Two Derivative Algorithms of Gradient Boosting Decision Tree for Silicon Content in Blast Furnace System Prediction

The background of the present study complies with silicon content prediction in hot metal in the blast furnace system. The blast furnace system is a highly complex industrial reactor in the conventional process. The system is subject to several problems (e.g., system automation, the thermal state of the blast furnace, and the life prediction of blast furnace) that should be addressed by professionals. To determine the prediction state of the heat in the blast furnace, the silicon content in the blast furnace molten iron commonly acts as a key indicator. Based on the assumption that the blast furnace system exhibits a stable state, the accuracy of hot metal silicon is analyzed by using a range of machine learning algorithms. In the present study, two derivative algorithms of gradient boosting decision tree are adopted to develop a strong boosting predictor based on the extreme gradient boosting (XGBoost) algorithm and the light gradient boosting machine (LightGBM) algorithm for prediction. Compared with the conventional algorithms (e.g., lasso, random forest, support vector machine and gradient boosting decision tree), the prediction by using the two boosting algorithms is capable of more effectively guiding and determining the state of the blast furnace. As revealed from experimentally simulated results, the mentioned two boosting algorithms exhibit better comprehensive prediction performance than the conventional algorithms on the datasets of two practical blast furnace systems, demonstrating that the R-square of the two blast furnaces in the training set is over 0.7. The mentioned two algorithms are of certain guiding significance for exploring blast furnace problems.