Mass Exchanger Network Synthesis Based on Random Walk Algorithm with Compulsive Evolution

doi:10.19596/j.cnki.1001-246x.8465

Abstract

Abstract:

Aiming at shortcomings of existing mass exchanger network optimization methods, a random walk algorithm with compulsive evolution for mass exchanger network synthesis is proposed, in which the mass transfer load, split ratio and the flow of lean streams of mass exchanger are increased or reduced randomly. A minimum threshold is set to realize synchronous optimization of network continuity and integer variables. A small probability is retained to accept the difference solution. It enhances mutation ability of the structure, and makes the algorithm taking into account global search and local search of mass exchanger network. Application in two mass exchanger network examples show that the optimization results are better than those in current literatures. The algorithm maintains independent evolution between individuals and has good global and local search ability.

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

Xiu-bao MA, Zhao-liang GAI, Guo-min CUI, Zhi-qiang ZHOU, Xin-yu HAN, Qi-guo YANG. Mass Exchanger Network Synthesis Based on Random Walk Algorithm with Compulsive Evolution[J]. Chinese Journal of Computational Physics, 2022, 39(4): 479-490.

Figures/Tables 14

References 35

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富流股	G_i/(kg·s^-1)	T_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/(＄·kg^-1)
R₁	0.9	0.07	0.000 3	S₁	2.3	0.000 6	0.031	0.004
R₂	0.1	0.051	0.000 1	S₂	∞	0.000 2	0.003 5	0.006

富流股	G_i/(kg·s^-1)	T_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/(＄·kg^-1)
R₁	0.9	0.07	0.000 3	S₁	2.3	0.000 6	0.031	0.004
R₂	0.1	0.051	0.000 1	S₂	∞	0.000 2	0.003 5	0.006

	L₁/(kg·s^-1)	L₂/(kg·s^-1)	No. of trays	No. of units	Spilts: rich/lean	TAC/(＄·a^-1)	difference/%
Ref.[31]	2.205	0.236 62	37	6	0/2	469 968	14.71
Ref.[9]	2.197	0.315	25	5	0/1	431 613	5.35
Ref.[15]	2.195	0.331	25	4	0/2	429 700	4.88
Ref.[32]	2.235 2	0.365 7	21	4	0/0	422 293	3.07
Ref.[26]	2.198	0.303	24	4	0/2	420 545	2.65
Ref.[21]	2.192	0.357	20	4	0/2	411 166	0.36
Ref.[14]	2.189 3	0.383 7	19	6	0/0	410 971	0.31
Ref.[25]	2.189 54	0.381 24	19	6	0/0	410 565	0.21
Fig. 9	2.195 87	0.320 42	21	4	0/1	409 704	0.00

	L₁/(kg·s^-1)	L₂/(kg·s^-1)	No. of trays	No. of units	Spilts: rich/lean	TAC/(＄·a^-1)	difference/%
Ref.[31]	2.205	0.236 62	37	6	0/2	469 968	14.71
Ref.[9]	2.197	0.315	25	5	0/1	431 613	5.35
Ref.[15]	2.195	0.331	25	4	0/2	429 700	4.88
Ref.[32]	2.235 2	0.365 7	21	4	0/0	422 293	3.07
Ref.[26]	2.198	0.303	24	4	0/2	420 545	2.65
Ref.[21]	2.192	0.357	20	4	0/2	411 166	0.36
Ref.[14]	2.189 3	0.383 7	19	6	0/0	410 971	0.31
Ref.[25]	2.189 54	0.381 24	19	6	0/0	410 565	0.21
Fig. 9	2.195 87	0.320 42	21	4	0/1	409 704	0.00

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	0.25	0.13	0.10	S₁	∞	0.030	0.070	58 680
R₂	0.1	0.06	0.02	S₂	∞	0.001	0.020	704 160