Terminal Temperature Prediction of Molten Steel in VD Furnace based on XGBoost and LightGBM Algorithms

Abstract

In the iron and steel industry, vacuum refining is the previous process of continuous casting, and the VD furnace is one of the key equipment for vacuum refining. The precise control of the end temperature is an important guarantee for the quality of subsequent cast steel products. In this paper, based on the XGBoost algorithm and the LightGBM algorithm, the VD furnace molten steel terminal temperature prediction model will be established respectively, and their performance will be compared. First, perform data preprocessing on the original industrial data, fill in missing values, use Tukey’s test method to detect and delete outliers, and use box-cox data transformation to normalize the data. Then, the random forest feature selection algorithm was used to select 8 variables as the input of the model. Finally, the temperature prediction models based on the XGBoost and LightGBM algorithms were established. The experimental results show that the models based on the XGBoost algorithm and the LightGBM algorithm both have good prediction effects, and the prediction accuracy within ±10℃ reached 89.86%, and the overall performance of the LightGBM model is better than XGBoost model.