Mingyu Wang, Jue Tang, Mansheng Chu, Quan Shi, and Zhen Zhang, Prediction and optimization of flue pressure in sintering process based on SHAP, Int. J. Miner. Metall. Mater.,(2024). https://doi.org/10.1007/s12613-024-2955-z
Cite this article as:
Mingyu Wang, Jue Tang, Mansheng Chu, Quan Shi, and Zhen Zhang, Prediction and optimization of flue pressure in sintering process based on SHAP, Int. J. Miner. Metall. Mater.,(2024). https://doi.org/10.1007/s12613-024-2955-z
Research Article

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

+ Author Affiliations
  • Corresponding author:

    Jue Tang    E-mail: tangj@smm.neu.edu.cn

  • Received: 16 April 2024Revised: 3 June 2024Accepted: 12 June 2024Available online: 13 June 2024
  • 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.
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