Flow fields control for bubble refinement induced by electromagnetic fields

Wang, Lianyu; Liu, Zeyi; He, Ming; Liu, Xiaoming; Sano, Masamichi; Zhao, Lijia; Wang, Qiang

doi:10.1007/s42243-021-00627-w

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…In the background of big data and strong computing computer developments, machine learning algorithms have made breakthrough advances in a variety of sectors such as computer visualisation, speech recognition and natural language processing, and their applications based on artificial intelligence (AI) technology have matured for industrial fields. Research on AI technology and its industrial applications are beginning to enter a new stage of development [10].…”

Section: Introductionmentioning

confidence: 99%

Application of deep learning in iron ore sintering process: a review

Gong,

Wang,

et al. 2024

J. Iron Steel Res. Int.

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In the wake of the era of big data, the techniques of deep learning have become an essential research direction in the machine learning field and are beginning to be applied in the steel industry. The sintering process is an extremely complex industrial scene. As the main process of the blast furnace ironmaking industry, it has great economic value and environmental protection significance for iron and steel enterprises. It is also one of the fields where deep learning is still in the exploration stage. In order to explore the application prospects of deep learning techniques in iron ore sintering, a comprehensive summary and conclusion of deep learning models for intelligent sintering were presented after reviewing the sintering process and deep learning models in a large number of research literatures. Firstly, the mechanisms and characteristics of parameters in sintering processes were introduced and analysed in detail, and then, the development of iron ore sintering simulation techniques was introduced. Secondly, deep learning techniques were introduced, including commonly used models of deep learning and their applications. Thirdly, the current status of applications of various types of deep learning models in sintering processes was elaborated in detail from the aspects of prediction, controlling, and optimisation of key parameters. Generally speaking, deep learning models that could be more effectively implemented in more situations of the sintering and even steel industry chain will promote the intelligent development of the metallurgical industry.

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Section: Introductionmentioning

confidence: 99%

Application of deep learning in iron ore sintering process: a review

Gong,

Wang,

et al. 2024

J. Iron Steel Res. Int.

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“…A uniform distribution of gas bubbles in steel flow could be certainly beneficial to realize a good pump effect on molten steel. [35][36][37][38] Also, it can enhance the refining process that happens at bubble/steel interface, such as degassing, inclusion removal, decarburization, and so on. With the conventional radial gas-injection nozzle, force interactions of gas bubbleliquid steel are normally in vertical direction, which produce an approximately steady state gas-liquid plume region.…”

Section: Introductionmentioning

confidence: 99%

Study on Multiphase Flow Characteristics During RH Refining Process Affected by Nonradial Arrangement of Gas‐Blowing Nozzle

Wang

Zhou

et al. 2023

steel research int.

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Bath stirring, degassing, and decarburization in steel refining are strongly related to flow behaviors. The bubble plume produced in Ruhrstahl–Heraeus (RH) up‐snorkel plays an important role during refining, since it not only acts as a bubble pump, but also provides the reaction interface. Herein, it is aimed to form a new flow pattern in the up‐snorkel by using a nonradially arranged gas‐injection nozzle to enhance the 260 ton RH refining process. The results show that an upward spiral steel flow is produced, when nonradial gas‐injection nozzles are used in the up‐snorkel. Meanwhile, some bubbles moved toward the center region of the up‐snorkel, which may be caused by the centripetal effect in a rotational steel flow. This leads to a more uniform bubble distribution on the cross section of the snorkel, compared to that of the conventional case. Specifically, the circulation flow rate is increased by about 18.0%, and the mixing time are shortened by about 26.2% (criteria of ±5%), compared to that of the conventional case. In addition, the inclusion removal rate is increased by 0.5%, 4.8%, and 11.3% for the inclusion size of 20, 50, and 100 μm, respectively, compared to the conventional radial nozzle case.

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Non-contact Bubble Refinement in Metallurgical Melt by the Forward–Reverse Rotating Electromagnetic Field

Wang

Liu

et al. 2022

Metall Mater Trans B

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Flow fields control for bubble refinement induced by electromagnetic fields

Cited by 6 publications

References 42 publications

Application of deep learning in iron ore sintering process: a review

Application of deep learning in iron ore sintering process: a review

Study on Multiphase Flow Characteristics During RH Refining Process Affected by Nonradial Arrangement of Gas‐Blowing Nozzle

Non-contact Bubble Refinement in Metallurgical Melt by the Forward–Reverse Rotating Electromagnetic Field

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