An Application of Simultaneous Stochastic Optimization at a Large Open-Pit Gold Mining Complex under Supply Uncertainty

Abstract

The simultaneous stochastic optimization of mining complexes optimizes various components of the related mineral value chain jointly while considering material supply (geological) uncertainty. As a result, the optimization process capitalizes on the synergies between the components of the system while not only quantifying and considering geological uncertainty, but also producing strategic mine plans, maximizing the net present value. This paper presents an application of simultaneous stochastic optimization at a large gold mining complex. The complex contains three open-pit mines, three stockpiles, a waste dump, and a processing facility. Material hardness management is integrated at the processing facility. The case study generated production schedules for each mineral deposit considered, as well as an overall assessment of the project and related forecasts. It resulted in an 18 year life-of-asset and identified the semi-autogenous grinder (SAG) mill as the bottleneck of the operation.

Technology Upgrade Assessment for Open-Pit Mines through Mine Plan Optimization and Discrete Event Simulation

“…Lastly, the third part of the function is related to capital expenditure options (k ∈ K): in other words, costs associated with investments in the mining project. Equation (1) has been the result of several efforts over the years [26][27][28][29][30][31][32][33]. Further details are left to the reader in [33].…”

“…When the best option is determined, it replaces the previous solution with the selected one, and a new iteration is performed until no prospective solutions are found. The literature shows a considerable amount of these metaheuristics in the neutral-risk approach, such as tabu search [39], particle swarm optimization [40], and simulated annealing, the latter being the most commonly employed [26][27][28][29][30][31][32][33].…”

“…It covers not only the mining sequence (production scheduling) but also how the material flows downstream, such as mineral beneficiation, separation and refinement [25]. Several subsequent studies have energized this approach [26][27][28][29][30][31][32][33]; however, the formulation of the mathematical problem possesses extraneous parameters, along with tuning hyperparameters added due to the solution methods (e.g., metaheuristics). To the best of our knowledge, the impacts of adding the mentioned parameters have not yet been analyzed in detail by any published work.…”

A Multi-Stage Methodology for Long-Term Open-Pit Mine Production Planning under Ore Grade Uncertainty

“…In the last few years, some authors have presented different models that aim to maximize NPV and minimize the negative effects of uncertainty [12,36,39,44,[62][63][64]. A common approach to these models is based on the incorporation of penalties that reduce the expected NPV when production or quality targets are not met.…”

“…Early works quantified the potential of including uncertainty in production scheduling [30][31][32], and several approaches have been proposed. One of the most widely used approaches is the minimization of deviation from production targets, which generates schedules that maximize NPV and minimize penalties for not fulfilling tonnage, grade, or quality goals [33][34][35][36][37][38][39]. Two-stage stochastic programs have also been implemented to include recourse actions when new information arrives [40][41][42].…”