Intelligent optimization method for the dynamic scheduling of hot metal ladles of one-ladle technology on ironmaking and steelmaking interface in steel plants

Li Zeng; Zhong Zheng; Xiaoyuan Lian; Kai Zhang; Mingmei Zhu; Kaitian Zhang; Chaoyue Xu; Fei Wang

doi:10.1007/s12613-023-2625-6

Volume 30 Issue 9

Sep. 2023

Turn off MathJax

Article Contents

Article Navigation > International Journal of Minerals, Metallurgy and Materials > 2023 > 30(9): 1729-1739

Li Zeng, Zhong Zheng, Xiaoyuan Lian, Kai Zhang, Mingmei Zhu, Kaitian Zhang, Chaoyue Xu, and Fei Wang, Intelligent optimization method for the dynamic scheduling of hot metal ladles of one-ladle technology on ironmaking and steelmaking interface in steel plants, Int. J. Miner. Metall. Mater., 30(2023), No. 9, pp. 1729-1739. https://doi.org/10.1007/s12613-023-2625-6

Cite this article as:

Li Zeng, Zhong Zheng, Xiaoyuan Lian, Kai Zhang, Mingmei Zhu, Kaitian Zhang, Chaoyue Xu, and Fei Wang, Intelligent optimization method for the dynamic scheduling of hot metal ladles of one-ladle technology on ironmaking and steelmaking interface in steel plants, Int. J. Miner. Metall. Mater., 30(2023), No. 9, pp. 1729-1739. https://doi.org/10.1007/s12613-023-2625-6

Citation:

PDF( 1076 KB)

Research Article

Intelligent optimization method for the dynamic scheduling of hot metal ladles of one-ladle technology on ironmaking and steelmaking interface in steel plants

+ Author Affiliations

Corresponding author:
Zhong Zheng E-mail: zhengzh@cqu.edu.cn
Received: 5 December 2022; Revised: 23 February 2023; Accepted: 6 March 2023; Available online: 9 March 2023

Abstract

Abstract

The one-ladle technology requires an efficient ironmaking and steelmaking interface. The scheduling of the hot metal ladle in the steel plant determines the overall operational efficiency of the interface. Considering the strong uncertainties of real-world production environments, this work studies the dynamic scheduling problem of hot metal ladles and develops a data-driven three-layer approach to solve this problem. A dynamic scheduling optimization model of the hot metal ladle operation with a minimum average turnover time as the optimization objective is also constructed. Furthermore, the intelligent perception of industrial scenes and autonomous identification of disturbances, adaptive configuration of dynamic scheduling strategies, and real-time adjustment of schedules can be realized. The upper layer generates a demand-oriented prescheduling scheme for hot metal ladles. The middle layer adaptively adjusts this scheme to obtain an executable schedule according to the actual supply–demand relationship. In the lower layer, three types of dynamic scheduling strategies are designed according to the characteristics of the dynamic disturbance in the model: real-time flexible fine-tuning, local machine adjustment, and global rescheduling. Case test using 24 h production data on a certain day during the system operation of a steel plant shows that the method and system can effectively reduce the fluctuation and operation time of the hot metal ladle and improve the stability of the ironmaking and steelmaking interface production rhythm. The data-driven dynamic scheduling strategy is feasible and effective, and the proposed method can improve the operation efficiency of hot metal ladles.
- hot metal ladles;
- ironmaking and steelmaking interface;
- one-ladle technology;
- dynamic scheduling;
- data-driven

FullText(HTML)

Supplements(0)

References(25)

References

[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]	R.Y. Yin, Review on the study of metallurgical process engineering, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1253. doi: 10.1007/s12613-020-2220-z
[4]	W.G. Han, X.P. Li, Y.X. Shi, W.D. Wang, and C.X. Zhang, Online ladle quantity of “one-open-ladle-from-BF-to-BOF” route by using queuing theory, Iron Steel, 48(2013), No. 5, p. 21. doi: 10.13228/j.boyuan.issn0449-749x.2013.05.015
[5]	Z.X. Gu, A.J. Xu, D.F. He, and K. Feng, A calculation model for determining number of hot metal ladle based on queuing theory with constraints of system capacity, J. Chongqing Univ., 40(2017), No. 8, p. 70. doi: 10.11835/j.issn.1000-582X.2017.08.009
[6]	J.J. Cui, S.Z. Luo, F. Liu, and X.H. Xu, Design and realization of the iron melt transport simulating system, J. Syst. Simul., 15(2003), No. 12, p. 1799. doi: 10.16182/j.cnki.joss.2003.12.037
[7]	J. Qiu, N.Y. Tian, A.J. Xu, et al., Development of transport scheduling application software for iron-steel interface at baosteel, Iron Steel, 38(2003), No. 5, p. 73. doi: 10.13228/j.boyuan.issn0449-749x.2003.05.020
[8]	H. Huang, T.Y. Chai, B.L. Zheng, Z.Y. Li, W. Xu, and W. Zhou, Design and development of molten iron scheduling simulation system, J. Syst. Simul., 24(2012), No. 6, p. 1192.
[9]	F. Wang, Y. Liu, A.J. Xu, D.F. He, and H.B. Wang, Modeling and calculation for the molten iron preparation problem based on production schedulling of steelmaking area, [in] Proceedings of the 2nd International Conference on Modeling and Simulation, Liverpool, 2009.
[10]	X.P. Li, W.G. Han, C.X. Zhang, J.H. Liu, Z.J. Wei, and C.Y. Wu, Optimization of interface material flow operation of BF–BOF section, J. Eng. Stud., 9(2017), No. 1, p. 53. doi: 10.3724/SP.J.1224.2017.00053
[11]	S.W. Lu and X.C. Luo, Design of multi-scenario simulation of molten iron logistics system with cranes and cross-train AGVs, J. Syst. Simul., 29(2017), No. 10
[12]	X.Y. Wang, A.J. Xu, D.F. He, and Z.X. Gu, Simulation optimization of logistics for iron-making plant based on plant simulation, Res. Iron Steel, 45(2017), No. 1, p. 17.
[13]	M. Pinedo and K. Hadavi, Scheduling: theory, algorithms, and systems, [in] Proceedings of the 20th Annual Meeting on Operations Research, Berlin, Heidelberg, 1992, p. 35.
[14]	W.H. Gui, C.H. Wang, Y.F. Xie, S. Song, Q.F. Meng, and J.L. Ding, The necessary way to realize great-leap-forward development of process industries, Bull. Nat. Nat. Sci. Found. China, 5(2015), p. 337.
[15]	Z.Y. Zhao, S.X. Liu, M.C. Zhou, and A. Abusorrah, Dual-objective mixed integer linear program and memetic algorithm for an industrial group scheduling problem, IEEE/CAA J. Autom. Sin., 8(2021), No. 6, p. 1199. doi: 10.1109/JAS.2020.1003539
[16]	J.P. Yang, Q. Liu, W.D. Guo, and J.G. Zhang, Quantitative evaluation of multi-process collaborative operation in steelmaking-continuous casting sections, Int. J. Miner. Metall. Mater., 28(2021), 8, p. 1353. doi: 10.1007/s12613-020-2227-5
[17]	Z.J. Xu, Z. Zheng, and X.Q. Gao, Operation optimization of the steel manufacturing process: A brief review, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1274. doi: 10.1007/s12613-021-2273-7
[18]	S.P. Yu and Q.K. Pan, A rescheduling method for operation time delay disturbance in steelmaking and continuous casting production process, J. Iron Steel Res. Int., 19(2012), No. 12, p. 33. doi: 10.1016/S1006-706X(13)60029-1
[19]	S.P. Yu, T.Y. Chai, and Y. Tang, An effective heuristic rescheduling method for steelmaking and continuous casting production process with multirefining modes, IEEE Trans. Syst. Man Cybern:Syst., 46(2016), No. 12, p. 1675. doi: 10.1109/TSMC.2016.2604081
[20]	S.P. Yu, A prediction method for abnormal condition of scheduling plan with operation time delay in steelmaking and continuous casting production process, ISIJ Int., 53(2013), No. 6, p. 1028. doi: 10.2355/isijinternational.53.1028
[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, art. No. 209. doi: 10.1109/TEVC.2013.2250977
[22]	J.H. Hao, M. Liu, S.L. Jiang, and C. Wu, A soft-decision based two-layered scheduling approach for uncertain steelmaking-continuous casting process, Eur. J. Oper. Res., 244(2015), No. 3, p. 966. doi: 10.1016/j.ejor.2015.02.026
[23]	S.L. Jiang, M. Liu, J.H. Lin, and H.X. Zhong, A prediction-based online soft scheduling algorithm for the real-world steelmaking-continuous casting production, Knowl. Based Syst., 111(2016), p. 159. doi: 10.1016/j.knosys.2016.08.010
[24]	J.Y. Long, Z. Zheng, and X.Q. Gao. Dynamic scheduling in steelmaking-continuous casting production for continuous caster breakdown, Int. J. Prod. Res., 55(2017), No. 11, p. 3197. doi: 10.1080/00207543.2016.1268277
[25]	K.K. Peng, Q.K. Pan, L. Gao, B. Zhang, and X.F. Pang, An improved artificial bee colony algorithm for real-world hybrid flowshop rescheduling in steelmaking–refining–continuous casting process, Comput. Ind. Eng., 122(2018), p. 235. doi: 10.1016/j.cie.2018.05.056