基于神经网络遗传算法的铝电解槽电压优化

Cell Voltage optimization of aluminum electrolysis Based on Neural Network-genetic Algorithm

为降低电解铝的生产成本,提出了一种基于神经网络遗传算法的电解铝生产过程槽电压优化方法,以寻找最优生产槽电压和对应的生产条件。采用核主元分析法确定影响电解铝生产的关键参数,建立槽电压的神经网络模型。利用遗传算法寻找电解铝槽电压的全局最优值及对应的生产条件。通过实际生产数据进行仿真实验,结果表明,基于神经网络遗传算法全局寻优的能力,该优化方法能准确预测电解铝槽电压,同时能够找到电解铝生产过程中的最优槽电压及其对应的优化生产条件。

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.