Mass Exchange Network Mass Transfer Load Infeasibility Treatment and Dynamic Regulation Strategy

doi:10.19596/j.cnki.1001-246x.8733

Abstract

Abstract:

In the optimization of mass exchange networks with mass transfer load as the optimization variable, using the penalty function method to deal with infeasible solutions that violate the feasible constraints on mass transfer will limit the generation of mass transfer units, and at the same time bring negative effects on the optimization algorithm's optimization path. In view of this, the negative impact of the penalty function method to deal with mass transfer constraints on optimization is analyzed, based on which a dynamic regulation strategy of mass transfer load is proposed to deal with the units mass transfer constraints by using different regulation methods, attenuating the mass transfer load of the units that violate the mass transfer constraints or eliminating the unit to ensure that the mass transfer units in the structure not violate the constraints. The validation is carried out by two mass exchange network arithmetic examples, and the obtained results demonstrate the effectiveness of the strategy by decreasing 2 943 ＄·a^-1 and 1 697 ＄·a^-1, respectively, compared with the optimal results in the literature, and increasing the number of mass transfer units in the structure.

Key words: mass exchange network, process integration, random walk algorithm with compulsive evolution, infeasible solutions

CLC Number:

TQ021.8

Siheng XIONG, Huanhuan DUAN, Guomin CUI, Yuan XIAO, Zhikang YI. Mass Exchange Network Mass Transfer Load Infeasibility Treatment and Dynamic Regulation Strategy[J]. Chinese Journal of Computational Physics, 2024, 41(4): 535-546.

Figures/Tables 17

Fig.1 Schematic of nodes-based non-structural model with streams split (a) initial empty structure; (b) matching structures

Fig.2 Flow chart of RWCE algorithm optimization

Fig.3 Four cases of mass transfer driving forces of the mass exchange unit (a) Δyleft > 0, Δyright > 0; (b) Δyleft > 0, Δyright < 0;(c) Δyleft < 0, Δyright > 0;(d) Δyleft < 0, Δyright < 0

Table 1 Stream data of Case 1

流股	最大流量/(kg·s^-1)	入口溶度/(kg·kg^-1)	目标溶度/(kg·kg^-1)	m	b	C^O/(＄·kg^-1)
R1	0.9	0.07	0.000 3
R2	0.1	0.051	0.000 1
S1	2.3	0.000 6	0.031	1.45	0	0.004
S2	∞	0.000 2	0.003 5	0.26	0	0.006

Table 2 Frequency of occurrence of two difference imperfect solutions

IT/10⁶	C1	C2	N1	N2	N3
1	3 115	11	10 122	630	2 037
2	6 589	31	20 179	1 205	2 873
3	10 087	71	30 109	1 935	4 070
4	13 679	105	40 142	2 456	5 068
5	16 045	158	49 967	3 014	5 794

Fig.4 Unit generated causes violation of the mass transfer feasibility constraint

Fig.5 Iteration of TAC with initial solution of the structure in Fig. 4

Fig.6 Continuous variable optimization causes violation of mass transfer feasibility constraints

Fig.7 Iteration curves for individual TAC, number of unit and constraint violations

Fig.8 Flow chart of the improved RWCE algorithm

Fig.9 MENS structure obtained using RWCE, TAC=410 369 ＄·a-1

Fig.10 MENS structure obtained by improvement of RWCE, TAC=406 761 ＄·a-1

Table 3 Result comparison of Case 1

文献	单元数	总塔板数/块	操作费用/(＄·a^-1)	TAC/(＄·a^-1)
Ref.[23]	5	25	313 292	431 613
Ref.[25]	4	21	326 700	422 293
Ref.[26]	4	24	311 297	420 545
Ref.[12]	4	20	317 986	412 500
Ref.[15]	4	21	314 114	409 704
Fig. 9	5	21	314 777	410 369
Fig. 10	6	20	315 757	406 761

Table 4 Steam data of Case 2

流股	最大流量/(kg·s^-1)	入口溶度/(kg·kg^-1)	目标溶度/(kg·kg^-1)	m	b	C^O/(＄·kg^-1)
R1	2	0.050	0.010
R2	1	0.030	0.006
S1	5	0.005	0.015	2	0	0
S2	3	0.01	0.030	1.53	0	0
S3	∞	0.001 3	0.015	0.71	0.001	0.01

Fig.11 MENS structure obtained using RWCE, TAC=337 570 ＄·a-1

Fig.12 MENS structure obtained by improvement of RWCE, TAC=336 471 ＄·a-1

Table 5 Result comparison of Case 2

文献	单元数	操作费用/(＄·a^-1)	投资费用/(＄·a^-1)	TAC/(＄·a^-1)
Ref.[27]	7	218 225	127 456	345 681
Ref.[24]	7	217 960	159 320	345 416
Ref.[28]	7	210 446	132 008	342 174
Ref.[23]	6	223 840	115 739	339 579
Ref.[7]	6	219 816	118 352	338 168
Fig. 11	6	223 531	114 039	337 570
Fig. 12	8	198 453	138 018	336 471

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