Judgement of process transition control strategies for large-range conditions change of chemical processes

doi:10.11949/j.issn.0438-1157.20181306

Abstract

Abstract:

When the chemical process changes in a wide range of working conditions, the complex migration control strategy will bring certain operational uncertainty, so it is necessary to make a selection decision on the control strategy. Intuitively, the simplest approach for the judgment of control strategies is mapping the relationship between the production index and the factors which cause the process transition. The fitting error of the mapping relationship is obvious for complicated chemical processes. The intermediate variables are introduced for decreasing fitting error, and intermediate variables based judgment method is proposed for complicated chemical processes. In details, there are three basic principles for building the intermediate variables. Firstly, the intermediate variables consist of measurable process variables; secondly, the intermediate variables are insensitive to the type of process transition factors; thirdly, the intermediate variables are sensitive to the production index of the process system. Furthermore, based on the simulation of a commercial ethylene column, the introduction of the intermediate variables is beneficial to decrease the estimation error of production index. The usability of the intermediate variables based judgment method is verified for the control of large-range conditions change.

Key words: process system, chemical processes, process control, dynamic optimization, process transition

摘要：

化工过程出现大范围的工况变化时，复杂的迁移控制策略会带来一定的操作不确定性，因此需要对控制策略进行选择判定。考虑到直接估计工况变化过程生产指标变化量带来的判定误差，引入了中间变量，提出了基于中间变量的控制策略选择判定方法，并分析给出了构建中间变量的基本准则。进而通过对某实际乙烯精馏塔工况变化过程的仿真研究，说明了中间变量的引入能够很大程度地降低对生产指标的估计误差，验证了基于中间变量的控制策略选择判定方法的可用性。

关键词: 过程系统, 化工过程, 过程控制, 动态优化, 工况迁移

CLC Number:

TQ 021.8

Dong HUANG, Xionglin LUO. Judgement of process transition control strategies for large-range conditions change of chemical processes[J]. CIESC Journal, 2019, 70(5): 1848-1857.

黄冬, 罗雄麟. 化工过程大范围工况变化的工况迁移控制策略判定[J]. 化工学报, 2019, 70(5): 1848-1857.

Figures/Tables 7

Fig.1 Mapping relationship between production index and transition factors

Table 1 Maximum value of different transition factors

Transition factor	Value (p ^ub)
maximum increment of feed flowrate (p ₁)	21.88%
maximum increment of ethylene component (p ₂)	3.934%
maximum decrement of feed flowrate (p ₃)	22.54%
maximum decrement of ethylene component (p ₄)	4.691%

Table 2 Data of potential intermediate variables and production index with different transition factors

Normalized transition factor				Potential intermediate variable								Production index/%
$p ? 1$	$p ? 2$	$p ? 3$	$p ? 4$	$s 1 p$	$s 2 p$	$s 3 p$	$s 4 p$	$s 5 p$	$s 6 p$	$s 7 p$	$s 8 p$	Production index/%
0	0	0	0	0	0	0	0	0	0	0	0	0
0.2				0.04441	0.04386	0.05349	0.04806	0.00054	0.00001	0.00924	0.00403	0.001930
0.4				0.08882	0.08771	0.10711	0.09650	0.00104	0.00002	0.01785	0.00810	0.003903
0.6				0.13323	0.13157	0.16086	0.14534	0.00150	0.00003	0.02591	0.01220	0.005904
0.8				0.17765	0.17543	0.21474	0.19458	0.00192	0.00004	0.03349	0.01633	0.007933
1.0				0.22206	0.21929	0.26877	0.24421	0.00232	0.00005	0.04063	0.02049	0.010000
	0.2			0.01072	0.04708	0.00475	0.00289	0.01072	0.04708	0.00475	0.00289	0.002173
	0.4			0.02143	0.09415	0.00908	0.00552	0.02143	0.09415	0.00908	0.00552	0.004098
	0.6			0.03215	0.14123	0.01294	0.00788	0.03215	0.14123	0.01294	0.00788	0.006070
	0.8			0.04287	0.18831	0.01628	0.00993	0.04287	0.18831	0.01628	0.00993	0.008063
	1.0			0.05358	0.23539	0.01905	0.01163	0.05358	0.23539	0.01905	0.01163	0.010000
		0.2		0.04575	0.04518	0.05498	0.04910	0.00061	0.00001	0.01027	0.00412	0.002076
		0.4		0.09150	0.09035	0.10982	0.09781	0.00128	0.00002	0.02142	0.00821	0.004089
		0.6		0.13725	0.13553	0.16455	0.14611	0.00202	0.00003	0.03359	0.01227	0.006090
		0.8		0.18300	0.18070	0.21915	0.19403	0.00284	0.00003	0.04696	0.01629	0.008064
		1.0		0.22876	0.22588	0.27364	0.24155	0.00376	0.00004	0.06174	0.02029	0.010000
			0.2	0.01278	0.05614	0.00616	0.00374	0.01048	0.04603	0.00616	0.00374	0.002171
			0.4	0.02555	0.11227	0.01281	0.00777	0.02095	0.09206	0.01281	0.00777	0.004096
			0.6	0.03833	0.16841	0.01990	0.01205	0.03143	0.13810	0.01990	0.01205	0.006068
			0.8	0.05111	0.22455	0.02738	0.01658	0.04191	0.18413	0.02738	0.01658	0.008061
			1.0	0.06389	0.28068	0.03521	0.02131	0.05239	0.23016	0.03521	0.02131	0.010000

Table 2 Data of potential intermediate variables and production index with different transition factors

Normalized transition factor				Potential intermediate variable								Production index/%
$p ? 1$	$p ? 2$	$p ? 3$	$p ? 4$	$s 1 p$	$s 2 p$	$s 3 p$	$s 4 p$	$s 5 p$	$s 6 p$	$s 7 p$	$s 8 p$	Production index/%
0	0	0	0	0	0	0	0	0	0	0	0	0
0.2				0.04441	0.04386	0.05349	0.04806	0.00054	0.00001	0.00924	0.00403	0.001930
0.4				0.08882	0.08771	0.10711	0.09650	0.00104	0.00002	0.01785	0.00810	0.003903
0.6				0.13323	0.13157	0.16086	0.14534	0.00150	0.00003	0.02591	0.01220	0.005904
0.8				0.17765	0.17543	0.21474	0.19458	0.00192	0.00004	0.03349	0.01633	0.007933
1.0				0.22206	0.21929	0.26877	0.24421	0.00232	0.00005	0.04063	0.02049	0.010000
	0.2			0.01072	0.04708	0.00475	0.00289	0.01072	0.04708	0.00475	0.00289	0.002173
	0.4			0.02143	0.09415	0.00908	0.00552	0.02143	0.09415	0.00908	0.00552	0.004098
	0.6			0.03215	0.14123	0.01294	0.00788	0.03215	0.14123	0.01294	0.00788	0.006070
	0.8			0.04287	0.18831	0.01628	0.00993	0.04287	0.18831	0.01628	0.00993	0.008063
	1.0			0.05358	0.23539	0.01905	0.01163	0.05358	0.23539	0.01905	0.01163	0.010000
		0.2		0.04575	0.04518	0.05498	0.04910	0.00061	0.00001	0.01027	0.00412	0.002076
		0.4		0.09150	0.09035	0.10982	0.09781	0.00128	0.00002	0.02142	0.00821	0.004089
		0.6		0.13725	0.13553	0.16455	0.14611	0.00202	0.00003	0.03359	0.01227	0.006090
		0.8		0.18300	0.18070	0.21915	0.19403	0.00284	0.00003	0.04696	0.01629	0.008064
		1.0		0.22876	0.22588	0.27364	0.24155	0.00376	0.00004	0.06174	0.02029	0.010000
			0.2	0.01278	0.05614	0.00616	0.00374	0.01048	0.04603	0.00616	0.00374	0.002171
			0.4	0.02555	0.11227	0.01281	0.00777	0.02095	0.09206	0.01281	0.00777	0.004096
			0.6	0.03833	0.16841	0.01990	0.01205	0.03143	0.13810	0.01990	0.01205	0.006068
			0.8	0.05111	0.22455	0.02738	0.01658	0.04191	0.18413	0.02738	0.01658	0.008061
			1.0	0.06389	0.28068	0.03521	0.02131	0.05239	0.23016	0.03521	0.02131	0.010000

Fig.2 Trend of potential intermediate variables with different transition factors

Fig.3 Trend of intermediate variables with different transition factors

Table 3 Verification of judgement of control strategies

Number	Normalize transition factor				Actual index (judgement)	Direct estimation		Indirect estimation
Number	$p ? 1$	$p ? 2$	$p ? 3$	$p ? 4$	Actual index (judgement)	Estimated index (judgement)	Result	Estimated index (judgement)	Result
1	0.5	0.45			0.00886 (C)	0.00950 (C)	true	0.00868 (C)	true
2	0.5	0.55			0.00955 (C)	0.01050 (T)	false	0.00951 (C)	true
3	0.5	0.5			0.00934 (C)	0.01000 (T)	false	0.00930 (C)	true
4	0.45	0.5			0.00856 (C)	0.00950 (C)	true	0.00860 (C)	true
5	0.55	0.5			0.00981 (C)	0.01050 (T)	false	0.00979 (C)	true
6			0.5	0.45	0.01015 (T)	0.00957 (C)	false	0.01030 (T)	true
7			0.5	0.55	0.01105 (T)	0.01058 (T)	true	0.01090 (T)	true
8			0.5	0.5	0.01052 (T)	0.01007 (T)	true	0.01039 (T)	true
9			0.45	0.5	0.00993 (C)	0.00957 (C)	true	0.00990 (C)	true
10			0.55	0.5	0.01117 (T)	0.01058 (T)	true	0.01089 (T)	true

Table 3 Verification of judgement of control strategies

Number	Normalize transition factor				Actual index (judgement)	Direct estimation		Indirect estimation
Number	$p ? 1$	$p ? 2$	$p ? 3$	$p ? 4$	Actual index (judgement)	Estimated index (judgement)	Result	Estimated index (judgement)	Result
1	0.5	0.45			0.00886 (C)	0.00950 (C)	true	0.00868 (C)	true
2	0.5	0.55			0.00955 (C)	0.01050 (T)	false	0.00951 (C)	true
3	0.5	0.5			0.00934 (C)	0.01000 (T)	false	0.00930 (C)	true
4	0.45	0.5			0.00856 (C)	0.00950 (C)	true	0.00860 (C)	true
5	0.55	0.5			0.00981 (C)	0.01050 (T)	false	0.00979 (C)	true
6			0.5	0.45	0.01015 (T)	0.00957 (C)	false	0.01030 (T)	true
7			0.5	0.55	0.01105 (T)	0.01058 (T)	true	0.01090 (T)	true
8			0.5	0.5	0.01052 (T)	0.01007 (T)	true	0.01039 (T)	true
9			0.45	0.5	0.00993 (C)	0.00957 (C)	true	0.00990 (C)	true
10			0.55	0.5	0.01117 (T)	0.01058 (T)	true	0.01089 (T)	true

Fig.4 Analysis of selection of control strategies

References 35

1	杨友麒, 项曙光 . 化工过程模拟与优化[M]. 北京：化学工业出版社, 2006.
	Yang Y L , Xiang S G . Modeling and Optimization of Chemical Processes[M]. Beijing：Chemical Industry Press, 2006.
2	罗祎青, 胡尊燕, 袁希钢 . 复杂化工过程系统的能值计算方法[J]. 化工学报, 2013, 64(1): 311-317.
	Luo Y Q , Hu Z Y , Yuan X G . Method for calculating stream energy in complex chemical process systems[J]. CIESC Journal, 2013, 64(1): 311-317.
3	许锋, 蒋慧蓉, 王锐,等 . 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 F , Jiang H R , Wang R , et al . Tradeoff between whole margin and control performance for chemical process[J]. CIESC Journal, 2014, 65(4): 1303-1309.
4	Huang D , Luo X L . Process transition based on dynamic optimization with the case of a throughput fluctuating ethylene column[J]. Industrial & Engineering Chemistry Research, 2018, 57(18): 6292-6302.
5	Chachuat B , Singer A B , Barton P I . Global mixed-integer dynamic optimization[J]. AIChE Journal, 2010, 51(8): 2235-2253.
6	Chu Y , You F . Integrated scheduling and dynamic optimization of complex batch processes with general network structure using a generalized benders decomposition approach[J]. Industrial & Engineering Chemistry Research, 2013, 52(23): 7867-7885.
7	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.
8	Huntington G T , Rao A V . Comparison of global and local collocation methods for optimal control[J]. Journal of Guidance,Control, and Dynamics, 2008, 31(2): 432-436.
9	Logsdon J S , Biegler L T . Accurate solution of differential-algebraic optimization problems[J]. Industrial & Engineering Chemistry Research, 1989, 28(11): 1628-1639.
10	Schlegel M , Marquardt W . Detection and exploitation of the control switching structure in the solution of dynamic optimization problems[J]. Journal of Process Control, 2006, 16(3): 275-290.
11	Li G D , Liu P , Liu X G . A control parameterization approach with variable time nodes for optimal control problems[J]. Asian Journal of Control, 2016, 18(3): 976-984.
12	Biegler L T . Solution of dynamic optimization problems by successive quadratic programming and orthogonal collocation[J]. Computers & Chemical Engineering, 1984, 8(3): 243-247.
13	Morison K R , Sargent R W H . Optimization of multistage processes described by differential-algebraic equations[C]//Proceedings of the Fourth IIMAS Workshop. Numerical Analysis. Heidelberg, Berlin： Springer， 1986: 86-102.
14	Cuthrell J E , Biegler L T . On the optimization of differential-algebraic process systems[J]. AIChE Journal, 1987, 33(8): 1257-1270.
15	Cuthrell J E , Biegler L T . Simultaneous optimization and solution methods for batch reactor control profiles[J]. Computers & Chemical Engineering, 1989, 13(1/2): 49-62.
16	Tanartkit P , Biegler L T . A nested, simultaneous approach for dynamic optimization problems(II): The outer problem[J]. Computers & Chemical Engineering, 1997, 21(12): 1365-1388.
17	Cervantes A M , Biegler L T . A stable elemental decomposition for dynamic process optimization[J]. Journal of Computational and Applied Mathematics, 2000, 120(1/2): 41-57.
18	Cervantes A M , Wächter A , Tütüncü R H , et al . A reduced space interior point strategy for optimization of differential algebraic systems[J]. Computers & Chemical Engineering, 2000, 24(1): 39-51.
19	Biegler L T , Cervantes A M , Wächter A . Advances in simultaneous strategies for dynamic process optimization[J]. Chemical Engineering Science, 2002, 57(4): 575-593.
20	Kameswaran S , Staus G , Biegler L T . Parameter estimation of core flood and reservoir models[J]. Computers & Chemical Engineering, 2005, 29(8): 1787-1800.
21	Kameswaran S , Biegler L T . Simultaneous dynamic optimization strategies: recent advances and challenges[J]. Computers & Chemical Engineering, 2006, 30(10/11/12): 1560-1575.
22	Zhao Y , Tsiotras P . Density functions for mesh refinement in numerical optimal control[J]. Journal of Guidance, Control, and Dynamics, 2011, 34(1): 271-277.
23	Darby C L , Hager W W , Rao A V . An hp-adaptive pseudospectral method for solving optimal control problems[J]. Optimal Control Applications and Methods, 2011, 32(4): 476-502.
24	Lazutkin E , Geletu A , Li P . An approach to determining the number of time intervals for solving dynamic optimization problems[J]. Industrial & Engineering Chemistry Research, 2018, 57(12): 4340-4350.
25	Fikar M , Latifi M A , Fournier F , et al . Control vector parametrisation versus iterative dynamic programming in dynamic optimisation of a distillation column[J]. Computers & Chemical Engineering, 1998, 22(12): S625-S628.
26	Fikar M , Latifi M A , Corriou J P , et al . CVP-based optimal control of an industrial depropanizer column[J]. Computers & Chemical Engineering, 2000, 24(2): 909-915.
27	Schlegel M , Marquardt W . Detection and exploitation of the control switching structure in the solution of dynamic optimization problems[J]. Journal of Process Control, 2006, 16(3): 275-290.
28	王平, 田学民 . 一种改进的CVP方法及其在动态优化中的应用[J]. 控制与决策, 2009, 24(11): 1757-1760.
	Wang P , Tian X M . Enhanced control vector parameterization method and its application in dynamic optimization[J]. Control and Design, 2009, 24(11): 1757-1760.
29	李国栋 . 基于控制向量参数化的动态优化研究[D]. 杭州：浙江大学, 2015.
	Li G D . Control vector parameterization based dynamic optimization research[D]. Hangzhou：Zhejiang University, 2015.
30	Chen X , Du W L , Tianfield H , et al . Dynamic optimization of industrial processes with nonuniform discretization-based control vector parameterization[J]. IEEE Transactions on Automation Science & Engineering, 2017, 11(4): 1289-1299.
31	Wang L , Liu X , Zhang Z . A new sensitivity-based adaptive control vector parameterization approach for dynamic optimization of bioprocesses[J]. Bioprocess & Biosystems Engineering, 2017, 40(2): 181-189.
32	Tian J , Zhang P P , Wang Y L , et al . Control vector parameterization based adaptive invasive weed optimization for dynamic processes[J]. Chemical Engineering & Technology, 2018, 41(5): 964-974.
33	黄冬, 赵民帅, 罗雄麟 . 乙烯精馏塔控制中降液管时滞效应影响分析[J]. 化工学报, 2016, 67(11): 4696-4704.
	Huang D , Zhao M S , Luo X L . Analyzing time-lag effect of downcomer for control of ethylene column[J]. CIESC Journal, 2016, 67(11): 4696-4704.
34	Huang D , Zhao X Y , Luo X L . Trade-off between energy consumption and ethylene recovery rate for quasi-plant wide operation optimization of the ethylene column with bottom circulatory system in ethylene complex[J]. Asia-pacific Journal of Chemical Engineering, 2017, 12(5): 694-708.
35	Huang D , Luo X L . A novel approach to promptly control product quality in precise distillation columns based on pressure dynamic modeling[J]. Asia-pacific Journal of Chemical Engineering, 2018, 13(4): e2212.

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