Prediction of blast furnace parameters using feature engineering and Stacking algorithm

ABSTRACT

Based on the unique characteristics of data within the BF ironmaking domain, this paper select hearth activity, [Si + Ti], and permeability index (PI) as target parameters to verify the effectiveness of the combination of feature engineering and Stacking algorithm in the field of BF process parameter prediction. Based on the actual production data stored in the enterprise database, this paper takes the actual production problems in the process of BF ironmaking as the application background. Through the combination of feature selection and ironmaking theory, the characteristic variables of the prediction model are selected for the preprocessed BF production data, and the accurate prediction of different machine learning algorithms is realized. The results show that the accuracy of stacking algorithm for classification and regression is more than 90%. The model process has good learning and generalization ability to effectively utilize BF ironmaking data and accurately predict BF process parameters.

KEYWORD:

• Feature engineering
• stacking
• data mining
• predicting models
• Classification prediction
• Regression prediction
• Ironmaking data preprocessing
• BF parameters

Acknowledgements

Thanks are given to the financial supports from the National Nature Science Foundation of China (52004096), Hebei Province High-End Iron and Steel Metallurgical Joint Research Fund Project, China (E2019209314), the Scientific Research Program Project of Hebei Education Department (QN2019200), Hebei Postgraduate Innovation Fund Project (CXZZBS2019142).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by national nature science foundation of China: [grant number 52004096]; Hebei provincial higher education fundamental research projects: [grant number JQN2020032]; Hebei province high-end iron and steel metallurgical joint research fund project, China: [grant number E2019209314]; Specialised Research Fund for the Doctoral Program of Higher Education of China: [grant number QN2019200].