A method of local anode effect prediction is proposed for the problem that it is difficult to detect the local anode effect in large aluminum reduction cell in real time. Firstly, a fuzzy classification of local anode effect prediction in terms of fuzziness level is proposed considering various working conditions of anode current in the region. Secondly, a current volatility detection method based on time-sliding window density is designed from the problem of uneven current distribution in the region, and the anode currents in the region are classified and tracked for prediction according to the different current volatility. Thirdly, an improved Gated Recurrent Unit (GRU) neural network structure is proposed to improve the prediction accuracy of fluctuating currents. Finally, simulation experiments are conducted based on actual data, and compared with Long Short-Term Memory (LSTM) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), the proposed method has certain advantages in both prediction time, training time, and the mean absolute error (MAE) and mean square error (MSE), which verifies the effectiveness of the proposed method.

With the continuous development of large-scale aluminum reduction cells, the problem of the uniform distribution of alumina concentration in the cell has become more and more serious for the reduction process. In order to achieve the uniform distribution of the alumina concentration, a data-driven distributed subspace predictive control feeding strategy is proposed in this paper. Firstly, the aluminum reduction cell is divided into multiple sub-systems that affect each other according to the position of the feeding port. Based on the subspace method, the prediction model of the whole cell is identified, and the prediction output expression of each sub-system is deduced by decomposition. Secondly, the feeding controller is designed for each aluminum reduction cell subsystem, and the input and output information can be exchanged between each controller through the network. Thirdly, under consideration of the influence of other subsystems, each subsystem solves the Nash-optimal control feeding quantity, so that each subsystem realizes distributed feeding. Finally, the simulation results show that, compared with the traditional control method, the proposed distributed feeding control strategy can significantly improve the problem of the uniform distribution of alumina concentration and improve the current efficiency of the aluminum reduction cell.