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16 March 2023 Optimization method of aluminum electrolysis current efficiency based on LightGBM-TPE
Yinglan Fang, Chenyang Liu, Zhenliang Li
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Proceedings Volume 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022); 125930Q (2023) https://doi.org/10.1117/12.2671649
Event: 2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 2022, Guangzhou, China
Abstract
The influencing factors of aluminum electrolysis production process are complex, and current efficiency is an important evaluation index. In order to study the influence of various parameters on the current efficiency in the aluminum electrolysis production process, a LightGBM-TPE current efficiency optimization model was established in this paper. First, the production data is preprocessed, and the industrial parameters are fitted using the LightGBM prediction model. Then, to further increase the model's prediction accuracy, the TPE optimization method is used to optimize the LightGBM hyperparameters. Finally, the optimization of current efficiency is realized through Optuna combined with TPE Bayesian optimization algorithm. The experimental results demonstrate that the model is capable of accurately identifying the realization conditions and process parameters of high current efficiency in the production process, as well as providing a parameter control foundation for the effective operation of the actual electrolytic aluminum production, ultimately achieving the goal of power consumption reduction.
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Yinglan Fang, Chenyang Liu, and Zhenliang Li "Optimization method of aluminum electrolysis current efficiency based on LightGBM-TPE", Proc. SPIE 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 125930Q (16 March 2023); https://doi.org/10.1117/12.2671649
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KEYWORDS
Aluminum
Mathematical optimization
Data modeling
Machine learning
Electrolytes
Industry
Detection and tracking algorithms
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