Operation optimization of the steel manufacturing process: A brief review

Zhao-jun Xu , Zhong Zheng , Xiao-qiang Gao

International Journal of Minerals, Metallurgy, and Materials ›› 2021, Vol. 28 ›› Issue (8) : 1274 -1287.

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International Journal of Minerals, Metallurgy, and Materials ›› 2021, Vol. 28 ›› Issue (8) : 1274 -1287. DOI: 10.1007/s12613-021-2273-7
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Operation optimization of the steel manufacturing process: A brief review

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Abstract

Against the realistic background of excess production capacity, product structure imbalance, and high material and energy consumption in steel enterprises, the implementation of operation optimization for the steel manufacturing process is essential to reduce the production cost, increase the production or energy efficiency, and improve production management. In this study, the operation optimization problem of the steel manufacturing process, which needed to go through a complex production organization from customers’ orders to workshop production, was analyzed. The existing research on the operation optimization techniques, including process simulation, production planning, production scheduling, interface scheduling, and scheduling of auxiliary equipment, was reviewed. The literature review reveals that, although considerable research has been conducted to optimize the operation of steel production, these techniques are usually independent and unsystematic. Therefore, the future work related to operation optimization of the steel manufacturing process based on the integration of multi technologies and the intersection of multi disciplines were summarized.

Keywords

intelligent manufacturing / operation optimization / steel manufacturing process / process simulation / production planning / production scheduling

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Zhao-jun Xu, Zhong Zheng, Xiao-qiang Gao. Operation optimization of the steel manufacturing process: A brief review. International Journal of Minerals, Metallurgy, and Materials, 2021, 28(8): 1274-1287 DOI:10.1007/s12613-021-2273-7

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References

[1]

W.Q. Sun, Q. Wang, Y. Zhou, and J.Z. Wu, Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives, Appl. Energy, 268(2020), art. No. 114946.

[2]

Xing Y, Zhang WB, Su W, Wen W, Zhao XJ, Yu JX. Research of ultra-low emission technologies of the iron and steel industry in China. Chin. J. Eng., 2021, 43(1): 1

[3]

Lasi H, Fettke P, Kemper H G, Feld T, Hoffmann M. Industry 4.0. Bus. Inf. Syst. Eng., 2014, 6(4): 239.

[4]

Yin RY. Metallurgical Process Engineering, 2011, Berlin, Heidelberg, Springer-Verlag

[5]

Li XF, Xu LY, Shao HH, Ren DX. Modeling and simulation of physical distribution system using Petri net & COM. Inf. Control, 2001, 30(3): 284

[6]

Zhao YQ, Guo L, Bi GH, Zhu DF. Analysis and design of steel-making complex logistics system based on multi-Agent. Metall. Ind. Autom., 2012, 36(2): 1

[7]

Zheng Z, He LM, Gao XQ. Cellular automata model for simulating logistics system in steel-making process. Iron Steel, 2004, 39(11): 75

[8]

M.P. Fanti, G. Iacobellis, G. Rotunno, and W. Ukovich, A simulation based analysis of production scheduling in a steel-making and continuous casting plant, [in] 2013 IEEE International Conference on Automation Science and Engineering (CASE). Madison, 2013, p. 150.

[9]

Fanti MP, Rotunno G, Stecco G, Ukovich W, Mininel S. An integrated system for production scheduling in steel-making and casting plants. IEEE Trans. Autom. Sci. Eng., 2016, 13(2): 1112.

[10]

Melouk SH, Freeman NK, Miller D, Dunning M. Simulation optimization-based decision support tool for steel manufacturing. Int. J. Prod. Econ., 2013, 141(1): 269.

[11]

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.

[12]

S.P. Wu, A.J. Xu, W. Song, and X.P. Li, Structural optimization of the production process in steel plants based on Flex-Sim simulation, Steel Res. Int., 90(2019), No. 10, art. No. 1900201.

[13]

Mohamed NMZN, Rashid MFFA, Rose ANM, Ting WY. Production layout improvement for steel fabrication works. J. Ind. Intell. Inf., 2015, 3(2): 133

[14]

Gelders LF, van Wassenhove LN. Production planning: A review. Eur. J. Oper. Res., 1981, 7(2): 101.

[15]

Liu SX, Tang JF, Song JH. Order-planning model and algorithm for manufacturing steel sheets. Int. J. Prod. Econ., 2006, 100(1): 30.

[16]

Yu CY, Xu MR, Qu RX. On the production order planning of integrated steel production SM-CC-HR-FF. J. Northeast. Univ. Nat. Sci., 2008, 29(11): 1548

[17]

Yu CY, Wang CE. Multi-objective order-planning model and algorithm for integrated steel production. Control Theory Appl., 2009, 26(12): 1452

[18]

Zhang B, Wang GS, Yang Y, Zhang S. Solving the order planning problem at the steelmaking shops by considering logistics balance on the plant-wide process. IEEE Access, 2019, 7, 139938.

[19]

Lin JH, Liu M, Hao JH, Gu P. Many-objective harmony search for integrated order planning in steelmaking-continuous casting-hot rolling production of multi-plants. Int. J. Prod. Res., 2017, 55(14): 4003.

[20]

Gargani A, Refalo P. Bessière C. An efficient model and strategy for the steel mill slab design problem. Principles and Practice of Constraint Programming — CP 2007, 2007, Berlin, Heidelberg, Springer, 77.

[21]

van Hentenryck P, Michel L. Perron L, Trick MA. The steel mill slab design problem revisited. Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 2008, Berlin, Heidelberg, Springer, 377.

[22]

Zhang WX, Li TK. Modelling and algorithm for the slab designing problem based on constraint satisfaction. J. Univ. Sci. Technol. Beijing, 2011, 33(5): 641

[23]

Dash S, Kalagnanam J, Reddy C, Song SH. Production design for plate products in the steel industry. IBM J. Res. Dev., 2007, 51(3.4): 345.

[24]

Wy J, Kim BI. Two-staged guillotine cut, two-dimensional bin packing optimisation with flexible bin size for steel mother plate design. Int. J. Prod. Res., 2010, 48(22): 6799.

[25]

Heinz S, Schlechte T, Stephan R, Winkler M. Solving steel mill slab design problems. Constraints, 2012, 17(1): 39.

[26]

Hu WZ, Zheng Z, Long JY, Gao XQ. Modeling and solving cutting stock problem considering specification uncertainties of mother plate and slab of medium and heavy plate. Comput. Integr. Manuf. Sys., 2017, 23(11): 2508

[27]

Zheng Z, Hu WZ, Long JY, Gao XQ. Research and application for medium steel plate-slab design system. J. Northeast. Univ. Nat. Sci., 2017, 38(10): 1405

[28]

Zheng Z, Wang YZ, Lu Y, Gao XQ. Intelligent optimization model and system of plate and slab design of medium steel plate. Iron Steel, 2020, 55(4): 53

[29]

Tang LX, Wang GS. Decision support system for the batching problems of steelmaking and continuous-casting production. Omega, 2008, 36(6): 976.

[30]

Tang LX, Wang GS, Liu JY, Liu JY. A combination of Lagrangian relaxation and column generation for order batching in steelmaking and continuous-casting production. Nav. Res. Logist., 2011, 58(4): 370.

[31]

Dong HY, Huang M, Ip WH, Wang XW. On the integrated charge planning with flexible jobs in primary steel-making processes. Int. J. Prod. Res., 2010, 48(21): 6499.

[32]

Li CX, Yuan BL, Ren JH, Sun LL, Wang J. Research on optimization of charge batch planning based on augmented Lagrangian relaxation algorithm. IFAC-PpprssOn-Line, 2019, 52(1): 814.

[33]

F. Yuan, S.P. Wu, W. Song, and A.J. Xu, Charge plan model for steelmaking-continuous casting section, Metals, 10(2020), No. 9, art. No. 1196.

[34]

Tang LX, Luo JX. A new ILS algorithm for cast planning problem in steel industry. ISIJ Int., 2007, 47(3): 443.

[35]

Yang F, Li QQ, Liu S, Wang GR. Hybrid heuristic-cross entropy algorithm for cast planning problem. Comput. Integr. Manuf. Syst., 2014, 20(9): 2241

[36]

Yang F, Li QQ, Wang GR. Hybrid improved algorithm for cast planning problem with flexible width. Control Decis., 2015, 30(2): 348

[37]

Yi J, Tan SB, Li WG, Du B. Hybrid optimization algorithm for solving combining tundish MTSP model on continuous casting plan. J. Northeast. Univ. Nat. Sci., 2012, 33(9): 1235

[38]

Xue YC, Zheng DL, Yang QW. Optimum steel making cast plan with unknown cast number based on the modified discrete particle swarm optimization. Control Theory Appl., 2010, 27(2): 273

[39]

Tang LX, Wang GS, Chen ZL. Integrated charge batching and casting width selection at Baosteel. Oper. Res., 2014, 62(4): 772.

[40]

Long JY, Sun ZZ, Chen HB, Bai Y, Hong Y. Variable neighborhood search for integrated determination of charge batching and casting start time in steel plants. J. Intell. Fuzzy Syst., 2018, 34(6): 3821.

[41]

Xu WJ, Tang LX, Pistikopoulos EN. Modeling and solution for steelmaking scheduling with batching decisions and energy constraints. Comput. Chem. Eng., 2018, 116, 368.

[42]

W.J. Xu, F. Zou, and L.X. Tang, A subpopulation-based differential evolution algorithm for scheduling with batching decisions in steelmaking-continuous casting production, [in] 2016 IEEE Congress on Evolutionary Computation (CEC). Vancouver, 2016, p. 2784.

[43]

Kosiba ED, Wright JR, Cobbs AE. Discrete event sequencing as a traveling salesman problem. Comput. Ind., 1992, 19(3): 317.

[44]

Ozsoy IC, Ruddle GE, Crawley AF. Optimum scheduling of a hot rolling process by nonlinear programming. Can. Metall. Q., 1992, 31(3): 217.

[45]

Tang LX, Liu JY, Rong AY, Yang ZH. A multiple traveling salesman problem model for hot rolling scheduling in Shanghai Baoshan Iron & Steel Complex. Eur. J. Oper. Res., 2000, 124(2): 267.

[46]

Jia SJ, Yi J, Yang GK, Du B, Zhu J. A multi-objective optimisation algorithm for the hot rolling batch scheduling problem. Int. J. Prod. Res., 2013, 51(3): 667.

[47]

Puttkammer K, Wichmann MG, Spengler TS. A GRASP heuristic for the hot strip mill scheduling problem under consideration of energy consumption. J. Bus. Econ., 2016, 86(5): 537

[48]

Hu WZ, Zheng Z, Gao XQ, Pardalos PM. An improved method for the hot strip mill production scheduling problem. Int. J. Prod. Res., 2019, 57(10): 3238.

[49]

Zhang R, Song SJ, Wu C. Robust scheduling of hot rolling production by local search enhanced ant colony optimization algorithm. IEEE Trans. Ind. Inform., 2020, 16(4): 2809.

[50]

Liu LL, Wan X, Gao ZG, Li XL, Feng BW. Research on modelling and optimization of hot rolling scheduling. J. Ambient Intell. Human. Comput., 2019, 10(3): 1201.

[51]

Pinedo M, Hadavi K. Gaul W, Bachem A, Habenicht W, Runge W, Stahl WW. Scheduling: Theory, algorithms and systems development. Operations Research Proceedings, 1991, Berlin, Heidelberg, Springer, 35

[52]

Ouelhadj D, Petrovic S. A survey of dynamic scheduling in manufacturing systems. J. Sched., 2009, 12(4): 417.

[53]

Bellabdaoui A, Teghem J. A mixed-integer linear programming model for the continuous casting planning. Int. J. Prod. Econ., 2006, 104(2): 260.

[54]

Harjunkoski I, Grossmann IE. A decomposition approach for the scheduling of a steel plant production. Comput. Chem. Eng., 2001, 25(11–12): 1647.

[55]

Tang LX, Luh PB, Liu JY, Fang L. Steel-making process scheduling using Lagrangian relaxation. Int. J. Prod. Res., 2002, 40(1): 55.

[56]

Mao K, Pan QK, Pang XF, Chai TY. A novel Lagrangian relaxation approach for a hybrid flowshop scheduling problem in the steelmaking-continuous casting process. Eur. J. Oper. Res., 2014, 236(1): 51.

[57]

Cui HJ, Luo XC. An improved Lagrangian relaxation approach to scheduling steelmaking-continuous casting process. Comput. Chem. Eng., 2017, 106, 133.

[58]

Pan QK, Wang L, Mao K, Zhao JH, Zhang M. An effective artificial bee colony algorithm for a real-world hybrid flowshop problem in steelmaking process. IEEE Trans. Autom. Sci. Eng., 2013, 10(2): 307.

[59]

Long JY, Zheng Z, Gao XQ, Pardalos PM. Scheduling a realistic hybrid flow shop with stage skipping and adjustable processing time in steel plants. Appl. Soft Comput., 2018, 64, 536.

[60]

K. Worapradya and P. Thanakijkasem, Proactive scheduling for steelmaking-continuous casting plant with uncertain machine breakdown using distribution-based robustness and decomposed artificial neural network, Asia Pac. J. Oper. Res., 32(2015), No. 2, art. No. 1550010.

[61]

Fazel Zarandi MH, Dorry F. A hybrid fuzzy PSO algorithm for solving steelmaking-continuous casting scheduling problem. Int. J. Fuzzy Syst., 2018, 20(1): 219.

[62]

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.

[63]

Tang LX, Zhao Y, Liu JY. An improved differential evolution algorithm for practical dynamic scheduling in steel-making-continuous casting production. IEEE Trans. Evol. Comput., 2014, 18(2): 209.

[64]

Mao K, Pan QK, Pang XF, Chai TY. An effective Lagrangian relaxation approach for rescheduling a steelmaking-continuous casting process. Control Eng. Pract., 2014, 30, 67.

[65]

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.

[66]

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.

[67]

S. Rahal, Z.K. Li, and D.J. Papageorgiou, Proactive and reactive scheduling of the steelmaking and continuous casting process through adaptive robust optimization, Comput. Chem. Eng., 133(2020), art. No. 106658.

[68]

Hadera H, Harjunkoski I, Sand G, Grossmann IE, Engell S. Optimization of steel production scheduling with complex time-sensitive electricity cost. Comput. Chem. Eng., 2015, 76, 117.

[69]

Tan YY, Zhou MC, Zhang Y, Guo XW, Qi L, Wang YH. Hybrid scatter search algorithm for optimal and energy-efficient steelmaking-continuous casting. IEEE Trans. Autom. Sci. Eng., 2020, 17(4): 1814.

[70]

Z.J. Xu, Z. Zheng, and X.Q. Gao, Energy-efficient steelmaking-continuous casting scheduling problem with temperature constraints and its solution using a multi-objective hybrid genetic algorithm with local search, Appl. Soft Comput., 95(2020), art. No. 106554.

[71]

G.S. Wang and L.X. Tang, A column generation for locomotive scheduling problem in molten iron transportation, [in] 2007 IEEE International Conference on Automation and Logistics. Jinan, 2007, p. 2227.

[72]

Huang H, Chai TY, Luo XC, Zheng BL, Wang H. Two-stage method and application for molten iron scheduling problem between iron-making plants and steel-making plants. IFAC Proc. Vol., 2011, 44(1): 9476.

[73]

Ge B, Wang K, Han Y. A design for simulation model and algorithm of rail transport of molten iron in steel enterprise. Comput. Mod. New Technol., 2014, 18(11): 1056

[74]

Liu YY, Wang GS. The mix integer programming model for torpedo car scheduling in iron and steel industry. Proceedings of the International Conference on Computer Information Systems and Industrial Applications, 2015, Bangkok, Atlantis Press, 731

[75]

Tang LX, Wang GS, Liu JY. A branch-and-price algorithm to solve the molten iron allocation problem in iron and steel industry. Comput. Oper. Res., 2007, 34(10): 3001.

[76]

Huang H, Chai TY, Zheng BL, Luo XC. Research on the molten iron scheduling system oriented to iron-steel correspondence and its application. J. Northeast. Univ. Nat. Sci., 2010, 31(11): 1525

[77]

Huang H, Chai TY, Zheng BL, Luo XC, Zhang H. Two-stage case-based reasoning for molten iron dynamic scheduling system oriented iron-steel correspondence. CIESC J., 2010, 61(8): 2021

[78]

Ning SS, Wang W, Liu QL. An optimal scheduling algorithm for reheating furnace in steel production. Control Decis., 2006, 21(10): 1138

[79]

Broughton JS, Mahfouf M, Linkens DA. A paradigm for the scheduling of a continuous walking beam reheat furnace using a modified genetic algorithm. Mater. Manuf. Process., 2007, 22(5): 607.

[80]

Yang YJ, Jiang ZY, Zhang XX. Model and algorithm of furnace area production scheduling in slab hot rolling. J. Univ. Sci. Technol. Beijing, 2012, 34(7): 841

[81]

Tang LX, Ren HZ, Yang Y. Reheat furnace scheduling with energy consideration. Int. J. Prod. Res., 2015, 53(6): 1642.

[82]

Ilmer Q, Haeussler S, Missbauer H. Optimal synchronization of the hot rolling stage in steel production. IFAC-PapersOnLine, 2019, 52(13): 1615.

[83]

Tang LX, Wang XP. A two-phase heuristic for the production scheduling of heavy plates in steel industry. IEEE Trans. Control Syst. Technol., 2010, 18(1): 104.

[84]

K. Li and H.X. Tian, Integrated scheduling of reheating furnace and hot rolling based on improved multiobjective differential evolution, Complexity, 2018(2018), art. No. 1919438.

[85]

Wang BL, Huang K, Li TK. Two-stage hybrid flow-shop scheduling with simultaneous processing machines. J. Sched., 2018, 21(4): 387.

[86]

Tanizaki T, Tamura T, Sakai H, Takahashi Y, Imai T. A Heuristic Scheduling Algorithm for steel making process with crane handling. J. Oper. Res. Soc. Jpn., 2006, 49(3): 188

[87]

Zheng Z, Xu L, Gao XQ. Simulation model of crane scheduling in workshop based on cellular automata. Syst. Eng. Theory Pract., 2008, 28(2): 137

[88]

Zheng Z, Zhou C, Chen K. Crane scheduling simulation model based on immune genetic algorithms. Syst. Eng. Theory Pract., 2013, 33(1): 223

[89]

Gao XQ, Li P, Long JY, Zheng Z. Multi-objective modelling and solving for crane scheduling with spatio-temporal constraints in casting workshop. Syst. Eng. Theory Pract., 2017, 37(9): 2373

[90]

Li J, Xu AJ, Zang XS. Simulation-based solution for a dynamic multi-crane-scheduling problem in a steelmaking shop. Int. J. Prod. Res., 2020, 58(22): 6970.

[91]

Tang LX, Xie X, Liu JY. Crane scheduling in a warehouse storing steel coils. IIE Trans., 2014, 46(3): 267.

[92]

Xie X, Zheng YY, Li YP. Multi-crane scheduling in steel coil warehouse. Expert Syst. Appl., 2014, 41(6): 2874.

[93]

Maschietto GN, Ouazene Y, Ravetti MG, de Souza MC, Yalaoui F. Crane scheduling problem with non-interference constraints in a steel coil distribution centre. Int. J. Prod. Res., 2017, 55(6): 1607.

[94]

Kuyama S, Tomiyama S. A crane guidance system with scheduling optimization technology in a steel slab yard. ISIJ Int., 2016, 56(5): 820.

[95]

Zhao GD, Liu JY, Tang LX, Zhao R, Dong Y. Model and heuristic solutions for the multiple double-load crane scheduling problem in slab yards. IEEE Trans. Autom. Sci. Eng., 2020, 17(3): 1307.

[96]

X. Wang, M.C. Zhou, Q.H. Zhao, S.X. Liu, X.W. Guo, and L. Qi, A branch and price algorithm for crane assignment and scheduling in slab yard, IEEE Trans. Autom. Sci. Eng., (2020). DOI: https://doi.org/10.1109/TASE.2020.2996227

[97]

Wang XY, Liu W, Zheng BL, Chai TY. Design and development of ladle scheduling software for steelmaking and continuous casting. J. Syst. Simul., 2007, 19(13): 2913

[98]

Tan YY, Wei Z, Wang S, Zhou W, Liu SX. Optimization algorithm for ladle scheduling based on the VRPTW-AT model. J. Syst. Eng., 2013, 28(1): 94

[99]

Wei Z, Zhu T, He TZ, Liu SX. A fast heuristic algorithm for ladle scheduling based on vehicle routing problem with time windows model. ISIJ Int., 2014, 54(11): 2588.

[100]

Tan YY, Cheng TCE, Ji M. A multi-objective scatter search for the ladle scheduling problem. Int. J. Prod. Res., 2014, 52(24): 7513.

[101]

Liu W, Pang XF, Chai TY. Research on the dephosphorization ladle scheduling algorithm of steelmaking-refining-continuous casting process. Control Eng. China, 2019, 26(4): 790

[102]

Liu W, Pang XF, Yu SP, Li CX, Chai TY. Steelmaking-casting of molten steel by decarburization ladle matching. Math. Probl. Eng., 2018, 2018, 1

[103]

W. Song, A.J. Xu, K. Feng, S.P. Wu, and X.S. Zang, Batch planning model and genetic algorithm for steelmaking and continuous casting, [in] 2019 The China Automation Congress (CAC), Hangzhou, 2019, p. 257.

[104]

Zhang WX, Li TK. Batch plan optimization of steel integrated production with multiple processes. Comput. Integr. Manuf. Syst., 2013, 19(6): 1296

[105]

Zhang WX, Li TK. Integrated batch planning optimization based on particle swarm optimization and constraint satisfaction for steel production. Comput. Integr. Manuf. Syst., 2010, 16(4): 840

[106]

Xu Y, Liu F, Huang GH, Cheng GH. An optimization model under interval and fuzzy uncertainties for a by-product gas system of an iron and steel plant. Eng. Optim., 2019, 51(3): 447.

[107]

Pena JGC, de Oliveira Junior VB, Salles JLF. Optimal scheduling of a by-product gas supply system in the iron- and steel-making process under uncertainties. Comput. Chem. Eng., 2019, 125, 351.

[108]

Z.Q. Wei, X.Q. Zhai, Q. Zhang, G. Yang, T. Du, and J.Q. Wei, A MINLP model for multi-period optimization considering couple of gas-steam-electricity and time of use electricity price in steel plant, Appl. Therm. Eng., 168(2020), art. No. 114834.

[109]

Han ZY, Zhao J, Wang W. An optimized oxygen system scheduling with electricity cost consideration in steel industry. IEEE/CAA J. Autom. Sin., 2017, 4(2): 216.

[110]

Han ZY, Zhao J, Wang W, Liu Y. A two-stage method for predicting and scheduling energy in an oxygen/nitrogen system of the steel industry. Control Eng. Pract., 2016, 52, 35.

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