Deep learning for electrolysis process anode effect prediction based on long short-term memory network and stacked denoising autoencoder  

作　　者：Gang Yin Yi-Hui Li Fei-Ya Yan Peng-Cheng Quan Min Wang Wen-Qi Cao Heng-Quan Xu Jian Lu Wen He 

机构地区：[1]School of Resource and Safety Engineering,and State Key Laboratory of Coal Mine Disaster Dynamics and Control,Chongqing University,Chongqing,400044,China [2]Guiyang Aluminium Magnesium Design and Research Institute Co.,Ltd.,Guiyang,550000,China [3]Aba Aluminium Factory,Aba,623001,China [4]Chongqing Qineng Electric Aluminium Co.,Ltd.,Chongqing,410420,China [5]Bomei Qimingxing Aluminium Co.,Ltd.,Meishan,620010,China

出　　处：《Rare Metals》2024年第12期6730-6741,共12页稀有金属(英文版)

基　　金：financially supported by the General Program of National Natural Science Foundation of China(No.62373069);the Major Projects for Technological Transformation(No.H20201555);Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project (No.CQYC202203091061)。

摘　　要：The anode effect is a common failure in the aluminium electrolysis industry.If the anode effect cannot be accurately predicted,it will cause increased energy consumption,harmful gas generation and even equipment damage in the aluminium electrolysis.In this paper,an anode effect prediction framework using multi-model merging based on deep learning technology is proposed.Different models are used to process aluminium electrolysis cell condition parameters with high dimensions and different characteristics,and hidden key fault information is deeply mined.A stacked denoising autoencoder is utilized to denoise and extract features from a large number of longperiod parameter data.A long short-term memory network is implemented to identify the intrinsic links between the realtime voltage and current time series and the anode effect.By setting the model time step,the anode effect can be predicted precisely in advance,and the proposed method has good robustness and generalization.Moreover,the traditional Adam algorithm is improved,which enhances the performance and convergence speed of the model.The experimental results show that the classification accuracy and F1score of the model are 97.14% and 0.9579%,respectively.The prediction time can reach 15 min.

关 键 词：Aluminium electrolysis Anode effect prediction Deep learning Improved Adam algorithm Merging model 

分 类 号：TF821[冶金工程—有色金属冶金] TP18[自动化与计算机技术—控制理论与控制工程]