# A Novel Method of Local Anode Effect Prediction for Large Aluminum Reduction Cell
## 5 Experimental Verification
### Simulation Experiment  
LSTM, Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and improved GRU-LSTM hybrid neural networks are respectively used to track and predict the current of fluctuating anode rods in each area, and determine the predictive value of the occurrence time of anode effect. As can be seen from the third part of the paper, the whole aluminum reduction cell is divided into six areas, and in order to compare the fluctuation of current prediction in each area, a fluctuating anode rod is selected in each region for current tracking prediction. In order to compare the differences between several methods more clearly, a local zoomed-in view of the prediction results is pointed out with arrows in the figure. The predicted results of anode rod current fluctuation are shown in Figure 9. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Area** & **AE Time** & **Position** \\ \hline
1 & Area1 & 27 September 2020/13:25:15 & B3 \\
2 & Area3 & 15 October 2020/16:14:30 & A12 \\
3 & Area6 & 3 November 2020/8:34:16 & B22 \\
4 & Area1 & 25 November 2020/18:28:45 & A2 \\
5 & Area4 & 6 December 2020/11:13:15 & A15 \\
6 & Area2 & 3 January 2021/1325:32 & B6 \\
7 & Area4 & 12 February 2021/14:33:15 & B14 \\
8 & Area5 & 18 February 2021/19:41:15 & A19 \\
9 & Area1 & 15 March 2021/06:25:15 & A2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: 9 groups of anode effect data. Figure 8: Block diagram of distributed anode rod current monitoring system for aluminum reduction Cells. According to the comparison of the three methods in subfigures a-f, the prediction curves of GRU-LSTM hybrid neural networks are closer to the actual current fluctuation in each area of the aluminum reduction cell. In addition, the enlarged area guided by the arrow in Figure 9a can be reflected more clearly. After testing, the time set to 50 rounds of training is reduced to 2.4 s, and this is quicker than another two methods. Taking area 1 as an example, the prediction time is analyzed. According to the current prediction results of fluctuating anode rods B3 in area 1 in Figure 9a, the ARIMA model is used to predict the current of seven non-fluctuating anode rods in area 1. The current prediction of eight anode rods in area 1 is shown in Figure 10.  
Figure 9: Prediction diagram of fluctuating current in 6 areas of aluminum reduction cell. Figure 10(A1) shows the comparison between the actual current situation of A1 anode rod in region 1 and the results predicted by ARIMA model. Figure 10(A2-A4,B1-B4) indicate the same meaning. It can be seen from Figure 10 that the ARIMA model can effectively predict the current of non-fluctuating anode rod, with consistent prediction trend and small prediction error. Combined with the prediction results of fluctuating anode rod current, the prediction time of anode effect can be advanced to 5-10 min. In order to quantify the prediction results, Table 2 shows the mean absolute error (MAE) and mean square error (MSE) of the prediction results of the three methods in six areas of the aluminum reduction cell. It can be seen from Table 2 that compared with LSTM and CNN-LSTM networks, the MAE of GRU-LSTM network is reduced by 0.9014 and 0.5162 on average, and the MSE is