化工过程总体裕量与控制性能的权衡优化

doi:10.3969/j.issn.0438-1157.2014.04.021

化工学报 ›› 2014, Vol. 65 ›› Issue (4): 1303-1309.DOI: 10.3969/j.issn.0438-1157.2014.04.021

化工过程总体裕量与控制性能的权衡优化

许锋, 蒋慧蓉, 王锐, 罗雄麟

中国石油大学自动化研究所, 北京 102249

收稿日期:2013-07-18 修回日期:2013-09-22 出版日期:2014-04-05 发布日期:2013-11-27
通讯作者: 许锋（1976—），男，博士，讲师。
作者简介:许锋（1976—），男，博士，讲师。
基金资助:
国家自然科学基金项目（21006127）；国家重点基础研究发展计划项目（2012CB720500）；中国石油大学（北京）基金项目（KYJJ2012-05-28）。

Tradeoff between whole margin and control performance for chemical process

XU Feng, JIANG Huirong, WANG Rui, LUO Xionglin

Research Institute of Automation, China University of Petroleum, Beijing 102249, China

Received:2013-07-18 Revised:2013-09-22 Online:2014-04-05 Published:2013-11-27
Supported by:
supported by the National Natural Science Foundation of China (21006127), the National Basic Research Program of China (2012CB720500) and the Science Foundation of China University of Petroleum, Beijing (KYJJ2012-05-28).

摘要/Abstract

摘要： 化工过程的总体裕量可以用操作优化的经济效益进行评价，根据稳态优化和动态优化的经济效益可进一步划分为服务于操作控制的控制裕量和表征过程可实现经济效益的工艺裕量，二者都与化工过程的控制性能有关。针对具有一定裕量的化工过程进行多目标动态优化，优化目标分别为操作点的经济效益与动态过程中的控制性能指标，采用0-1变量描述控制结构，将控制结构和控制器参数也作为优化变量进行混合整数动态优化，采用Constrained NSGA-Ⅱ算法求解非劣解集，根据非劣解集分析总体工艺裕量、总体控制裕量与控制性能指标的关系。通过催化裂化装置的实例分析发现，对于具有一定裕量的化工过程，控制性能越高，所需的总体控制裕量越多，表征操作优化可实现经济效益的总体工艺裕量越少，只有通过对总体控制裕量和总体工艺裕量进行权衡，才能找到兼顾工艺要求和控制性能的工艺操作点和控制设计方案。

关键词: 总体裕量, 控制性能, 操作优化, 多目标优化

Abstract: The whole margin of a chemical process can be evaluated by the economic benefits of operation optimization, and based on steady-state optimization and dynamic optimization it can be further divided into control margin for dynamic operation and process margin for achievable economic benefits, both of which are relevant to control performance of the chemical process. In this paper, multi-objective dynamic optimization was implemented for a chemical process with design margin, whose optimization objectives were the achievable economic benefits of operating point and the control performance of dynamic process. The control structure described by 0-1 variables and control parameters were the optimization variables to form a mixed-integer dynamic optimization. Constrained NSGA-Ⅱ was used to solve the multi-objective dynamic optimization problem. Based on the Pareto optimal solution set, the relationship between control margin, process margin and control performance was analyzed. The case study of FCCU showed that, for a chemical process with design margin, when control performance was improved, the process would need more control margin but less process margin, which meant less achievable economic benefits from operation optimization. Through the trade-off between control margin and process margin, an optimum operating point and control scheme could be found to fulfill both process and control demand.

Key words: whole margin, control performance, operation optimization, multi-objective optimizationn

中图分类号:

TQ021.8

许锋, 蒋慧蓉, 王锐, 罗雄麟. 化工过程总体裕量与控制性能的权衡优化[J]. 化工学报, 2014, 65(4): 1303-1309.

XU Feng, JIANG Huirong, WANG Rui, LUO Xionglin. Tradeoff between whole margin and control performance for chemical process[J]. CIESC Journal, 2014, 65(4): 1303-1309.

参考文献

[1] Bansal V, Perkins J D, Pistikopoulos E N. A case study in simultaneous design and control using rigorous, mixed-integer dynamic optimization models[J]. Industrial & Engineering Chemistry Research, 2002, 41(4): 760-778
[2] Ulasa S, Diwekar U M. Integrating product and process design with optimal control: a case study of solvent recycling[J]. Chemical Engineering Science, 2006, 61(6): 2001-2009
[3] Miranda M, Reneaume J M, Meyer X, Meyer M, Szigeti F. Integrating process design and control: an application of optimal control to chemical processes[J]. Chemical Engineering and Processing, 2008, 47(11): 2004-2018
[4] Sakizlis V, Perkins J D, Pistikopoulos E N. Parametric controllers in simultaneous process and control design optimization[J]. Industrial & Engineering Chemistry Research, 2003, 42(20): 4545-4563
[5] Sanchez-Sanchez K B, Ricardez-Sandoval L A. Simultaneous design and control under uncertainty using model predictive control[J]. Industrial & Engineering Chemistry Research, 2013, 52(13): 4815- 4833
[6] Chawankul N, Budman1 H, Douglas P L. The integration of design and control: IMC control and robustness[J]. Computers and Chemical Engineering, 2005, 29(2): 261-271
[7] Bansal V, Sakizlis V, Ross R, Perkins J D, Pistikopoulos E N. New algorithms for mixed integer dynamic optimization[J]. Computers and Chemical Engineering, 2003, 27(5): 647-668
[8] Lu X J, Li H, Yuan X. PSO-based intelligent integration of design and control for one kind of curing process[J]. Journal of Process Control, 2010, 20(10): 1116-1125
[9] Asteasuain M, Brandolin A, Sarmoria C, Bandoni A. Simultaneous design and control of a semibatch styrene polymerization reactor[J]. Industrial & Engineering Chemistry Research, 2004, 43(17): 5233- 5247
[10] Asteasuain M, Sarmoria C, Brandolin A, Bandoni A. Integration of control aspects and uncertainty in the process design of polymerization reactors[J]. Chemical Engineering Journal, 2007, 131(1/2/3): 135-144
[11] Flores-Tlacuahuac A, Biegler L T. Integrated control and process design during optimal polymer grade transition operations[J]. Computers and Chemical Engineering, 2008, 32(11): 2823-2837
[12] Grosch R, Monnigmann M, Marquardt W. Integrated design and control for robust performance: application to an MSMPR crystallizer[J]. Journal of Process Control, 2008, 18(2): 173-188
[13] Ricardez-Sandoval L A, Douglas P L, Budman H M. A methodology for the simultaneous design and control of large-scale systems under process parameter uncertainty[J]. Computers and Chemical Engineering, 2011, 35(2): 307-318
[14] Ricardez-Sandoval L A. Optimal design and control of dynamic systems under uncertainty: a probabilistic approach[J]. Computers and Chemical Engineering, 2012, 43(10): 91-107
[15] Sánchez-Sánchez K, Ricardez-Sandoval L A. Simultaneous process synthesis and control design under uncertainty: a worst-case performance approach[J]. AIChE J., 2013, 59(7): 2497-2514
[16] Muñoz D A, Gerhard J, Marquardt W. A normal vector approach for integrated process and control design with uncertain model parameters and disturbances[J]. Computers and Chemical Engineering, 2012, 40(11): 202-212
[17] Sharifzadeh M, Thornhill N F. Integrated design and control using a dynamic inversely controlled process model[J]. Computers and Chemical Engineering, 2013, 48(10): 121-134
[18] Kuhlmann A, Bogle I. Controllability evaluation for nonminimum phase-processes with multiplicity[J]. AIChE J., 2001, 47(11): 2627- 2631
[19] Figueroa J L, Bahri P A, Bandoni J A, Romagnoli J A. Economic impact of disturbance and uncertain parameters in chemical processes—a dynamic back-off analysis[J]. Computers and Chemical Engineering, 1996, 20(4): 453-461
[20] Soliman M, Swartz C, Baker R. A mixed-integer formulation for back-off under constrained predictive control[J]. Computers and Chemical Engineering, 2008, 32(10): 2409-2419
[21] Li Zhihong(李志红), Hua Ben(华贲). Flexible synthesis of heat exchanger networks for multi-period operation[J]. Journal of Chemical Engineering of Chinese Universities(高校化学工程学报), 1999, 13(3): 252-258
[22] Xu Qiang(徐强), Chen Bingzhen(陈丙珍), He Xiaorong(何小荣). Flexible synthesis in chemical processing systems[J]. Journal of Tsinghua University(清华大学学报), 2001, 41(6): 44-47
[23] Karafyllis I, Kokossis A. On a new measure for the integration of process design and control: the disturbance resiliency index[J]. Chemical Engineering Science, 2002, 57(5): 873-886
[24] Xu Feng(许锋), Luo Xionglin(罗雄麟). Manipulation margin and control performance analysis of chemical process under advanced process control[J]. CIESC Journal(化工学报), 2012, 63(3): 881-886
[25] Xu Feng(许锋), Luo Xionglin(罗雄麟). Margin analysis and control design of FCCU regenerator based on dynamic optimization (Ⅰ): Mathematical description of dynamic optimization[J]. CIESC Journal(化工学报), 2009, 60(3): 675-682
[26] Xu Feng(许锋), Luo Xionglin(罗雄麟). Margin analysis and control design of FCCU regenerator based on dynamic optimization (Ⅱ): Solution and result analysis[J]. CIESC Journal(化工学报), 2009, 60(3): 683-690
[27] Xu Feng(许锋), Jiang Huirong(蒋慧蓉), Wang Rui(王锐), Luo Xionglin(罗雄麟). Margin evaluation and bottleneck analysis for FCCU[J]. CIESC Journal(化工学报), 2013, 64(6): 2131-2144
[28] Deb K, Pratap A, Agrawal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197

化工过程总体裕量与控制性能的权衡优化

Tradeoff between whole margin and control performance for chemical process

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	诸程瑛, 王振雷. 基于改进深度强化学习的乙烯裂解炉操作优化[J]. 化工学报, 2023, 74(8): 3429-3437.
[2]	张生安, 刘桂莲. 高效太阳能电解水制氢系统及其性能的多目标优化[J]. 化工学报, 2023, 74(3): 1260-1274.
[3]	林渠成, 廖祖维. 基于分解算法的功热交换网络多目标优化[J]. 化工学报, 2022, 73(11): 5047-5055.
[4]	柳旭, 许松林, 王燕飞. 原甲酸三甲酯-醋酸萃取精馏全局多目标优化[J]. 化工学报, 2022, 73(10): 4518-4526.
[5]	谢府命, 许锋, 罗雄麟. 工艺调度对乙炔加氢反应器优化运行策略的影响分析[J]. 化工学报, 2021, 72(5): 2718-2726.
[6]	魏彬, 周鑫, 王耀伟, 郭振莲, 陈小博, 刘熠斌, 杨朝合. 基于改进NSGA-Ⅱ算法的FCC分离系统多目标优化[J]. 化工学报, 2021, 72(5): 2735-2744.
[7]	赵杨, 熊伟丽. 基于多策略自适应差分进化算法的污水处理过程多目标优化控制[J]. 化工学报, 2021, 72(4): 2167-2177.
[8]	朱鹏飞, 郭磊磊, 尧兢, 杨福胜, 张早校, 吴震. 以生物质为燃料的SOFC和发动机热电联供系统：参数分析和性能优化[J]. 化工学报, 2021, 72(2): 1089-1099.
[9]	谢府命, 许锋, 罗雄麟. 慢时变化工过程裕量释放机制分析[J]. 化工学报, 2020, 71(S2): 216-224.
[10]	吴乐, 王竞, 王玉琪, 郑岚. 生物质油与蜡油在FCC装置共炼的多目标优化[J]. 化工学报, 2020, 71(5): 2182-2189.
[11]	杨路, 刘硕士, 罗小艳, 杨思宇, 钱宇. MTO烯烃分离过程的多目标操作优化[J]. 化工学报, 2020, 71(10): 4720-4732.
[12]	宋泽雨, 李国庆, 刘凌轩. 一种新的无模型自适应控制模型参数整定方法[J]. 化工学报, 2019, 70(9): 3430-3440.
[13]	叶昊天, 董以宁, 许爽, 邹雄, 李振花, 董宏光. 考虑本质安全的换热网络多目标优化[J]. 化工学报, 2019, 70(7): 2584-2593.
[14]	李鹏, 韩中合, 贾晓强, 梅中恺, 韩旭. 动态透平效率对有机朗肯循环系统性能的影响[J]. 化工学报, 2019, 70(4): 1532-1541.
[15]	朱坚, 杨博, 王永健, 唐晓婕, 李宏光. 一种新型的基于Levenshtein距离层次聚类的时序操作优化方法[J]. 化工学报, 2019, 70(2): 581-589.