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

Zhidong Feng (), Ge Liu, Luofeng Wang, Qinghua Gu and Lu Chen
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Zhidong Feng: School of Resources Engineering, 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
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
Lu Chen: Xi’an Key Laboratory for Intelligent Industrial Perception, Calculation and Decision, Xi’an University of Architecture and Technology, Xi’an 710055, China

Sustainability, 2023, vol. 15, issue 6, 1-20

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.

Keywords: strip mine; ore blending; multiagent; deep reinforcement learning; multiobjective optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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