A BP Neural Network Based on Improved PSO for Increasing Current Efficiency of Copper Electrowinning

Abstract

In this paper, to improve the current efficiency in the copper electrowinning process is taken as the research objective. In the traditional production process, sulfate ion concentration, copper ion concentration and current density are carried out according to the empirical value, which cannot ensure the current efficiency to reach the optimal level. Therefore, firstly, this paper proposes a BP neural network model to improve the current efficiency according to the relationships between sulfate ion concentration, copper ion concentration, current density and the established BP neural network model is trained by using real data from the enterprise. The simulation results indicate that there is a definite error between the predicted current efficiency and corresponding to the current efficiency measured at the production site. It is proposed that the BP neural network improved by the improved PSO to further improve the prediction accuracy of the BP neural network. Simulation results indicate that the prediction error of the current efficiency is greatly reduced that meets the accuracy requirements. On the premise of guaranteeing the quality of copper electrowinning, the current density, sulfate ion concentration and copper ion concentration corresponding to the maximum current efficiency accurately predicted by this method can be respectively adjusted in real-time in the copper electrowinning process, which realizes the optimization of current efficiency in the process of copper electrowinning under the background of low carbon and environmental protection.