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Comprehensive Utilization of Mineral Resources: Optimal Blending of Polymetallic Ore Using an Improved NSGA-III Algorithm

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  • Lu Chen

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
    Xi’an Key Laboratory for Intelligent Industrial Perception, Calculation and Decision, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Qinghua Gu

    (Xi’an Key Laboratory for Intelligent Industrial Perception, Calculation and Decision, Xi’an University of Architecture and Technology, Xi’an 710055, China
    School of Resources Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Rui Wang

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Zhidong Feng

    (Xi’an Key Laboratory for Intelligent Industrial Perception, Calculation and Decision, Xi’an University of Architecture and Technology, Xi’an 710055, China
    School of Resources Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Chao Zhang

    (China Molybdenum Co., Ltd. (China), Luoyang 417500, China)

Abstract

A serious problem faced by the metal mineral mining industry is the challenge to the sustainable development of resource mining due to the continuous decline of ore geological grade. In the case of producing concentrates of the same quality, compared with using only high-grade raw ore, ore blending is a way to slow down the decline of ore geological grade by combining high- and low-grade raw ore. There are many ore blending models considering cost minimization or profit maximization as the target value, ignoring the fact that ore blending is intended to obtain a homogenized product. Moreover, the ore blending model cannot be solved by traditional operational research methods when blended grade stability of multiple elements is considered in the ore blending program. In this paper, a multi-objective ore blending optimization model is constructed for the comprehensive utilization of associated resources in ores. It minimizes the deviation of the grade of each metallic element in the blended associated ore from the beneficiation grade and the percentage of different types of rocks at the unloading point. To solve this multi-objective optimization model, an intelligent optimization method is proposed that is an improved multi-objective optimization algorithm based on the Non-dominated Sorting Genetic Algorithm III (NSGA-III). The case study shows that the proposed model and algorithm can effectively solve the mixing problem of polymetallic ores and obtain a satisfactory ore blending solution.

Suggested Citation

  • Lu Chen & Qinghua Gu & Rui Wang & Zhidong Feng & Chao Zhang, 2022. "Comprehensive Utilization of Mineral Resources: Optimal Blending of Polymetallic Ore Using an Improved NSGA-III Algorithm," Sustainability, MDPI, vol. 14(17), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10766-:d:901100
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    References listed on IDEAS

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    1. Michelle L. Blom & Adrian R. Pearce & Peter J. Stuckey, 2016. "A Decomposition-Based Algorithm for the Scheduling of Open-Pit Networks Over Multiple Time Periods," Management Science, INFORMS, vol. 62(10), pages 3059-3084, October.
    2. Moreno, Eduardo & Rezakhah, Mojtaba & Newman, Alexandra & Ferreira, Felipe, 2017. "Linear models for stockpiling in open-pit mine production scheduling problems," European Journal of Operational Research, Elsevier, vol. 260(1), pages 212-221.
    3. Sotoudeh, Farzad & Nehring, Micah & Kizil, Mehmet & Knights, Peter & Mousavi, Amin, 2020. "Production scheduling optimisation for sublevel stoping mines using mathematical programming: A review of literature and future directions," Resources Policy, Elsevier, vol. 68(C).
    4. Danish, Abid Ali Khan & Khan, Asif & Muhammad, Khan & Ahmad, Waqas & Salman, Saad, 2021. "A simulated annealing based approach for open pit mine production scheduling with stockpiling option," Resources Policy, Elsevier, vol. 71(C).
    5. Michelle L. Blom & Christina N. Burt & Adrian R. Pearce & Peter J. Stuckey, 2014. "A Decomposition-Based Heuristic for Collaborative Scheduling in a Network of Open-Pit Mines," INFORMS Journal on Computing, INFORMS, vol. 26(4), pages 658-676, November.
    6. Gaurav Singh & Rodolfo García-Flores & Andreas Ernst & Palitha Welgama & Meimei Zhang & Kerry Munday, 2014. "Medium-Term Rail Scheduling for an Iron Ore Mining Company," Interfaces, INFORMS, vol. 44(2), pages 222-240, April.
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    Cited by:

    1. Jiang Yao & Zhiqiang Wang & Hongbin Chen & Weigang Hou & Xiaomiao Zhang & Xu Li & Weixing Yuan, 2023. "Open-Pit Mine Truck Dispatching System Based on Dynamic Ore Blending Decisions," Sustainability, MDPI, vol. 15(4), pages 1-12, February.

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