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炼钢–连铸区段3种典型工序界面技术研究进展

杨建平 张江山 刘青

杨建平, 张江山, 刘青. 炼钢–连铸区段3种典型工序界面技术研究进展[J]. 工程科学学报, 2020, 42(12): 1542-1556. doi: 10.13374/j.issn2095-9389.2020.05.08.001
引用本文: 杨建平, 张江山, 刘青. 炼钢–连铸区段3种典型工序界面技术研究进展[J]. 工程科学学报, 2020, 42(12): 1542-1556. doi: 10.13374/j.issn2095-9389.2020.05.08.001
YANG Jian-ping, ZHANG Jiang-shan, LIU Qing. Research progress on three kinds of classic process interface technologies in steelmaking-continuous casting section[J]. Chinese Journal of Engineering, 2020, 42(12): 1542-1556. doi: 10.13374/j.issn2095-9389.2020.05.08.001
Citation: YANG Jian-ping, ZHANG Jiang-shan, LIU Qing. Research progress on three kinds of classic process interface technologies in steelmaking-continuous casting section[J]. Chinese Journal of Engineering, 2020, 42(12): 1542-1556. doi: 10.13374/j.issn2095-9389.2020.05.08.001

炼钢–连铸区段3种典型工序界面技术研究进展

doi: 10.13374/j.issn2095-9389.2020.05.08.001
基金项目: 国家自然科学基金资助项目(50874014);中央高校基本科研业务费资助项目(FRF-BR-17-029A)
详细信息
    通讯作者:

    E-mail: qliu@ustb.edu.cn

  • 中图分类号: TF758

Research progress on three kinds of classic process interface technologies in steelmaking-continuous casting section

More Information
  • 摘要: 面对钢厂智能化发展的时代要求,炼钢–连铸区段工序界面技术受到越来越多冶金学者的关注,其不仅是解决工序关系集合协同–优化问题的重要手段,也影响着工序功能集合解析–优化和流程工序集合重构–优化的效果。本文对炼钢–连铸区段3种典型工序界面技术,即钢包运行控制、天车运行控制和生产运行模式优化的研究进展进行阐述,其中,钢包运行控制包括钢包热状态监测、钢包选配以及钢包调度,天车运行控制包括吊运任务的分配和同跨/异跨天车的协同调度,生产运行模式优化包括工序/设备产能、时间节奏与炉–机对应模式的匹配设计。此外,针对炼钢–连铸区段多工序协同运行的制约因素,指出工序界面技术协同的必要性,并对上述工序界面技术的协同机制与协同方案进行了阐述。
  • 图  1  炼钢−连铸区段钢包运行示意图

    Figure  1.  Schematic of ladle cycling in SCCS

    图  2  钢包包壳温度与热流量分布图[18]。(a)温度分布;(b)热流量分布

    Figure  2.  Distributions of temperature and heat flux on ladle shell[18]: (a) temperature distribution; (b) heat flux distribution

    图  3  钢包调度示意图

    Figure  3.  Schematic of ladle scheduling

    图  4  钢包运行管控系统技术架构图

    Figure  4.  Technical framework of the control system for ladles cycling

    图  5  国内某钢厂车间布置和天车配置

    Figure  5.  Workshop layout and crane configuration in steelmaking plant

    图  6  生产模式在钢厂系统中的地位及其逻辑关系[70]

    Figure  6.  Status of operation mode in steelmaking plant system[70]

    图  7  “层流”运行模式

    Figure  7.  “Laminar flow” operation mode

    图  8  模型应用前后炉–机对应关系[75]。(a)应用前;(b)应用后

    Figure  8.  Furnace‒caster coordinating scenario[75]: (a) before application; (b) after application

    图  9  钢包、天车运行控制与生产运行模式优化的协同机制

    Figure  9.  Collaboration mechanism among ladle cycling control, crane running control, and operation mode optimization

    表  1  钢包热状态影响因素的研究

    Table  1.   Study on influence factors on thermal state of ladles

    No.Authors (Year)Influencing factorsMethods (tools)/Model typesRefs.
    1Xia (2001)Initial temperature of ladle lining, heat dissipation rate of slag layer, and bottom blowing or notCFX software/Two dimensional
    heat transfer model
    [22-23]
    2Volkova (2003)Lining thickness and working layer materialsTwo dimensional heat transfer model[24]
    3Björn (2011)Lining thickness, distance from cover to ladle edge,
    and preheating time
    COMSOL software/Two dimensional
    heat transfer model
    [25]
    4Tripathi (2012)Thickness of slag layer, tapping temperature, ladle life,
    and initial temperature of ladle lining
    Software of Gambit and Fluent/Two
    dimensional heat transfer model
    [26]
    5Huang (2016)Repair time, preheating time, baking gas temperature,
    and cooling time
    Two dimensional heat transfer model[27]
    6Phanomchoeng (2016)Thermal resistance for different materials and thermal resistance for the same material with different temperaturesBounded Jacobian nonlinear observer/One dimensional heat transfer model[28]
    7Gong (2016)Online/offline preheating time, cooling time, and erosion degree of ladle liningAnsys software with ParaMesh/Two
    dimensional heat transfer model
    [29]
    8Wang (2017)Materials and structures of ladle liningFluent software/Three dimensional
    heat transfer model
    [30]
    9Yuan (2018)Ladle preheating methodsFluent software/Three dimensional
    heat transfer model
    [31]
    10Santos (2018)Working layer materials and insulation layer or notAbaqus software/Two dimensional
    heat transfer model
    [32]
    11Hou (2018)Thickness and thermal conductivity of ladle liningAbaqus software and Taguchi approaches/Two dimensional heat transfer model[33]
    下载: 导出CSV

    表  2  近年来关于天车调度的代表性研究工作

    Table  2.   Study on crane scheduling in recent years

    No.Authors (Year)Modeling methodsSolving methodsCharacteristicsRefs.
    1Xu (2007)Cellular automataHeuristic and genetic algorithmsVerify the feasibility of results through
    cellular automata
    [55]
    2Ma (2010)Multi-agentHeuristic algorithmImprove the reliability of results by frequent interaction among different agents and
    parallel computing strategy
    [56]
    3Liu (2011)Mathematical programmingHeuristic algorithmCoordinated scheduling between
    ladles and cranes
    [57]
    4Sun (2011)Mixed-timed Petri NetBranch-and-cutmethodTransform crane scheduling problem
    into the linear model
    [58]
    5Xie (2012)Mathematical programmingVariable neighborhood search algorithmOptimize algorithm parameters by
    artificial neural network
    [59]
    6Yu (2012)Mathematical programmingGenetic and heuristic algorithmsSolve the static and dynamic crane scheduling models respectively by genetic
    and heuristic algorithms
    [60]
    7Zhu (2013)Petri Net with UMLHeuristic algorithmMake up the deficiency of UML on formal expression by Petri Net[61]
    8Zheng (2013)Mathematical programmingImmune genetic algorithmProminent local search ability of the algorithm and strong global diversity of solutions[62]
    9Wang (2014)Mathematical programmingImproved Memetic algorithmDesign decoding operator based on task allocation and conflicts eliminating rules[63]
    10Jiang (2016)Mathematical programmingImproved genetic algorithmDesign encoding operator based on matrix form, and solve task priority and crane
    selection in parallel
    [64]
    11Gao (2017)Mathematical programmingImproved genetic algorithmMinimize the total transfer times of tasks and balance the task allocations among cranes[65]
    12Li (2019)Mathematical programmingHeuristic algorithmApply the predictive reactive
    rescheduling strategy
    [66]
    13Pang (2019)Mathematical programmingHeuristic algorithmApply analytical hierarchy process (AHP) fuzzy comprehensive evaluation to
    analyze crane scheduling
    [67]
    14Yang (2019)Plant simulationHeuristic algorithmCoordinated scheduling among ladles,
    cranes, and heat plans
    [68]
    下载: 导出CSV
  • [1] Zhong R Y, Xu X, Klotz E, et al. Intelligent manufacturing in the context of Industry 4.0: a review. Engineering, 2017, 3(5): 616 doi: 10.1016/J.ENG.2017.05.015
    [2] 殷瑞钰. 关于智能化钢厂的讨论—从物理系统一侧出发讨论钢厂智能化. 钢铁, 2017, 52(6):1

    Yin R Y. A discussion on “smart” steel plant—view from physical system side. Iron Steel, 2017, 52(6): 1
    [3] 殷瑞钰. 冶金流程工程学. 2版. 北京: 冶金工业出版社, 2011

    Yin R Y. Metallurgical Process Engineering. 2nd Ed. Beijing: Metallurgical Industry Press, 2011
    [4] 刘青, 田乃媛, 殷瑞钰. 炼钢厂的运行控制. 钢铁, 2003, 38(9):14 doi: 10.3321/j.issn:0449-749X.2003.09.004

    Liu Q, Tian N Y, Yin R Y. Running control for steelmaking workshop. Iron Steel, 2003, 38(9): 14 doi: 10.3321/j.issn:0449-749X.2003.09.004
    [5] Semura K, Matsuura H. Past development and future prospects of secondary refining technology. Tetsu-to-Hagane, 2014, 100(4): 456 doi: 10.2355/tetsutohagane.100.456
    [6] 王锋, 田乃媛, 贺东风, 等. 在线双工位LF有效利用系数的讨论. 炼钢, 2011, 27(1):71

    Wang F, Tian N Y, He D F, et al. Discussion on effective utilization coefficient of online two-operating position LF. Steelmaking, 2011, 27(1): 71
    [7] 王刚, 王彬, 王宝, 等. 基于“炉机对应”原则的炼钢–连铸调度模型. 北京科技大学学报, 2013, 35(8):1080

    Wang G, Wang B, Wang B, et al. Scheduling model for steelmaking-continuous casting process based on “furnace-caster matching” principle. J Univ Sci Technol Beijing, 2013, 35(8): 1080
    [8] 余相灼, 杨建平, 李想, 等. 基于“炉机对应”原则的连浇优化方案. 中国冶金, 2019, 29(4):22

    Yu X Z, Yang J P, Li X, et al. Optimized scheme of sequence casting based on "furnace-caster matching" principle. China Metall, 2019, 29(4): 22
    [9] Feng K, He D F, Xu A J, et al. End temperature prediction of molten steel in LF based on CBR-BBN. Steel Res Int, 2016, 87(1): 79 doi: 10.1002/srin.201400512
    [10] Bao Y P, Li X, Wang M. A novel method for endpoint temperature prediction in RH. Ironmaking Steelmaking, 2019, 46(4): 343 doi: 10.1080/03019233.2017.1392104
    [11] 付国庆, 刘青, 汪宙, 等. LF精炼终点钢水温度灰箱预报模型. 北京科技大学学报, 2013, 35(7):948

    Fu G Q, Liu Q, Wang Z, et al. Grey box model for predicting the LF end-point temperature of molten steel. J Univ Sci Technol Beijing, 2013, 35(7): 948
    [12] Fredman T P. Heat transfer in steelmaking ladle refractories and steel temperature: a literature review. Scand J Metall, 2000, 29(6): 232 doi: 10.1034/j.1600-0692.2000.d01-28.x
    [13] 吴晓东, 周丹, 郑建忠. 炼钢–连铸过程300 t钢包热状态测试研究. 炼钢, 2009, 25(4):49

    Wu X D, Zhou D, Zheng J Z. Research on thermal status of 300 t ladle in the process of steel-making and continues casting. Steelmaking, 2009, 25(4): 49
    [14] Mazzetti-Succi V. Insulation board investigation and trials in 300 tonne steel ladles at Arcelormittal Dofasco // Proceedings of the Unified International Technical Conference on Refractories. Victoria, 2014: 703
    [15] Zhou J A, Xie J B, Wang B, et al. New insight into investigation of thermal transfer of molten steel inside a ladle with vacuum shell. J Therm Anal Calorim, 2017, 128(1): 481 doi: 10.1007/s10973-016-5853-4
    [16] Fredman T P, Saxen H. Model for temperature profile estimation in the refractory of a metallurgical ladle. Metall Mater Trans B, 1998, 29(3): 651 doi: 10.1007/s11663-998-0100-4
    [17] Zabadal J R S, Vilhena M T M B, Leite S Q B. Heat transfer process simulation by finite differences for online control of ladle furnaces. Ironmaking Steelmaking, 2004, 31(3): 227 doi: 10.1179/030192304225012150
    [18] Lu B Y, Meng X N, Zhu M Y. Numerical analysis for the heat transfer behavior of steel ladle as the thermoelectric waste-heat source. Catal Today, 2018, 318: 180 doi: 10.1016/j.cattod.2017.10.038
    [19] Song G W, Tama B A, Park J, et al. Temperature control optimization in a steel-making continuous casting process using a multimodal deep learning approach. Steel Res Int, 2019, 90(12): 1900321 doi: 10.1002/srin.201900321
    [20] 张壮, 曹玲玲, 林文辉, 等. 基于IPSO-RELM转炉冶炼终点锰含量预测模型. 工程科学学报, 2019, 41(8):1052

    Zhang Z, Cao L L, Lin W H, et al. Improved prediction model for BOF end-point manganese content based on IPSO-RELM method. Chin J Eng, 2019, 41(8): 1052
    [21] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436 doi: 10.1038/nature14539
    [22] Xia J L, Ahokainen T. Transient flow and heat transfer in a steelmaking ladle during the holding period. Metall Mater Trans B, 2001, 32(4): 733 doi: 10.1007/s11663-001-0127-2
    [23] Xia J L, Ahokainen T. Thermal stratification in a steel ladle. Can Metall Q, 2001, 40(4): 479 doi: 10.1179/cmq.2001.40.4.479
    [24] Volkova O, Janke D. Modelling of temperature distribution in refractory ladle lining for steelmaking. ISIJ Int, 2003, 43(8): 1185 doi: 10.2355/isijinternational.43.1185
    [25] Björn G, Mårten G, Du S C. Thermal modelling of the ladle preheating process. Steel Res Int, 2011, 82(12): 1425 doi: 10.1002/srin.201100198
    [26] Tripathi A, Saha J K, Singh J B, et al. Numerical simulation of heat transfer phenomenon in steel making ladle. ISIJ Int, 2012, 52(9): 1591 doi: 10.2355/isijinternational.52.1591
    [27] Huang Y F, Xiong W, Fan Z Y. Effect of ladle thermal state on the LF endpoint temperature of molten steel // Proceedings of the 2015 4th International Conference on Sustainable Energy and Environmental Engineering. Shenzhen, 2016: 533
    [28] Phanomchoeng G, Chantranuwathana S, Charunyakorn P. On-line ladle lining temperature estimation by using bounded Jacobian nonlinear observer. J Iron Steel Res Int, 2016, 23(8): 792 doi: 10.1016/S1006-706X(16)30122-4
    [29] 龚华超, 徐安军, 袁飞, 等. 钢包热状态对钢水温降影响因素分析算法优化. 炼钢, 2016, 32(6):19

    Gong H C, Xu A J, Yuan F, et al. The analysis about effect of ladle thermal status on liquid steel temperature drop and algorithm optimization. Steelmaking, 2016, 32(6): 19
    [30] 王淼. 新型耐火材料改善钢包热特性的研究[学位论文]. 沈阳: 东北大学, 2017

    Wang M. Study on Improvement of Thermal Properties of Ladle with New Refractory Materials[Dissertation]. Shenyang: Northeastern University, 2017
    [31] 袁飞. 钢包蓄热式烘烤及周转过程温度模拟和优化研究[学位论文]. 北京: 北京科技大学, 2018

    Yuan F. Temperature Simulation and Optimization Research on the Processes of the Ladle Regenerative Preheating and Turnover[Dissertation]. Beijing: University of Science and Technology Beijing, 2018
    [32] Santos M F, Moreira M H, Campos M G G, et al. Enhanced numerical tool to evaluate steel ladle thermal losses. Ceram Int, 2018, 44(11): 12831 doi: 10.1016/j.ceramint.2018.04.092
    [33] Hou A D, Jin S L, Harmuth H, et al. A method for steel ladle lining optimization applying thermomechanical modeling and Taguchi approaches. JOM, 2018, 70(11): 2449 doi: 10.1007/s11837-018-3063-1
    [34] 王恩会, 陈俊红, 侯新梅. 钢包工作衬用耐火材料的研究现状及最新进展. 工程科学学报, 2019, 41(6):695

    Wang E H, Chen J H, Hou X M. Current research and latest developments on refractories used as ladle linings. Chin J Eng, 2019, 41(6): 695
    [35] Yan W, Wu G Y, Ma S B, et al. Energy efficient lightweight periclase-magnesium alumina spinel castables containing porous aggregates for the working lining of steel ladles. J Eur Ceram Soc, 2018, 38(12): 4276 doi: 10.1016/j.jeurceramsoc.2018.05.002
    [36] Gruber D, Harmuth H. Thermomechanical behavior of steel ladle linings and the influence of insulations. Steel Res Int, 2014, 85(4): 512 doi: 10.1002/srin.201300129
    [37] 刘青, 赵平, 吴晓东, 等. 钢包的运行控制. 北京科技大学学报, 2005, 27(2):235 doi: 10.3321/j.issn:1001-053X.2005.02.025

    Liu Q, Zhao P, Wu X D, et al. Control strategy for ladle running. J Univ Sci Technol Beijing, 2005, 27(2): 235 doi: 10.3321/j.issn:1001-053X.2005.02.025
    [38] Wang B, Wang B, Mu Y Q, et al. Optimization and control of ladle operation for special steel plants. Appl Mech Mater, 2014, 602-605: 899 doi: 10.4028/www.scientific.net/AMM.602-605.899
    [39] Huang B F, Tian N Y, Shi Z, et al. Steel ladle exchange models during steelmaking and continuous casting process. J Iron Steel Res Int, 2017, 24(6): 617 doi: 10.1016/S1006-706X(17)30093-6
    [40] 蔡峻, 汪红兵, 贺东风, 等. 炼钢厂钢包周转率的影响因素. 北京科技大学学报, 2013, 35(8):1072

    Cai J, Wang H B, He D F, et al. Affecting factors of the turnover rate of steel ladle in steelmaking plants. J Univ Sci Technol Beijing, 2013, 35(8): 1072
    [41] 蔡峻, 汪红兵, 徐安军, 等. 炼钢厂钢包红包出钢率的影响因素仿真. 钢铁研究学报, 2014, 26(1):27

    Cai J, Wang H B, Xu A J, et al. Simulation on influencing factors of rate of hot steel ladle in steelmaking plant. J Iron Steel Res, 2014, 26(1): 27
    [42] 赵天恒, 徐安军, 蔡峻. 炼钢厂钢包管理系统研究综述. 工业加热, 2015, 44(2):12 doi: 10.3969/j.issn.1002-1639.2015.02.004

    Zhao T H, Xu A J, Cai J. Survey on steel ladle management system. Ind Heat, 2015, 44(2): 12 doi: 10.3969/j.issn.1002-1639.2015.02.004
    [43] 刘建. 炼钢厂钢包跟踪与调度研究[学位论文]. 杭州: 杭州电子科技大学, 2009

    Liu J. Research on Ladle Tracking and Scheduling in Steelworks[Dissertation]. Hangzhou: Hangzhou Dianzi University, 2009
    [44] 刘炜, 柴天佑. 基于规则学习的炼钢–连铸钢包选配方法. 东北大学学报: 自然科学版, 2018, 39(11):1521

    Liu W, Chai T Y. Steelmaking continuous casting ladle matching method based on rule-learning. J Northeast Univ Nat Sci, 2018, 39(11): 1521
    [45] 刘炜, 庞新福, 柴天佑. 炼钢–精炼–连铸脱磷钢包调度算法研究. 控制工程, 2019, 26(4):790

    Liu W, Pang X F, Chai T Y. Research on the dephosphorization ladle scheduling algorithm of steelmaking‒refining‒continuous casting process. Control Eng China, 2019, 26(4): 790
    [46] 谭园园, 魏震, 王森, 等. 基于VRPTW-AT模型的钢包优化调度方法. 系统工程学报, 2013, 28(1):94 doi: 10.3969/j.issn.1000-5781.2013.01.013

    Tan Y Y, Wei Z, Wang S, et al. Optimization algorithm for ladle scheduling based on the VRPTW-AT model. J Syst Eng, 2013, 28(1): 94 doi: 10.3969/j.issn.1000-5781.2013.01.013
    [47] Tan Y Y, Cheng T C E, Ji M. A multi-objective scatter search for the ladle scheduling problem. Int J Prod Res, 2014, 52(24): 7513 doi: 10.1080/00207543.2014.939238
    [48] 肖阳. 基于UML与Plant Simulation的钢包周转调度研究[学位论文]. 重庆: 重庆大学, 2012

    Xiao Y. Research on Ladle Scheduling Based on UML and Plant Simulation in Steel Plant[Dissertation]. Chongqing: Chongqing University, 2012
    [49] 张涛. 涉及钢包周转的炼钢–连铸生产作业计划优化方法研究[学位论文]. 重庆: 重庆大学, 2009

    Zhang T. Study on Optimical Method of Steelmaking-Continuous Casting Production Planning Involved in Ladle Turn-around[Dissertation]. Chongqing: Chongqing University, 2009
    [50] 张媛. 考虑钢包分配的炼钢‒连铸调度问题研究[学位论文]. 沈阳: 沈阳工业大学, 2018

    Zhang Y. A Scatter Search Algorithm for A Steelmaking‒Continuous Casting Schedule Problem with Combination of Ladle Allocation[Dissertation]. Shenyang: Shenyang University of Technology, 2018
    [51] 蔡峻, 汪红兵, 徐安军, 等. 基于钢包跟踪的钢水温度在线补偿系统. 冶金自动化, 2013, 37(5):37

    Cai J, Wang H B, Xu A J, et al. Molten steel temperature on-line compensation system based on steel ladle tracking. Metall Ind Autom, 2013, 37(5): 37
    [52] Kuyama S, Tomiyama S. A crane guidance system with scheduling optimization technology in a steel slab yard. ISIJ Int, 2016, 56(5): 820 doi: 10.2355/isijinternational.ISIJINT-2015-466
    [53] Maschietto G N, Ouazene Y, Ravetti M G, et al. Crane scheduling problem with non-interference constraints in a steel coil distribution centre. Int J Prod Res, 2017, 55(6): 1607 doi: 10.1080/00207543.2016.1193249
    [54] 刘青, 田乃媛, 王英群, 等. 天车调度在优化钢厂物流管制中的重要作用. 北京科技大学学报, 1998, 20(1):36

    Liu Q, Tian N Y, Wang Y Q, et al. Important role of crane schedule in optimizing mass flow control of steel plant. J Univ Sci Technol Beijing, 1998, 20(1): 36
    [55] 徐乐. 基于元胞自动机的钢厂车间天车调度仿真方法研究[学位论文]. 重庆: 重庆大学, 2007

    Xu L. Study on Simulation Method of Crane Scheduling in Workshop of Steel-making Plant Based on Cellular Automata[Dissertation]. Chongqing: Chongqing University, 2007
    [56] Ma C B, Zhu D F, Wang H, et al. Simulation model for crane scheduling in workshop of steel-making plant based on MAS // 2010 International Conference on Computer Application and System Modeling. Taiyuan, 2010: V5-404
    [57] Liu W, Sun L L, Ding J L, et al. Study on ladle schedule of steel making process using heuristic scheduling algorithm. IFAC Proc Vol, 2011, 44(1): 8211 doi: 10.3182/20110828-6-IT-1002.02306
    [58] Sun L L, Liu W, Chai T Y, et al. Crane scheduling of steel-making and continuous casting process using the mixed-Timed Petri net modelling via CPLEX optimization. IFAC Proc Vol, 2011, 44(1): 9482 doi: 10.3182/20110828-6-IT-1002.00170
    [59] Xie X, Li Y P, Zhou H B, et al. Variable neighborhood search based multi-objective dynamic crane scheduling // Proceedings of 2012 International Conference on Measurement, Information and Control. Harbin, 2012: 457
    [60] 俞侠. 炼钢–精炼–连铸生产过程天车调度问题研究[学位论文]. 沈阳: 东北大学, 2012

    Yu X. Research on Crane Scheduling of Steelmaking‒Refining‒Continuous Casting Production Process[Dissertation]. Shenyang: Northeastern University, 2012
    [61] 朱道飞, 王华, 王建军, 等. 基于Petri网和UML的钢厂天车调度系统仿真. 昆明理工大学学报: 自然科学版, 2013, 38(3):5

    Zhu D F, Wang H, Wang J J, et al. Simulation of crane scheduling systems for steel plant based on Petri Nets and UML. J Kunming Univ Sci Technol Sci Technol, 2013, 38(3): 5
    [62] 郑忠, 周超, 陈开. 基于免疫遗传算法的车间天车调度仿真模型. 系统工程理论与实践, 2013, 33(1):223 doi: 10.3969/j.issn.1000-6788.2013.01.028

    Zheng Z, Zhou C, Chen K. Crane scheduling simulation model based on immune genetic algorithms. Syst Eng —Theory Pract, 2013, 33(1): 223 doi: 10.3969/j.issn.1000-6788.2013.01.028
    [63] 王旭, 刘士新, 王佳. 求解具有时空约束的天车调度问题Memetic算法. 东北大学学报: 自然科学版, 2014, 35(2):190

    Wang X, Liu S X, Wang J. Memetic algorithm for crane scheduling problem with spatial and temporal constraints. J Northeast Univ Nat Sci, 2014, 35(2): 190
    [64] 姜海远. 炼钢–连铸车间天车调度的仿真与优化[学位论文]. 青岛: 青岛理工大学, 2016

    Jiang H Y. Optimization and Simulation on Crane Scheduling of Steelmaking–Continuous Casting Production Process[Dissertation]. Qingdao: Qingdao University of Technology, 2016
    [65] 高小强, 李盼, 龙建宇, 等. 时空约束下连铸车间天车调度的多目标建模与求解. 系统工程理论与实践, 2017, 37(9):2373 doi: 10.12011/1000-6788(2017)09-2373-11

    Gao X Q, Li P, Long J Y, et al. Multi-objective modelling and solving for crane scheduling with spatio-temporal constraints in casting workshop. Syst Eng —Theory Pract, 2017, 37(9): 2373 doi: 10.12011/1000-6788(2017)09-2373-11
    [66] Li J, Xu A J, Zang X S. Simulation-based solution for a dynamic multi-crane-scheduling problem in a steelmaking shop. Int J Prod Res, 2019. https://doi.org/10.1080/00207543.2019.1687952
    [67] 庞新富, 刘炜, 李海波, 等. 炼钢–连铸生产过程运输设备天车调度方法. 信息与控制, 2019, 48(6):745

    Pang X F, Liu W, Li H B, et al. Crane scheduling method in steelmaking‒continuous casting process. Inform Control, 2019, 48(6): 745
    [68] Yang J P, Zhang J S, Guan M, et al. Fine description of multi-process operation behavior in steelmaking‒continuous casting process by a simulation model with crane non-collision constraint. Metals, 2019, 9(10): 1078 doi: 10.3390/met9101078
    [69] 刘青, 田乃媛, 殷瑞钰. 炼钢厂系统的运行原则与调控策略. 过程工程学报, 2003, 3(2):171 doi: 10.3321/j.issn:1009-606X.2003.02.015

    Liu Q, Tian N Y, Yin R Y. Operation principle and control strategy for steelmaking workshop system. Chin J Process Eng, 2003, 3(2): 171 doi: 10.3321/j.issn:1009-606X.2003.02.015
    [70] 刘青, 黄星武, 富平原. 炼钢厂系统生产模式优化. 北京科技大学学报, 2005, 27(6):736 doi: 10.3321/j.issn:1001-053X.2005.06.024

    Liu Q, Huang X W, Fu P Y. Production mode optimization of a steelmaking workshop system. J Univ Sci Technol Beijing, 2005, 27(6): 736 doi: 10.3321/j.issn:1001-053X.2005.06.024
    [71] 殷瑞钰. 冶金流程集成理论与方法. 北京: 冶金工业出版社, 2013

    Yin R Y. Theory and Methods of Metallurgical Process Integration. Beijing: Metallurgical Industry Press, 2013
    [72] 刘青, 尹佳, 田新中, 等. 转炉炼钢厂工序产能和品种钢铸机配置. 北京科技大学学报, 2007, 29(8):845 doi: 10.3321/j.issn:1001-053x.2007.08.021

    Liu Q, Yin J, Tian X Z, et al. Matching of productive capacity among working procedures and allocating of continuous casting machines for quality steel in a converter plant. J Univ Sci Technol Beijing, 2007, 29(8): 845 doi: 10.3321/j.issn:1001-053x.2007.08.021
    [73] 董金刚, 郑贻裕, 蒋晓放. 宝钢产品专线化生产实践//2015连铸装备的技术创新和精细化生产技术交流会. 西安, 2015: 30

    Dong J G, Zheng Y Y, Jiang X F. Production line practice of Baosteel // Proceedings of 2015 Continuous Casting Equipment Technical Innovation and Refined Production Technology Exchange Meeting. Xi’an, 2015: 30
    [74] 董金刚. 炼钢生产模式比较及应用//第12届中国钢铁年会. 北京, 2019: 1

    Dong J G. Comparison and application of steelmaking production patterns // Proceedings of the 12th CSM Steel Congress. Beijing, 2019: 1
    [75] 穆衍清, 尹佳, 谢飞鸣, 等. 特殊钢厂炉机匹配研究. 北京科技大学学报, 2013, 35(1):126

    Mu Y Q, Yin J, Xie F M, et al. Research on matching between furnaces and casters in special steel plants. J Univ Sci Technol Beijing, 2013, 35(1): 126
    [76] Gu Z X, Xu A J, Chang J B, et al. Optimization of the production organization pattern in Tangshan Iron and Steel Co., Ltd. J Iron Steel Res Int, 2014, 21(Suppl 1): 17
    [77] 芦永明, 田乃媛, 徐安军, 等. 棒线材生产流程炉机匹配模型的建立. 冶金自动化, 2009(增刊 2): 832

    Lu Y M, Tian N Y, Xu A J, et al. Establishment of the BOF-caster matching model for the process producing rods and wires. Metall Ind Autom, 2009(Suppl 2): 832
    [78] 芦永明, 王锋, 贺东风, 等. 全板带型钢厂炉机匹配模型的建立. 北京科技大学学报, 2009, 31(9):1189

    Lu Y M, Wang F, He D F, et al. Establishment of the BOF-caster matching model for the steelmaking plant producing plates and strips. J Univ Sci Technol Beijing, 2009, 31(9): 1189
    [79] 陈若冰, 齐欢. 炼钢–连铸流程的炉机匹配. 中南大学学报: 自然科学版, 2011, 42(6):1650

    Chen R B, Qi H. Matching between converters and continuous casters. J Cent South Univ Sci Technol, 2011, 42(6): 1650
    [80] Liu Q, Yin J, Wang B, et al. Research on rational collocation of LF in medium and small size BOF process // AISTech 2009-Proceedings of the Iron and Steel Technology Conference. St. Louis, 2009: 995
    [81] 郑忠, 徐兆俊, 高小强, 等. 钢厂生产运行模式的仿真研究//第11届中国钢铁年会. 北京, 2017: 1

    Zheng Z, Xu Z J, Gao X Q, et al. Simulation-based research on the production operation mode in steel plant // Proceedings of the 11th CSM Steel Congress. Beijing, 2017: 1
    [82] 袁帅鹏, 李铁克, 王柏琳. 多目标炼钢–连铸生产调度的改进带精英策略的快速非支配排序遗传算法. 计算机集成制造系统, 2019, 25(1):115

    Yuan S P, Li T K, Wang B L. Improved fast elitist non-dominated sorting genetic algorithm for multi-objective steelmaking‒continuous production scheduling. Comput Integr Manuf Syst, 2019, 25(1): 115
    [83] 刘倩, 杨建平, 王柏琳, 等. 基于“炉–机对应”的炼钢–连铸生产调度问题遗传优化模型. 工程科学学报, 2020, 42(5):645

    Liu Q, Yang J P, Wang B L, et al. Genetic optimization model of steelmaking‒continuous casting production scheduling based on the “furnace‒caster coordinating” strategy. Chin J Eng, 2020, 42(5): 645
    [84] 刘青, 刘倩, 杨建平, 等. 炼钢‒连铸生产调度的研究进展. 工程科学学报, 2020, 42(2):144

    Liu Q, Liu Q, Yang J P, et al. Progress of research on steelmaking–continuous casting production scheduling. Chin J Eng, 2020, 42(2): 144
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  • 收稿日期:  2020-05-08
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