# A Novel Method of Local Anode Effect Prediction for Large Aluminum Reduction Cell
## 1 Introduction
[10] proposed the distribution of alumina concentration in the feeding process based on the finite element mechanism analysis, however the finite element algorithm takes a long time to calculate. So it will take a long time to get the training data, and it is not feasible to predict the anode effect. [PERSON] et al. [11] obtained the influence of fluoride bubbles on the cell voltage by simulating the equivalent circuit of aluminum electrolytic cell, and then predicted the occurrence of local anode effect. By analyzing the distributed anode current, [PERSON] [12] divided the anode effect into conventional anode effect and low-voltage anode effect according to the cell voltage setting threshold of 8 V. The detection of low-voltage anode effect is analyzed by monitoring the non-uniformity of the current distribution of the anode rod, which proves the feasibility of the distributed anode rod current to predict the anode effect. Aiming at the problem of uneven alumina concentration distribution in large-scale aluminum reduction cells, [PERSON] et al. [13] proposed the idea of uniform alumina concentration control based on single point feeding and combined with the effect prediction system based on anode current distribution, which greatly reduces the probability of anode effect in the in aluminum reduction cells. By analyzing the power spectrum of the distributed anode rod current, [PERSON] et al. [14] divided the aluminum reduction cell into areas, and determined the occurrence of local anode effect by setting the current fluctuation threshold. [PERSON] et al. [15] developed a low-voltage anode effect prediction method based on the periodic average current deviation ratio by averaging the distributed anode current in time windows, and proposed a multi-point feeding strategy based on the probability distribution of low-voltage anode effects. Due to the complex on-site environment of high temperature, high magnetism and multi-field coupling in the aluminum electrolysis industry and the complex physicochemical process reaction in the cell, it is difficult to achieve online real-time measurement of alumina concentration, the core parameter that reacts to local anodic effect. While distributed current, as an important parameter affecting the occurrence of anode effect, can be collected in real time. By tracking and predicting the fluctuation of anode current data, the occurrence time and intensity of local anode effect can be effectively predicted, so as to prevent the occurrence of global effect through reasonable and effective measures. This paper proposes a prediction method of local anode effect of aluminum reduction cell based on distributed current fluctuation detection. Firstly, through the analysis of the aluminum electrolysis production process, a current fluctuation detection method based on the density of the time sliding window is designed to detect the fluctuation of anode current. Secondly, according to the difference of current fluctuation, the anode current in the area is classified and predicted. The Gated Recurrent Unit-Long Short-Term Memory (GRU-LSTM) hybrid neural network is used to track and predict the anode rod current with fluctuation, and the Autoregressive Integrated Moving Average (ARIMA) algorithm is used to track and predict the anode rod current without fluctuation. The occurrence time and intensity of local anode effect are given by combining these two algorithms. Finally, the local anode effect prediction simulation experiment is carried out based on the actual industrial aluminum plant data. Innovations: (1) The local anode effect is predicted, and the fuzzification level classification of the local anode effect prediction is proposed. (2) A current volatility detection method based on time-sliding window density is designed to classify and track the prediction of anode current in the region according to the current volatility.