Evaluation of Feature Selection Methods for Oxygen Supply Prediction in BOF Steelmaking

作者：Yujie Liu, Xinggan Zhang, Qian Peng, Yunjin Xia, Huatai Wang, Aijun Deng · 发表于：ISIJ International · 年份：2025 · DOI：10.2355/isijinternational.isijint-2025-165 · 被引用次数：3 · 研究领域：Metallurgical Processes and Thermodynamics

Accurate prediction of oxygen supply in BOF steelmaking is essential for precise endpoint control, energy efficiency, and product quality. However, the high dimensionality and strong feature coupling of industrial data pose significant challenges for effective feature selection. This study proposes a comprehensive evaluation framework that integrates four filter-based methods: Pearson correlation coefficient (PCC), Spearman rank correlation coefficient (SCC), mutual information (MI), and maximal information coefficient (MIC), with five widely used regression models: elastic net (EN), support vector regression (SVR), extreme gradient boosting (XGBoost), deep neural networks (DNN), and k-nearest neighbors (KNN). The framework evaluates prediction accuracy, model sensitivity, and feature importance. Results show that MIC consistently outperformed the other methods, achieving the lowest average RMSE (197.4 m3) and highest R2 (0.649), particularly improving the robustness of models sensitive to input features. In contrast, MI resulted in significantly higher errors across all models, with SVR reaching an RMSE of 231.1 m3. Furthermore, the study introduces a hybrid PI-SHAP interpretability approach to construct feature sets that are both predictive and mechanistically meaningful, further reducing DNN prediction error by 2%. The derived feature importance rankings align closely with metallurgical principles, highlighting the dual benefits of interpretable feature selection for accur...