COSMO-Stochastic Mine Planning Laboratory Optimization of ......Open Pit Mine Planning and Design....
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Optimization of LOM Open Pit Mine Plan Under Geological Uncertainty COSMO-Stochastic Mine Planning Laboratory Iain Farmer: [email protected] McGill University Introduction: Historically, 60% of mines have had a production rate that was 70% less than their planned rate. Moreover, in the firs year of production, 70% of mills and concentrators had an aver- age rate of production that was 70% less than their designed capacity. The principal reason for these dis- crepancies is that there is poor un- derstanding and incorporation of risk related to the orebody. Procedure: Step 1: Modeling Both kriged and simulated models are created for Cu deposit and the model statistics are verified against the drill hole data. Step 2: Ultimate Pit Limits Step 3: Pushback Design Step 4: Simulated Annealing Scheduling Algorithm ) ( s n ) ( s n : Ore and waste production of per- turbed sequences S: Number of simulations P: Number of Periods Simulated Annealing Results and Discussion: Statistical Differences Between Models Pushback Schedule Performance Schedule Performance by Period Stochastic Schedule Results vs. Base-Case Schedule 0.E+00 1.E+06 2.E+06 3.E+06 4.E+06 5.E+06 6.E+06 7.E+06 8.E+06 0 5 10 Ore Tonnage Period Krig Sim1 Sim2 Sim3 Sim4 0.E+00 5.E+07 1.E+08 2.E+08 2.E+08 3.E+08 3.E+08 0 5 10 NPV ($) Period Krig Sim1 Sim2 Sim3 Sim4 Value Added When the traditional and stochastic optimizations are compared for the copper deposit, the stochastic approach is seen to generate an increase of 18% in total discounted project value. In dollar terms this represents $55M in profits. An- other advantage to the stochastic approach is that it provides a distribution of possible NVPs that can be examined to further evaluate the risk involved in the project. Conclusion: This report has introduced geological risk as the main risk factor for a mining project and presented a way in which it can be incorporated in a LOM plan for an open pit mine. The accounting for risk in the mine design has added 18% or $55M in project value for the Cu deposit case study and reduced the mine life by almost one year. This is due to the risk-based schedule’s ability to minimize deviations from production targets. Geological risk was able to be considered through the use of multiple orebody simulations that better represent the grade distribution and spatial variability of the raw data when compared to the smoothed estimated model generated by kriging. Accounting for risk allowed for a further increase in value by the use of ex- tended pit limits resulting in a more sustainable utilization of the resource. References: Albor, F. (2010). Exploring stochastic optimization in open pit mine design. Montreal: McGill M.Eng Dissertation. Batista, D. W. (2010). Strategic Risk Management in Mine Design: From Life of Mine to Global Optimization. Montreal, Canada. Hustrulid, W. A. (1995). Open Pit Mine Planning and Design. Rotterdam: A.A. Balkema. R, Dimitakopoulos. (2007). Ore Reserve and Mine Planning Optimization: Stochastic Models and Optimization with Applications . Montreal: McGill COSMO Stochastic Mine Planning Laboratory. R, Dimitakopoulos., et al. (2007). Orebody Modeling and Strategic Mine Planning Second edition. Victoria: The Australasian Institute of Mining and Metallurgy.
Transcript of COSMO-Stochastic Mine Planning Laboratory Optimization of ......Open Pit Mine Planning and Design....
Optimization of LOM Open Pit Mine Plan Under Geological Uncertainty
COSMO-Stochastic Mine Planning Laboratory
Iain Farmer: [email protected] McGill University
Introduction:
Historically, 60% of mines have had a production rate that was 70% less than their planned rate. Moreover, in the firs year of production, 70% of mills and concentrators had an aver-age rate of production that was 70% less than their designed capacity. The principal reason for these dis-crepancies is that there is poor un-derstanding and incorporation of risk related to the orebody.
Procedure: Step 1: Modeling Both kriged and simulated models are created for Cu deposit and the model statistics are verified against the drill hole data.
Step 2: Ultimate Pit Limits
Step 3: Pushback Design
Step 4: Simulated Annealing Scheduling Algorithm
)(sn
)(sn : Ore and waste production of per-
turbed sequences
S: Number of simulations
P: Number of Periods
Simulated Annealing
Results and Discussion:
Statistical Differences Between Models
Pushback Schedule Performance
Schedule Performance by Period Stochastic Schedule Results vs. Base-Case Schedule
0.E+00
1.E+06
2.E+06
3.E+06
4.E+06
5.E+06
6.E+06
7.E+06
8.E+06
0 5 10
Ore
Ton
nage
Period
Krig
Sim1
Sim2
Sim3
Sim4
0.E+00
5.E+07
1.E+08
2.E+08
2.E+08
3.E+08
3.E+08
0 5 10
NP
V ($
)
Period
Krig
Sim1
Sim2
Sim3
Sim4
Value Added
When the traditional and stochastic optimizations are compared for the copper deposit, the stochastic approach is seen to generate an increase of 18% in total discounted project value. In dollar terms this represents $55M in profits. An-other advantage to the stochastic approach is that it provides a distribution of possible NVPs that can be examined to further evaluate the risk involved in the project.
Conclusion: This report has introduced geological risk as the main risk factor for a mining project and presented a way in which it can be incorporated in a LOM plan for an open pit mine. The accounting for risk in the mine design has added 18% or $55M in project value for the Cu deposit case study and reduced the mine life by almost one year. This is due to the risk-based schedule’s ability to minimize deviations from production targets. Geological risk was able to be considered through the use of multiple orebody simulations that better represent the grade distribution and spatial variability of the raw data when compared to the smoothed estimated model generated by kriging. Accounting for risk allowed for a further increase in value by the use of ex-tended pit limits resulting in a more sustainable utilization of the resource.
References:
Albor, F. (2010). Exploring stochastic optimization in open pit mine design. Montreal: McGill M.Eng Dissertation. Batista, D. W. (2010). Strategic Risk Management in Mine Design: From Life of Mine to Global Optimization. Montreal, Canada. Hustrulid, W. A. (1995). Open Pit Mine Planning and Design. Rotterdam: A.A. Balkema. R, Dimitakopoulos. (2007). Ore Reserve and Mine Planning Optimization: Stochastic Models and Optimization with Applications . Montreal: McGill COSMO Stochastic Mine Planning Laboratory. R, Dimitakopoulos., et al. (2007). Orebody Modeling and Strategic Mine Planning Second edition. Victoria: The Australasian Institute of Mining and Metallurgy.
