关键词: 电解铝, 槽电压, 遗传算法, 神经网络建模

Abstract: In order to reduce the production cost of electrolytic aluminum, an optimization extreme method was proposed based on neural network and genetic algorithm, to find the optimal production cell voltage and the corresponding production conditions. Using kernel principal component analysis method to determine the key parameters affecting of aluminum electrolysis production, a neural network model of electrolytic aluminum was established. Using the genetic algorithm, the global optimal value of the cell voltage of the electrolytic aluminum and the corresponding production conditions were found. The simulation results show that the neural network and genetic algorithm can predict the cell voltage of electrolytic aluminum accurately, at the same time it can find the optimal cell voltage, and the production conditions.

Key words: electrolytic aluminum, cell voltage, genetic algorithm, neural network modeling