Work

Incorporation of Control Parameters into a Kinetic Model for Decarburization During Basic Oxygen Furnace (BOF) Steelmaking

Year: 2025

Type: article

Abstract: 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.

Source: Processes

DOI: 10.3390/pr13103048

Authors: Keqing Cai, Kai Feng, Dongfeng He, Lingzhi Yang, Meng Zhang

Institutions: University of Science and Technology Beijing, Central South University, China Metallurgical Planning Net (China)

Language: English

Topic: Metallurgical Processes and Thermodynamics

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,896