2.793

2018影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

复杂工业过程运行优化与反馈控制

柴天佑

柴天佑. 复杂工业过程运行优化与反馈控制. 自动化学报, 2013, 39(11): 1744-1757. doi: 10.3724/SP.J.1004.2013.01744
引用本文: 柴天佑. 复杂工业过程运行优化与反馈控制. 自动化学报, 2013, 39(11): 1744-1757. doi: 10.3724/SP.J.1004.2013.01744
CHAI Tian-You. Operational Optimization and Feedback Control for Complex Industrial Processes. ACTA AUTOMATICA SINICA, 2013, 39(11): 1744-1757. doi: 10.3724/SP.J.1004.2013.01744
Citation: CHAI Tian-You. Operational Optimization and Feedback Control for Complex Industrial Processes. ACTA AUTOMATICA SINICA, 2013, 39(11): 1744-1757. doi: 10.3724/SP.J.1004.2013.01744

复杂工业过程运行优化与反馈控制


DOI: 10.3724/SP.J.1004.2013.01744
详细信息
    作者简介:

    柴天佑 中国工程院院士, 东北大学教授, IEEE Fellow, IFAC Fellow. 1985 年获得东北大学博士学位. 主要研究方向为自适应控制, 智能解耦控制, 流程工业综台自动化理论、方法与技术.

  • 基金项目:

    国家重点基础研究发展计划(973计划)(2009CB320601)资助

Operational Optimization and Feedback Control for Complex Industrial Processes

More Information
  • Fund Project:

    Supported by National Basic Research Program of China (973 Program) (2009CB320601)

  • 摘要: 过程控制不仅使被控对象的输出尽可能好地跟踪控制器设定值, 而且要对整个工业装置的运行进行控制, 使反映产品在该装置加工过程中质量、效率与消耗等指标, 即运行指标在目标值范围内, 尽可能提高质量与效率指标, 尽可能降低消耗指标, 即实现工业过程运行优化控制. 本文在综述了已有的运行优化与控制方法的基础上, 重点介绍了复杂工业过程的数据驱动的混合智能运行优化控制和运行控制半实物仿真系统, 并以赤铁矿磨矿过程为应用研究案例, 仿真实验和工业应用结果表明所提方法的有效性, 并指出了复杂工业过程运行优化控制研究需要关注的问题.
  • [1] Engell S. Feedback control for optimal process operation. Journal of Process Control, 2007, 17(3): 203-219
    [2] Darby M L, Nikolaou M, Jones J, Nicholson D. RTO: an overview and assessment of current practice. Journal of Process Control, 2011, 21(6): 874-884
    [3] Scattolini R. Architectures for distributed and hierarchical model predictive control——a review. Journal of Process Control, 2009, 19(5): 723-731
    [4] Mercangöz M, Doyle F J III. Real-time optimization of the pulp mill benchmark problem. Computers and Chemical Engineering, 2008, 32(4-5): 789-804
    [5] Hasikos J, Sarimveis H, Zervas P L, Markatos N C. Operational optimization and real-time control of fuel-cell systems. Journal of Power Sources, 2009, 193(1): 258-268
    [6] Jäschke J, Skogestad S. NCO tracking and self-optimizing control in the context of real-time optimization. Journal of Process Control, 2011, 21(10): 1407-1416
    [7] Tatjewski P. Advanced control and on-line process optimization in multilayer structures. Annual Reviews in Control, 2008, 32(1): 71-85
    [8] Adetola V, Guay M. Integration of real-time optimization and model predictive control. Journal of Process Control, 2010, 20(2): 125-133
    [9] Alvarez L A, Odloak D. Robust integration of real time optimization with linear model predictive control. Computers and Chemical Engineering, 2010, 34(12): 1937-1944
    [10] Wu M, Cao W H, He C Y, She J H. Integrated intelligent control of gas mixing-and-pressurization process. IEEE Transactions on Control Systems Technology, 2009, 17(1): 68-77
    [11] Bischoff K B, Denn M M, Seinfeld J H, Stephanopoulos G, Chakraborty A, Peppas N, Ying J, Wei J. Advances in Chemical Engineering. vol.26. San Diego: Academic Press, 2001
    [12] Skogestad S. Plantwide control: the search for the self-optimizing control structure. Journal of Process Control, 2000, 10(5): 487-507
    [13] Findeisen W, Bailey F N, Bryds M, Malinawski K, Tatjewski P, Wozniak A. Control and Coordination in Hierarchical Systems. New York: John Wiley, 1980
    [14] Marlin T E, Hrymak A N. Real-time operations optimization of continuous processes. In: Proceedings of the 5th International Conference on Chemical Process Control. New York: American Institute of Chemical Engineers, 1997. 156-164
    [15] Nath R, Alzein Z. On-line dynamic optimization of olefins plants. Computers & Chemical Engineering, 2000, 24(2-7): 533-538
    [16] Hartmann J C M. Distinguish between scheduling and planning models. Hydrocarbon Processing, 1998, 77: 93-100
    [17] Bartusiak R D. NLMPC: a platform for optimal control of feed-or product-flexible manufacturing. Assessment and Future Directions of Nonlinear Model Predictive Control Lecture Notes in Control and Information Sciences. Berlin, Heidelberg: Springer, 2007, 358: 367-381
    [18] Qin S J, Badgewell T A. A survey of industrial model predictive control technology. Control Engineering Practice, 2003, 11(7): 733-764
    [19] Li H X, Guan S P. Hybrid intelligent control strategy. Supervising a DCS-controlled batch process. IEEE Control Systems Magazine, 2001, 21(3): 36-48
    [20] Wang Z J, Wu Q D, Chai T Y. Optimal-setting control for complicated industrial processes and its application study. Control Engineering Practice, 2004, 12(1): 65-74
    [21] Yang C H, Gui W H, Kong L S, Wang Y L. A two-stage intelligent optimization system for the raw slurry preparing process of alumina sintering production. Engineering Applications of Artificial Intelligence, 2009, 22(4-5): 786-795
    [22] Wu M, Xu C H, She J H, Yokoyama R. Intelligent integrated optimization and control system for lead-zinc sintering process. Control Engineering Practice, 2009, 17(2): 280-290
    [23] Zhou P, Chai T Y, Sun J. Intelligence-based supervisory control for optimizing the operation of a DCS-controlled grinding system. IEEE Transactions on Control Systems Technology, 2013, 21(1): 162-175
    [24] Chai Tian-You, Ding Jin-Liang, Wang Hong, Su Chun-Yi. Hybrid intelligent optimal control method for operation of complex industrial processes. Acta Automatica Sinica, 2008, 34(5): 505-515 (柴天佑, 丁进良, 王宏, 苏春翌. 复杂工业过程运行的混合智能优化控制方法. 自动化学报, 2008, 34(5): 505-515)
    [25] Chai T Y, Liu J X, Ding J L, Su C Y. Hybrid intelligent optimising control for high-intensity magnetic separating process of hematite ore. Measurement and Control, 2007, 40(6): 171-175
    [26] Chai T Y, Ding J L, Wu F H. Hybrid intelligent control for optimal operation of shaft furnace roasting process. Control Engineering Practice, 2011, 19(3): 264-275
    [27] Yan A J, Chai T Y, Yue H. Multivariable intelligent optimizing control approach for shaft furnace roasting process. Acta Automatica Sinica, 2006, 32(4): 636-640
    [28] Wu F H, Chai T Y. Soft sensing method for magnetic tube recovery ratio via fuzzy systems and neural networks. Neurocomputing, 2010, 73(13-15): 2489-2497
    [29] Zhou P, Chai T Y, Wang H. Intelligent optimal-setting control for grinding circuits of mineral processing process. IEEE Transactions on Automation Science and Engineering, 2009, 6(4): 730-743
    [30] Chai T Y, Wu F H, Ding J L, Su C Y. Intelligent work-situation fault diagnosis and fault-tolerant system for the shaft-furnace roasting process. Proceedings of the Institution of Mechanical Engineers Part I: Journal of Systems and Control Engineering, 2007, 221(16): 843-855
    [31] Ding J L, Chai T Y, Wang H. Offline modeling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization. IEEE Transactions on Neural Networks, 2011, 22(3): 408-419
    [32] Ding J L, Chai T Y, Wang H, Chen X K. Knowledge-based global operation of mineral processing under uncertainty. IEEE Transactions on Industry Informatics, 2012, 8(4): 849-859
    [33] Chai T Y, Zhang Y J, Wang H, Su C Y, Sun J. Data-based virtual unmodeled dynamics driven multivariable nonlinear adaptive switching control. IEEE Transactions on Neural Networks, 2011, 22(12): 2154-2172
    [34] Liu Q, Chai T Y, Wang H, Qin S Z J. Data-based hybrid tension estimation and fault diagnosis of cold rolling continuous annealing processes. IEEE Transactions on Neural Networks, 2011, 22(12): 2284-2295
    [35] Yu G, Chai T Y, Luo X C. Multiobjective production planning optimization using hybrid evolutionary algorithms for mineral processing. IEEE Transactions on Evolutionary Computation, 2011, 15(4): 487-514
    [36] Chai T Y, Zhao L, Qiu J B, Liu F Z, Fan J L. Integrated network-based model predictive control for setpoints compensation in industrial processes. IEEE Transactions on Industrial Informatics, 2013, 9(1): 417-426
  • [1] 李玉帅, 李天义, 高炜, 高文忠. 基于异步动态事件触发通信策略的综合能源系统分布式协同优化运行方法[J]. 自动化学报, 2020, 46(9): 1831-1843. doi: 10.16383/j.aas.c200172
    [2] 陈晓露, 王瑞璇, 王晶, 周靖林. 基于混合型判别分析的工业过程监控及故障诊断[J]. 自动化学报, 2020, 46(8): 1600-1614. doi: 10.16383/j.aas.c180089
    [3] 褚菲, 赵旭, 代伟, 马小平, 王福利. 数据驱动的最优运行状态鲁棒评价方法及应用[J]. 自动化学报, 2020, 46(3): 439-450. doi: 10.16383/j.aas.c180018
    [4] 邹筱瑜, 王福利, 常玉清, 郑伟. 基于两层分块GMM-PRS的流程工业过程运行状态评价[J]. 自动化学报, 2019, 45(11): 2071-2081. doi: 10.16383/j.aas.2018.c170412
    [5] 原豪男, 郭戈. 交通信息物理系统中的车辆协同运行优化调度[J]. 自动化学报, 2019, 45(1): 143-152. doi: 10.16383/j.aas.c180354
    [6] 姜艺, 范家璐, 贾瑶, 柴天佑. 数据驱动的浮选过程运行反馈解耦控制方法[J]. 自动化学报, 2019, 45(4): 759-770. doi: 10.16383/j.aas.2018.c170552
    [7] 代伟, 陆文捷, 付俊, 马小平. 工业过程多速率分层运行优化控制[J]. 自动化学报, 2019, 45(10): 1946-1959. doi: 10.16383/j.aas.2018.c180300
    [8] 富月, 杜琼. 一类工业运行过程多模型自适应控制方法[J]. 自动化学报, 2018, 44(7): 1250-1259. doi: 10.16383/j.aas.2017.c160763
    [9] 丁进良, 杨翠娥, 陈远东, 柴天佑. 复杂工业过程智能优化决策系统的现状与展望[J]. 自动化学报, 2018, 44(11): 1931-1943. doi: 10.16383/j.aas.2018.c180550
    [10] 刘强, 卓洁, 郎自强, 秦泗钊. 数据驱动的工业过程运行监控与自优化研究展望[J]. 自动化学报, 2018, 44(11): 1944-1956. doi: 10.16383/j.aas.2018.c180207
    [11] 杨亚茹, 李少远. 切换非线性系统全局优化运行的经济预测控制[J]. 自动化学报, 2017, 43(6): 1017-1027. doi: 10.16383/j.aas.2017.c170112
    [12] 张亚军, 柴天佑, 杨杰. 一类非线性离散时间动态系统的交替辨识算法及应用[J]. 自动化学报, 2017, 43(1): 101-113. doi: 10.16383/j.aas.2017.c150759
    [13] 范家璐, 姜艺, 柴天佑. 无线网络环境下工业过程运行反馈控制方法[J]. 自动化学报, 2016, 42(8): 1166-1174. doi: 10.16383/j.aas.2016.c150771
    [14] 范家璐, 张也维, 柴天佑. 一类工业过程运行反馈优化控制方法[J]. 自动化学报, 2015, 41(10): 1754-1761. doi: 10.16383/j.aas.2015.c150061
    [15] 刘德荣. 复杂工业过程的先进控制[J]. 自动化学报, 2014, 40(9): 1841-1482.
    [16] 代伟, 柴天佑. 数据驱动的复杂磨矿过程运行优化控制方法[J]. 自动化学报, 2014, 40(9): 2005-2014. doi: 10.3724/SP.J.1004.2014.02005
    [17] 柴天佑, 李少远, 王宏. 网络信息模式下复杂工业过程建模与控制[J]. 自动化学报, 2013, 39(5): 469-470. doi: 10.3724/SP.J.1004.2013.00469
    [18] 周平, 柴天佑, 陈通文. 工业过程运行的解耦内模控制方法[J]. 自动化学报, 2009, 35(10): 1362-1368. doi: 10.3724/SP.J.1004.2009.01362
    [19] 柴天佑, 丁进良, 王宏, 苏春翌. 复杂工业过程运行的混合智能优化控制方法[J]. 自动化学报, 2008, 34(5): 505-515. doi: 10.3724/SP.J.1004.2008.00505
    [20] 何敏, 吕勇哉. 基于混合知识表达模型的启发式优化控制策略及其应用[J]. 自动化学报, 1992, 18(3): 371-375.
  • 加载中
计量
  • 文章访问数:  2677
  • HTML全文浏览量:  388
  • PDF下载量:  3390
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-07-19
  • 修回日期:  2013-08-28
  • 刊出日期:  2013-11-20

复杂工业过程运行优化与反馈控制

doi: 10.3724/SP.J.1004.2013.01744
    基金项目:

    国家重点基础研究发展计划(973计划)(2009CB320601)资助

    作者简介:

    柴天佑 中国工程院院士, 东北大学教授, IEEE Fellow, IFAC Fellow. 1985 年获得东北大学博士学位. 主要研究方向为自适应控制, 智能解耦控制, 流程工业综台自动化理论、方法与技术.

摘要: 过程控制不仅使被控对象的输出尽可能好地跟踪控制器设定值, 而且要对整个工业装置的运行进行控制, 使反映产品在该装置加工过程中质量、效率与消耗等指标, 即运行指标在目标值范围内, 尽可能提高质量与效率指标, 尽可能降低消耗指标, 即实现工业过程运行优化控制. 本文在综述了已有的运行优化与控制方法的基础上, 重点介绍了复杂工业过程的数据驱动的混合智能运行优化控制和运行控制半实物仿真系统, 并以赤铁矿磨矿过程为应用研究案例, 仿真实验和工业应用结果表明所提方法的有效性, 并指出了复杂工业过程运行优化控制研究需要关注的问题.

English Abstract

柴天佑. 复杂工业过程运行优化与反馈控制. 自动化学报, 2013, 39(11): 1744-1757. doi: 10.3724/SP.J.1004.2013.01744
引用本文: 柴天佑. 复杂工业过程运行优化与反馈控制. 自动化学报, 2013, 39(11): 1744-1757. doi: 10.3724/SP.J.1004.2013.01744
CHAI Tian-You. Operational Optimization and Feedback Control for Complex Industrial Processes. ACTA AUTOMATICA SINICA, 2013, 39(11): 1744-1757. doi: 10.3724/SP.J.1004.2013.01744
Citation: CHAI Tian-You. Operational Optimization and Feedback Control for Complex Industrial Processes. ACTA AUTOMATICA SINICA, 2013, 39(11): 1744-1757. doi: 10.3724/SP.J.1004.2013.01744
参考文献 (36)

目录

    /

    返回文章
    返回