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

Abstract

Concentrate copper grade (CCG) is one of the important production indicators of copper flotation processes, 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 operational 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.