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

Lu Chen, Qinghua Gu (), Rui Wang, Zhidong Feng and Chao Zhang
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Lu Chen: School of Management, 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
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
Chao Zhang: China Molybdenum Co., Ltd. (China), Luoyang 417500, China

Sustainability, 2022, vol. 14, issue 17, 1-19

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.

Keywords: ore blending problem; mining sustainability; multi-objective optimization; CM-NSGA-III; evolutionary algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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