Predictive Modeling of the Hot Metal Sulfur Content in a Blast Furnace Based on Machine Learning

Abstract

The sulfur content of hot metal in a blast furnace is an important index that reflects the production effects and quality of the hot metal. Establishing an accurate prediction model for hot metal sulfur content can guide the production process. In the present study, the blast furnace production data were collected and then preprocessed using box plotting. Cross-validation was used in the training process of the model to improve the generalization performance and robustness of the model. Two models for predicting the sulfur content in hot metal were established based on extreme gradient boosting (XGBoost) and multilayer perceptron (MLP) algorithms. The results show that coal consumption (CC), coal ratio (CLR), and sinter consumption (SC) are all positively correlated with hot metal sulfur content. The oxygen enrichment rate (OER) was negatively related to hot metal sulfur content. Both the extreme gradient boosting (XGBoost) and multilayer perceptron (MLP) models predicted hot metal sulfur content effectively; however, the extreme gradient boosting (XGBoost) model had a higher hit rate, accuracy, and stability, with the hit rate achieving 95.07%.