基于梯度信息的实时优化与控制集成策略

doi:10.3785/j.issn.1008-973X.2019.05.004

浙江大学学报(工学版)

2019, Vol. 53

Issue (5): 843-851 DOI: 10.3785/j.issn.1008-973X.2019.05.004

计算机与控制工程

基于梯度信息的实时优化与控制集成策略

李啸晨(

),苏宏业*(

),邵寒山,谢磊

浙江大学智能系统与控制研究所，浙江杭州 310027

Gradient information-based strategy for real time optimization and control integration

Xiao-chen LI(

),Hong-ye SU*(

),Han-shan SHAO,Lei XIE

Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China

全文: PDF(1298 KB) HTML

摘要：

针对工业过程的最优操作问题，分析过程系统的层次模型，提出实时优化与控制集成的级联结构. 对于优化层采用基于梯度信息的稳态实时优化方法，通过在线采集过程测量值，估计过程的梯度信息，更新设定值. 不需要使用显式的过程模型，可以有效抑制模型失配对优化目标的影响. 利用最小二乘的思想求解梯度向量，降低计算成本，可应用于大规模工业过程的稳态实时优化. 提出非线性过程中被控变量的选取方法，利用非线性模型计算平均损失，优化效果具有全局性. 为了快速求解非线性规划问题，对某些条件进行合理假设，从而获得次优解，给出求解被控变量的解析方法，提高计算效率，同时将优化层与控制层联系起来. 通过对数值算例、蒸发过程和放热反应过程的研究，验证所提出方法的有效性.

关键词： 最优操作; 层次模型; 级联结构; 梯度信息; 最小二乘; 被控变量

Abstract:

The hierarchy model of process system was analyzed, and the cascade structure of real time optimization and control integration was proposed, aiming at the optimal operation problem for industrial process. Gradient information-based steady state real time optimization approach was installed in the optimization layer. The setpoint was updated by collecting measurements online and estimating the gradient information of process. The proposed approach can effectively suppress the impact of plant model mismatch on optimization objective, since it avoided using an explicit process model. A least square thought was introduced to compute the gradient vector. The proposed algorithm not only had a low computational burden, but also can be applied to the steady state real time optimization of large-scale industrial processes. A method for selecting controlled variables of nonlinear process was discussed. The proposed method minimized the global average loss based on the nonlinear model. Reasonable assumptions were made for some conditions, so that the suboptimal solution was obtained, in order to solve the nonlinear programming problem efficiently. The analytical solution of controlled variables was given and the calculation efficiency was improved as well as the optimization layer was connected with the control layer. A numerical example, evaporation process and exothermic reaction process were studied to illustrate the effectiveness of the proposed method.

Key words: optimal operation hierarchy model cascade structure gradient information least square controlled variables

收稿日期: 2018-03-30 出版日期: 2019-05-17

CLC:

TP 273

通讯作者: 苏宏业 E-mail: lixiaochen@zju.edu.cn;hysu@iipc.zju.edu.cn

作者简介: 李啸晨（1992—），男，博士生，从事工业过程先进控制与优化技术研究. orcid.org/0000-0003-0826-2868. E-mail： lixiaochen@zju.edu.cn

	服务
	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	作者相关文章
	李啸晨
	苏宏业
	邵寒山
	谢磊

引用本文:

李啸晨,苏宏业,邵寒山,谢磊. 基于梯度信息的实时优化与控制集成策略[J]. 浙江大学学报(工学版), 2019, 53(5): 843-851.

Xiao-chen LI,Hong-ye SU,Han-shan SHAO,Lei XIE. Gradient information-based strategy for real time optimization and control integration. Journal of ZheJiang University (Engineering Science), 2019, 53(5): 843-851.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.05.004 或 http://www.zjujournals.com/eng/CN/Y2019/V53/I5/843

图 1 一般过程系统的层级模型

图 2 实时优化与控制集成的级联结构

图 3 本研究所提出方法与文献[26]所提出方法的性能损失对比

图 4 蒸发过程反应原理图

表 1 蒸发过程中变量的物理含义和标称值

表 2 本研究方法与文献[26]方法的计算效果对比

图 5 连续搅拌釜式反应器反应原理图

表 3 CSTR模型中变量的标称值

图 6 扰动变量的变化轨迹

图 7 文献[28]方法得到的测量变量的变化轨迹

图 8 本研究方法得到的测量变量的变化轨迹

图 9 本研究方法与文献[28]方法得到的操纵变量对比

图 10 本研究方法与文献[29]方法得到的目标函数对比

1	VARMA V A, REKLAITIS G V, BLAU G E, et al Enterprise-wide modeling and optimization: an overview of emerging research challenges and opportunities[J]. Computers and Chemical Engineering, 2007, 31 (5/6): 692- 711
2	SHOKRI S, HAYATI R, MARVAST M A, et al Real time optimization as a tool for increasing petroleum refineries profits[J]. Petroleum and Coal, 2009, 51 (2): 110- 114
3	ZHANG Y, MONDER D, FORBES J F Real-time optimization under parametric uncertainty: a probability constrained approach[J]. Journal of Process Control, 2017, 12 (3): 373- 389
4	BRDYS M A, TATJEWSKI P. Iterative algorithms for multilayer optimizing control [M]. London: Imperial College Press, 2005: 128–144.
5	ROBERTS P D An algorithm for steady-state system optimization and parameter estimation[J]. International Journal of Systems Science, 1979, 10 (7): 719- 734 doi: 10.1080/00207727908941614
6	TATJEWSKI P Iterative optimizing set-point control: the basic principle redesigned[J]. IFAC Proceedings Volumes, 2002, 35 (1): 49- 54
7	GAO W, ENGELL S Iterative set-point optimization of batch chromatography[J]. Computers and Chemical Engineering, 2005, 29 (6): 1401- 1409 doi: 10.1016/j.compchemeng.2005.02.035
8	YE L, CAO Y, YUAN X, et al. Retrofit self-optimizing control of Tennessee Eastman process [C] // IEEE International Conference on Industrial Technology. Piscataway: IEEE, 2016: 866–871.
9	BINETTE J C, SRINIVASAN B, BONVIN D On the various local solutions to a two-input dynamic optimization problem[J]. Computers and Chemical Engineering, 2016, 95: 71- 74 doi: 10.1016/j.compchemeng.2016.09.003
10	SKOGESTAD S Plantwide control: the search for the self-optimizing control structure[J]. Journal of Process Control, 2000, 10 (5): 487- 507 doi: 10.1016/S0959-1524(00)00023-8
11	GE X, ZHU Y A necessary condition of optimality for uncertain optimal control problem[J]. Fuzzy Optimization and Decision Making, 2013, 12 (1): 41- 51 doi: 10.1007/s10700-012-9147-4
12	VERHEYLEWEGHEN A, J?SCHKE J Self-optimizing control of a two-stage refrigeration cycle[J]. IFAC PapersOnLine, 2016, 49 (7): 845- 850 doi: 10.1016/j.ifacol.2016.07.295
13	ARAúJO A C B D, GOVATSMARK M, SKOGESTAD S Application of plantwide control to the HDA process: I. steady-state optimization and self-optimizing control[J]. Control Engineering Practice, 2007, 15 (10): 1222- 1237 doi: 10.1016/j.conengprac.2006.10.014
14	SKOGESTAD S Selection of controlled variables and robust setpoints[J]. Industrial and Engineering Chemistry Research, 2002, 35 (1): 351- 356
15	HALVORSEN I J, SKOGESTAD S, MORUD J C, et al Optimal selection of controlled variables[J]. Industrial and Engineering Chemistry Research, 2003, 42 (14): 3273- 3284 doi: 10.1021/ie020833t
16	YE L, CAO Y, SKOGESTAD S Global self-optimizing control for uncertain constrained process systems[J]. IFAC PapersOnLine, 2017, 50 (1): 4672- 4677 doi: 10.1016/j.ifacol.2017.08.691
17	SRINIVASAN B, PRIMUS C J, BONVIN D, et al Run-to-run optimization via control of generalized constraints[J]. Control Engineering Practice, 2001, 9 (8): 911- 919 doi: 10.1016/S0967-0661(01)00051-X
18	SRINIVASAN B, BONVIN D, VISSER E, et al Dynamic optimization of batch processes: II. role of measurements in handling uncertainty[J]. Computers and Chemical Engineering, 2003, 27 (1): 27- 44 doi: 10.1016/S0098-1354(02)00117-5
19	ALSTAD V, SKOGESTAD S, HORIA E S Optimal measurement combinations as controlled variables[J]. Journal of Process Control, 2009, 19 (1): 138- 148 doi: 10.1016/j.jprocont.2008.01.002
20	XIN S, GUO Q, SUN H, et al Cyber-physical modeling and cyber-contingency assessment of hierarchical control systems[J]. IEEE Transactions on Smart Grid, 2017, 6 (5): 2375- 2385
21	SKOGESTAD S Control structure design for complete chemical plants[J]. Computers and Chemical Engineering, 2004, 28 (1/2): 219- 234
22	KEADTIPOD P, BANJERDPONGCHAI D. Design of supervisory cascade model predictive control for industrial boilers [C]// Automatic Control Conference. Piscataway: IEEE, 2017: 122–125.
23	DARBY M L, NIKOLAOU M, JONES J, et al RTO: an overview and assessment of current practice[J]. Journal of Process Control, 2011, 21 (6): 874- 884 doi: 10.1016/j.jprocont.2011.03.009
24	KNAPP R, HU Y. Numerical optimization [M]// Berlin: Springer, 2018: 29–76.
25	BEBIANO N. Applied and computational matrix analysis [M]. Berlin: Springer, 2017: 58.
26	ALSTAD V, SKOGESTAD S Null space method for selecting optimal measurement combinations as controlled variables[J]. Industrial and Engineering Chemistry Research, 2007, 46 (3): 846- 853 doi: 10.1021/ie060285+
27	KARIWALA V, CAO Y, JANARDHANAN S Local self-optimizing control with average loss minimization[J]. Industrial and Engineering Chemistry Research, 2008, 47 (4): 1150- 1158 doi: 10.1021/ie070897+
28	FRAN?OIS G, SRINIVASAN B, BONVIN D Use of measurements for enforcing the necessary conditions of optimality in the presence of constraints and uncertainty[J]. Journal of Process Control, 2005, 15 (6): 701- 712 doi: 10.1016/j.jprocont.2004.11.006

[1]	粟序明,方成刚,潘裕斌,吴伟伟,李亚萍,朱浪. 变照度下的视觉测量系统误差建模[J]. 浙江大学学报(工学版), 2020, 54(10): 1929-1935.
[2]	李竟成,吴越,胡斯登,石健将. 基于递推最小二乘法与核密度估计的同步整流Boost变换器回流功率抑制[J]. 浙江大学学报(工学版), 2020, 54(1): 169-177.
[3]	许爱东,黄文琦,明哲,陈伟亮,胡浩基,杨航. 基于级联网络和残差特征的人脸特征点定位[J]. 浙江大学学报(工学版), 2019, 53(12): 2365-2371.
[4]	周朝君,黄明辉,陆新江. 基于低维约束嵌入的分布参数系统建模[J]. 浙江大学学报(工学版), 2019, 53(11): 2154-2162.
[5]	孙颖,胡艳香,张雪英,段淑斐. 面向情感语音识别的情感维度PAD预测[J]. 浙江大学学报(工学版), 2019, 53(10): 2041-2048.
[6]	吴晨睿, 张树有, 何再兴. 基于梯度分类的复杂背景椭圆快速检测方法[J]. 浙江大学学报(工学版), 2018, 52(5): 943-950.
[7]	陈然, 连光耀, 秦子龙, 李宝晨, 张西山. 基于层次模型的外场可更换模块故障注入方法[J]. 浙江大学学报(工学版), 2017, 51(7): 1390-1396.
[8]	李滔, 王士同. 增量式0阶TSK模糊分类器及鲁棒改进[J]. 浙江大学学报(工学版), 2017, 51(10): 1901-1911.
[9]	谢罗峰, 徐慧宁, 黄沁元, 赵越, 殷国富. 应用双树复小波包和NCA-LSSVM检测磁瓦内部缺陷[J]. 浙江大学学报(工学版), 2017, 51(1): 184-191.
[10]	熊海贝,曹纪兴,张凤亮. 含加强层框筒结构位移监测方法[J]. 浙江大学学报(工学版), 2016, 50(9): 1752-1760.
[11]	张阿龙, 章明, 乔明杰, 朱伟东, 梅标. 基于视觉测量的环形轨底座位姿标定方法[J]. 浙江大学学报(工学版), 2016, 50(6): 1080-1087.
[12]	金鑫, 梁军. 基于动态PLS框架的多变量无静差预测控制[J]. 浙江大学学报(工学版), 2016, 50(4): 750-758.
[13]	吴尧锋,王文,卢科青,魏燕定,陈子辰. 边界聚类椭圆快速检测方法[J]. 浙江大学学报(工学版), 2016, 50(3): 405-411.
[14]	卢雨，胡安康，刘亚冲. 基于有限点法的自由面流动的数值模拟[J]. 浙江大学学报(工学版), 2016, 50(1): 55-62.
[15]	王国芳, 方舟, 李平. 基于批量递归最小二乘的自然Actor-Critic算法[J]. 浙江大学学报(工学版), 2015, 49(7): 1335-1342.

Viewed

Full text

Abstract

Cited

Shared

Discussed