Advances in Hard Rock Mining Technology...These authors used Fischer Discriminant Analysis to transform the sensor data into orthogonal, linear and non-linear projections with criteria

MINERAL ECONOMICS AND MANAGEMENT SOCIETY THIRTEENTH ANNUAL CONFERENCE

21-23 APRIL, 2004

Advances in Hard Rock Mining Technology

Professor Michael Hood CEO, CRC Mining

PO Box 5234 Kenmore East Queensland 4069, AUSTRALIA

([email protected])

Introduction Many would argue that the words mining and high-technology do not sit comfortably in the same sentence. It is often stated that if a miner (or a minerals’ processor) working during the first decade of the 20th Century was transported to a hard rock mine (or plant) of today they would be impressed by the size and scale of the modern operations but otherwise they would not be surprised by anything that they saw. The unit operations are, generally, basically the same: the rock is drilled, the holes are loaded with explosives, the rock is blasted, the fragmented rock is then loaded using a digging machine (powered by electricity or diesel rather than steam) and transported (by truck rather than train), often to a crusher. In many mines the ore is then further comminuted in a rotating mill and concentrated using gravity separation units and flotation cells. All of these processes would be familiar to our old-timer. The continual decrease in the real (inflation-adjusted) price of all mineral commod-ities over time, together with the ever-decreasing reduction in the grades at which it is economic to extract these commodities, demonstrate that the superficial view – that mining technology has not changed much in at least 100 years – is wrong. The analogy would be a statement that a modern day Mercedes Benz is the same basic technology as a Model T Ford because both are wheeled vehicles powered by internal combustion engines. Clearly the technology in all areas of the Mercedes is vastly superior to that in the Model T. Similarly, it is only because mining companies have always deployed the cutting-edge technology of the day that they have been able to profitably discover, extract and process new deposits at ever-lower grades at ever-lower real prices. This remains true today. What has changed over the past 10-15 years is that many of the larger mining companies and mining equipment manufacturers used to maintain in-house R&D groups; today they have mostly disbanded or re-focused these groups because they did not find value in developing and owning intellectual property. Nowadays most mining companies rely on keeping abreast of new technologies developed by third parties and accessing these technologies for use in their own operations. Many of the equipment manufacturers still maintain R&D groups but they concentrate much more up the “D” end of the “R&D” spectrum than they used to. The closure or re-focusing of these industrial mining R&D groups has been accom-panied, over this same time period, by the widespread closure of world-renowned government and industry-club-funded mining research organisations. For example, the Bureau of Mines (USA) and British Coal’s TSRE were shut down. Germany’s

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Bergbauforschung and South Africa’s COMRO both greatly scaled back their research capabilities when they were subsumed into other, more general, research organisations. Concurrently, mining programs at many universities throughout the developed world have been disbanded because, with low student numbers, they were not financially viable. Formerly famous mining schools, including The Royal School of Mines (UK) and Penn State (USA), as well as many less-well-known mining programs have closed. Most remaining mining degree programs in the developed world remain endangered, in the sense that enrolments in these programs remain low and therefore, from a university perspective, they are uneconomic. In summary, contrary to widespread opinion, mining today is a capital intensive, high technology industry utilising sophisticated and productive equipment – albeit equipment that is superficially similar to that from a former era. At the same time there are current opportunities for step-change improvements to both mining productivity and mine safety. One of these would be to utilise the information technology that has radically changed the manufacturing and retailing industries. Mining has yet to adopt the system-wide approaches required to achieve these improvements. Where today these other businesses frequently operate on a just-in-time basis – keeping inventories low by maintaining excellent communication between manufacturer/retailer and customer and manufacturer/retailer and supplier – mining frequently operates in a just-in-case manner – maintaining high inventories (an extra stockpile here or a shovel there) – just in case a piece of equipment breaks down unexpectedly, or just in case the mine runs into a geological disturbance. It follows, therefore, that another opportunity would be to increase the reliability of the expensive capital equipment. Compared to other industries, the availability of mining equipment ranges from poor to abysmal. Much of this problem is attributable to unplanned maintenance, ie breakdowns. Studies have shown that much of the damage that leads to these breakdowns is inflicted, unwittingly, by the machine drivers when they operate the equipment in ways that were not intended by the machine designers. A third opportunity would be to develop radically new methods of extracting, transporting, and processing ores – disruptive technologies for mining. It can be argued that the most recent example of such a disruptive technology was the development of open pit hard rock mining. This occurred in 1908 when Bingham Canyon was first mined using the steam shovels that had been deployed to excavate the Panama Canal. It allowed the bulk mining and processing of what previously had been uneconomic, low-grade ore. The time would seem to be right for consideration of novel mining systems based on radically different approaches to those used today. This paper describes the approach being taken by the Cooperative Research Centre—Mining (CRCMining) in Australia to develop technology for the global mining industry. CRCMining is an incorporated joint venture between most of the world’s largest mining companies1, some large mining equipment manufacturing companies2,

1 BHP Billiton Innovation Pty Ltd, Anglo Coal Australia Pty Ltd, Anglo Gold Pty Ltd, WMC Resources Ltd,

Hamersley Iron Pty Ltd, Rio Tinto (Technological Resources Pty Ltd), Phelps Dodge Corporation 2 P&H (Harnischfeger of Australia Pty Ltd), Komatsu Australia Pty Ltd


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a drilling contractor3, and three Australian universities4 – with a U.S. university5 as an associate member. Examples of ongoing work in this Centre are given. However, the problems to be addressed are formidable and, as noted above, the research community available to address these problems has declined substantially. The question is posed, therefore, whether the most appropriate approach would be to develop a consortium of researchers from countries where mining continues to be a significant industry.

Smart Mining Systems General Approach Most modern mining equipment is fitted with a large array of different sensors; these monitor a range of parameters from machine health to machine production. In most instances the data collected from these sensors is not used to best advantage and, in many cases, it is not used at all. For example, on a typical Australian longwall some 5,000 measurements – of hydraulic pressures, motor currents, etc – are collected routinely in intervals ranging from seconds to minutes. These data are recorded in a ring buffer and, every few hours, when the buffer is full, they are over-written with new data. These data are rarely interrogated or examined. Perhaps the most common use of the sensor data collected today is diagnosing machine failures after they have occurred. The approach that we are adopting in our research program is illustrated in Figure 1. We seek to analyse existing sensor data in different ways to create information and knowledge for different users. For example, the data might be analysed one way for the machine operator, to provide real-time information to allow them to drive the equipment more efficiently – machine control. It might be analysed another way for the shift supervisors to give them, say, ten updates every hour on the performance of different pieces of equipment in the fleet. They would use this information to modify the shift plan as changes occurred in the mine – shift control. These data might be analysed a different way again for the mine manager – mine control. We are engaged in a number of projects using this approach. One of these uses the longwall data, referred to above, to predict failures of equipment on the longwall face, Bongers et al (2001). These authors used Fischer Discriminant Analysis to transform the sensor data into orthogonal, linear and non-linear projections with criteria to maximise the separation between the classes of data. One of their results, using this method to study an impending stall of a tailgate drive of an armoured face conveyor, is shown in Figure 2. This plot contains 44,640 data points but because measurements in the same class overlie each other relatively few points are visible. In normal operation the points lie on the vertical line above the zero on the abscissa, concentrated at about the value -3 on the ordinate. When the drive is about to stall the data points jump to the approximate value: 4.5, -4.5. Then, for the next few minutes the data plots in the manner indicated in this figure until the fault occurs at approximately: 3, -3.

3 A J Lucas Group Limited 4 The University of Queensland, The University of Sydney, The University of Newcastle 5 The University of Arizona


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Figure 1 Simplified outline of concept for the transformation of data

Figure 2 Prediction of face conveyor drive using Linear Fischer Discriminant

Analysis Clearly the interesting feature of this analysis is that it was able to predict the stall several minutes before it occurred. Had this information been available to the face workers at the time they could have prevented the stall by stopping the conveyor and clearing the coal lumps that had fallen from the face. In addition to face conveyor stalls this analysis approach has been successfully demonstrated to give advance warning of failures for the stage loader and other face equipment. Furthermore, the data could be processed to automatically generate weekly downtime statistics, including the cause of the downtime as well as the actual time spent down. This information, which would be much more accurate than the current record system, hand-written notes by the face personnel, would be a valuable planning tool for the


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mine manager. These data could also be processed using a Weibull analysis to optimise maintenance scheduling and to identify design improvement needs. This information would be of value to the maintenance personnel and to the original equipment manufacturers. The general approach that we are using in this and similar projects is designed to allow mine operators to recognise changes in mining conditions in real-time and then react by adapting the mine operations, either manually or automatically, in an optimal and timely manner. Smart mining systems need to have the following characteristics:

• The ability to acquire and represent in real-time the actual state of the mining operation.

• The ability to assess the performance of the mining operation relative to the existing mine plan and relative to where the operations key performance limits lie.

• The ability to formulate the required short term mine plans and operating sched-ules that drive the mine operational strategies to their optimal performance limits.

System for fragmentation optimisation The size distribution of the rock fragments produced by blasting has a substantial impact on mining and processing costs. This size distribution is heavily influenced by the discontinuities in the virgin rock mass: blasting two rocks with similar strength properties (based on testing of intact small samples), one highly jointed and the other with few discontinuities, will typically produce two very different size distributions. Research in rock blasting generally has concentrated on understanding the propagation of stress waves – as they travel through, and induce cracking in, the rock – and the heave action of the gas pressure that follows this wave action; this propagates cracks and displaces the fragmented rock to form the muck pile. Much of this research has been conducted in homogeneous, isotropic materials, including unfractured rock, because the experimental work is more repeatable and the system is easier to understand. Consequently, when designing a blast a mining engineer typically employs an empirical approach that combines formulaic learnings from these research findings (mostly in unfractured rock) with experience at the particular mine site. Unsurprisingly, the consequence is often unpredictable. The size distribution of the rock produced is very variable; the shape and location of the muck pile is not always as intended; and the damage caused to the surrounding rock – slope, hangingwall, footwall, etc – can be considerable. A more rational approach would be to incorporate into the blast design knowledge of the rock properties – particularly the nature (frequency, orientation, thickness and strength) of the rock discontinuities but also the strength and brittleness of the intact rock. This approach has not been adopted because, until now, it has been too difficult to measure these properties. Clearly this approach won’t work unless the required rock properties can be collected easily and inexpensively during the drill-and-blast process, preferably by measurements made on the drill.


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Drill monitors exist which interpret drill measurements – pulldown force (thrust), torque, rotary speed, penetration rate, etc – as rock strength. These monitors typically use a low sampling rate and do not give any information on rock structure. We are engaged in a long-term project to develop a real-time drill measurement sy-stem to acquire all of the rock properties necessary to optimise blast fragmentation. The system envisaged will interpret drill measurements as rock strength/discontin-uities and feed these data into a (computer) blast model that will reside on the drill. The model will respond with an output that might say “open up the drill pattern here because the rock is weak”, or it might suggest “place decking across a region 10 m down the hole because of a weak shear zone”. In other words the model will: (i) help design the drilling pattern on-the-fly, (ii) recommend the amount of explosive loading per hole, how the loading should be placed, and the timing pattern for the holes, and (iii) predict the fragmentation and the shape of the muck pile. After the blast the actual muck pile shape and the size distribution of the rock particles will be measured (using image analysis and/or automated digging machines). These measurements will be compared with the predictions; the model might then be adjusted to improve the prediction for the next blast. Our optimism that rock fracture information can be obtained comes from work performed by Hatherly et al (1997) in which full waveform sonic logs of holes were obtained. These workers showed that these logs gave much better information about the occurrence of fractures in the holes than traditional RQD measurements (Figure 3). In fact these logs gave information about the hole that was often better than that obtained using the much more expensive, acoustic scanner. Furthermore, these logs also gave good information on whether the fractures were open or closed: open fractures are very likely to separate and therefore form discrete rock fragments during the heaving action in a blast. Unfortunately, in order to take these sonic logs, Hatherly and his colleagues had to run

a probe along a water-filled hole; not something that would be practical for the blasting system envisaged. Our approach has been to see whether we can obtain information about the fractured nature of the rock from monitoring the vibrations of the drill string. The initial work is being performed using a large-scale laboratory drill rig (

Figure 4). Figure 5 (from Smith, 2001) shows an early result of measured string vibrations drilling in layered samples of concrete of different strengths. The interface between the layers can be clearly seen in the sample with the sand aggregate and is still distinguishable, although less clearly, in the sample with the pebble aggregate. This work is continuing. The other approach that we are using is computer-based simulation of the drilling process. A number of such drilling simulators exist. We are collaborating with Professor George Cooper at The University of California, Berkeley using a simulator, Payzone, that he and his students have developed (Cooper et al, 1995; Abouzeid and Cooper, 2001). Typically model simulation of the drilling process relates operational drilling inputs (weight-on-bit [wob], rotary speed, etc) and rock property inputs (rock type, strength, and abrasivity) to the outputs (rate of penetration [rop], bit wear rate). This is the traditional method of using Payzone. Our interest is to use the simulator to


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predict rock property information given known values for wob, rotary speed, rop, etc. We would then use this rock data as input to the blasting model.

Figure 3: Full waveform sonic log of borehole drilled through a stratiform deposit

showing clearly zones of weak rock and open fractures


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Figure 4 Laboratory drill test rig

Figure 5 Drill string vibration data drilling in concrete samples As a first step we have used Payzone to see how accurately this model predicted actual rop and bit wear. The field data used was from a North Sea oil well drilled off the Scottish coast. The rock property information was derived from interpretation of a combination of geological and geophysical logs (Cooper and Hatherly, 2003). The actual values of the drilling inputs – wob, rotary speed, mud properties, and flow rate – were taken from the drilling records. The model was used to predict rop and bit wear; these predictions then were compared with the measured values. A typical rop result is given in Figure 6. In general the model predictions for rop were good. The predictions for bit wear were inconsistent. For some bit runs they were accurate, for others, less so. This work has given us the confidence to continue with this drilling simulator approach. Systems for Geological Vision A big problem in mining can arise when the workings encounter an unexpected geologic disturbance – a fault, a roll, etc. Surprisingly, most mining operations know relatively little about the detailed geometry of the orebody that is being mined; conse-quently these problems arise frequently. This orebody will have been delineated initially by a pattern of exploration boreholes. For sensible economic reasons, the total volume of ore samples taken from these holes is trivial compared with the volume of the ore to be mined; also the spacing between the holes is substantial. The interpolation of ore grades and orebody position/thickness between these widely-spaced, point samples will have been made using geostatistics, or some other technique. This analysis will have allowed the development of a model of the orebody. As mining proceeds additional drilling is often performed to update the model and provide more detailed information about the ground that is about to be


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mined. Despite this, geological surprises are common. These can have a substantial, and sometimes a devastating, negative impact on the viability of the mine. At the very least the knowledge that the operations may encounter such disturbances leads to the costly just in case mentality described above.

Figure 6 Field and Payzone simulator rate of penetration values as a function of depth

for bit run 9 using a PDC bit (from Cooper and Hatherly, 2003) The only way to overcome this problem is to develop technology that gives the mining engineer the ability to “see” through a rock mass. We are working on a number of techniques to achieve this geological vision. One of these is borehole radar: a profil-ing technique conceptually similar to marine echo sounding. A pulsed signal genera-ted by a borehole transmitter is reflected and acquired by a receiver in the same hole. Conventional exploration tools lack the resolution to reveal the geological features with a throw of less than 20 m that need to be recognised ahead of mining operations in narrow vein orebodies. In the appropriate geological environments, synthetic aperture and interferometric borehole radar are emerging as a tactical tool with which to ensure the safe, economic extraction of ore from narrow vein stopes. Professor Iain Mason and his team at the Universities of Sydney (Australia) and Stellenbosch (South Africa) have built ultra-slim digital borehole radars to map sub-metre scale objects on target horizons from small diameter boreholes over distances of 50-100 m. Figure 7 shows the trajectory of an exploration hole drilled in a deep-level mine to locate the plane of the VCR gold reef ahead of mining. The problem is clear. After more than 2 km of drilling the mine acquires three reef intercepts, probably each about 1 m long. This is the only knowledge that the mining engineer has to plan the operations. Any rolls in the reef or faults are unknown.


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Figure 7 A borehole nearly 2 km in length (the LIB hole) has been drilled from the

left-hand to the right-hand side of this diagram. After a distance of some 250 m this hole has intercepted the VCR gold reef (shown as a dashed line) and some dykes. This hole, now below the reef, has been angled back to intercept it again in the two branches at the end of the hole.

This technology has been demonstrated in both gold and platinum mines in South Africa. It shows great promise in both environments and it has the potential to find widespread use in mines around the world. In the platinum mines it has been shown to give mining engineers advance warning of potholes – rolls in the reef plane that cause considerable interruption to mine production when, as frequently occurs, they are encountered unexpectedly (Zindi, 2003). This author notes that, in the study conducted, borehole radar provided essential planning information; in addition to identifying potholes it delineated: dykes, shear zones, faults, acquifers, and water fissures.

Smart Mining Machines Diggers Large capacity excavators (cable shovels, hydraulics excavators, wheels loaders) are the workhorses of most hard rock surface mines. The performance of these machines is often a key to overall mine productivity, since it starts the materials handling process. Typically, the performance of an excavator operator is related directly to the amount of material moved per unit time: mines often reward operators based on their amount of material moved per shift.


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Figure 8 Borehole image of the VCR reef obtained from the hole shown in Figure 7.

Unfortunately this geophysical image is a mirror reflection. It shows the hole straight at the top of the image and the reef plane dipping away. Also it shows the hole as being drilled from right to left. On the vertical scale 1 sec corresponds to about 100 m distance.

Assessing true machine productivity is more complex than just short term rate of material moved. Associated with excavator operation is a relationship between duty (cumulative machine damage) and productivity (material moved per unit time). In this relationship, higher productivity achieved by working a shovel harder is often offset by increases in duty and hence maintenance costs. The duty vs productivity relationship is often quantified by mines on a periodic basis in terms of cost/tonne of material loaded. For an excavator to achieve its full potential (lowest cost/tonne of material moved) operators have to balance duty and productivity for each loading cycle. Some operators are more successful at achieving this balance than others. Our studies characterizing excavator performance (Jessett, 2001; Hall, 2002) have revealed there is considerable variation between operators, and, moreover, that operators show high levels of variation from cycle-to-cycle. For example, Figure 9 shows the variation in dig trajectories across 4 operators working on the same hydraulic excavator over consecutive shifts. The figure shows that operator D has a very consistent dig trajectory that is clearly different from the other operators. What is interesting is that operator D achieves the highest instantaneous productivity (tonnes/hr) but uses a style that generates considerably more damage to the machine than the other operators.


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Figure 9 Bucket tooth trajectories for four different hydraulic shovel operators (from

Hall, 2002) There is considerable scope for improving excavator performance. At the highest level, the Centre is working on excavator automation projects that remove the operator completely from the machine or, at least, automate the tasks that operators find difficult from the perspective of duty or productivity. In the shorter term we are developing tools that attempt to: (i) bring each operator’s mean performance to a level commensurate with ‘best practice’, and (ii) reduce the cycle-by-cycle variation of each operator. The following section presents some details of the capabilities of these operator-assist tools. Stall Feedback Project Previous work (Jessett, 2001) had revealed the potential for performance improvement using feedback systems to adjust the way operators control the shovel, particularly during digging. This technique encourages operators to fine-tune their individual ‘styles’ to allow for good dipper fills and, at the same time, reduce the frequency of events that are likely to cause excessive damage to the shovel (that is, duty). Presently, shovel operators have limited feedback on their individual operating style and how it impacts on productivity, machine duty or both. Existing feedback systems are based mostly around the accumulation of operator statistics and not focused on improving operating technique. In addition, this information is presented in such a way that it is difficult to use owing to the limited time operators have during the operating cycle to process this information. It is important for the feedback system to provide useful information and to present appropriate information in a form that can be effectively used by the operator. The objective is to assist operators in making correct decisions when unfavourable working conditions occur, thereby operating more consistently at site-specific “operator best practice”.


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The CRCMining feedback system passively monitors machine activity by observing current and voltage signals in a shovel’s main electrical drives. The signal waveforms are then analysed to identify and diagnose various classes of operator behaviour. Thus this system is capable of:

• Tracking shovel activity in real time, thereby providing a capability for basic shovel monitoring including cycle time analysis.

• Detecting events that result in significant duty loading on the machinery, so providing a basic tool for performance and duty monitoring.

• Warn operators of undesirable events by providing corrective feedback information.

Data from field trials has determined that the highest machine stresses and the largest performance variations occur during the digging phase. A prevalent problem amongst all operators was stalling of the swing machinery whilst digging. This “swing stall” condition occurs when the operator commands the shovel to swing whilst the dipper is engaged in the bank. It induces very high stresses in the boom and inhibits the smooth motion of the dipper through the bank. This both increases the frequency of machine failure and reduces productivity. The shovel operator is unaware of the swing stall condition as there are no visual or other cues present. A prototype system was developed to present swing-during-dig feedback information to an operator. We worked with cognitive psychologists to design a display that presents real-time knowledge-of-performance feedback to operators in an easily acceptable and understandable format. The displayed information shows the operator’s current joystick position. When the operator sees a red wedge form (Figure 10) this means that a swing-stall is occurring. The required response is to move the joystick in the opposite direction to the wedge to correct the stall. Additional summary data for the shift (in one hour intervals) provides a graph of the percentage of “correct cycles”, ie cycles without swing stall. Field trials of this system at several mines in Australia and at a large copper mine in Arizona have successfully demonstrated the effectiveness of this particular feedback tool. For example, the feedback system operating on a P&H 4100A in a coal mine generated reductions in swing-during-dig ranging from 47 percent up to 69 percent depending on the operator. In addition, a decrease in cycle time was experienced. Based on the effectiveness of this system the Centre is actively commercialising this tool and improving its capability to provide feedback on other unwanted operator traits such as hoist and crowd stall. In addition, a parallel development of similar tools for hydraulic excavators is proceeding.


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Figure 10 Operator display of swing-stall feedback system Scale Model Testing The loading performance of an excavator can vary dramatically, based on factors that include: operating skill, digging material characteristics, machine capabilities and dipper geometry. Often these factors are inter-related which complicates the effect on loading performance. For example, the muck characteristics (size distribution, adhesion, angle of repose, etc) can often affect the fill-ability of different bucket geometries and the required optimum bucket trajectories. Also, selected bucket features may only increase loading performance if the bench geometries and the operators’ digging patterns are correctly matched. Potentially a better understanding of these inter-related bucket loading effects could be gained using analytical or numerical methods. Unfortunately, effective models that realistically describe bucket/rock loading interactions are extremely complex and require excessive computational effort. To keep the computational requirements to days rather than months these models need to be simplified to the point that they are ineffectual. CRCMining has built up considerable capabilities and experience with scale modelling of excavators in realistically re-created digging conditions. These scale model technologies offer a controlled, time efficient, low cost approach to bucket loading studies and to bucket design. Machine specific parameters/limitations can be input into the scaled machine control algorithm. Field dig conditions can be characterized, scaled, and reproduced in the laboratory for loading studies without causing undesired interruptions to field productivity. We have developed a laboratory loading bin ( Figure 11) as a tool for scale model dig testing. This facility allows the Centre’s researchers to control the movement of scaled excavator buckets to follow the specific motions of any excavator type. A 3-axis force/torque sensor measures the forces at the interface between the material and the bucket. In addition, internally-developed, automated dig algorithms control the dipper trajectory during the dig cycle. This


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control system uses forces acquired from the force/torque sensor to respond as the real excavator would in the field. In this way the effects of changes in material properties, machine design, and mine environment can be studied without the overwhelming effect on excavator productivity introduced by operator performance. The automated digging algorithm can utilize force feedback to drive the excavation trajectory to allow the excavator to acquire the maximum possible payload in the shortest time. Thus, best practice operator digging strategies for different excavators can be linked to material characteristics and mine conditions.

Figure 11 The scale model loading bin We have performed numerous studies comparing laboratory dig results to field dig data from mines in the USA and Australia and shown that dig energies, bucket payloads, and dig times can be matched consistently to within 10-15 percent. This approach can be effective in quantifying the effect of material changes on loading performance. The resulting data can then be used to predict machine productivity or to better understand the severity of the dig condition. Importantly, new bucket designs can be trialled economically in a variety of dig conditions and evaluated in terms of dig performance – capability, productivity, and reliability. Smart Trucks Tyre wear is an important component of haul truck operating costs. While it is not unknown to get upwards of 7,000 operational hours from a haul-tyre, typical life expectancy is 1,500 to 4,500 hours, depending on pit conditions and driver behaviour. Assuming an average life of 3,000 hours, the annual tyre replacement cost for a typical haul truck with six tyres could be as high as AUD$300,000. Tyre manufacturers’ literature suggests that tyre damage is strongly influenced by dynamic tyre forces. For example, Bridgestone claims that a 20% reduction in tyre loading will increase tyre life by 50% while a 30% increase will halve it. Excessive tyre forces are caused by: (i) poor quality haul roads; (ii) bad driver practices, (iii)


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poor tuning of the truck control system; (iv) truck overloading. Each of these causal areas can be analysed further. Some road surfaces are poor because their foundations are unsound; others are poorly maintained. Drivers differ in their skill levels and driving habits, for example, the speeds at which they corner and their braking patterns. Tuning of the truck control systems significantly affects the way in which truck and road interact; poor tuning may result in, for example, wheel spin on acceleration. In addition to causing damage to the truck, large tyre forces initiate and propagate damage to the surface and sub-structure of the road. It is a self-degenerating process; as a haul roads starts to deteriorate the tyre forces increase which accelerates the rate of damage to both truck and road. Therefore, a proactive tyre management strategy needs to monitor and control the dynamic tyre forces. We have recently developed a technology to monitor the contact forces and moments at the tyre-road interface for off-road trucks (Siegrist, 2003). The difficulty in using tyre forces as a basis for controlling damage has been that these forces could not be measured directly. Siegrest’s methodology, which was developed and demonstrated on a virtual truck, allows indirect measurement using sensors that measure truck motion. When implemented this technology will provide information that should result in substantial savings to the fleet owner. The technology will:

• identify, log and report incidents, road conditions and driving actions that cause a rapid tyre wear,

• monitor the tyre traction and report when the tyres need to be changed, • map the variation of tyre rolling resistance along the haulage path, • identify, log and report incidents, road conditions and driving actions that

cause abnormal loading on critical mechanical and structural truck components,

• log the duty on critical truck components and automatically produce maintenance reports that recommend preventive maintenance action based on actual damage rates.

Drills At present blasthole drilling accounts for some 10 to 15% of the total surface mining cost, although for each operation these costs will vary (Morrison, 1998). A technique that reduces the cost and increases the productivity of blasthole drilling would be valuable to the mining industry. We have been developing a technology that uses a high pressure waterjet to assist the conventional rotary rock cutting action of a tricone drill bit to increase its overall penetration rate. Research into combining waterjets with tricone bits has been attempted by several researchers notably Maurer (1973), Summers (1988), and Veenhuizen (1997). Our approach uses a waterjet to cut a slot in the rock face that is being drilled by the bit. (Figure 12 shows the waterjet nozzle attached to a tricone bit.) This creates an additional free face for the rock to break to which allows the rock to break in tension rather than confined compression (the advantages of this are discussed in the ODC section below). The lower bit forces that result can increase productivity (faster drilling rates) or reduce bit wear (better cooling, lower specific energy), or both.


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Another consequence could be better hole straightness (less hole deviation) which should improve rock fragmentation.

igure 12 Tricone bit with waterjet nozzle attached

n experimental program using the experimental drill rig ( ue can increase the rate of

isruptive Technologies for Mining

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F AFigure 4) has demonstrated that the waterjet assisted techniq

drill penetration by up to 40%. Plans to trial this technology on a production drill rig at a mine site are underway.

DOscillating Disc Cutting (ODC) A mining method that would surpfrom the early 20th Century is longwall coal mining. This mining system approaches the concept of an underground factory with continuous production from an excavation(cutting) machine discharging directly onto a face conveyor, which in turn discharges onto a panel conveyor and then onto outbye belts. These transport the coal directly to a surface washing plant or loading station. One hundred years ago coal was typically hewn from the face by hand – a physically arduous and dangerous job. The benefits of the modern approach are enormous. Workers are relieved of almost all of the physically challenging aspects of the job. They operate in much safer more pleasenvironments. The productivity gain derived from the mechanised equipment is orders of magnitude higher. Tcapable of cutting the coal face. Underground hard rock miners, who still rely on drill-and-blast methods to fragment much of the rock they mine (albeit today, typically using mechanised rather than hand-held drills) aspire to emulate theircolleagues and machine the rock instead of blast it. If this could be achieved then in many underground hard rock mines benefits similar to those realised in coal would bederived. For example, Figure 13 shows a decline driven by the Robbins Mobile Miner. The quality of this excavation is evident. The smooth floor makes vehicutravel more akin to driving on a concrete highway that the usual rough mine road.


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The smooth walls and back require little in the way of rock support. The even profile minimises ventilation losses.

Figure 13 Decline at the Broken Hill mine driven by the Robbins Mobile Miner,

MM130 Unfortunately, the problem of cutting hard rock is significant and few machines exist that are capable of performing this task economically. For example, the Mobile Miner described above was taken out of service because the cutter costs were excessive. The fundamental problem is that hard rock is a strong, and often abrasive, material. It is important to understand the properties of rock materials in some detail because this can lead to a solution to this difficult problem. Most hard rocks respond to load in an elastic-brittle manner. Brittle materials are strong when loaded in compression, very strong when loaded in confined-compression, but weak when loaded in tension. In a rock cutting operation, therefore, it would seem sensible to attack the rock in a manner that caused it to fail in its weakest mode, ie in tension. In practice, most drills and rock cutting tools break hard rock using an indentation process in which the tool applies a compressive load normal to a rock surface. Indentation loads the rock in confined-compression, that is, in its strongest configuration. The consequence is that the drill or machine force required to break the rock is much (more than an order of magnitude) higher than if the tool attacked the rock in a manner that promoted tensile breakage. The equipment manufacturers that build these drills and cutting machines understand this rock physics. They are constrained by the properties of the cutting tools, which are themselves brittle materials. Consequently, these tools are susceptible to brittle failure. The easiest way to resist brittle failure of the cutting tool is to load it in com-pression – hence the use of indentation as the preferred cutting technique. As noted above, a major downside of this approach is that very high tool forces, and therefore


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machine forces, are required to excavate the rock in this manner. This means that the machines developed for cutting hard rock need to be massive to resist these high forces; the Mobile Miner weighed some 280 t. Large machines are generally unsuited to the mining environment because they have limited manoeuvrability. We have developed a technique that is capable of excavating strong rock with very low tool and machine forces. This technique was originally conceived by David Sugden and is termed oscillating disc cutting (ODC). It uses the robust type of disc cutters that are used successfully on tunnel boring and raise boring machines. However, instead of using them in the usual indentation mode, we use these cutters to undercut the rock, thereby promoting tensile fracture in the rock. As the ODC name implies we also oscillate the cutter during the rock cutting action. This motion cyclically loads the rock, which lowers the effective rock strength. In addition we direct a series of moderate pressure (90 MPa) water jets at the interface between the advancing cutter and the rock. These jets serve two purposes. One, they remove the layer of crushed rock that always forms beneath a rock cutting tool. This crushed material acts as a cushion between the tool and the rock, requiring the application of higher tool forces to generate the critical (tensile) stress in the rock to cause failure. Because the jets continually remove this crushed rock layer they allow the cutter to press directly on the intact rock and produce rock chips at lower tool forces (Hood, 1976; Fenn et al, 1985). The other action the jets provide is effective continuous cooling of the cutter during the cutting operation; this serves to reduce the rate of cutter wear. Cutting tools are replaced in service either because they break (brittle failure) or because they wear. The rate of tool wear accelerates as the temperature of the tool material rises. Consequently, cutter life is greatly enhanced when direct cooling is provided at the interface where the frictional heat is generated during the cutting process (Morris and MacAndrew, 1986). We have performed extensive testing of this novel cutting system in the laboratory and we have performed trials using a field test rig at two hard rock quarries (Figure 14). The results, from both the lab and the field, show that the machine forces are reduced significantly using this novel cutting system. We are continuing to develop the technology with mining company sponsors.
Figure 14 Quarry testing with ODC rig


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Conclusions Hard rock mining today is a capital-intensive, highly productive industry. It may also be an industry in transition. We may be moving away from the mining methods that we have used throughout the 20th Century. Today many open pit mines are very deep, necessitating very long hauls by increasingly expensive trucks. Also, there is an increasing trend towards underground bulk mining (caving). New methods will need to be found to control rock fragmentation in both open pit and underground mines. Similarly new methods will be required to transpoprobably, from underground. It is not clear where the technologies that will be needed for these future mining systems will come from because the global mining research community has shrunk dramatically over the past two decades. To an extent Australia has resisted this trend. CRCMining is one of the few thriving mining research organisations in the world today. Its success is based on its joint venture status. The problems that need to be tackled are large and need the resources of flexible, multidisciplinary research teams. We secure these resources by working with academic staff and students in many university departments (these include mining departments, but they also include mechanical and electrical engineering, and earth science departments) at a number of universities. The problems that we tackle are also real because they are identified and supported by our mining company members. This ensures that transfer of technologies once they are developed, a common pitfall for many research organisations, is not a problem for us. The customers helped identify the need and then worked side-by-side with the researchers developing the technologies. Many of the technologies that we develop are incorporated into pieces of mining equipment. Hence, our recent strategy of inviting original equipment manufacturers to join the Centre as full members. An advantage of membership for these manufacturers is that they regain access to the full spectrum of R&D, rather than just concentrating on ‘D’. They also get the opportunity to interact with their customers on technology issues on a regular basis. The advantage to the mining companies and the Centre in having manufacturers as members involved with the whole life-cycle of projects is that it provides a much faster route to commercial products and services and thereby makes the technologies available more quickly to the industry. A question is, can and should this model of cooperation in mining research be extended globally? The Centre now operates with a group of global mining company and mining equipment company members. It also has the University of Arizona as a supporting member. We have active projects outside Australia, in South Africa and the United States. Is there an opportunity or a need to work with, say, Chilean or Canadian researchers to solve mining problems faced by the mines in those countries? Would the mining industry be better served if we established a truly global mining research joint venture? References Abouzeid, A.A. and Cooper G.A. The use of a drilling simulator to optimise a well drilling plan, presented at the Geothermal Resources Council 2001 Annual Meeting, San Diego, CA, USA, 26-29 Aug, 2001.

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Bongers, D., Gurgenci, H., Wilson, R. and Orlowska, M. Event identification and

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ummers, D. A., Waterjetting technology. London, E & FN Spon. 1995.

identification from multivariate data with missing values, Proceedings 2nd International ICSC Symposium on Advances in Intelligent Data Analysis, CIMA 2001. Cooper, G.A., Cooper, A.G., and Bihn, G. An interactive simulator for teaching and research, SPE Paper 30213, presented at the 10th Petroleum Computer Conference, Houston, TX, USA, 11-14 June, 1995. Cwireline data and their use in drilling, SPE Paper 83509, presented at SPE WesterRegional/AAPG Pacific Section Joint Meeting, Long Beach, CA, USA, 19-24 May 2003. Fenn, O., Protheroe, B.E., and Joughin, N.C. Enhancement of roller cutting by meanof water jets, Proceedings Rapid Excavation and Tunnelling Conference, New York, NY, June 16-20, 1985, 1: 341-356. Hall, A. S. Characterizing the performance of large hydraulic excavators. M. Phil Thesis, The University of Queensland, Department of Mechanical Engineering, 2002. Hatherly, P.J., Fallon, G.N., Fullagar, P.K., and Zhou, B. Geotechnical chaisation of the rock mass with sonic logging, Fourth International Symposium on MinMechanisation and Automation, Brisbane, Qld, Australia, 6-9 July, 1997, A9 Hood, M. Cutting strong rock with a drag bit assisted by high pressure water jets,Afr. Inst. Min. and Met., 77, 4, 1976: 79-90. Jessett, A. Tools for performance monitoring of electric rope shovels, M. Eng. Sci. Thesis, The University of Queensland, Department of Mechanical Engineering, 2001 Morris, A.H. and MacAndrew, K.M. A laboratory study of high pressure water jet assisted cutting, Proceedings of the 8th International Symposium on Jet Cutting Technology, Durham, UK, Sep 2-11, 1986: 1-8. Morrison. G. Developments in Rotary Blasthole Drill Efficiency, Svedala, AusBrisbane, 1998: 26. Maurer, W. C. et al., Hydraulic jet drilling. 4th Conference Drilling and Rock Mechanics, University of Texas, Austin, 1973 Smith, B. Improvements in blast fragmentation using measurement while drilling parameters, ProceedingsA Siegrist, P. ,APhD Thesis, The University of Queensland, Department of Mechanical Engineering, 2003. S


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Documents

Advances in Hard Rock Mining Technology...These authors used Fischer Discriminant Analysis to transform the sensor data into orthogonal, linear and non-linear projections with criteria