【摘要】 铝电解氧化铝浓度的有效预测和控制对电解槽稳定运行和提高电流效率具有重要作用。构建氧化铝浓度的BP神经网络预测模型,依据电解槽运行参数数据确定预测模型的输入变量。使用L-M优化算法训练神经网络,表明有效提高预测模型的预测速度和精度,且均方误差较小。更多还原

【Abstract】 The effective prediction and control of aluminum electrolytic aluminum oxide concentration plays an important role in the stable operation of the electrolytic cell and the improvement of current efficiency.By establishing a BP neural network prediction model of alumina concentration,the input variables of the prediction model are determined according to the characteristics of the electrolytic cell parameter data.The L-M optimization algorithm is used to train the neural network model.The results show that the prediction speed and accuracy of the prediction model are effectively improved,and the mean square error is small.更多还原