CIM 2015 (Michael Spleit, New Millennium Iron)
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This document summarizes work to optimize the mine production schedule and reduce risk at the LabMag magnetite deposit. It discusses: 1. Using a mathematical optimization model to generate a schedule that considers haul cycle times, minimizing equipment needs and costs. 2. Developing a strategy to create in-pit space for waste disposal over the mine life. 3. How the optimized scheduling approach reduced the risk profile for concentrate tonnages and sales by 14.9% compared to the conventional schedule.
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SPE 192047 technical presentation - 2018 SPE Conference (Brisbane, AU)
SPE 192047 technical presentation - 2018 SPE Conference (Brisbane, AU)
2. Jonathan Nortier and Ali Sirkeci, AMC - Evaluate Your Options with Minemax
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Similar to CIM 2015 (Michael Spleit, New Millennium Iron)
Norway2000
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1658902048613_MBAKO INDUSTRIAL ATTACHMENT PRESENTATION.pptx
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MNRF CWRA Technical Workshop March 6 2018 Rob Grech and Robert Muir City of M...
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2023 CalAPA Spring Conference presentation by John Harvey on compaction of as...
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1658902048613_MBAKO INDUSTRIAL ATTACHMENT PRESENTATION.pptx
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CIM 2015 (Michael Spleit, New Millennium Iron)
- 1. Reducing risk and optimizing the mine production schedule at the LabMag magnetite deposit CIM Convention - May 12, 2015 Michael Spleit Senior Engineer, Mine Planning
- 2. Outline 2 • LabMag deposit • Tailings management • Truck fleet • Geological and financial risk • Mathematical optimization model • Comparison of optimized schedule to conventional • Conclusions and future Work
- 3. 5 New Millennium Iron’s Projects Determinstic (kriged) model Mesabi Iron Range Marquette Iron Range Millennium Iron Range Labrador Trough NML Project Areas: Northern Québec & Labrador
- 4. 6 LabMag Taconite Iron Ore Deposit
- 5. 7 Previous LabMag Site Layout
- 6. 6 Equipment Fleet 150m deep: trucks drive slower on the incline productivity is lower Better: • Directly consider truck hours during scheduling • Mine blocks with higher truck productivity in earlier periods • Delay equipment purchases
- 7. 14 Equipment Fleet 0 5 10 15 20 25 30 35 40 45 #oftrucks Year HAUL TRUCKS Trucks Smoothed trucks
- 8. 6 Geological Risk Structural Units: 7 lithologies correlated thicknesses Metallurgical Qualities: 4 correlated qualities FeH (head iron) DTWR (weight recovery) FeC (concentrate iron) SiC (concentrate silica) Deposit is stochastically simulated 10 times
- 9. Risk Analysis with Stochastic Simulation Early period risk of shortfall
- 10. Cumulative DCF Comparison 23 -250% -200% -150% -100% -50% 0% 50% 100% 150% Y-4 Y-2 1 3 5 7 9 11 13 15 17 19 21 23 25 CumulativeDCF($) Year 80% confidence range (Conventional schedule) Expected value (Conventional schedule) Deterministic model (Conventional schedule) 14.9%
- 11. 4 Considerations in this study: 1.Maximize mine profitability 2.Address geological (and thus financial) risk 3.Minimize equipment fleet 4.Provide space in-pit for replacing waste/tailings New Optimized Production Schedule
- 12. Changes in Criteria • No cut-offs • Different silica targets (flowsheet changed) • Avoid MS layer • Tailings considered dry-stacked instead of wet 8
- 13. 28 10 Year Optimization and Pit Designs 10 Year Optimization Based on Block Model Each colour is a different period Manual Pit Designs Based on Optimization After 5 Years After 10 Years After 25 Years Process Plant
- 15. Ore Tonnes 17 Deterministic model consistently over-estimates
- 16. Waste and Stockpile Tonnes 21 (Stochastic) (Conventional) (Conventional) 0 10 20 30 40 50 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Millionsoftonnes Period Waste Stockpile Waste
- 18. Cumulative DCF Comparison 23 -250% -200% -150% -100% -50% 0% 50% 100% 150% Y-4 Y-2 1 3 5 7 9 11 13 15 17 19 21 23 25 CumulativeDCF($) Year 80% confidence range (Conventional schedule) Expected value (Conventional schedule) Deterministic model (Conventional schedule) 14.9%
- 19. Cumulative DCF Comparison 23 -250% -200% -150% -100% -50% 0% 50% 100% 150% Y-4 Y-2 1 3 5 7 9 11 13 15 17 19 21 23 25 CumulativeDCF($) Year 80% confidence range (Optimized schedule) 80% confidence range (Conventional schedule) Expected value (Optimized schedule) Expected value (Conventional schedule) Deterministic model (Conventional schedule) 16.9%
- 20. 25 Conclusions 1. Optimized sequencing considers haul cycle times: Reduced equipment CapEx, reduced OpEx 2. Strategy for creating in-pit space for waste disposal 3. Reduced risk profile for concentrate tonnes (and thus sales) 4. Blending control was incorporated 5. Stochastic scheduling has significant economic benefit
- 21. 26 Future Work Global optimization -Manage waste explicitly -Semi-mobile crusher instead of fixed plant: include crusher locations and moves as part of the optimization -Consider cost implications to DTWR variations in the plant