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Research on the Multiobjective and Efficient Ore-Blending Scheduling of Open-Pit Mines Based on Multiagent Deep Reinforcement Learning

Author

Listed:
  • Zhidong Feng

    (School of Resources Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
    School of Information Engineering, Yulin University, Yulin 719000, China
    Xi’an Key Laboratory for Intelligent Industrial Perception, Calculation and Decision, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Ge Liu

    (School of Resources Engineering, 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)

  • Luofeng Wang

    (CMOC Group Limited, Luoyang 417500, China)

  • Qinghua Gu

    (School of Resources Engineering, 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)

  • Lu Chen

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

Abstract

In order to solve the problems of a slow solving speed and easily falling into the local optimization of an ore-blending process model (of polymetallic multiobjective open-pit mines), an efficient ore-blending scheduling optimization method based on multiagent deep reinforcement learning is proposed. Firstly, according to the actual production situation of the mine, the optimal control model for ore blending was established with the goal of minimizing deviations in ore grade and lithology. Secondly, the open-pit ore-matching problem was transformed into a partially observable Markov decision process, and the ore supply strategy was continuously optimized according to the feedback of the environmental indicators to obtain the optimal decision-making sequence. Thirdly, a multiagent deep reinforcement learning algorithm was introduced, which was trained continuously and modeled the environment to obtain the optimal strategy. Finally, taking a large open-pit metal mine as an example, the trained multiagent depth reinforcement learning algorithm model was verified via experiments, with the optimal training model displayed on the graphical interface. The experimental results show that the ore-blending optimization model constructed is more in line with the actual production requirements of a mine. When compared with the traditional multiobjective optimization algorithm, the efficiency and accuracy of the solution have been greatly improved, and the calculation results can be obtained in real-time.

Suggested Citation

  • Zhidong Feng & Ge Liu & Luofeng Wang & Qinghua Gu & Lu Chen, 2023. "Research on the Multiobjective and Efficient Ore-Blending Scheduling of Open-Pit Mines Based on Multiagent Deep Reinforcement Learning," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5279-:d:1099065
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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