Preprint Article Version 1 This version is not peer-reviewed

Multi-Objective Optimization for Metal Mine Production Technical Indicators with NSGA-II and ANN Algorithms

Version 1 : Received: 29 June 2018 / Approved: 3 July 2018 / Online: 3 July 2018 (10:23:12 CEST)
Version 2 : Received: 27 November 2018 / Approved: 29 November 2018 / Online: 29 November 2018 (10:59:56 CET)

A peer-reviewed article of this Preprint also exists.

Wang, X.; Gu, X.; Liu, Z.; Wang, Q.; Xu, X.; Zheng, M. Production Process Optimization of Metal Mines Considering Economic Benefit and Resource Efficiency Using an NSGA-II Model. Processes 2018, 6, 228. Wang, X.; Gu, X.; Liu, Z.; Wang, Q.; Xu, X.; Zheng, M. Production Process Optimization of Metal Mines Considering Economic Benefit and Resource Efficiency Using an NSGA-II Model. Processes 2018, 6, 228.

Journal reference: Processes 2018, 6, 228
DOI: 10.3390/pr6110228

Abstract

The selection of the best mine production technical indicators is crucial to increasing a mine’s economic benefit and saving resources for sustainability. Therefore, this research proposes a ‘multi-objective optimization model’ based on a ‘fast and elitist Non-dominated Sorting Genetic Algorithm’ (NSGA-II) and ‘Artificial Neural Networks’ (ANN) for the optimization of production technical indicators in the entire geology, mining and beneficiation metal mine production processes. The multi-objective optimization model has decision variables including ‘cut-off grade,’ ‘industrial grade’ and ‘loss rate,’ with objectives being ‘economic benefit (profit)’ and ‘resource benefit (metal volume).’ First, the relationship between the technical indicators of mine production is studied. The REG model, MATLAB’s own ksdensity function and the BP neural network are used to calculate the ore weight, the probability density of grade distribution, the dilution rate, the concentration ratio and the concentrate grade, and to further calculate geological reserves, profit and metal volume. Then, the NSGA-II is applied to maximize profit and metal volume simultaneously. Finally, the model is applied to the Huogeqi copper mine. The optimization result is a set of multiple optimal solutions called Pareto optimal solutions. Compared with the plan data, the profit and metal volume of partial optimization results increased by 2.89% and 2.64% simultaneously. These Pareto optimal solutions can help decision makers in bettering the actual process of metal mine production.

Subject Areas

Multi-objective optimization; metal mine; production technical indicators; NSGA-II; artificial neural networks

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