Intelligent Optimization of Cell Voltage for Energy Saving in Process of Electrolytic Aluminum

Abstract

Based on the characteristic of cell voltage fluctuations in the process of electrolytic aluminum, a new method based on neural-network-genetic-algorithm (NNGA) for the optimization of cell voltage is proposed in this paper. First, the method of kernel principal component based on analysis of electrolytic aluminum process is used to determine the operating parameters. Second, in order to predict cell voltage in real time, back propagation neural network (BPNN) is used to establish the cell voltage prediction model. Third, the model of the optimization control of cell voltage is constructed, and then, genetic algorithm is used to optimize cell voltage and obtain corresponding operating conditions. Finally, the actual production data is used to perform experimental verification. The results show that the proposed method based on NNGA is effective. The process of electrolytic aluminum can operate under the optimal production conditions, and the goal of saving energy is achieved.