复杂工业过程运行优化与反馈控制

柴天佑

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

柴天佑

1.
东北大学流程工业综合自动化国家重点实验室沈阳 110819

基金项目:

国家重点基础研究发展计划(973计划)(2009CB320601)资助

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.

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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

Operational Optimization and Feedback Control for Complex Industrial Processes

CHAI Tian-You

1.
State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University), Shenyang 110819

Funds:

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

摘要

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.
- 复杂工业过程 /
- 运行优化 /
- 运行反馈控制 /
- 运行指标预报 /
- 半实物仿真系统
Abstract: Process control should ensure not only controlled variables to follow their setpoint values, but also the whole process plant to meet operational requirements optimally (e.g., quality, efficiency and consumptions). Process control should also enable operational indices for quality to and efficiency to be improved continuously, while keeping the indices related to consumptions at the lowest possible level. This paper starts with a survey on the existing operational optimization and control methodologies and then presents a data-driven hybrid intelligent optimal operational control for complex industrial processes where process operational models are difficult to obtain. Applications via a hybrid simulation system and an industrial grinding process for hematite ore mineral processing are presented to demonstrate the effectiveness of the proposed operational control method. Issues for future research on the optimal operational control for complex industrial processes are outlined before concluding the paper.
- Complex industrial process /
- operational optimization /
- operational feedback control /
- operational indices prediction /
- hybrid simulation system

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参考文献(36)

[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