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Increasing the sustainable value and facilitating the decision-making process of mining projects since 2010
Increasing the sustainable value and facilitating the decision-making process of mining projects since 2010
This MiningMath use case originates from an academic study developed at the Universidad de Santiago de Chile (USACH), within the Department of Mining Engineering. The study was conducted by Fabián Ignacio González Recabarren as part of his thesis to obtain the title of Mining Engineer, under the supervision of Professor Moisés Álvarez Becerra.
Carried out in 2022, the work applied advanced mine planning methodologies to the La Cocota deposit, a large copper porphyry located in northern Chile. The research sought to compare, in a quantitative and methodologically rigorous way, the results of traditional planning techniques with the more recent Direct Block Scheduling (DBS) approach, a concept equivalent to MiningMath’s Single-step Mining Optimization.
This case translates that academic thesis into a structured report for technical teams and managers, highlighting the economic gains, methodological consistency, and practical applicability of the solution.
The strategic study of the La Cocota deposit demonstrated that Single-step Mining Optimization, implemented by MiningMath, overcomes the limitations of traditional methods such as Lerchs & Grossmann and Whittle-Milawa. While sequential methods impose artificial restrictions and generate plans subject to arbitrariness, single-step optimization simultaneously integrated sequencing, destinations, and operational constraints.
The result was a significant increase in NPV from 439.4 to 518.6 million dollars, combined with greater operational consistency and the anticipation of positive cash flows by two years. In addition to maximizing value, the methodology provided more transparent, replicable, and operationally aligned solutions.
Open-pit mining projects involve long investment cycles and decisions that impact the entire life of the operation. The choice of mining rates, plant capacity, and pit shells directly influences economic return and technical feasibility. Mistakes in these definitions can result in significant financial losses, imbalance between mine and plant, and increased operational risk.
The traditional Lerchs & Grossmann methodology, applied in traditional software, structures mining into nested pits and depends on manual definition phases. This logic presents important limitations:
Dependence on subjective decisions in phase definition.
Results that do not directly account for the time value of money.
Risk of inconsistency between mine-plant capacity and sequencing.
Problems such as the Gap Problem, which require the creation of artificial sub-phases.
These points reduce economic robustness and widen the gap between theory and operational practice.
Greater computational capacity has opened space for more comprehensive methodologies, such as DBS, which simultaneously integrate technical, economic, and operational variables. MiningMath took this concept further with its Single-step Mining Optimization approach, which eliminates the need for intermediate steps, reduces manual biases, and delivers more consistent solutions.
Block model: 1,040,820 blocks (15×15×10 m).
Resources: 143 Mt @ 0.67% CuT (cut-off 0.2%).
Technical parameters: metallurgical recovery 84%; concentrate grade 29%.
Economic assumptions: price USD 3.6/lb; discount rate 10%.
Scenarios: plants of 15, 20, 25, and 30 ktpd, with mining rates from 35 to 95 Mtpa.
MiningMath applies a hybrid three-step process:
Initial assessment: exclusion of regions without economic value.
Linearized optimization (MILP): approximation of the nonlinear problem, integrating geometric, economic, and operational constraints.
Nonlinear refinement (Branch & Cut): transformation into feasible and operational solutions, respecting all project parameters.
Slope angles: 48°–52°.
Minimum bottom width: 40 m.
Maximum vertical rate: 15 benches/year.
Stockpiles without maximum limit.
Mine-plant capacities adjusted by scenario.
This integration reduced the space for subjective biases and eliminated the need for predefined pushbacks.
MiningMath generated an increment of 79 MUSD, equivalent to an 18% increase in NPV, for the same plant capacity.
| Methodology | Plant (ktpd) | Mine (Mtpa) | NPV (MUSD) |
|---|---|---|---|
MiningMath | 30 | 95 | 518.6 |
Traditional | 30 | Content | 439.4 |
Rock movement: 568.1 Mt (MiningMath) vs. 664.9 Mt (traditional).
Fine copper: 630.6 kt (MiningMath) vs. 737.7 kt (traditional).
Cash flow: starts in year 5 (MiningMath) vs. year 7 (traditional)
Despite lower fine copper, the reduction in waste and sequencing consistency ensured higher net return.
The results of the La Cocota study highlight several practical advantages delivered by the Single-step Mining Optimization. Beyond the numerical gains in NPV and cash flow, the methodology reshapes how mining projects are analyzed and managed. The following points summarize the key benefits observed when applying MiningMath in this context:
Reduced subjectivity in phase definition.
Agility to test multiple scenarios.
Models better aligned with operational reality.
Significant increase in project value.
Anticipation of revenues and greater liquidity.
Reliable scientific basis to justify investments.
The study of the La Cocota deposit clearly shows that MiningMath’s Single-step Mining Optimization represents an evolution over traditional mine planning methodologies.
The results prove that full integration generates mine plans with higher economic return, greater technical consistency, and lower risk of imbalance between mine and plant. At La Cocota, this translated into an increment of 79 million dollars in NPV, along with the anticipation of positive cash flows by two years. These figures are significant in terms of competitiveness: they represent greater liquidity, more room for reinvestment, and increased security against market uncertainties.
MiningMath challenges planning paradigms. It allows engineers to act less as “phase adjusters” and more as scenario analysts, exploring combinations of capacity, geotechnical constraints, economic parameters, and market variations that enable well-founded strategic decisions with comparable variations. The methodology turns each study into an opportunity to generate new knowledge, reduce risks, and increase returns.
Contact us today to boost value and cut risks in your mining project.
Windows 64-Bit (x86_64) - 121 MB
Windows 64-Bit (x86_64) - 121 MB
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