# A Novel Method of Local Anode Effect Prediction for Large Aluminum Reduction Cell
## 1 Introduction  
Anode effect prediction is one of the research focuses in the production of modern aluminum electrolysis industry. Anode effect is a special phenomenon in the production process of aluminum electrolysis. If the anode effect occurs in one or some anodes, the voltage and anode current density on these anodes will rise sharply in a short time, resulting in a decrease in the current efficiency and a shortened life of the aluminum electrolytic cell. In addition, the anode effect will produce two kinds of perfluorocarbon gases, CF\({}_{4}\) and C\({}_{2}\)F\({}_{6}\), with strong greenhouse effect, which have a global warming potential 6630 and 11,100 times higher than that of CO\({}_{2}\)[1]. Therefore, the earlier detection of anode effect for reducing its number has important theoretical value and practical significance. In the last few years, the research on anode effect mostly focused on the prediction of global anode effect, due to the small scale of aluminum electrolytic cell. The current detecting methods for the occurrence of anode effect is to monitor the changes of cellvoltage and cell resistance. Until now, the direct prediction of anode effect is not realized in industrial production [2]. In literature [3], a model was proposed to extract the fault signatures of anode effect based on the combination of digital filter and local mean decomposition, which lays the foundation for anode effect prediction. [PERSON] et al. [4] used generalized regression neural network to identify and simulate the cell voltage before and after the anode effect of aluminum electrolytic cell. After training and verifying the model with field data samples, the occurrence of anode effect can be predicted online about half an hour in advance. [PERSON] et al. [5] proposed a robust dictionary learning method, which improved the fault detection rate of anode effect and reduced the false alarm rate. In literature [6], a nonlinear process anode effect monitoring method was proposed based on Kernel dictionary learning algorithm. [PERSON] et al. [7] proposed a data fusion algorithm based on collaborative prediction model to predict the global anode effect by using the similarity search principle. [PERSON] et al. [8] excavated the key feature information of the anode effect by constructing a stacked denoising autoencoder, and sorted the key features by the random forest algorithm to solve the problem of uneven sample distribution in the prediction of the anode effect. At the same time, the Long Short-Term Memory (LSTM) neural network was used to predict the global anode effect. The above research was aimed at the prediction of global anode effect. Due to the violent reaction when the global anode effect occurs, a large number of bubbles overflow, and the slot controller alarms, which is convenient for observation and recording. At present, the study of global anode effect based on data-driven has been relatively mature, and the prediction point of global anode effect can be advanced to about 20 min. Neural network, machine learning, and other methods are used to predict the occurrence of anode effect, which is feasible and accurate. With the development of computer technology and the improvement of the production process of aluminum electrolytic cell, the aluminum electrolytic cell is gradually developing towards large scale [9], which leads to the difficulty of controlling the production of aluminum electrolysis. In addition, because of the increasing scale of the cell, the key parameters of the aluminum electrolytic cell are unevenly distributed, and the local anode effect happened frequently. The occurrence of local anode effect often precedes the global anode effect. The effective prediction of local anode effect is conducive to preventing the occurrence of global effect. Therefore, with the development of large-scale electrolytic cell, the prediction of local anode effect has gradually become the focus of research. [PERSON] et al.