Deep learning for electrolysis process anode effect prediction based on long short-term memory network and stacked denoising autoencoder

The anode effect is a common failure in the aluminium electrolysis industry. If the anode effect cannot be accurately predicted, it will cause increased energy consumption, harmful gas generation and even equipment damage in the aluminium electrolysis. In this paper, an anode effect prediction frame...

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Vydáno v:Rare metals Ročník 43; číslo 12; s. 6730 - 6741
Hlavní autoři: Yin, Gang, Li, Yi-Hui, Yan, Fei-Ya, Quan, Peng-Cheng, Wang, Min, Cao, Wen-Qi, Xu, Heng-Quan, Lu, Jian, He, Wen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Beijing Nonferrous Metals Society of China 01.12.2024
Témata:
Aluminium electrolysis
Anode effect prediction
Biomaterials
Chemistry and Materials Science
Deep learning
Energy
Improved Adam algorithm
Materials Engineering
Materials Science
Merging model
Metallic Materials
Nanoscale Science and Technology
Original Article
Physical Chemistry
Deep learning
Improved Adam algorithm
Aluminium electrolysis
Merging model
Anode effect prediction
ISSN:1001-0521, 1867-7185
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Shrnutí:The anode effect is a common failure in the aluminium electrolysis industry. If the anode effect cannot be accurately predicted, it will cause increased energy consumption, harmful gas generation and even equipment damage in the aluminium electrolysis. In this paper, an anode effect prediction framework using multi-model merging based on deep learning technology is proposed. Different models are used to process aluminium electrolysis cell condition parameters with high dimensions and different characteristics, and hidden key fault information is deeply mined. A stacked denoising autoencoder is utilized to denoise and extract features from a large number of long-period parameter data. A long short-term memory network is implemented to identify the intrinsic links between the real-time voltage and current time series and the anode effect. By setting the model time step, the anode effect can be predicted precisely in advance, and the proposed method has good robustness and generalization. Moreover, the traditional Adam algorithm is improved, which enhances the performance and convergence speed of the model. The experimental results show that the classification accuracy and F1 score of the model are 97.14% and 0.9579%, respectively. The prediction time can reach 15 min. Graphical abstract
Bibliografie:Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self‐archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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ISSN:1001-0521
1867-7185
DOI:10.1007/s12598-024-02766-x