Predictive modeling of the hot metal silicon content in blast furnace based on ensemble method

Abstract

The silicon content of the hot metal is not only an important indicator of the quality of the hot metal and blast furnace (BF) operation but also reflects the level of energy utilization and the thermal state within a BF. It is important to develop an accurate prediction model for hot metal silicon content. In present study, two models for predicting hot metal silicon content are developed based on two ensemble learning methods, random forest regression (RFR) and extreme gradient boosting (XGBoost). First, Box plot was used to visualize the collected data and determine extreme outliers in the raw data. Extreme outliers are replaced with null value, and all null value are filled by linear interpolation. Secondly, feature selection is performed using recursive feature elimination. Cross-validation is performed to optimize machine learning hyperparameters and having a robust accuracy measure. Based on this, two hot metal silicon content prediction models are developed. Finally, the prediction results of the two models are compared and evaluated. The results show that both ensemble learning models show good prediction performance in predicting hot metal silicon content, but the prediction performance of the RFR model is better than that of the XGBoost model and reaching 98.77%.