Abstract
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.
Highlights
T HE The Blast furnace (BF) ironmaking process, an essential process in the iron and steel industry, refers to a complex physical and chemical process extensively applied for pig iron production
To address the problems in the above models, the present study proposes two derivative algorithms based on gradient boosting decision tree (GBDT), i.e., Extreme Gradient Boosting (XGBoost) algorithm and Light Gradient Boosting Machine (LightGBM) algorithm, for silicon content prediction in BF molten iron [33], [34], [41]–[49]
In this study, XGBoost and LightGBM algorithms based on GBDT algorithm are proposed for silicon content prediction in molten iron of two BF systems, which is formulated as
Summary
T HE The Blast furnace (BF) ironmaking process, an essential process in the iron and steel industry, refers to a complex physical and chemical process extensively applied for pig iron production. Shihua Luo et al.: Two Derivative Algorithms of Gradient Boosting Decision Tree For Silicon Content in Blast Furnace System Prediction They applied Bayesian network model to predict the silicon content in molten iron [5]. To address the problems in the above models, the present study proposes two derivative algorithms based on gradient boosting decision tree (GBDT), i.e., Extreme Gradient Boosting (XGBoost) algorithm and Light Gradient Boosting Machine (LightGBM) algorithm, for silicon content prediction in BF molten iron [33], [34], [41]–[49]. Based on these two points of innovation, it is necessary and feasible to select these two algorithms to study the state of BF.
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