Adaptive simultaneous stochastic optimization of a gold mining complex: A case study

Abstract

An innovative strategic mine planning approach was applied to a multi-mine and multi-process gold mining complex that simultaneously considers feasible capital investment alternatives and capacity management decisions. The simultaneous stochastic optimization framework determines the extraction sequence, stockpiling, processing stream, blending, waste management, and capital investment decisions in a single mathematical model. A production schedule branches and adapts to uncertainty based on the likelihood of purchasing a number of feasible investment alternatives that may improve mill throughput or blending, or increase the tailings capacity. Additionally, the mining rate is determined simultaneously by selecting the number of trucks and shovels required to maximize the value of the operation. The mining complex contains several sources - two open-pit gold mines and externally sourced ore - stockpiles, waste dumps, tailings, and three different processing streams. The simultaneous optimization framework integrates the blending of sulphates, carbonates, and organic carbon at the autoclave for refractory ore while managing acid consumption. The production schedule generated branches over an investment in the autoclave expansion; the first branch undertakes a capacity expansion at the autoclave resulting in a 6.4% increase in NPV, whereas the second branch results in a 27.5% increase in NPV without the investment. The adaptive approach is compared to a base case production schedule generated using a non-branching two-stage stochastic integer program.