Optimizing Mass Exchange Network Using RWCE Algorithm with Individual Elimination

doi:10.19596/j.cnki.1001-246x.8576

Abstract

Abstract:

In the process of optimizing the mass exchange network by random walk algorithm with compulsive evolution, some individuals are in a long-term disadvantaged state in the competition and the individual structure is highly similar. RWCE algorithm with individual elimination is proposed to optimize the mass exchange network. That is, in a certain period, through the real-time monitoring of the optimal state of the individuals in the population, the network structure of the individual is first standardized, so as to identify the similar quality exchangers in the structure, and then evaluate the structural similarity of the individual according to its number. Divide the individuals in the population into several groups, and use the total annual cost as an indicator to evaluate the individual optimization performance, and eliminate the disadvantaged and similar individuals in the population. Strengthen the information exchange of individuals between populations, improve individual optimization vitality and population diversity, and effectively enhance the global search ability of the algorithm to optimize the mass exchange network. Applying this method to two examples of mass exchange networks, the optimization results are better than the best results in the literature, indicating that the method can change the evolution direction of the structure, stimulate the differential evolution of individuals between populations and maintain the optimization vitality of individuals, and effectively improve the global optimization performance of the algorithm.

Key words: mass exchange network, nodes-based nonstructural model, random walk algorithm with compulsive evolution, mass transfer, simultaneous optimization

Xiubao MA, Guomin CUI, Zhiqiang ZHOU, Yuan XIAO, Yue XU, Qiguo YANG. Optimizing Mass Exchange Network Using RWCE Algorithm with Individual Elimination[J]. Chinese Journal of Computational Physics, 2023, 40(3): 376-388.

Figures/Tables 16

References 31

1	贾小平, 石磊, 杨友麒. 工业园区生态化发展的挑战与过程系统工程的机遇[J]. 化工学报, 2021, 72 (5): 2373- 2391.
2	SHORT M , ISAFIADE A J . Thirty years of mass exchanger network synthesis-A systematic review[J]. Journal of Cleaner Production, 2021, 304 (7): 127112.
3	杨友麒. 质量交换网络[J]. 化工进展, 2007, 26 (2): 284- 289.
4	ISAFIADE A J , SHORT M . Review of mass exchanger network synthesis methodologies[J]. Chemical Engineering Transactions, 2019, 76, 49- 54.
5	EL-HALWAGI M M , MANOUSIOUTHAKIS V . Synthesis of mass exchange networks[J]. AIChE Journal, 1989, 35 (8): 1233- 1244. DOI
6	HALLALE N , FRASER D M . Supertargeting for mass exchange networks: Part Ⅰ: Targeting and design techniques[J]. Chemical Engineering Research and Design, 2000, 78 (2): 202- 207. DOI
7	YANWARIZ A , OLADOSU W A , ALWI S R W , et al. A new graphical approach for simultaneous targeting and design of mass exchange networks[J]. Computers & Chemical Engineering, 2020, 142, 107061.
8	PAPALEXANDRI K P , PISTIKOPOULOS E N , FLOUDAS A . Mass exchange networks for waste minimization: A simultaneous approach[J]. Chemical Engineering Research & Design, 1994, 72 (3): 279- 294.
9	薛东峰. 废物最小化为目标的质量集成方法研究[D]. 大连: 大连理工大学, 2001.
10	杜红彬, 薛东峰, 姚平经. 改进的自适应模拟退火算法及其在过程综合中的应用[J]. 高校化学工程学报, 2002, 16 (1): 106- 110.
11	李绍军, 阳永荣. 利用改进的遗传算法进行质量交换网络的最优综合[J]. 化工学报, 2002, (1): 60- 65.
12	李绍军, 王惠, 姚平经. 求解全局最优化的遗传(GA)-Alopex算法的研究[J]. 信息与控制, 2000, 29 (4): 304-308+314 DOI
13	王江峰, 沈静珠, 李有润, 等. 不相容多组分质量交换网络综合[J]. 化工学报, 2004, 55 (2): 297-300, 304
14	CHEN C L , HUNG P S . Simultaneous synthesis of mass exchange networks for waste minimization[J]. Computers & Chemical Engineering, 2005, 29 (7): 1561- 1576.
15	QU Y C , CHEN J X , CUI G M , et al. Weighted differential evolution algorithm for heat exchanger network synthesis[J]. Chinese Journal of Computational Physics, 2021, 38 (1): 79- 88.
16	高志辉. 费用最小的质量交换网络综合研究[D]. 大连: 大连理工大学, 2007.
17	LIU L , DU J , EL-HALWAGI M M , et al. Synthesis of multi-component mass-exchange networks[J]. Chinese Journal of Chemical Engineering, 2013, 21 (4): 376- 381.
18	TAO S , JIANG M , LIU M , et al. A mixed discrete PSO algorithm for synthesis of mass exchange network with incompatible multicomponent[J]. Chemical Engineering Transactions, 2017, 61, 709- 714.
19	MA X B , GAI Z L , CUI G M , et al. Mass exchanger network synthesis based on random walk algorithm with compulsive evolution[J]. Chinese Journal of Computational Physics, 2022, 39 (4): 479- 490.
20	XU Y , CUI G M . Heat exchanger network optimization using structural perturbation strategy[J]. Chinese Journal of Computational Physics, 2020, 37 (6): 734- 744.
21	FARRAG N M , KAMEL D A , GHALLAB A O , et al. Graphical design and analysis of mass exchange networks using composition driving forces[J]. South African Journal of Chemical Engineering, 2021, 36, 94- 104.
22	AZEEZ O S , ISAFIADE A J , FRASER D M . Supply and target based superstructure synthesis of heat and mass exchanger networks[J]. Chemical Engineering Research and Design, 2012, 90 (2): 266- 287.
23	HOU C , XU W , LUO M . Integral function to optimize mass exchange network synthesis model[J]. Journal of Chemical Engineering of Japan, 2020, 53 (6): 254- 266.
24	LIU L , EL-HALWAGI M M , DU J , et al. Systematic synthesis of mass exchange networks for multicomponent systems[J]. Industrial & Engineering Chemistry Research, 2013, 52 (39): 14219- 14230.
25	AZEEZ O S , ISAFIADE A J , FRASER D M . Supply-based superstructure synthesis of heat and mass exchange networks[J]. Computers & Chemical Engineering, 2013, 56, 184- 201.
26	侯创, 罗明生, 徐文星. 取整函数优化基于超结构模型的质量交换网络[J]. 化学反应工程与工艺, 2020, 36 (2): 108- 116.
27	谢会, 史彬, 鄢烈祥, 等. 列队竞争算法综合质量交换网络[J]. 计算机与应用化学, 2010, 27 (12): 1617- 1620.
28	HALLALE N , FRASER D M . Supertargeting for mass exchange networks: part Ⅱ: applications[J]. Chemical Engineering Research and Design, 2000, 78 (2): 208- 216.
29	HALLALE N. Capital cost targets for the optimum synthesis of mass exchange networks[D]. Cape Town: University of Cape Town, 1998.
30	SZITKAI Z , FARKAS T , LELKES Z , et al. Fairly linear mixed integer nonlinear programming model for the synthesis of mass exchange networks[J]. Industrial & Engineering Chemistry Research, 2006, 45 (1): 236- 244.
31	ISAFIADE A J , FRASER D M . Interval based MINLP superstructure synthesis of mass exchange networks[J]. Chemical Engineering Research and Design, 2008, 86 (8): 909- 924.

富流股	G_i/(kg·s^-1)	Y_iⁱⁿ/(kg·kg^-1)	Y_i^out/(kg·kg^-1)	贫流股	L_j^c/(kg·s^-1)	X_jⁱⁿ/(kg·kg^-1)	X_j^out/(kg·kg^-1)	C_j^L/(＄·s·kg^-1·a^-1)
R₁	0.9	0.07	0.000 3	S₁	2.3	0.000 6	0.031	117 360
R₂	0.1	0.051	0.000 1	S₂	∞	0.000 2	0.003 5	176 040

富流股	G_i/(kg·s^-1)	Y_iⁱⁿ/(kg·kg^-1)	Y_i^out/(kg·kg^-1)	贫流股	L_j^c/(kg·s^-1)	X_jⁱⁿ/(kg·kg^-1)	X_j^out/(kg·kg^-1)	C_j^L/(＄·s·kg^-1·a^-1)
R₁	0.9	0.07	0.000 3	S₁	2.3	0.000 6	0.031	117 360
R₂	0.1	0.051	0.000 1	S₂	∞	0.000 2	0.003 5	176 040

参考文献	L₁/(kg·s^-1)	L₂/(kg·s^-1)	No. of units	TAC/(＄·a^-1)
Ref. [25]	2.205	0.236 62	6	469 968
Ref. [24]	2.23	0.46	4	433 730
Ref. [17]	2.192	0.37	4	413 450
Ref. [18]	2.190 9	0.369 2	4	413 160
Ref. [23]	2.192	0.357	4	411 166
Ref. [11]	2.189 3	0.383 7	6	410 971
Ref. [27]	2.189 54	0.381 24	6	410 565
Fig. 10(b)	2.196 22	0.319 6	4	409 603

参考文献	L₁/(kg·s^-1)	L₂/(kg·s^-1)	No. of units	TAC/(＄·a^-1)
Ref. [25]	2.205	0.236 62	6	469 968
Ref. [24]	2.23	0.46	4	433 730
Ref. [17]	2.192	0.37	4	413 450
Ref. [18]	2.190 9	0.369 2	4	413 160
Ref. [23]	2.192	0.357	4	411 166
Ref. [11]	2.189 3	0.383 7	6	410 971
Ref. [27]	2.189 54	0.381 24	6	410 565
Fig. 10(b)	2.196 22	0.319 6	4	409 603

富流股	G_i/(kg·s^-1)	Y_iⁱⁿ/(kg·kg^-1)	Y_i^out/(kg·kg^-1)	贫流股	L_j^c/(kg·s^-1)	X_jⁱⁿ/(kg·kg^-1)	X_j^out/(kg·kg^-1)	C_j^L/(＄·s·kg^-1·a^-1)
R₁	3.3	0.05	0.001 5	S₁	10	0.001 3	0.025	58 680
R₂	0.6	0.07	0.003	S₂	10			417 060

R₃	1.4	0.02	0.003	再生流股	M₁^c/(kg·s^-1)	Z₁ⁱⁿ/(kg·kg^-1)	Z₁^out/(kg·kg^-1)	C₁^M/(＄·s·kg^-1·a^-1)
R₄	0.2	0.03	0.002	H₁	10	0	0.005	88 020