Short-term schedule optimization with nonlinear blending models for improved metallurgical recovery in mining

Traditional linear approaches oversimplify the complexities of ore blending. Adopting nonlinear blending models in mining and mineral processing is critical to enhancing prediction accuracy and allowing optimization regarding nonadditive variables, such as metallurgical recovery. This study implements a simulated annealing algorithm in short-term mine planning that seeks to optimize the metal recovery by considering how to blend the ore in the mine better. Two nonlinear metallurgical recovery models are used as inputs to the algorithm to represent synergistic and antagonistic blending. The results of the case study demonstrate that the optimized schedule exhibits improvements in both blending behaviors when contrasted with conventional linear-based scheduling plans.