Optimization method of aluminum electrolysis current efficiency based on LightGBM-TPE

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.