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., 32(2025), No. 2, pp.346-359. https://dx.doi.org/10.1007/s12613-024-2955-z
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., 32(2025), No. 2, pp.346-359. https://dx.doi.org/10.1007/s12613-024-2955-z
引用本文 PDF XML SpringerLink

基于SHAP的烧结过程烟道压力预测与优化

摘要: 烧结矿是高炉的主要原料。烟道压力是影响烧结矿质量的重要状态参数。本文基于SHAP对烟道压力预测和优化进行了研究,实现了烟道压力预测与操作参数反馈。首先收集并处理烧结过程数据。在对比了不同的特征选择方法与机器学习算法后,采用SHAP结合极限树算法建立烟道压力预测模型。在±0.25kPa的误差范围内,预测模型精度为92.63%。通过SHAP分析提升模型的可解释性。分析了不同烧结操作参数对烟道压力的影响、关键操作参数与烟道压力间的数值范围关系、烧结操作参数对烟道压力的协同影响以及烟道压力预测模型在单个样本上的预测过程。构建并分析烟道压力优化模型。在预测结果达到调整条件时,反馈操作参数组合调整方案。在验证过程中烟道压力被提升了5.87%,优化效果良好。

 

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

Abstract: 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.

 

/

返回文章
返回