Prediction model for the current efficiency of aluminum electrolysis based on the adaptive double layer unscented Kalman filter neural network
FANG Xiao-yan
YAO Li-zhong
LUO Hai-jun
ZHANG Yu-ze
YI Jun
Abstract：This paper presents a novel modeling method based on an adaptive double layer unscented Kalman filter neural network(ADLUKFNN),which tackles the challenges of poor model accuracy and stability resulting from strong interference and time-varying disturbances in the aluminum electrolysis process.Firstly,this method constructs a dou-ble layer unscented Kalman filter neural network(DLUKFNN)model to enhance the stability of the model towards the disturbance system.Specifically,the weights and thresholds of the neural network are updated online using the double layer unscented Kalman filter.Then,a constraint adjustment parameter is introduced into the mean square error of the state variables in DLUKFNN.Meanwhile,by employing the gradient descent method to adaptively adjust the constraint adjustment parameter,the mean square error is constrained within a smaller range,thereby weakening the impact of error accumulation during the filtering recursive calculation on the model.Finally,the accuracy and stability of the proposed method are verified through aluminum electrolysis current efficiency prediction.
Keywords：aluminum electrolysisadaptive modelingdouble layer unscented Kalman filteringneural networkcurrent efficiency
Publication Date：2025-03-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 579-589 )