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

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.

Keywords

hot metal ladles / ironmaking and steelmaking interface / one-ladle technology / dynamic scheduling / data-driven

References

[1] Yin RY. Metallurgical Process Engineering, 2011, Beijing, Metallurgical Industry Press

[2] Yin RY. A discussion on “smart” steel plant - View from physical system side. Iron Steel, 2017, 52(6): 1.

[3] Yin RY. Review on the study of metallurgical process engineering. Int. J. Miner. Metall. Mater., 2021, 28(8): 1253.

[4] Han WG, Li XP, Shi YX, Wang WD, Zhang CX. Online ladle quantity of “one-open-ladle-from-BF-to-BOF” route by using queuing theory. Iron Steel, 2013, 48(5): 21.

[5] Gu ZX, Xu AJ, He DF, Feng K. A calculation model for determining number of hot metal ladle based on queuing theory with constraints of system capacity. J. Chongqing Univ., 2017, 40(8): 70.

[6] Cui JJ, Luo SZ, Liu F, Xu XH. Design and realization of the iron melt transport simulating system. J. Syst. Simul., 2003, 15(12): 1799.

[7] Qiu J, Tian NY, Xu AJ, et al. Development of transport scheduling application software for iron-steel interface at baosteel. Iron Steel, 2003, 38(5): 73.

[8] Huang H, Chai TY, Zheng BL, Li ZY, Xu W, Zhou W. Design and development of molten iron scheduling simulation system. J. Syst. Simul., 2012, 24(6): 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] Li XP, Han WG, Zhang CX, Liu JH, Wei ZJ, Wu CY. Optimization of interface material flow operation of BF-BOF section. J. Eng. Stud., 2017, 9(1): 53.

[11] J. Syst. Simul., 2017, 29(10)

[12] Wang XY, Xu AJ, He DF, Gu ZX. Simulation optimization of logistics for iron-making plant based on plant simulation. Res. Iron Steel, 2017, 45(1): 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] Gui WH, Wang CH, Xie YF, Song S, Meng QF, Ding JL. The necessary way to realize great-leap-forward development of process industries. Bull. Nat. Nat. Sci. Found. China, 2015, 5, 337.

[15] Zhao ZY, Liu SX, Zhou MC, Abusorrah A. Dual-objective mixed integer linear program and memetic algorithm for an industrial group scheduling problem. IEEE/CAA J. Autom. Sin., 2021, 8(6): 1199.

[16] Yang JP, Liu Q, Guo WD, Zhang JG. Quantitative evaluation of multi-process collaborative operation in steelmaking-continuous casting sections. Int. J. Miner. Metall. Mater., 2021, 28(8): 1353.

[17] Xu ZJ, Zheng Z, Gao XQ. Operation optimization of the steel manufacturing process: A brief review. Int. J. Miner. Metall. Mater., 2021, 28(8): 1274.

[18] Yu SP, Pan QK. A rescheduling method for operation time delay disturbance in steelmaking and continuous casting production process. J. Iron Steel Res. Int, 2012, 19(12): 33.

[19] Yu SP, Chai TY, Tang Y. An effective heuristic rescheduling method for steelmaking and continuous casting production process with multirefining modes. IEEE Trans. Syst. Man Cybern:Syst., 2016, 46(12): 1675.

[20] Yu SP. A prediction method for abnormal condition of scheduling plan with operation time delay in steelmaking and continuous casting production process. ISIJ Int., 2013, 53(6): 1028.

[21] IEEE Trans. Evol. Comput., 2014, 18(2) art. No. 209

[22] Hao JH, Liu M, Jiang SL, Wu C. A soft-decision based two-layered scheduling approach for uncertain steelmaking-continuous casting process. Eur. J. Oper. Res, 2015, 244(3): 966.

[23] Jiang SL, Liu M, Lin JH, Zhong HX. A prediction-based online soft scheduling algorithm for the real-world steelmaking-continuous casting production. Knowl. Based Syst., 2016, 111, 159.

[24] Long JY, Zheng Z, Gao XQ. Dynamic scheduling in steelmaking-continuous casting production for continuous caster breakdown. Int. J. Prod. Res., 2017, 55(11): 3197.

[25] Peng KK, Pan QK, Gao L, Zhang B, Pang XF. An improved artificial bee colony algorithm for real-world hybrid flowshop rescheduling in steelmaking-refining-continuous casting process. Comput. Ind. Eng., 2018, 122, 235.