Prediction and optimization of flue pressure in sintering process based on SHAP

• Published in: International journal of minerals, metallurgy and materials Vol. 32; no. 2; pp. 346 - 359
• Main Authors: Wang, Mingyu, Tang, Jue, Chu, Mansheng, Shi, Quan, Zhang, Zhen
• Format: Journal Article
• Language: English
• Published: Beijing University of Science and Technology Beijing 01.02.2025; Springer Nature B.V; Liaoning Low-Carbon Steelmaking Technology Engineering Research Center,Northeastern University,Shenyang 110819,China%School of Metallurgy,Northeastern University,Shenyang 110819,China; School of Metallurgy,Northeastern University,Shenyang 110819,China%School of Metallurgy,Northeastern University,Shenyang 110819,China; Engineering Research Center of Frontier Technologies for Low-Carbon Steelmaking(Ministry of Education),Shenyang 110819,China
• Subjects: Algorithms; Ceramics; Characterization and Evaluation of Materials; Chemistry and Materials Science; Composites; Corrosion and Coatings; Furnaces; Glass; Materials Science; Metallic Materials; Natural Materials; Optimization; Prediction models; Pressure effects; Process parameters; Raw materials; Research Article; Sintering; Surfaces and Interfaces; Thin Films; Tribology; prediction; shapley additive explanation; optimization; sintering process; flue pressure
• ISSN: 1674-4799; 1869-103X
• DOI: 10.1007/s12613-024-2955-z

Sinter is the core raw material for blast furnaces. Flue pressure, which is an important state parameter, affects sinter quality. In this paper, flue pressure prediction and optimization were studied based on the shapley additive explanation (SHAP) to predict the flue pressure and take targeted adjustment measures. First, the sintering process data were collected and processed. A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP + extremely randomized trees (ET). The prediction accuracy of the model within the error range of ±0.25 kPa was 92.63%. SHAP analysis was employed to improve the interpretability of the prediction model. The effects of various sintering operation parameters on flue pressure, the relationship between the numerical range of key operation parameters and flue pressure, the effect of operation parameter combinations on flue pressure, and the prediction process of the flue pressure prediction model on a single sample were analyzed. A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions. The operating parameter combination was then pushed. The flue pressure was increased by 5.87% during the verification process, achieving a good optimization effect.