Anode effect prediction of aluminum electrolysis using GRNN

Aimed to improve accuracy rate and time advance of the Anode Effect (AE) prediction in aluminum electrolysis cell, a new prediction method by means of the Generalized Regression Neural Network (GRNN) is proposed. The structure and advantages of the GRNN are introduced, then modeling the anode effect system of aluminum electrolysis cell by means of system identification based on the GRNN. The structure of samples is analyzed emphatically in the process of modeling, and the influence of sample size to model prediction accuracy is analyzed by experiment. The prediction model based on GRNN is trained and tested by sufficient samples extracted from the production data of the 400kA aluminum electrolysis cell. It´s proved that the accuracy rate of the AE prediction is more than 90% on average in advance 30 minutes. In addition, results tested for different aluminum electrolysis cells show that the method has extensive applicability while keep high prediction accuracy and its effective.