Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks

Slag foaming is a key factor in terms of quality and productivity in the electric arc furnace (EAF) steelmaking process. Optimal control of slag foaming is required, but is difficult due to the absence of practical on-line measuring methods and the broad process variability. In this study, a soft se...

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書誌詳細

出版年:	Journal of Materials Research and Technology
主要な著者:	Kyungchan Son, Jaegak Lee, Haejin Hwang, Wonseok Jeon, Hyunseok Yang, Il Sohn, Younghwan Kim, Hyungsic Um
フォーマット:	論文
言語:	英語
出版事項:	Elsevier 2021-05-01
主題:	Electric arc furnace (EAF) Slag foaming height Long short-term memory (LSTM) network Soft sensor model Machine learning
オンライン･アクセス:	http://www.sciencedirect.com/science/article/pii/S2238785421002118