Morales et al. (2019) proposed a two-step methodology based on direct block scheduling that integrates throughput rate and metallurgical recovery as uncertain variables into the stochastic mine planning framework. This is a notable addition to the stochastic optimization problem, as it addresses the performance of the downstream recovery processes directly during the extraction planning. The geometallurgical variables are simulated separately from the grade, as no clear correlation was found between these two categories, and are based on tests performed on site. Evaluation of the method in a case study performed on a copper-molybdenum mining complex resulted in a 9.4% increase in NPV with significantly lower probability of deviations from established production targets. A different approach, leveraging material behaviour is introduced by Levinson et al. (2023). Preconcentration relies on separating mined-out material before it enters the comminution circuit. This effectively leads to more selective extraction, with a lower amount of waste being unnecessarily processed, therefore lowering the energy demand of the comminution and milling stages. The researchers proposed a simultaneous stochastic optimization framework that incorporates a preconcentration facility into the objective function, with the material being separated based on grade-by-size behaviour. The underlying mechanism relies on different fracture behaviour in the blasting process of the ore based on its mineralogy. This results in coarse separation of mineralized and barren fractions (Carrasco, Keeney, and Napier-Munn 2016). The use of a preconcentration facility, along with better integration of other elements of the mining complex, resulted in reduced risk of exceeding operational capacity in the leaching facilities and lowered the demand for stockpile use. Additionally, the optimized schedule yielded a $140 M increase in NPV of the project. Given the large size of the optimization problem, the function was solved using a reinforcement learning algorithm, which significantly decreased solution time and resulted in a schedule that can be further reutilized in the next planning horizons. The techniques discussed in this section, summarized in Table 2, highlight the potential of jointly optimizing multiple components of the mining complex to reduce operational risk and increase the NPV of the project. Although none of the studies quantify the impact on energy consumption directly, the integration of geometallurgical properties into the objective function can be used to leverage more selective extraction to decrease the energy demand in the downstream processes. Table 2 – Summary of methodologies connected to material considerations Author Year Objective function Planning scope Model type Solution methodology/Solver Kumar and Dimitrakopoulos 2019 Maximize DCF, penalize deviation from capacity constraints, penalize deviations from geometallurgical targets, Longterm SMIP Metaheuristics (multineighbourhood simulated annealing with adaptive neighbourhood search) Morales et al. 2019 Maximize DCF, minimize cost of deviations, Longterm SIPDBS Heuristics (ETInc)
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