A Novel Method of Local Anode Effect Prediction for Large Aluminum Reduction Cell
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Non-Destructive Testing Techniques · Advanced Battery Technologies Research · Machine Learning and ELM
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Guiyang Aluminum Magnesium Design and Research Institute Co., Ltd., Guiyang 550081, China
School of Information Engineering, Jingdezhen University, Jingdezhen 333000, China
Funding
China Postdoctoral Science Foundation · Guizhou Province Science and Technology Plan Project · National Natural Science Foundation of China · Chinese Society of Academic Degrees and Graduate Education
References (23)
Show source-provided references
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