基于深度学习的铝电解槽阳极效应预测方法研究

Research on prediction method of anode effect of aluminum electrolytic cell based on deep learning

阳极效应在铝电解生产中最为频发,对其进行准确预测能够稳定电解铝生产,降低能耗,减少事故。本文从深度学习入手,提出一种基于堆叠降噪自动编码器和长短时记忆网络的预测模型,利用堆叠降噪自动编码器挖掘关键故障特征信息,同时利用长短时记忆网络实现故障诊断。本文通过采集某铝厂的历史生产数据对模型进行性能验证,结果表明,该模型预测准确率和F1分数分别为97.56%和0.9686。对比分析BP神经网络、广义回归神经网络、LSTM和SDAE-RF,本文构建的SDAE-LSTM的模型表现最佳,能准确地对阳极效应进行预报,在铝电解实际生产中具有重要的指导意义。

Anode effect is the most frequent fault in the aluminum electrolysis production,and accurate prediction of the anode effect can reduce energy consumption and reduce accidents.Starting from deep learning,this paper proposes a prediction model based on stacked noise reduction autoencoder and long short term memory network.The stacked noise reduction autoencoder is used to find key fault feature information,and meanwhile the long short term memory network is used to realize fault diagnosis.Finally,the historical production data of an aluminum factory is collected to verify the performance of the model.The experimental results show that the prediction accuracy and F1 score of the model are 97.56%and 0.9686,respectively.This paper makes a comparative analysis of the BP neural network,generalized regression neural network,LSTM and SDAE-RF.The experimental results show that the SDAE-LSTM model constructed in this paper has the best performance with more accurate prediction of the anode effect,which has important guiding significance for the actual production of aluminum electrolysis.