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| Modeling of LF refining process: a review |
| Zi-cheng Xin1,2, Jiang-shan Zhang1, Kai-xiang Peng2, Jun-guo Zhang3, Chun-hui Zhang4, Qing Liu1 |
| 1 State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China 2 School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China 3 Tangshan Branch, Hebei Iron and Steel Co., Ltd., Tangshan 063000, Hebei, China 4 Xinjiang Bayi Iron and Steel Co., Ltd., Baosteel Group, Wulumuqi 830022, Xinjiang, China |
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Abstract With the increasing demand for energy conservation and emission reduction, more attentions have been paid to the intelligentization, greenization and low carbonization during the transformation and upgrading of steelmaking plants. Ladle furnace (LF) refining is one of the key procedures in steelmaking process and has been widely used in steelmaking plants for its high equipment matching degree, low equipment investment and outstanding refining performance. According to the main tasks of LF refining process, the modeling methods of temperature prediction model, slag-making model, alloying model, argon blowing model and model of inclusions behavior were systematically reviewed, and the advantages and disadvantages of each modeling method were summarized. In addition, the technical framework for the future has also been proposed based on existing works, including classification of raw materials, graphic representation of knowledge, introduction, upgradation and management of device/equipment, customization of steelmaking, modeling of refining process, synergy of models, intelligentization of decision-making, automation of control, and digitization of processes and operations, aiming to provide a reference for the modeling and intelligent development of LF refining process.
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| Cite this article: |
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Zi-cheng Xin,Jiang-shan Zhang,Kai-xiang Peng, et al. Modeling of LF refining process: a review[J]. Journal of Iron and Steel Research International, 2024, 31(02): 289-317.
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2024, Vol. 31 
