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Mining Plan Optimization of Multi-Metal Underground Mine Based on Adaptive Hybrid Mutation PSO Algorithm

Author

Listed:
  • Yifei Zhao

    (School of Resource and Safety Engineering, Central South University, Changsha 410083, China)

  • Jianhong Chen

    (School of Resource and Safety Engineering, Central South University, Changsha 410083, China)

  • Shan Yang

    (School of Resource and Safety Engineering, Central South University, Changsha 410083, China)

  • Yi Chen

    (School of Resource and Safety Engineering, Central South University, Changsha 410083, China)

Abstract

Mine extraction planning has a far-reaching impact on the production management and overall economic efficiency of the mining enterprise. The traditional method of preparing underground mine production planning is complicated and tedious, and reaching the optimum calculation results is difficult. Firstly, the theory and method of multi-objective optimization are used to establish a multi-objective planning model with the objective of the best economic efficiency, grade, and ore quantity, taking into account the constraints of ore grade fluctuation, ore output from the mine, production capacity of mining enterprises, and mineral resources utilization. Second, an improved particle swarm algorithm is applied to solve the model, a nonlinear dynamic decreasing weight strategy is proposed for the inertia weights, the variation probability of each generation of particles is dynamically adjusted by the aggregation degree, and this variation probability is used to perform a mixed Gaussian and Cauchy mutation for the global optimal position and an adaptive wavelet variation for the worst individual optimal position. This improved strategy can greatly increase the diversity of the population, improve the global convergence speed of the algorithm, and avoid the premature convergence of the solution. Finally, taking a large polymetallic underground mine in China as a case, the example calculation proves that the algorithm solution result is 10.98% higher than the mine plan index in terms of ore volume and 41.88% higher in terms of economic efficiency, the algorithm solution speed is 29.25% higher, and the model and optimization algorithm meet the requirements of a mining industry extraction production plan, which can effectively optimize the mine’s extraction plan and provide a basis for mine operation decisions.

Suggested Citation

  • Yifei Zhao & Jianhong Chen & Shan Yang & Yi Chen, 2022. "Mining Plan Optimization of Multi-Metal Underground Mine Based on Adaptive Hybrid Mutation PSO Algorithm," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2418-:d:860218
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    References listed on IDEAS

    as
    1. W. Matthew Carlyle & B. Curtis Eaves, 2001. "Underground Planning at Stillwater Mining Company," Interfaces, INFORMS, vol. 31(4), pages 50-60, August.
    2. Newman, Alexandra M. & Kuchta, Mark, 2007. "Using aggregation to optimize long-term production planning at an underground mine," European Journal of Operational Research, Elsevier, vol. 176(2), pages 1205-1218, January.
    3. Nesbitt, Peter & Blake, Lewis R. & Lamas, Patricio & Goycoolea, Marcos & Pagnoncelli, Bernardo K. & Newman, Alexandra & Brickey, Andrea, 2021. "Underground mine scheduling under uncertainty," European Journal of Operational Research, Elsevier, vol. 294(1), pages 340-352.
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    Cited by:

    1. Wen Zhang & Xiaofeng Xu & Jun Wu & Kaijian He, 2023. "Preface to the Special Issue on “Computational and Mathematical Methods in Information Science and Engineering”," Mathematics, MDPI, vol. 11(14), pages 1-4, July.
    2. Juan Pérez & Héctor López-Ospina, 2022. "Competitive Pricing for Multiple Market Segments Considering Consumers’ Willingness to Pay," Mathematics, MDPI, vol. 10(19), pages 1-32, October.

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