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The optimization of investment strategy for resource utilization and energy conservation in iron mines based on Monte Carlo and intelligent computation

He, Yong, Liao, Nuo, Rao, Jiwen, Fu, Feifei, Chen, Zhihao
Journal of cleaner production 2019 v.232 pp. 672-691
Monte Carlo method, algorithms, decision making, empirical research, energy, energy conservation, iron, mining, models, China
How to improve resource utilization and reduce energy consumption are two challenging tasks in iron mines. The realization of resource utilization and energy conservation goals depends on proper investment strategy on key production processes with relatively high resource loss or energy consumption. However, none of literature have explored the decision-making issue of investment strategy combined with the mining and dressing grades from the perspective of system engineering. Focusing on the entire mine system, this study links the investment decision-making of resource utilization and energy conservation with the determination of the grades combination, and obtains the investment strategies to maximize the goals of resource utilization and energy conservation. A nonlinear multi-objective constrained optimization model is established, and then Monte Carlo simulation and intelligent computation methods are fused to be MC-IO algorithm, to find out the optimal investment strategy and grades combination. Taking DA iron mine in China as an empirical study, five different investment strategies with grades combination are obtained. The results indicate the feasibility and validity of the proposed methodology. This study has provided a scientific and feasible approach for improving resource utilization and energy conservation in iron mines.