An Intelligent Partition-and-Prediction Framework for Ultra-Low-Phosphorus High-Purity Iron: Improved Interpretability and Accuracy

Ultra-low-phosphorus high-purity iron (ULP-HPFe) is essential for advanced electromagnetic, aerospace, and defense systems, yet stabilizing basic-oxygen-furnace (BOF) dephosphorization remains challenging. To address this instability, we present an intelligent partition-and-prediction framework (iDePP) that first auto-classifies 5102 industrial data records into medium-phosphorus (iDePP-MP), low-phosphorus (iDePP-LP), and ultra-low-phosphorus (iDePP-ULP) subsets, and dedicated ensemble prediction models are then developed for each subset based on representative machine learning algorithms, including random forest (RF), extreme gradient boosting (XGBoost), and neural networks (NNs). Compared with a single global predictor, iDePP reduces the mean absolute error from 0.0018% to 0.0011%, 0.0007%, and 0.0004% for the three classes, respectively, and increases the iDePP-ULP hit rate (HR) to 82.7% within ±6 ppm. Shapley additive explanations (SHAP) and quantitative feature coupling analysis reveal two critical mechanisms governing extreme dephosphorization: limestone-induced thermal penalties and furnace-age effects. Guided by these insights, three consecutive 200-ton BOF industrial trials preliminarily verified the practical feasibility of producing ULP-HPFe, with model plant deviations of approximately 4 ppm, 1 ppm, and 1.5 ppm, respectively. Notably, this work demonstrates the value of automatic domain partitioning combined with subset-specific ensemble learning for complex BOF control, highlighting the potential applicability of iDePP to other data-sparse industrial processes.