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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Research on Molten Steel Carbon Content Prediction for Multi-Procedure Steelmaking Process
Author(s) Zhengjiang Yang, Mingmei Zhu, Xianhong Qin
On-Site Speaker (Planned) Zhengjiang Yang
Abstract Scope Accurate prediction of end-point molten steel carbon content is the core of steel quality control. This paper proposes a multi-procedure carbon content prediction method for the KR-BOF-LF production route of a steel plant. Guided by the decarburization mechanism, the random forest algorithm is used for feature dimensionality reduction. After comparing multiple intelligent algorithms, the extreme gradient boosting (XGBoost) algorithm is adopted to construct end-point carbon content prediction models for BOF and LF processes, and the particle swarm optimization (PSO) algorithm is used to optimize the model hyperparameters. Combined with the carbon content transfer law, the single-process models of the basic oxygen furnace (BOF) and ladle furnace (LF) are coupled to realize full-process carbon prediction. Validation results show that the proposed model achieves a prediction hit rate of 83.28% within an error of ±0.01 wt%, offering reliable theoretical and technical support for multi-procedure carbon control in steelmaking.
Proceedings Inclusion? Planned:
Keywords Iron and Steel, Machine Learning, Modeling and Simulation

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