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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| Published in: | Rare metals Vol. 43; no. 12; pp. 6730 - 6741 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Beijing
Nonferrous Metals Society of China
01.12.2024
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| Subjects: | |
| ISSN: | 1001-0521, 1867-7185 |
| Online Access: | Get full text |
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