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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Vydáno v:	Journal of iron and steel research, international Ročník 32; číslo 7; s. 1856 - 1868
Hlavní autoři:	Zhang, Jian-bo, Khaksar Ghalati, Maryam, Fu, Jun, Yang, Xiao-an, El-Fallah, G.M.A.M., Dong, Hong-biao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Singapore Springer Nature Singapore 01.07.2025 Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
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