Data‐Driven Exploration of Slag and Carbon Deoxidation Regularities in Steel Melts

Based on the atmospheric pressure carbon deoxidation process in steelmaking, this study calculated thermodynamic calculations under multi‐variable conditions and combined permutation feature importance (PFI) analysis to evaluate the feature importance of machine learning models. It revealed the dependence of key parameters for carbon deoxidation in molten steel on slag system composition, providing a basis for selecting appropriate slag systems tailored to the composition of target steel grades. This study also established a paradigm for high‐throughput slag system optimization research, aiding future selection of suitable slag systems for different deoxidation methods. Five machine learning algorithms were used to establish relevant models, among which Gradient Boosting‐2 demonstrated the best predictive performance on the test set, with R 2 and root mean square error values of 0.992 and 0.005, respectively. PFI analysis based on this model indicated that features such as CaO/SiO 2 , SiO 2 , and MgO exhibit significant influence under different scenarios. This study provides new insights for customized slag system design and customized steel grade production in novel clean deoxidation processes.