Release mechanism analysis of design margin for slowly-time-varying chemical processes

doi:10.11949/0438-1157.20200440

Abstract

Abstract:

Slowly-time-varying characteristics are common in chemical processes, and the changes of slowly-time-varying parameters in an operating cycle gradually decrease the performance of chemical process. So, enough margins must be added for design variables during the phase of process design according to the possible worst-case influence of slowly-time-varying parameters. The design margins will be released gradually compensating the worse influence of slowly-time-varying parameters in an operating cycle. It can be called as a perfect operation that the operating point is on the boundary of process constraints when an operating cycle is ending. In this paper, the margin release mechanism of slowly-time-varying chemical processes is analyzed. Based on the universal dynamic model containing slowly-time-varying parameters, the full cycle operation optimization is solved by minimum principle of optimal control. It is found that the optimal margin release trajectory is related to the curve of slowly-time-varying parameter, ensuring that the optimal margin release is only dependent on the operating cycle. This mechanism is verified by the example of acetylene hydrogenation reactor. For slowly-time-varying chemical processes, the shorter the operating cycle is set, the faster the design margin is released, the higher temporary economic benefit is obtained; otherwise, the longer the operating cycle is set, the more integrated economic benefit is accomplished.

Key words: process systems, operation optimization, design margin, slowly-time-varying process, acetylene hydrogenation reactor

摘要：

化工过程普遍存在慢时变特性，在一个运行周期内慢时变参数的变化造成化工装置性能逐渐下降。为此，过程设计时需要按照慢时变参数可能的“最坏”影响对设计变量留出足够的设计裕量，在一个运行周期内通过操作逐渐释放，补偿慢时变参数的不利影响，且理想操作是保证到运行周期结束时化工装置性能恰好达到过程约束边界。本文对慢时变过程设计裕量的释放机制进行了分析，考虑含慢时变参数的全周期操作优化通用动态模型，通过最优控制的极小值原理求解该优化问题，建立了最优裕量释放轨迹和慢时变参数变化曲线之间的联系，从而证明最优裕量释放只与慢时变化工过程的运行周期有关。以乙炔加氢反应器为例验证了该裕量释放机制，对于慢时变化工过程，设定的运行周期越短，设计裕量释放越快，仅能获得较高的短期经济效益；反之，设定较长的运行周期，设计裕量缓慢释放，能获得更高的长期经济效益。

关键词: 过程系统, 操作优化, 设计裕量, 慢时变系统, 乙炔加氢反应器

CLC Number:

TQ 021.8

Fuming XIE, Feng XU, Xionglin LUO. Release mechanism analysis of design margin for slowly-time-varying chemical processes[J]. CIESC Journal, 2020, 71(S2): 216-224.

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.

Figures/Tables 5

Fig.1 Margin consumption of full cycle

Fig.2 Problem illustrated of optimal margin consumption characteristic

Fig.3 Structure of slow-time-varying system

Fig.4 Optimal process margin consumption

Fig.5 Optimal economic benefit curve of 120 and 180 d operation

References 31

1	涂飞, 青红英, 罗雄麟, 等. 乙炔加氢反应器的先进控制(Ⅰ): 动态机理模型的建立[J]. 化工自动化及仪表, 2003, 30(1): 20-24.
	Tu F, Qing H Y, Luo X L, et al. Advanced process control of acetylene hydrogenation reactor (Ⅰ): Construct dynamic model [J]. Control and Instruments in Chemical Industry, 2003, 30(1): 20-24.
2	Weiss G. Modeling and control of the acetylene converter [J]. Journal of Process Control, 1996, 6(1): 7-15.
3	Gobbo R, Soares R P, Lansarin M A, et al. Modeling, simulation, and optimization of a front-end system for acetylene hydrogenation reactors [J]. Brazilian Journal of Chemical Engineering, 2004, 21(4): 545-556.
4	Szukiewicz M, Kaczmarski K, Petrus R. Modeling of fixed-bed reactor: two models of industrial reactor for selective hydrogenation of acetylene [J]. Chemical Engineering Science, 1998, 53(1): 149-155.
5	罗雄麟, 刘建新, 许锋, 等. 乙炔加氢反应器二维非均相机理动态建模及分析[J]. 化工学报, 2008, 59(6): 1454-1461.
	Luo X L, Liu J X, Xu F, et al. Hydrogenation, two-dimensional dynamic modeling and analysis of acetylene hydrogenation reactor [J]. Journal of Chemical Industry and Engineering (China), 2008, 59(6): 1454-1461.
6	田亮, 蒋达, 钱锋. 乙炔加氢反应系统操作优化策略[J]. 化工学报, 2015, 66(1): 373-377.
	Tian L, Jiang D, Qian F. Reactor system switch strategy for acetylene hydrogenation process [J]. CIESC Journal, 2015, 66(1): 373-377.
7	田亮, 蒋达, 钱锋. 催化剂失活条件下的碳二加氢反应器模拟与优化[J]. 化工学报, 2012, 63(1): 185-192.
	Tian L, Jiang D, Qian F. Simulation and optimization of acetylene converter with decreasing catalyst activity [J]. CIESC Journal, 2012, 63(1): 185-192.
8	Luo X L, Xia C K, Sun L. Margin design, online optimisation, and control approach of a heat exchanger network with bypasses [J]. Comput. Chem. Eng., 2013, 53(11): 102-121.
9	谢府命, 许锋, 梁志珊, 等. 乙炔加氢反应器全周期操作优化[J]. 化工学报, 2018, 69(3): 1081-1091.
	Xie F M, Xu F, Liang Z S, et al. Full-cycle operation optimization of acetylene hydrogenation reactor [J]. CIESC Journal, 2018, 69(3): 1081-1091.
10	Narraway L T, Perkins J D. Selection of process control structure based on linear dynamic economics [J]. Ind. Eng. Chem. Res., 1993, 32(11): 2681-2692.
11	Narraway L T, Perkins J D, Barton G W. Interaction between process design and process control: economic analysis of process dynamics [J]. J. Process Contr., 1991, 1(5): 243-250.
12	Bahri P A, Romagnoli J A, Bandoni J A, et al. Back-off calculations in optimising control: a dynamic approach [J]. Comput. Chem. Eng., 1995, 19: 699-708.
13	Figueroa J L, Bahri P A, Bandoni J A, et al. Economic impact of disturbances and uncertain parameters in chemical processes - a dynamic back-off analysis [J]. Comput. Chem. Eng., 1996, 20(4): 453-461.
14	Kookos I K, Perkins J D. Control structure selection based on economics: generalization of the back-off methodology [J]. AIChE J., 2016, 62(9): 3056-3064.
15	Mehta S, Ricardez-Sandoval L A. Integration of design and control of dynamic systems under uncertainty: a new back-off approach [J]. Ind. Eng. Chem. Res., 2016, 55(2): 485-498.
16	Rafiei-Shishavan M, Mehta S, Ricardez-Sandoval L A. Simultaneous design and control under uncertainty: a back-off approach using power series expansions [J]. Comput. Chem. Eng., 2017, 99: 66-81.
17	Galvanin F, Barolo M, Bezzo F, et al. A backoff strategy for model-based experiment design under parametric uncertainty [J]. AIChE J., 2009, 56(8): 2088-2102.
18	Shi J, Biegler L T, Hamdan I, et al. Optimisation of grade transitions in polyethylene solution polymerization process under uncertainty [J]. Comput. Chem. Eng., 2016, 95: 260-279.
19	Koller R W, Ricardez-Sandoval L A, Biegler L T. Stochastic back-off algorithm for simultaneous design, control and scheduling of multi-product systems under uncertainty [J]. AIChE J., 2018, 64(13): 2379-2389.
20	Xu F, Luo X L, Wang R. Design margin and control performance analysis of a fluid catalytic cracking unit regenerator under model predictive control [J]. Industrial & Engineering Chemistry Research, 2014, 53(37): 14339-14350.
21	罗雄麟, 夏车奎, 孙琳. 有旁路换热网络全周期节能的动态优化控制实现方法[J]. 化工学报, 2013, 64(4): 1340-1350.
	Luo X L, Xia C K, Sun L. A dynamic optimization control approach of life cycle energy saving for heat exchanger network with bypasses [J]. CIESC Journal, 2013, 64(4): 1340-1350.
22	夏车奎, 罗雄麟, 孙琳. 基于全周期节能的有旁路换热网络裕量优化设计[J]. 化工学报, 2012, 63(5): 1449-1458.
	Xia C K, Luo X L, Sun L. Margin optimal design of heat exchanger network with bypasses based on life cycle energy saving [J]. CIESC Journal, 2012, 63(5): 1449-1458.
23	Xu F, Jiang H R, Wang R, et al. Influence of design margin on operation optimization and control performance of chemical processes [J]. Chinese Journal of Chemical Engineering, 2014, 22(1): 51-58.
24	Xie F M, Xu F, Liang Z S, et al. Full cycle dynamic optimisation maintaining the operation margin of acetylene hydrogenation fixed-bed reactor[J]. J. Taiwan Inst. Chem. E, 2020, 108: 29-42.
25	Pollard G P, Sargent R W H. Off line computation of optimum controls for a plate distillation column [J]. Automatica, 1970, 6(1): 59-76.
26	Morison K R, Sargent R W H. Optimization of multistage processes described by differential-algebraic equations [C]// Proceedings of the Fourth IIMAS. Berlin Heidelberg: Springer, 1984: 86-102.
27	Teo K L, Jennings L S, Lee H W J, et al. The control parameterization enhancing transform for constrained optimal control problems [J]. Journal of the Australian Mathematical Society, 1999, 40(3): 314-335.
28	Binder T, Cruse A, Villar C A C, et al. Dynamic optimization using a wavelet based adaptive control vector parameterization strategy [J]. Comput. Chem. Eng., 2000, 24(2): 1201-1207.
29	Chen T W C, Vassiliadis V S. Inequality path constraints in optimal control: a finite iteration ε-convergent scheme based on pointwise discretization [J]. J. Process Contr., 2005, 15(3): 353-362.
30	Hartwich A, Schlegel M, Würth L, et al. Adaptive control vector parameterization for nonlinear model-predictive control [J]. International Journal of Robust and Nonlinear Control, 2008, 18(8): 845-861.
31	Chen X, Du W L, Tianfield H, et al. Dynamic optimization of industrial processes with nonuniform discretization-based control vector parameterization [J]. IEEE T. Autom. Sci. Eng., 2014, 11(4): 1289-1299.

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