Boosting algorithms for predicting end-point temperature in BOF steelmaking using big industrial datasets Boosting algorithms for predicting end-point temperature in BOF steelmaking using big industrial datasets

The application of machine learning was investigated for predicting end-point temperature in the basic oxygen furnace steelmaking process, addressing gaps in the field, particularly large-scale dataset sizes and the underutilization of boosting algorithms. Utilizing a substantial dataset containing...

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Veröffentlicht in:	Journal of iron and steel research, international Jg. 32; H. 7; S. 1856 - 1868
Hauptverfasser:	Zhang, Jian-bo, Khaksar Ghalati, Maryam, Fu, Jun, Yang, Xiao-an, El-Fallah, G.M.A.M., Dong, Hong-biao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Nature Singapore 01.07.2025 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Applied and Technical Physics Artificial intelligence Basic converters Continuous casting Datasets Engineering Ensemble learning Industry 4.0 Machine learning Machines Manufacturing Materials Engineering Materials Science Metallic Materials Multilayer perceptrons Neural networks Original Paper Oxygen steel making Physical Chemistry Prediction models Processes Steel production Support vector machines Steelmaking Basic oxygen furnace Boosting algorithm Machine learning
ISSN:	1006-706X, 2210-3988
Online-Zugang:	Volltext
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