaunit.Thelightgrayzoneintheroofindicatesfailurebytensionand/oryielding,andtheblackcurveshowsthemeasuredoverbreakeach5malongthetunnelaxisinthisparticularsector.Differenttunnelorientationswithinthesamegeotechnicalunitwereconsideredforthisanalysis.

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TheseresultsindicatethatthegeomechanicalpropertiesofthedifferenttypeofbrecciaspresentedinTable3areagoodestimateoftherockmasspropertiesforthesetypesofmassiverock.

Table 4. Summary of rock mass strength and deformability parameters for the different geotechnical units according to the generalized Hoek-Brown method —see Hoek et al., 2002; Hoek & Diederichs, 2006.

UGTBγ GSI σci

mi

σt Emv

c φ

(KN/m3) Mean value (MPa) (MPa) (GPa) (kPa) (°)

BBS 25.9 70 81.1 11.00,768

29.31 0,207,336 34

0,384* 5,180* 33*

BBC 26.6 72 77.4 12.00,782

25.60 0,207,578 35

0,391* 5,350* 34*

BBT 25.4 70 100.0 8.01,302

23.01 0,207,448 33

0,651* 5,260 32*

PDAC 25.8 73 144.5 28.50,662

34.55 0,2012,078 48

0,331* 8,500 43*

(*)UbiquitouspropertiesconsidersJennings(1970)criterionwithak=0.3.

3 Support requirements for the crusher cavern according to empirical methods

Figure6showsanisometricviewforthecrushercavernthatconsidersmainlythedumpingchamber,apronfeeder,crusherchamber,mainsilo,mainfeederandlift.

BasedonthelargeexperienceofexcavationoftunnelsandcavernsindifferentrockunitsatElTenientemine,usingthetraditionalmethodoffullfaceblastinganappropriate(temporary)supportconsistinginrockbolts,steelwiremeshandshotcretewereproposedfor thecavern(SGM-I-011/2006,VCP,2010c,amongothers).

Apreliminaryestimationofthequantityofpermanentsupporttouseduringexcavationwasdoneusingempiricalmethods.ThemethodsconsideredwerethosedescribedbyBarton(1974),Palmström&Nilsen(2000),Unal(1983),Hoek(2007)andHönish(1985),amongothers.Thesemethodsgiveguidelinesforpermanentsupportrequirementbasedonseveralofthegeotechnicalindexesdiscussedearlieron,suchasvaluesofRQD,QandRMR.Table5summarizesthecharacteristicsoftherecommendedsupportforSChNº1accordingtotheabovementionedmethods.

Duetotheintrinsiclimitationsoftheempiricalmethods(particularlyinregardtotheassumptionofisotropyofstressesandrockmasscontinuity),thesemethodswereusedasafirststepinselectingasupporttypefor theSCHNº1; the actual verificationof theproposed supportwas carriedout using tri-dimensionalnumericalmodelsasdescribedinthenextsections,whichamongothers,allowedincorporationofseveralgeotechnicalunitsexistingintherockmassandinsitustressfieldshowedinTable3.

Theacceptabilitycriterionforpermanentsupportwasestablishedbasedonfactorsofsafetywithrespecttofailure(incompression)ofthesupport.Basedontypesofsupportsusedandsuggestedlengthspansfromempiricalmethods,factorofsafetyof2.0forpermanentsupport(forstaticloadinganddryground)werejudgedappropriate.Inthisregard,aliteraturesurveydidnotrevealtheexistenceofestablishedrulesforfactorsofsafetytoconsiderforcavernoflargedimensions(asthecaseoftheSChNº1).Forexample,Hoek


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(2007),suggestanacceptabledesignisachievedwhennumericalmodelsindicatethattheextentoffailurehasbeencontrolledbyinstalledsupport,thatthesupportisnotoverstressedandthatthedisplacementsintherockmassstabilize.Pariseau(2007)suggeststhattheloadactingonthesupportforlargeexcavationshouldnotexceedhalfthevalueofthestrengthofthesupportmaterialof(shotcreteorconcrete)—i.e.,thiswouldmeanconsideringafactorofsafetyofatleast2.Forwedgeandblocksfailuresinalargecaverndesignafactorofsafetyof1.5to2.0iscommonlyusedasacceptabilitycriteria(Hoek,2007).

Figure 4 Results from a finite element back-analysis of one of the sectors considered for the TAP tunnel in BBT unit. The light gray zone surrounding the tunnel section indicates failure by tension and/or shear, and the

blue curves show the measured overbreak each 5 m along the tunnel axis in this particular sector

Figure 5 Infrastructure considered for the geomechanical analysis in relation with the main geotechnical units

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Table 5. Summary of preliminary permanent support recommended for the SCh Nº1 as derived from application of empirical methods.

Excavation B × H (m) Sector

Barton (1974) Palmstrom & Nilsen

(2000)

Hoek (2007)

Unal (1983) Hönisch (1985)

PatternLc (m)

Lc (m) Shotcrete Thickness (mm)

BBS BBC Lb (m) Lb / Lc (m) BBS BBC BBS BBC

DumpingChamber 24,3×8,8

Roof 1,3x1,3to1,7x1,7m;Shotcrete

120-150mm

1,7x1,7to2,1x2,1m;Shotcrete

50-120mm

7.5–8.1 5.8 5.6/9.74.1–14.2 6.3–11.2

100-150 100a150

Walls 2.4–2.6 4.4 N/A 50(min) 50(min)

StorageHooper 14,3×21,2 Walls


120-150mm

1,7x1,7to2,1x2,1m;Shotcrete50-90mm

5.7–6.2 4.0 5.2/7.4 3.8–12.5 6.0–9.8 50-150 50-100

ApronFeeder 9,2×10,8


90-120mm


2.8–3.1 3.2 N/A2.1–6.2 2.8–5.0

50(min) 50(min)

Walls 2.9–3.2 3.0 3.6/3.8 50-100 50(min)

CrusherChamber 16,8×43,6


150-250mm


90-120mm

5.2–5.6 4.4 4.5/6.7N/A N/A

50-150 50-100

Walls 11.7–12.7 5.3 8.5/15.3 150-200 150-200

LoadingHooper 17,0 Walls


90-150mm


5.2–5.7 4.6 4.6/6.8 3.1–10.0 4.5–7.9 50-150 50-100

B: SectionLength. H: SectionHeight. Lb: BoltLength. Lc: CableLength.

4 Three-dimensional numerical analysis of the crusher cavern excavation

Three-dimensional models implemented in the finite difference software FLAC3D (Itasca 2007) wereconstructed for the main infrastructure of the SCh Nº1 (see Figure 6). The three-dimensional modelsincorporated only the permanent support (with characteristics described in the next section) and theproposedexcavationadvance,coincidingwiththeminingdesignexcavation.

Thepurposeofthismodelwastoaccountfortheactualthree-dimensionalnatureoftheexcavationproblem;themodelallowedwalldisplacementsonthelargeexcavation,extentoftheplastic-failurezonearoundthewallsofthelargeexcavations,andtheperformanceofthepermanentsupporttobequantified—i.e.,theverificationoftheacceptabilitycriteriaintermsoffactorofsafetydescribedinSection3.Ingeneral,majorprincipalstress(s1)reaches60to80MPaintheupperpartofcrusherchamberandapronfeeder(seeFigure7a).Unconfinedstress(s3<4.0MPa)areobservedbelowofthefloorofthedumpingchamber(seeFigure7b).Also,amaximumdisplacementof4cmisobservedinthefloordumpingchamberaftertheexcavationofthecrusherchamber(seeFigure7c).Maximumdisplacementsof5cmareobservedintheintersectionof thecrusherchamberwallsandapronfeederandintersectionof loadinghooperandmainfeeder(seeFigure7d).


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Figure 6 Three-dimensional numerical model of the crusher cavern. The figure shows the 93 advance intervals considered for the excavation in different colors. The model, which incorporates only permanent support, was

constructed using the finite difference code FLAC3D —see Itasca (2007)

Analysis of results from these three-dimensional models allowed to conclude that the support (withcharacteristicsdescribedinthenextsection)satisfiestheacceptabilitycriterion—i.e.,afactorofsafetyof2.0forpermanentsupport.Figure8aand8bshowntheresultsforthedoublecablesinstalledintheroofofthecrusherchamberandthefinalexcavationofthemodel.

Thevaluesof loads resulting inpermanent liners (i.e., thevaluesof thrust,bendingmomentandshearforce) were recorded for each of the large excavations analyzed. The values of support loading wereplottedincapacitydiagramstoverifythatthefactorofsafetyvalueswerebelowadmissiblelimits—foradiscussiononthemethodologyinvolvingverificationofsupportusingcapacitydiagrams,seeHoeketal. (2008);Carranza-Torres&Diederichs (2009).For example,Figure8c represents capacitydiagramsforapermanentsupportofthickness0.3mintheapronfeederroofforthefinalexcavationofthemodel.Inbasicallyallthelargeexcavations,loadingintheproposedsupportanalyzedwiththecapacitydiagramapproachwasfoundtobewithintheadmissiblelimitsoffactorofsafetymentionedearlieron.

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Finally,toverifythesupportrecommended,awedge/blockanalysiswasperformedbasedonthestructuralinformationprovidedinTable2usingkeyblockteory(Goodman&Shi,1985)andthesoftwareUnwegde(Rocscience2009).Figure9showstheapplicationofkeyblocktheorytothedumpingchamberroof.Allthekeyblocksintheroofsandwallsforallthelargeexcavationswereverified.

a) b)

c) d)

Figure 7 Representation of the results in the model sliced by a cross section plane located at the midpoint of the apron feeder. Represented are: a) major principal stresses after crusher chamber excavation, b) minor

principal stresses after crusher chamber excavation. c) displacements after crusher chamber excavation and d) displacements for the final excavation model


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a) b)

c)

Figure 8 Support performance for some of the main large excavations. a) Axial force for cables in the crusher chamber roof at the end of excavation. b) Resulting axial force for cables installed in the crusher chamber at the end of excavation (yielding load, pre-stressing load and factors of safety of 1.5 and 2.0 also are shown). c)

Capacity diagrams for shotcrete liner in apron feeder at the end of excavation

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Figure 9 Dumping chamber section showing maximum removable blocks for each JP superimposed on the stereographic projection of the JPs. To the upper left, the analysis for the roof with Unwedge program to verify

the support recommendations for the JP 1011 block (shaded in red)

5 Proposed crusher cavern support

Basedonexperienceindesignoflargeexcavationssupportandontheapplicationofempirical,analyticalandnumericalmodelsdescribedinprevioussections,forthelargeexcavationscrossingthegoodqualityrockmassunits(BBS,BBCandBBTunits),permanentsupportwith thecharacteristicssummarizedinTable6wereproposed.Thetemporarysupportconsistsmainlyofrockbolts(andwiremesh)withquiteuniformcharacteristicsformostofthelargeexcavations.

Forthelargeexcavations(dumpingchamber,storagehooper,crusherchamberandapronfeeder),inwhichhighstressconfinementintherockmasscouldtranslateintogroundinstability,heavierpermanentsupportproposed.

Table 6 Summary of permanent support proposed for the Crusher Cavern SCh Nº1

Excavation B (m) H (m) SectorCables*

ShotcretePattern Length (m)

DumpingChamber 24,3 8,8

Roof 1,0x1,0 10 H30t=300mmWalls 2,0x2,0 8

StorageHooper 14,3 21,2 Walls 1,5x1,5 14 H30

t=150mm

ApronFeeder 9,2 10,8Roof 1,0x1,0 14 H30

t=200mmWalls 1,5x1,5 12

CrusherChamber 16,8 43,6

Roof 1,0x1,0 15 H30t=300mmWalls 1,5x1,5 15

LoadingHooper 17 - Walls 1,5x1,5 12 H30

t=200mmB: SectionLength. H: SectionHeight.(*) Allthecablesaredoublessinglestrandoff=15.6mm,additionallyasteelwiremeshC443wasrecommended.


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6 Conclusions

ThispaperhasdescribedseveralaspectsoftheprocessofdeterminingthepermanentsupportforthelargecrushercavernSChNº1attheNewMineLevelprojectatElTenientemine.Thecrushercavernistobeexcavatedinarockmassofgenerallygoodquality(BBS,BBCandBBTunits),inamediumtohighstressenvironment.

Thesupport recommendedforcrushercavern,asdescribed in thispaper isnotdefinitiveandwillhavetobeoptimizedonceconstructiontechniquesareselectedinafuturephaseofdesignoftheundergroundinfrastructure.

Thecharacteristicsofthesupportrecommendedforthecrushercavernarebasedontheassumptionoftherockmassisdryandthatdynamicloadingonpermanentliner(e.g.,duetoblastingduringfuturecavingoperations)isneglected.Also,asensitivityanalysisforHoek-Browmparameters,ubiquitousmodelandan incrementof the in situ stresswasconsideredand theproposedsupportwas found tobewithin theadmissiblelimitsoffactorofsafetymentionedearlieron.

Intermsofpermanentsupport,consideringthecriticalimportanceofcontinuousoperationofthecrushercavernforatleast50years,apermanentconcretelinerofatleast0.3metersthicknesswasjudgedappropriate.Thispermanentsupportthicknesswasestablishedbasedoncurrentpracticeusedincivilengineeringtunnelprojects,andnotbasedontheempiricalmethodsdescribedabove.

Acknowledgements

TheauthorswouldliketothankCODELCOandinparticular,Mr.PabloVasquezChiefoftheEngineeringDepartmentofVP-NNMProject,forgrantingpermissiontopublishthispaper.

References

Barton,N,Lien,R&Lunde,J1974,Engineeringclassificationofrockmassesforthedesignoftunnelsupport.6(4),189–236.

Barton,N2002,‘SomenewQ-valuecorrelationstoassistinsitecharacterizationandtunneldesign’,Int.J.RockMech.&Min.Sci.,vol.39,Nº2,pp.185-216.

Bieniawski,ZT1989,EngineeringRockMassClassifications,JohnWiley&Sons.

Bieniawski, ZT 1993, ‘Classification of RockMasses for Engineering: The RMR System and FutureTrends’,ComprehensiveRockEngineering,(J.A.HudsonEd.),vol.3,pp.553–573.PergamonPress,Oxford.

Carranza-Torres,C&Diederichs,M2009,‘Mechanicalanalysisofacircularlinerwithparticularreferencetocompositesupports.Forexample,linersconsistingofshotcreteandsteelset’,TunnellingandUndergroundSpaceTechnology,vol.24,Nº4,pp.506–532.

Deere,DU1963,‘Technicaldescriptionofrockcoresforengineeringpurposes’,RockMech.Eng.Geol.,vol.1,pp.18-22.

DiederichsM 2013b, ‘SummaryReport of Findings andRecommendationsBased onNNMTechnicalAdvisoryMeetingsElTenienteNewMineTunnelProject’,21-25October2013.

Floody,R2000,‘EstudiodeVulnerabilidadGeológico-GeotécnicadeChimeneadeBrechaBraden.FaseI:GeologíaComplejodeBrechasBraden’,ReportGL-044/00,SuperintendenceofGeology,DivisionElTeniente,Codelco.

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Goodman,R&Shi,GH1985,‘Blocktheoryanditsapplicationtorockengineering’,PrenticeHall.USA.

Grimstan, E & Barton 1993, ‘Updating the Q-system for NMT’, Proceedings Int. Symp. On sprayedconcrete–ModernUseofWetMixSprayedConcreteforUndergroundSupport,Fagemes,(Kompen,OpsahlandBergeds),Oslo:NorwegianConcreteAssn.

Hoek,E1994,‘Strengthofrockandrockmasses’,ISRMNewsJournal,vol.2,Nº2,pp.4-16.

Hoek,E&Brown,ET1997, ‘Practicalestimatesof rockmassstrength’, InternationalJournalofRockMechanicsandMiningSciences,vol.34,Nº8,pp.1165–1186.

Hoek,E,Carranza-Torres,C&Corkum,B2002,‘Hoek-Brownfailurecriterion–2002edition’,NARMS-TAC2002,MiningInnovationandTechnology,(H.R.,W.Bawden,J.Curran,&M.TelesnickiEds.),Toronto–10July2002,pp.267–273.UniversityofToronto.(AvailablefordownloadingatHoek’sCorner,www.rocscience.com).

Hoek,E&Diederichs,MS2006,‘Empiricalestimationofrockmassmodulus’,InternationalJournalofRockMechanicsandMiningSciences,vol.43,Nº2,pp.203–215.

Hoek,E.Kaiser,PK&Bawden,WF1995,SupportofUndergroundExcavationsinHardRock.Rotterdam:Balkema.

Hoek,E2007,PracticalRockEngineering,coursenotesavailableonlineathttp://www.rocscience.com.

Hönisch, K 1988, ‘Rockmassmodelling for large underground powerhouses’, NumericalMethods inGeomechanics,EditedbyG.Swodoba,Innsbruck,Austria,vol.3,A.Balkema,Rotterdam.

Itasca2007,FLAC3D.FastLagrangianAnalysisofContinua.Version3.1.User’smanual.(www.itascacg.com).Minneapolis,Minnesota.

JenningsJE1970,Amathematicaltheoryforthecalculationofthestabilityofslopesinopencastmines.PlanningOpenPitmines.ProceedingsofInternationalSymposium(ed.PWJVanRensburg),Johannesburg,pp.87-102.Balkema,CapeTown.

Karzulovic,A2000,EstimacióndelaspropiedadesgeomecanicasdelasbrechasqueconformanlapipaBraden,TechnicalNoteNºDT-CG-00–04A.Karzulovic&Asoc.Ltda.Chile,submitedtoDivisionElTeniente,Codelco.

Palmström,A&Nilsen,B2000,EngineeringGeologyandRockEngineeringHandbook.NorwegianRockandSoilEngineeringAssociation.

Pariseau,W2007,Designanalysisinrockmechanics,Taylor&Francis/Balkema.

Pereira,J,Russo,A&Karzulovic,A2003,‘GeomechanicalPropertiesoftheBradenBrecciasatElTenienteMine,Chile’,SoilandRockAmerica2003.12thPanamericanConferenceonSoilMechanicsandGeothecnicalEngineering.39thU.S.RockMechanicsSymposium.Cambridge,EEUU.22-26june2003,pp723-727.

Rocscience2009,Unwedge.UndergroundWedgeStabilityAnalysis,Version3.0,Toronto,Canada.

Rocscience2010,Phase2,FiniteElementAnalysisforExcavations,Version7.0,Toronto,Canada.

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VCP 2010b, ‘Caracterización geológica y geotécnica Sala deChancadoN° 1 - Fase II’,T09E205-F1-VCPNNM-36000-INFGO04-3100-002-P. Feasibility Stage NLM project, Codelco. (inspanish)

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Design of 3-D models in mining

E Córdova Codelco, ChileP González, Codelco, ChileC Pardo Codelco, Chile

Abstract

The importance of planning and designing and optimized model form its conception, has the advantage that the model is developed and thought from the beginning to be interchangeable between software used in the analysis, making the model transfers easier among the different applications (AutoCAD, Vulcan, 3D Studio Max, Mine2-4D, Abaqus, etc.), while minimizing the information re-interpretation time. The process of learning and understanding the adaptability of the different applications must consider an initial trial period to verify the interactions between them. The future of modeling is in being able to develop an interactive unified model that can easily be adapted and transferred, maintaining an acceptable resolution for the different types of analyses required. In the search of optimizing the creation of models a PLM (Product Lifecycle Management) philosophy can be adopted and modified to establish a MLM (Modeling Lifecycle Management) philosophy that can assure that the different models created are related between them, having parent models that serve as a foundation to create detailed models (child models).

1 Introduction

Amodelcanbethoughtasarepresentationofreality,thatcouldvaryfromaverysimpleandbasicmodeltoadetailedandcomplexone.Astherequireddetailincreasesandmorecharacteristicsfromrealityareneededaspartofthemodel,thecomplexityandtimerequiredtodevelopitalsoincreasessubstantially.

Theknowledgeofthedifferentsoftwaretobeusedintheanalysesandthewaythemodelisconceptualizedfromthebeginningcanplayanimportantroleinthefinalefficiencyofthemodelingprocess.Sincethesamemodelmightbeusedwithdifferentapplicationsthatsometimesdonotworkseamlesslywitheachother,itisimportanttospendtimefiguringoutwhatisthebestwaytodevelopamodelandwhatisalsothebesttechniquetotransfertheworkfromoneapplicationtotheother.

Arobustmodelisbuiltfromthebeginningbyunderstandingtheprosandconsofamodel,thewaytheinformation is transferredbetweenapplications, thechanges required tomakeanavailablemodelworkwhenissenttoadifferentapplication,whileoptimizingthemodelingprocessbyavoidingtheduplicationofwork.

Aunifiedmodelshouldhaveaninherentcombinationofcomplexityandsimplicitywheretheresultcomesfromtransformingsomethingdetailedintosomethingsimplethatcapturesthemostimportantaspectsfromrealitywhilesimplifyingthepartsthatmightnotbeneededintheanalyses.Asanexample,dependingontheresolutionoftheproblembeinganalyzed,atunnelmightbeasimplifiedregularshape(likeasquareof4moneachside)oramorecomplexprimitive,withasquareshapeatthebottom,andtheuppersidecurvedwithaseriesofpointsthatreallyrepresenttheshapethetunnelwillhaveintheend,amorerefinedapproachtothisdetailwouldbetohaveactuallaserscansofthetunneljoinedtogethertosimulatetherealshapeatacertainintervalofmeters.

Astheshapesgetmoreandmorecomplex,usuallythenumberofnodesorpointsinvolvedalsoincreases,thisproducesanincreaseinthenumberoftrianglescreatedtoformatriangulationwiththe3-Dinformation,andifthesamevolumebeingdeveloped,isusedinafiniteelementsapplication,thenumberofelementsalsoincreases.


502

Thebestapproachistoplanaheadandunderstandthefinalpurposeofthemodel.

2 Model conception

Modelsusuallystartonpaperandarefirstrepresentedintwodimensionalviews(2-D)thatarebuilt todevelopasimplerepresentationonofthemainaspectsofthefinaldesign.

Figure 1 Representative vertical section of a Crinkle-Cut mining method combined with conventional undercut

Figure 2 North-South Section of a Crinkle-Cut method and the connection to an existing cave

Figure 3 Plan view of the production level

Thefirst tasktobuildthemodelistotakewhatisin2-Danduseit tobuilda3-Drepresentationofit.The“basicmodel”canconsistof themaingeneralareas tomodel indetail just toprovidea feelingofhoweverythingshouldlookin3-Dintheend.Tobuildthefirstmodeldifferentsoftwarepackagescanbeused,fromthemostcommoncommercialpackagesuchasAutoCAD(withincreased3-Dmodelingtools

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in its latestversions), tomoreadvancedandspecializedminingpackages suchasDatamine,Gemcom,Minesight,orVulcan.

Aplanviewofthedifferentareastomodel(productionandundercutlevel)isusefultodefinethemainextentsfromthemodel,andtovisualizetheinformationthatsectionsin2-Dcan´tcapturesuchasspacingbetweenthedevelopments,andshapeoftheundercutandproductionlevel.

3 Three dimensional modeling

Beforestartingtodevelopthe2-Dinformationintoa3-Dmodel, itmustbedecidedtheendresult thatisrequiredforthemodel.Dependingonthefinalresultneeded,theconceptualizationofthemodelwillchange.

A robustmodelwill try to combineandplan fordifferentoptions and future requirements, taking intoaccountthatiftimepermitsit,itismucheasiertorebuildasimplermodelfromamoredetailedonethantheotherwayaround.

Building a general model of the area as a visual model is a good practise that will provide valuableinformationonwheretofocuswhenbuildingamodelwithmoredetails.

3.1 Visualization models

Visualmodelscanbethoughtofasamodelthatisbuilttoplaceitin3-Dwhereeverythingcanbevisualizedtogiveanimpressionofhowitlooksinreality.Thesemodelsareusuallyfocalizedintheexternaldetailofthegeometriesandbasedinachievinganoptimalexternallook.Thefocusontheexternallookssometimesmeansthatnotenoughcareistakentoobtainconsistentgeometriesandsolidsthatcanbeeasilytransferredwithouterrors.

Figure 4 Isometric view of an Autocad model of the Crinkle Cut and conventional area

3.2 Time dependent models (TDM)

Thesemodelsmightbeavariantofthevisualmodelsandaredifferentinthesensethatgeometriesandsolidsarecutorsectionedatcertaintimeintervals.Asanexampleinamodelatamonthlyresolution,one


504

developmentasadriftcouldconsistofdifferentpiecesthatrepresentthemonthlyadvance.Thismodellingapproachisusuallyusedtocheckifconsiderationsoftheminingmethodusedarebeingcorrectlyfollowed,toseeifshowndelaysarerelatedtootheractivities,andtounderstandaprojectevolvesovertime.InaTDMalltheactivitiesareseparatedandtheneachactivityissectionedonamonthlybasis,allowingtheanalysisofindividualactivitiesoragroupofthemunderacertaintimeframe.

Figure 5 Time dependent model of the area at monthly resolution

3.3 Design Models (DM)

Thesemodelsarebasedinachievingareasonableoverallgeometrythatwillrepresentthemainaspectsof the area under study.The end use of thesemodels are engineering andmodelling applications likeBoundaryElement(BEM)orFiniteElement(FEM),wherethequalityofthegeometriesandvolumesplayanimportantroleintheexpectedresult.Thecreationofthesemodelsshouldtakeintoaccountthefollowingaspects:

• Typeofgeometriesanditscomplexity:shouldlaserscannedtopographybeusedtoanalysedevelopmentsthatlie400mbelowintothegroundormost

• Solidsdefinition:solidsshouldcomplytocertainstandardstoensurethebestcompatibilitybetweenapplications.Solidsshouldatleastbeclosed(havingallitstrianglesconnectedcreatingaclosedshell),consistent(makingsuretherearenooverlappingtrianglesoroneedgeconnectedtomorethantwotriangles),andwithoutcrossingtriangles.

• Contacts:formostengineeringapplicationsthecontactbetweendifferentsolidsmustbeconsistenttoavoidhavingonesolidoverlappinginthespacewithanotherbody.Specialcaremustbetakenwhendoing“boolean”operationswithintwogeometriestomakesurethattheoriginalgeometriesandthebooleanresultcomplieswiththebasicsolidgeometryquality.

• Intersections:creatingcleanintersectionsitisveryimportanttomakesurevolumesarenotcountedtwiceinspaceandtomakesureerrorsarenotpresentwhenbuildingelementsinsidethesoldgeometry.

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Figure 6 Crinkle-Cut geometries to be used in a FEM model

4 Unified models

TheunifiedmodelpresentedinFigure6wasbuiltwithaspecializedminingpackage.Themainreasonforusingathistoolwasthatallthegeneralinformationspeciallygeologicalandstructuralwasbeingalreadydevelopedwithit,soitwasonlynaturaltousetheundergrounddevelopingtoolsalreadyavailableintheapplication.

AfewsolidgeometrieswereimportedfromotherCADapplications,buttheywereonlyusedasabaseforbuildingafinalgeometrywithin themining tool.Themainreason torebuild thegeometrieswas tomakesurethatthenewvolumeswouldfullycomplywiththeminingpackagedefinitionsforconsistentgeometries(closed,notcrossing,edgecompliance).

Themainconcernatthisstageistomakesurenottogointogreatdetailsifthesedetailsaregoingtobeactuallylostintheend.

Agoodwaytoseeifthemodelingwillbeeffectiveistocomparetheoriginalmodelresolutiontotherequiredresolutioninthefutureanalyses,asanexampleamodelcanhavethegreatestdetailintheintersectionsandthedevelopmentsectionscouldbeperfectlyshapedtocorrespondasmuchaspossiblewithreality,butifintheendintheFEMmodelthesmallestelementwillbeofoneortwometers(becausetheanalysisisnotcenteredinthedevelopments),allthisdetailwillbelostandsimilarresultscouldbeachievedwithsimplershapes(Figures7and8).

Asthemodelingprogressesandmoredetailareneeded,“child”modelscanbecreatedusingtheoriginalsimplified“parent”models.Thistechniquebuildsa“family”ofmodelsatdifferentresolutionsdependingontheirsimplifiedpredecessors.

5 Model application

ThefirstmodelgeneratedwasfocusedonexplainingoperationalchallengesencounteredintheareabytheLHDoperatorandtheremovalofmaterialfromtheflatandinclinedundercutintheareawheretheCrinkle-Cutmethodwastested(Figure9).


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Figure 7 Detailed modeling of crossing intersections

Figure 8 Detailed modeling of crossing intersections

Figure 9 Side view showing the LHD position with respect to the flat (red) and inclined (yellow) undercut

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AlineofdrawbellsinthewestareawasisolatedtoshowtheuseadrawbellwiththeHendersonlayout,toassuretheconnectiontotheexistingcavefromtheSoutharea(Figure10).

Figure 10 NS Section showing the different drawbells used in the west area of the model

Thedifferenttypesofcrownpillars(CP)associatedtotheConventionalandCrinkle-Cutundercutweremodelledin3-DtoestablishtheapproximatevolumeofCPleftbyeachvariant.

Figure 11 Different drawbells and crown pillars used in the model

Themodelwasalsousedtoshowthepositionoftheundercutwithrespecttothedrawbellincorporationatcertaintimes(Figure12).

Figure 12 Position of the undercut front v/s drawbell incorporation at a certain period in a time dependent model


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6 Finite element models

Whenbuildingsolidsfornumericalmodels,specialcaremustbetakentoensurethemodelbeingcreatedisveryconsistentintheareaswherethedifferentpiecesaretouchingeachother.Theessenceofthefiniteelementmodelisthatthesolidswillbefilledbysmallerelements,suchastetrahedrons,andtheywillbeallinterconnectedbetweenthemthroughthenodesintheelements.

Incomplexareaswherebooleanoperations(differenceorintersectionbetweensolids)tookplace,athinlayerofthesolidswillcreatecomplexelementsthatmighthaveanearzerovolume,orverylongedgesthatwillcreatedistortedminorelements,sometimesincreasingtheoverallnumberofelementsinthemodel.Nodesbetweentwodifferentsolidsmustconnectwitheachother,aligningtheelementsbetweenthem.

Infiniteelementapplications,arulethatmightcomeoutveryoftenisthat“yougetwhatyoupayfor”,dependingonthespecifictaskonhand,ifaverycomplexmeshmustbebuiltforthemodel,sometimesapre-processormustbeusedtocreatesuchamesh.Thetimespentdevelopingamodelandmeshingitalltogethermighttakesometimes50%ormoreofefforttodoananalysis.

Themodelin3-Dcanbesimplifieddependingonthescaleofthingsthatareneededtoanalyze.Ifamodelisbuilttoanalyzethestabilityof20mbenchesinanopen-pit,asmallscaleofelementof10cminthenearsurfaceofareasthatarenotofinteresttotheanalyseswillcreatemillionsofelementsthatwillonlyslowdownallthecalculations.Theresolutionmustbeincreasedalwaysthinkingabouttheproblemandaccuracyneededontheresults,whiletakingintoaccountnottocrowdthemodelinareaswherenodetailisneeded.

Thenumberofelementscanalsobemanagedontheinternalgrowthofeachsolid,thismeansthatasolidcanhaveacertainsizeofelementsonthesurface,andasmoreelementsarecreatedinsideofthesolid,andgetawayfromthesurface,theystartincreasinginsizetooptimizetheoverallnumberofelementsinthemodel.

7 Finite element model characteristics

Themodeldevelopedconsistsofanareaof5.3x5.3km,andatotalelevationofaround3km(Figure13)Thesolids insideof themodel representacentralpipeofadifferentmaterial than thehost rock,andasubsidencecavesurrounding thepipe(Figure14).Themain lithologiesaddedto themodelbesides thebaserockofthemodelaredacite,tonalite,andfourseparatedioritebodies(Figure15),wherethedacitesurroundspartofthepipe(thatappearstranslucentinFigure15),andatonaliteonthesouthside.

Figure 13 View of the area modeled in Abaqus

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Figure 14 View of the subsidence cave and the central pipe

Figure 15 Top view of the main lithology of the model

Acentraldioriteintersectstheplacewheretheminedesigntobestudiedisplaced,theminedesigntakesanapproximateareaof250x250m(Figure16).


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Figure 16 Top view of where the mine development is located intersected by a central diorite

Theminedevelopmentsbuiltintothemodelrepresentaproductionlevelwithconnectionsforthedrawbells,anundercutlevel(UCL)withacrinkle-cutundercutdesign,anapexlevelontopoftheUCL,andaseriesofdrawbellsbetweentheproductionandUCLlevel.

Figure 17 View of the mine design elements of the model

8 Conclusions

Planningarobustmodelallowstheuseofthesamebasemodeltogeneratetheneededgeometriesforthedifferentanalysesrequired(generalvisualization,timedependent,numericalmodeling,back-analysis).

Modelsmustbecentralizedanddevelopedhavinginmindtherequiredresolutionforthedifferentsub-modelsthatmightbegeneratedfromthebasemodel.Arobustmodelwillcapturetheessentialdetailsinthegeometrieswhileoptimizingthemforeaseoftranslationformoneanalysispackagetotheother,withoutlosingtheessentialcharacteristicsinthem.

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Thegenerationofabasemodelusingonedesignpackagewillservetominimizetheuncertaintygeneratedwhendevelopingmodelsfromdifferentsources,andoptimizethetimerequiredtocreatesubmodelsusedindifferentanalyses.

Finiteelementmodelingcanbeatimeconsumingtaskespeciallyifthegeometriesarecomplexandnotoptimizedtospeedupthemodelingprocess.

Anoptimized set of geometries can be almost automaticallymeshedwithout running into any kind oftrouble,allowingthemodelertofocusinothertasksoftheprocess,andreachingtheresultsinashortertime.

Amodelermustalwaysthinkonhowthesolidsthatarebeingcreatedwillinteractinthefuture,takingspecialcareonwhatisbeingsimplifiedordesignedatahigherresolution,alsopreservingtheintegrityoftheoriginaldataincasethemodelneedstobeupdatedandre-createdinthefuture.

Themodelingprocesswillbeoptimizedinthefuturebyhavingmodelsthatcanbetraceableandlinkedbetweenthem,sochangesareupdatedautomaticallybetweenthesolidsandtheirinteractingmeshes.

Thefuturemodelingphilosophyisbasedinthecorrectmanagementofthelifeofthemodels,wherethemodelingprocessisdividedintoacoherentstructurewheretheinteractionsbetweentheelementsandtheirpropertiesarewelldefined.

Theideaistoapplywhatothercompanies(aerospaceandautomotive)alreadyuseandhavelearnedtobuildcomplexmodelswiththousandsofelementsinteractingbetweenthem,andtoexpandthisphilosophytominemodels.Intheend,themaindifferencebetweentheaerospaceandautomotivemodelsandtheonesbeingdevelopedinminingisthesizeoftheelementsbeingcreated,wheremostofthetimeinmining,largeglobalmodelsrangingfromkilometerstometersaredevelopedfirst,andsub-modelsaredoneinamuchmoredetailedscale(cm)forveryspecificareasoranalyses.

Themodelingphilosophyallowstherapiddevelopmentofdifferentfiniteelementanalysestounderstandandstudyproblemssuchasmacro-sequencesdefinition,undercuttinggeometrieseffect,undercutadvance,crown-pillars,andexpectedstressdistributionfromarangeofundercuttingdesigns.

References

Beck,D 2012, ‘Applications of RockMechanics’,Geotechnical EngineeringCentre Presentation,TheUniversityofQueensland,September2012.

Córdova, EA, Constanzo, HE 2013, ‘Optimized Design of Models in Mining’, Mine Planning 2013Conference,24-26July,Santiago,Chile.

Córdova,EA2012,‘3-DModellingoftheCrinkle-CuttestinTTE4SouthExtensionArea’,SIN-I-005/2012,InternalReport,DivisionElTeniente,Codelco,Chile.

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Preconditioning

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Study of the impact of rock mass preconditioning on a Block Caving Mine operation

C Castro IM2-Codelco, ChileF Báez Codelco, ChileE Arancibia Codelco, ChileV Barrera, Im2-Codelco, Chile

Abstract

Nowadays, Codelco´s underground mines that apply caving methods are located mainly within primary ore. This extremely hard environment needs the application of rock mass preconditioning techniques (PC), to improve its caveability and the extraction process. To achieve this objective, two main technologies are applied to the rock mass: hydraulic fracturing (HF) and confined blasting (CB).

In this work, six variations of the preconditioning technologies are simulated to study their effects on the mine operation: HF with a distance of 0.5 m between fractures, HF with 0.75 m between fractures, HF with 1.0 m between fractures, HF with 1.5 m between fractures and two applications of both techniques (HF + CB): HF with 1.0 m between fractures + CB and HF with 1.5 m between fractures + CB. The effect of each variation is simulated for the predicted secondary fragmentation, hang-ups and over sizes at the draw points, productivity and operation costs. The results are compared with a base case without preconditioning.

1 Introduction

Since 1999, Codelco has been developing preconditioning techniques (PC) for the primary ore of itsundergroundmines,Andina,SalvadorandElTeniente.Twomaintechnologieshavebeenappliedtotherockmass:hydraulicfracturing(HF)andconfinedblasting(CB)orcombinationsofboth.

The conclusion obtained is that there are benefits in terms of the seismic magnitude and frequency,caveability, draw rate, fragmentation, hang-ups and oversize occurrences at the drawpoints.However,thefinalfragmentationisnotoptimal,thusCodelcodecidedtobeginaresearchefforttodeterminehowtoimprovethecurrentpreconditioningtechniquestoobtainabetterfragmentationatthedrawpoints.

Themainobjectivesforthisstudyare:

• To perform a comparative assessment of the preconditioning improved techniques for thefragmentation,flowinterruptioneventsfrequency,productivityandoperatingcosts.

• To develop a tool to make technical/economical comparisons in different scenarios for theapplicationofthePC-improvedtechniques.

2 Methodology

Sixvariationofthepreconditioningtechnologiesaresimulatedtostudytheireffectsonthemineoperation:HFwithadistanceof0.5mbetweenfractures(hereinafter,HF0.5),HFwith0.75mbetweenfractures(HF0.75),HFwith1.0mbetweenfractures(HF1.0),HFwith1.5mbetweenfractures(HF1.5)andtwo


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applicationsofboth techniques:HFwith1.0mbetween fractures+CB (Mix1.0) andHFwith1.5mbetweenfractures+CB(Mix1.5).Theeffectofeachvariationissimulatedforthepredictedsecondaryfragmentation,hang-upsfrequencyandoversizeatthedrawpoints,productivityandoperatingcosts.Theresultsarecomparedwithabasecasewithoutpreconditioning.

ThemodelconsideraLHDproductionmoduleofatypicalblockcavingmine,witheightdrawpointsandadumpingpointattheproductiondriftendandanorepasswithagrizzlylimitingthesizeofrocksupto1.4mindiameter.Twocasesarestudied:unloadingtoanorepassandunloadingtoasizercrusher.Inbothcases,newtechnologiesforrockoversizereductionatdrawpointsareconsidered.

TheprimaryandsecondaryfragmentationcurvesfortheHFandbasecasesweresimulatedusingtheBlockCavingFragmentationsoftware(BCF).ThegeotechnicalandgeologicalinputparameterswereobtainedfromarealproductionareafromCodelco’sAndinamine.

ThefragmentationcurvefortheCBcasewasobtainedfromaJKSimblastsoftwaresimulation.TherealinputparametersforthepreconditionedprojectedareainAndinaminewereconsidered.

Formixedcases(HF+CB),acompositefragmentationcurveisconstructedbyconsideringtheinfluenceofthevolumeoftherockmassforeachPCtechnique(inthiscase,68%forCBand32%forFH).

Fromthe fragmentationcurves,flowinterruptioneventsareobtained.Then,productionsimulationsareperformedforeachcaseofpreconditioningtechnologies.

Finally,theoperatingcostsarecalculatedconsideringdevelopment,LHDextraction,secondaryblasting,orepassing,haulage,mineservices,maintenanceandrepair,crushing,beltconveyorandmanpower.

3 Data

3.1 Fragmentation

The fragmentation curves are in order from finer to coarser as expected from the hypothesis for thealternativePCtechniques.FH,withtheshortestdistancebetweenfractures(0.5m)hasthefinestfragmentsforthehydraulicfracturingcases,themixedcaseshavethefinestfragments,whilethebasecaseisthecoarsest(Figure1).

Figure 1 Fragmentation curves for preconditioning technologies alternatives

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Totransformthevolumeofparticlesinm3tolengthinm,theparameterslistedinTable1wereconsidered.

Table 1 Transformation factors

Density 2.65 (t/m3)

Formfactor1 1.00

Formfactor2 0.85

Formfactor3 0.75

Then,thelengthoftheparticleLinmwasobtainedusingtheformula:

V(m3)=L(m)3*1*0.85*0.75

WhereVisthevolumeoftheparticle.

3.2 Definition of flow interruption events

3.2.1 LHD dumping to an ore pass

Highhang-upoccursatthetopofthedrawbell,obstructingthedrawpointandstoppingthenormalflowofore.Itcorrespondsto100%oftherockswithsizessmallerthan4.65m(64.11m3).Thehigherareaofthedrawbellis173m2.

Lowhang-upoccursatthebottomofthedrawbellanditalsostopstheflowofore.Itcorrespondsto50%oftherockswithsizesbetween2.37mand4.65m(8.50m3upto64.11m3).Thelowerareaofthedrawbellis45m2.

Bigbouldercorrespondsto67%ofrockswithsizesbetween1.4mand2.37m(1.75m3upto8.50m3)and50%oftherockswithsizesbetween2.37mand4.65m.Itstopstheflowoftheore.

SmallboulderisarockatthedrawpointabletobemovedtoanotherplacebytheLHD.Itcorrespondsto33%oftherockswithsizesbetween1.4mand2.37m.

AsevencubicyardLHDisconsidered,asshowninFigure2.

Figure 2 Flow interruption events, LHD to ore pass case


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3.2.2 LHD dumping to a Sizer Crusher case

Inthiscase,thedefinitionofoversizeisincreased,wherethemaximumsizeoforethesizeracceptsis1.8m.

3.3 Simulation area

The production of eight draw points was simulated using heuristic techniques considering two cases:LHD(7yd3)unloadingtoanorepassandLHDunloadingtoasizer.Newtechnologiesforthesecondaryreductionofoversizesareconsidered:aboulderbreakingequipmentandahang-upsbreakingequipment,bothconceived,designedandconstructedbyCodelco.

Theinfluenceareaofeachdrawpointis13mx17m(221m2),andthetotalareais2210m2,consideringtheorepassorsizerareaatthedumpingpoint.Figure3showsthesimulatedextractionmodule.

Figure 3 Extraction module simulation

4 Results

4.1 Flow interruption events

Figure4and5showthefrequencydistributionofflowinterruptioneventsatthedrawpoint,innumberofeventsfor1000tonnesoforepassing,forthecaseofLHDdumpingtotheorepassandthecaseofLHDdumpingtoasizercrusher.

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Figure 4 Flow interruption frequency, LHD to ore pass option

Figure 5 Flow interruption frequencies, LHD to sizer crusher option

4.2 Productivity

Figure6and7aswellasTable2and3showstheproductivitiesintonnesperdayforeachtechnologyanddumpingoptions.Thedifferencesinpercentagesareobtaineduncomparedtothebasecasewithoutpreconditioning.

Figure 6 Extraction module productivity, LHD to ore pass case


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Table 2 Extraction module productivity comparisons, LHD to ore pass

tpd/module Rate (tpd/m2) Dif.(%)CASEBase 1.185 0,54 -

HF1.50 1.577 0,71 33%MIX1.50 2.093 0,95 77%HF1.00 2.480 1,12 109%MIX1.00 2.506 1,13 111%HF0.75 2.756 1,25 132%HF0.50 2.964 1,34 150%

Figure 7 Extraction module productivity, LHD to sizer crusher case

Table 3 Extraction module productivity comparison, LHD to sizer crusher case

tpd/module Rate (tpd/m2) Dif (%)

Case

Base 1.360 0,62 -HF1.50 2.777 1,26 104%MIX1.50 2.907 1,32 114%HF1.00 2.984 1,35 119%MIX1.00 2.901 1,31 113%HF0.75 2.955 1,34 117%HF0.50 2.928 1,32 115%

4.3 Costs

InTable4, theminingcostisbrokendowninitsdifferentitemswithvaluesfortheconventionalpanelcavingmethod(basecase).Thetotaloperatingcostis8.86US$/t.

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Table 4 Operating cost itemization for the base case

Mine Cost Value Units

Development 2,42 US$/tonExtraction 0,55 US$/ton

SecondaryBlasting 0,51 US$/tonOrepass 0,25 US$/tonHaulage 0,65 US$/ton

Mineservices 0,25 US$/tonMaintenanceand

repairs0,31 US$/ton

Crushing 0,30 US$/tonBeltconveyor 1,10 US$/tonWorkforce 2,53 US$/tonTOTAL 8,86 US$/ton

Figure 8 andTable 5 show the comparison of themining cost between the different scenarios for thedumpingtoorepasscase.

Figure 8 Mine Cost comparison, LHD to ore pass case

Table 5 Mine Cost comparison, LHD to ore pass case

Mine Cost Value Unit Dif. (US$/t) Dif.(%)HF0.75 8,53 US$/ton -0,33 -3,77%HF1.00 8,54 US$/ton -0,32 -3,65%HF0.50 8,58 US$/ton -0,28 -3,21%HF1.50 8,81 US$/ton -0,05 -0,61%Base 8,86 US$/ton 0,00 0,00%

Mix1.00 9,13 US$/ton 0,27 3,03%Mix1.50 9,19 US$/ton 0,33 3,68%

Figure9andTable6showthecomparisonoftheminingcostbetweenthedifferentscenariosfortheLHDdumpingtosizercase.


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Figure 9 Mine Cost comparison, LHD to sizer crusher case

Table 6 Mine Cost comparison, LHD to sizer crusher case

Mine Cost Value Units Dif. (US$/t) Dif.(%)

HF1.50 8,74 US$/ton -0,12 -1,39%

HF1.00 8,76 US$/ton -0,11 -1,21%

HF0.75 8,80 US$/ton -0,06 -0,72%

Base 8,86 US$/ton 0,00 0,00%

HF0.50 8,91 US$/ton 0,05 0,52%

Mix1.50 9,33 US$/ton 0,47 5,26%

Mix1.00 9,36 US$/ton 0,50 5,59%

5 Conclusions

SimulationresultsshowafragmentationcurvesorderfromfinertocoarserasexpectedforthealternativePCtechniques.FHhastheshortestdistancebetweenfractures(0.5m)andhasthefinestfragmentsforthehydraulicfracturingcases;themixedcaseshavethefinestfragments,whilethebasecaseisthecoarsest.Thedifferentcasesofalternativetechnologiesareorderedbythesizeofthefragmentationfromfinertocoarserasfollows:Mix1.0,Mix1.5,HF0.5,HF0.75,HF1.0,HF1.5andthebasecase.

Fortheflowinterruptioneventsfrequency,thelessfavourablecaseisthebasecase,withthehighestvalueforthisindicator.ThefrequencydescendsinthefollowingorderforHF1.5,Mix1.5,HF1.0,Mix1.0,HF0.75andHF0.5,thelatterwiththelowestvalue.ThemixedcasesimprovetheresultscomparedtotheHFcases,withshorterfrequencyforhang-upsandoversizes.Thehighhang-upoccurrencesarerareforeachoneofthestudiedalternativetechnologies.ThesetrendsarealsoobservedintheLHDtosizercase.

IntheLHDtoOPscenario,theproductivityshowsitssmallestvalueforthebasecase(0.54tpd/m2);thenthevaluesincreaseinthefollowingorder:forHF1.5,Mix1.5,HF1.0,Mix1.0,HF0.75andHF0.5withthelargestvalue(1.34tpd/m2),150%abovethebasecase.ThemixedcasesalsoimprovetheproductivitycomparedtotheHFcases.

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Fromtheitemizationoftheoperatingcost,weobservethatpreconditioningtechniques(withoutdistinctionbetweentheLHDdumpingtoorepassorsizercases)havehigherdevelopmentcoststhanthebasecase,withthehighestvalueforthemixedcases.However,thecostsassociatedtosecondaryblasting,orepassandmaintenanceandrepair,areextremelysmallerthanthebasecasevalues.Weconcludedthatthelowerfrequency of flow interruption events and higher draw rate of the preconditioning techniques, have animpactoverthementionedcosts,reducingthemupto50%.

Thecomparisonof theoperatingcosts for thepreconditioning techniques in theLHDtoorepasscase,indicatesthattheHFcasesvaluesaresmallerthanthebasecasevalues,withaminimumvalueof8.53US$/tforHF0.75andareductionof0.34US$/t(3.80%)comparedto thebasecase(8.86US$/t).Thelargestvaluesoftheoperatingcostareobtainedforthemixedcases,withthehighestvaluefortheMix1.5withanoperatingcostof9.19US$/t,0.33US$/tabovethebasecasevalue(3.69%).

Theseoperatingcosts trendsarealsoobserved in theLHDtosizercase,butwith largervaluesfor thisvariable, due to the higher development cost associated to the excavation this equipment needs for itsoperation.Inthiscase,thesmallestvalueisforHF0.75with8.74US$/t,withareductionof0.12US$/tcomparedtothebasecase(1.39%).TheMix1.0isthelessfavourablecasewithanoperatingcostof9.36US$/t,5.63%higherthanthebasecasevalue.

ThesmallervaluesfortheoperatingcostintheHFcasescomparedtothebasecaseareobtainedduetothesecondaryblastingandmaintenanceandrepairlowercosts.Themixedcaseshavehighercoststhanthebasecaseduetothehigherdevelopmentcostofthesealternatives.

Inbrief,forthesimulatedpreconditioningtechnologies,theHFwithadistanceof0.5mbetweenfracturesshowsthesmallestsizesforfragmentationamongstthehydro-fracturingtechniques,thelargestproductivityandthelowestfrequencyfortheflowinterruptionevents(hang-upsandoversizes).Thebasecaseisthelessfavourable,withthelargestfragmentationsize,thelowestproductivityandthehighestfrequencyforinterruptionevents.Ingeneral,themixedcasesshowthefinestfragmentationandlargerproductivity(drawrate)thanthecaseswithoutconfinedblasting.HFtechniquesshowthesmallestoperatingcostwhilethemixedalternativesarethemostexpensiveones,bothcomparedwiththebasecase.

References

Raña, F 2011, ‘Análisis de la Implantación de Nuevas Tecnologías en los Proyectos Subterráneos deCodelco’,IM2,Chapters3,4.


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Pre-conditioning with hydraulic fracturing — when and how much?

C Valderrama Pontificia Universidad Católica de Chile-IM2 Codelco, ChileF Báez Codelco, ChileE Arancibia Codelco, ChileV Barrera IM2-Codelco, Chile

Abstract

Rock mass pre-conditioning by means of hydraulic fracturing is increasingly used, generating several benefits in caving mines, one of which is the reduction of fragments size. However, what conditions of the rock mass pre-conditioning will be more useful? What is the optimum reduction of spacing between hydraulic fractures?

Through a numerical fragmentation assessment tool, we analyse how successful is pre-conditioning depending on two characteristics of the rock mass: the orientation and density of the pre-existing discontinuities. Furthermore, we examine the influence of the hydraulic fracture spacing (a design parameter) in the fragmentation. To analyse the importance of each parameter in fragmentation, a numerical factorial experiment was carried out.

General guidelines are given to know in which cases we could expect the largest reductions in fragment size, and when a reduction in the spacing of hydraulic fractures will have a better performance.

1 Introduction

The caving industry ismoving towards a next generation of deeper and bigger caving geometries andscenarios,wherehardrockmasseswithhighstressenvironmentsandlowdensityofdiscontinuities(orwithstrong infill)areencountered(Chitombo,2010).Theseunfavourableconditionsgenerateproblemslike,suchas:

• Increaseinseismicityduetothemorebrittlebehaviouroftherockmassandhighstresses.

• Slownessorstallingofcaving,whichcouldtoproduceareductioninproductionratesorairblasts.

• Increaseinfragmentssize(fragmentation),requiringasubsequentcomminution.

Particularly,fragmentationisfundamentalinthedesignoftheminelayout,dimensionsofdrawpointsandtheirspacing,andadditionally, it is important in thematerialhandlingscheme(Brown2007).Toavoidtheproblemsfromthenewsurroundingsincavemines,thepre-conditioningofrockmassesbyhydraulicfracturingisbeingused,andtheresultshavebeenpositive(Araneda&Sougarret2007).

However, the requirementsof the currentminingmake it necessary to studywhich is the limitofpre-conditioninginmoredetailandinwhichconditionsitismostfavourabletoapplyit.Ifwecanreducethesizeoffragments,theobvioussolutionistodecreasethespacingbetweenhydraulicfractures,however,howgoodisthissolution?

For all these reasons, we study how hydraulic fractures change the in-situ fragmentation for differentscenariosofpre-existingdiscontinuities.Thedifferentscenarioswereconstructedvaryingtheorientation

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anddensityofpre-existingdiscontinuitiesandthespacingofhydraulicfractures.Forallthevariableswechooselevelsorcases,andthefactorialcombinationofthesecasesgeneratesasetofscenariosforwhichwecarriedoutnumericalexperimentstocomparetheimportanceofeachoneofthefragmentationvariables.

Thenumericalexperimentconsistsonbuilding10DiscreteFractureNetworks(DFN)foreachscenarioandcalculatingtheirin-situfragmentation.Afterthat,weanalysethe80%and60%passingsize(P80andP60,respectively).Tomeasuretheuniformityofgradation,wealsoconsidertheuniformitycoefficientUC=(P60/P10)1/3.Withtheseresults,wepresentgeneralguidelinesabouttherockmassconditionsinwhichhydraulicfracturingpre-conditioninghasbetterresults.

The paper is organized as follows: in Section 2 we describe the methodology, indicating the mainassumptions,thecasesthatwereconsideredandhowthefragmentationcurveswereobtained.InSection3,weshowandanalysetheresults,toconcludewithSection4,wheretheconclusionsandadiscussionaboutthefuturetrendsarepresented.

2 Methodology

Thisstudytriedtobeconceptual;therefore,thecharacteristicsofrockmassdiscontinuitiesarenotrelatedwith any particular case study. However, the values used are representative of conditions generallyencounteredincavingmines.Theanalysispresentedisbasedonthefactorialdesignmethodology,whichconsistsofdetermining the factors that influence the responseof thestudiedparameter,assigning themdiscretevaluesorlevels,andtakeonallpossiblecombinationsoftheselevelsintheexperimentation.

Thestudiedparametersarethe80%and60%passingsize,P80andP60,whichareconsideredrepresentativeofthebiggestandmediumblocksportion,respectively,andtheuniformitycoefficientUC=(P60/P10)1/3,whichisameasurementoftheparticlesizerange.Ontheotherhand,thechosenfactorsthatinfluencetheresponseare:dipofpre-existingsets,densityofpre-existingdiscontinuitiesintherockmass(measuredthroughtheaveragefracturefrequencypermeter)andspacingofhydraulicfracturesgeneratedinthepre-conditioning.TheselectedlevelsforeachfactorarepresentedinTable1.

Table 1 Levels selected for the factors to be studied

Low value Medium value High value

Dip (°) 10-30 - 60-80

Average Fracture frequency per meter 4 - 6

Hydraulic fracture spacing (m) NoHF 0.7 1

Furthermore, three sets of pre-existing fractures are considered: S1, S2 and S3, which orientation ismodelledbyaFisherdistribution,withmeandipdirectionsof0°,60°and120°,respectively.Therangeof20°forthedipissimulatedbymeansofaFisherparameterofK=100.Inthispaper,thelowestdipcasewillbecalledgentlydipping(GD),whilethehighestdipcase,steeplydipping(SD).Figure1showsthepoledensityplotsofthesetsS1,S2,S3andhydraulicfractureswhenallaresteeplyorgentlydipping.

The hydraulic fractures are considered nearly horizontal (dip=0° with K=1000) and their radius wasassumedtobe20maccordingtoCodelco’sfieldexperimentalresults.Additionally,basedonthemethodproposedbyBungeretal(2012),weestimatethat,undertheusualcharacteristicsofChileanminesandpre-conditioning,no-curvingofhydraulicfracturesoccursduetotheirinteraction,thereforehydraulicfractureswereconsideredasstraightfractures.Certainly,thehydraulicfracturesthatareperpendiculartotheminorprincipalstressα3,arenotnecessarilynearlyhorizontal,buttheideaofouranalysisistodefinetheresultswithrespecttothedirectionofα3.


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Ontheotherhand,thegivenvaluesfortheaveragefracturefrequencyistheonemeasuredforallthepre-existingsets,andthespacingofeachsetismodelledbyanexponentialprobabilitydensityfunction(pdf).Forthetracelength,anexponentialpdfisused,withameanvalueof15m.

Figure 1 Pole density plot of hydraulic fractures with: (a) gently dipping sets, and (b) steeply dipping sets

We choose two levels for dip and fracture frequency and three levels for spacing between hydraulicfractures;therefore,weneedtomodel48differentscenarios.The48scenariosstudiedarethecombinationsofthecasespresentedinTable2(numbers)andTable3(letters).

Table 2 Scenarios for orientation of pre-existing discontinuities

Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 Case 7 Case 8

S1 (DDIR=0°) SD SD SD SD GD GD GD GD

S2 (DDIR=60°) SD SD GD GD SD SD GD GD

S3(DDIR=120°) SD GD SD GD SD GD SD GD

Table 3 Scenarios for density of pre-existing fractures and hydraulic fracture spacing

Case A Case B Case C Case D Case E Case F

ff per meter 4 6 4 6 4 6

Spacing of HF NoHF NoHF 0.7 0.7 1 1

Moreover,giventhestochasticnatureofDFN,itisnecessarytomakeatleast10runsforeachscenario,resultinginatotalof480runs.

Thisanalysisdoesnotincludethepropagationofpre-existingfracturesgeneratedbytheinteractionwithhydraulic fracturing,due to the requiredcomputationalefficiency is restrictive for thenumberofcasestobestudied.Nevertheless,thislimitationisnotsorestrictiveintheassessmentofin-situfragmentation,becausethepre-existingdiscontinuitiesinthecurrentcavingminesusuallyareclosedorsealedwithstronginfill,whichavoidspropagation.AsimplifiedanalysiswasconductedthroughtheJointStats(Eadie2002)

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software,which takes as input core loggingsor scanlines, generating statistics for spacing,orientation,trace length and termination, and later, it creates theDFN. Finally, bymeans of a tessellationmethod(constrainedDelaunaytriangulation)itcalculatesthecurveoffragmentssize.

Forthisstudy,wehaveprescribedstatisticalcharacteristicsofthediscontinuitiesinsteadoffieldscanlines.Forthisreason,amethodwasdevelopedtogenerateartificialscanlines,whichfollowthedesiredstatisticalparameters.

3 Results

Becausethemodelisstochastic,itisnecessarytostudythedatavariability.Figure2showstheboxplotsobtainedforP80andP60inoneofthestudiedcases(case1),wherethelowerquartile,medianandupperquartileareshown,andthe+signrepresentsoutlierdata.Ahighvariabilityexistsintheresultsofthebiggerfractionoffragmentswhenthetotaldensityoffractures(pre-existingandhydraulicfractures)islow,whichhappensincaseA.Thesametrendwasobservedforcases2,3,4,5,6,7and8.However,forthemediumfractionoffragments(P60),thevariabilityismoresimilarbetweenthedifferentcases.Anotherobservationisthattheresultsnotnecessarilydistributenormallyand,usually,theyhaveanasymmetrythatfavoursthesmallestsizes.

Figure 2 Scatter of the P80 and P60 results between runs

Figure 3 shows the average uniformity coefficient (UC) obtained in each scenario, and also showsfragmentationcurvesfordifferentuniformitycoefficients,onefortheobtainedfortheaverageUC≈3.8andtheotherfortheoutlierUC≈5.4.

InspiteofthevariabilityofthedatashowninFigure3,wecanconcludethat:a)thehighestvariabilityisobtainedforthecasewithlowtotaldensityoffractures(CaseA);b)largely,UCvaluesarewithinthe3.2<UC<4.5range,whichisaveryuniformgradationandc)onaverage,thesmallestvaluesofUCwereobtainedforcasesCandD,whichcorrespondtotheintensepre-conditioning(spacingof0.7m),therefore,hydraulicfracturescontributetoimprovetheuniformityoftherockblocks.

Toclarifythedisplayofresults,theobtainedpassingsizeof60%and80%weredividedintothreegroups:1)Cases1and8,wherethethreesetshavethesameaveragedip;2)Cases2,3and5,wheretwosteeplydippingsetsexist;and3)Cases4,6and7,wheretwogentlydippingsetsexist.


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Figure 3 Uniformity coefficient UC for the studied cases, and fragmentation curves for different values of UC

InFigure4,5and6,foreachoftheaforementionedgroups,weshowthevariationofP60andP80dependingonthepre-conditioningcharacteristics,forthetwoselectedlevelsoffracturefrequencypermeter.

Figure 4. P60 and P80 obtained for the cases where the three sets have the same average dip

Figure4showsthecaseswhereallthesetsaresteeplyorgentlydipping.Bothconditionsaretheworstforin-situfragmentation,generatingthebiggerblocks.Theadditionofahorizontalset,ashydraulicfractures,inarockmassthatonlyhassteeplydippingsets,obviouslyhasastronginfluencebecausethisnewsetcuts

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theblocksinadifferentdirection.Surprisingly,thesameeffectisobservedwhenallthepre-existingsetsaregentlydipping.Ontheotherhand,areductioninthespacingofhydraulicfracturesisusefultoo,obtainingreductionsbetween1.5–5m3intheP80,decreasing30centimetresofspacing.

Figure 5 P60 and P80 obtained for the cases where two steeply dipping sets exist.

InFigure5,weshowtheresultswhentwosteeplydippingsetsexist.Inthisscenario,thein-situfragmentationwithouthydraulicfracturesismuchfinerthantheoneobtainedincases1and8.Despitethis,theadditionofhydraulicfractureshasverygoodeffects.However,thereductionofthehydraulicfracturingspacinginthiscasehassmalleffectsandreductionsbetween0.2–0.5m3wereobtainedintheP80.

Figure 6 P60 and P80 obtained for the cases where two gently dipping sets exist.

When twogently pre-existing sets exist, the results (Figure6) show twodifferent behaviours: thefirstforthecase4whichissimilartotheoneobservedincases1and8,andsecond,thebehaviourofcases


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6,7whichissimilartocases2,3and5.Webelievethattheresultsobtainedforcase4,canbeduetotheparticularvaluesofthedipdirectionsconsidered,inwhichthecuttingeffectofthesteeplysetovertheothertwogentlysetsisnotcorrectlyaddressed,therefore,theanalysisismadetakeintoaccounttheresultsofcases6and7.Thisscenariowasfoundtobethemostfavourableintermsofthein-situcondition,obtainingthesmallestfragmentation.Theaddingofhydraulicfracturesimprovesfragmentationbutnotinthesamemagnitudethanfortheothercases,andthesameconclusioncanbemadeforthereductionofhydraulicfracturespacing.

Theformerresultsareintermsofabsolutevalues.Forthisreason,inFigure7weshowtheP80obtainedwithpre-conditioning,normalizedbytheP80obtainedfromthein-situcondition.Theeffectofhydraulicfracturesincases1and8remainsbeingthebestone.Theeffectinthecases2and5arebiggerthantheoneobservedpreviouslyintermsofabsolutevalues.

Figure 7 Normalized results of P80 for all the scenarios, and fracture frequency per meter of 4

4 Conclusions

Theanalysismadeontheinfluenceofpre-conditioningwithhydraulicfracturingonthein-situfragmentationallowsustopointoutseveralideasaboutthisprocedure.WeindicatetheimportanceoftakingintoaccountthevariabilityofthefragmentationcurveswhenweuseDFNsimulation,mainlywhenthedensityofthefracturesconsideredislow.ThisvariabilityisrelatedtothestochasticnatureoftheDFNgenerationandnottotheuncertaintyoftheinputdata.Insomecases,forexample,bigblocks,itmaytakealotoftimetoobtainafragmentationcurve.Despitethat,weemphasizethefactthatitisnecessarytodomorethanonesimulation.

TheuniformityinthegradationmeasuredbyUCisimprovedwiththehydraulicfracturing.Thesmalleristhespacingbetweenhydraulicfracturesthemorehomogeneousarethesizedistributionofthecurves.

Consideringthreemainsets,theworstin-situconditionforfragmentationisinwhichallthesemainsetsofdiscontinuitieshaveasimilardip.Whentwosetsaresteeplydippingortwoaregentlydipping,thein-situfragmentationismuchbetter,withP80valuesthatcanbeeven10–20timeslowerthantheformercase.Thisinitialconditionisveryimportantintheevaluationtoknowifpre-conditioningwillbenecessaryoruseful.Inabsoluteterms,thebiggerthein-situblocks,thebetteristheperformanceofhydraulicfracturing,whichistobeexpected.

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FromtheP80normalizedcurves,wecanconcludethatinallthecases,theadditionofhydraulicfractureshasasimilargoodperformanceinrelationtothein-situcondition.Ontheotherhand,thereductioninthehydraulicfracturesspacingfrom1meterto0.7mgeneratesadecreasebetween5–30%inP80.

Theloweristhedensityofpre-existingfractures,thebetteristheeffectofhydraulicfracturingand,withanincreaseindensity,theeffectsofhydraulicfracturingarelower.

Asasummaryoftheresults,forthreedefinedcases,generalguidelinesaregivenfortheapplicationofpreconditioningbyhydraulicfracturing:

• Allsetswithsimilardip:Thisisthebestcasetoapplypre-conditioningandthereductioninspacingisveryeffective,too.Theonlyexceptioniswhenthesepre-existingsetsareperpendiculartos3.

• Twosetsnearlyparalleltos3andonenearlyperpendiculartos3:Thisin-situconditionisgood,buttheadditionofhydraulicfractureshasverygoodresults.Areductioninthespacingisnoteffective.

• Twosetsnearlyperpendiculartos3andonenearlyparalleltos3:Thein-situconditionismildlybetterthantheformersituationandtheadditionofhydraulicfractureshasgoodeffectsbutlesseffectsthantheotherconditions.Areductioninspacingisnoteffective.

References

Chitombo,G2010,‘Cavemining–16yearsafterLaubscher´s1994paper´CaveMining–stateofart´’,Caving2010(Potvin,Y.ed),AustralianCentreforGeomechanics,Perth,pp.45-61.

Brown,ET2007,BlockCavingGeomechanics, JuliusKruttschnittMineralResearchCentre,Brisbane,696p.

Araneda,O&Sougarret,A2007,Keynotesaddress:Lessonslearnedincavemining,ElTeniente1997-2007,CaveMining,SAIMM,CapeTown,pp.59-71.

Bunger,AP,Zhang,X&Jeffrey,RG2012, ‘Parametersaffecting the interactionamongcloselyspacedhydraulicfractures’,SPEHydraulicFracturingTechnologyConference,TheWoodlands,pp.292-306.

Eadie,BA2002,Modellingprimaryandsecondaryfragmentationforblockcaving,PhDThesis,UniversityofQueensland,Brisbane.


532

Caving propagation and dilution control through the pre-conditioning technology

V Barrera Codelco, ChileC Valderrama Codelco, ChileP Lara IM2 Codelco, ChileE Arancibia Codelco, ChileF Báez Codelco, ChileE Molina Codelco, Chile

Abstract

Caving propagation and dilution control are extremely important phenomena in cave mining, because their correct estimate improves the ore body recovery. The pre-conditioning technology, based on the decrease of the mechanical competence of the rock mass through the creation of fractures, allows the in situ stress redistribution to enhance the ore body caveability and fragmentation. This paper presents the influence of pre-conditioning, namely, hydraulic fracturing, in these phenomena through the analysis of the operational data on draw points obtained during the application of this technique in North Inca, West Central Inca and West Inca sectors at El Salvador mine in the 2011-2012 period.

1 Introduction

Theplanningincavemining,whichaimstorecoveralargevolumeofresources,mustpredicttheflowphenomenaof theorebodywithcertainty.Byusinggravityas thedrivingforceandafinitenumberofdrawpoints,selectivityinthisflowisanimportantparametertoconsider.However,boththeentrainmentofwastematerialduringdilutionandtheprecisecontrolofthecavingpropagationwhenamaterialwithinadequateparticlesizeflows,introducesuncertaintyintheprocesswiththeconsequentlossofselectivity.

Priortothestartoftheextractionphaseofanewarea,inordertocontrolthemineralflow,thepreconditioningtechniquewasapplied,specificallyhydraulicfracturing(HF).HFisatechniquethatinvolvespressurizingasectionofanexistingdrillholeorfracturewithaspecificfluid,usuallywater,whichisinjecteduntilanetpressureenoughtoinitiateatensilefractureandpropagateitintotherockmassisreached.NewfracturesproducedbyHFactasfreesurfacesthatfacilitateorincreasetheformationofablock,therebyreducingthesizeofthefragmentstocave(Baez2011).

AtElSalvador,Codelco-ChileDivision,inthe2011-2012period,theexpansionoftheminewasprojectedto nearby zones in North Inca,West Central Inca andWest Inca sectors with challenging productionplans,takingminingtothelimitrates.Theseareasconsistedonverycompetentrocks,whichaddedtotheinformationaboutthehang-ups(20,000m2involvedarea)andsubsequentairblastin1999atNorthIncamineintroducedlimitstotheambitiousminingplan.Additionally,intheareasurroundingWestCentralInca,theorientationofthedrawpointdriftswaschangedcausingtheformationofirregularpillarswiththeconsequentappearanceofareasofhighstressconcentration.Forthisreason,asawaytostimulatethecavingandimprovetheparticlesizeofthebrokenmaterial,HFwasused.

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Figure 1 Inca level layout and commissioning of new areas at El Salvador mine

2 Methodology

ToachievetheoptimumfromtheHFtechnique,thefollowingparametersshouldbeconsidered:

2.1 Mining layout

ThemininglayoutdeterminesthedrillingparametersneededforHF,i.e.,lengthandspatialorientation,becausetheHFdesignmustenhancethestabilityoftheexistingworksattheexpenseoftheareaprojectedtocollapse.TheinfrastructurearrangementisalsorelevantbecausetheproximityoftheHFequipmenttosupplies(air,waterandelectricity)isanoperationalvariabletoconsider.

Figure 2 Mining layout conditioning design of HF drillholes (North Inca sector)


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2.2 Primary – secondary contact zone and subsidence effect

AlthoughCodelcocurrentlyoperatesitsminesinaprimaryenrichmentzoneintheorecolumn,thedistancefrom the currentworks to the drawpoints generated by the primary - secondary contact zone and thesubsidencecontrolsthelengthofHFdrillholes.

2.3 HF´s influence radius

Ithasbeendetermined[1]thatfracturesgeneratedbytheHFwitharadiusof20mprovideanoptimalinteractionofthedrillholes.TheinfluenceradiidesignedforNorthIncasectorcanbeseeninFigure3.

Figure 3 Drillhole design for HF (Plano 2012)

2.4 Fracture spacing

ThespacingoftheHFfracturesaffectstheblocksizetocave.Thisparameterisoperationallylimitedbytheminimumdistanceallowedbythestraddlepackersystem(inthiscase,1.5m).Thespacingalsodependsontheboreholeconditions(boreholestability).Asectioninthedrillholewiththepresenceofimportantstructures(deepdiscontinuitiesorexistenceoffragmentsthatcandamagethepackersystems)suggeststhatweshouldomitthesesectorsandcontinuethefracturingwherethedrillholeisingoodcondition.

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2.5 Stress condition

ElSalvadorminehasarichgeomechanicaldatabasethatprovidesinformationonthestatusofstressinminingsectors,whichcanpredicttheorientationoffracturesgeneratedbyHF.ThisinformationisshowninFigure4.

Figure 4 Stress measurement plane at Inca level, El Salvador mine

Through theanalysisof thedrawcurvesand theparticle size summary, the influenceofHFoncavingpropagationanddilutioncontrolwasstudied.

3 Results

Theanalysisperformedafterpre-conditioningandundercutting in theWestcentral Incasector,showedthatoftheplanned2,303,255tons,2,317,959tonswereextracted,whichcorrespondstotheentiremineralblock.ThisisshowninFigure5,wherethepercentageofdrawntonnage(bluecurvefortheminingplanandredcurvefortheactualdrawcurve)isshown.


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Figure 5 Draw curves, actual (red), scheduled (blue)

Thefactthatthetwocurvescoincidereflectsthatthedilutionphenomenonisprevented.

TheinformationgatheredatthedrawpointsinWestCentralIncasector(crosscut5to16,betweenJanuaryandJuly2012)allowsidentifyingthefragmentationobtainedforthefoursizeclasses(<3“,6”to12“,12”32“>32”)inthemineralcolumn,asshowninFigure6.

Figure 6 Particle size summary at West Central Inca sector

Theuniformgrainsizeshowninthewholecolumncausesaslightincreaseinoversizeandfinematerial.TheoverallgrainsizewasobtainedaccordingtotheexpectedcontributionofHF.

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4 Conclusions

With the information available, it can be concluded that HF allows an efficient caving propagation,consideringthatitwaspossibletoextractthemineralcolumncommittedintheminingplancompletelyavoidingordelayingtheentryofdilution.

Regardingthesieveanalysis,withtheapplicationofHF,itispossibletoobtainauniformdrawfragmentationatalldrawpoints.Lessthan25%ofthemineraldrawninthelastperiodcorrespondstofragmentslargerthan32”andapproximately70%ofthedrawwassmallerthan12”.

Acknowledgement

Theauthorsacknowledge thesponsorshipof IM2 in thecontextof thecompletionof the IM2P-64/10project,“ApplicationofNewTechnologiesinPreparationandExtractionSystems,ElSalvadorMine”.

References

F.Báez,PreacondicionamientodelMacizoRocoso–DesarrolloTecnológico1999-2010,Codelco,2011.


538

Numerical analysis of pre-conditioning using blasting and its relationship with the geomechanical properties of the rock mass and its interaction with hydraulic fracturing

F Báez Codelco, ChileE Arancibia Codelco, ChileI Piñeyro IM2 S.A., ChileJ León IM2 S.A., Chile

Abstract

In caving operations, the rock mass pre-conditioning (PC) has been adopted in a variety of situations, mainly to mimic the geotechnical properties of the secondary rock. The two techniques used to PC the ore body are confined blasting (CB) and hydraulic fracturing (HF).

In this work, a numerical analysis of the technique was done in order to identify the key optimal design parameters considering the in-situ geomechanical conditions for the CB and its interaction with the HF. The analysis was done with the Hybrid Stress Blasting Model (HSBM), which is a blast simulation tool aimed to analyse the role played by different explosive formulations in fragmenting and/or damaging various rock types under different degrees of confinement. The criteria used in this analysis to evaluate the impact of the blasting mainly due to the shock wave, was the peak particle velocity (PPV) response of the medium.

Initial results show that the stresses present in the medium are the main geomechanical conditions that impact the extent of the damage. The effect of the presence of joints sets and also Hydraulic fractures in the extent of the damaged zone can be identified only when the orientation of both of them is against the propagation of the shock wave. Simulations were done with different scenarios looking for: interaction between blast holes, interaction of blast holes with free faces and also changing the distance between primers. A strong inverse correlation has been found between the primers distance and the damaged zone. The results of this work are key elements to consider for an optimal PC campaign where the design can be adjusted to the specific conditions of the ore body and the mine requirements.

1 Introduction

Thecurrentandfuturecavingoperationsarefacedtogreaterchallengesmainlyduetoafundamentalchangeintheconditionsoftherockthatiscaved.ThisisthecaseofCodelcoandagroupofminingcompanieswhichoperationshaveevolvedfromsecondaryrockdepositstodeeperdepositswherethegeotechnicalandminingconditionsarechallengingbecauseofthestrongrockmassesandthepresenceofhighstresses,conditionsthatarecharacteristicsofaprimaryrock.

Someoftheproblemsfacedintheseconditionsareofasafetyandalsooperationalnature,wherestabilityandfragmentationarekeyissuesthatneedtobesolvedinordertoensureproduction.

Toaddresstheseissues,methodologieshavebeenadoptedsuchastherockmasspre-conditioning(PC),tomimicthegeotechnicalpropertiesofthesecondaryrock.Therearetwotechniquesusedtopreconditiontheorebody,namely,confinedblasting(CB)andhydraulicfracturing(HF),whichareusedindependentlyandalsowithacombinedconfiguration.Becausethenatureoftheimpactintherockmassofbothtechnologies

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isdifferent,thebenefitsofthedifferentconfigurationsofthesecombinedtechnologiesareunderanalysis.

MajorminingcompaniesaredoingexperimentaltrialsinordertolookforgeneralguidelinestoimplementthesePCmethodologies.Asacomplement to thesefield trials, computermodelshavebeen introducedmainlyduetotheirversatilitytorepresentawiderconfigurationandconditionspectrumforanalysis,aspointedoutby(Catalanetal.2012).

ThisworkusestheHybridStressBlastingModel,HSBM,asatoolfortheanalysisoftheimpactoftheblastunderdifferentrockconditionsthatincluderockstress,structures,andalsohydro-fractures.

TheHSBMisanumericalmodelfortheentireblastingprocessthatinitscurrentformrepresentstherockmassbyacontinuousmediumneartheblastholesandalatticeschemeelsewhere.Thesoftwaretakesintoaccounttherockmassgeotechnicalpropertiesanditscapabilitiesallowavibrationalanalysisthatcanbedonethroughsimulatedgeophones,a3DmapofthePeakParticleVelocity(PPV)reachedbyeverypointofthesimulationandalsoincludesarockmassbreakagecriterion.

Initscurrentstateofdevelopment,theHSBMsoftwaredoesnotintendtogivespecificquantitativeresults,butitcangiveimportantinsightsabouttheimpactofkeyparametersthatgoverntheblasting.Therefore,thesoftwarecanbeusedtolookforblastdesignguidelines.

2 Methodology

This paper looks for some general guidelines on how to maximize the impact of PC under differentconditions;sodifferentblastscenariosweresimulatedinordertocomparetheresultsbetweenthem.Therewasnocomparisonofresultsbetweenfieldtrialswiththesimulations,exceptforcalibrationpurposes.

Everysimulationconsistsonavolumethatrepresentstherockmassbytheinclusionofitsspecificrockmassproperties,thelocationoftheblastholes,theirprimersandtiming.ThedimensionsofthevolumescorrespondtothoseofactualtrialsinordertorepresentrealPCimplementations.WhiletheHSBMmodelcanconsidertheimpactofthegasesinblasting,thisstudyonlymakesananalysisoftheimpactofthestresswave.

Tomeasuretheimpactofablastintherockmass,twocriteriawhereconsidered;theHolmberg&PerssonCriteria(PPV)anddamage.

Inordertoensurethattheresultsobtainedwiththemodelareclosetowhatcanbeexpectedinfieldtrials,themodelwascalibratedusingdatafromaPCfieldtestdonebyDivisionAndinaofCodelcoChilein2001.

Withthemodelalreadycalibrated,simulationsweredonewithdifferentscenariosinordertoanalysetheimpactalongablasthole.

2.1 Damage Criteria

2.1.1 Holmberg & Persson Criteria (PPV)

ThissoftwareallowstheuseofthePPVcriteria(Holmberg&Persson1979),whichrelatesacriticalPPVwiththedamageinducedtotherockmass.ThecriticalPPVisobtainedfromthefollowingexpression:

(1)

ECPPV tp s×

=max


540

Where:

Cp=Compressivewavevelocity,

σt=Tensilestrength

E=Young`sModulus

2.1.2 Internal HSBM damage

The code defines a fragment as a collection of lattice nodes that are connected through bonds, so thebreakageofthesebondsallowsthegenerationofdamage,fracturesandnewfragments.

2.2 HSBM calibration methodology

ForcalibrationpurposesafieldtrialdonebyDivisionAndinaofCodelcoChilewassimulated.Inthistrial,anarrayofgeophoneswasinstalledinthenearablasthole,allowingtheintensityanddecaycalibration.

Theblastdesignconsideredablastholeofapproximately23.3minlength,withastemmingof12m.Threeprimerswereusedandtheywereplacedincontactwiththestemming,at12[m],inthecentreofthehole,at18manddownthehole23.3m.Twoboreholesweredrilledparalleltotheblastholetoinstallasensorarray.Thefirstholewasdrilledat9mfromtheblastholeandthesecond17mfartherfromthesecondblastholeandinlinewithbothofthem.ThegeneraldesignisshowedinFigure1.

Figure 1 Calibration test blast design

ThecalibrationwasdonematchingthePPVresultsobtainedfromthefieldtrialandfromthesimulations.TherockpropertiesandexplosivesusedinthetestarepresentedinTable1.

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Table 1 Calibration parameters

Parameter ValueUniaxialCompressiveStress(UCS) 150MPa

TensileStrength(σt) 17.6MPaYoung’sModulus(E) 60GPaPoisson’sratio(ν) 0.2

In-situStress(σH,σh,σV) 30MPa,20MPa,15MPaCompressivewavevelocity(Cp) 4,979m/s

Explosivetype ANFOExplosiveVoD 4,000m/sExplosivedensity 0.78g/cm3

2.3 Rock mass response analysis

Differentscenariosweresimulatedinordertoanalysetheextentoftheimpactoftheblasthole(damage).Theparametersanalysedwere:

• Stressesofdifferentmagnitudeandorientation:twomainsituationswereconsideredinordertoanalysetheimpactofthestressesintheextentofthedamage.ThefirstofthemistheonepresentinmostoftheChileancopperoredepositsandcorrespondstoahorizontalprincipalstress.Thesecondsituationispresentinotheroredeposits,wherethemainreasonforstressesisgravity,thustheprincipalstressisvertical.

• Thepresenceofjointsandtheirorientation:thesimulationsweredoneconsideringonlytheChileancasefors1,i.e.,horizontal.Eachjointsetconsideredinthisanalysiswasasetofplanesthatchangetherockmasspropertiesineachpointwheretheyinteractwiththelatticethatdefinesthevolumeofanalysis.Threeorientationsforthejointsetswereconsideredinthisanalysis:horizontaljointsets,andaddition,jointsetsparallelandperpendiculartos1.

• Thedistancebetweenprimers(boosters):lookingforamechanismtoextendtheradiusofinfluenceoftheblast,theimpactofthedistancebetweenprimerswasanalysed.Forthisanalysis,twosetsofsimulationsweremade;bothsetsconsidereddifferencesinthelengthoftheblasthole,50mforthefirstoneand70mforthesecond.Thedistancebetweenprimersvariesfrom2mto12m.

• Thepresenceoffreefaces:simulationswithfreefacesweredoneinordertolookforsomenewblastdesigns.Thesimulatedblastdesignswereagainstafullfreefaceandagainsttheraise.

• Interactionbetweenblastholes:simulationswherethedistancebetweentheblastholeswereanalysedlookingforthebestinteractionbetweenthem.

• PresenceofHF:becauseamixedapproachofPCiscurrentlybeingadoptedbytheindustry,whichincludesblastandHF,somesimulationsweredonelookingfortheinteractionbetweenthem.

AndinamineprovidedtheinformationaboutexplosivesanditissummarizedinTable2.Therockpropertiesarethesameonesusedinthecalibrationmodel.


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Table 2 Simulations parameters

Parameter Value

PPVmax 1,461mm/s

Explosivetype Emulsion

ExplosiveVoD 5,600m/s

Explosivedensity 1.15g/cm3

PrimerTiming Simultaneous

3 Calibration

ThefieldmeasurementsofthePPVofeachgeophoneanditscorrespondingsimulationarelistedinthefollowingtablewherethedifferenceobtainedaftercalibrationisalsoindicated.

Table 3 Calibration results

Geophone Measured PPV (mm/s) Simulated PPV (mm/s) Difference

G1 359.03 307.43 -14%

G2 683.74 659.73 -4%

G3 649.15 620.78 -4%

G4 434.28 373.35 -14%

G5 205 128.71 -37%

G6 259.81 213.47 -18%

G7 321.87 180.26 -44%

G8 220.45 132.25 -40%

Fromthesecalibrationresults,wecanexpectaveryclosecorrespondenceofthesimulationsinthenearfieldoftheblastwithreality,eventhoughsomeconsistentnegativebiascanbeexpected.

So,inthenearfieldwecanexpectagoodcorrespondencewithrealtrialseventhoughsomeunderestimationcanbeexpected.Fordistancesfartherthan25[m]wecansaythatthemodelisunderestimatingthedamage.

4 Results

4.1 Stresses of different magnitude and orientation

Theimpactofthevariationofthedirectionoftheprincipalin-situstresscouldbeseeninFigure2.InthisthedamagezoneoritsradiusiscalculatedfromthePPVmaxcriteria.

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Figure 2 Damage extent vs. σ1 orientation

FromFigure2,wecanseebigdifferencebetweenthetwoorientations,findingthattheverticalorientationgenerateslessdamage.Inthecaseoftheverticalσ1,acloserlookatthedamagewasdoneconsideringnowthebrokenlinkscriteria(Figure3).

Figure 3 Damage extent vs. Vertical σ1

Theresultsshowthateventhoughtheextentofthedamageissimilarinallthecases,thelevelofdamageinsidethedamagedzonevariesastheverticalstresschanges.Thus,lessbrokenlinksarefoundwhentheverticalstressisbigger.

4.2 Presence of joints and their orientation

Foursetsofsimulationsweredone:withoutjointsets,horizontaljointsets,paralleltoσ1andperpendiculartoit.Theextentofthedamagewasmeasuredalongtheσ1directionandperpendiculartoit.


544

Figure 4 Damage extent vs. Joints Orientation

TheresultsshowinFigure4thatthehorizontalstructureslimittheextentofthedamagebutonlyinanorderofafewcentimetres,whichisofnorealimpact.

Inthecaseofverticalstructures,theresultsshowalossofsymmetryintheextentofthediameter,wherethebiggestimpactisinthedirectionthatisperpendiculartotheplanethatcontainsthejoints.Thiseffectwasfoundinbothcasesanalysedregardingtheprincipalstressbeenverticalorhorizontal.

4.3 Distance between primers boosters

Theresultsshowthattheextentofthedamageincreasesasthedistancebetweenprimersdecreases,andalsothatthelengthofthecolumnisofnorealimpact.ThePPVcriteriaforthetwosetsofsimulationsareshowninFigure5.

Figure 5 Damage extent vs. distance between primers

4.4 Presence of free faces

Figure6showsasectionperpendiculartothefreeface.ThePPVcriteriausedconsidered.

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Figure 6 From left to right: Base Case, blast hole against full free face and against raise (right)

Comparedtothebaseline,inbothcases(fullfreefaceandraise),someinteractioncanbeobserved,theactualimpactofitneedstobeanalysedconsideringthedistancetothefreefaceasadesignparameter.

4.5 Interaction between blast holes

Thevariationofthedistancebetweentwoblastholesshowssomeinteraction(Figure7).

Figure 7 Distance between blast holes and it’s relation with damage extent

4.6 Presence of HF

TheinteractionbetweenblastandHFwasanalysedconsideringthechangeintheextentofthedamagecomparedwithabaselinewithnoHF(Figure8).

Theresultsshowsomeminorimpactsintheextentofthedamage.Inthecaseofanon-horizontalHF,somebiggerimpactcanbefound,andalsosomelossofsymmetryasinthecaseofinteractionwithjoints.


546

Figure 8 Damage extent vs. HF orientation

5 Conclusion

Themodellingofdifferentblast scenariosprovides some important insights about the relevanceof thedifferentparametersinvolvedinthedesignofPC.

• Stressesandtheirorientationsplayanimportantrolelimitingtheextentofthedamage.

• Thenon-horizontaljointsalsolimittheextentofthedamage,andsomelossofsymmetryisfound.

• ThedistancebetweenboostershasanimportantroleintheimpactofPC.

• FreefacescanbeanalternativetoincreasetheimpactofPC;adeeperanalysisshouldbedone.

• NobiginteractionbetweenHFandblasthasbeenfoundinthedifferentscenariosmodelled.

• Someinteractionbetweenblastholeshasbeenfound,butnotalwaysenoughtosatisfysomeofthedamagecriteriaselected.

ThemodellingofPChasshowntobeanimportanttooltoidentifythekeyparametersfortheresultsofaPCcampaign.Mostoftheparametersanalysedarealreadyfixedfromtheoreconditionsandcannotbefinetuned,soimportantdifferencesshouldbefoundifasinglePCdesignisused.

Newdesignsconsideringthedistancebetweenboostersandfreefacesneedtobeincludedinthebatteryofoptionstoconsider.Recommendationsabouthowtoimplementtheseresultsmayvaryfromsitetosite,therefore,adeeperanalysisisneeded.

References

Catalan,A,Onederra,I&Chitombo,G2012,‘AproposedmethodologyforevaluationofthepreconditioningbyblastattheCadiaEastpanelcavemine’,Massmin2012.

Holmberg,R&Persson,PA1979,‘DesignofTunnelPerimeterBlastholePatternstoPreventRockDamage’,Tunnelling’79’,ProceedingsoftheSecondInternationalSymposium,London,England,12-16March,London:InstituteofMiningandMetallurgy,pp.280-283.

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Intensity rock mass preconditioning and fragmentation performance at the El Teniente Mine, Chile

A Brzovic Codelco, ChileJP Hurtado Universidad de Santiago de Chile, ChileN Marín Codelco, Chile

Abstract

Fragmentation measurements have been undertaken at the Sur Andes Pipa mine sector (SuaPi) within the El Teniente mine to validate the effect of rock mass preconditioning. SuaPi mine sector has been mining out primary and secondary ore since September 2010 at around 6000 tpd. The primary ore is called Dacita, which is considered as the stronger and massive rock mass for caving at the El Teniente mine, and that is one of the main reasons for preconditioning (to improve caving and fragmentation performance). Two different preconditioning techniques were implemented over the Dacita rock mass; hydraulic fracturing (HF) and confined blasting called DDE (Debilitamiento Dinámico con Explosivos).

Fragmentation analyses were undertaken considering main geological features of the sector, and finally compared/correlated to the variable intensity of rock mass preconditioning undertaken over the primary rock mass. This paper describes the applied methodologies and main results of the investigations, which shows a clear and direct relationship between preconditioning intensity and fragmentation performance at the El Teniente mine.

1 Introduction

TheprimarycopperoreattheElTenientemineisdescribedasverycompetentandmassive,itexhibitsabrittlebehavior,oftenviolentfailureunderhighstressconditions(Rojasetal.,2001).Thisdescriptioniscoherentwiththegeologicaldescriptionoftherockmass,whichdoesnothavediscontinuities(joints)thatmatchasthedefinitionprovidedbyInternationalSocietyofRockMechanics(ISRM,1981).Onlyfaultscanbeclassifiedasdiscontinuities,buttheyarewidelyspacedwithinrockmass.Theprimarycopperorehasahighfrequencyofveins,where thecoopermineralizationishosted, theseveinnetworkstructuresareknownasstockwork(Figure1).Ithasbeenobservedanddocumentedthatsoftveinscontainingweakmineralsasinfill(chalcopyriteandanhydritemainly)controlthedisassemblingoftherockmassduringcaving(BrzovicandVillaescusa2007;Brzovic2011).

Differentpreconditioningtechniqueshavebeenappliedattheminesite,aimingmoistlytoreduceseismichazard,butalsotoimprovecavabilityandfragmentation.HydraulicFracturing(HF)iscurrentlyappliedminewidesince2008,andtheconfinedblastingcalledDDE(DebilitamientoDinamicoconExplosivos)havebeenappliedonlyas industrial trial tostudy its impacton fragmentationperformance.Thispaperdescribe the result of the industrial trial of preconditioning (HF+DDE) applied at the SurAndes Pipa(SuaPi)minesector,mainlyoverDacitarocktype,whichisthestrongerandmassiverockmassforcavingattheElTenientemine.

Afragmentationmeasurementscampaignatthedrawpointsoftheproductionlevelwasimplementedtoevaluate the fragmentationperformance.Fragmentationmeasurement,undertakenbyminingengineers,startedinOctober2010andfinishedinJuly2013consideringtwomainrocksizedistributions;a)thefinefraction,whichiscollectedattheproductiondrawpointitselfbyvisualinspectionusingflipcharttechniques


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andbackedwithphotographies, andb) thecoarse fraction, that represent allbigblocksundertaken forsecondary blasting and hang up reduction. Both combined data provide the final fragmentation curveobservedatdrawpointsofthepreconditionedvolume.

Figure 1 a) Panel caving method currently used at the El Teniente Mine (b) Intense vein network “stockwork” at a development ahead of the cave front (c) Rock block found in the draw points at the production level (d)

Weak Veins as faces of caved rock blocks (e) Laboratory scale sample showing a Weak veins (from Brzovic & Villaescusa 1997)

Preconditioningintensityiscalculatedbycountingtheareaofcreatednewfracturesperunitvolume,whichisaparameterusedcommonlyinstructuralgeology;itiscalledP32m2/m3accordingtoDershowitzandEinstein(1988).Detailedloggingofcoresboredoverthepreconditioningvolumeprovidetheinsightoftherockdamagebythosetechniques,whichwasusedtobuildupDiscreteFractureNetwork(DFN)modelofthecreatednewfractures.ThevalueofP32m2/m3thatrepresentsthepreconditioningintensityisfinallyobtainedfromtheDFNmodel.

TheaimofthispaperistocompareandcorrelatefragmentationperformanceobservedagainstpreconditioningintensityappliedoverDacitarocktypeattheElTenientemine.

2 Fragmentation measurements methodology

FragmentationmeasurementshavebeencollectedcontinuouslysinceOctober2010consideringtwomainrocksizedistributions;a)thefinefractionthatrepresentthemuckmaterialatthedrawpoint,andb)thecoarsefraction,thatrepresentallbigblocksundertakenforsecondaryblastingandhangupreduction.Themethodologyproceduretocollectfragmentationinformationandundertakedataanalysisisafollow:

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Preconditioning

• Thesizedistributionofthefinefractionaremeasuredbyvisualinspectionoffullydrawpoints(“puntosabocados”)infoursizeranges;[<0.25m],[0.25mto1.0m],[1.0mto1.5m]and[1.5m>].Acomparative“flipchart”isusedtohelptoestimatethepercentagesofeachsizerange(Figure2).

• Thecoarsefractionrepresentallrockblockidentifiedduringsecondaryblasting;theonesthatformthehangupatthedrawbell,andalllargerockblockovertheproductionlevelfloorthattheLHDcannotcarryout.Thecoarsefractioniscountinginrangesof;[1.0mto2.0m],[2.0mto3.0m]and[3.0m>].Eachsinglerockblockischaracterisedbyitssizedimensions(threemayoraxis).

• Withtherockblocksizedata,theshapefactor“f”(Gy1967)iscalculated.“f”isadimensionless“particleshapeparameter”,whichvaryingbetween0and1.Theshapefactorisobtainedbythemultiplication of the ratio of rock blockmajor axes divided by the large axes recorded. Thisparameterisnecessarytoconvertthetwodimensionalobservationsofarockblock,inhangupforinstance,intoathreedimensionalvolumeandfurthertonnage.ItisimportanttonotethatalllargerockblockovertheproductionlevelfloorthattheLHDcannotcarryoutwerefullycharacterised,thenstatisticalanalysisisundertakentoestimatethefineandcoursefractiontonnage.

• Bothsizedataarecombinedandcorrelatedtothedatabaseofthemineproduction,whichallowtocorrelateeachdrawpointwith;date,shift,columnheight,extractiontonnagesamongothersparameters.Data analysis is undertaken for a certain number of draw points that have similargeologicalandpreconditioningconditions.

Figure 2 Scheme showing fragmentation measurement methodology. A fully draw points is shown at the left (upper) and a rock block for secondary blasting at the left and right bottom. Hung up at the upper-right and

the Flip-Chart at the centre

3 Rock mass damage by preconditioning

Coreloggingandboreholecamera(BHC)recordsofseveraldrillcoresboredafterpreconditioningwasappliedtotheprimaryrockmassallowedtoidentifyandtocharacterizetherockdamagebytheapplicationofbothtechniques.Rockmasspreconditioningresultedinacreationofnewandfreshopenfractures(Figure


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3), that thenaturalprimaryoredoesnotcontain.HFfractures tendtohaveasub-horizontalorientationaccordingtotheinducedstressfieldattheminesector.DDEfracturestendtohavesub-verticalorientationaccordingtoatypicalpre-splitblastingtechnique(Figure4)ratherthanmicrocrackswithintheintactrock.Microcrackneverwereobservedneithermeasured.HFisalsocharacterisedwithalowroughnessprofilethantheDDEfracturesascanbeseeninFigure3.

Figure 3 Pictures of both core and bore hole camera showing the fresh and new fractures created by preconditioning techniques. Primary rock mass without fractures (only stockwork veins) is also shown at the

BHC´s pictures

Figure 4 The progressive creation of a fracture plane during pre-split blasting technique (from Matheson 1983 in Hudson & Harrison 1997)

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Preconditioning

3.1 Intensity preconditioning estimates

Preconditioning by both techniqueswere not homogenous at SuaPimine sector, becauseHF injectioncoresandDDEblastholewereplacedwithdifferentspacingthroughtheentireDacitarockmass(Figure5).CloserspacingofDDEblastholeandlargenumberofthemwasconsideredatthenorthpartofSuaPIincomparisonwiththesouthpart.DDEblastingperformancewerealsodifferentatthenorthparte,moreblastholeweredetonatedatthesametimeinthisminesectortoo.HFwasalsonothomogenousthroughthecolumnheight,becausesomeFHcouldnotbecreatedbyoperationalissuesascanbeseeningeologicalcrosssectionofFigure6(left).

Figure 5 Geological plan view of SuaPi mine sector showing; rock types (Dacita as yellow and Cmet as grey colours), HF injection core (black dots) and DDE blast hole (red and blue dots). North direction is along SuaPi

(from bottom to top in the plan view)

Figure 6 Geological cross section showing different HF intensity (left) and isometric 3D view of the HF created at SuaPi mine sector based on mine design and real HF performance


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MinedesignandrealHFperformanceinformedbytheoperationstaffwasusedtoestimateHFintensityasisshowninFigure6(left).HFwereassumedascirculardiscwith20metersradio(φ)accordingminedesing.Dataanalysis(Figures4and5)andcorelogging(Figure7)wereusedtobuildupDFNmodelofthenewfracturescreatedbyDDEpreconditiontechnique(fromBrzovicetal,2014a).DDEdataanalysiscouldonlybedoneatthenorthpartofthestudiedminesector,andthenassumedsimilaratthesouthpart.

Figure 7 (Left) Different isometric 3D view of DDE blast holes (green colour) showing DDE fractures identified on both Cores (black) and BHC. (Right) Final DFN model of both HF (sub-horizontal) and DDE (sub-vertical)

fractures

Different sub-sectors were redefined within SuaPi mine sector considering the following criteria:Preconditioningtechniqueapplied(onlyHFandHF+DDE)andDDEspacing.Ateachsub-sectorandbasedontheDFNmodel,intensitypreconditioningwascalculatedastheP32parameter.Ateachsub-sectorthestructuralgeologicalintensity(insituorwithinrockmass)alsowascalculatedbasedontheDFNmodel(BrzovicandSchachter2013,Brzovicetal,2014b)astheP32parametertoo.Veinandfaultintensityareassumedassimilarthroughtheentirearea,preconditioningintensitywereestimateonlyoverprimaryore.Thecompleteintensityinformationofeachsub-sectorisshowninTable1.Thefragmentationperformanceisthenstudiedateachsub-sector.

Table 1 Structural Intensity (rock mass and preconditioning) at SuaPi mine sub-sectors

SuaPi mine

Sub-Sectors

Structural Intensity P32 (m2/m3)Veins Faults HF (φ 20m) DDE HF+DDE

DacitePrimarywithHF+DDE(closer

spacing)3.1 0.06 0.39 0.11 0.50

DacitePrimarywithHF 3.1 0.06 0.22 - 0.22

DacitePrimarywithHF+DDE

HF+DDE3.1 0.06 0.35 0.08 0.43

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Preconditioning

4 Fragmentations results

Fragmentationdataanalysiswerecarriedoutconsideringdifferentvariablessuchashangupfrequency,largerockblockoccurrenceinhangup,tonnageperhangup,hangupheight,explosiveconsummation,fragmentationcurve,amongother,butonlythetwofirstonesareshownhere.

4.1 Hang up frequency

Thehangupfrequencyofthe3studiedsubsectorastheextractedcolumnheightincreasecanbeseeninFigure8.Primaryoreandpreconditioningeffectareobservedduringthefirst100metersofthecolumnheight,secondaryoreinfluencesbyfinemigrationoccurabovethatheight.

Figure 8 Hung up frequency observed of the 3 studied sub sector through the extracted column height. Above 100m of the column height the secondary ore influence appear

Figure8clearlyshowsthatdecreasingDDEspacing(closerblasthole)improvedfragmentationperformance.ItisalsoshowninFigure8thatFHpluswiderDDEspacing(redline)doesnotdiffermuchincomparisonwithDacitaonlywithHFintermofthenumberofhangupperformance.However,DaciteprimaryonlywithHFtendstobelessproductiveabove80metersofcolumnheight,andevenduringthesecondaryoreinfluence.

4.2 Large rock block in hang-up

Duringthefragmentationmeasurementcampaignaspecialattentionwasmadeoverthelargerockblocksidentifiedinhangup,especiallyofthoseinwhichtookmorethanoneshifttobringdownfromthedrawbell.Thoselargerockblockswerealsodefinedwhenthelargeaxesobservedwasabove6meterslong.Morethan40caseswerereportedduringthestudy,someofthemtookmorethan12shiftstoclearthedrawbell,andlargeaxesmeasuredwereupto14meterslong(calledasextremecaseshereafter).


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BasedonDFNapproach,itwasalsopossibletocreateamapofthelocalpreconditioningintensitymodel(3D10mx10mx10mblockmodel)asisshowninFigure9(planviewoflevel30metersaboveUCL),ThelocalpreconditioningintensitywasalsocorrelatedtothelargerockblockoccurrenceatthestudiedareaofSuaPiminesector.

ItisveryclearfromFigure9thatlargerockblockoccurrenceattheSuaPiminesectoriscontrolledbypreconditioningintensity.Wherethereislowpreconditioningintensity,morenumberonlargerockblocksappearedatthedrawpointsoftheproductionlevel.Inotherwords,thereisadirectrelationshipbetweenpreconditionintensityandfragmentationperformanceinthestrongerandmassiveDacitarocktypeatthestudiedminesector.

ItcanbeinferredfromdataanalysisthatDDEfractureshelpinfragmentationreduction.DespitethatDDEfractureshavelessintensity(fewerandshorterthanHFfractures),thesearepositionedinaperpendicularorientationrespectHFfractureshelpingtodefinesmallerrockblocksizes.

Figure 9 Plan view of SuaPi mine sector showing local preconditioning intensity and large rock block occurrence in hang up at the production level draw points. It is also shown detailed location and information

of the extreme cases

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Preconditioning

5 Conclusions

FragmentationmeasurementswereundertakenattheSurAndesPipaminesectorwithintheElTenienteminetovalidatetheeffectofrockmasspreconditioning.TwodifferentpreconditioningtechniqueswereimplementedovertheDacitarockmass,whichisconsideredasthestrongerandmassiverockmassforcavingat themine site.Thesewerehydraulic fracturing (HF)andconfinedblastingcalledDDE.Mainconclusionsoftheworkwere:

• Rockmass preconditioning resulted in a creation of new and fresh open fractures (Rockmassdamage).Thisisthemostimportantfindingsinceprimaryrockmasspracticallydoesnotcontainopenfractures.

• HFfracturestendtohaveasub-horizontalorientationandDDEfracturestendtohavesub-verticaldisposition.Rockmass fracturing by pre-split blasting techniques is a close comparison to thefracturingbyDDE.

• RockmassdamagebypreconditioningwasquantifiedbyanintensityparametercalledP32,whichrepresenttheareaoffracturing(m2)pervolumeunit(m3).RockmassfracturingbyHFresulted4timesgreaterthanDDEfracturing.ThenpreconditioningP32ofstudiedSuaPisub-sectorwascorrelatedtofragmentationperformance.

• Itwasmeasuredaconsiderablereduction(50%)ofhangupfrequencybycloserDDEblasthole,butitwasnotobservedtomuchdifferencebetweenprimaryrockmasswithHF+DDEandonlywithHF.TheamountofblastholesblastedduringDDEimplementationmayalsoplayedanimportantroleinrockdamage,analysisthatwasnotundertakeninthiswork.

• Largeandextremerockblock inhangupappearedwhere low intensityofpreconditioningwasidentified, that confirms the clear and direct relationship between preconditioning intensity andfragmentationperformance.

Acknowledgement

TheauthorsacknowledgetheElTenienteDivisionofCodelco-Chilefor theirpermissiontopublishthedataandforsupportingthiswork.ThisstudywasfundedbyDacitaProyect(contracts4501138457and4501236828)andbyAPIT10E202bothofCodelco-Chile.PaulinaSchachter,JoseAlvarez,MiguelCastro,BrendaCerda,CristobalIgnacioRiquelmearealsoacknowledgedfortheircontributiontothiswork.

References


Brzovic,A2009,‘RockmassStrengthandSeismicityduringCavingPropagationattheElTenienteMine,Chile‘,InProceedingsof7thInternationalSymposiumonRockburstandSeismicityinMines(RaSiM07),(Tang,C.A.ed.),DalianUniversity,vol.2,pp.838-52.

Brzovic,A&Schachter,P2013,‘RockMassGeotechnicalCharacterizationbasedontheWeakStockworkVeinsattheElTenienteMine,Chile’,Proceedingsof3thInternationalSeminaryofGeologyfortheMiningIndustry,GEOMIN,Santiago,Chile.


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Brzovic,A,Alvarez,J,Schachter,P,Webb,G,&Rogers,S,2014a,‘DiscreteFractureNetworkModellingto Quantify the Impact of Intensive RockMass Preconditioning at the El TenienteMine,Chile’,Abstractacceptedforthe1stInternationalConferenceonDiscreteFractureNetworkEngineering,Vancouver,October2014.

Brzovic,A, Schachter, P, de los Santos, C, Vallejos, J &Mas Ivars, D 2014b, ‘Characterization andSyntheticSimulationstoDetermineRockMassBehaviourattheElTenienteMine,Chile.PartI’,Proceedingsofthe3rdInternationalSymposiumonBlockandSublevelCaving,Santiago,Chile.


Gy,PM1967,‘L’échantillonnagedesmineraisenvrac’,Int.Rev.Ind.Miner.,Jan.1967,188p.

Hudson,J,andHarrison,J1997,‘EngineeringRockMechanics,anIntroductiontothePrinciples’,Oxford,PergamonPress.

ISRM1981,‘Suggestedmethodsforthequantitativedescriptionofdiscontinuitiesinrockmasses’,Rockcharacterization, testingandmonitoring, ISRMSuggestedmethods, (editedbyETBrown),PergamonPress,pp.3-52.

Matheson,GD1983,‘PresplitblastingforHighwayRoadExcavation’,DepartmentoftheEnvironment,DepatmentofTransportandRoadResearchLaboratoryReportLR1094.

Rojas,E,Cavieres,P,Dunlop,R,&Gaete,S2000,‘ControlofInducedSeismicityattheElTenienteMine,CodelcoChile’, InProceedingMassmin,(Chitombo,Ged.),Brisbane,Australia,AusIMM,777-781.

VallejosJ,Suzuki,K,Brzovic,A&MasIvars,D2014,‘CharacterizationandSyntheticSimulations toDetermineRockMassBehaviourattheElTenienteMine,Chile.PartII’,Proceedingsofthe3rdInternationalSymposiumonBlockandSublevelCaving,Santiago,Chile.


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Seismicity

Improved microseismic event hypocentre location in Block Caving Mines using local earthquake tomography

J Philippe Mercier, Golder Associates, CanadaW de Beer Golder Associates, CanadaJ Pascal Mercier Advanced GeoScience Imaging Solutions, Canada

Abstract

In production data processing, event hypocentre locations are usually calculated by considering a homogeneous (constant) velocity within the volume of rock monitored. However, the rock mass is far from homogeneous and, in the block caving context, its state can change rapidly as caving progresses. The consequent large discrepancies between the homogeneous velocity approximation and the true velocity distribution can considerably hamper the characterization of cave induced microseismic activity. Local earthquake, or passive source, tomography provides an efficient way to estimate the 3D seismic velocity distribution and simultaneously refine estimates of microseismic event hypocentre locations. It is a robust inversion method that uses information readily available in the microseismic data. It requires no a priori knowledge of the rock mass composition and stress state and provides a comparatively easy way to estimate the 3D velocity distribution using only seismic data. We present the results of locating microseismic event hypocentres in a block cave using local earthquake tomography. In addition, the 3D velocity model(s) calculated provide information on the rock mass state and the distribution and evolution of stresses as caving progresses. We first use a synthetic example to demonstrate the method’s ability to estimate the 3D seismic velocity distribution and simultaneously correct the hypocentre location. We then discuss results obtained using real data collected at a block caving operation.

1 Introduction

In hard rockmines,microseismicity provides useful informationon the behaviour and response of therockmasstomining.Inblockcaving,itisrecognizedthatthelocationandcharacteristicsofmicroseismiceventsinducedbyminingcouldbeusedtobetterunderstandtheevolutionofthecavingprocessandtheoverallrockmassresponse,bothduringthedevelopmentoftheundercutandextractionlevelsandduringproduction.Thishasbeenputintopracticeatseveralblockcaves(e.g.,H.Whiteetal.2004;HudymaandPotvin,2010a,2010b)(Glazer&Hepworth,2006;Glazer&Townsend,2008;Glazer2008;HudymaandPotvin,2008;Hudymaetal.2007a,2007b;Potvin&Hudyma2008;Trifuetal.2007;HyltonWhiteetal.2004).Theamountandqualityofinformationextractedfromthemicroseismicitylargelydependsontheabilitytoaccuratelycalculatetheeventhypocentrelocations.Inturn,theaccuracyoftheeventhypocentrelocationsisdirectlyrelatedtohowrepresentativeofrealitythevelocitymodelusedtocalculatethelocationsis:themorerepresentativethemodel,themoreaccuratethelocationofthemicroseismicevents.

Inblockcavemines(asinothertypeofmines),theeventhypocentrelocationsareusuallycalculatedbyconsideringahomogeneous(constant)velocitywithinthevolumeofrockmonitored.Intheblockcavingcontext,therockmasscanbefarfromhomogeneous,anditsstatecanchangerapidlyascavingprogresses.Potentially largedifferencesbetween thehomogeneousvelocityused to calculate the eventhypocentrelocationsandtruevelocitiesatdifferentlocationsintherockmasscanconsiderablylimitamicroseismicmonitoringsystem’sabilitytocharacterizecave-inducedmicroseismicactivity,yieldingsignificanterrors


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ineventhypocentrelocationsandsourceparametercalculations.Forthatreason,moresophisticated3Dheterogeneousvelocitymodelsthatbetterrepresenttherockmassshouldbeused.

Localearthquake,orpassivesource,tomography(LET)providesanefficientwaytoestimate3Dseismicvelocity distributions and simultaneously refine estimates of microseismic event hypocentre locations.Comparedtootherapproaches,LETprovidesaneasywaytoestimatethe3Dvelocitydistributionemployingonlyseismicdata.Ithasbeenappliedusingmine-inducedseismicity((Huangetal.2013;MaxwellandYoung,1996,1993;Maxwelletal.1998)).OurLETmethodiscomputation-efficient.Itusesonlyreadilyavailableinformationcollectedfrommicroseismicdata,namelyinitialeventhypocentrelocationsandP-and/orS-waveonsettimes.Itrequiresnoaprioriknowledgeoftherockmasscompositionorstressstate.

We first verify the capabilities of this technique by applying it to a synthetic example.We then showhowitcanbeappliedtorealdatacollectedatablockcavingmineduringthecavingprocess.Ourresultsclearlyshowthatourmethodhelps to improve theaccuracyofmicroseismiceventhypocentre locationestimatesandobtain informationon the3Dvelocitydistribution,yieldingabetterunderstandingof therockmass stateand thedistributionandevolutionof stressesas cavingprogresses.Weshow thatLETprovidesanalternativetoanapproachthatinvolvesmanualbuildingofa3Dvelocitymodelfromavailablegeotechnicalinformation.

Anoteonterminology:by“locationerror”wemeanthedifferencebetweenarealsourcelocationandthecalculatedlocationforthesamesource.Inpractice,locationerrorscanonlybedeterminedforsyntheticsources,controlledsources(e.g.,surveyedblastsormechanically-inducedvibrations)andmined-throughinducedornaturalseismiceventsources.“Locationuncertainty”referstoastatisticalmeasureofthesizeoferrorellipsoidwithinwhich,toahighdegreeofconfidence,theactuallocationofthesourceis.“Residual”or“traveltimeresidual”referstothegoodness-of-fitmeasureemployedinaninversion.

2 Method

Therelationbetweenarrivaltime, ,velocity, ,andorigintime, ,foranevent locatedat recordedatasensor locatedat isasfollows:

(1)

Where:

representsthetravel-time,

,and istheray-path.

Equation1isnon-linear,sincethetrajectorybetweenasource andareceiver alongwhichtheseismicenergytravelsdependsontheunderlyingvelocitymodel, , theeventhypocentrelocation, andthesensorlocation, ,andsincethecalculatedhypocentrelocationandeventorigintimedependonthevelocitymodel.

Theinverseproblemconsistsincalculatingsimultaneouslythe3Dvelocitydistribution,eventhypocentrelocationandeventorigintimecorrectionsfromtravel-timemeasurements.Tosolvethisinverseproblem,weadoptedapopularapproach(e.g.,Eberhart-Phillips,1993,1993;Kisslingetal.1994;Thurber&Eberhart-Phillips,1999)consistingoflinearizingEquation1andcorrectingthemodelparameters(velocities,eventhypocentrelocationsandeventorigintimes)toreducethedifferencebetweentheobservedandpredictedarrivaltimeswhileimposingconstraintontheresultingmodelinaseriesoflinearinversionsandforwardmodellings.

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3 Synthetic example

3.1 Synthetic examplesetting

ThepurposeofthesynthetictestistoshowthatLETcanrecovercomplexvelocitydistributionsandcorrecteventhypocentrelocationswithoutanyaprioriknowledgeofthevelocitydistribution.Forthesynthetictest,webuilta100x100x100m3syntheticvelocitymodelwitha20x20x20m3cubiclowvelocityanomalyin themiddle.Weset thevelocityof thebackgroundand the lowvelocityanomaly to5,000ms-1and1,000ms-1,respectively.Wethendistributed25sensorsand200eventsinsidethemodelbutoutsidethelowvelocityanomalyusinguniformandGaussianrandomdistributions,respectively.Figure1showsthesyntheticvelocitymodel,thelocationofsensorsandthemicroseismicevents.

Figure 1 Oblique view of the synthetic velocity model. Blue and red represent low and high velocities, respectively. Inverse cones represent sensors and dots microseismic event hypocentre locations

3.2 Synthetic travel time data and initial event hypocentre locations

Usingthesettingsdiscussedpreviously,wegeneratedasetofsynthetictraveltimesusingaFastMarchingEikonalsolver(Sethian1999)along40%oftheallpossibleraypaths,whichrepresentsonaverage10traveltimesperevent.Notethateveryevent-sensorpairyieldsoneraypath.Employingthesynthetictraveltimes,wethencalculatedtheeventhypocentrelocationsinahomogeneousgridwithavelocityof5,000ms-1.

3.3 Inversion setting

Joint velocity, event hypocentre and event origin time inversionwasperformedon the synthetic traveltimedataset.Weusedahomogeneousgrid,withavelocityof5,000ms-1asastartingvelocitymodelandtheeventhypocentrelocationscalculatedfromthesynthetictraveltimeonthishomogeneousgridas


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thestartingpointforeventhypocentrelocations.Thespacingoftheinversiongridwassetto1mineverydirection,yieldingalittlemorethan1milliongridnodes.

3.4 Synthetic test inversion results

Figure 2a compares the true velocity profile and the velocity profiles obtained after 1, 10 and 50iterationsmeasured along the Z axis forX andY constant and equal to 50m (middle of themodel).Therecoveredvelocitymodelconvergestowardsthetruevelocity.Themeanrelativeerrorinpercent(

)betweenthetrue, ,andrecovered, ,velocitymodelis7%,1.5%and0.8%after1,10and50iterations,respectively.

Figure2bshowstheevolutionofthemeaneventhypocentrelocationerrorduringtheinversionprocessandcomparestheresultstothemeanlocationerrorforaneventhypocentrelocatedusingtheinitialmodelhomogeneous velocity (~10.5m) and the true velocitymodel (~2m). Note that a non-linear locationprocedure(Lomaxetal.,2000)wasusedtolocatetheeventhypocentreinthetruevelocitymodel.Themeanerrorfor theeventhypocentrelocationcalculatedusingLETdecreasesfromapproximately10.5m, themeanhypocentrelocationerrorinthehomogeneousmodel,toslightlymorethan3.5m.Themeanlocationerrorobtainedafter50iterationsisroughly1.5mhigherthanthemeanhypocentrelocationerrorcalculatedusingthetruemodel,andabout3timessmallerthantheinitialmeanerrorfortheeventhypocentrelocatedinthehomogenousvelocitymodel.

Figure 2 Comparison of original and recovered model parameters: (a) Velocity along the Z-axis for constant X and Y in the middle of the model at three stages of the inversion process. (b) Evolution of the mean event

hypocentre location error during the inversion process

4.0 Real data example: Block Caving

4.1 Context

Weusedadata setcontainingP-waveonset timemeasurementscollectedoveraweekat theheightofseismicactivityduringproduction,andcorrectedtheeventhypocentrelocationsandeventorigintimes.

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4.2 Microseismic system

Themicroseismicactivityattheminewasmonitoredbyanarraycomposedof19triaxialaccelerometersandnineuniaxialgeophones.Thesensorsweredeployedrelativelyclosetotheorebodyina3Dgeometrydesignedtoensureaccuratedetectionandlocationofmicroseismiceventsthroughoutthecavingprocessandtomitigatetheshadowingeffectexpectedfromthegrowingcave.

4.3 Inversion setting

TheP-wavevelocitymodelscoveravolumeextendingover550minthenorthandeastdirectionsand500mintheZdirection,fullyencompassingtheorebodybeingmined.Thespacingbetweenadjacentnodeswassetat5mineverydirection,yieldingamodelcomprising1.21millionnodes.Thestartingvelocitymodelwaschosentobehomogeneous(i.e.,constant),withavelocityvalueof3,900ms-1attributedtoeverynode.ThisvelocitycorrespondstotheaverageP-wavevelocityobtainedwithcalibrationblasts.Theinversionswereperformedindependentlyoneachofthevelocitymodels,and20non-lineariterationswereused.Theregularizationparameterswereset to1x10-13and1x10-7forvelocityandeventhypocentrecorrection,respectively.Thesevalueswereselectedusingaheuristicapproachbasedontheso-calledtrade-offcurve(seeRawlinsonandSambridge,2003).

4.4 Inversion results

Figure3presentstheinversionresultsandshowsin(a),theevolutionofthecumulativetraveltimeresidual,(b),thedifferenceintheestimatedeventhypocentrelocationuncertaintyatthebeginningandendoftheinversionprocess,and(c),(d)and(e),threeperpendicularcut-slicesshowingtheresultingvelocitymodelsandthelocationofthecorrectedmicroseismiceventhypocentres.

Theinversionyieldedadecreaseinthecumulativetraveltimeresidualfromtheinitialvalueof0.65msto0.25msattheendoftheinversionprocess.Notethatthecumulativetraveltimeresidualmeasuresthefit between the predicted andobserved travel times ( ).The event hypocentrelocationuncertainty,whichisestimatedfromthecovariancematrix,decreasedfrommorethan30mforeventslocatedinthehomogeneousgridtoapproximately10mattheendoftheinversionprocess.Thisrepresentsathree-foldimprovement.

In addition, the3DseismicP-wavevelocitydistributionwas calculated.Thevelocitymodel features alarge,lowvelocity(blue)regionsurroundedbyhighvelocity(red).Themainlowvelocityregionislocatedat the bottom, near the extraction level. The range of recovered velocity extends from approximately2,900ms-1to4,550ms-1,withastandarddeviationofcloseto150ms-1.

Thesyntheticand realdataexamplespresented in theprevioussections show thatLETcanbeused tosignificantly improve event hypocentre location in complexmediawith strong velocity contrast, usinginformationreadilycollectedbyamicroseismicmonitoringsystemandwithouttheneedforexplicitmanualconstructionofavelocitymodelusingestimatesofcavegeometry,rockmassproperties,stressstateand(simplified)geologicalunits.


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Figure 3 Tomographic inversion results for the period extending from 6 October 2004 to 13 October 2004. (a) Event hypocentre location uncertainty distribution at the beginning and at the end (after 50 non-linear

iterations). (b) Travel-time residual evolution during the inversion process. (c), (d) and (e) Three perpendicular slices in the velocity model perpendicular to the east, north and z directions, respectively. The black rectangles

give an indication of the volume encompassed by the cave during the time spanned by the data

5 Discussion

For the syntheticcase,LETwasable to reduce the locationerrorbya factorof threecompared to thehomogeneousvelocitymodel.Inaddition,thelocationerrorachievedbyLETisonlyslightlyhigherthanthe smallestpossible locationerrorobtainedusing the truevelocitymodel. In thecaseof the realdataexample,although thevastmajorityof locationerrorscannotbedeterminedsince the true locationsofthemicroseismiceventsinquestionaregenerallynotknown,wehaveshownthattheestimatedlocationuncertaintywasalsoimprovedbyafactorofaboutthreecomparedtothehomogenousvelocitymodel.

Apartfromallowingrelocationoftheeventhypocentre,LETimagesthe3Dvelocitydistributionoftherock,providinginsightsintostressdistributionandcavegeometry.Thevelocitydistributioncanbeusedtosupplementthegeotechnicaldatacollectedduringthecavingprocessandprovideinsightintotherockmassresponsetominingactivity,theprogressionofthecavingfrontandthegeometryofthecave.Wheninversionisrepeatedfordatasetscoveringdifferenttimeperiods,LETcanalsoprovideinformationonthevariationofthe3Dvelocitydistribution.

Theexplicitconstructionofamodelrepresentingthe3Dvelocitydistributionduringthecavingprocessrequiresconsiderablelogisticsandaddstotheburdenofdutiesofatechnicalservicesdepartment.Largeamounts of geotechnical and geological data must be collected (generally manually), rapidly quality-controlled and then distributed andmanaged.To properly build a velocitymodel that is representativeofthetruerockmassvelocity,preciseinformationisrequiredonrockmassproperties,thecavingfrontlocation,thecavegeometryandthestressstateoftherockmass.Inaddition,velocitymodelsneedtobe

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updatedregularlytoaccountfortheprogressionofthecavingfront,thechangingcavegeometryandstressredistributionaroundthecave.Evenafterthiseffort,withthebestdataqualityanddensity,inaccuraciesinthevelocitymodelareinevitable.

6 Conclusions

In this paper,we have demonstrated that local earthquake tomography (LET) can be used to improvetheaccuracyofeventhypocentre locationwithvery little informationabout thevelocitymodelandnoinformationonthecavegeometryorstressstateoftherockmass.WehaveappliedLETtotwodatasets,one synthetic andone froma real block cave.Our results show that event locationuncertainty canbesignificantlyimprovedbyusingLETratherthanhomogeneousvelocitymodels.AnadditionaloutcomeofLET is a 3Dvelocitymodel that provides important insights on the rockmass response tomining,complementingothergeotechnicaldatacollected.Insummary,ourresultsshowthatLETcanprovideanalternativetoanapproachinvolvingthemanualbuildingofa3Dvelocitymodelfromavailablegeotechnicalinformation.

References

Eberhart-Phillips,D1993, ‘Local earthquake tomography: earthquake source regions’, Seism,Tomogr.TheoryPract,pp.613–643.

Glazer,S,HepworthN,2006, ‘Crownpillar failuremechanism–casestudybasedonseismicdata fromPalaboraMine’,Min.Technol,vol.115,pp.75–84.

Glazer,SN2008,‘Seismicallyactivevolumearoundthecaveanditsrelationtothecavingstages’,MassMin2008,LuleåSweden9-11June2008,LuleåUniversityoftechnology,LuleåSweden,pp.983–992.

Glazer,SN,Townsend,P2008,‘TheapplicationofseismicmonitoringtothefutureLift2blockcaveatPalaboraminingcompany’,MassMin2008,LuleåSweden9-11June2008,LuleåUniversityoftechnology,LuleåSweden,pp.919–930.

Huang, J.-W, Reyes-Montes, J,Young, R 2013, ‘Passive three-dimensional microseismic imaging formining-induced rock-mass degradation’, Rock Mechanics for Resources, Energy andEnvironment,CRCPress,1000p.

Hudyma,M,Potvin,Y,2008,’CharacterizingcavinginducedseismicityatRidgewaygoldmine’,MassMin2008,LuleåUniversityoftechnology,LuleåSweden,pp.931–942.

Hudyma,M, Potvin,Y 2010a, ‘An EngineeringApproach to Seismic RiskManagement in HardrockMines’,RockMech.RockEng.,vol.43,pp.891–906.

Hudyma,M, Potvin,Y 2010b. ‘An EngineeringApproach to Seismic RiskManagement in HardrockMines’,RockMech&RockEng.,vol.43,pp.891–906.

Hudyma,MR,Potvin,Y,Allison,DP2007a,‘SeismicmonitoringoftheNorthparkesLift2blockcave-part1undercutting’,1st InternationalSymposiumonBlockandSub-LevelCavingCaveMining,CapeTown,pp.303–334.

Hudyma,MR,Potvin,Y,Allison,DP,2007b,‘SeismicmonitoringoftheNorthparkesLift2blockcave-part2productioncaving’,1stInternationalSymposiumonBlockandSub-LevelCavingCaveMining,CapeTown,pp.335–354.


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Kissling, E, Ellsworth,W, Eberhart-Phillips, D, Kradolfer, U 1994, ‘Initial reference models in localearthquaketomography’,J.Geophys.Res.SolidEarth1978–2012,vol.99,pp.19635–19646.

Lomax,A,Virieux,J,Volant,P,Berge-Thierry,C2000,‘Probabilisticearthquakelocationin3Dandlayeredmodels’,Mod.ApproachesGeophys,vol.18,pp.101–134.

Maxwell,S,Young,R1993,‘Acomparisonbetweencontrolledsourceandpassivesourceseismicvelocityimages’,Bull,Seismol.Soc.Am.,vol.83,pp.1813–1834.

Maxwell,S,Young,R1996,‘Seismicimagingofrockmassresponsestoexcavation’,Int.J.RockMech.Min.Sci.Geomech.Abstr.,vol.33,pp.713–724.

Maxwell,S,Young,R,Read,R1998,‘Amicro-velocitytooltoassesstheexcavationdamagedzone’,Int.J.RockMech.Min.Sci.,vol.35,pp.235–247.

Potvin, Y, Hudyma, M 2008, ‘Interpreting caving mechanisms using microseismic monitoring data’,MassMin2008,LuleåUniversityoftechnology,LuleåSweden,pp.971–982.

Rawlinson,N,Sambridge,M2003, ‘Seismic traveltime tomographyof thecrustand lithosphere’,Adv.Geophys,vol.46,pp.81–198.

Sethian,JA1999,‘Fastmarchingmethods’,SIAMRev.,vol.41,pp.199–235.

Thurber,C,Eberhart-Phillips,D1999, ‘Local earthquake tomographywithflexiblegridding’,Comput.Geosci,vol.25,pp.809–818.

Trifu,C-I,Shumila,V,Burgio,N2007, ‘Characterisationof theCavingFrontatRidgewayMine,NewSouthWales,BasedonGeomechanicalDataandDetailedMicroseismicAnalysis’,ChallengesinDeepandHighStressMining,AustralianCentreforGeomechanics,Perth,Australia,pp.443–453.

White,H, deBeer,W,White,H, vanAs,A2004, ‘Design and ImplementationofSeismicMonitoringSystemsinaBlock-CaveEnvironment’,MassMin2004,Santiago,Chile.

White,H.deBeer,W,White,H,vanAs,A,Allison,D2004,‘Implementationofseismicmonitoringsystemsinablock-caveenvironment’,PresentedattheMassmin2004,Santiago,Chile,pp.559–554.

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Seismic risk management for underground mining projects - Codelco Chile Division El Teniente

AE Espinosa CODELCO Chile Division El Teniente, ChileRA Fuentes CODELCO Chile Division El Teniente, ChileEG Moscoso ERDBEBEN Ltda, Chile

Abstract

The main objective of this article is to propose a hazard seismic description, for each project stage, so they are useful in taking decision at the scopes of design and planning. The final purpose is to minimise the exposure and increase the safety conditions according to each engineering project’s stage. As an example of this proposal, the application of this method for Dacita project is presented. This process has allowed apply a methodology based on geomechanical vulnerability descriptions for mine design and mitigate those vulnerabilities by installing a proper ground support system and safety re-entry times after blasting.

1 Introduction

Miningprojectsneeda seismic riskmanagement.The risksarepresents inminingprojects since theseconsistinauniqueprocesswithobjectivesandtimespanswelldefined.Then,theyhaveuncertainty.Theminingprojectsmanagementconsistsofapplyingknowledge,skills,toolsandtechniquestoachievetheproductionobjectives,assuminguncertaintiesandcosts.According to theProjectManagementInstitute(PMI2008),theriskmanagementbasicallyconsistsofidentifyinghazards,risksevaluationundercertaincriteria,andtheirimpactsandadministration.Thisprocessmustbeiterativeandfed-backwithresults.

TheUKAssociationforProjectManagementestablishesthattheriskmanagementanditsimplementationmustbecarriedoutduringtheearlystagesoftheproject,whenitsdevelopmentismoreflexible.Theriskanalysismustbedoneinthesestagesandmustbeupgradedinthenextstages(Brown2003).

Ingeneral, theriskmanagementofaminingproject takesintoaccountnotgeotechnical issues,suchaspricevariations and exchanges rates (Butcher&Smith2010).However, some researchers suggest thatgeotechnical issues are themost important to be considered in a riskmanagement of aminingproject(Bartlett2010;Catalanetal.2010;Hormazabaletal.2010).

Aminingprojectdevelopmentinvolvesrisksduringminingmethodselection,minedesign,andoperations.The risks could be: geological and geotechnical data, cave-ability, cave propagation, fragmentation,excavation stability, and operational and environmental hazards.Amethodology called CaveRisk wasproposed during the International Caving Study to manage the risks in block caving projects, whichconsidersthepreviousgeomechanicaltopics,andthemoredangerousriskslikerockburst(Brown2003).

Theseismicriskisrelatedtorockburst.Thisoccursinundergroundminingasacombinationofstressesandrockmassconditions.Seismichazardrequiresmanagementfromtheearlyprojectstages.Abouttheconceptualframework,thisproposalisbasedonDunlop&Gaete(2000)concept.Theyproposethattheinduced-seismicitycontrolmustbedoneconsideringundercutandextractionrates,inordertoreducetheactiverockmassvolume,accordingtoitsownmechanicalcharacteristicsandinducedstresses(Dunlop&Gaete2000).

Currently, at El Teniente mine, the seismic hazard estimation for productive sectors is based on themaximummagnitudeexpectedbyusingtheGutenberg-Richterlaw.Thisestimateconsidersavolumeand


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timeperiodassumingthisrockmassvolumehasbeensubjectedtotheentirecavingmineprocessduringthattimeperiod,uptoitstotalfragmentation.Besides,itassumestheminemaintainssimilarundercutandextractionrates.Thismethodallowstoobtainacertaindangerousnessleveltoaprojectinevaluation,butisnotenoughtotakesomecontrolactionsaccordingtogeologicalconditions,geomechanicalenvironment,designandminesequence,foreachengineeringstage,constructionandexecutionofanundergroundmine.

Themainobjectiveofthisarticleistoproposeahazardseismicdescriptionforeachprojectstagetomakedecisions in design andmine planning,which allows to carry out a seismic riskmanagement in eachprojectengineeringstagetominimisetheexposureandrecommendgroundsupportsystemsaccordingtoexpectedrequirements.Finally,itisnecessarytomonitortherockmassresponsetomining.Asanexampleofthisproposal,theevolutionofdecisionsforDacitaprojectispresentedsincethefirststage.Thisprocesshasallowedanapplicationofamethodologybasedongeomechanicalvulnerabilitydescriptionsforminedesignandmitigatethosevulnerabilitiesbyinstallingapropergroundsupportsystemandsafetyre-entrytimesafterblasting.

2 Methodology

Fromageomechanicalpointofview,oneof themain threats for anundergroundcavingproject is theinducedseismicityandrockburst.Thismethodologypresentshoweachengineeringstagetakesintoaccounttheriskmanagementofseismichazardaccordingtoavailabledataandminedesignrequirements.

Thepre-feasibilityandfeasibilityarethemainstagesconsideringtheseismichazard.Inthesestages,thefollowingissuesmustbeconsidered:

1. Groundsupportingalleries.

2. Growthstrategiesandextractionrates.

3. Tolerabledistancesandalternativedrifts.

4. Postblastingisolatedtimesforre-entry.

5. Geomechanicalmonitoring.

Inthisway,theseismichazardmanagementhasobjectiveswelldefined.Theseobjectivesmustbeachievedineachengineeringstageaccordingtoavailabledataandanalysistools.Inthefollowing,thepurposes,scopesandanalysistoolstomanagetheseismicriskaredescribedforeachprojectstage.

Themainconceptsofthismethodologyare:

1. SeismicEvent:rockmassfracturethatreleasesenergyinelasticwaves.Theseelasticwavesaredetectedbyaseismicnetwork.

2. Seismichazard:itisathreatforpeopleandminingplanperformanceduetoseismicevents.Themaximummagnitudeexpectedisameasureofseismichazard.

3. Seismicrisk:combinationofseismichazardprobabilityandnegativeconsequencesforpeopleandminingplan.

4. RiskControlPlan:managementtoavoid,transfer,reduceand/oracceptthehazardconsequences.

5. Residualseismicrisk:quantificationofseismichazardaftertakingthecontrolactions.

Inthefollowing,activities,methodologyandfinalproductsforeachtopicaredescribedtofinallyintegrateeverythinginastrategicplanmatrixofresources.

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2.1 Profile engineering

Themainobjectiveofthisstageistohaveaqualitativedescriptionallowingidentifyrelevantfaultsaboutmine exploitationy achieve a50%of risk certainty.The appliedmethodologies arehazarddescriptionregardinggeologicalandgeotechnicaldataandstresses.

2.1.1 Seismic hazard description

Seismichazarddependsontheseparameters:

• Columnheight:estimateofpre-miningstresses.

• Cavitiesinteraction:estimateofinducedstresses.

• Rockmassgeotechnics(lithologyandmaingeologicalfaults):estimateofrockmassresponsetomining,strainandfracture.

• Gutenber-Richterdistributionsforseismicdatafromproductivesectorsnexttotheproject.

2.1.2 Identify risk potentials

Therisksevaluationisbasedondesignelementsbeingconsideredinthisstage.Someelementsinthisriskqualitativeevaluationare:

• Undercutlevelelevationyverticaldistanceamonglevels.

• Pillarssizeandshapeinextractionandundercutlevels.

• Locationofcavernsandothercivilbuildings.

• Alternativesforhaulagelevelandmineraltransport.

• Startingpointandgrowthsequencetocavepropagation.

2.2 Prefeasiblity Engineering

Designoptionsareevaluatedinthisstage.Therefore,theseismichazardandriskmustbedoneforverydifferentdesignoptions.Thisideacouldbeambiguous,butitisnecessarytocarryouttheanalysiscasebycase,becausedifferentminingmethodscouldinducesimilarseismicrockmassresponse.

2.2.1 Seismic hazard estimate

Inthisstage,ageographicallocationoftheminingproject,andsomespatialandtimelimitsaredefined.Withthisbackground,themethodologyproposedbyGaete(2009)isappliedbyDunlop(Dunlop2010)toestimatethemaximummagnitudeforaseismicevent,byusingsomeparameterscomparingstress,mining,rockmassproperties,andaseismicdatacatalogue.

Inducedseismicityislocatedaroundthecavitygeneratedbymining.Thisseismicitydefinesanactiverockmassvolumeinfailure,Vf,duetoinducedstresses.Then,themaximumseismicmomentexpected,M0,insideofthisactivevolumeis:

M0 = kVf(1)


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wherekisaconstant.Theactivevolumemainlydependsonpre-miningstress,miningratesandrockmasscharacteristics.Iftheseparametersareknownfortheproject(newmine),itsvolumeinfailure p

fV is:

(2)

whereaSis thepre-miningstressvariationfactor,amis theminingratefactor,andar is therockmassvariationfactor.Therefore,themaximumseismicmomentexpected, pM 0 ,fortheprojectis:

(3)

Assumingthestructuralpropertiesoftherockmasscontrolthefracturesgenerationandcavingpropagation,andsimilarminingratesbetweenthereferencemine(known)andtheminingproject:

(4)

whereαcf isrelatedtocohesionandfrictionangleofstructures,andαff isrelatedtofracturesfrequency.ThemomentmagnitudeusedatElTeniente´smineis:

(5)

Then,themaximummagnitudeexpectedforaminingprojectis:

(6)

(7)

Thefirsttermofthesecondmemberinequation(7)isthemaximummagnitudeexpectedrmmax forthe

referencemine.ThismagnitudecanbeestimatedbyapplyingaGutenberg-Richter lawtoseismicdata.Therefore,

(8)

2.2.2 Qualitative risk evaluation - risk matrix

Thequalitativeevaluationofriskconsistsinobtaintheprobabilityofoccurrenceanditseconomicimpactin theminingprocess,due to induced-seismicitynotexpected.Somecontrolactionsaredevelopedandappliedtothenextengineeringstage.Inthisexample,theriskareclassifiedin:

• Tolerable(ProbabilityxImpact≤2

• 2<Moderado(ProbabilityxImpact<4

• Unacceptable≥4

2.2.3 Seismic risk control - instrumentation requirements

Considering the hazard antecedents and qualitative evaluation of seismic risk, a first approach forinstrumentationrequirementscanbedonetodifferentalternativesoftheminingproject.

pfV =(1+αs)(1+αr)Vf

pfV=k=k(1+αs)(1+αm)(1+αr)VfpM 0

=k(1+αs)(1+αcf)(1+αff)Vf()()()fffcfspf VkM aaa +++= 111

m=logM0-6.012_3

=log[k(1+αs)(1+αcf)(1+αff)Vf]-6.012_3()()()[ ]01.6111log

32

max �+++= fffcfsp Vkm aaa

=log[kVf]-6.01+log(1+αs)(1+αcf)(1+αff)]2_3

2_3()()()[ ]01.6111log

32

max �+++= fffcfsp Vkm aaa

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2.3 Feasibility engineering stage

Thefeasibilitystagedoesthedesignoftheminealternativechosenorselectedfromthepreviousstage.Inthisstep,itisnecessaryidentifysectorswelldefinedwheretherearesuitablegeological,geotechnicalandstressconditions.Theseconditionscouldassistingettingadifferentseismicriskresponse.Besides,itisnecessarytoestablishactioncontrolsandanticipatepossiblenegativeeffectsintheminingplan.

2.3.1 Seismic risk - application

Theobjectiveinthisstageisidentifyriskzones,applyingthepreviousconceptsandelements,whichwereestimated.Theresultsarevulnerabilitymapsallowingguide theactioncontrols inminedesignaswellasextractionprocess. In thisstage, the locationandsizesofgalleriesanddriftsmustberevisited,withmodificationsinlayouts.

2.3.2 Seismic risk control - application on mine design

Theminedesignmust incorporate solutions tocontrol the seismic risk.This is applied in locationandgeometryofexcavationsinordertoreducethenegativeconsequencesincaseofsevereseismicity.Thisprocessisiterativebecausethevulnerabilitymapsmustincludethemininglayout,whichcouldbemodifiedaccordingtoriskevaluations.

2.3.3 Residual seismic risk

Theresidualseismicriskestimateandthecontrolactionsmustbeconsideredingeomechanicalguidesforminingplan.Forexample,inthissection,thetransitionzones,there-entrytimes,themaximumextractionrates,theadvancingfrontorientationandgeomechanicalmonitoringtoolsaredefinedandimplemented.

2.4 Detail engineering stage

Thepreviousresultsareconsolidatedintechnicalspecifications,designplanes,budgetsandconstructionsissuesoftheproject.Aboutseismicriskmanagement,thevulnerabilitymustbeconsideredinlayouts,mineadvancingandcontrolactions.

2.4.1 Seismicriskcontrol–specifications

Thesemainlyare:

• Type(brandandmodel)andlocationofseismicsensorsandsesmicnetwork.

• Defineseismicnetworksensibility

• Seismicnetwork installationonfieldaccording todevelopmentsandenergypoweravailability.Itwouldbeusefulanevaluationofgeomechanicalstabilityconditionsofoldergalleriestoinstallseismicsensorsthere.

• Hardware and software requirements for properly performance of seismic network. Suppliersavailabilityanddeliverytimesofproducts.


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Table 1 Sumary of principal elements to be incorporated in each engineering stages.

Engineering stage

Key aspect in the seismic risk control Products Scope of the

evaluationValue for the

project

SCOPING

Location,sizeandtemporalityformining(developmentsand

extraction)

SizingImpactofseismicrisk

Quantifytheexpectedseismic

risk,.

Decisiononthetechnical

feasibilityoftheproject

PRE-FACTIBILITY

(Conceptual)

conceptualgeomechanicalmodel Deskreviewsfor

dimensioningseismicresponse(Deterministic,statistics.)

Usingknownedmethodologies.Atleast50%ofthebackgroundmustcomefromthe

sectorunderstudy.

EvaluationandconsiderationforalternativetradeoffIdentifykeyaspectsin

seismicresponse

FACTIBILITY

(Básica)

Geological,geotechnicaland

mininginfrastructurelayoutmodels.

Vulnerabilityplansaccordingtoplangrowthmining.

Todevelopplansforevaluationandcontrolofseismicriskduetomining

Providescontrolelementsfordimensionedseismicrisk

DETAIL

Technicalspecificationsandbudgetsdirectedtomanagementof

seismicrisk

Providesallthetechnicalbackgroundforacquisitions

andoperationalimplementation

ofcontrolmeasures

3 Application to Dacita´s project

Afirst approach toapply thismethodologywasdoneduring the feasibility stageof theDacitaproject.In thisstage, theseismichazardconceptcarriedoutat twostages.First thedefinitionof themaximummagnitudeexpectedandvulnerabilityplansconstructionsaccordingtogeotechnicaldataandmining.Table2presentsthevaluesoftheseismiccoefficients(Equation8)asusedintheprojectfortheDaciteandtheAndesitelithologies.Itisnotedthatacfdecreasesfrom3.7to1.6(131%incrementregarding1.6)andthataffincreasesfrom0.62to0.85(37%increment).Bothnumbersmeansanincreaseforthe“seismichazard”.

Table 2 Values to estimate maximum magnitude at Dacita project.

Lithology FF/mweakveins(αcf)

Ff/mstrongveins(αff) MaximumMagnitude

Dacite 1.6 0.85 Table3

CMET 3.7 0.621.5(AbovePA(*))

1.1(InPA)2.2(BelowPA)

(*)PA:Preconditioning

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Inrelativeterms,themaximummagnitudeincrementcouldbeobtainedbyreplacingintoEquation(8):

Then,thevaluesfortheprojectarederived(asshowninTable3).

Table 3 Values for expected maximum magnitude at Dacita´s project

Zone Maximummagnitudeexpected

AbovePA 1.5+0.3

InPA 1.1

BelowPA 2.2+0.3

Regarding the vulnerability plan these indicate specific locations for each mine level where it couldbe affected by seismic activity owing to factors, such as: excavation geometry, litholigic environment,presence of geological faults and contact zones. This meant changes in the design according to theidentifiedvulnerabilities.Otherwise,insectorswherewouldimpracticabletomakechangesoflayoutwereimplemented controlmeasures.Thesemeasures include installing additional fortification geometries ofgreatervulnerabilityandachangeofthestartingpointofminingandconditionsforcontinuityofproduction.

4 Conclusions

Theseismicriskmustbeconductedfromtheprofileengineeringstageandincreasethecontrolleveloverpotentiallooseswhilegoingforwardinthedifferentstagesofengineering.Theestimationofmagnitudeformaximumexpectedseismicevent isnotuseful if it isnotaccompaniedwithestimationof location,mining condition (mining advance) and control measurements. This control measurements includealternativesinthelayoutdesigntomitigateloosesinmineralextraction,useofsupportsystemaccordingtotheriskpotentialidentifiedanduseofdifferentisolationtimesbeforeblastingaccordingtogeologicalorgeotechnicalconditions.

References

ProjectManagementInstitutePMI,2008,Guíadelosfundamentosparaladireccióndeproyectos,PMIInc,Pennsylvania.

Brown,ET2003,Block caving geomechanics: InternationalCavingStudy 1997 - 2004, JKRMC (ed),Queensland.

Butcher, RJ,& Smith, G 2010, ‘Strategic considerations in block caving’, Proceedings of the SecondInternationalSymposiumonBlockandSublevelCaving,(Potvined),Perth,pp.231-236.

Bartlett,PJ2010, ¡Considerations inplanningand implementingmassiveundergroundminesatdepth’,Caving2010:Proceedings of theSecond InternationalSymposiumonBlock andSublevelCaving,(Potvined),Perth,pp.359-370.


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Catalan, A, Sinaga, F, & Qudraturrahman, I 2010, ‘The role of geotechnical engineering during theprefeseabilitystudiesandearlyworksofCanadiaEastpanelcavingproject’,Caving2010:ProceedingsoftheSecondInternationalSymposiumonBlockandSublevelCaving,(Potvined),Perth,pp.389-406.

Hormazabal,E,Villegas,F,Rovira,F,&Carranza-Torres,C2010,‘Geomechanicalevaluationofmacro-block cavingoptions using3Dnumericalmodelling atChuquicamata undergroundprojectinChile’,Caving2010:Proceedingsof theSecondInternationalSymposiumonBlockandSublevelCaving,(Potvined),Perth,pp.469-482.

Dunlop, R&Gaete, S 2000, ‘An estimation of the induced seismicity related to a cavingmethod, inDynamicrockmassresponsetomining’,ProceedingsoftheFifthInternationalSymposiumonRockburstandSeismicityinMinesProceedings,RaSiM5,(vanAswegen,DurrheimandOrtleppeds),Johannesburg,pp.281-285.

VanAswegen,G2005,‘RoutineseismichazardassessmentinsomeSouthAfricanmines,inControllingSeismic Risk’, Sixth International Symposium on Rockburst and Seismicity in MinesProceedings,RaSiM6,(Potvin&Hudymaeds),Perth,pp.437-444.

Durrheim,RJ,Cichowicz,A,Ebrahim-Trollope,R,Essrich,F,Goldbach,O,Linzer,LM,Spottiswoode,SM,&Stankiewicz,T2007,‘Guidelines,standardsandbestpracticeforseismichazardassessmentandrockburstriskmanagementinSouthAfricanmines’,DeepMiningProceedings,(Potvined),Perth,pp.249-262.

Spottiswoode,S2009,‘Mineseismicity:predictionorforecasting?’,HardRockSafe:SafetyConference,TheSouthernAfricanInstituteofMiningandMetallurgy,pp.81-98.

Kijko,A&Funk,CW1994,‘Theassessmentofseismichazardinmines’,TheJournalofTheSouthAfricanInstituteofMiningandMetallurgy,July1994,pp.179-185.

Hudyma,M&Potvin,Y2004,‘SeismichazardinWesternAustralianmines’,TheJournalofTheSouthAfricanInstituteofMiningandMetallurgy,June2004,pp.265-276.

Albrecht, J & Potvin,Y 2005, ‘Identifying the factors that control rockburst damage to undergroundexcavations’, Controlling Seismic Risk: Sixth International Symposium on Rockburst andSeismicityinMinesProceedings,RaSiM6,(Potvin&Hudymaeds),Perth,pp.519-528.

Heal,D,Potvin,Y&Hudyma,M2006,‘Evaluatingrockburstdamagepotentialinundergroundmining’,Proceedings of the 41st U.S. Symposium on RockMechanics (USRMS),American RockMechanicsAssociation,Colorado.

Mendecki,A2008,Forecastingseismichazardinmines,inTheFirstSouthernHemisphereInternationalRockMechanicsSymposiumProceedings,Perth,pp.1-17.

Mendecki,A&Lötter,E2011,‘Modellingseismichazardformines’,AustralianEarthquakeEngineeringSociety2011,ConferenceProceedings,BarossaValley,pp.1-20.

VanasAswegen,G&Mendecki,A1999, ‘Mine layout,geological featuresandseismichazard’,FinalReport,SafetyinMinesResearchAdvisoryCommittee,SIMRAC.

Wang,JA&Park,HD2001,‘Comprehensivepredictionofrockburstbasedonanalysisofstrainenergyinrocks’,TunnellingandUndergroundSpaceTechnology,Elsevier,vol.16,pp.49-57.

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Seismic hazard analysis at the El Teniente Mine using a clustering approach

J Cornejo Codelco, ChileJ Vallejos University of Chile, ChileX Emery University of Chile, ChileE Rojas Codelco, Chile

Abstract

Cave mining methods induce changes in rock mass equilibrium conditions around cavities created by mining exploitation. These changes are often represented as seismic activity and its characterization is one of the major challenges related to control people exposure to rock burst risk. At El Teniente mine, important advances have been reached on this field with monitoring and short term protocols, but the study of characterization and identification of zones with higher seismic latency and/or with higher rock burst probability of occurrence is still on progress.

This work explains a proposal for the identification of different levels of seismic hazard, using the agglomerative hierarquic clustering technique. This methodology includes the application of reliability filters and temporal locations, besides the use of pre-processing of residual estimation of hypocenter positions.

Finally, by using back analysis, it is possible to separate groups of seismic events with different characteristics and hazard levels based on statistical criteria, allowing to improve actions in order to mitigate rockburst risk.

1 Introduction

Thechangesinducedbytheapplicationofamassivecavingmethod,suchaspanelcaving,inaprimaryrockmass,generateanimportantredistributionofstressesaroundthecave-back.Thiscanbeseenmostlyintheseismicactivity,generatingrelevantinterferenceswiththeminingbusiness,mainlyforrisksrelatedwithpeople,whichareaconstantfactorofanalysisatCODELCO–ElTeniente.Inthiscontext,zonesnextto themainfaultsand lithologycontactswithdifferentgeotechnicalcharacteristicsareamong themostcomplexzonesformining.Forthatreason,itisprimordialtoidentifythemostdangerousforexposuretopeopletosuddenenergyreleases,toapplymitigationactions.

Fromthenineties,differentauthorshaveinvestigatedthespatialdistributionofseismicactivitythroughclusteranalysis,makingsignificantadvancesintheinclusionofclusteringpatternsrelatedwiththegenesisof seismic activity.At El Teniente, this analysis technique was introduced only in the last five years,achievingsignificantimprovementsintheidentificationoftheseismicsourceassociatedwitheachcluster.However, thepositionof a seismic event is an estimatedparameter subject touncertainties and errors,whichcanbeminimizedbutnotremoved.Onewaytominimizetheeffectofuncertaintiesisnottoconsiderthelocationswheretheyappearfarfromtheseismicsource.Thismethodologyiscompletelysubjective,canproducealossof informationandstronglyinfluencetheresultsofanalyses.Fortheabovereasons,inthisresearch,amethodologyofrepositioningseismiceventsisproposed,basedontheuncertaintyinthepositionestimation,calledstatisticalcollapse.Thissolvestheproblemoftheinformationeffectand


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integratestheuncertaintyintheanalysis.TheresearchwasmadeinasectorcalledEsmeraldaSurBloque1,inthesoutheastofthedeposit.Theresultspermittoapplyriskmitigationmeasuresintheproductionprocess and minimize personnel exposure, knowing beforehand the areas with less favorable seismicresponses.

2 El Teniente Mine overview

ElTenienteMineisaCodelcoChileundergroundcoppermine.ItislocatedintheAndesrangeinthecentralzoneofChile,about70kmSSEfromthecapitalcity,Santiago.ElTenienteisthelargestknowncopper–molybdenumdepositintheworld.Itishostedinacopperporphyrysystem.Themainrocktypesincludeandesite,dioriteandhydrothermalbrecciasoftheMioceneera.Since1906,morethan1,100milliontonsoforehavebeenmined.Themineiscurrentlyextractingaround140,000tons/dayusingmechanizedcavingmethods.Panelandpost-undercutcavingmethods,variationsofthestandardblockcaving,wereintroducedin1982and1994,respectively,toexploitprimarycopperore(InformePlanMinero2014).

3 Background

3.1 Spatial identification of seismically active zones

Mendecki et al. (1999) arguet that the “mainpurposeof spatial analysisof the seismicity is todelimittheareasorvolumesofinterestfromthepointofviewofstability.”Inthemedium-termseismichazard,thespatiallocationsofseismiceventswerevisuallyidentified,oraredelimitedusingtechniquessuchasclusteringorcontrolpolygons(vanAswegen2005).

Thecontouroftheseismicparametersinthespatialdatasetscanbecarriedouttoidentifyareasofmaximumvaluesoftheparametersortofindgradientsofchangeintheparameters.Bothanomalousvariationsinthelocalmaximaandtheareaofgreatestchangehavebeenidentifiedasareaswithrockburstpotential.

Clusteringisthesearchforsetsofobjects,suchthattheobjectsinagroupwillbesimilarto(orrelated)witheachotheranddifferent(orunrelated)totheobjectsofothersets(Haldikietal.2001).Thedefinitionofthegroupsmaybeimpreciseanddependsonthenatureofthedata,andexpectedresults(Wittenetal.2011).Inthiswayforareliablemethodandisnotdependentontheuser’sinfluenceiscrucialbecauseitallowsthereproducibilityofanalysis.Clusteringmethodshavealargenumberofapplications,whichhavebeendocumentedandpublishedespeciallyintheareasofcomputing,biology,economics,rockmechanics,amongothers.Thesemethodscanbeclassifiedintotwowaysofgrouping;agglomerativeanddivisive.Inthecaseoftheagglomerative(oralsoknownashierarchical)called,pairsofindividualdataarecombinedbasedonacriterionofsimilaritytocreategroups.ThesimilaritycriterionisthenappliedtothegroupstocreateahierarchyofAscendingcloseness.InFigure1,sevenelementsA,B,C,D,E,FandGwiththeircorrespondingmapofhierarchyordendrogramareshown(fromJainetal.1999).

Figure 1 Hierarchical clustering of individual items (a) and its corresponding hierarchical dendrogram (b) their relative levels of similarity (Jain et al. 1999)

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Seismicity

Inliteratureseveralmethodsofclusteringanalysisappliedtoseismologyandseismicityinminingcanbefound.Thesestudieshavesoughttomainobjectiveinvestigationofthedistributionofspace-time,energyandmagnitudeofseismiceventsindifferentregionsandscales(FrohlickandDavis1990;Kijko&Funk1996;Falmagne2001;Vasaketal.2004;Hudyma2002,2008,2009;Fuentes2008).

Clusteringconventionalmethodologies,usingthesimilaritymeasureofdistancebetweenthedata,haveadvantagesandintrinsiclimitations.However,allthetechniquesdevelopedinthisfieldconcurthatwhenusedinclusteringdataassociatedwithseismicevents,assumethat thepositionof theseismicevents isunequivocal.

Theassumptionthattheestimatedpositionsofseismiceventsareunequivocalisfarawayfromreality.Thisisbecausethepositionofaseismiceventisestimatedandissubjecttouncertaintiesanderrors,whichresultin,forexample,poorestimateofthetimeofarrivalofPandSwaves,incorrectvelocitystructureorpoorgeometryofnetworkmonitoring.Theseestimationerrorscouldbeminimizedbyperformingthelocationprocesscarefully,buttheuncertaintiescanneverbeeliminatedfromtheobserveddata,i.e.,thearrivaltimesofthewavearethemselvessubjecttouncertainties.

Thecollapsestatisticalmethodologyincorporatesuncertaintyintheestimationofhypocenteroftheseismiceventasafundamentalpartoftheanalysis(Jones&Stewart,1997).PetersandCrosson(1972)proposeamethodologytoestimateuncertaintyfromtheresidueof locationestimationalgorithmtodatafromaseismicmonitoringnetwork.Thusthestandarderrorineachofdimensions,isequalsthesumofsquaresof residualdividedby (n-4),wheren is thenumberof stationsused to locate theseismicevent.Giventheabove,foreachseismiceventwillfeatureanellipsoidofuncertainty,whichequiprobablehypocenterlocationcanberelocatewithoutlosingitscharacteristics.

Thepurposeof thisapproach is to“collapse” the locationofseismicevents tosimplify theanalysisofthesourceorspatialinteraction.Inparticular,thismethodusesadistributionfitperformingahypothesistestaftereachiterations,wherethehypocenterisvariedinsidetheuncertaintyellipsoid,interactingwithenvironmenteventsuntiladjustmentofthedistributionofoveralluncertaintyofeventsismaderelativetoagivendistribution(seeFigure2).

Figure 2 Operation scheme statistical collapse methodology (Jones & Stewart 1997)


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Thealgorithmconsistsofthefollowingstages:

1. Optimizetheobjectivefunctionofthelocationofaparticularseismicevent.

2. Searchevents,wheretheiruncertaintyellipsoidsinteractwiththeeventdefinedinstep1.

3. Calculatethecentroidoftheevents,wheretheiruncertaintyellipsoidsintersect,giventhesameweightforeachofthem.

4. Relocatetheseismiceventdefinedinstep1inthecentroidcalculatedinstep3.

The output of the algorithm consists of 3 steps, which are repeated for each generation of collapsedhypocenter:

1. Performsteps1-4ofthealgorithmtocreateagenerationofcollapsedhypocenter.

2. Foreachseismicevent,calculatethedistancebetweentheiroriginallocationandthenewhypocenter,intermsofstandarddeviationoftheoriginalerrorellipsoid.

3. Compare the distribution of movements with a Chi-squared distribution with three degreesof freedom,usingaKolmogorovhypothesis test,which isbasedon the largedifferences in thecumulativeprobabilitydistribution.Thisoutputalgorithmisrepeateduntilthedifferencesarenotsignificantaccordingtothetest.

3.2 Analysis methodology

To identify relevant groups in the database of seismic data used in this paper, the uncertainty in theestimationofthelocationforrepositioningthroughthestatisticalcollapsealgorithmisused,thenapplytheagglomerativehierarchicalClusteringmethodology.Theapproachconsistsofthefollowingsteps:

1. Definitionofperiod,studyarea,andvolumeofseismicmonitoring,whichcompletelyenclosestheareaofconcern.

2. Exploratorydataanalysis,reliabilityandfiltersspacetime.

3. Relocationusingstatisticalcollapseandclusteringbycompletelinkclustering

4. Categorizationofseismichazardfortheidentifiedclustersusinghistoricalinformation.

5. Identificationofseismichazardzones.

4 Application of the methodology to El Teniente mine

Forthecurrentstudy,wasdefinedananalysiszoneBlock1oftheEsmeraldaSurlocatedinthesouthernpartoftheElTenientemine,whichisoperatedbypanelcavingconventionalpre-conditioningbyhydraulicfracturing,thissectorhasabout26Mtinanareaofapproximately42,500m2(Gallardoetal.2010).Inthisarea,wasisolatedavolumecenteredontheproductionlevelwitharadiusof200m,formingasphere,whichenclosesthelithologicalbodiesandmajorfaultsthatarelocatedinthearea,whichdirectlyaffecttheseismicbehaviorofBlock1associatedwithminingperformedintheinitiationstageofcaving(betweenFebruaryandSeptember2012),whichcorrespondto10,123records.

Table1 showa summaryof theexploratory study results,using these resultswasdefinedasminimumthresholdmagnitudeforthefilterdatareliabilityof-0.65andanuncertaintyof34meters,uncertaintythatallowshavingatleast95%ofthedataandalsodeletesdatabyover2.5timesthemeanuncertainty.

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Table 1 Exploratory study results

Variable/statistical average Standarddeviation Mode P95

Error 12.87 6.776 13.15 34

Mw -0.59 0.306 -0.65 -0.2

The initial filter is related to the spatial location of each event and overall uncertainty in the locationestimation; thus, such adistance is estimated that95%of thedatahave anuncertaintyof less than34meters.Moreover,theminimumsensitivityofseismicmonitoringsystemcalculatedforthestudyareawasused,whichcorrespondstothemodeofthedistributionrecordedintheperiodofanalysis,whichinthiscasecorrespondstothelocalmagnitude-0.65,excluding4,331events(42.8%ofthedatabase).Inordertospace-timefilter,atimecorrespondingtosixmonthsrecurrencewasused,consideringacceptedalleventscontainedwithinanarea,whichinthiscasecorrespondstothesumoftheaverageuncertaintyoftheseismicsystemandthestandarddeviationoftheoveralldistributionofestimationuncertainty.Inthiswayanyeventthatmeetsthesecharacteristicsisconsideredtointeractwiththeeventsoftheirsurroundingseffectively,eliminatingjustsoyouhavenointeractionwithanyeventsintheirenvironment,intotal138recordswereexcluded(1.4%ofthedatabase).Finally,afterapplicationofthefilters5,654eventsdatabasewereaccepted(55.9%),theresultsofapplyingthefilterisdisplayedinFigure3.

Figure 3 Applying reliability and space-time filter.

Then a relocationof the positionof seismic events is performedusing the algorithmcalled “statisticalcollapse”(Jones&Stewart1997).Thisusestheuncertaintyintheestimateofhypocenterasneighborhoodsearch for a global optimization of the position, searching to converge to a known distribution of theestimationerrors(inthiscase,isthesquarerootofachi-squaredistributionofthreedegreesoffreedom)andbyaKolmogorovhypothesistestevaluatesgoodnessoffitofdistributions.Oncethetestresultunderagivenrangeisfound,itisthespatialconfigurationoftheseismiceventswhichbestrepresenttheirpositionwithinellipsoidsofuncertainty,theresultsareshowninFigure4.

Afterrepositioning,weproceededtogroupeventsusingthehierarchicalagglomerativetechnique,calledclusteringbycomplete linkage (CLC). InFigure5, the summaryof theEuclideandistancesof seismiceventsstandardizeddendrogramshown,appliedafterthere-positioningalgorithm.Itcanbeseenthatunderthethreshold2.5atleastthreedistinctgroupsandmorediffusetwoseparategroups.Thentheupperboundrulewasapplied to identify theoptimalnumberof clusters (Figure5),whereonecanobserve that thefirstchangeintrendisproducedaftertheconstructionoffiveclusters.However,betweenfiveandsevengroupshavethesamecoefficientofpurebond,whichisconsideredastheoptimalvalueasmanygroupingsbeforeagainchangingtrendcoefficientlevelpurebond(Mojena,1977).Clusteringofseismiceventsis


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thenperformedinsevengroups,obtainingthedistributionofthenumberofeventspergroupshowninthehistograminFigure5,whereonecanseethatofthesevengroupsidentifiedonlyfourhavethenumberofminimumelementsfortheestimationofseismichazard(atleast250events(Felzer2006)),thusdiscardinggroups1,6and7.Whereupon,forsubsequentanalysisisconsideredasvalidclusters2,3,4and5.

Figure 4 Outcomes from application of statistic colapse

Figure 5 Identification of main groups

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Theobtainedresultswerecomparedusingthefollowingconcepts:

1. Goodness-of-fitofthelinearportionofthecumulativefrequencydistribution-eventmagnitude(Gutenberg-Richterrelationship)foreachgroup.

2. Associationwithminingintermsofitstemporaltrend.

Figure 6 shows, the goodness-of-fit obtained in the linear part of the frequency-magnitude relation fortheclusters identified. Itcanbeseen that ingeneral theclustersdonotpresentmajorchanges inslopeat its bottom (evenwhen adjusting the relation ofGutenberg-Richter to events nonclustered),with theexceptionofgroup3,whichalsohasthesecondlargestmagnitudeestimated(1.6MW).Asfortheestimatedmagnitudes,whichareobtainedforgroups3and4thehighestestimatedmagnitudes,andinthecaseofgroup4to2.0MW,andthisgroupisalsothehighestrelativeprobabilityofexceedingtheestimatedvaluesinceithasthelowestvalueinitsparameterb(b=1.11).Furthermore,itcanbeobservedthatthegrouphasalowerrelativehazardisgroup5,withanestimated1.0maximummagnitude[Mw]andthehighervalueb(b=1.92),whichwasestimatedforthisgrouptheleastrelativeprobabilityofexceedingthemaximumestimatedvalue.Regardingthegoodnessoffitobtainedforgroups,anaveragevalueof0.86,wherethemaximumvaluecorrespondstogroup1(R=0.91)andtheminimumvaluetogroup5(0.82)wasobtainedinwhichthemethodologystatisticalagglomerativehierarchicalclusteringcollapseandrecordsanaverageacceptablefit.

Figure 6 Outcomes for Gutenberg-Richter relation fit

InFigure7,itcanbeseenthatafterthefirstevidenceofconnectiontoupperlevelhasastrongchangeintheslopeofthecumulativefrequencyofgroups2and5,andtoalesserextentingroup3,butnotingroup4,whichisnotaffectedtheirrateofeventsduringtheconnectionprocess,andithasbeenfoundthattheactivefromthebeginningoftheincorporationarea.Giventhis,itispossibletoassociatetheseismicactivityofgroups2and5directlywiththeconnectionprocesstotheupperlevel,inthecaseofgroup4withmining


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on the surroundings of undercut level (especially geometrical changes as undercut or incorporation ofarea)andinthecaseofgroup3thiswouldberelatedinafirststeptominingactivityinthevicinityoftheundercutlevelandthenbeaffectedbytheconnectionprocess.Furthermoreispossibletoseethatgroup2issituatedinthesurroundingsofintrusivebodyofdioritewestlimitedbyfaultsBandP,mainlyneartheleveloftheundercutlevel.Asforgroup3,thisislocatedinthenorth-easternborderofCentraldioriteintrusivebodynorthofthefaultandlowaltitudeJinteractingwithmininginanenvironmentwithstiffnesscontrastbetweenthehostrock(CMET)andintrusivebodyinthevicinityofmajorfaults,asgroup2.Asforthegroup4,thisislocatedatlowaltitude,inthesurroundingsofundercutfront,anddirectlyaffectedtheirbehaviorduetoundercuttask.Finally,group5islocatedsouthofthefaultJ(mainlyheight),andtothenorthofthePfaultontheedgeandinsidethecentraldioriteintrusivebodyinteractingdirectlywiththespreadofthecavityandconnectedtotheupperlevel.

Figure 7 Evolution of mining and identified clusters

Usingthepreviousresultsforclassifyingtheclustersobtainedintermsofmagnitudeenergy,andgeologicalassociationofgroups,anarrayof seismichazardwasconstructed (seeFigure8). In thismatrix, itwasconsideredthehighestlevelofhazardthatthecasecompliancewithallconditionsdefinedaboveasnecessaryand /or sufficient togenerate a rockburst, theaverage levelofhazardwasconsidered theareaswheretheyenergyisatleastestimated10^6[J]and/orestimatedMw1.5orgreater,orgeologicallycomplex

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areas (Landeros&Cornejo2013;Cornejo2013).Concerning the seismichazardareasconsidered low,consideredthosewithoutenergyandmagnitudeestimatedwiththecharacteristicsofrockbursthistoricallyrecorded in theElTenientemine, andalsonot ingeologically complexareas.The resultsobtainedaresummarizedinFigure8.

Figure 8 Classification of seismic hazard

Finally,acleardifferentiationof twozoneswithdifferenthazards in theproduction levelandundercutlevelwasobtained; theseresultsallowdifferentiationhazardmanagementdependingontheareawheremining takes place.With the above, including the possible consequences, as production stop,materialdamageand/orpersonalinjury,itispossibletoestimatetherelativeriskofareaswithactiveminingandimplementmitigationmeasureswhichallowriskmanagementsystems to throughminimizingexposureand/ormodificationofmining,inordertoavoidgeometricalchangesthatunleashanunhandledseismicresponse.

5 Conclusions

Basedontheresultsobtaineditmaybeconcluded:

• For seismic applications, the use of cluster analysis as a pre-processing of data, allows asimplificationoflargedatabasesallowinglargelyreducecalculationtimesforfurtheranalysis.

• The cluster analysis using the similarity function “Euclidean distance”, performs well in thecharacterization of seismic groups. However, given the limitations of the seismic monitoringnetworkoftheElTenientemine,theinclusionofthewholedatabaseconsiderablydecreasestherecognitionperformanceofinterestgroups,inthiswayisimportanttoperformthefiltersbasedoncharacteristicsofseismicinformationthuscarryouttheidentificationofgroupsonareliabledatabase.

• Theclustermethodologywithpre-processingusingthecharacteristicsofthedatabaseand/ortheuncertaintyofhypocenterestimationcanstandardizetheanalysisandobtainbetterperformanceonidentifyingthemostrelevantgroupswithinaseismicdatabase.This,sinceitdecreasesinformationbiaslinkedtowhoperformstheclusteranalysis,therebyminimizingresultsbasedonconclusionspreconceivedwithrespecttoseismicbehavioranalysiszonesandpreventingtheanalysisbeledtoanoutcomeinparticular.

Acknowledgement

TheauthorswouldliketothankCodelcoChile,ElTeniente,toauthorizethepublicationofthisdocumentand,inparticular,toallthepeoplewhomaketheSuperintendencyofGeomechanicsGRMD.


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References

Cornejo,J2013,‘Identificacióndezonasdepeligromedianteanálisisdeagrupamientodeeventossísmicos,minaElTeniente’,MasterThesis,Departmentodeingenieríademinas,UniversidaddeChile.(inspanish)

Cornejo, J & Landeros P 2013, ‘Aplicación de criterio sísmico para estimación de la interferenciaoperacionalasociadaatronadura,minaelteniente’,XVIIISimposiumdeingenieríademinas,SIMIN2013,Santiago,Chile.(inspanish)

Falmagne, V 2001, ‘Quantification of rockmass degradation using microseismic 370 monitoring andapplicationsforminedesign’,PhDThesis,QueensUniversity,Kingston,Canada.400p.

Felzer,K2006,‘CalculatingtheGutenberg-RichterbValue’,AmericanGeophysicalUnionMeeting2006,SanFrancisco,CA,USA.

Frohlick,C&Davis,SD1990,‘Single-linkclusteranalysisasamethodtoevaluatespatialandtemporalpropertiesofearthquakecatalogues’,GeophysicsJournalInternational,vol.100,pp.19-32.

Fuentes,RA2008,‘MineSeismicityRiskAnalysisProgram(MS-RAP)inReno’,TechnicalAssessment.UnpublishedPowerPointpresentation,TenienteTechnicalAdvisoryBoard(TTAB),Rancagua.

Gallardo,M,DíazS,CuelloD&CavieresP2010,IngenieríageomecánicaproyectoEsmeraldaSur,NotaInternaSGM-255/2010.

Halkidi,M,Batistakis,Y&Vazirgiannis,M2001,‘Onclustervalidationtechniques’,JournalofIntelligentInformationSystems,vol.17,Nº2/3,pp.107-145.

Hudyma,MR,Mikula,P&Owen,M2002,‘SeismichazardmappingatMountCharlotteMine’,Proceedingsof the5thNorthAmericanRockMechanicsSymposium,Toronto,07-10 July, (Editors:R.Hammah,W.F.Bawden,J.CurranandM.Telesnicki),UniversityofTorontoPress,Canada,pp.1087-1094.

Hudyma,MR2008,Analysis and InterpretationofClustersofSeismicEvents inMines,PhDThesis–DepartmentofCivilandResourceEngineering,UniversityofWesternAustralia.

Hudyma,MR2009,Esmeraldaseismic riskstudy,1999-2000,ACGreport submitted to theNewMineLevelProject,Perth.

Jain,AK,Murty,MN&Flynn,PJ1999,‘Dataclustering:Areview’,ACMComputingSurveys,vol.31,Nº3,pp.264-323.

Jones,RH&Stewart,RC1997,‘Amethodfordeterminingsignificantstructuresinacloudofearthquakes’,JournalofGeophysicalResearch,vol.102,Nº134,pp.8245-8254.

Kijko,A&Funk,CW1996,‘Space-timeinteractionamongstclustersofmininginducedseismicity’,PureandAppliedGeophysics,vol.147,Nº2,pp.277-288.

Mendecki,AJ,vanAswegen,G&Mountfort,P1999,‘Aguidetoroutineseismicmonitoringinmines’,AHandbookonRockEngineeringPracticesforTabularHardRockMines,(EditorsA.J.JagerandJ.A.Ryder),CredaCommunications,CapeTown.

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Mojena,R1977,‘Hierarchicalgroupingmethodsandstoppingrules:Anevaluation’,ComputerJournal,vol.20,pp.359-363.

Peters,DC&Crosson,RS1972,‘Applicationofpredictionalanalysistohypocenterdeterminationusingalocalarray’,Bull.Seismol.Soc.Am.,vol.62,pp.775-788.

VanAswegen,G2005, ‘Routineseismichazardassessment in someSouthAfricanmines’,ControllingSeismicRisk-RockburstsandSeismicityinMines,(Editors:Y.PotvinandM.R.Hudyma),Perth:AustralianCentreforGeomechanics,pp.437-444.

Vasak, P, Suorineni, FT, Kaiser, PK & Thibodeau, D 2004, ‘Hazard map approach using space-timeclustering analysis of mining-induced microseismicity’, Canadian Institute ofMining andMetallurgyAnnualGeneralMeeting,Edmonton,8p.

Witten,IH,Eibe,F&Hall,MA2011,Datamining:practicalmachinelearningtoolsandtechniques,(3rded.),ELSEVIER,2011.


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Modeling induced seismicity in 4D

E Cordova Codelco, ChileM Nelson University of Utah, USA

Abstract

A new technique has been developed to estimate how seismicity evolves through the mine, making the technique an interesting addition to defining areas with high, medium, and low damage potential due to their embedded seismic history. The use of solid triangulations in representing the areas of interest makes the developed methodology a simple and powerful addition to the study of seismicity in mines. The research illustrates a new technique to model seismic events and combine them into block models, providing the user with the ability to analyze these data as a function of time (4-D) model, with the possibility of combining different analysis criteria to display the data, create sections of the information in any direction needed, cut the data at any elevation to see what has happened through the life and development of the mine. The seismic history of the mine can be displayed and analyzed using the developed technique, defining areas of progressive deterioration associated to the energy levels released by the seismic events.

1 Introduction

Evolutioncanbedefinedasaprogressionorsuccessionofeventsthathaveshapedthewaysomethingistodayduetothechangesithassufferedthroughtime.Evolutionrelatestochange,andifchangecanbestudiedandunderstoodwemightbeabletorealizehowthechangesmightshapefutureevents.

Ifweapplythisthinkingtothewayanundergroundmineevolves,allthedifferentactivitiestakingplacearerelatedtothefinalprocessofextractingtheore.Inminesthatusecavingminingtechniquestheextractionandevolutionoftheminewillproduceacavethatwillinteractwiththesurroundingrockandtherockwillalsorespondandaccommodatetothechangestakingplace.

2 Information

Theseismicresponsetotheevolutionofthemineiscapturedbyaseismicnetworkinstalledinordertorecordtheseismiceventstakingplacealloverthemineduetotheworkingsanddevelopmentstakingplaceonadailybasis.

Theseismicnetworkprovidesaninstantfeedbackofthepulseofthemineandtheworkingconditionsintheareasunderdevelopment,evenreachingapointwheretherecordeddataisusedtoconditionifcertainactivitiesmightbedelayedforsafetyreasons.

Theseismicmonitoringsystemprovidesthedate,time,spatiallocation,associatedlocationerror,moment,energy,triggersactivated,etc.foreachevent,makingitpossibletodefinetheir locationwithrespecttodifferentareasofthemine.

3 Conceptual approach

The succession of seismic events can be thought as the responses from the rock to how themine haschangedovertime,whereareasthatpresentedincreasedseismicactivityhaveahigherdamagepotential.

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Iftheseismichistoryoftheminecanbeorderedandaccountedthroughtime,thenitcanbeusedtostudyhowtherockhasrespondedtothemodifyingconditionsoftheminingprocess.

Datacanbeeasilydisplayedbut there isawholenewspectrumof informationif thesamedatacanbeorderedintimeandanalyzedindifferentwaystoachieveabetterunderstandingofit.

Blockmodelshavebeenwidelyusedinresourceestimationtoestablishwherethegradesareandtodefineareasofinterestwherethegradesshowthattheremightbeapotentialforextraction.

Onasimpledefinition,ablockmodelisabigboxthatcoversthedatathatneedstobemodeled,withthisboxcomposedofsmallerblocks,thebigboxisdefinedbyitsoriginandextentoneachdirection(x,y,z)andthesmallerblocksaredefinedbyitssizeandlocationrelativetotheoriginoftheblock(Figure1).

Figure 1 Block model parameters and sub blocks

Themainadvantageofablockmodelisthatitcanbeusedtostoreinformationfromdifferentvariableslocated in3D, sinceeachblockcanbeassignedsetsofvariableswhere the information is stored,newvariablescanbealsocreated tomanipulateandperformcalculations from theoriginalvariables,whileusingrestrictionsfromotherstoredparameters.

Tostoreinformationintotheblocks,interpolationtechniquesmustbeusedinordertodefinewhichvaluesareusedintheestimationofeachblock.Therearedifferenttechniquesthatrangefromthebasicnearestneighboralgorithmthatassignstheclosestvalueofdatatothecenteroftheblocktomorecomplexonesthattakeintoaccountthedifferentdatatrendswhileminimizingtheerrorintheestimationslikekriginganditsvariations.

Theinversedistancemethodisatechniquethatgivesmoreimportancetothedatathatisclosertothecenteroftheblock,byusinganexponentthatwhenincreasedassignsahigherweightoclosestsamples.

(1)


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Where:

d=distancefromsampletoblockcenter.

w=samplevaluetointerpolate.

x=inversedistancepower.

n=totalnumberofsamples.

4 Modeling methodology

Inordertoaccountfortheseismiceventsandtheirlocationarestrictedinterpolationtechniqueisused,whereonly thevalues that fall insideof theblocksareused in theestimation.Ofcourse this relates totheselectionoftheblocksize,wherebigblocksmightcovertoomuchdataandcouldhidetherequiredchanges,whileverysmallblocksmightnotbeabletoaccumulatearepresentativenumberofeventsovertime.

Asetofvariablesthatrepresentthetimearecreatedtostoretheestimationsatdifferenttimesdependingonthedesiredresolutionoftheanalyses(weekly,monthly,oryearly).

Thesametimevariablesarethenmanipulatedtocalculatethevaluesonaccumulationvariableswheretheeffectoftheprogressiveseismicityisstored.

Restrictingvariablesarealsocreatedtostoredefiningparametersoftheblocklikeanassociatedaveragelocationerrorandthenumberoftriggersforeachevent,thesevariablescanbeusedtorestricttheestimateddatatoblockswithahigherlocationconfidencebyusingonlyblocksthathavealowerlocationerror.

5 Results

Figures2through5showtheprogressiveseismicityaccountedforyear1992upto2012.Withinthemodel,thesameanalysiscanbecarriedoutforanydesiredtimerangeorbetweenspecifictimeperiods(i.e.2004to2012).

Therawinterpolationshowstheaccumulatedenergyreleasesforasectionofthemine,withhigheractivityatthe2,400levelbetween1,000-1,400East(Figure2).

Figure 2 Progressive energy for the model up to year 2012

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Seismicity

Thesamedatacanbereloadedbyrestrictingthatthenumberoftriggersactivatedbytheprogressiveeventsshouldbegreaterthan5(Figure3.)

Figure 3 Progressive energy up to year 2012 with an average of five or more triggers

Thedatacanalsobefilteredtoashowonlyblocksthathavealocationerrorof20morlessbetweenthesamplesusedtointerpolatethevaluestoeachblock(Figure4)

Figure 4 Progressive energy up to year 2012 with location error less than 20 m

Therestrictionscanbecombinedtouseblocksthathaveagreateraccuracyinthevaluesusedtoestimatethem,withalowerlocationerrorandhighernumberoftriggers(Figure5).


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Figure 5 Progressive energy up to year 2012 with location error less than 20 m and an average of five or more triggers

6 Three dimensional modeling

Thedatawasmodeledinthree-dimensionsusingvolumestodisplaytheprogressionoftheeventsmagnitudefrom1992 to2012 ina specificarea.Thedifferentcontours showblockswithaccumulatedmagnitudevalueshigherthanone(blue),five(yellow),andten(red)(Figure6).

Figure 6 Progressive seismic analysis of events magnitude of one (blue), five (yellow), ten (red)

Forthesamearea,themagnitudeevolutionatacut-offvalueof1orhigherismodeledintovolumestoshowhowtheprogressionoccurredforyears,1995,2000,2005,and2010(Figures7to10).

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Seismicity

Themodeledvolumesatdifferentyearsshowthattheevolutionoftheseismicityhasoccurredfromthetopoftheareafromlevel2550to2300uptotheyear1995,tocontinuetolevel2200duringthenextfiveyears,reachinglevel2125atyear2005andexpandingtotheeastduringthelastfiveyears.

Theadvantageofthemodelingisthatthesametypeofanalysescanbeperformedfordifferentyears;thevariablesusedcanberestrictedforothervaluesofinterest(i.e.accumulatedmagnitudegreaterthan5orenergygreaterthan10,000J.

Figure 7 Progressive seismic magnitude up to year 1995 greater or equal than 1

Figure 8 Progressive seismic magnitude of events up to year 2000 greater or equal than 1


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Seismicity

Theevolutionshowsthattheseismicityhasmovedonatop-downmannerforthevolumeunderstudyinlightred.Oncetheseismicityreachedthebottomofthevolume,thenitextendedtotheeastsideofthevolumeatyear2010.

Abetterdetailoftheevolutioncanbeachievedbyapplyingthemethodologyinamonthlybasisoverthewholesetofdata.

7 Applications

Thedefinedmethodologycanbeusedindifferenttypeofanalysestodisplayandunderstandhowseismicityhasmovedthroughdifferentperiodsoftime(Figures11throughto13).

7.1 Seismicity evolution

Checkinghowtheseismicityhasdevelopedovertimeatacertainareaofthemine,thecurrentseismicstateofafutureareacanbedeveloped.

Figure 11 Accumulation of seismicity in the central area due to mining on the yellow project to the right

Figure 12 Accumulation of seismicity in the central area due to mining on the blue (left project) and yellow (project to the right)

7.2 Accumulation of events

Evaluating the accumulation of events according to themagnitude these events have had over certainperiodsoftime.


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Figure 13 Accumulated magnitudes inside the volume under study for different cut-off values

7.3 Relevant events

Evaluatingtherelationshipbetweenrelevantseismiceventsandtheaccumulatedseismicenergybeforetheeventtookplace,tocheckifthemodelhasblocksthatrepresentsuddenincreasesofenergy(Figure14).

Thirty-fourgeomechanicaleventsinoneareawerelocatedwithinthedevelopedblockmodel.Therelevanteventswereanalyzedwiththemodeleddataandthelocationsoftheeventswereusedtolookforblocksthatshowedunusualseismicactivitypriortotheoccurrenceofeachevent.Amonthlyresolutionmodelwasusedtofindtheblocksthathadsuddenincreasedseismicactivityuptoonemonthbeforetherelevanteventoccurred.

Thestudyoftheeventsandtheircorrelationsshowedthefollowingresults:

• Therewere34relevantgeomechanicaleventsinthemineareastudied,whichhadafootprintof160,000m2.Thoseeventswereusedtocomparethelocationsoftheserelevanteventsandtheaccumulatedenergydistributionsintheblockssurroundingtheeventonemonthbeforetheoccurrenceoftheevent.

• Outof34majorstudiedforthesamelevelofthemine,16events(47%)tookplacenearblocksthatshowedincreasedseismicactivitypriortotherelevanteventtakingplace.

• The 16 relevant geomechanical events took place fromMarch 1997 toDecember 2012,with twoeventsin1997,threein1998,threein1999,twoin2001,onein2002andfivein2003.

• Outofthe16events,therewerefoureventsthatshowedseismicactivityinthe10monthsprecedingtheevent,inablockclosetotherelevantseismicevent.

• Outofthe16events,12(calledmainevents)showedanincreaseofenergyinanearbyblocksinaperiod4.1monthsorlessbeforetheevent.

• Ofthese12mainevents,75%showedanincreaseinenergyfrom1to6monthsbeforetheeventtookplace.

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Figure 14 Seismic event location in green with high energy block on top (103,158 J)

7.4 Undercutting face advance

Checkingthebehavioroftheseismiceventsinvolumesduetotheadvanceofanundercuttingfront,todetermineiftheseismicityismovingbehindorinadvanceoftheundercutface.

Figure 15 Seismic energy model (for year 2004) with undercut advance from two projects south (blue) and north (red), showing three energy cut-offs in different views (Top, Front, and Left side views)


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8 Discussion of results

Theapproachofstoringtheseismichistoryofamineinastandardblockmodelcreatesaneffectivetoolforanalyzingandunderstandinghowvariousseismiceventshavemigratedandaffecteddifferentareasofthemineovertime.Theprogressionofseismicitycanbeusedtoestablishtheseismichistoryofareasthatmighthavebeenaffectedbypreviousminingactivities.

Analyzinghowtheseismicityhasaffectedthesurroundingsofnewareastobeminedbycavingtechniquescanbeusefulinestablishingthemostsuitableforthestartoftheundercuttingoftheblock.

Whentheseismicitydataareembeddedintheblocks,seismicactivitycanberelatedinspaceandtimetoactivitiesinthemine,andtheblockviewingfilterscanbemanipulatedtodisplaytheinformationcontainedincertainareastoshowtheseismiceffectsindesiredareasandtimes.Thebehaviorofagivenareaandtheenergyassociatedwithothercavingareasnearbycanbetrackedtoshowtheeffectsfromthecavingprocessovertime.

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Theanalysescanbefilteredfortheindividualblockstoachieveahigherconfidenceinthedata.Thetwofiltersstudiedhere,locationerrorandthenumberoftriggersactivatedbyagivenevent,canbeusedaloneortogethertoincreasetheconsistencyofthedatabeingusedinaparticularanalysis.

9 Conclusions

Themodelingapproachused in this studyhas shown that theenergiesandmagnitudesassociatedwithseismicevents,alongwiththenumbersoftriggersandthelocationerrors,canbeeffectivelyinterpolatedintoablockmodelandthattheresultantdatacanbeusedtodetermineareaswithhistoricseismicitythatmayhaveresultedinaccumulateddeteriorationoftherockmass.

Theability tomodel theseismicenergyassociatedwiththeblocksover timeallowstheanalysisof theevolutionoftheseismicityindifferentareasofthemine.Theresultinginformationcanbedisplayedastwo-dimensionalsectionsinanydirectionrequiredorastriangulatedsolidstobetterunderstandhowtheseismiceventsevolvethroughthemine.

Thenewmethodpresentedherefor interpolationof theseismicdatafacilitates theaccumulationof theseismichistorywithinablockmodel,andthemodelingofpotentialdeteriorationsolids.Thesecanbeusedtostudyhowpreviousminingactivitieshaveinfluencedareaswherenewprojectsarebeingplannedforthefuture.

Theseismicdatacanbedisplayedandlocatedatdifferentlevelsoftheminewhereseismicityhasbeenrecordedthroughtime,forexampleintheundercut,production,haulage,andventilationlevelsofapanelorblockcavingmine.Thesevisualizationscanbeused todefineareaswheresignificantseismicityhasoccurredinthepast,indicatingwherepotentialproblemsmayoccurinthefuture.

Theinterpolateddataprovideapowerfultoolthatfacilitatesanalysisofhowtheseismicityhasevolvedinanareawhereminingwithacavingtechniqueisplanned.Thiswillallowtheidentificationofpreferredlocationsfortheinitiationoftheundercuttingoftheblock,leadingtooptimizedcavingperformance.

Theprogressiveanalysisofseismicactivityaspresentedshowsanewwayoflookingattheevolutionofseismicdatabycombiningthedatawithinverse-distanceinterpolationsandblockmodelingtechniques.Inducedseismicityoccursmainlyasaconsequenceofcavingandundercutting,bothofwhicharedynamicprocesses.Undercuttingeventscanvarydependingontheundercutmethodusedandwhenundercuttingtakesplace in relation tootherdevelopmentactivities.Thesevariations inundercuttingprocedureswillaffectthe“cavability”oftherockmass.

Themethodspresentedhereconstituteanewapproachtothestudyofseismicinformation,byallowingtheassociationofseveralvariablesrelatedtoseismicitywiththeblocksinablockmodel.Thisisconvenientandusefulbecausemineoperators,planners,andengineersuseblockmodelsregularly,andarefamiliarwiththeorganizationandpresentationofdatainthismanner.Theassociationofseismicvariableswiththeblocksinamodelallowstheseismicinformationtobefilteredbasedononeormoreparameters.Suchfilteringcaneliminateminororunimportantseismicevents,allowingamuchclearervisualizationoftheaccumulationofseismicenergyinaparticularareaofinterest.

Thereisagreatpotentialinapplyingthismodelingmethodtostudyingthecorrelationbetweenrelevant,geo-mechanical events thathavecausedproblemsat themineand theblocks thathave shownunusualincreasesinseismicitypriortotheoccurrenceoftherelevanteventtakingplace.TheexamplepresentedinSection7.3considered34relevantgeomechanicalevents,andthemodelshowedthatin16ofthem,therewasanearbyblockthatexperiencedasuddenaccumulationofenergypriortotheoccurrenceoftherelevantevent.


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Though examples are not shown here, it is clear that this modeling approach can be readily used inconjunctionwithnumericalmodelingpackages,suchas,Flac,Map3DorAbaqus,todefinezoneswhererock mass properties have changed, and damage potential might be increased by mining resulting ingeomechanicaleventsleadingtoproblemsinproductionandoperation.

Thevolumesderivedwiththisapproach,showingprogressiveseismicactivity,canbeusedinfiniteelementmodelinganalysestodefineareaswheretherockmasshasbeenchangedovertime,providinganimportanttoolforenhancingnumericalanalysesinthefuture.

References

AppliedGeostatistics,E.H.IsaaksandR.M.Srivastava,OxfordUniversityPress,1989.

Barnes,MP1979,‘DrillHoleInterpolation:EstimatingMineralInventory’,OpenPitMinePlanningandDesign,NewYork:SMEofAIME.

Brown, AR 2004, Interpretation of Three-Dimensional Seismic Data, 6th ed. AAPG Memoir 42,Investigations inGeophysics,Nº9,AmericanAssociationofPetroleumGeologistsand theSocietyofExplorationGeophysicists.

Codelco,2001,FundamentalstotheSeismicityConductionResponseinaCavingMethod,InternalMineReport,Codelco–DivisiónElTeniente,Rancagua,Chile.

Durheim,RJ,Spottiswoode,SM,Roberts,MKC&Brink,A.vanZ2006,‘ComparativeSeismologyoftheWitwatersandBasinandBushveldComplexandEmergingTechnologiestoManagetheRiskofRockbursting’,JournalofSouthAfricanInstituteofMiningandMetallurgy,vol.105,Nº6,pp.409-416.

Essrich,F2005,‘MineSeismologyforRockEngineers–AnOutlineofRequiredCompetencies’,ControllingSeismicRisk,ProceedingsoftheSixthInternationalSymposiumonRockburstandSeismicityinMines,March9–11,Perth,WesternAustralia:AustralianCentreforGeomechanics.

Gibowicz,SJ&Kijko,A1994,‘AnIntroductiontoMiningSeismology’,1sted.InternationalGeophysicsSeries,vol.55,SanDiego:AcademicPress.

Glazer, S&Hepworth,N 2004, ‘SeismicMonitoring ofBlockCaveCrown Pillar – PalaboraMiningCompany,RSA’,ProceedingsofMassmin2004,Santiago,Chile,pp.565-569, InstitutodeIngenierosdeChile.

Gutenberg,B&Richter,CF1954,SeismicityoftheEarthandAssociatedPhenomena,2nded.Princeton,N.J.:PrincetonUniversityPress.

Hudyma,MR,Frenette,P&Leslie,I2010,‘MonitoringOpenStopeCavingatGoldexMine’,Proceedingsof Caving 2010, Second International Symposium on Block and Sublevel Caving, Perth,WesternAustralia:AustralianCentreforGeomechanics.

Hughes,WE&Davey,RK.1979,‘DrillHoleInterpolation:MineralizedInterpolationTechniques’,OpenPitMinePlanningandDesign,NewYork:SMEofAIME.

Moss,A, Diachenko& Townsend, P 2006, ‘Interaction between the block cave and the pit slopes atPalaboraMine’,SymposiumSeriesS44,StabilityofRockSlopes inOpenPitMiningandCivilEngineeringSituations,Johannesburg:SAIMM.

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Spottiswoode,S2009,‘MineSeismicity:PredictionorForecasting?’,Proceedingsofthe1stHardrockSafeSafetyConference,SunCity-Mine.

Stanley, BT 1979, ‘Mineral Model Construction: Principles of Ore-Body Modeling’, Open Pit MinePlanningandDesign,NewYork:SMEofAIME.

Swanson,PL&Sines,CD1991,CharacteristicsofMining-InducedSeismicityandRockBursting inaDeepHard-RockMine,ReportofInvestigations,RI-9393,Washington,DC:U.S.BureauofMines.

Turner,MH&Player,JR2000,‘SeismicityatBigBellMine’,ProceedingsofMassmin2000,Melbourne,Victoria:AusIMM.

White,H,VanAs,A&Allison,D2004, ‘Design and ImplementationofSeismicMonitoringSystemsinaBlock-CaveEnvironment’,ProceedingsofMassmin2004,Santiago,Chile,InstitutodeIngenierosdeChile.

Whyat, JK, White, BG & Blake, W 1996, ‘Structural Stress and Concentration of Mining-InducedSeismicity’,Trans.SME,vol.300,pp.74-82.

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Application of InSAR technologies to measure the subsidence at El Teniente´s Mine

AE Espinosa Codelco, ChileO Mora Altamira Information, España F Sánchez Altamira Information, España

Abstract

The generation of a subsidence crater, derived from an underground mine explotation, has been under study since the 70’s with works done by Peck (1969), Shandbolt (1978), Kvapil et al (1989) and Flores (2005), among the most famous. All of them have been developed using available observations together with the characterization of rock massifs and materials disposition, to elaborate rules that would allow the estimation of the extension of motion caused by the phenomena of subsidence.

With the incorporation of data obtained through high resolution radar satellites and the application of interferometry, it has been possible to make precise measurements over large extensions of land and monitor ground motion. The application of this technique in the measurement of deformations in and around a subsidence crater, has allowed estimating quantitatively the extension on the surface of the effect of underground mining. The utility of this type of information can be identified in the following points:

1. Support in the safety of personnel and equipment working near a subsidence crater.

2. Register the limit at the surface of the extension of the subsidence generated and provide with records for estimating the same effect on the surroundings of the underground mine.

3. Indirect monitoring over the evolution of cavities and register the decrease of the crater bottom.

The use of this technique at El Teniente Codelco’s division in Chile since 2010, has allowed for, among others, proposing a model of the behaviour of the effect of the underground extraction on the surface. With that information, it has been decided as viable, the exploitation of Rajo Sur mine and the positioning of a waste dump at the base of the subsidence crater. This articles shows the results of the measurements of the subsidence that challenges past thinking at the mine. This document summarizes the process starting with the conceptual preparation done by the Geomechanical Superintendence of El Teniente’s Division, up to the practical application and the elaboration of several products that has been done by ALTAMIRA INFORMATION.

1 Introduction

The implementation of techniques that use satellite interferometry to monitor ground motion in thesubsidence crater of El Teniente mine has allowed for the visualization of the extension process (orgrowth)oftheedgeofthesubsidencecrater,attributedtotheextractionfromtheundergroundmine,aswellaslandslidesonthewallsandthedescentofthecraterbottom.Thisdifferentiationtranslatesitselfinidentifyingthreeareaswithdifferentmotionpatterns:

1. Edgeofthecrater(orzoneoftheoreticalbreakage):itisdefinedbetweenthemorphologicedgeofthesubsidencecrater(lossoftheoriginaltopography)andthedetectablelimit(visualorthroughtools)ofthesubsidenceeffect.

2. Exposedwall:itislocatedbetweenthemorphologicedgeofthecrater(Edgeofthecrater)and


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3. Bottomofthecrater:itcorrespondstothesurfacethatmodifiesitstopographyduetoadirectactionoftheundergroundextraction.

Withallthesedataavailable,aconceptualmodeloftheevolutionofthesubsidencecratergrowthhasbeenbuilt,whichallowstosustaincontrolmeasuresoveritsextension,especiallyinthehorizontalcomponentofthegrowth(strictlyoverthetopographicmap).Additionallyandbaseduponthisavailabledata,estimationsoftheverticaldescentweredoneandthelocationoflandslideareasduetotheexposedwallinstabilitiesweredefined.

TheresultofthisprojectisorientedtowardsthefunctionalimplementationoftheInSARdataintheperiodicevaluationoftheriskconditionsinthesubsidencecraterareasandintheuseofpriorinformationtodevelopcontrolactionsforannualand5-yearminingplans.Intheseminingplans,thereisaninteractionbetweenundergroundminingactivitiesandthosedoneinthesubsidencecraterarea.

2 Theoretical background

SpaceborneSARsareactivesystemsonboardsatellites,whichilluminatetheEarth’ssurfacewithaseriesofmicrowave pulses in a side-looking geometry (Duro 2010).While the sensor ismoving through itsorbitalpath,ittransmitsmicrowavepulses.TheemittedsignalinteractswiththeelementsoftheEarth’ssurfaceandpartofthisenergyisbackscatteredtowardsthesatellite.

Presently,thereisalargenumberofspaceborneSARsensors,offeringdatasetsofvaryingsuitabilityforrepeatpassinterferometry.SARimagesacquiredatdifferentwavelengths,withdifferentrangesofswathcoverage,resolutionsandrevisittimesarehighlyaccessible.

2.1 SAR interferometry

SARinterferometry(InSAR)isasignalprocessingtechnique.ItusestwodifferentSARacquisitionsofthesamegroundsurfacefromslightlydifferentpointofviewstocreateanimageofthephasedifferences.Thisphasedifferenceisknownastheinterferogramortheinterferometricphase.

2.2 InSAR main applications

ThemainInSARapplicationstakebenefitofthecapacityofmeasurementdifferencesinthetravelphasebetween repeat passes.Within themining industry, themost important application is the detection ofmovementsofthegroundsurface.Thereareotherapplicationsbasedonradarinterferometryasforexamplechangedetectionbasedontheinterferometriccoherence,classification,soilmoistureanalysis,andothers.

2.3 Estimation of ground deformation maps

The interferometric phase can be directly related to the difference of travel phase between the twoacquisitions.Ifthetwoimageshavebeenacquiredunderthesamepointofview,possiblechangesinthetravelphasewouldmeanthatthegroundtargethaschangeditsposition.Inotherwords,thattherewasadisplacementoftheilluminatedgroundsliceofterrainbetweenthetwoepochs.

2.4 PSI technology

Persistent Scatterer Interferometric techniques are very powerful geodetic tools for land deformationmonitoringthatofferthetypicaladvantagesofthesatelliteremotesensingSAR(SyntheticApertureRadar)systems:awidecoverageatarelativelyhighresolution.ThosetechniquesarebasedontheanalysisofasetofSARimagesacquiredoveragivenarea.

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Inlate1990sandbeginningof2000s,anewInSARprocessingtechniqueappearedbasedonthemulti-imageSARimagescomparisonoverstableorcoherentscatterers,calledPersistentScatterersInterferometry(PSI).ThePermanentScatterertechniquewasthefirstPSImethodologyintroducedinbetween1999and2000.

Duringthepastyears,theStablePointNetwork(SPN)technique,aPSItechniquedevelopedbyALTAMIRAINFORMATION,hasbeentestedinaverylargevarietyofscenariosandinsomecasesunderverydifficultconditions.Therefore,therobustnessandtheflexibilityofthechainiswellknown,andthusarethemainlimitationsandconstrainswithintheactualprocessingworkflow.

3 Methodological development

Theapplicationofsatelliteinterferometrytodeterminethegroundmotionaroundthesubsidencecraterwastheresultoflookingfortoolsthatwouldallow:

1. Obtaininggroundmotiondataremotely,withoutputtingatriskpersonnel.

2. Obtainingmillimetricprecisionofthegroundmotionestimation.

3. Havingatleastaweeklymeasurementfrequency.

4. Fullycoveringtheareaofinterestforeachimageacquisition.

5. Reducingcostscomparedtoaerophotogrametrymeasurements(LIDAR,orthoimagecorrected)inmannedflights.

Thedataacquiredareusedtorecreatetheextractionprocessofthesubsidencecrater,takingintoaccountagroupofeventsthatcausethemodificationofthecrateredgeandthebottomdescent.SincetheuseofInSARmeasurementsforthesubsidencecraterofElTenientemine,theeventspresentedinTable1havebeendeveloped.

Table 1 Stages of development

Phase Objective Tasks Product

Assessmentofthetechnicalfeasibility

(Nov2012–Feb2011)

DeterminetheapplicabilityofInSARmeasurementsforthecraterofelTenientemine.

Images’acquisition,satellitegeometricaldistortionsanalysis,coherenceevaluation.

Decisionaboutthecontinuityoftheproject,satelliteselection,imageacquisition.

Firstestimationofthegroundmotioninthesubsidencecraterarea

(Jan2011–Apr2011)

Obtainamotiondistributionaroundthecrater.

Images’acquisitionandprocessingcoveringtheperiodJanuary-April2011.

InSARestimationofcrateredge(limitofthemotionatthesurface)

Applicationforthecraterbottommeasurements

(July2011)

Obtainamotiondistributionatthebottomofthecrater.

ApplicationofSPNInSARandclassicalDInSAR

Estimationofthedescentmagnitudeanddirectionofthemotionaffectingthecraterbottom.

ALTAMIRAINFORMATIONInSAR’smonitoringprogram-

(July2011todate)

Monitoringtoassessthecraterbehaviour(bottomandsurroundings)accordingtotheminingextraction.

Processinganddeliveryofreports,databaseandspecificassessmentsincaseofanomalies.

Reportseverysixmonthsfortheaccumulativemotion,forspecialeventsevery8days(average)ifrequired


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Asrelevantevents,thereistheestimationoftheareasthatarevisiblebythesatellite.Thesatellitebeamhascoveredcompletelythecraterarea.AnotherfactorthatreinforcestheapplicationoftheInSARtechnologyiswhenanestimationofthecrateredgeisdoneusingthegroundmotiondata,makingthemcoincideinshapeanddimensionwiththeestimationattheedgedonebyanalysingorthorectifiedimages.Asfarasthemonitoringdataisavailable,ithasbeenconfirmedthroughsitevisits,ahighlevelofcoincidencebetweentherealmotionandthedatadeliveredbyInSAR;theseobservationsarecommentedinthispaper.

3.1 Phase 1 – Technical feasibility of InSAR application in measuring ground motion around the subsidence crater of El Teniente mine

TheareaofinterestiscompletelycoveredbyTerraSAR-XandCosmo-SkyMedsatellites,theybothworkwithsimilarparameters,thedatausedtodefinethecoverageandvisibilityarelistedinTable2.

Table 2 Data adquisitions

Cosmo-Skymed TerraSAR-X

02/05/2011 11/27/201002/13/2011 12/19/201002/14/2011 01/10/2011

02/12/2011

Bothsatellitescoveredtheareaofinterest,however,Cosmo-SkyMedofferedabettervisibilityreducingdistortions thanks to amore appropriate incidence angle chosen.Togetherwith this advantage and foravailabilityreasons,itwasdecidedtocontinuetheacquisitionswithCosmo-SkyMed,acquiringanimageevery8days.Figure1shows,fromlefttoright,thecoverageofthesatellitesandthevisibilitymasks.

Figure 1 Analysis for coverage and masks

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3.2 Phase 2 – First estimation of the edge of the crater

Having clear that the edge of the crater establishes the border between the visualmanifestation of thesubsidence (surface sinking) and the areawhere there are only small deformations, as settlements andcracksofafewcentimetres,itispossibletoestimatewhichwouldbetheedgeofthecraterusingInSARdata;wheretheareaswithlowcoherenceareassociatedwithdeformationsatthebottomofthecraterandthehighercoherenceareascorrespondtosmalldeformationsthatareexpectedtooccurattheupperpartofthecrater.Figure2showsaninterferogramobtainedusingtwoTerraSAR-Ximages(January10–February12,2011),theprojectionontheDEMandaviewwiththeundergroundmininginfrastructures.

Figure 2 Interferogram and crater border estimation

TheimageontherightinFigure2showsthedegreeofcoincidencebetweentheedgeofthecraterestimated“manually”andtheedgeobtainedthroughtheinterferometricanalysis.Theassessmentoftheresultallowsproposinganewedgeofthecrater,named“coherent”,thatincorporatesdeformationdatathatisnotseenonsite,andthatarenotconsideredforthedefinitionofthetraditionalcrateredge.

3.3 Phase 3 – Measurements at the bottom of the crater

Once the objective of determining the limit of the subsidence caused by the undergroundminingwasachieved,thefocuswasonestimatingthemotionoccurringatthebottomofthecrater.Animageamplitudeanalysishadtobeusedforthispurpose;furthermore,theresultaddedthemotionvectors(Figure3).

Figure 3 Bottom crater subsidence estimation


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Finally,intheintegrationofthephases,aproductthatcoversthetechnicalexpectationsisobtained,intermsofprecision,onsitecheckingandextensionofthemotionareainthesameevent(Figure4).Intermsofcost,anestimationobtainedbyapplyingInSARtechnologyisaround25%lessthanapplyingtraditionaltools.Figure4showstheresultobtainedinthetechnicalassessmentphase.

Figure 4 Summary of InSar technique evaluation and applications

Afterthethreeevaluationstageswerecompleted,thedecisionwasmadetoapplyInSARforthreeyearsoverthecraterareaandtoobtainthefollowingproducts:timeseriesofthedisplacementatthecrateredgeandbottomandtimeseriesofmotionforeachnaturalreflectorregistered.ThepresentstatusoftheInSARmonitoringcanbedescribedassuccessfulintermsoffulfilmentofradaracquisitions,deliveryofreportsandconsistentdatainrelationwithon-siteobservations.

4 Current application

Presently,theresultsoftheinterferometricanalysisaredisplayedinaveryfunctionalapplicationwhereit ispossibletolookandidentifythegeneralperformanceofsubsidenceinandoutofthecraterandtoreviewtimeseriesfordisplacement(Figures5and6).Thisapplicationisbeingusedtodefinetheborderofmovementbeyondthecrateredge.Forthenextstages,themovementestimationatthebottomofthecraterwillbeusedtorelatethisdatawithundergroundmining,withtheideatocorrelatedescentandextensionofsubsidencewithverticalflowduetoextraction.

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Figura 5 TSViewer data display

Figura 6 TSViewer data display


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5 Conclusions

Aftertheevaluationstage,InSARtechnologyhasprovedappropriatetoestimatetheeffectofsubsidencearoundtheedgeofthecraterintermsofsatisfyingthemainrequirementsforsubsidencemonitoring:

• Obtaininggroundmotiondataremotely,withoutputtingpersonnelatrisk.

• Gettingmillimetricprecisionofthegroundmotionestimation.

• Havingatleastaweeklymeasurementfrequency.

• Fullycoveringtheareaofinterestforeachimageacquisition.

• Reducingcostscomparedtoaerophotogrametrymeasurements(LIDAR,orthoimagecorrected)inmannedflights.

Thiskindofmonitoringallowstogetanimportantamountofdatawhichcanbeusedinstudiesfor:

• Identifyingriskzonesduetowaterormudaccumulationoverthecratersurface.

• Linkingupundergroundextractionwiththesubsidenceeffectoverthesurface(estimatingaratebetweenmineralextractedanddescentandextensiononthesurface).

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Chuquicamata Underground Project subsidence analysis

A Aguayo Codelco, ChileD Villegas Codelco, Chile

Abstract

The superficial expression of underground mining mined by Block Caving, is represented by a depression in the ground called subsidence crater, its perimeter is defined by a failure plane that start at the undercutt level and finish at the surface. These planes originate in the undercut level and have an inclination in relation to the horizontal line, this angle is referred to as the collapse angle, and another angle that defines the limit zone of the effect of the subsidence or zone of influence, which is defined by the fracturing angle. In the Chuquicamata Underground Project, where the crater is located within the current open pit operation, the subsidence angles were estimated considering various aspects including the following:

• Benchmarking information from other similar underground mines.

• The application of empirical methodologies in order to apply experiences from similar mines.

• Results of two-dimensional models, through which it is possible to estimate displacement, settlements and distortions.

The result of the analysis shows a projection for the subsidence angles separated by the current pit wall and elevation, since it depends directly on the geological - geotechnical characteristics of the rock mass in each case. In addition, a zone of influence is defined by the effect of subsidence and a criterion for abandonment of the site is recommended once the underground mining is completed, considering 100 % of the extraction.

1 Introduction

Aftermanyyearsofengineeringstudiesanddevelopment,CodelcoChileisintheprocessofconstructinganundergroundminethatwillreplacethecurrentopenpitextractionmethodwithBlockCavingMethod.

Oneof theplanningvariablesmost relevant for thismethod is thedefinitionofsubsidence thatwillbegeneratedbyundergroundmining,particularlyforthisprojectthatincludesminingintwosimultaneouslevels.

ThegreatamountofinterferencegeneratedbysubsidenceonupperlevelsandinfrastructureofDivisiónChuquicamata,requireknowledgeofthesubsidenceangleswithaccuracygreaterthantheoneprovidedby empiricalmethods. Therefore, it was necessary to estimate these angles by using two-dimensionalnumericalmodels.

2 Methodology

In thecaseofmassiveundergroundminingusingcavingmethods,cavinggenerateacave thatendsupconnectingtothesurface.Thisconnectiontosurfacedefinesacraterusuallycalledsubsidencecrater.Inthegroundadjacent to thecraterperimeteranoticeablecrackingzoneoccurs.Thisnoticeablecracking


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correspondstothemaximumexpressionofdisplacementanddeformationsexperiencedbythelandlocatedwithintheinfluencezoneofthecrater,asthepresenceofthecraterenablestheconvergingdisplacementsoflandtowardsit.Figure1showssomeexamplesofsubsidencecausedbycavingmethods(blockandpanelcaving).

Figure 1 Examples of subsidence caused by caving methods (block and panel caving). Left: Mina Andina, Chile. Right: Mina El Teniente, Chile

ThesurfaceexpressionofminingandundergroundorebodyisrepresentedbyadepressioninthegroundcalledSubsidenceCraterandfromapracticalpointofview,itisinterestingtoevaluatethemagnitudeandextensionofthissubsidenceaswellasitsprobableevolutionovertime.

Itssurfaceexpressionisdefinedbytheintersectionofaseriesofinclinedplaneswithrespecttothegroundsurface.Theseplanesareoriginatedintheundercutlevelandareinclinedwithrespecttothehorizontal;theangleiscalled:“CollapseAngle”(ΨA).Anotherangle,whichdefinesthelimitzoneofsubsidenceeffectorinfluencezoneiscalled“FracturingAngle”(ΨB).

A brief description of parameters defining the geometry of the subsidence crater for ChuquicamataUndergroundprojectasillustratedinFigure2,includes:

• Height of Broken Material:averageheightbetweencavinglevelandsurfaceofbrokenmaterialcolumn.

• Crater Perimeter: surface contour of zone affected by block falling and spillage inside thesubsidencecrater.

• Base Perimeter:basecontourofsubsidencecrater,definedbytheundercutareaintheundercutlevel(UCL).

• Crater Height: verticaldistancebetweenthecraterperimeterandbaseperimeter.

• Collapse Angle (ΨA):averageinclinationofcraterwallsmeasuredbetweenhorizontallineandimaginarylinethatconnectsthebaseandtheedgeofthecrater.Alsoknownasbreakingangle.

• Fracturing Zone (Large Scale): zone adjacent to the crater, where the rock mass has largedeformationsandthereisevidenceoflargesizecracksgenerated(>1m).Thewidthofthiszonevariesindepth,showingthelargerextensioninthesurfaceandthesmallerextensionabovetheundercutlevel.

• Fracturing Angle (ΨB): averageinclinationbetweenthehorizontalandimaginarylinesconnectingthelimitoflargescalefracturingzoneandthebaseofthecrater.

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• Fracturing Zone (Small Scale): Zoneadjacent tofracturingzone,wheretherockmassshowscontinuousdeformationandthereisevidenceofsmallsizecracksgenerated(<1m).Thewidthofthezonelimitsthesurfacesubsidence.

• Subsidence Angle (Ψc): Average inclination between the horizontal and imaginary linesconnectingthesmallscalefracturingzonelimitandthecráterbase.

• Subsidence Angle between Levels (ΨD): Averageinclinationbetweenthehorizontallineoflowerundercutlevels(UCL)anditsprojectiontothesurface.Thisanglecanconnecttothefracturingangleorsubsidenceangleandshouldbesteeperthanboth.

Figure 2 Parameters defining the geometry in a subsidence crater (modified from Vyazmensky 2008)

3 Data

3.1 Characterization of rock mass

ForgeotechnicalcharacterizationofChuquicamataMine,thefollowingBasicGeotechnicalUnits(UGTB)have been defined,which consist of relatively homogeneous ore bodies resulting from overlapping ofalterationunitsonlithologyunits.

Based on the aforesaid, considering the gravel units and leached materials, the following UGTB arerecognized:


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• FortunaGranodiorite(GDF)

• ElenaSurGranodiorite(GES)

• ElenaNorteGranodiorite(GEN)

• Metasediments(MET)

• ModerateShearZone(ZCM)

• IntenseShearZone(ZCI)

• BrecciaBetweenFaults(BEF)

• QuartzGreaterthanSericite(Q>S)

• QuartzEqualtoSericite(Q=S)

• QuartzLessthanSericite(Q<S)

• SericiticEastPorphiry(PES)

• PotasicEastPorphiry(PEK)

• ChloriticEastPorphiry(PEC)

• Leached(HomogeneousandHeterogeneous)

• Gravels

Figure 3 Left: Plan view with geotechnical units of Chuquicamata Mine, pit 2005 (DCN, 2005). Right: Plan view of major VIF structures in Chuquicamata Mine (DCN, 2005).

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Ontheotherhand,structuraldomainsidentifiedinChuquicamataMinecorrespondto:MesabiDomain,NoroesteDomain, BalmacedaDomain, Estanques BlancosDomain, ZaragozaDomain andAmericanaDomain.WhiletheFortunaNorteDomainandFortunaSurDomainarelocatedintheWestSlope,VIF(VeryImportantFaults)structuralsystemshaveapersistencyof500mormoreandFT(Faults)haveapersistencyof100mapproximately.

3.2 Geotechnical properties of rock mass

Properties of the rock mass were calibrated as a function of analysis sections covering the differentgeotechnicalzonesofChuquicamatapit.WiththeseanalysissectionsthebehaviorobservedinslopesofeastandwestwallsofChuquicamatapitwasverified.Table1presentsasummaryofcharacteristicvaluescorresponding tostrengthandstrainproperties forallgeotechnicalunitsaccording to thecalibrationofpropertiesobtainedfrombackanalysesandpitanalysesinyear2012.

Table 1 Rock Mass Properties at Chuquicamata Pit

UGB g (kN/m3) UCS (Mpa) mi GSI D E (Gpa) v

GDF 25.3 100 20 430.70 8.00

0.230.00 10.00

GES 26 100 14 430.70 5.10

0.240.00 8.00

MET 25.9 49 20 400.70 3.10

0.250.00 5.00

ZCM 23.5 50 22 500.70 5.00

0.260.00 6.00

ZCI 22.6 20 22 400.70 4.00

0.270.00 5.00

BEF 24.6 45 20 540.70 8.50

0.230.00 10.00

Q<S 25.4 30 16 500.70 5.00

0.260.00 8.00

Q=S 25.8 60 20 600.70 8.00

0.250.00 10.00

Q>S 26.2 90 25 650.70 8.20

0.230.00 19.50

PES 25 78 20 680.70 15.00

0.220.00 20.00

PEK 25.2 90 21 580.70 8.10

0.230.00 10.00

PEC 25.8 80 17 530.70 13.40

0.220.00 15.00

LIXHOM 25.6 31 20 450.70 8.00

0.230.00 10.00

LIXHET 25.6 25 20 300.70 4.00

0.230.00 6.00


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4 Results

4.1 Empirical methods

Therearedifferentempiricalmethodologiesusedinminingindustrytoestimatethesubsidenceassociatedtomassiveundergroundmining,suchas:Laubscher(2000),methodologyusedbyDivisiónElTenienteandDivisiónAndina,beingthelattertheoneusedinthisanalysis.Estimationofthebreakingangleisdonebyaprocedurethatrelatestopographyorelevationyouwishtoknowtheprobableeffectofsubsidence,theheightoftheprimaryrockcolumnandtheextractionpercentageofeachproductionareaormacroblockatthetimetheprojectionisdone.Itmustbementionedthatthepredictivedesigncurvesofthebreakingangleareclassifiedaccordingtothecompetenceoftherockmass,usingtheRMRindexfromBieniawski(1989)(Figure4).

Figure 4 Methodology of División Andina to estimate the breaking angle or rupture angle (modified from Karzulovic et al. 1997)

ConsiderationsmadeforestimationofsubsidencethroughempiricalmethodologyofDivisiónAndinaarethefollowing:

• ExtractionpercentageofeachMacroBlockused10yearsperiods.

• FinaltopographyofpitaccordingtoPND2013wasconsidered.

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Subsidence

ItmustbementionedthatthemethodologyofDivisiónAndinawaspreparedandcalibratedwithsubsidencedatafordepthslessthan500meters,soforbiggerdepthsthemethodmustbeextrapolated.

4.2 Numerical methods

For two-dimensional numerical analysis software Phase2was used, this considers finite elements.Theanalysisconsideredthefollowing:

• Geotechnicalsectionsandundergroundminingsequencewereconsidered

• Foreachoftheanalysissections,theinsitutensilestatusisdefinedbythefollowingmainstresses:

ο Gravitationalverticalstress.

ο StressratioinEWdirectionwasKEW=1.2andinNSdirectionwasKNS=0.8.

• StructuralsystemsVIFandFTwereexplicitlyconsideredwiththemostunfavorableorientationsfortheslopeaccordingtothecurrentstructuralmodel.

• CurrentrockmasspropertiesfromDivisiónChuquicamatawereconsideredandcalibratedaccordingtothemostimportantinstabilitiesregisteredinthepitandbehaviorobservedinpitslopes.

• AnalterationfactorD=0.7wasconsideredforlowconfinementzones(<3MPa)andD=0valueforhigherconfinementareas(>3MPa).Thicknessofthiszonewasestimatedbetween100and120mthroughanelasticbi-dimensionalmodelthatcorrespondstothemobilizedzoneaccordingtothefieldrecordsfromextensometersinstalledinthearea.

First,itwasnecessarytoreachtheequilibriumconditionofthemodel,consideringthein-situtensilestatusorbeforethemining.Oncethemodelforagivensectionwasbalanced,theminingeffectwassimulatedandtheminingofbencheswiththegeometryconsidered.

Asaresultoftheanalysisforthebasecaseofeachsection,thestrainfield,displacementsandpossiblefailuremechanismsoftherockmasswereobtainedforthedifferentyearsanalyzed.

Figure5showsthedistributionofdifferentgeotechnicalunits,majorVIFstructures,simplifedgeometryofslopesandminingsequenceofundergroundmineforsectionPcorrespondingtoBaseCaseYear2060.

Figure 5 Two-dimensional model Phase2 of profile P that shows the mesh of finite elements with the different geotechnical units, major structures and mining sequence corresponding to Base Case for Year 2060


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Fortheanalysisandinterpretationofresultshorizontaldisplacementswereconsideredaswellasmaximumsheardeformation.TheaforesaidaccordingtohistoricalinstabilitiesregisteredandbehaviorobservedintheslopesofChuquicamatapit.

The thresholdvalueforhorizontaldisplacements for thewestwallhasbeendefinedas5minorder todefinethelimitofthefracturingzonewhichinturnwilldefinethefracturingangle.TheaforesaidwasbasedmostlyoninstabilitiesregisteredinthewestwallandspecificallyontheinstabilityoccurredinNovember2006.Likewise,todefinethesubsidenceangle,athresholdvalueof1mhasbeenconsidered.

Thethresholdvaluefortheeastwallhasbeendefinedas2mforhorizontaldisplacementsinordertodefinethelimitofthefracturingzonewhichinturnwilldefinethefracturingangle.

The aforesaid is based mainly on the instabilities registered in the east wall and specifically on theinstabilityoccurredinMay2010.Similarly,todefinethesubsidenceangleathresholdvalueof1mhasbeenconsidered.

To define the breaking angle and the angle between levels, we have considered the maximum sheardeformation and presence of major VIF structures, which in some cases control deformations or the“connection” to the surface. Figure 6 shows a schematic of structural control that can be present in asubsidencecraterduetothepresenceofmajorstructures.

Figure 6 Schematic that illustrates the structural control in a subsidence crater (modified from Stacey 2007)

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Subsidence

Ingeneralterms,fracturingandsubsidenceanglesinsectorWandNWarelesssteepthanintheeastsectormostlyduetothepresenceoftheWestFault,poorgeotechnicalqualityunits(ZCIandZCM)andGDFunitwhichpresentsa“strong”structuralcontrol.SubsidenceandfracturinganglesinSWsectorwouldbecontrolledbythepresenceoftheWestFaultandpoorgeotechnicalqualityunits(ZCIandZCM)andmajorVIFstructurespresentinthesector.

SubsidenceandfracturinganglesinEandNEsectorswouldbecontrolledbypoorgeotechnicalqualityunits (LIXHOM,LIXHETandMET)andmajorVIFstructurespresent in the sector.SubsidenceandfracturinganglesintheSEsectorwouldbecontrolledbythepoorgeotechnicalqualityunitMETandmajorVIFstructurespresentinthesector.SubsidenceandfracturinganglesinNsectorwouldbecontrolledbymajorVIFstructurespresentinthesectorandWestFault.

Figure7showsanexampleoftheresultsfromfiniteelementstwo-dimensionalmodelswithanestimationofsubsidenceandfracturinganglesasafunctionofhorizontaldisplacementscorrespondingtoBaseCaseandundercutlevel1409.

Figure 7 Two-dimensional model of finite elements in Profile P showing the estimation of subsidence and fracturing angles as a function of horizontal displacements corresponding to Base Case and undercut level

1409


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Table 2 Summary of subsidence angles for different sectors of the Chuquicamata pit – Base Case

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Subsidence

5 Conclusions

Accordingtotheinformationavailableandanalysesperformedthefollowingconclusionscanbeobtained:

• ThemethodologyusedinDivisiónAndinawasappliedforestimationofsubsidenceenvelopesofPMCHSconsideringtheproductionprogramandthecurrentminingmacrosequence.

• Thedesigncriteriadefined in thebasicengineeringstagewereapplied inorder toestimate thesubsidenceenvelopesofPMCHS.

• Inorder toanalyze subsidence through two-dimensionalnumericalmodels,various sectionsofinteresthavebeendefinedwhicharerepresentativeofthedifferentgeotechnicaldesignzonesofChuquicamatapit.Thesesections,likeProfilePdescribedinthisdocument,considerundergroundminingofthefourexploitationlevelsbasedonthecurrentproductionprogramofPMCHS,whichwillbeconsideredas“BaseCase”.Interpretationofresultsindicatesthefollowing:

ο Ingeneralterms,subsidenceandfracturinganglesofWandNWsectorsarelesssteepthanEastsectormostlyduetothepresenceoftheWestFault,poorgeotechnicalqualityunits(ZCIandZCM)andGDFunitwhichpresentsa“strong”structuralcontrol.

ο SubsidenceandfracturinganglesinSWsectorwouldbecontrolledbythepresenceofWestFaultandpoorgeotechnicalqualityunits(ZCIandZCM)andmajorVIFstructurespresentinthesector.

ο SubsidenceandfracturinganglesinEandNEsectorswouldbecontrolledbypoorgeotechnicalqualityunits(LIXHOM,LIXHETyMET)andmajorVIFstructurespresentinthesector.

ο Subsidence and fracturing angles in SE sector would be controlled by poor geotechnicalqualityunitMETandmajorVIFstructurespresentinthesector.

ο Subsidenceand fracturingangles inNsectorwouldbecontrolledbymajorVIF structurespresentinthesectorandtheWestFault.

• The subsidence analysis shown in this document is look at two differentways.The empiricalanalysistakeoldexperiencesinanotherminingoperationsanduseittoplanthenewsubsidencebehaviortakingaccountthemassrockcharacteristics.Bytheothersidethebidimensionalanalysisillustrate the stressanddisplacementsoccur in the rockmasscontourasa resultof thecavingshown like subsidence.The initiative is synchrony the old experiences and the results of themodelingintheprojectionofsubsidenceandbothmustbecalibratedoncethecavingstarts.


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References

BCTecIngenieríayTecnología2013,PropiedadesdeRelavesFiltrados,NotaTécnica.

Bieniawski,Z1989,EngineeringRockMassClassifications,JohnWiley&Sons,NewYork,251p.

Flores,G&Karzulovic,A2002,GeotechnicalGuidelinesforaTransitionfromOpenCuttoundergroundMining:Benchmarkingreport,ReporttoInternationalCavingStudyII,JKMRC:Brisbane.

Itasca2009,‘ChuquicamataUndergroundProject,2009GeotechnicalUpdate’,SubsidenciaporEfectodelCavingMinaElTeniente,XISimposiumdeIngenieríaenMinas(SIMIN’99),(Karzulovic,A.Cavieres,P.&Pardo,C.eds),UniversidaddeSantiagodeChile.

Karzulovic,A1997,SubsidenciaAsociadaalIIIPaneldelaMinaRíoBlancoysuEvoluciónenelTiempo,InformeTécnico,A.Karzulovic&Asoc.Ltda.paraDivisiónAndinadeCODELCO-CHILE.

Laubscher,DH2000,BlockCavingManual,PreparedforInternationalCavingStudy,JKMRCandItascaConsultingGroup,Inc:Brisbane.

Rocscience2010,PHASE2v8.0,FiniteElementAnalysisforExcavationsandSlopes,Canada.

SRKConsulting Chile 2010, Criterios y Parámetros de Subsidencia, Ingeniería Básica ProyectoMinaChuquicamataSubterránea,N09DM41-F11-HATCH-7129-CRTGE04-2000-001,Rev.P.

SRKConsultingChile2013,EstudiodeRiesgoGeotécnicoMinaChuquicamata,DivisiónChuquicamata,CODELCO,InformeTécnicoemitidoenRev.A.

Stacey,TR2007,‘SlopeStabilityinHighstressandhardRockconditions.Keynoteaddress’,ProceedingsoftheInt.Symp.OnRockSlopeStabilityinOpenpitMiningandCivilEngineering,Perth.AustralianCentreforGeomechanics,pp.187-200.


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Unit Mine Operations

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Methodology for up-hole drilling accuracy measurements at Kiruna SLC mine

M Wimmer LKAB, SwedenAA Nordqvist LKAB, SwedenD Billger Inertial Sensing One AB, Sweden

Abstract

Blast function, fragmentation and gravity flow are core elements for sublevel caving (SLC). A high ore recovery and a low waste rock dilution is possible if all three elements work as planned. These elements are affected by a number of given factors and controllable factors. The effect of rock mass characteristics and a semi-confined blasting situation are largely unknown. The controllable factors are related to mine planning (SLC layout and ring design), charge and initiation pattern, performance of drill and blast work, and mucking (draw control). The scale and layout of SLC has changed tremendously over the years which makes accurate upwards production drilling outermost important.

Undesirable borehole deviations are dependent upon errors related to the collaring, alignment and in-hole trajectory deviations. A methodology to separately measure these different components is suggested. The collar and collar alignment is measured with a newly developed system. A set of two inflatable packers are aligned along a rod which is pushed into the borehole. As the packers are inflated by compressed air they adjust to the irregular borehole wall and centralize the system. Its alignment is then measured along a mounted base with pivoting prisms. In-hole deviations are measured by a gyro based system which allows high accuracy measurements also in a magnetically disturbed environment. The geo-referencing of this trajectory is based upon the collar and collar alignment measurement and the total borehole deviation can be quantified. Its implications on the blast result and subsequent gravity flow can then be analysed.

The results of a systematic, in-depth quality control of 282 boreholes are presented.

1 Introduction

Sublevel caving (SLC) is amassminingmethod based upon the utilization of gravity flow of blastedoreandcavedwasterock(Hustrulid&Kvapil,2008).Itreliesontheprinciplethatoreisfragmentedbyblastingwhiletheoverlyinghostrockfracturesandcavesundertheactionofmineinducedstressesandgravity.Therebythecavedwasteoriginatingfromtheoverlyingrockmassfillsthetemporaryvoidcreatedbyoreextraction.TheSLCextractionprocessmaybesimplifiedasinFigure1.AttheheartoftheSLCprocessliethethreecoreelements:blastfunction,fragmentationandgravityflow(Wimmer2012).Theseelementsareaffectedbyanumberofgivenfactorsandcontrollablefactors.

Theuseofbestavailabletechnology(BAT)hasresultedinincreasedminingproductivitybydecreasingdevelopmentandminingcosts.Thereby,thescalehasincreasedtremendouslyattheLKABKirunamine,forexamplefroma12msublevelheightin1983(Hustrulid&Kvapil2008)to28.5minthemid-1990s.Inaddition,theSLClayoutandringdesignwasaltered(Wimmer2012).Onthisaccounthighdemandsaremadeontheperformanceofdrillinglongboreholes(Ø115mm)intermsofqualityandquantity,i.eaccuratelydrilledlongholeswithoutdecreasingthepenetrationrate.

Bothgivenandcontrollablefactorsaresubjecttochange.Miningiscarriedoutatgreaterdepthsatwhichtheminingmethodhasnotpreviouslybeentested.In-situandconfiningstressesincreaseatthesedepthsandthisaffectstheblastperformance.


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Figure 1 System description of the SLC extraction process

Baseduponthesechangesinboundaryconditions,itwouldbeexpectedthatflowbehaviourhasalsochangedduringtheyears.Forthisreason,acomprehensivestudyofgravityflowofbrokenrockwasstartedwithintheframeoftheEU-project“I2Mine”(InnovativeTechnologiesandConceptsfortheIntelligentDeepMineoftheFuture;Hejny2013).SLCmaterialflowfromaspecifictestareaistherebymonitoredbymarkersbasedonRFIDtechnique.Themarkersareinstalledwithintheburdenofblastringsandlaterrecoveredatthedrawpoint.Detailsaboutthedevelopmentandfinalshapeofextractionzoneareobtainablebasedupontherecoveredmarkers.EssentialinputdataforthisexperimentisgoodknowledgeofthedrillingdeviationsinboththeSLCblastringsandmarkerringsin-between.

Themethodologytomeasureup-holedrillingaccuracyanditsresultsaredescribedherein.

2 Measurement of up-hole drilling accuracy

BlastedgeometryandfragmentationitselfarehiddeninthecontrolledSLCoperation.Drillingaccuracyasanessentialcontrollablefactor,isthoughunpredictable.Withoutspecialtools,itise.g.impossibletosayhowlargetheactualburdenandspacingsfortheblastholesare.Generally,boreholedeviationconsistsofvariouscomponents(Ouchterlony2002):

• Collardeviation,i.e.componentduetosetoutofcollar,set-upandcollaring.

• Alignmentdeviation,i.e.componentduetocollarangleerror.

• Trajectorydeviation,i.e.componentduetoin-holedeviations.

Deviation is defined as “measured” – “planned” throughout the paper. Figure 2 shows the individualcomponents.Inparticular,adistinctionbetweentrajectorydeviationandboreholedeviationfromplanisimportant.Theformerisrelatedtotheabilitytodrillstraightholeswhereasthelatterisrelatedtotheabilitytodrillstraightholesaccordingtoplan.

In-hole trajectorydeviations for theWassaraDTHdrillingsystemarenormallywithin1–1.5%of itslengthfor54mlongboreholes(Quinteiro&Fjellborg2008).Thisestimateisbaseduponthemeasurementofmaximumdepthofsightinaborehole(visualobservationofreflectivematerialinthehole).Withtheassumption that in-holedeviations followsacirculararc, thedeviationat thismaximumdepthof sightcorrespondstofourtimestheholediameter.Thetotalboreholedeviationwasquantifiedbyholesdrilled

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throughtoupperlevelsandthesuperioraccuracyofaDTHversusatophammerwasconfirmed.However,thelackofpropermeasuringmethodsatthistime,didnotallowforadistinctionbetweenalignmentandtrajectorydeviation.

Figure 2 Borehole deviation and definitions of error components in SLC (not to scale)1

Themethodologydescribedinthefollowingshouldthoughmeettheseconcerns.

2.1 Collar and collar alignment survey instrument (“C2ASI”)

Tomeasurepost-drillingcollarandcollaralignment,anewsurveyinstrument(“C2ASI”,Figure3)wasdesigned,custom-builtbyComdrillBohrausrüstungenGmbHandcommissioned.

Figure 3 Schematic picture of the collar and collar alignment instrument “C2ASI”

1 TerminologyandcoordinatesystemsrelatedtoanSLCringareexplainedinsection6.


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Apairofinflatablehosepackers(diameter72-160,length500mm)isaxiallymountedona2.5mlongprecisionsteeltube(40x3mm).Afterinsertionintotheborehole,bothpackersareinflatedatthesamemomentusingasmallcompressor.Thesystemgraduallycentralizesintheboreholeandfastensatapressureofaround5barsina115mmborehole.Arodwithtwoprisms(centre-centredistance1m)isattachedtoanadapterattheendofthetube.Theprismsaretiltable(±45°)together,whichfacilitatesmeasurementwithatotalstationfromdifferentviewingdirections.Both,collarandcollaralignmentarededucedfromthesemeasurements.

With respect to SLCproduction holes, the instrument is inserted into the boreholes using a telescopichandlerplatform.Twomeasurements areperformed in the samehole (system rotatedby180°). In thisway,themeasurementerrorcanbereducedbycalculatingtheaveragevalueofmeasurement1and2.Thegoodrepeatabilityofresultsisdemonstratedbythelowangledifferenceofmeasurement1and2whichisonaverage0.498°±0.083°(282measurementpairs).Thelowestmeasuredlimitat0.3°canberelatedtoamarginalfalsepositionoftheadapter.Baseduponthemeasurementtechniqueincombinationwithrelativelylongpackers(0.5m)thesystemcompensatesforirregularboreholewallsandcentralizeswellinmostboreholes.Anexceptiontothatareboreholeswithlargercavities.

2.2 Borehole survey system (“isGyroTM”)

The “isGyroTM” (Inertial SensingOneAB) is a system suited for borehole surveying inmagneticallydisturbed environment, e.g. surveys inside drill rods or in otherwisemagnetically disturbedholes.Thesystemhasaccessoriesandoperationalproceduresthatallowstheusertorunsurveysinvertical,inclinedandhorizontalboreholesandiscommonlyusedininmineralexploration,civilengineeringaswellasoilandgasexploration.

Thestandardsystemconsistsofrunninggear,thesurveyinstrumentwithrechargeablebatteryandaruggedcomputer.Therunninggearconsistsofaprotective38mmpressurebarrelfortheinstrumentplusasetofaccessoriesusedtowinch,pumporbyothermeansgettingthesystemin-andoutoftheborehole.Tofacilitateasmootherrunintheboreholein-linecentralizers(bowsprings)mightbeattached.

Theinstrumentitselfisbasedonso-calledMEMS(micro-electro-mechanicalsystems)sensortechnology.There are three gyro and accelerometer components inside the system, which are mounted alongperpendicularaxes therebyprovidingcontinuousmeasurementof rotationspeedandaccelerationalongthex-,y-andz-axesoftheinstrument.Thesensorsaremountedonseparateboardsmakingthesystemmodular.Thesystemalsocontainsamotherboardwhichhandlestheinternalprocessing,datastorageandBluetoothcommunication.

Priorasurvey, thecomputerand instrumentaresynchronized in time.Acommunicationduringsurvey,whiletheinstrumentisencasedandinthehole,istherebynotnecessary.Thesystemisinsertedintheup-holesbymeansofthehosefromachargingtruck.

The actual survey is started at the hole bottom.The system is then slowly and continuously retractedto pre-set survey stations (measured depths) forwhich the time stamps are recorded on the computer.Ameasuringlengthof2mprovedtobeidealforshortholeswithdesiredhighlyaccuratemeasurementresults.Theinstrumentisheldstationaryateachsurveystation.Thedatarecordedatthesestationsisusedtocomputeinclinationandgravityhighsidebasedonaccelerometerdata.Gyrodatafromthesestationsisusedtoanalyzeandcompensateforanygyrooffsetsignals.Thedatarecordedwhenmovingthesystemfromonestationtothenextisusedtonavigatetheattitudeoftheinstrument(integrategyrosignals)inordertocomputethechangeinazimuthandgyrotoolface.Afterthesurvey,whenBluetoothcommunicationisrestored,thedataistransferredtothecomputer.

Oncethedatahasbeentransferred,itisprocessedbythesurveysoftware.Afastdataprocessing(~1/60ofsurveytime)isduetospecialsignalprocessingalgorithms,whichincludeefficientmemoryhandlingand

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navigationfilters(Kalmanfilter).Finally,coordinatesforallsurveystationsarecalculatedbasedupontheminimumcurvaturemethod.

All boreholes were measured twice with the intention to limit possible measuring errors. If the geo-referencedmeasurements(seeSection2.3)didnotmeetapre-definedrequirement(holebottomswithinaradiusof<0.5m),anothermeasurementforthisspecificholewasmade.

Thesurveyingprecision(seeSection2.4)isrelatedtoseveralfactors,someoftheminherenttothesystemandsomerelatedtothemeasurementprocedure.Theaccuracyofindividualmeasuredcomponentsoftheboreholesurveyareasfollows:inclination±0.15°,gravityhighside±0.2°,gyrotoolface±0.2°andfortheazimuth±0.5°.Thisshould,inprincipal,allowforapositionaccuracyintheorderof0.5%or0.5mfora100mlongborehole.

2.3 Calculation method for final borehole deviation

Thesurveydata(seeSection2.2)isstillnotgeo-referencedsincethegyroinstrumentisnotnorthseeking.

Initially,theorientationoftheboreholesurveysystemwasdirectlydeterminedastheprobelefttheborehole(lastsurveystation).Eithertwopointsweremeasuredalongthepressurebarrel(shortbase)orstartandendpointofalineprojectedbyaparallelalignedlaser(longerbase).Allcoordinateswereadjustedbaseduponthemeasuredazimuthanda3Dpointatthecollar.Thismethodbecamethoughobsoleteasitwasfoundthatthealignmentmeasurementson-sitedidnotprovedtobeaccurateandreproducible.

By contrast, the collar and collar alignment measurements (see Section 2.1) are regarded as highlyaccurateandarethereforethebasisforthecalculationofthefinalboreholedeviation.Thecalculationsandvisualizationarepartofarecentlyin-housedevelopedsoftware.Themaincalculationstepsare:

• A3Dlineofbestfitiscalculatedforthefirstpartoftheborehole(e.g.10m).Theassumptionofnearlystraightholesclosetothecollarseemstobevalidbaseduponearlierobservations(Wimmeretal.2012)andascalculatedRMSvaluesarestillsmall.Iftheunderlyinglengthfortheregressionisreasonablylongalsosmallirregularitiesfromtherunoftheprobeintheboreholearebalanced.

• Theboreholesurveydataisrotatedaroundthez-axissothatthelineofbestfitisparallelwiththemeasuredholealignment.Itsoriginisshiftedtothemeasuredcollar.

• Thesoshifteddataisvisualizedinabull`seyeplot,i.e.theboreholetrajectoryisplottedinrelationtotheintendedposition(centre).Thefinalboreholedeviationisthencalculatedasthemeanforallsurveysthatendwithinaradiusof<0.5m.

2.4 Surveying precision

Thesurveyingprecisionoftheentiremeasurementsystemwasquantifiedbytwotests.

2.4.1 Test 1, survey of boreholes drilled through to upper levels

Twointentionallycurvedtophammerdrilledholesweredrilledfrom907mlevelthroughtoupperlevels(Table1).Hole1showedanexcessivelylargedeviationinxdirection.

ThecomputedresultsaresummarizedinTable2.Withthedescribedmethod(rotationroundzaxis,seeSection3)andstandardparameters(highlightedinTable2),thedeviationΔrfrombreakthroughis0.41mforhole1andrespectively0.46mforhole2.Δxisthemajorcomponentandsuggeststhatthecalculatedtrajectoryadditionallydipsforward(Figure4).


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It should be noted that themeasureddeviation from the real breakthrough is a combinationof severalmeasurementerrorswhichmightberelatedtothesystemorthecomputationalmethod.Mentionableisalsotheuncertaintyinthesetupofthetotalstationandreferencetofixpointsatdifferentminelevelswhichmightbeintheorderof0.1m(Gustafsson2013)2.

Byincreasingthenumberofmeasurements(from2to4)andreducingtheusedlengthforlinearregression(from10to6m)Δrcouldbefurtherdecreasedbelow0.4m.Thelatterassumptionmightwellbejustifiedconsidering that these specificholesweredrilledwith a tophammerwhich allows for strongly curvedboreholes. The results also indicate that with increased station intervals (from 2 to 4 m) measuringinaccuracymightgetlargeraswell.

Table 1 Borehole data for the test of surveying precision3

Boreholeid - 1 2

Minelevel collar m 907breakthrough m 849 878

Holelength l m 55,1 24,3

Measuredcollaralignment sideangle,εside ° 92,5 110,8frontangle,εfront ° 80,5 79,7

Verifieddeviationatbreakthroughfromalignment

Δx m 3,31 -0,17Δy m -0,08 -0,32

Figure 4 Surveying precision, X versus Y (left) and Z versus X (right)

2 Inparticular,thereareindicationsthatthefixpointsatZ=849mhaveanerrorofaround+0.1minsuchawaythatthedeviationΔrforthelongerholewouldbefurtherincreased.

3 Calculationofsideandfrontangleisbasedoncangle=0(seeFigure13).

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Table 2 Data for surveying precision, test 1 4

Numberofmeasurements Hole Station

intervalsTest1:DeviationfromBreakthrough:calculated-real6mlinearregression 10mlinearregression

N id [m] Δx[m] Δy[m] Δr[m] Δx[m] Δy[m] Δr[m]

21 2 0.44 0.15 0.46 0.46 0.05 0.461 4 0.46 0.42 0.62 0.48 0.31 0.572 2 0.29 -0.05 0.30 0.40 -0.11 0.41

41 2 0.33 0.19 0.39 0.31 0.33 0.451 4 0.36 0.36 0.51 0.34 0.47 0.582 2 0.32 -0.20 0.38 0.30 -0.19 0.36

2.4.2 Test2,surveyofanartificialtestsite

A55mlongplasticpipewasburiedinanundergroundrampwiththeintentiontocreateanextremelylargedeviation(trajectorydeviationof19.5m).Inordertoallowforalinearregressionline,theinitial5mofthepipewerestillkeptstraight.Startandend-pointweredeterminedbyatraverselinewhichassureshighaccuracy.

Theresults(Table3)showaremarkablywellagreementofthecalculatedandrealdataforthischallengingtestsite.Thecalculatedend-pointwaswithin0.35m.Themeasureddeviationsoccurprimarilyinz-direction,whichsuggestthatminorproblemsseemtoexistwiththeaccuracyofrepeatedinclinationmeasurements.

Table 3 Data for surveying precision, test 2 5

Numberofmeasurements

Stationintervals Test2:Deviationfromendpoint:calculated-real

N [m] Δx[m] Δy[m] Δz[m] Δr[m]2 1 -0.15 0.02 -0.32 0.352 2 0.03 -0.05 -0.30 0.31

3 Analysis of measurements results

3.1 Data set

Anexperimentaltestareaforanin-depthstudyofgravityflow(seeSection1)wasestablishedinproductionblock9atlevel820mintwoadjacentdrifts(99and101).RFIDmarkerswereinstalledwithintheburdenof5consecutiveSLCblastringsineachdriftandtheirappearanceduringextractionatthedrawpointwasrecorded.

Figure5 shows thedrillpattern for theblast and“marker” rings.Threemarker ringsaredrilled in theburden.Standardblastdesign,aso-called“silo-shaped”ringdesignwasapplied. It involvesdrillingof8holeswithfairlysteepsideangles(73°)andlongmidholes(54m).Theindividualringshaveafrontangleof80°andaprojectedburdenalongthedriftof3m.Specificdrillingamountstoabout0.03m/tonneforafull-sizedring,yieldingatonnageof10,000tonnesofore.Threemarkerringswithtotalof17holes(diameter155mm)weredrilledparalleltotheblastringplaneineachburden.Thedesignfortworings

4 Boreholesaredepthcorrected(Δz=0).5 Geo-referencingisdoneby5mlinearregression(seesection2.3).Thedeviationiscalculatedasorthogonaldistancefromthe

(extrapolated)measuredlinetotheknownend-point.


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(ring1and3)wasidenticalwith6holeseachwhereasring2wasstaggeredwithonly5holes.Allholeshadadiameterof115mm.Theyweredrilledwithanautomateddrillrig(SimbaW6C,AtlasCopco)equippedwithadown-the-holehammer(W100DTH,Wassara)andpoweredbyahigh-pressurewaterpump.Therockqualitywasclassifiedthroughoutthetestareaasgood.

Figure 5 Drill pattern for blast and marker rings

Knowledgeoftheactualplacementoftheseholesinrelationtotheblastholeswasoftheutmostimportanceifdetailedconclusionsoninternalflowmechanisms,e.g.shallowdraworbackbreak(Wimmer2012)weretobemade.Adistinctionbetweenblast-ormarkerholeswasnotmadeinthefurtheranalysis.

3.2 Collar deviation

MeasuredcollardeviationsareplottedinFigure6andFigure7.Tocompensateforpossibleirregularitiesofthedriftroof,themeasuredzcoordinatewasshiftedtotheplannedone.

AnoffsetbetweensubsequentringstowardspositiveΔx(0.28m),i.e.forwardinthelongitudinaldirectionofthedrift,exists.Inexceptionalcases,itcanbeaslargeas0.85m.Aslongastheoffsetremainsconstant,thisimpliesessentiallyanunchangedburden.

Deviations inydirection,Δy, aremuch larger indicatedby thevariabilityoutside theupperand lowerquartiles.Clearly,atrendexiststhatdeviationsincreasetowardsthesidesinsuchawaythattheboreholecollars are successively shifted towards themidline. Primarily, this type of deviation causes a reducedwidthoftheSLCringsatthecollarby0.68±0.09m.Ifthealignmentdeviation(seeSection3.3)doesnotcompensatewithflattersideholes,thiswillultimatelyimplynarrowerboundariesoftheblastedringface.

ThesystematiccharacterofthedeviationsinbothΔxandΔydirectionsuggestsproblemsrelatedtosetoutofthedrillplaneandset-upofthedrillrig.Onthecontrary,collaringerrorswouldbeassumedtobemorestochastically.WithrespecttodeviationsinΔy,theuseddrillrig(SimbaW6C,AtlasCopco)isamajorinfluencingfactorasthedrillingpatternwasactuallyplannedbaseduponthecapacitiesforthestandarddrillrig(Solo8-W100,Sandvik,seeSection6).Fortheuseddrillrigtheoffsetfromthemid-lineislimitedto1.5m(insteadof1.8m)whichmakesitnecessaryfortheoperatortoshiftthesideholesinwards.Thesideanglesarethoughunchangedfordrilling,whichthenresultsinaparallelshiftoftheseholes.

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Figure 6 Collar deviation. Data points: 282 (each)6

Figure 7 Collar deviation Δx and Δy versus side angle

3.3 Alignment deviation

Alignmentdeviations(Figure13)intermsofholesandringsareplottedinFigure8.

Forindividualholesitwasfoundthatthesideanglesareslightlylargerthanplanned,i.e.Δεside=0.14°.Thiscausestheholestoberotatedinthedrillplanetowardspositiveydirection.Minimumoutliers(31samples)wereidentifiedtooccurinparticularforholesattherightsideofthedriftwithasideanglefrom95–107°.Alsothefrontanglesarelargerthanplanned(Δεfront=0.30°)whichimpliessteeperholescloseatthecollar.Someoutliers(±2°)existbothtowardslargerandsmallerfrontangles.

Abestfitplanewascalculatedforallalignmentmeasurementsinasinglering.AcalculatedRMSvalueof0.02±0.01millustratesthatdeviationswithintheringplaneareinsignificant.Thedeviationsforthefrontangle(Δεfront=0.24°)stillshowthattheringplaneatthecollarissomewhatsteeperbutwithasmallervariability.Additionallyaminorrotationoftheringplaneoccurs(Δc=-0.17°,i.e.rotationtowardsnegativex).Asbothofthesedeviationsarereferredtotheringtheycouldbeconsideredtobeaset-uperror.

6 Themedianisrepresentedwithan“x”markerandhorizontalmarkersareusedforthefirstquartile(Q1)andthirdquartile(Q3).Theendsofthewhiskeraresetat1.5*IQR(interquartilerange)aboveQ3and1.5*IQRbelowQ1.Iftheminimumormaximumvaluesareoutsidethisrange,thentheyareshownasoutliers(minandmaxvaluesshownonly).


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Figure 8 Alignment deviation. Data points: 282 (holes), 44 (rings)

3.4 Trajectory deviation

Thetrajectorydeviationmeasuredfordifferentholelengths(20,30and40m)isshowninFigure9.Itisassumedthatthemedianfollowacirculararcandthusbeingquadraticallyproportionalwithlength:

Where:

k=bending.

r=bendradius.

d=boreholelength.

θ=angleatspecificboreholelength.

Figure 9 Trajectory deviation, measured and calculated. Data points: 275/156/64

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Forthelongestholes(48m),thetrajectorymediandeviationisestimatedtobe1.56mifthebendradiusis740m.Theshowndeviationsarebasedonallmeasuredholes(Ø115mm)anddonotdistinguishbetweendifferentside-andfrontangles.

Figure10displaysthetrajectorydeviationforanindividualring(blastring20,drift99)inabull`seyeplot7.

Figure 10 Trajectory deviation, drift 99, blast ring 20, holes 1-8

Foremost,itcanbeobservedthatallholesareflatterthanplanned.Thiseffectislengthdependentwiththelargestdeviationsforthelongestholes(Down=-1.6m).Themidholes(hole4and5)donothaveanysignificantdeviationtothesides.Bycontrast, thesideholes1-3deviatetowardstherightsideandsideholes6-8totheleftrespectively.Hole3hasthelargestdeviationtotheside(Right=0.6m).Figure11summarizesthedescribedeffectforallmeasurementswiththedeviationsplottedat20mboreholelengthandgroupedwithrespecttothesideangle.

Figure 11 Deviation at 20 m length for different side angles, confidence region (95%)

Theactualmechanismsfortheobserveddeviationsarenotunderstood.Furtherinvestigationsareplanned.Thesymmetriccharactersuggeststhatthedirectionoftorquehasaratherinsignificanteffectontrajectorydeviations.Inaddition,changesinfeedforceandrockparametersseemtohaveminoreffect.Bycontrast,effectsrelatedtotheactualset-upofthemachine,e.g.thefixationofdrillboominthedrift,closematch

7 Thecentrerepresentsthemeasuredactualcollaralignment(seeFigure2andsection2.1)asadirectionvector( ). isa

vectorinhorizontalplaneandperpendicularto .“Positive”isdefinedtotherightwhenlookinginwardsthehole. isa

vectorinverticalplane,perpendicularto( )andLRpositiveupwards( = × ).


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ofdrillbitandhammer,etc.mightberelevant.Likely,alsothegravitationfieldplaysanimportantrole.Itmightbeseenasavaryinglateralloadonabeaminbending(DTHhammer-drillrod–feeder)which,isdependentonthelocalchangeinanglealongthestructure.

3.5 Drilled burden

From a blasting point of view, the relative accuracy in between theSLC rings is a decisive factor forcompleted breakage.This concerns both deviationswithin and betweenSLC rings.The drilled burdenmightbeestimatedbymeansofahorizontalxyplot(Figure12).

Figure 12 Drilled burden for blast ring 20, drift 99, front view (left) and top-view (right)

Forthespecificexample(blastring20,drift99)themaximumprojectedburdenvariedatdifferentheightsbetween2.6–3.1m. It shouldnot infer anybreakageproblems.Also, the aforementioned symmetriccurvatureofholes(seeSection3.4)canbeidentifiedwhichresultsinasomewhatnarrowerringarea.

Ingeneral,thesystematiccharacterofdeviationsbetweenrings,i.e.apositivecollaroffset(seeSection3.2)andflatterdrilledholes,wasfound(seeSection3.4),whichimpliesaconstantoffsetwithanessentiallyunchangedburden.

4 Conclusions and future work

Amethodologytomeasureup-holedrillingaccuracywaspresented.Itcomprisesofthedeterminationofallcomponentsforboreholedeviation,i.e.collar,collaralignmentandin-holetrajectory.Aninstrumentwasdevelopedtoaccuratelymeasurepostdrill-drillingcollarandcollaralignment.In-holetrajectoriesweremeasuredusingagyrobasedsystem.Forthetotalboreholedeviation,thesetwomeasurementsarelinked.Surveyingprecisionwasverifiedandfoundtobeacceptable(<0.7%ofitslengthfora55mlonghole).Bycontrast,themeasuredin-holetrajectorydeviationsnormallyshowedtobeafactorof3-4larger.

Theresultsofasystematic,in-depthqualitycontrolof282boreholeswerepresented.Implicationsontheblastresultandlatergravityflowwasshownbasedonanexample.Amoredetailedoverallinvestigationispending.

Withthepresentedmethodology,itisnowpossibletoreliablyandaccuratelysurveyboreholesofalltypes.Thisalsoprovidesabasisforfuturedevelopments,e.g.tocontrolfuturisticSLCdesignswithcurvedholes(Hustrulid&Kvapil2008)ortopreciselypositionsensorsintheblastedburdentomonitortheeffectsofconfinedblastinginfull-scale(Wimmer2012).

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TheterminologyandcoordinatesystemsrelatedtoanSLCringareexplainedinFigure13.

Figure 13: Terminology for an SLC ring, schematic

References

Gustafsson,J2013,Personalcommunication.

Hejny,H2013,‘I2Mine–Innovativetechnologiesandconceptsfortheintelligentdeepmineofthefuture’,23rdWorldMiningCongress,paper246,onCD,Montréal,Canada:CIM.

Hustrulid,W&Kvapil,R2008,‘Sublevelcaving–pastandfuture’, 5thInternationalConferenceandExhibition onMassMining, (H. Schunnesson & E. Nordlund Eds.), pp. 107-132, Luleå,Sweden:LuleåUniversityofTechnology.

Kvapil,R1998,‘Themechanicsanddesignofsublevelcavingsystems’,Techniquesinundergroundmining.Selectionsfromundergroundminingmethodshandbook,(R.E.Gertsch&R.L.Bullock),pp.621-653,Littleton,USA:SocietyforMining,Metallurgy,andExploration,Inc.

Ouchterlony,F2002,Borrhålsavvikelservidsprängningavslänter,ErfarenheterfrånmätningariSödertäleDrillholedeviations in a roadcutperimeter, experiences frommeasurements atSödertälje,SveBeFoReport53,Stockholm,Sweden:SwedishRockEngineeringResearch.

Quinteiro,C&Fjellborg,S2008,‘MeasurementsofboreholedeviationinsublevelcavingfansatKirunamine’, Proceedings of the 5th International Conference and Exhibition on Mass Mining,(H. Schunnesson& E. Nordlund Eds.), pp. 543-551, Luleå, Sweden: Luleå University ofTechnology.

Wimmer,M2012,Towardsunderstandingbreakageandflowinsublevelcaving(SLC)–Developmentofnewmeasurementtechniquesandresultsfromfull-scaletests,PhDThesis,LuleåUniversityofTechnology,Luleå.

Wimmer,M,Nordqvist,A,Ouchterlony,F&Selldén,H2012,3Dmappingofsublevelcaving(SLC)ringsandflowdisturbancesintheLKABKirunamine,SwebrecReport2012:P1,Luleå,Sweden:LuleåUniversityofTechnology.


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Analysis of geometric design in ventilation raises for Block Cave production level drifts

JP Hurtado Universidad de Santiago de Chile, ChileYH San Martín Universidad de Santiago de Chile, Chile

Abstract

Block Cave mining methods and their ventilation systems have both evolved through time, starting in the early 50’s, with manual operation system, until nowadays with highly mechanized LHD system. Ventilation systems are designed according to the mining method and the country of residence legal requirements. In a mechanized LHD Block Cave, the production level drifts are the most polluted area of the mine. Every production drift contributes gases from LHD equipment and dust from extracting, transporting and dumping the ore. As a result production drifts have one of the largest airflow volume requirements at the mine. This paper analyses the impact of different raise diameters in terms of energy consumption of the system through the use of an experimental scale model coupled with a commercial mine ventilation network software. Additionally, different curvature radiuses are introduced to the experimental models generating important improvements in terms of energy consumption. The methodology here developed could be used to improve the future designs of mine ventilation systems to save energy and to help improve the underground mine environment.

1 Introduction

VentilationsystemsinBlockCavemineshavebeenstudiedbyseveralauthors.Calizaya&Mutama(2004)presentacomparativeevaluationoffourventilationsystemsforBlockCavemineoperations.Thesystemsareillustratedwithrealmineexamplesshowingthecriticaldesignaspects,thebasicrequirements,andthelimitationsbeyondwhichthesystembecomesinefficient.Inthisway,mechanizedBlockCaveinvolvesactivitiesonmanylevels,allofthemrequiredtoachieveproduction.Fromthoselevels,theproductionleveldriftsareoneofthemainventilationconcerns,becauseoftheairflowvolumerequirementneededtodiluteandremovecontaminantsasgasesanddustduetoload,haulanddumpactivities(Hurtadoetal.2010).Thecircuittheairfollowsfromthefreshairintaketotheexhaustisverytortuous,withsingularitiesresultinginhighshocklosses,whichareusuallynotproperlyaccountedforinventilationmodelsresultingfromalackofavailabletabulateddataforparticulargeometries.ForElTenienteMine,theairvolumecirculatingintheproductiondriftscanbetheorderof14-30m3/s,dependingofthenumberofloadequipmentworkinginthesamestretch(usually1or2).

Shocklosseshavebeenlessstudiedinmineventilationthaninpiping,butsomepreviousworkshavegivenimportantinformationaboutshocklossesforBlockCavemines.Hurtadoetal.2010)studiedtheintakeandexhaustshocklossesofProductionLeveldrifts,mainlyfocusedatElTenienteperformanceventilationsystem,bymeanofCFDtechniques.Thisworkhelpsunderstandtheturbulentbehaviourofairflowinadrift,butthevaluesofshocklosseswerenon-calibrated.Subsequently,Hurtadoetal.(2012a;2012b;2014)developedanexperimentalandCFDmodelling,whichallowscalibratingtheshocklossesvaluestoarealscalesizedrift.ValuesofshocklosseswereobtainedandalsoincludedtheimpactofasimplegeometrymodificationtothecurvatureradiusoftheElbow-Split,whichwasmodelledusingCFD.TheCFDresultsofthesestudieswereintroducedinacommercialventilationnetworkprogram,provinganenergyreductionof25%fortheparticularcircuitpresentedinFigure1.

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This studydevelops amethodology to establish the operational cost per cubicmeter of the circulatingairflow, in termsofenergyusedbytheventilationsystemfans,consideringdifferentcurvatureradiusesanddiameterstoventilateastretchoftheProductionLevelaccordingtoElTenienteminelayout.Figure1showthecircuitstudiedthatconsidersthreemaingeometricalsingularities,namelyelbow-split/T-joint,fan-chamber-raiseandcrosscut(Diaz2011).Thestretchconsidersfourcrosscutsdrifts(drawpoints) toventilate from the inletventilation raise to theexhaustventilation raise.Additionally, it isnecessary toincludethefan-chamber-raiseslocatedintheintakeandexhaustairways.Theshocklossattheelbow-split/T-jointandfan-chamber-raisedependsonthedirectionofflow,whicharestudiedseparatelybyHurtadoetal.(2014).

Figure 1 General scheme of production drift ventilation system (Diaz 2011)

Ineconomicterms,thestudiedgeometry(Figure1)representsthemostimportantcircuittostudyinBlockCave exploitation systems, because it is repeated dozens or hundreds of times in aBlockCavemine.Concordantly,diminishingthecostsassociatedwithoperatingthiscircuitwillimpacttheoperationaltotalcosts,resultinginseveralUSDmillionsperyearinsavings.

2 Experimentation

2.1 Experimentation set-up

Thesingularitiesexperimentallystudiedconsiderthegeometriesmentionedintheprevioussection.Thefan-chamber-raisepresentsageometricdifference,intheexperimentalcircuititisopened,aspresentedinFigure2,butinthepreviousworksitwasclosedwithanentranceonlyforthefan.Itisimportanttotakethisaspectintoaccountintheanalysisbecauseitreducestheshocklossinthegeometry.Crosscutgeometryisnotconsideredinthescalecircuitbutitisconsideredfurtherintheresistanceestimation.Airwaytunnel(intakeandexhaustairways)hassectionsof5.5mx5.5m,productiondrifts3.8mx4.0mandraises1.50mindiameter.

Figure2showstheexperimentalfacilities.Theyarecomposedofascalemodel(1:52)tokeepconstantthegeometricdimensionless,whichweremadewithevenwood,PVCsplittubesandPVCtubesforraises;an“AmericanFanCompany”fanmodelVP0404,withaTD–5006impellermodelVP1usedtogeneratethe airflow and pressure.A calibratedVenturi flowmeter serves tomeasure the airflow in the system.


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Modifications in thecurvatureradiusweremoldedwithaheatguninPVC.Figure3showsamodifiedraisewithcurvatureradius(PVCtube)overlappedinachamber’sroof.Dynamicdimensionlesscan’tkeepconstantbecausethescalemodelcanreachaReynoldsnumbernearto50,000buttheminedriftscanreachaReynoldsnumbernearto200,000.However,previousCFDworkhassolvedthisproblem(Hurtadoetal.2014).

Figure 2 Experimental facilities for the studied circuit

Figure 3 Modified raise with curvature radius PVC tube

LossescanbeobtainedfromEquations(1)and(2),accordingtothestaticanddynamicpressuresmeasuredinthedifferentstretches.ThepressurelossandpowerareobtainedfromthesquarelawEquation(3)andthepowerEquation(4),inPascalsandkW.Figure4showspressuretapsthatallowsquantifyingheadlossestoobtainthelossesofthesystem(McPherson1993;Acuña&Lowndes2014).

(1)

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(2)

(3)

(4)

Where:

Ẇ = Power (Watts)V = Average Velocity (m/s) P =Pressure (Pa)p = Density (kg/m3)Re = Reynolds Number (dimensionless)Q = Flow Rate (m3/s)R = Resistance (Ns2/m8)X = Shock Factor (dimensionless)

Figure 4 Studied circuit diagram with measure points

2.2 Raise modifications

Modifications were conceived considering an operational and constructability point of view. First,modificationcorrespondingtotheradiusofcurvature,weredonewithminorrequirementsofdrillingandblasting.Diaz(2011)obtainedenergysavingsintheorderof25%withacurvatureradiusof1.0R(radiusofraise),whichwasfoundtobetheoptimumcurvatureradius.Asaresult,modifiedgeometriescorrespondtoacurvatureradiusof1.0R.

Thesecondmodificationcorrespondstoincreasingraisediameters,whichweassumedwouldnotgenerateanexcessiveextracostortimetodevelop.Theactualdiameterofraiseswas1.50mandthemodificationsconsidered2.0mand2.5m.Scaledraisediameterswere28.4mm,36mmand48.2mm,respectively.Figure 5 shows the raises’ modifications. It is important to highlight that, including the tests for thedifferentdiametersandmodificationstotheradiusesforallthetesteddiameters,atotalofsixtestshadtobeimplemented.


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Figure 5 Experimental facilities for the studied circuit

It isknown that fanschange theiroperationalpointaccording to thecircuit losses (Acuñaetal.2010).Asexpected,thecarriedouttestsshowdifferentpressures,airflowratesandhydraulicpoweraccordingto raisediameter and themodificationsof curvature radius.Table1 shows theobtained results.Taking1.50mdiameterasthebasecaseanddividingbythehydraulicpowertoobtainanincrementofhydraulicpowerofthesystem,Figures6and7wereobtained.Hydraulicpowerincrementwithdiametercanreachapproximately90%.If increaseindiameterandcurvatureradiusisconsidered, theincrementcanreachalmost200%atmaximumdiameterof2.5mand1.0Rcurvatureradiusmodification(2.5misequivalentto48mm).

Table 1. Results obtained in the scale tested circuit

Circuit Pressure drop (Pa)

Flow rate (m3/s)

Resistance

(Ns2/m8)Hydraulic

power (kW)28.4mm 3339 0.0263 4823894 0.08836mm 3269 0.0379 2271463 0.12448.2mm 3276 0.0503 1293536 0.16528.4mmMod. 3326 0.0315 3361937 0.10536mmMod. 3258 0.0539 1120777 0.17648.2mmMod. 3234 0.0788 520574 0.255

Figure 6 System hydraulic increment for different diameters tested

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Figure 7 System hydraulic increment for different diameters and curvature radiuses tested

3 Ventilation modelling

3.1 Ventilation network program

Theventilationcircuitwasmodeledwithacommercialventilationnetworkprogramcommonlyusedformineventilationnetworkmodeling(VentSim).Onelimitationofventilationnetworkprogramsisthelackofcapabilitytoassignshocklossesfromcomplexgeometries,usuallyresultingfromtheturbulencegeneratedbetweenclosesingularities,whichmadeshocklossesnotpredictable.ThatisthereasontodeterminethemexperimentallyandwithCFDtechniques,asmentionedinthecitedstudiesinprevioussections.

ThefanusedfortheventilationnetworkprogramsimulationsisanAlphair4500-VAX1800FullBladewith30ºbladeangle,whichoperatesatrangesof20,000to80,000cfmand0to4inchesofwatergage.Figure8presentsthegeometriesimplementedusingtheventilationnetworkprogram.

Figure 8 Ventilation model of studied circuit


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3.2 Tested modifications

The testedmodifications have been selected frompreviousworkswhich gives the values necessary toestimatethetotallossofthecircuit.Table2showsthevaluesofshocklosses(Hurtadoetal.2012).

Oncethecircuitwassimulated,thepressuredrop,airflowrateandtotalresistanceofthesystemtoeachcircuitanditsmodificationswereobtainedandpresentedinTable3.

Table 2 Values of shock losses (Hurtado et al. 2012)

CircuitX1

Fan/Chamber/Raise

X2

Elbow/Split

X3

Elbow/T-joint

X4

Raise/Chamber/FanCircuit1(Raise1.5m) 2.0 1.5 1.0 1.5Circuit2(Raise2.0m) 2.0 1.5 1.0 1.5Circuit3(Raise2.5m) 2.0 1.5 1.0 1.5Circuit 4 (Raise 1.5mMod.) 1.2 1.2 0.9 1.2

Circuit 5 (Raise 2.0mMod.) 1.15 1.15 0.9 1.15

Circuit 6 (Raise 2.5mMod.) 1.2 1.2 0.9 1.2

Table 3 Results obtained from ventilation network program

Circuit Pressuredrop(Pa)

Flowrate(m3/s)

Resistance

(Ns2/m8)Hydraulicpower(kW)

28.4mm 602 31.9 0.59137 19.2

36mm 233 35.7 0.18286 8.3

48.2mm 104 36.8 0.0767 3.8

28.4mmMod. 479 33.2 0.43466 15.9

36mmMod. 170 36.2 0.13001 6.2

48.2mmMod. 77 37.1 0.05576 2.9

3.3 Power loss of the circuit

TheresistancecurvesofeachcasewereobtainedfromtheresistancevaluesestimatedpreviouslyinTable3,consideringpressureandairflowrate.Also,thefanconsumedpoweranddeliveredairflowwasgraphed.DividingtheconsumedPowerbyairflowhelpsdeterminetheenergycostpercubicmeterforeachresistancecurve,inkWh.Figure9showshydraulicpowerpercubicmeterairflowinkWhforthescalecircuitandFigure10fortheventilationnetworkprogram.

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Figure 9 urve of kW-h per cubic meter supplied for the scale circuit

Figure 10 Curve of kW h per cubic meter supplied for the network model

4 Analysis of results

ThegraphsinFigures9and10showthesamebehaviorbetweenthescaleexperimentalcircuitandtheventilationnetworkprogrammodel.There isamagnitudedifference in thescalemaking theairsupplymoreexpensivethroughthescalemodelbecauseofthehighresistanceofscaledcircuit,whichareaisverysmallcomparedtotherealsizemodel.

Fromtheexposedresults,anotabledifferencecanbeappreciatedforenergyconsumption,whencurvatureradiusesaremodifiedordiametersizeschanged.Concordantly,alargerradiusoralargerraisediameterdiminishestheenergyconsumption.

However,inthecaseofminecircuititisveryimportanttonoticethattheresultshereexposedareonlyusefulforthetestedcircuits.Itisbecauseventilationcircuitrespondsindifferentwaytotheturbulence,whichdependsmainlyonvelocitiesandshapeofgeometry.Additionally, longitudesanddimensionsofdriftsandraisescanvaryaccordingtotheminelayout.


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5 Conclusions

AmethodologywasdevelopedandimplementedthatallowstheusertoquantifythecostpercubicmeterofairflowsupplyinaBlockCaveventilationsystem.Withthesevalues,itispossibletochoosethemostappropriatedesignorimprovementtoobtaincosteffectiveminingdesignstodiminishoperationalcost.Toreachthesevalues,itisnecessarytocalibrateandevaluatethespecificlayoutofeachmineinordertonotmisunderstandormakemistakesintheventilationdesigninputparameters.Eachdesignanditsrespectivegeometrymustbeseparatelytoobtainshocklossvalues.

Acknowledgement

ThisresearchworkhasbeensupportedbytheFondecytResearchProject11085050ofConicytChileandDicytResearchProject051215HCofUniversidaddeSantiagodeChile.

References

Acuña,E,Hardcastle,S,Fava,L&Hall,S2010,‘TheapplicationofaMIPmodeltoselecttheoptimumauxiliaryfanandoperationalsettingsformultipleperiodduties’,INFOR,vol.48,Nº2,pp.89-96.

Acuña,EI&Lowndes,IS2014,‘Areviewofprimarymineventilationsystemoptimization’,INTERFACES,INFORMS,vol.44,Nº2,pp.163-175.

Calizaya, F & Mutama, KR 2004, ‘Comparative evaluation of Block Cave ventilation systems’,Proceedingsofthe11thU.S./NorthAmericanMineVentilationSymposium,(Eds.Ganguli&Bandopadhyay),pp.3-14,Taylor&FrancisGroupPlc.,ISBN:9058096335.

Díaz,N2011,‘MejoramientoaerodinámicodelsistemadeventilacióndelascallesdeproducciónenminaElTeniente’,Thesis,UniversidaddeSantiagodeChileSantiago,Chile.(inspanish)

Hurtado,JP,Gutiérrez,O&Moraga,NO2010,‘NumericalSimulationofShockLossesattheintakeandexhaustRaisesofBlockCavingProductionLevelDrifts’,Proceedingsofthe13thUS/NorthAmericanMineVentilationSymposium,Sudbury,vol.1,pp.425-432.

Hurtado,JP,Díaz,N&Acuña,E2012,‘3DCharacterizationofMineVentilationCircuitsforBlockCavingProduction Levels’,MassMin 2012, Proceedings of the Sixth International Conference &ExhibitiononMassMining,Sudbury,Ontario,Canada.June10-14,vol.1,pp.896-911.

Hurtado,JP,Díaz,N,Maya,C&Acuña,E2012,‘Caracterizaciónnuméricayexperimentaldepérdidasde carga enelniveldeproducciónenmétodoBlockCaving’,Proceedingof the14thUS/NorthAmericanMineVentilationSymposium.SaltLakeCity,Utah,UnitedStatesofNorthAmerica,June17-20,vol.1,pp.553-559.

Hurtado,JP,Díaz,N,Acuña,E&Fernández,J2014,‘ShocklossescharacterizationofventilationcircuitsforBlockCavingproductionlevels’,TunnellingandUndergroundSpaceTechnology,vol.41,pp. 88-94, ISSN0886-7798.Available from: (http://www.sciencedirect.com/science/article/pii/S0886779813001946).

McPherson,MJ1993,SubsurfaceVentilationandEnvironmentalEngineering,Chapman&Hall,London.

647


Simulating the Logistic of an Underground Mine

M Moretti Paragon Decision Science, BrazilL Franzese Paragon Decision Science, BrazilM Capistran Paragon Decision Science, BrazilJ Cordeiro Alkmim/AngloGold Ashanti, BrazilB Penna Alkmim/AngloGold Ashanti, BrazilG Mendes Alkmim/AngloGold Ashanti, Brazil

Abstract

This paper describes a logistic study of an underground gold mine, belonging to AngloGold Ashanti, where four different layout options could be applied to the tunnels with different transportation strategies. Each evaluated layout had its own configuration for shaft and truck fleet. The study was made individually for each year of the mine operation life, determining the necessary transportation capacity to achieve the planned production for that year. Due to the very restrictive traffic options in the tunnels, a framework was developed to represent the tunnels and traffic rules in a discrete-event simulation model. A KPI named Total Transportation Capacity was developed to compare scenarios with different truck types. The results pointed to the scenario with the lowest necessary transportation capacity to achieve the planned production.

1 Introduction

Theundergroundminingisaverydefyingchallenge.Inadditiontoallconcernsaboutsafety,thetunnelnetworkhastobewellplannedinordertoachievefeasibilityoftheminingoperations.Theexcavationofgalleriesisanexpensiveandcomplexoperation.Thus,thetunnelnetworkhastobedesignedtominimizeitsextension,allowingthebestpossibletrafficoptions.

AsearchforthebestlayoutoptiontothetunnelnetworkwastheproblemfacedbyAngloGoldAshanti,agoldminingcompanywithoperationsinBrazil.Inadditiontothetunnellayoutitself,theminecouldhaveshaftsindifferentpositions,differenttransportationstrategieswithintermediarysilos,andalsodifferenttruckfleets.Thegoalwas tofind thebest layoutoption to achieve the scheduledproductionusing thelowest investment in trucks.The truck fleet should be sized for each one of the fourteen years of theminingoperation.Sincetheundergroundtrafficisaverydynamicprocess,itisverydifficulttostudywithdeterministictools,andthediscrete-eventsimulationwasthechosenoption.

Theconcernaboutundergroundtrafficinminesisnotnew.Itisalsosubjectofsimulationstudiessincethe early days of this technique appliedwith computers.Hayashi andRobinson (1981) documented asimulation study regardinganunderground railroad in a coalmine.Theyaddressed trafficproblems indetail,consideringcrossinglines,singlelinesandtunnellayouts.Theirobjectivewasalsotoachievethebesttrainconfigurationsanddispatchingstrategiestosustaincoalproductionwithminimumresources.

ThestudyconductedbyMiwaandTakakuwa(2011)isalsoaboutacoalmine.Theyhaveevaluatedanundergroundconveyornetwork,anotheroptiontoretrievemineralsfromthemine.Inthiscase,thestudywasfocusedintheconveyorvelocity,workingunderapredefinedlayout.Wuetal.(2013)havedevelopedasimulationstudyregardingtunnelvisualizationofundergroundmines,butthetransportationandtrafficwerenotdiscussed.


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Whenanundergroundmineusestrucksasthemaintransportationresource,thetunnelnetworkmayhavetrafficproblemssimilartoarailroadnetwork.Usually,thetunnelsarelargeenoughtoallowonlyonetrucktopass,sometimestwo.Trafficsituations,suchas,passingorcrossing,arenoteasyinsidethemine.Almosteverytunnelhasstructurescalled“muckingbays”or“passingbays”,whicharestrategicallylocatedspacesthatcanaccommodateonetruck,sometimesmorethanone.Whenatruckisinatunnelandanothercomesfromtheoppositedirection,oneofthemparksintothepassingbayandallowstheothertopass.Thisissimilartoasinglerailroadlinewithacrossingline,aspresentedinFigure1.

Figure 1 Comparison between crossing vehicles in a mine gallery and a railroad

Sincethetrafficproblemsaresimilar,thesolutionsdevelopedforrailroadcouldalsobeappliedtothiscase,withthenecessaryadjustments.Eventheprioritizationbehaviouristhesame:loadedtrucksshouldpassandemptytrucksshouldwait.ThechosenalgorithmwastheoneproposedbyFioronietal.(2008),whichaddressestheline/tunnelrestrictions,crossingrulesandtrafficbehaviour.Thefollowingsectionsdescribehowthisstudywasconducted.

2 Main structures in the mine

TheundergroundmineusedtosupportthisstudyislocatedinBrazil,intheMinasGeraisstate.Theavailablescenariostobeevaluatedareacombinationofthefollowingcomponents:

• Tunnellayout.

• Trafficdirections.

• Shaftloadingposition.

• Intermediarysilos:quantityandposition.

• Trucktypeandcapacity.

Thetruckshavemainlythreetaskstoaccomplish:carrythegoldoretoashaftorhopper,carrywastetotheshaftorhopperandcarrywastetosomeminedoutareasthatneedtobefilledagain.Trucksnevergoloadedtosurface.Theminehasalimitednumberofloaders,whichisthesameforallscenarios.Theloadingpointsarechangedaccordingtotheproductionschedule,goingdeeperinthemine.

Afterinternaldiscussionsandstudies,theAngloGoldteamhasselectedfourscenariostobeevaluatedwithsimulation:

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2.1 Scenario 1: Original design

Thisscenarioistheoriginaldesignforthemine,withfourmainaccesstunnelsandamixoftruckswithcapacityof30and45tons.Itisconsideredthebasescenario,andisusedasareference.

TheschematicofthetunnelnetworkispresentedinFigure2.Eachcolorsquareisaminingpointatthelevel,andabrownsquaremeansapassingbayposition.

Thisscenariohasahopperatlevel9andtheshaftispositionedatlevel11,providingtwounloadingpointstothetrucks.

Figure 2 Tunnel schematics for the scenario 1, the base scenario

2.2 Scenario 2: Deeper shaft position

Thisscenariousesthesamemixoftrucks,butaddsanewunloadingpositionatlevel16,providingmoreoptions for the trucks,minimizingcongestions. It isalsonearest to thebottomof themine.The tunnellayoutisthesameasforScenario1.

2.3 Scenario 3: Intermediary silos

ThisscenariousesthesametunnelnetworklayoutandunloadingpositionsasScenario1,butintermediarysiloswereaddedatlevels15,18,20and22.Afleetof30tonstrucksisusedtobringgoldoretothesesilosand,afterthat,afleetof60tonstrucksisresponsibletoconveyittotheshaftpositionatlevel11.


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2.4 Scenario 4: Additional access tunnel and traffic changes

ThisscenarioaddsanewaccesstunneltoScenario1layout,assigningitasunidirectionalgoingdownandanotherpre-existingtunnel,asunidirectionalgoingup.ThetruckfleetmixisalsothesameasofScenario1with30and45tonsofcapacity.

3 The mine simulation model

ThesimulationtoolchosentobuildthemodelswasArena,fromRockwellAutomation.TheapproachtomodelthetunnelnetworkwastheonedescribedbyFioronietal.(2013),theSignalOrientedApproach.Itwaschosenbecausethenetworkhadsomeparticularitiesthatshouldbeaddressedlocallyandthisapproachallowed that.Situations,suchas,prioritizationbetween trucksand theaccess to thehoppers requiredalocal set of decisions different from the regular truckmovement.This approach focused on the signalintelligence,lettingthemdecidesifthetruckwasallowedtopassornot.Signalsweredistributedalongthemodelnetworkandeachoneofthemhadadifferentdecisionexpression,consideringtheothersignal’sstatus, the nearby tunnels situation and other relevant factors to its specific location.At the realmine,theydon’treallyhavethisamountoflightsignals,butthetruckadvanceisdecidedvisuallyorbyradioinstructions,resultinginthesamebehavior.

Themodelhasconsideredmorethan2000individualpositions,wherethetruckcouldload,unload,parkorwaitforothertruckstocross.TheanimationstructuresofthetunnelnetworkarepresentedinFigure3,wherethesignalscanbeseenalongthelines.

Figure 3 Partial view of model animation

Therealnetworkwastoobigtoberepresentedandgreatpartofitwasnotimportanttothestudy.Therefore,notalltunnelswererepresentedbutonlytheonesrelevanttotheprocessandwithtruckcirculation.Itwasfurthersimplifiedbyremovingirrelevantconnectionsandaggregatingcommonpoints.

Furthermore,itwasassumedthatthetruckshoulduseonlyonepath/routebetweenpositions.Thishelpedtosimplifythemodelandgivesome“room”totheresults,since,attherealminethetruckscouldavoidtunnelswithmoretraffic,makingbetterdecisionsthanthemodel.However,itwasnotconsideredrelevantenough to affect thedecision.The routeswereassignedbyAngloGoldpersonnel, since theyhadmore

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knowledgeaboutthemineandwherethetrucksshouldpassoneverytripbetweenpositions.Morethan10,000routeswerecreated,coveringeachpossibleorigin-destinationpairinthemodel.

Anindividualmodelwasbuiltforeachscenario,duetostructuraldifferencesbetweenthem.Evidently,theroute’slisthadtobeupdatedforeachmodel.

Alltrucksandloaderswereaffectedbydowntimesandmaintenanceandeverymovementofthetruckhadachancetobeaffectedbydisturbingvehicles,impactingitstraveltime.Besidesthepriorityinthemine,the trucks, sometimesmay be affected by the other vehicles, such as, personnel transportation, tunnelmaintenanceequipment,cars,etc.

3.1 Model output

AsetofKPIswereimplementedwithinthemodeltohelpthesystemvalidationandcomparisonbetweenscenarios, especially, travel and activities times and utilizations. Also, the scheduled production andsimulatedproductionwerecomparedtoconfirmthegoalachievement.ApartialviewoftheoutputinterfacecanbeseeninFigure4.

Figure 4 Partial view of the output interface

Inaddition, themodeloutput included thenumberof tripsperformedforeachroute inside themine toprovidetheuserwithusefulinformationaboutpotentialtrafficproblemsandthemostproblematicroutes,ascanbeseeninFigure5.

3.2 Model validation

Themodelwasvalidatedbycomparingitsresultswithdeterministiccalculationsmadeforthebasescenario(Scenario1).Allresultswereanalysedbytheminingexpertstocheckforcoherency.Themodelbehaviourwasevaluatedwithsensitivityexperiments.

Subsequently,AngloGoldteamhasapprovedthemodeltoproceedwithscenarioexperiments.


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Figure 5 Usage count for each route at the tunnel network

4 Scenario results

Several experimentsweremadewith each scenario to determine the optimal truck fleet for each yearofoperation.Theobjectivewas tofind the lowestfleet, able toachieve95%ormoreof the scheduledproduction.

Inordertocomparethescenarios,anewKPIwasproposed,sincethetrucktypewasnotthesameforallscenariosand thedirectcomparisonwouldnotbepossible.ThisKPIwasnamed“TotalTransportationCapacity”(TTC)andisasumofcapacitiesofalltrucksofthetwodifferentfleetsmeasuredintons.

TTC = (F1*C1)+(F2*C2) (1)

Where:

F1=Trucksoffleet1.

C1=Truckcapacityatfleet1.

F2=Trucksoffleet2.

C2=Truckcapacityatfleet2.

TheTTCwascalculatedforallscenariosandusedtogeneratethechartpresentedatFigure6.

EvaluatingthisKPI,Scenario2and4performednoticeablybetterthan1and3.Theproductionhasapeakat2024andareductionat2025.Itcanbenotedatthetransportationcapacityrequiredforthisyearinallscenarios.Thefollowingyear,2025,isn’tsodemanding,requiringlesstrucks.Thesesuddenchangesinthenumberoftrucksfromoneyeartoanotherareinconvenientandshouldbeavoided.

Inthecomparisonbetweenscenarios2and4,ispossibletonotethatscenario4ismorestable.Itrequireslesschangesinthenumberoftrucksduringtheentiremineoperationperiod.TheTable1showsanotherKPI:thepeakcapacityrequiredforeachscenario.

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Figure 6 TTC comparison between scenarios

Table 1 Peak capacity required for each scenario

Scenario Peak TTC (tons)1 9182 5523 10044 466

ByevaluatingthisKPI,thebestisalsoScenario4,whichachievedthescheduledproductionforallyearswiththelowestTTC,meaningthesmallestfleet.

AnotherKPIusedtocomparethescenariosunderthesamebasiswasthetonsperkilometerpertruck(tkmpertruck).Itwascalculatedusingthetruckcycletimes,averagedistancetraveledandtruckfleetforeachyear.Thesearealsomodeloutputs.TheresultispresentedinFigure7.

Figure 7 Comparison between scenarios


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ThisKPIalsoconfirmsthatScenario4hasthebestoperationalperformance.

5 Conclusions

By the results obtained andmodel behavior, it is possible to conclude that the railroad algorithms andapproachadoptedwereappropriatetorepresentanundergroundminetrucktrafficbehavior.Allscenarioscouldbemodeledandconsideredvalidatedbytheminespecialists.Thisisarelevantachievement,becausetomodelrestrictivemovementisalwaysachallenge,nottherestrictionitself,buttheentiredecisionprocessthathavetobepresenttoallowthetruckortraintomoveinthisstructure.

Thisstudyhavefocusedonthetruckfleetasthemainfactortodecidewhichscenariowasthebest,butthereareotherfactorsinvolved,suchas,theinvestmenttoimplementtheinfrastructurerequiredforeachoneofthem.Forthisstudy,allscenarioswereassumedtohavesimilarinvestmentlevels.

Oneweakpointinthisstudywastheabsenceofadispatchsysteminthemodel,whichwillprobablyexistintherealsystem.Evenifitwasnotperfectoroptimal,thiscouldallowthetruckstochooseabetterpathor decide a different destinationdependingon thepresent situation at themine. In this case, however,asmentionedbefore,thiswasnotconsideredrelevanttothestudy.Allofthescenariossharedthesameweakness,whichbecomesirrelevantwhencomparingscenariodata.Theyareallaffectedinthesamewayandatthesamelevel,meaningthecomparisonisveryreliable.

Thisstudyhasconfirmedthevalueofadiscrete-eventsimulationtool,suchas,Arena,toevaluatetrafficproblemsinundergroundmines.Computationaltools,FPCandTALPAC,areusefultodeterminethefleetofloadersandtrucksforaspecificsector,butlackthenecessaryresourcestodeeplyconsiderthetrafficattheentiremine.Thisstudycouldbeappliedtoanyundergroundmineusingblockandsublevelcavingaswellasothermethods.

Theconclusionisthatthisresultpointedtothebesttechnicaldecision.However,thebestbusinessdecisionshouldbetakenafteraddingcoststoallthisdata.

Acknowledgement

TheauthorsthankAngloGoldAshantibysupportingthisprojectandbyauthorizingtheuseofitsinformationinthispaper.

References

Fioroni,MM2008,SimulaçãoemciclofechadodemalhasferroviáriasesuasaplicaçõesnoBrasil,PhDThesis,EscolaPolitécnica,UniversidadedeSãoPaulo,SãoPaulo,SP.Available at: http://www.teses.usp.br/teses/disponiveis/3/3135/tde-03062008-180002/pt-br.php. [Accessed :February27,2014].(inPortugal).

Fioroni,MM,Quevedo, JG,Santana, IR,Franzese,LAG,Cuervo,D,Sanchez,P&Narducci,F2013,‘Signal-OrientedRailroadSimulation’,Proceedingsofthe2013WinterSimulationConference,(Eds.Pasupathy,S.-H.Kim,A.Tolk,R.Hill,andM.E.Kuhl),pp.3533–3543.Piscataway,NewJersey:InstituteofElectricalandElectronicsEngineers,Inc.

Hayashi,F&Robinson,D1981,‘ComputerSimulationofMineRailHaulageSystem’,Proceedingsofthe1981WinterSimulationConference,(Eds.T.I.Oren,C.M.Delfosse,C.M.Shub),pp.121–127.Piscataway,NewJersey:InstituteofElectricalandElectronicsEngineers,Inc.

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Miwa, K & Takakuwa, S 2011, ‘Operations Modeling andAnalysis of an Underground Coal Mine’,Proceedings of the 2011 Winter Simulation Conference, (Eds. S. Jain, R.R. Creasey, J.Himmelspach,K.P.White,andM.Fu),pp.1685–1695,Piscataway,NewJersey:InstituteofElectricalandElectronicsEngineers,Inc.

Wu,S,Lu,M,Mao,S&Shen,X2013,‘As-BuiltModelingandVisualSimulationsofTunnelsUsingReal-TimeTBMPositioningData’,Proceedingsofthe2013WinterSimulationConference,(Eds.byR.Pasupathy,S.-H.Kim,A.Tolk,R.Hill,andM.E.Kuhl),pp.3066–3073,Piscataway,NewJersey:InstituteofElectricalandElectronicsEngineers,Inc.


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Engineering approach for the design and analysis of drawbell blasting in block and panel caving

Á Altamirano BCTEC Ingeniería y Tecnología SpA, ChileR Castro Universidad de Chile, ChileI Onederra University of Queensland, Australia

Abstract

In Block Caving development and to achieve planned production, several drawbells need to be built. In recent years, the concept of rapid development have also include the possibility of building drawbells using a single phase. This article describes an engineering approach to design and analyze single phase drawbells. It combines the use of empirically based damage models with estimates of swell factors. A damage/breakage criterion is proposed based on the back analysis of successfully extracted single phase drawbells. Design parameters are evaluated using this criterion and recommendations outlined for implementation in the and further validation.

1 Introduction

Inminingsystems,suchas,BlockandPanelCaving,itisextremelyimportanttoperformdrawbellblastingeffectively.Thereare significantproductivitybenefits if thedrawbellsblasting is conducted ina singlephase.Thisisalsoreferredtoassingleshotfiring.Inaddition,singleshotfiringofdrawbellscaneliminateriskstopersonnelworkingwithexplosivesunderfragmentedmaterial.

Drillingandblastingincavingoperationsdependonthevariantthatisused.InthecaseofaPanelCavingwith pre-undercut, the excavation of the drawbell has to be done only from the level of production.Therefore,itisnecessarytocreateaslottoprovidethenecessaryfreefacefortheexpansionoffragmentedmaterial;inaddition,theblastholeshavetobeabout14mto18mlongwithanexplosivechargeadequatetoprovide fracturing rockandsuitableconnection to theundercut level.Failure to implementaproperprocedureinblastingisgoingtoconfinethefragmentedmaterialblockingtheflowofore.Additionally,insomecasesitcandamagetheCrownPillarandthedrawpoint.WithintheconventionalortraditionalPanelCavingmethod,therearetwostagestotheconstructionofthedrawbell:initially,thedevelopmentfromtheproductionlevelwithverticalblastholesofabout12mto15mabovethelevelofproduction;secondly,thedevelopmentfromtheundercuttinglevelcanproceedtodrillblastholeswithnegativeorientationtocompletethegeometryofthedrawbell.

Thecurrentpracticeofdrawbelldesignhasconsideredtheuseofdifferentgeometries(Jofreet.al.2000).Itshouldbenotedthatbetween1985and1994,differentdesignsofdrawbellwereimplementedinSectorTeniente4Sur,withthemainobjectivetodealwithspecificsingularitiesintheminedesign,suchas,changesinthedirectionofdrawbelldrift,changesinorientationinloadandhaulageortransport,modificationofextractionpointspositionsandconnectionsbetweentwomethodsofextractions.

Theapplicationsofsingleshotfiringhasbeendrivenbyadvancesinbothdrillingandblastingtechnology,inparticularbytheavailabilityofpreciseinitiationsystems.Singleshotfiringisroutinelyimplementedbysomeofthemajorcavemineoperators.ExperiencewiththistechniqueisdescribedbyLovitt(2005),wherehedescribestheimplementationofoneshotdesignsinLift#2atNorthparkesMines.Thiswaspossible

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throughtheuseofelectronicdetonators.Thesuccessoftheprocedurewasattributedtoaredistributionoftheblastholestoimproveefficiency.Amongsttheoperationalparameters,highlightsinclude:areductioninthetimeblastholesdeparturefrom5to20ms,thedensityofexplosiveusedbetween0.8and1.2g/cm3,thefreefacetoarchivetheblastingincreaseto1,1m(slot)and,finally,the64mmdiameterblastholes,whichplayedanimportantpartinthisachievementasitreducedthepercentageofmisfiredholesafterblastingthedrawbellsandsavedconsiderabletimeandmoney.Popa(2012)proposedadrillingandblastingdesignforatcone–shapeddrawbellsCadiaEastProject.Theoperationalparameterswere:16.5mhigh;17.5mdiameterand2,100m3drawbell,whichwassuccessfullyestablishedinasingleblast,using136blastholes76mmindiameterand7reliefholes200mmindiameter.Thetotaldrillingwas2,057mandthechargeweight6,000kgresultinginapowderfactorof1.0kg/m3.

Thereviewoftheliteratureshowsthatthereisalackofmethodologyappliedtothedesignandanalysisofdrawbellblasting.Thispaperdiscussesanengineeringapproach thatcouldbeapplied toothermineconditions.

2 Methodology

TheframeworkoftheproposedapproachindescribedinFigure1.Thefirststagebeginswiththeanalysisoftherockmassproperties.Thisfirststepisrequiredtodeterminekeyparametersforthecalibrationoftheempirically-baseddamagemodels.Inthisinstance,theHolmbergandPerssonapproachisused(Perssonetal.1994).

Thesecondstageistodefinetheexplosiveproperties,inparticular,thematerialdensity.

The third stage involves the definition of preliminary design parameters such as burden, spacing, anduncharged collar lengths. This is mainly driven by rules of thumb and geometry constraints. Prior toconductingsimulationsofbreakageenvelopes,theHolmberg-Perssonmodeliscalibratedtodeterminethemainintensity(K)andattenuation(alpha)constants.Inthiscase,theparametersweredeterminedfromthebackanalysisofdamagezonesgeneratedinproductionordevelopmentdrifts.

Inthiswork,thesimulationstagewasconductedwiththeJKSimBlast(2DRing)software.Theanalysisgives theestimateddamagezones inhorizontalandverticalprofiles,highlighting thepossibleeffectofblastingonadjacentpillarsandtheinteractionoftheblastholeswithcontiguousfreefaces.Inthisprocess,a criteria defined from previous experience are applied to the analysis.This is based on the breakagecoverageandswell.

Asmentionedpreviously,theprocessbeginswiththecollectionofgeotechnicalinformationthatshouldberelevantforthemodellingofblastdamage.

Thefollowingconsiderationsweretakenintoaccountduringtheanalysis:

• Thecalibrationofthemodelparameters(Kandα)isperformedbasedonthetheoreticaldiagramofdrillingandblastingfordriftdevelopment;thereisnoinformationaboutthedeviationoftheblastholes.

• ThecalibrationwasperformedwithANFOatadensityof0.82g/cm3,sinceitisthemainexplosiveusedinblastdesignforthishorizontaldevelopment.

• Itwasassumedthatestimatesofbreakageenvelopesusingthissimplemodelcouldbeextrapolatedto production blasting and undercutting, provided that the rockmass and explosive possessedsimilarcharacteristics.


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Figure 1 Design and analysis framework for drawbell blasting

2.1 Evaluation and definition of breakage criteria

Astandarddrawbellcasestudywasused toevaluate theability toachievefullbreakageandextractionthrough single shot firing. The approachwas applied to the first phase of a standard drawbell, whichconsistedof54blastholesintotal,distributedin9rows.Theburdenbetweenrowsvariedfrom1.5mto1.8m.

Inthiscase,theanalysisofblastinginasinglephasewasfocusedonthefirstphaseofastandarddrawbell,whichconsistedof20blastholescorrespondingto4,5and6rows,asseeninFigure2,withtheburdenis1.5m.

Blasting analysis allowed todefine thebreakarea and interactionofdamagezoneswith respect to thedescribedCaseStudy.TheresultsoftheanalysisareshowninFigure3.

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Figure 2 Drawbell Case Study

Figure 3 Result of analysis of “Drawbell Case Study” showing Contour of PPV at the mid-plane

ThesimulationofPPVcontours(peakparticlevelocity)inthemid-planeofDrawbellCaseStudy(Figure3)showsthattheinteractionbetweentheslot(freeface)andtheareaofdamagebyblastholesishigh.Inthecontoursoftheslot(redcolorinFigure3),particlevelocitywasfourtimesthecriticalPPV(PPVc).Fromexperience,thisisconsideredtobeareasonableindexthatdefinestheextentofbreakage.Inthisparticularcase,thecoverageisoftheorderof53%.

4 Application of proposed methodology to an alternative drawbell design

Forillustrativepurposesthepresentedmethodologywasappliedtoasyntheticcase,whereaminerequiredtodesignaoneshotblasting.InTable1,theconditionsorgeometrytobereachedareshown.



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Table 1 Geometric conditions and description of example drawbell

Specifications DrawbellwithSingleSlot

PlanView

DiameterSlot(m) 1,5LongDrawbell(m) 10WidthDrawbell(m) 5

DiameterBlastholes(mm) 63,5Numberblastholes 25DrillingLength(m) 11,96

Specificdrilling(m/m3) 0,74

Thenextstepsincludethecalibrationofdamagemodel,thedesignofanewdrillandblastpatternandtheanalysisofswellcondition.

4.1 Damage Model parameters

Geomechanics informationprovided included:propertiesof the rockmass and intact rockaswell as areport of preconditioningbyASPBlastronics, providing theCriticalPeakParticleVelocityof the rock(PPVC),asshowninTable2.PPVCrepresentsthepeakparticlevelocitythatcanbesustainedbytherockbeforetensilefailureoccurs.

Table 2 Variables for simulation JKSimBlast (Holmberg & Persson 1989)

tensilestrength[Mpa]

wavespeedpropagation

[m/s]

Young’smodulusofelasticity[Gpa]

PPVc[mm/s]DisturbedAreaisconsidered

4*PPVc[mm/s]

FractureZoneisconsidered

K(Factorof

Velocity)

α(AttenuationconstantoftheRock)

17,6 4.979 60 1.461 5.844 600 0,9

4.2 Analysis of drabell design alternative

Anewdesignfordrillingandblastingisconsidered.Similarly,ananalysisoftherateofswellingisperformedassuminganangleofreposeattheextractionpointofabout30°.Finally,thetimesequencingbetweenblastholesisconsideredasakeyfactoroftheblastinginonephase,thusfollowedbytherecommendationforatimeandoutputsequencingofblastholes.

Regardingthegravitationalflow,conventionalcavingdesignsindicatethattheapexvaluesmustbeminimuminordertoavoidapointchargeduetothegeometryofthepillar.Thenewdesignproposeschangesinthegeometryofdrawbells.Basically,itisrecommendedtoadjustthedesignsandreducethesizeofthecurrentapexfrom7.0mto2.5m,asshowninFigure4.

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Figure 4 New geometric configuration of drawbell

Thealternativedrawbelldesignconsiders48blastholesloadedwithANFOatadensityof0.82g/cm3with9rowsdistributedacrossthewidthofthedrawbell(Figure5).Theblastholesnearthelimithaveincreasedoffset, inorder toprotect that area.Thisdesign ensures ahighgradeof fragmentationof thematerial,facilitatingtheejectionandflowduringblasting,allowingasingleshotevent.Itdevelopsasequenceofdetonationinconjunctionwiththedesigninordertooptimizetheinteractionbetweenzonesoffracturingofeachblasthole.

Figure 5 Layout top view of the new D&B design drawbell, View Profile of rows 1 and 9

Theanalysisof thesinglephasedrawbellextraction isperformed in twoways.First, thepercentageofbreakageareaiscalculated,thenthefreefaceavailableforthesequenceisestimated.Table3showsthemid-planesimulationofthisdrawbell,showingtheareasubjectedtoaparticlevelocitygreaterthanfourtimesthePPVCoftherock.Additionally,thelowestbreakagearoundthelimitsofthedrawbell(Visera)isdisplayed.ComparativeresultswithindexdrawbellcasestudyareshowninTable3.



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Table 3 Comparative index Drawbell Case Study vs new design drawbell apex 2.5 m

Comparative indices

Specific perforation (m/m3 rock)

Breakage Area (%)

Interaction breakage

areaPlane of Study

Conventional Drawbell Case

Study (slot phase)

1,05 53,01 High

Alternative Drawbell design 0,93 58,5 High

SequencingofeachblatsholecanbeseeninFigure6,wherethedetonationtimesaredisplayed.

Figure 6 Detonation sequence new design drawbell apex 2.5 m

Thenewdesigndrawbellhasahighfracturezone(breakagearea).Theblastholesaredistributedtoincreasetheinteractionbetweenthezonesoffracturing,whileadvancingtheblastingsequence.Thisdesignwouldensureafinefragmentationofthematerial,improvingtheratioofspaceavailableformovementofswellmaterial.Thepercentageofareaofbreakreaches58.5%,whichiswellabovethe50%definedbyotherbackanalysisworkand53.01%whencomparedtotheslotphase“DrawbellCaseStudy”.

4.3 Swell Factor analysis of alternative drawbell

Theobjectiveofthisanalysiswastoinvestigateiftherewasenoughfreespaceforthefragmentedmaterialtomoveandflow.Initially,itwasconsideredasemptyvolume,thevolumeofairavailableinthedriftandtheslot,withthefinalconsiderationwasthattheangleofreposeofthematerialwas32˚.

AccordingtoHustrulidandKvapil(2008),incavingblastingtherearetwomainswellmodesfortheore,the available free swell space asprovidedby the subleveldrift, and the confined swell,which is scaleindependentaslongasthedesignpowderfactoratthetoesoftheblastholesremainsthesame.Newmanetal(2008)conductedafieldtestinwhichasliceoforewasblastedhorizontallytowardsacaverockfilleddrift.Thisresultedintheconfinedswellvaluesofaround17%,whichisaminimalpercentageofswellthatisneededtoachieveablastsingleshotandallowtheoreflow.

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Basedon the above, itwasdecided that the swell factorwas20% for thefirst stages and30% for theremainingstages.Theswellfactorwaslessinthefirststage,becausetheinteractionbetweentheblastholesisgreaterinthezoneclosetotheslotand,therefore,thesizeofrockfracturingwaslarger.Additionally,itwasconsideredthattherewillbeacompactionofthematerialproductoftheblastinthelaststage,whichgeneratedanadditional10%oftheavailablevolumeforstagethree;thisissupportedbyblastingtestinasublevelcavingmine(Hustrulid&Kvapil2008).

Itisconsideredthatthelevelofinteractioninthedamagezonesishighandtheenergylevelissufficienttoproduceafinefragmentationofthematerial;theseassumptionsweresupportedbythehighpercentageofbreakageestimatedandthehighlevelofinteractionwiththefreeface.Foranalysispurposes,theblastwasseparatedintothreestageswith3volumesofmaterialgivenbytheiso-linesoftimeaccordingtothedetonationsequence,asshowninTable4(consideredStageI:180-200ms;StageII:480-500msandStageIII=730ms).

Table 4 Volumes of material to move by stage and displacement volumes

Stage I (Swell 20%)

Stage II (Swell 30%)

Stage III (Swell 30%)

Top View

Volume of Material (m3) 104 200 266

Swell (m3) 125 260 345

Volume Available (m3) 108 310 350

Volume Difference(m3) -17 +50 +5

5 Conclusions

ThispaperdiscussedtheevaluationofmethodologytoevaluatesinglephaseextractionofDrawbells.Aprocedureisdescribedincorporatingtheuseofsimplebreakagecriteria.Theapproachisinitiallyverifiedwiththeanalysisoftheslotphaseofaconventionaldrawbellandthenappliedtoanalternativedrawbelldesign.Thisnewdrawbelldesignisproposedforasitespecificlayout.

Theanalysisindicatedthatthealternativedrawbelldesignhasasignificantbreakagezone.Theblastholesweredistributedinordertoincreasetheinteractionbetweenthezonesoffracturing.Thedesignindicatedthepotentialforafinefragmentationofthematerial.Thepercentageofareaofbreakagereached58.5%,whichwaswellabove the53.01%presented in theslotphaseofaconventionaldrawbellcasestudy. Itisalsoconsistentwiththebackanalysisofsinglephasedrawbellsinotheroperations.Theanalysisdid,however,indicatedthepotentialforanincreaseareaofdisturbedzoneintheapexpillars.Inordertoreducedamagetothepillars,theuseofdistributedexplosivechargesorlowerdensityarerecommendedinrowsneartheperimeterofthedrawbell,atthetimeofimplementation.Theanalysisalsoindicatedthatitwasveryimportanttoconductadetailedstudyofthestabilityofthepillars,consideringthepotentialincreasesintheextentofdamage.Furtherworkiscurrentlyunderwaytovalidatetheproposeddesignandanalysisapproachtobetterdefinedrawbellblastingparametersinsinglephaseextraction.


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Acknowledgment

Deepest gratitude tomyworkmateFranciscoMarco.Without his knowledge and assistance, this studywouldnothavebeensuccessful.ThankyoutoengineersGermanPúga,MarioVicuñaandFelipeDiazwhocollaboratedonthestudy.

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Music,A&SanMartin,J2010,‘GreatVolumeDrawBellsBlastatElTeniente’,CODELCO,DivisionElTeniente.

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Lovitt,M& Silveira,A 2005, ‘Off to a good start with Lift #2:Drawbell Extraction –Northparkes’,ProceedingsoftheNinthUndergroundOperatorsConference,pp.75-80.Perth,WA.

Holmberg, R& Persson, PA 1980, ‘Design of Tunnel Perimeter Blast Hole Patterns to Prevent RockDamage’,Trans.Inst.MiningMetall,vol.89,pp.A37–A40.

Villaescusa,I&Onederra2003,‘BlastInducedDamageandDynamicBehaviourofHangingwallsinBechStoping’,Fragblast2003.

Onederra,I&EsenS2003,‘AnalternativeApproachtoDeterminetheHolmberg-PerssonConstantsforModellingNearFieldPeakParticleVelocityAttenuation’,Fragblast2003.

Villaescusa&OnederraI2003,‘BlastInducedDamageandDynamicBehaviourofHangingwallsinBechStoping’,Fragblast2003.

Hustrulid,W&KvapilR2008,‘Sublevelcaving–pastandfuture’,MassMin2008,LuleåSweden.

Persson,Holmberg&Lee,RockBlastingandExplosivesEngineering,CRCPress,1994.

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Analysis of induced damage due to undercut blasting

D Morales Hatch, ChileR Olivares Codelco, Chile

Abstract

Operational issues related to the exposure of personnel to hazards at the undercut face and risks related to a typical caving operation (rockfall, collapses, high induced stresses, damage at excavations due to blasting, among others) impact significantly on both safety and productivity of a Panel Caving operation. The analysis of current practices at undercutting resulted in a proposal to modify the standard undercut drilling pattern with the objective to reduce the damage around the undercut drift (brow and walls) and also to avoid the over break. Since some operations are conducted by personnel at the undercut level, it’s crucial to maintain the working zone in optimal conditions. This paper aims to incorporate a new proposed design for the blasting of a Post Undercutting sequence in order to reduce risks related to this operation and to improve productivity and effectiveness for the advance of the undercut face. The analysis and comparison of both designs (standard and proposed) blasting simulations were conducted using JKSimblast software. The results of the simulations show that the proposed design diminishes substantially the damage around the brow and walls of the undercut drift improving brow conditions. Moreover, the proposed design shows no undercutting blasting induced damage at the extraction level.

1 Introduction

UndercuttingisoneofthecriticaloperationswithintheproductiveprocessofaPanelCavingOperation.Theunderstandingoftheundercuttingprocessderiveslargelyfromoperationalexperience,andmanyempiricalattempts to improve theprocess (Rivero2008).According toButcher (Butcher2000), theundercuttingprocesshas3mainobjectives:

1. Togenerateanexcavationlargeenoughtoallowandensurethecavingprocess.

2. Toachievetherequireddimensionsoftheareatostartthecavingprocess,minimizingdamageintheproximityoftheundercutarea.

3. To reach as fast as possible the hydraulic radius required to generate caving; to propagate thecavingprocessandconsequentlyreducetheinducedstressesderivedfromthisoperation.

IfaPostUndercutstrategyisused,theproductionlevelsmustbefullydevelopedandconstructedpriortotheundercuttingprocess.Inordertoachieveacontinuousadvanceoftheundercutface,theunitoperationsrelatedtothedevelopmentoftheselevelsmustbecarriedoutoptimizingthedevelopmentandconstructionrateandthesafetyconditionsforpersonnelexposedtorockfallhazards,collapsesandinstabilityinherenttotheexploitationmethod.Inaddition,sincedrawbellsareopenedbeforethepassageofundercutface,thissequenceexposestheproductionleveltohighlevelsofabutmentstressduringagiventimecausingpotentialdamageintheproductionlevelpillars(Jofré2000).

Amajoroperationalsafetyissue,relatedtotheadvanceoftheundercutfaceandpropagationofcaving,istheexposureofthepersonaltopoorbrowandwallsconditionsafterfiringaring.Sincethechargingoftheblastholesforundercuttingisconductedmanuallybyoperators,iscommonthatafterblastingaring,thepersonnelgetsexposedtopoorbrowconditionsandhazardsattheundercutfacewhilechargingthenextrings,aspresentedinFigure1.


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Figure 1 Scheme of the side view of a typical cave face, using systematic reinforcement of the top of the drift by means of wooden pillars

Instabilityconditionsattheundercutlevelintheproximityofaworkingarea,inadditiontotherisksrelatedto a typical caving operation (rockfall, collapses, high induced stresses, damage at excavations due toblasting,amongothers),impactsignificantlyinsafetyandproductivityofaPanelCavingoperation.Theseconditionscreatetheimperativenecessitytoincorporateimprovementsbothintheundercuttingdesignandinoperationalpractices.

Thisstudywillfocusonoptimizingtheundercutdrillingdesign,analyzingblastingdamageattheundercutlevel,bymeansofblastingsimulationsoftware,withtheobjectivetodiminishthedamageofthewallsandbrowoftheundercutdrift.Thiswillimprovesafetytotheworkersandminimizedamageatthedrawbell.

2 Background

2.1 Undercutting in a Post Undercut mining sequence

Atypicalundercutdesign(Andina,CodelcoDivision)foraPanelCavingoperationusingaPost-Undertcutstrategy,considersanundercutheightof10mwithflatroof,usingafandrillingpattern(Figure2).Theundercutdriftdimensionsare4mx3.6mandthereinforcementconsistofSplitSetBoltsandtheinstallationofapreventivemesh.

Afterblastingasetofrings,andwiththepurposetocontinuetheundercutfaceadvance,theundercuttingprocessconsistsonthefollowingactivities:

1. Scalingthetopoftheundercutdrift

2. Set-upofamuckingplatform.

3. Installationof temporary reinforcement for theexcavation,by installingwoodenpillars,whosepurposeistosupportthebrowandtheroofduringchargingoftheblastholes(Figure3).

4. Firingoftheblastholestocontinuetheundercutfaceadvance.

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Figure2,presentsastandarddrillingpatternforundercuttinginaPostUndercuttingPanelCavingoperation.The design includes 19 radial blast holes, drilledwith a Simba pivoting 1.8m.This particular designgeneratesa10mheightflatundercut.TheundercutblastholesarechargedusingANFOat4%,leavingastemmingof1mloadedwithsand.

Figure 2 (Left) Standard drilling pattern for undercutting, (Right) Induced damage due to blasting at undercut and production level

Figure 3 Typical undercut face, using reinforcement during the charging of the blast holes.

2.2 Induced damage relative to blasting in undercutting.

Undercuttingstandardprocedureshaveresultedinsignificantdamageatthebrowandwallsoftheundercutdrift.Figure2presentsthisdesignandillustratesthedamagegeneratedalongexcavation.

Whenablastholeisfired,acompressionalwavegeneratedbytheexplosiveexpandsallowingcrackstopropagatetowardsin-siturock.Afterwards,explosivesgassesgeneratedbydetonationswellthecrackedrockandanegativepressureejectsmaterial towards theoppositedirection (Kay2000)aspresented inFigure4.


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Figure 4 Systematic blasting mechanisms (Kay 2000)

Thepropagationwave related toblasting, impacts the surroundingworkingzonebyactivatingexistingstructuresandpromotingthegenerationofinstablerocksblocksatthebrow(undercuttingblast)andwalls(drawbellsblast)oftheundercutdrifts.

Inaddition,thehighstressenvironmentofaPostUndercutsequenceencouragesthetransmissionofinducedstressesoverthebrowoftheundercutdrift;increasingtherisksofrockfallandinstabilityandexposingpersonneltothishazardworkingcondition.Riskmanagementstrategiesconsistofrestrictingaccesstotheworkingzone,modifyingthesupportandadjustingtheminedesigns.

2.3 Main criteria for the analysis of blasting damage

Inordertoprovethehypothesisthatdrillingandblastingdesigninfluencesthestabilityandsafetyoftheundercutfaceworkingarea,simulationswerecarriedoutusingtheJKSimblastsoftware.

Thesimulationswillcomparetwoscenarios:thestandardpatternandanewdesignproposedbytheauthors.JKSimblastallowstoevaluatetheinducedblastingdamageattheundercutdriftsresultingfromblasting.TheattenuationmodelisbasedontheHolmerandPerssonModel,estimationwhichestimatestheparticlevelocityinthenearfield.Themainparameterstobeusedforestimatingdamageare:

• Drillingpatterngeometry.

• Explosivecharacteristics.

• Attenuationparametersoftheeachrocktypeforestimatingtheimpactofthepropagationwaveduetoblasting.

For thecasestudy, itwasconsidered theattenuationparametersfor the“StrongSandstone”attenuationparametersduetothelackofapropagationmodelforblastingatIIIPanel,RioBlancoMine.EventhoughtheAndina´srocktypeisnotStrongSandstone,theattenuationparameterscouldbeconsideredverysimilar,sincetheirtensilestrengthparametersarewithintheorderofmagnitude.

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Table 1 Attenuation parameters for different rock types, according to the Holmer and Person Model

3 Methodology

Thisinvestigationaimstoevaluateandcontrasttwoscenarios(originalandproposeddesign),bymeansoftheJKSimblastsoftware.Thesoftwareallowsquantifyingtheinduceddamageduetoblastingintheundercutandextractiondrifts.

Thesimulationallowscomparingbothbehaviours,intermsofthegeneratingsuitablecavingconnection,andtheoptimizationoftheoperationoftheundercutprocessbydiminishingthedamageattheundercutdriftexcavationface(wallsandbrow).

Themethodologyisasfollows:

1. Analysisof a standarddrillingpattern currentlyused inPanelCavingOperations and thenewproposeddesign,bymeansoftheJKSimblastsoftware.

2. Incorporatingbothdrillingpatternsinasimulation,definingboundaryconditions.

3. SimulatingbothdesignsusingANFOat4%forundercutandANFOat10%fordrawbellblasting,withthepurposetoquantifydamageattheundercutlevel.

4. Conductingcomparativeanalysisbetweenbothdesigns,checkingtheirperformanceintermsoftheextractionlevelstabilityandundercutfaceadvance.

4 Proposed improved design

Inordertodeveloptheproposeddrillingpattern,thefollowingvariableswereconsidered:

1. Undercutheight.

2. Undercutsideview.

3. Diminishingdamageintheundercutdrifts.

4. Creationofanoptimaloperationalfreeface,fornextringstobeblasted.

5. Ensuringtheadvanceoftheundercutface.

Theproposeddesignconsidersverticaldrillingringsincluding14radialblastholesperpendiculartodriftwalls.Thedesignhasthepeculiarityofnotincludingblastholesatthetopoftheundercutdrift(Figure4)andaddsnewblastholeswithnegativeinclination(-42°)thatdecreasethedrawbellheightin3.5mversustheoriginaldesign.


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Theundercutheightabovetheundercutlevelis3.6mand17.6mabovetheextractionlevel.

Figure 5 (Left) Proposed design for a Post Undercutting mining sequence. (Right) Undercutting and blasting of the drawbell resulting geometry

TheproposeddesignispresentedinFigure5.TheadditionofblastholeswithnegativeinclinationallowsalsoshapingthecrownpillarbydecreasingthecreationsupportingpointsasinthecaseofflatundercuttingaspresentedinFigure6.

Figure 6 Scheme of the creation and effect of supporting points after undercutting (Karzulovic 1998)

Thenewdrillingdesignenhancestheflowofblastedmaterialintothedrawbellbymodifyingthedrawbellgeometry; thisupgradedflowconditionallowsblastedmaterial tomovedownwardseasily, creatinganoptimalfreefaceforthenextringstobeblasted.

5 Simulation results

AccordingtothewavepropagationmodelpresentedinSection2.3,adamagecriterionispresentedinTable2.Thiscriterionwillbeadoptedtoevaluateinduceddamageduetoblastinginthesimulatedscenarios.

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Table 2 Criteria for evaluating damage in an excavation, adjusted for Strong Sandstone

5.1 Simulation inputs

InordertoconductthedamageanalysisinJKSimblast,certaininputparametersareneeded;theseparametersarerelatedtothedrillingpatternandtotheexplosiveused.

Table 3 Inputs for JKSimblast

5.2 Simulation results

TheresultsfromthesimulationsconductedinJKSimblastarepresentedinFigure7.


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Figure 7 Standard design simulation results

The results from the simulations for the proposed design, conducted in JKSimblast are presented inFigure8.

Figure 8 Proposed design simulation results

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6 Conclusions and further work

Basedontheconductedanalysis,itispossibletoconcludethatthenewdesignhasthefollowingadvantagesoverthestandarddesign:

• Theremovalfromthedrillingpatternoftheblastholesofthetopoftheundercutdriftimprovessignificantlythestabilityconditionatthebrow.

• The propagationwave related to the drawbell firing and its induced damage do not affect theundercutdrift.

• Thereisnoevidenceofdamageduetoundercuttingblastingattheextractionlevel.

• Theproposeddesignminimizestheexposureofpersonneltopoorbrowconditionsandhazardsattheundercutface.

• In economic terms, the newdesign diminishes the drilledmeters in about 20m, lowering theoveralldevelopmentcost.

Theauthorsrecommendconductingin-situmeasurestodetermineproperlytheattenuationparametersfromtheHolmer&Persson’smodelforeachparticularrocktype.Therefore,simulationscanbecomparedtoin-situdamagemeasurementsandcalibratethesoftwareinaparticularscenarioforvalidationpurposes.

Currentdrillingpracticesforundercuttingmustbereviewedinordertominimizeoperationalhazardsandtoensuresafetyworkingconditions.

Acknowledgements

TheauthorswouldliketothankMontserratPinedaforhervaluableandhelpfulsupportduringthewritingofthisarticle.

References

Rivero,V2008,EvaluaciónGeomecánicadeEstrategiasdeSocavaciónenMineríaSubterránea,MemoriadeTituloIngenieroCivildeMinas,UniversidaddeChile,32p.(inspanish)

Soft-Blast2006,UndergroundUserManual,JKSimBlast,p.57-117.

Onederra,I2010,Apuntesdetecnologíaytécnicasdetronadura,UniversityofQueensland,DiplomadoIngenieríadelBlockCavingUniversidaddeChile.(inspanish)

Butcher,RJ2000,‘BlockCaveUndercutting-Aims,Strategies,MethodsandManagement’,ProceedingsofMassmin2000,pp.405-414,Brisbane,Australia.

Jofre,J,etal.2000,‘EvolutioninPanelCavingUndercuttingandDrawbellExcavation,ElTenienteMine’,ProceedingsofMassmin2000,pp.249-260,Brisbane,Australia.

Kay,D2000,‘DigitalBlasting-AnOpportunitytoRevolutioniseMassUndergroundMining’,ProceedingsofMassmin2000,pp.155-161,Brisbane,Australia.

Karzulovic,A1998,EvaluaciónGeotécnicaMétodosdeSocavaciónPreviayAvanzadaMinaElTeniente,EstudioDT–CG–98–003,ElTeniente,CODELCOCHILE,pp.1-19.(inspanish)


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How high a draw column in Block Caving?

C Cerrutti AMEC International, Chile

A Ovalle AMEC International, Chile

Y Vergara Universidad de Chile, Chile

Abstract

The heights of block cave columns have been steadily increasing, since the initial 50 m column heights to the current 400 m heights and in one case heights of between 800 and 1,000 m. Thus, what is the maximum limit? What are the factors driving even higher columns, what are the advantages and disadvantages? The need to achieve the highest possible production capacity is the main driver for higher column heights, as they have a direct relationship with each other. Furthermore, higher column heights reduce preparation costs per tonne of ore. On the other hand, factors that may influence column heights include: geomechanics risks, grade distribution, subsidence, global mine strategy, implementation rates, financial reasons, and cultural reasons. This paper presents a benchmarking study of column heights from different mines as well as a discussion on some of the factors that drive column heights.

1 Introduction

Theheightsofblockcavecolumnshavebeensteadilyincreasing,sincetheinitial50mcolumnheightstothecurrent400mheightsandinonecaseheightsofbetween800and1,000m.Whatisdrivingthisincrease?Undoubtedly,onemotivationistolowercostpertonne,asthebasepreparationcostsaredistributedinmoretonnesby the increase incolumnheights.However, the realdriving force is thatmaximumcapacity isgainedthroughhighercolumnheightsasthereisadirectrelationbetweenthem.

Achievingthemaximumproductioncapacitypossibleformassiveundergroundminesisaveryimportantformining of the future.Many large open pits are decreasing their ore production, principally due toreaching the limits of open pit depths, and this trendwill increase in the future. Therefore, it is veryimportanttoexplorethelimitsofthemaximumproductionthroughputofundergroundminingmethods,inordertocontinuetofeedandfullyutiliselargeprocessingplants,replacingorefromopenpitproductionas itdecreases.Furthermore, inanenvironmentof lowering resourcegrades, an increase inproductionthroughputisoneofthemainwaystolowercostsandkeeptheindustryoperating.

2 Maximum Production Capacity

Themaximumlong-termproductioncapacitythatcanbeachievedinanorebodyminedbyblockcavingdirectlydependsonfourfactors,asshowninequation1(OvalleandPesce2004).Itshouldbenotedthatthegenerictermblockcavingcanbeusedtodesignateallvariantsofcavingsuchasblockcaving,panelcaving,macro-blockcavingandothers,andthatalthoughtheformulaprovidedwasdevelopedstrictlyforpanelcaving,itcanbeusedforothervariantsofblockcaving.

MPCLP=H×Vp×γ×RO(1)

Where:

MPCLP (t/a) = Maximumlongtermproductioncapacity.

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H (m) = Heightoforecolumn.Vp (m2/a) = Preparationrate.γ (t/m3) = Densityofinsitumaterial.RO (fractionof1) = Operationalrecovery.

Thedimensionalanalysisofthisformulayields:

MPCLP=[m]×[m2/a]×[t/m3]×[1]=[t/a]

Thedensity(γ)hasanaturalinvariantvalueandcannotbechanged.Whiletheoperationalrecovery(RO)shouldbeascloseaspossibletoone,itactuallyhasalowervalueandprudenceisrecommended.Experienceshowsthatitiscloserto0.85(basicallyduetolossesduetocollapses,prematureclosureofdrawpoints,andotheroperationalissues).

Thisthereforeonlyleavestwocontrollableparameterstodeterminethemaximumlong-termproductioncapacity;theheightoftheorecolumn(H)andtherateofpreparation(Vp).

Figure1graphically shows themaximumpossible long-termproduction capacity that canbe achievedwithdifferent ratesofpreparation.Thegraph indicates thatproductioncapacitycan reach800kt/d forblockcavingwherethecolumnheightis2,050m,providedthattherateofpreparationis60,000m2/a.Isitpossibletoattaincolumnheightsof2,000m?

Figure 1 Maximum Production Capacity for BC as a function of column height, for different preparation rates

Ofthetwocontrollabledesignfactors,wegenerallylookatthecolumnheight,asitisthefirstfactorweshouldfix in the block cavedesign.Once the footprint elevation is selected, themaximumproductioncapacitythatcanbeachievedismoreorlessdefined.Wethenshouldbeabletoaddresstheseconddesignfactor, thepreparationrate,whichisnotdiscussedfurther in thispaper.Hereweanalyse themaximumcolumnheightvaluescanbeachievedandthefactorsthatdeterminetheselimits.Wealsomusthighlightthathydraulicpreconditioningapplied toblockcaving is the technologicaladvancement thatallowsanincreaseincolumnheightstothevalueswearelookingat.


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3 Block caving column heights and effects

3.1 Definitions

Economic column height

Inblockcavingthisisdefinedastheheightofcolumnthatcanbeextractedeconomically.Thisisdoneinpracticeatdrawpoints in thedetailedengineeringstages,andsometimesfeasibilitystages,orbyevaluatingcolumnsformedbyavailableblocksinscoping,conceptual,prefeasibilityandoccasionallyfeasibilitylevelstages(blocksbetween10mx10mx10mand30mx30mx30m).Dependingontheorebody,mineralizationandtheexistenceofproductionlevelsabove,theindividualcolumnscanhaveawiderangeofheights.Inordertosimplifytheproblem,theaverageeconomically extractable columnheight isgenerally considered.However, one shouldalsobeconsciousofthevariabilityofcolumnsheightsthatshouldbeconsideredinordertocalculatethevariabilityinthemaximumproductioncapacity.

Column heights to surface or production level above

Fromaphysicalstandpoint,theheightofcolumnwhichisimportantistheheighttosurfaceorthepreviousproductionlevelabove.Thisheightneedstobeconsideredwhencalculatingdilution(forexample,dilutionmodelsthatareappliedtothecolumnsarephysicalmodelsandcannotascertainwhattheeconomiccolumnheightwillbe),orinordertocalculatetheinsitustressesthatmininglevelswillbesubjectedto(under-cut,production,ventilation,haulage,etc).

Thisconceptualdistinctionbetweeneconomiccolumnheightandheighttosurfaceisimportanttokeepinmindwhencomparingcolumnheightsbetweendifferentdeposits.

3.2 Values from some deposits

TheevolutionofcolumnheightsatElTeniente is illustrated inFigure2. Inonehundredyearscolumnheightshaveincreasedfrom60mto300m,yetweknowthatintheedgesofsomesectorsofthemine,wheretherearenouppersectorspresent,mineralizedandextractedcolumnshavereachedover800m.

Figure 2 Evolution of the column heights at the El Teniente mine (low column Pilares sectors needed to compensate production losses due to Sub 6 failure by rockburtsing)

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Acomparisonofcolumnheightsofotherdepositsisshowninthefollowingdiagram:

Figure 3 Mineralized column heights and column heights to surface from other deposits

3.3 Costs affected by variation in column height

Thecomponentsofcostpertonnethatcanbeaffectedbyvariationincolumnheightare:

• Preparationcostpertonne

• Extractioncost,affectedbyvariationinfragmentationsizewithheight

• Infrastructuremaintenanceandrehabilitationcosts

Mine preparation costs per tonne of ore

Ifweassumeauniquepreparationcostforallcolumns, thenthepreparationcostper tonnevarieswithcolumnheightasshowninFigure4.However,itisnotedthatpreparationcostsforhighercolumnsmaybehigher,duetotheeffectsofhigherinsitustressesandhigherwearingatdrawpoints.Thesepointsarediscussedlater.

Figure 4 Preparation cost per tonne as a function of column height


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Extraction costs affected by variation in fragmentation size with column height

Fragmentation size is an important aspect in engineeringdesign, productivity andoperating costs.Theinitial fragmentation isobviouslyaffectedbyundercutblasting, inwhich the initialdrawcanbeeasilyextracted,afterwhichtherockbeginstofragmentduetothedynamicsofcaving.Thesizeofrocksreportingtothedrawpointisdeterminedbyprimaryfragmentationcausedbyinsituforcesandgravity,andlaterbysecondaryfragmentationcausedbyattrition.Ascolumnsgethigher,sodoestheeffectofcomminution,as rock needs to travel further to the draw point and there is a higher probability of attrition betweenfragmentsandconsequentreductioninsize.Basedonthisargument,itisreasonabletoassumethathighercolumnheightswillhaveafavourableeffectonfragmentationsize.Thisreductioninfragmentationsizewillresultinlesssecondaryblasting,lowerloadingtimes,higherperformanceofloadingequipmentandlowerextractioncosts.

Figure 5 Grizzly productivity of secondary ore, as a function of percent extraction

AlthoughitwasnotpossibleobtaindatathatshowsincreasedLHDproductivityduetoincreaseincolumnheight, there is certain evidence to demonstrate this occurs. Figure 5 shows old El Teniente grizzlyproductiondata,whereproductivityisafunctionofcolumnheightorpercentageoforebodyextraction.Wehypothesize that the shapeof these curves canbe extrapolated in the someway to representLHDextraction.Therefore,tallercolumnsshouldlowerextractioncostperton.

Maintenance and rehabilitation of mine infrastructure

Withincreasingcolumnheights,theusefulservicelifeofinfrastructurewillneedtobehigher,sosomecomponentswillrequirebeingmoredurable,suchas:drawpoints;orepasses;productionlevelroadways;groundsupportandrockreinforcement.

Withrespect todrawpoints, theflowofmaterialfromthecavewillprincipallyweardraw-pointbrowsandnearbysupportelements,plussignificantdamagecanoccurduetoremovinghang-upsandsecondarybreakageinthedrawpoint(e.g.byblasting).Asthecolumnheightincreasessoshouldthenumberofrepairs,however,itisconsideredthatthiswillnotbedirectlyproportionaltothetonnesmined,aswearshouldbelessdue toreducedfragmentationsizewithcolumnheight.Theeffectofdamagedue toblastinghang-upsandsecondarybreakageshouldcorrespondtoafixedcostincurredfortheinitialstagesofextraction.Thelaterstagesshouldseeadecreasedcostforhighercolumnsasthesecostsaredistributedoverhighertonnages.

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Ore passes suffer fromwearwhen the fragment size is coarse, and especially if they are not operatedcorrectly or left empty, as impact from large fragments does themost damage.As the column heightincreases,fragmentsizeshoulddecrease,soshouldthewearrate.

Withtheseconsiderations,itcouldbearguedthatthemaintenancecostpertonneforbothdraw-pointandorepassmaintenancedecreaseswithincreasingcolumnheight.However,itshouldbenotedthatthesecostsarenotcontinuous,astheyaresubjecttooverhauling,whicharetime-consumingandcausedisruptiontoproduction.Thehypothesis is that theseoverhaulsare requiredat lesser intervals as thecolumnheightincreases.

Thedamagetoroadwaysintheproductiondrivesisdirectlyrelatedtothetonnagecarriedonthem,soitcouldbearguedthatthecostisfixedintermsofcostpertonneandthereforedoesnotdependontheheightof the column.There are other factors that influence roadwaydamage: design, quality of construction,weightofloadedequipment,andmainlywaterandpoordrainage.

Deterioration of ground support and rock reinforcement in production levels, especially in walls, areprincipallyduetoinsitustressesandrockmasscreep,whichwouldindicateanincreaseincostpertonnewithincreaseincolumnheight.Thisfactorisworthyofmodelling.Secondly,thecostofrepairstowallsisinfluencebydamagebyequipment,however,thiscouldbenegligibleifexcavationsweredesignedwithsufficientspace.

Summingupallthefactors,webelievethatthemaintenancecostsofinfrastructuremaydecreaseintermsofcostpertonnewithincreasingcolumnheight.However,pendingtheavailabilityofanevidencedatabasetosupportthis,itishypothesizedthatthecostofrepairstoinfrastructureshouldbefixedbasedoncostpertonne.

3.4 Dilution

Thevariablethataffectsblockcavingrevenueasafunctionofcolumnheightisdilution.

AccordingtoLaubscher(2000),iftheratioofthevolumeoftheorebodytoore-wastecontactboundaryincreases,thentheoveralldilutiondecreases.

V1/S1>V2/S2àD1<D2(2)

Where:

V1,V2:Volumeofcolumnforcases1and2.

S1,S2: Surfaceboundaryexposedtowasteforcases1and2

D1,D2:Dilutionforcolumns1and2,accordingtoconditionsofeach.

WewillcomparedilutionfortwocaseswithmaincolumnheightsofHandH/2,forcase1and2,respectively.

Theusualsituationinpanelcavingorblockcavingisshowninthe3-DviewofFigure6.Theextractioncolumn under study, in dark, is surrounded by four other extraction columns, in grey. There are twosidesexposed towaste(the2 lowerneighbouringcolumns)and twosidesexposed toore(the2higherneighbouringcolumns).Therooffaceisexposedtowaste.


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Figure 6 3-D view of usual situation for an extraction column or block

Inordertoobtaintherelationshipbetweenheightanddilution,wehavethefollowingvaluesforcase1(heightH)andcase2(heightH/2),accordingtoformula(2).

V1/S1=H×a×b/(H×a+H×b+a×b)

V2/S2=H×a×b/(H×a+H×b+2×a×b)

Assumingcarefuldrawforbothcases,theheightofthecolumncorrespondstotheonlyvariablethatdefinesthehigherorlowerpercentageofoveralldilution.

Thus,ifthecolumnheightincreases,theV/SratioalsoincreasesandaccordinglytostatementsmadebyLaubscher,dilutiondecreases.

4 Geomechanical Aspects

4.1 In situ and induced stresses during development

In situand induced stressesduringdevelopmentareusuallynot aproblemwith increasingorecolumnheight or increasingminedepth, but of course there are limits and special conditions.There aremanyreportedpoppingandrockburstingphenomenainminedevelopmentsofdeepminesorwithunusualrockconditions,likeveryfragilerocksandhighinsitustresses.Theseconditionsrequireanextraengineeringeffort.

4.2 Induced stresses during caving

The induced stresses caused by caving are particularly crucial in the undercutting period of the blockcave.Thedynamicsof thisoperational stepareextremelycomplex (relative to theamountofdifferentexcavations),whichcausesdifferentvariationsofstressfargreaterthanthosegeneratedbydevelopmentalone.Furthermorethereisanincreaseintheedgesofthecave(abutmentstresses)andlossofconfinement.Afterthecavefronthaspassed(i.e.intheshadowzone),stressesdecrease,andcancauseinstabilityissuesand/orcollapsesinthemineinfrastructureandpropagationofcaving(seeFigure7)

Withdepth(increasedcolumnheight)theundercutphasecangeneratehighervariationsofinducedstresses,theabutmentstressesincreasebyaround2to3timestheinsituverticalstress,andastheconfinementstress

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decreases there is ahigherprobabilityof failure (e.gproduction levelpillars).Theseaspects shouldbestudiedinmoredetailforfeasibilityleveldesign(e.g.adequatesupportandreinforcement,and/orstrategiespreoradvancedundercut,and/orpreconditioningstudies).

Figure 7 Example states of induced stresses during caving over production level pillars

4.3 Other risks

Rock bursts

Theriskofrockburstscouldbeathreattoincreasingcolumnheights.Rockburstsareconceptuallycausedbythereleaseofstoredstrainenergyduringseismicevents.Atthemomentofaseismiceventaportionof the available stored strain energy is consumed in the collapseof the event source (power collapse),andtheotherportionispropagatedasstresswavesthattravelthroughtherockmass,whosepulseenergydiminishes(attenuationanddispersion)asittravelsthroughthesurroundingrock(vibrationenergy).Whenstresswaves intercept excavations in the rockmass,dependingon its formandenergy, theycancauseviolentruptureandejectionoftherockmassintotheexcavation(i.e.rockburst).Theriskofrockburstscanbereducedwithadequateseismicmonitoring,tocontroltherateofproductionandundercutting,whichhasadirectrelationshiponseismicity.Inaddition,theuseofhydraulicpreconditioningdefinitelyassistsbyreducingthelikelihoodofhighenergyseismicevents.Also,theuseofspecialdynamicrockreinforcementcanreduceseismicrisks.

Cave stalling and air blasts

Independentlyof thehydraulic radius relationship fordifferent typesof rocks, abovewhich thecavingprobabilityapproachesone,apparentlythereisarelationshipbetweenthefootprintareaandthecolumnheight thatdeters thinkingabouthighcolumns for small footprints areas,becauseof theprobabilityofcavestalling,formationofstablearchesandprobableunwantedairblastphenomena.However,hydraulicpreconditioningisthetooltomitigatethissituation.

Propagation of caving

Wetendtothinkthatblockcavingpropagatesvertically,butweknowofcases(Northparkes,Palabora)thatthecavinghaspropagatedoffthevertical,causingunwantedearlydilution.Highercolumnshavegreaterprobabilityofnon-verticalcavingpropagation,especiallyifmainfaults,aclosebyopenpitorspecialstress


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fieldsarepresent.Hereagain,hydraulicpreconditioningisthetooltomitigatethissituation.

Subsidence

Thesubsidenceeffectmightbeabigdeterrentforhigherorecolumns;especiallyifinternalminefacilitiesorsurfaceinstallationsmustbeprotected.

5 Strategic aspects

Wehavestatedtechnicalfactors,mostofwhichpointinthedirectionofincreasingcolumnheightsinblockcaving.Buttherearealsostrategicfactorsthatmustbetakenintoaccountintheoverallequationtosetcolumnheights.

Themainstrategicconsiderationsaregradedistribution,globalminestrategy,project´s implementationtime,financialreasons,andculturalconsiderations.

Thegradedistributionisanimportantconcern.Ifhighergradesareabove,itmightbemoreconvenienttohavealowercolumnlifttorecoverthesehighgradesfirst,eventhoughyoumightpaythepriceofanextraproductionlift.

Theglobalminestrategyisveryimportant.Inbrownfieldprojects,existingmaterialshandlinginfrastructure,subsidenceeffects,overallminingsequenceorspecialproductionrequirementsmightforcethesituationforalowcolumnheightinanewsector.Ingreenfieldprojects,explorationmightnothaverecognizedthebottomofthedeposit,ortheinitiationpointofthefirstcavingliftdefines,toagreatextent,thegeneralsequenceofthemineexploitationandtheoverallminegeometry,andthusthepossibleheightsoffutureproductionlevels.

Highcolumncavestakealongconstructiontime,especiallyifthesurfacetopographyisflat.Manycompaniescannot endure long implementation times (financial or production reasons), and this consideration isimportanttooptforlowercavingcolumns.

Lastbutnotleast,thereareculturalreasonsfornotoptingforhighercavingcolumns.Theminingindustryisveryconservative,wehavealwaysdonelikethis,showmewheretheyhavedoneit,etc.Butasengineers,andvalueproviderstotheindustry,wemustsafelyexplorethelimits.Higherorecolumnsforblockcavingwillopenthedoorforveryhighproductionundergroundminesinthefuture.

6 Conclusions

There isgreatpressure to replace thehighproductionofopenpits thatwill soon reach theireconomiclimitbyundergroundmethods,andthereisalsopressuretolowerminingcosts,whichcanbeattainedbyincreasingminingsize.Herethereisanopportunityforblockcavingtosatisfythechallengesofhigherproductionintheextractiveminingindustryofthefuture.

Themainincentive tousehighercolumnsinblockcaving, is itsdirectrelationshiptoachievinghigherlong termproductionrates.Furthermore, there isa reduction inpreparationcostsper tonneoforewithincreasingcolumnheight.Thecostofmaintenanceand rehabilitationof infrastructureper tonneoforepossibly decreases slightlywith column height, however, for practical purposesmay be regarded as aconstantcostpertonneofore.

Dilution isanother importantvariable inblockcaving,which tends todecreasewith increasingcolumnheight,providedthatdrawiscarefullycontrolledaswithlowercolumnheights.However,itmaybemoredifficulttohavecarefuldrawcontrolwithhighercolumnheights,andsuchthesetwofactorsmaycanceleachother.

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Thegreatestaspectthatinhibitshighercolumnheightsarea)geomechanicalrisks;b)longtimeneededtodevelopprojectswithhighcolumnheightsrequirehigherinitialcapitalcosts(canbemitigatedwithrapiddevelopmentorifthereisadequatetopographytoenablelateralaccess);c)longrampuptimetoachievenominalproductioncapacity (canbemitigatedbyopeningupmore initial area);d)other strategic andculturalreasons.

With respect to geotechnical risks, we believe that these should be addressed at the early stages ofengineering.Inthiscase,quantifythemagnitudeandlikelihoodofoccurrenceoftheindividualconditionsspecifictoeachorebody.Thiswouldinvolveidentifyinglinesofresearchthatwillbenecessarytoincreaseknowledgeandanalysethesolutionsthatneedtobeincorporatedintotheengineeringanddesign.

Ashistoryhasshown thatblockcavecolumnheightshavebeen increasing,and that the limitsof theirapplicationaretemporaryanddependinmanyrespects,onotherbranchesofengineering,technologicaldevelopmentandgenerationofknowledge.

Giventhecurrenttechnology,especiallyduetopreconditioning,webelievethatwecanfeasiblyachievecolumnheightsgreaterthan1,000mwithadequatedesignsandprecautions,withapotentialproductioncapacityof400kt/d.

References

Ovalle,A2012,‘Masscavingmaximumproductioncapacity’,MassMin2012,Sudbury,Canada.

PretoriusD.&Ngidi,S.2008,‘CavemanagementensuringoptimallifeofmineatPalabora’,Massmin2008,pp.63-71.

RossI.&vanAs,A.2005,‘NorthparkesMines-Design,SuddenFailure,AirBlastandHazardManagementattheE26BlockCave’,NinthUndergroundOperatorsConference,Perth,Australia,pp.7-18.

PesceJ.&Ovalle,A.2004,‘ProductionCapacityofaMassCaving’,MassMin2004,Santiago,pp.75-78.

Laubscher,DH2000,CaveMiningHandbook,InternationalCavingStudy,TheUniversityofQueensland,pp.115-118.


Many operations are considering, or have decided, to use block caving as their preferred mining method. Currently, about 400,000 tons per day are extracted by caving methods. It is estimated that this figure will increase to a rate of 1 Million tons per day by 2018. Production rates would also increase. This will present new and exciting challenges and opportunities for the mining industry.

In June 2014, the Third International Symposium on Block and Sublevel Caving will be held in Santiago, Chile the Block Caving’s country. Chile has three large block cave operations; El Teniente, Andina and Salvador, with an annual production of 74 Mt. Codelco, the largest copper producer, is developing two new block caving mines at El Teniente and Chuquicamata, that will produce additional resources for Chile’s future. This book contains the work of authors from all over the globe which summarizes the international state of the art on Block and Sublevel Caving as in 2014.