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Digital twin-driven smelting process management method for converter steelmaking

Author

Listed:
  • Tianjie Fu

    (Zhejiang University)

  • Shimin Liu

    (The Hong Kong Polytechnic University)

  • Peiyu Li

    (Zhejiang University)

Abstract

The converter is an indispensable key equipment in the steel manufacturing industry. With the increasing demand for high-quality steel, there is an increasing demand for monitoring and controlling the status of the converter during the smelting process. Compared to other manufacturing industries, such as food processing and textile, converter steelmaking requires a larger keep-out zone due to its ultra-high temperatures and harsh smelting environment. This makes it difficult for personnel to fully understand, analyze, and manage the smelting process, resulting in low production efficiency and the inability to achieve consistently high-quality results. Aiming at the low virtual visualization level and insufficient monitoring ability of the converter steelmaking process, a process management method based on digital twin technology is proposed. Firstly, a digital twin system framework for full-process monitoring of converter steelmaking is proposed based on the analysis of the process characteristics of converter steelmaking. The proposed framework provides critical enabling technologies such as point cloud-based digital twin model construction, visual display, and steel endpoint analysis and prediction, to support full-process, high-fidelity intelligent monitoring. After conducting experiments, a digital twin-driven smelting process management system was developed to manage the entire smelting process. The system has proven to be effective as it increased the monthly production capacity by 77.7%. The waste of smelting materials has also been greatly reduced from 34% without the system to 7.8% with the system. Based on these results, it is evident that this system significantly enhances smelting efficiency and reduces both the costs and waste associated with the process.

Suggested Citation

  • Tianjie Fu & Shimin Liu & Peiyu Li, 2025. "Digital twin-driven smelting process management method for converter steelmaking," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2749-2765, April.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02366-7
    DOI: 10.1007/s10845-024-02366-7
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    References listed on IDEAS

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    1. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
    2. David Cemernek & Sandra Cemernek & Heimo Gursch & Ashwini Pandeshwar & Thomas Leitner & Matthias Berger & Gerald Klösch & Roman Kern, 2022. "Machine learning in continuous casting of steel: a state-of-the-art survey," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1561-1579, August.
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    4. Ayman AboElHassan & Soumaya Yacout, 2023. "A digital shadow framework using distributed system concepts," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3579-3598, December.
    5. A. J. H. Redelinghuys & A. H. Basson & K. Kruger, 2020. "A six-layer architecture for the digital twin: a manufacturing case study implementation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1383-1402, August.
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