Explainable machine learning model for predicting molten steel temperature in the LF refining process

Accurate prediction of molten steel temperature in the ladle furnace (LF) refining process has an important influence on the quality of molten steel and the control of steelmaking cost. Extensive research on establishing models to predict molten steel temperature has been conducted. However, most re...

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Veröffentlicht in:	International journal of minerals, metallurgy and materials metallurgy and materials, 2024-12, Vol.31 (12), p.2657-2669
Hauptverfasser:	Xin, Zicheng, Zhang, Jiangshan, Peng, Kaixiang, Zhang, Junguo, Zhang, Chunhui, Wu, Jun, Zhang, Bo, Liu, Qing
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Ceramics Characterization and Evaluation of Materials Chemistry and Materials Science Composites Corrosion and Coatings Glass Graphical representations Ladle metallurgy Liquid metals Machine learning Materials Science Metallic Materials Natural Materials Optimization Performance evaluation Prediction models Predictive control Refining Research Article Steel making Surfaces and Interfaces Thin Films Tribology
Online-Zugang:	Volltext
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