Domain-knowledge-enhanced random forest model for basic oxygen furnace material prediction

Abstract

The non-linear influence of hot metal composition and scrap ratio on flux addition requires accurate predictive models to optimise charging operations. A random forest (RF) model was developed using multi-heat industrial data to predict lime and light-burned dolomite additions. The model effectively captured non-linear interactions between key process variables and flux inputs but showed slight systematic deviations under fluctuating operating conditions. To improve prediction accuracy and physical consistency, metallurgical mechanisms were embedded into the data-driven framework. Empirical features for light-burned dolomite were obtained by polynomial fitting, while theoretical lime additions were derived from quaternary basicity theory and used as mechanistic constraints. This hybrid model retained the non-linear learning capability of RF while enhancing interpretability and robustness. After feature enhancement, all performance indicators improved markedly: for dolomite, the coefficient of determination (R²) increased from 0.4801 to 0.5675, the mean absolute error (MAE) decreased from 123.17 kg to 116.52 kg and the root mean square error (RMSE) from 152.90 kg to 139.46 kg; for lime, R² rose from 0.5843 to 0.7553 and MAE and RMSE dropped by 24.1% and 23.3%, respectively. The proportion of samples within ±5% error increased significantly, confirming improved reliability for basic oxygen furnace (BOF) charge prediction and physically consistent steelmaking control.