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Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Additive Manufacturing Modeling, Simulation and Artificial Intelligence
Presentation Title BOF Endpoint Carbon Content Prediction Model Based on Data and Mechanism Driven
Author(s) Zhengbiao Hu, Changhe Li, Tingting Lu, Lili Jiang
On-Site Speaker (Planned) Zhengbiao Hu
Abstract Scope The development of accurate prediction model of end-point carbon content in converter steelmaking is one of the key technologies to realize the intelligentization of converter steelmaking process. Due to the difficulty in obtaining the hyperparameters of the metallurgical mechanism model, the data model has poor interpretability. Therefore, this paper combines the advantages of the two, and constructs a hybrid model for predicting the end-point carbon content of the converter based on data and mechanism. Firstly, the unmeasurable parameters in the mechanism model are searched based on the genetic algorithm. Secondly, the mechanism model and random forest are coupled to obtain a hybrid model. The actual production data of a steelmaking plant are verified. The results show that the performance of the hybrid model based on data and mechanism is better than that of the simple mechanism model and data model.
Proceedings Inclusion? Planned:

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