|
|
Economic-based performance assessment of hierarchical control systems |
FU Ruo-wei, SONG Zhi-huan |
State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou310027, China |
|
|
Abstract In order to ensure multivariable industrial processes operating in a safe and economic mode, a method for control performance assessment of hierarchical control systems was proposed. The three-layer structure of a hierarchical control system: direct control layer, constraint control layer and real-time optimization layer, was analyzed to formulate the control objective functions of three aspects: suppressing disturbances, keeping constraints and maximizing process profits, respectively. A control performance assessment benchmark called “best to worst performance range” was established to monitor the economic performance of industrial processes, and to evaluate how much potential would be improved. To avoid the degradation of control performance due to model-plant mismatch, a method to compute generalized object model through open loop model and regulatory parameters was presented. The reliability and efficacy of the proposed performance assessment technique is demonstrated on a case study on Shell heavy oil fractionator control problem.
|
Published: 08 September 2011
|
|
基于经济指标的分层递阶控制系统性能评估
为了解决复杂多变量过程工业平稳安全运行与经济效益最大化共同实现的问题,提出一种分层递阶控制结构的性能评估方法.通过分析分层递阶控制的直接控制、约束控制和实时优化三层垂直结构,构建出抑制扰动、不违反约束和经济效益最大化的控制目标函数.通过计算该控制系统的最优-最差性能区间,给出了一种面向经济性能的评估基准和新的评估指标,提出一种根据开环模型和调节器参数计算被控系统广义对象模型的方法,在此基础上实时监测和评估过程的生产经济效益,并分析存在的最大提升潜力,能有效减少模型与真实对象失配引起的控制性能下降.对Shell重油分馏塔模型的仿真结果表明了本文方法的可靠性和有效性.
|
|
[1] 柴天佑.生产制造全流程优化控制对控制与优化理论方法的挑战[J].自动化学报,2009,35(6): 641-649. CHAI Tianyou. Challenges of optimal control for plantwide production processes in terms of control and optimization theories [J]. Acta Automatica Sinica, 2009, 35(6): 641-649. [2] TATJEWSKI P. Advanced control of industrial processes: structures and algorithms [M]. London: Springer, 2007: 1-29. [3] SCATTOLINI R. Architectures for distributed and hierarchical Model Predictive ControlA review [J]. Journal of Process Control, 2009, 19(5): 723-731. [4] TATJEWSKI P. Advanced control and online process optimization in multilayer structures [J]. Annual Reviews in Control, 2008, 32(1): 71-85. [5] HARRIS T J. Assessment of control loop performance [J]. Canadian Journal of Chemical Engineering, 1989, 67(10): 856-861. [6] HARRIS T J, BOUDREAU F, MACGREGOR J F. Performance assessment of multivariable feedback controllers [J]. Automatica, 1996, 32(11): 1505-1518. [7] XU Fangwei, HUANG Biao. Performance assessment of model predictive control for variability and constraint tuning [J]. Industrial & Engineering Chemistry Research.,2007, 46(4): 1208-1219. [8] ZHAO Chao, ZHAO Yu, SU Hongye et al. Economic performance assessment of advanced process control with LQG benchmarking [J]. Journal of Process Control, 2009, 19(4): 557-569. [9] CAMPO P J, HOLCOMB T R, GELORMINO M S, et al. Decentralized control system design for a heavy oil fractionator: the Shell control problem [C]∥ The Second Shell Process Control Workshop: Solutions to the Shell Standard Control Problem. Stoneham,MA: Butterworths, 1990: 315-365. [10] PRETT D M, MORARI M. Shell process control workshop [M]. Stoneham: Butterworth Publishers, 1987: 350-360. [11] VLACHOS C, WILLIAMS D, GOMM J B. Solution to the Shell standard control problem using genetically tuned PID controllers [J]. Control Engineering Practice, 2002, 10(2): 151-163. [12] YING C M, JOSEPH B. Performance and stability analysis of LPMPC and QPMPC cascade control systems [J]. AIChE Journal, 1999, 45(7): 1521-1534. [13] KETTUNEN M, ZHANG P, JAMSAJOUNELA S L. An embedded fault detection, isolation and accommodation system in a model predictive controller for an industrial benchmark process [J]. Computers and Chemical Engineering, 2008, 32(12): 2966-2985 |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|