Top-bottom combined blowing converter steelmaking involves complex thermodynamic and kinetic processes, and predictive modeling has long been a key focus in steelmaking research. This paper proposes a kinetic process prediction model with on-site applicability. Based on actual production data, machine learning models (BP neural network, random forest, XGBoost) are employed to predict Tapping Steel Oxygen (TSO) content, which is then used as input for the kinetic model. An optimized theoretical decarburization kinetic model is selected and validated against measured Tapping Steel Carbon (TSC) data. The key innovation lies in the integration of converter control parameters into the kinetic model through a data-driven cyclic iteration algorithm. Comparison of prediction accuracy before and after integration shows that the model’s TSC prediction within the range −0.2, +0.2 improves by 6.26%. This work presents a novel approach for enhancing kinetic models via control parameter integration, offering effective guidance for real-time monitoring and optimization in converter steelmaking.
Cai et al. (Wed,) studied this question.