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The use of ARENA® Simulation to estimate Drawpoint Construction Rate, Production Rate and Costs for the Hugo North Lift 1 Panel Cave

Pierre Labrecque Labrecque Technologies, Sudbury, Canada Troy Newman Rio Tinto, Oyu Tolgoi Project, Vancouver, Canada Dudley, Jo-Anne Rio Tinto, Oyu Tolgoi Project, Vancouver, Canada

Abstract This paper describes how ARENA ® simulation was used as a key and integral part of mine planning for the Oyu Tolgoi Feasibility Study. The purpose of this paper is not to explain details of the simulation process, rather to highlight how simulation was used as a central information repository to drive mine design changes, mine operating plan changes and to support cost estimating in an iterative planning process. The close links of simulation development to mine planning have enabled the mine design team to develop a robust design and production plan. Key trade-off studies are summarised in this paper.

Introduction The Oyu Tolgoi Project is located in the South Gobi region of Mongolia. Oyu Tolgoi contains a number of predominantly copper-gold mineral deposits including Hugo North, Hugo South, Heruga and Southern Oyu. Hugo North, the first underground mine to be developed, will be mined using panel caving techniques and will consist of two lifts. An exploration shaft currently provides access to the first mining area, Lift 1, which is under development. A Feasibility Study is nearing completion for the first 2 panels to be mined on Lift 1. The full production rate of 90,000 tonnes per day is achieved through operating two caving panels. The extraction level horizon is at -100 RL, 1300 metres below surface with LHDs dumping from drawpoint to orepass. A truck haulage level collects ore from central orepasses and delivers to one of two gyratory crushers. Conveyors are used to transport crushed ore to one of two production shafts. Full production from the first two panels is sustained for eight years, and as production ramps down, future panels will ramp up to maintain overall production. The Oyu Tolgoi Feasibility Study team commenced work on detailed mine design in early 2010 and engaged Labrecque Technologies to develop an ARENA ® simulation of all on-footprint activities in parallel to mine design and planning work. Eighteen months and four major simulation models later (incorporating multiple interim trade-off models), a final feasibility study simulation has been produced, reflecting the outcome of the over 50 trade-offs and sensitivity analyses conducted. An ore handling system simulation including underground crushing and conveying, hoisting and surface conveying to mill was developed by AMEC, and both models were integrated to give a complete ore flow production schedule from cave to mill, as illustrated in Figure 1. This paper concentrates only on the footprint simulation.

Background

How is the simulation different from the typical approach during studies? Although it is common for discrete event simulation models to be used to simulate a portion of a production system, the Oyu Tolgoi study team worked with Labrecque Technologies to extend and refine

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the simulation to include development, undercutting, extraction level construction, production LHDs, secondary breaking, and truck haulage. The simulations key strength is in managing the interactions between these different activities on different levels over time, based on clearly defined cave abutment and activity sequence rules. Simulations for such large mining operations are typically built to study full production for one year. This ARENA ® model simulates preproduction activities, production ramp-up and sustaining full production, starting when development drill rigs reach the footprint in 2014 through until depletion of the first two panels in 2036.

Figure 1: Ore flow process and tie between footprint simulation and ore handling simulation

The simulation has allowed the study team to develop a base case drawpoint construction rate and production rate along with a valuable understanding of the drivers of these rates. It has been used for a multitude of trade-offs to understand the key factors influencing drawpoint construction rates and production rates. It has been used to predict equipment build up and operating hours each year, thus proving to be a valuable tool for study cost estimation. The simulation is based on the footprint layout and mining block extents that are planned to be mined and therefore reflects the true feasibility study mine plan. Why dynamic simulation? Simulation models take into account competition for resources, queuing, traffic, variability in process times, random breakdowns and events, and changing operating conditions over time. This allows for a realistic assessment of the type of production performance that can be expected. “What-if”

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experimentation can easily be carried out allowing best configurations and operating strategies to be worked out before the actual operation begins. The simulation provides a central information repository for design and operating assumptions, processes and outputs. Like any database system, this provides benefits of ensuring all information is in one location, and all processes are using the same base data for calculation. Any changes made to inputs flow automatically through different areas of simulation. An important outcome of the simulation work for the OT study has been the rigor that is necessary to build a simulation model of this magnitude, and the in-depth understanding of the process needed. Figure 2 shows the simulation structure with multiple inputs and outputs. How is the Model Verified and Validated? Model inputs primarily come from historical data and benchmarking data from other caving and underground operations. Wherever possible, Oyu Tolgoi site data has been used to formulate model inputs but at this stage this is limited to underground lateral development activities. Inputs that had limited measured data have had significant input from mine planning staff and subject matter experts who have worked in cave construction and production operations. The processes that are built into the simulation, that convert model inputs into activities and control how these activities interact, are based off similar ‘advanced undercutting’ caving projects. Outputs are benchmarked to ensure results are reasonable and credible. Reviews of inputs, logic, assumptions and results have been used extensively. Sensitivities have also been undertaken to test logic and the model robustness.

Figure 2: Simulation Structure, showing key inputs and outputs

Key Study Outputs Design Figure 3 shows a plan view of the three levels within the final simulation model. The simulation trade-offs and sensitivities undertaken during the feasibility study had a direct influence on the final design. Drawpoint Construction Rate Drawpoint construction rate and block height are key factors in determining the production rate for a cave. The interdependencies of undercutting rate, drawpoint construction rate and production rate make identifying the drawpoint construction rate extremely important. High abutment stresses coupled with highly fractured ground at Oyu Tolgoi places extra focus on undercut speed and undercut face lengths to

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manage ground conditions. Simulation was used to manage the activity interaction to produce drawpoint construction rates on a month by month basis over the 20 years of production. Results showed a variation in drawpoint construction rate with changing geometry (cave face length and number of working areas) of between 4 to 6 drawbells per month, per advancing cave front. Model results were calibrated to industry actuals and results were adjusted so that drawpoint construction rates were modelled between 3 to 5 drawbells per month, per advancing cave face.

Figure 3: Feasibility Design within ARENA ® – Undercut Level, Extraction Level and Haulage Level

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Undercut North Face Undercut South Face Undercut Total Undercut Drives Lateral Development Figure 4: Undercut Level Summary Graph (Normalized to Report Drawbells per Month)

Production Ramp-up Rate and Full Production Rate Oyu Tolgoi is using Gemcom’s Personal Computer Block Cave (PCBC) software for production scheduling. Within PCBC, the Production Rate Curve (PRC) is used to to inform the software on expected rates of draw from the cave. The PRC typically represents an increasing draw rate based on

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drawpoint maturity, which is a proxy for factors affecting the cave’s ability to provide production tonnes such as caveback progression and fragmentation. Additional adjustments need to be made to incorporate production influences outside the cave (e.g. influence of secondary breaking, orepass rebuild or ore handling availability) to get a complete production schedule. The simulation allowed the team to more accurately calibrate the PCBC production ramp-up to match the productivity of the entire system.

Trade-offs Influencing Study Outcomes Over 50 Mine planning trade-offs were simulated, each one contributing to mine planning decisions. Major decisions assisted by simulation include: Orepass location (centre, rim, in-between), Drawpoint layout (El Teniente vs Herringbone) and Undercut Optimisation. Other trade-offs can be found in Figure 5. Ore Pass Location (Within Feasibility Model #1) To evaluate improvements in production schedule, due to improved loader productivity, compared against cost of additional orepasses and development.

Figure 5: Sensitivities and Trade-offs completed as the ARENA ® simulation evolved.

The PFS mine design was based on rim production orepasses, located on one side of the orebody. Average one way tramming distances were in the order of 170 metres and two extraction drives shared one rim orepass (OP). Three alternate ore pass locations were investigated, as shown in Figure 6, and simulation models set up to evaluate each option. Moving orepasses internal to the rim drive resulted in doubling the number of orepasses, and additional haulage level infrastructure, the cost of which was included in the evaluation of the benefit of any productivity improvement.

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Figure 6: Orepass Locations tested

Results PCBC Production schedules called for mucking rates consistently up to 5,000 tonnes per day per drive to achieve the planned production ramp-up profile. Simulation runs based on the rim orepass design identified that the production LHDs couldn’t meet the required draw, due to long tramming distances and limitation of one loader operating per extraction drive. Model experimentation showed that there was a benefit from moving the orepass from the west perimeter to the edge of the footprint and a significant benefit from moving the orepass to 1/3 of the crosscut. There was only a small benefit from moving the orepass directly into the center of the extraction drive, as the average cycle distance is very close for the 1/3 case and the center case. Results are displayed in Table 1. It is of note that this simulation comparison was carried out with a limitation of one loader per extraction drive. The Base OP (rim) and Edge OP can only have one loader per extraction drive, however 1/3 OP and Centre OP can support 2 loaders at one time, resulting in higher drive tonnage ability.

Key Results 1 - Base OP 2 - Edge OP 3 - 1/3 OP 4 - Center OP Number of LHDs 30 21 17 17 Crosscut TPD 3,500 4,050 5,400 5,550

Table 1: Results from Orepass location tests, limited to one loader operating In addition to examining changes on the extraction level, simulation was used to analyse impacts on the haulage level to understand the impacts on trucking distances effecting number of trucks, truck operating costs and development capital difference Outcome Centre orepass location was selected based on ability to meet production schedule and upside capacity El Teniente Vs Herringbone Drawpoint Layout (Within Feasibility Model #2) Herringbone and El Teniente drawpoint geometry are both applicable to Oyu Tolgoi. The study team needed to evaluate the impact on production and construction of each layout. Herringbone and El Teniente models were created, as shown in Figure 8. Separate micro models were also created as a verification to compare upside capacity of both layouts. Results The simulation comparison identified that the change in geometry to El Teniente resulted in a 0.5 drawbell per month increase in construction rate, due to congestion reduction (by opening of a third access into the footprint work areas). Simulation models were set up to evaluate extraction drive productivity (mm/day maximum draw ability). For a central ore tipping design, under normal operating conditions, the reverse herringbone layout

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produced the greatest productivity, up to 600mm/day average for all drawpoints utilising 14t electric LHDs’ and up to 500mm/day for 14t diesel LHD’s. The El Teniente design produced up to 500mm/day for 14t diesel LHD’s and up to 300mm/day for 14t electric LHD’s (due to cable interaction). Both diesel LHD layouts allowed for upside potential well in excess of the base 300mm/day drawpoint maturity call. The two electric LHD’s, and also one diesel LHD per drive, achieved the required production ramp-up rate and full production tonnage requirement, but limited upside opportunity for future cave tonnage expansion.

Figure 7: Drawpoint Layout comparison showing construction access

Outcome The simulation comparison, from a drawpoint construction and production perspective, supported the change to El Teniente design. Undercut Optimisation (Within Feasibility Model #3) Accelerating the start of production date is a key value driver for the Oyu Tolgoi project. Two areas were examined. The first area was undercut development, and minimising the initial development required to start undercut blasting, while still maintaining development required for rapid ramp-up. The second area was undercut longhole blast initiation sequence. Results The introduction of the temporary rim drive reduced the time to blast the first drawbell by 4 months due to minimising preproduction lateral development metres and delaying to a time when ventilation is less of a constraint and more equipment can be added to the undercut level. In order to increase the ramp up rate of undercutting a few scenarios were tested. The best option was a “V” shape undercut option that initiated 4 drawbells in from the west perimeter. This doubles the working faces initially by allowing equipment to work to the east and west of the initiation slot, allowing the cave to reach Critical Hydraulic Radius 2 months earlier than starting on the cave edge.

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Undercut  Level Extraction  Level

Figure 8: Undercut Initiation simulation results on both Undercut and Extraction levels

On the extraction level this also allows drawpoint and drawbell construction to progress sooner as the undercutting is immediately taking place directly above the drawbells, reducing the time to first drawbell blast and supporting a faster drawbell construction rate ramp up. Congestion issues, like that experienced with the diamond shape undercut initiation at Palabora Cave, were closely monitored. The difference in design of Oyu Tolgoi with central tipping (compared to Palabora layout with rim tipping) and initiating the cave 4 bells in from the edge (rather than the centre) did not raise cause for concern. Outcome Undercut sacrificial drives and ‘V’ shape undercut initiation sequence were included into the Oyu Tolgoi Feasibility Study Estimate and Development Schedule (within Feasibility Model #4) Results Direct outputs from the simulation include Equipment fleet ramp up and operating hours per year which are used in the cost estimate for the Feasibility Study, as shown in Table 2. The Undercut advance rates predicted by ARENA ® are followed in the Mine2-4d/EPS schedule instead of using average advance rates per period.

# hrs # hrs # hrs # hrs # hrs # hrs # hrs # hrs # hrsLHDs 1 2300 4 3600 7 3850 11 4000 16 4000 20 4300 20 4700 20 5000 20 5000Trucks 1 3400 3 3400 6 3500 10 3700 13 3900 17 4500 17 4900 17 4900 17 4900

2022 2023 20242016 2017 2018 2019 2020 2021

Table 2: LHD and Truck buildup to full production

Conclusion This paper is not about an ARENA ® simulation working in isolation, it aims to share how a simulation has evolved hand-in-hand with the design to produce a robust mine design and operating plan where there is a better understanding of what drives the key outcomes for the operation. The ARENA ® simulation at OT has tested a number of mine planning trade-offs and system capacities in a way that can't be done using traditional mine planning techniques, resulting in changes to the mine design that improved productivity and reduced risk to achieving operational goals, both during ramp up and in full production phases. The simulation has provided confidence that the mine will be able to operate as planned, with

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upside potential identified. It has allowed the mine design team to evaluate ranges of outcomes as sensitivity cases so that critical aspects of mine design and production philosophy can be focussed on items that impact production rate and drawpoint construction rate. Key Learnings about the use of simulation for the mine design team were:

• More than one simulation model is required as the process of mine design and analysis is an iterative process

• The simulation model should be built as early as possible so that it can be used to make critical design decisions. If the model is built too late than the lessons learned from the model will not be able to be implemented in the mine plan and design.

• Don’t limit the simulation to simply validation at the end of study. Simulate the entire production ramp up and life of mine schedule.

Simulation runs can have unexpected outputs and if time is spent to understand the results, significant improvements can be made to the mine design and operating strategies. Understanding the outputs allows for experimentation in the form of trade-off studies to optimise the mine design and operating strategies. Sensitivities have been very powerful, helping the study team understand what factors drive ramp up, production rate and other important key performance indicators for the operation to focus work on those areas that drive value. The use of this simulation can extend from study phase into construction and operation, introducing measured activity times to predict physicals and costs for construction, development and production throughout the mine life.