首页|Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

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Concentrate copper grade(CCG)is one of the important production indicators of copper flotation pro-cesses,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an opera-tional optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

Weighted Gaussian process regressionIndex-oriented adaptive differential evolutionOperational optimizationCopper flotation process

Zhiqiang Wang、Dakuo He、Haotian Nie

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College of Information Science and Engineering,Northeastern University,Shenyang 110004,China

State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenyang 110004,China

National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

2021YFC290270362173078617731056153300761873049618730536170308561374147

2024

10.1016/j.cjche.2023.09.010

中国化学工程学报(英文版)
中国化工学会

中国化学工程学报(英文版)

CSTPCDEI
影响因子：0.818
ISSN：1004-9541
年,卷(期)：2024.66(2)
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