一类面向多级过程系统的实时进化方法

doi:10.3969/j.issn.0438-1157.2013.12.017

摘要/Abstract

摘要： 针对传统的实时优化执行前需要达到稳态、系统整体优化时间长、难以实时调整控制参数等问题提出一类面向多级过程系统的实时进化方法。把多级过程系统分成多个相互关联的子系统，当发生扰动时将系统等待稳态的过程分为若干个“拟稳态”。在每个拟稳态区间内，应用粒子群算法依次优化每一个子系统，其他各子系统设为静态，对子系统间的共享变量和内部变量进行协调，最终得到系统的最优解，把优化操作解送入控制器，连续改变系统设定值。给出了算法的详细步骤以两级串联反应器为对象进行了实例研究，验证了方法的有效性。

关键词: 多级过程系统, 实时进化, 粒子群优化, 协调

Abstract: Traditionally,real-time optimization needs to reach steady state before being implemented. While,regarding multivariable and high dimensional systems global optimization is always time-consuming and too long to adjust the control parameters in real time.To solve these problems,this paper proposes a real-time evolution approach towards multilevel process system,which divides a multilevel process system into several interconnected subsystems.When the system is disturbed,the process waiting for steady state is divided into several pseudo-steady states.In each steady state,a particle swarm optimization algorithm is adapted to optimize each sub-system in turn,while the rest of sub-systems are regarded as the static system.Then coordinate the shared and internal variables between the sub-systems,and put the finally optimal solution into the controller to change the system set-point continuously.This paper gives the detailed algorithm steps and the two stage series reactor is carried out to demonstrate the benefits of the proposed methodology.

Key words: multilevel process system, real-time evolutionary, particle swarm optimization, coordination

中图分类号:

TP202

李强, 李宏光, 黄静雯. 一类面向多级过程系统的实时进化方法[J]. 化工学报, 2013, 64(12): 4385-4389.

LI Qiang, LI Hongguang, HUANG Jingwen. A real-time evolution approach towards multilevel process systems[J]. CIESC Journal, 2013, 64(12): 4385-4389.

参考文献 20

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[1]	Cutler C R，Perry R T.Real-time optimization with multivariable control is required to maximize profits[J].Computers and Chemical Engineering，1983，7：663-667
[2]	Biegler L T，Zavala V M.Large-scale nonlinear programming using IPOPT：an integrating framework for enterprise-wide dynamic optimization[J].Computers and Chemical Engineering，2009,33:575-582
[3]	Darby M L，White D C.On-line optimization of complex process units[J].Chemical Engineering Progress，1988，84(10)：51-59
[4]	Sequeira S，Graells M，Puigjaner L.Real-time evolution for on-line optimization of continuous processes[J].Industrial Engineering and Chemical Research，2002，41(7)：1815-1825
[5]	Sequeira S，Graells M，Puigjaner L.On-line process optimization：parameter tuning for the real time evolution(RTE)approach[J].Computers and Chemical Engineering，2004，28：661-672
[6]	Guo J，Li H G. A PSO based evolutionary RTO approach[J].Automation Instrumentation,2011,32(8):17-21
[7]	Aris R.Studies in optimization(Ⅲ)：The optimum operating conditions in sequences of stirred tank reactors[J].Chemical Engineering Science，1960，13(2):75-81
[8]	Zasadzinski M，Damuach M，Keller J Y.A comparative study of decentralized data coordination algorithm//15th Annual Conference of IEEE Industrial Electronics Society[C].1989
[9]	Becerra V M，Roberts P D.Novel developments in process optimization using predictive control[J].Process Control，1998，8(2):117-125
[10]	Dantzig G B，Wolfe P. Decomposition principle for linear programs[J].Operations Research，1960，8:101-111
[11]	Shen Jingzhu(沈静珠).Process System Optimization(过程系统优化)[M].Beijing：Tsinghua University Press,1994
[12]	Wan Baiwu(万百五).Industrial Systems Optimization and Quality Control of Products(工业大系统优化与产品质量控制)[M].Beijing：Science Press,2003
[13]	Ramezani M H，Sadati N.Hierarchical optimal control of large-scale nonlinear chemical processes[J].ISA Transactions，2009,48:38-47
[14]	Moro?an P D，Bourdais R，Dumur D，Buisson J. A distributed MPC strategy based on Benders’ decomposition applied to multi-source multi-zone temperature regulation[J].Journal of Process Control，2011，21(5):729-737
[15]	Cheng R，Forbes J F，Yip W S. Price-driven coordination method for solving plant-wide MPC problems[J].Journal of Process Control，2007，17(5)：429-438
[16]	Cheng R Y，Forbes J F，Yip W S. Dantzig-Wolfe decomposition and plant-wide MPC coordination[J].Computers & Chemical Engineering，2008，32(7):1507-1522
[17]	Zhang Q，Mahfouf M.A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels[J].Engineering Applications of Artificial Intelligence，2010，23(5):660-675
[18]	Jamali A，Nariman-zadeh N，Darvizeh A，Masoumi A，Hamrang S.Multi-objective evolutionary optimization of polynomial neural networks for modeling and prediction of explosive cutting process[J].Engineering Applications of Artificial Intelligence,2009,22(4/5):676-687
[19]	Stewart B T，Venkat A N，Rawlings J B，Wright S J，Pannocchia G. Cooperative distributed model predictive control[J].Systems & Control Letters，2010，59(8)：460-469
[20]	Ali M M，Kaelo P.Improved particle swarm algorithms for global optimization[J].Applied Mathematics and Computation，2008，196：57