Jian-ping Yang, Qing Liu, Wei-da Guo, and Jun-guo Zhang, Quantitative evaluation of multi-process collaborative operation in steelmaking–continuous casting sections, Int. J. Miner. Metall. Mater., 28(2021), No. 8, pp. 1353-1366. https://doi.org/10.1007/s12613-020-2227-5
Cite this article as:
Jian-ping Yang, Qing Liu, Wei-da Guo, and Jun-guo Zhang, Quantitative evaluation of multi-process collaborative operation in steelmaking–continuous casting sections, Int. J. Miner. Metall. Mater., 28(2021), No. 8, pp. 1353-1366. https://doi.org/10.1007/s12613-020-2227-5
Research Article

Quantitative evaluation of multi-process collaborative operation in steelmaking–continuous casting sections

+ Author Affiliations
  • Corresponding author:

    Qing Liu    E-mail: qliu@ustb.edu.cn

  • Received: 18 August 2020Revised: 16 November 2020Accepted: 18 November 2020Available online: 26 November 2020
  • The quantitative evaluation of multi-process collaborative operation is of great significance for the improvement of production planning and scheduling in steelmaking–continuous casting sections (SCCSs). However, this evaluation is difficult since it relies on an in-depth understanding of the operating mechanism of SCCSs, and few existing methods can be used to conduct the evaluation, due to the lack of full-scale consideration of the multiple factors related to the production operation. In this study, three quantitative models were developed, and the multi-process collaborative operation level was evaluated through the laminar-flow operation degree, the process matching degree, and the scheduling strategy availability degree. Based on the evaluation models for the laminar-flow operation and process matching levels, this study investigated the production status of two steelmaking plants, plants A and B, based on actual production data. The average laminar-flow operation (process matching) degrees of SCCSs were obtained as 0.638 (0.610) and 1.000 (0.759) for plants A and B, respectively, for the period of April to July 2019. Then, a scheduling strategy based on the optimization of the furnace-caster coordinating mode was suggested for plant A. Simulation experiments showed higher availability than the greedy-based and manual strategies. After the proposed scheduling strategy was applied, the average process matching degree of the SCCS of plant A increased by 4.6% for the period of September to November 2019. The multi-process collaborative operation level was improved with fewer adjustments and interruptions in casting.

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  • [1]
    R.Y. Yin, Metallurgical Process Engineering, Metallurgical Industry Press, Beijing, 2011.
    [2]
    R.Y. Yin, A discussion on “smart” steel plant—View from physical system side, Iron Steel, 52(2017), No. 6, p. 1.
    [3]
    J.P. Birat, Steel cleanliness and environmental metallurgy, Metall. Res. Technol., 113(2016), No. 2, art. No. 201. doi: 10.1051/metal/2015050
    [4]
    D.S. Liao, S. Sun, S. Waterfall, K. Boylan, N. Pyke, and D. Holdridge, Integrated KOBM steelmaking process control, [in] Proceedings of the 6th International Congress on the Science and Technology of Steelmaking, Beijing, 2015, p. 127.
    [5]
    J.G. Speer, The continuing development of modern steel products, [in] AISTech 2018 Iron and Steel Technology Conference and Exposition, Philadelphia, 2018, p. 1.
    [6]
    X.H. Wang, J.Z. Li, and F.G. Liu, Technological progress of BOF steelmaking in period of development mode transition, Steelmaking, 33(2017), No. 1, p. 1.
    [7]
    Q. Liu, Q. Liu, J.P. Yang, J.S. Zhang, S. Gao, Q.D. Li, B. Wang, B.L. Wang, and T.K. Li, Progress of research on steelmaking‒continuous casting production scheduling, Chin. J. Eng., 42(2020), No. 2, p. 144.
    [8]
    Z. Zheng, J.Y. Long, X.Q. Gao, Y.M. Gong, and W.Z. Hu, Present situation and prospect of production control technology focusing on planning and scheduling in iron and steel enterprise, Comput. Integr. Manuf. Syst., 20(2014), No. 11, p. 2660.
    [9]
    Q. Liu, B. Wang, Z. Wang, B. Wang, F.M. Xie, and J. Chang, Fine production in steelmaking plants, Mater. Today:Proc., 2(2015), Suppl. 2, p. S348.
    [10]
    R.Y. Yin, Theory and Methods of Metallurgical Process Integration, Metallurgical Industry Press, Beijing, 2016.
    [11]
    J.G. Dong, Y.Y. Zheng, and X.F. Jiang, Product line practice of Baosteel, [in] 2015 Continuous Casting Equipment Technology Innovation and Fine Production Technology Exchange Conference, Xi’an, 2015, p. 30.
    [12]
    Z.X. Gu, A.J. Xu, J.B. Chang, S.W. Li, and Y.B. Xiang, Optimization of the production organization pattern in Tangshan Iron and Steel Co., Ltd., J. Iron Steel Res. Int., 21(2014), Suppl. 1, p. 17.
    [13]
    Y.M. Lu, Precise Design and Integrated Production for Steel Manufacturing Process of all Plate and Strip [Dissertation], University of Science and Technology Beijing, Beijing, 2011, p. 66.
    [14]
    F. Wang, Y.M. Lu, A.J. Xu, D.F. He, and N.Y. Tian, Evaluation model of layout of steelmaking workshop based on factors analysis, China Metall., 26(2016), No. 7, p. 15.
    [15]
    K.J. Yan, Study on Evaluation Method and Making Re-plan for Production Operation of Steelmaking Plant [Dissertation], Chongqing University, Chongqing, 2014, p. 18.
    [16]
    Y.Q. Mu, J. Yin, F.M. Xie, B. Wang, B. Wang, C. Wang, Q. Liu, X.C. Lu, L.Q Zhang, and S.H. Bai, Research on matching between furnaces and casters in special steel plant, J. Univ. Sci. Technol. Beijing, 35(2013), No. 1, p. 126.
    [17]
    G. Wang, B. Wang, B. Wang, C. Wang, Y.Q. Mu, B.L. Wang, Q. Liu, F.M. Xie, H.W. Li, X.W. Nie, and X.C. Lu, Scheduling model for steelmaking–continuous casting process based on “furnace-caster matching” principle, J. Univ. Sci. Technol. Beijing, 35(2013), No. 8, p. 1080.
    [18]
    Q. Liu, N.Y. Tian, and R.Y. Yin, Operation principle and control strategy for steelmaking workshop system, Chin. J. Process Eng., 3(2003), No. 2, p. 171.
    [19]
    K. Mao, Q.K. Pan, T.Y. Chai, and P.B. Luh, An effective subgradient method for scheduling a steelmaking-continuous casting process, IEEE Trans. Autom. Sci. Eng., 12(2015), No. 3, p. 1140. doi: 10.1109/TASE.2014.2332511
    [20]
    S.P. Yu and T.Y. Chai, Heuristic scheduling method for steelmaking and continuous casting production process, Control Theory Appl., 33(2016), No. 11, p. 1413.
    [21]
    L.X. Tang, Y. Zhao, and J.Y. Liu, An improved differential evolution algorithm for practical dynamic scheduling in steelmaking–continuous casting production, IEEE Trans. Evol. Comput., 18(2014), No. 2, p. 209. doi: 10.1109/TEVC.2013.2250977
    [22]
    S. Deng, A.J. Xu, and H.B. Wang, Simulation study on steel plant capacity and equipment efficiency based on plant simulation, Steel Res. Int., 90(2019), No. 5, art. No. 1800507. doi: 10.1002/srin.201800507
    [23]
    J.P. Yang, B.L. Wang, Q. Liu, M. Guan, T.K. Li, S. Gao, W.D. Guo, and Q. Liu, Scheduling model for the practical steelmaking–continuous casting production and heuristic algorithm based on the optimization of “furnace-caster matching” mode, ISIJ Int., 60(2020), No. 6, p. 1213. doi: 10.2355/isijinternational.ISIJINT-2019-423
    [24]
    G.H. Liu and T.K. Li, A steelmaking–continuous casting production scheduling model and its heuristic algorithm, Syst. Eng., 20(2002), No. 6, p. 44.
    [25]
    J.P. Yang, J.S. Zhang, M. Guan, Y.J. Hong, S. Gao, W.D. Guo, and Q. Liu, Fine description of multi-process operation behavior in steelmaking–continuous casting process by a simulation model with crane non-collision constraint, Metals, 9(2019), No. 10, art. No. 1078. doi: 10.3390/met9101078
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