Fine search strategy applied to mass exchange network synthesis

doi:10.11949/0438-1157.20220024

Abstract

Abstract:

Mass exchange network is an important part of chemical process system, and its optimal design is of great significance to reduce pollution emissions. When the heuristic algorithm is used to optimize the mass exchange network, there is a problem that it is difficult to take into account both the global search and the local search. By analyzing the optimization results under different precision optimization parameters, this paper reveals the causes of the problem, and proposes a fine search strategy for the in-depth optimization of the structure obtained by the basic algorithm. The strategy includes two methods. Method 1 adopted a high-precision random walk algorithm with compulsive evolution (RWCE) with individual back substitution and differentiation, which can retain the ability of individual structure variation. Method 2 used the deterministic approach to perform a one-dimensional search for each variable in the multidimensional objective function in turn, which has the advantages of high precision and fast convergence. Applying this strategy to coke oven gas sweetening and ammonia removal problem, the results are 407308 USD·a^-1 and 127807 USD·a^-1, which are better than the best results in the current literature. The effectiveness of the proposed strategy is verified.

Key words: mass exchange network, process system, optimization, random walk algorithm with compulsive evolution, deterministic method

摘要：

质量交换网络是化工过程系统的重要组成部分，其优化设计对降低污染排放具有重要意义。采用启发式算法优化质量交换网络时，存在难以兼顾全局搜索和局部搜索的问题。通过分析不同精度优化参数下的优化结果，揭示了该问题的成因，并提出一种精细搜索策略用于基础算法所得结构的深度优化。该策略包含两种方法，方法1采用具有个体回代与分化的高精度强制进化随机游走算法，可保留个体结构变异能力；方法2采用确定性方法依次对多维目标函数中的每个变量进行一维搜索，具有精度高收敛快的优点。将该策略应用于焦炉气脱硫和空气除氨算例，得到的结果分别为407308 USD·a^-1和127807 USD·a^-1，经济性优于现有文献中的结果，验证了本策略的有效性。

关键词: 质量交换网络, 过程系统, 优化, 强制进化随机游走算法, 确定性方法

CLC Number:

TQ 021.8

Ling YANG, Guomin CUI, Zhiqiang ZHOU, Yuan XIAO. Fine search strategy applied to mass exchange network synthesis[J]. CIESC Journal, 2022, 73(7): 3145-3155.

杨岭, 崔国民, 周志强, 肖媛. 精细搜索策略应用于质量交换网络综合[J]. 化工学报, 2022, 73(7): 3145-3155.

Figures/Tables 15

References 30

1	张贤, 郭偲悦, 孔慧, 等. 碳中和愿景的科技需求与技术路径[J]. 中国环境管理, 2021, 13(1): 65-70.
	Zhang X, Guo S Y, Kong H, et al. Technology demands and approach of carbon neutrality vision[J]. Chinese Journal of Environmental Management, 2021, 13(1): 65-70.
2	薛东峰, 陈理, 袁一, 等. 质量交换网络综合[J]. 现代化工, 2001, 21(6): 16-19, 21.
	Xue D F, Chen L, Yuan Y, et al. Synthesis of mass exchange network[J]. Modern Chemical Industry, 2001, 21(6): 16-19, 21.
3	El-Halwagi M M, Manousiouthakis V. Synthesis of mass exchange networks[J]. AIChE Journal, 1989, 35(8): 1233-1244.
4	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.
5	Velázquez-Guevara M Á, Uribe-Ramírez A R, Gómez-Castro F I, et al. Optimal synthesis of mass exchange networks through a state-task representation superstructure[M]//Computer Aided Chemical Engineering. Amsterdam: Elsevier, 2018: 331-336.
6	Short M, Isafiade A J. Thirty years of mass exchanger network synthesis—a systematic review[J]. Journal of Cleaner Production, 2021, 304: 127112.
7	Hallale N, Fraser D M. Capital and total cost targets for mass exchange networks(Ⅰ): Simple capital cost models[J]. Computers & Chemical Engineering, 2000, 23(11/12): 1661-1679.
8	Hallale N, Fraser D M. Capital and total cost targets for mass exchange networks(Ⅱ): Detailed capital cost models[J]. Computers & Chemical Engineering, 2000, 23(11/12): 1681-1699.
9	Gadalla M A. A new graphical-based approach for mass integration and exchange network design[J]. Chemical Engineering Science, 2015, 127: 239-252.
10	Yanwarizal, Oladosu W A, Wan Alwi S R, et al. A new graphical approach for simultaneous targeting and design of mass exchange networks[J]. Computers & Chemical Engineering, 2020, 142: 107061.
11	El-Halwagi M M, Manousiouthakis V. Automatic synthesis of mass-exchange networks with single-component targets[J]. Chemical Engineering Science, 1990, 45(9): 2813-2831.
12	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.
13	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, 53(1): 60-65.
	Li S J, Yang Y R. Mass exchanger networks synthesis using genetic-alopex algorithms[J]. Journal of Chemical Industry and Engineering (China), 2002, 53(1): 60-65.
14	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.
	Xie H, Shi B, Yan L X, et al. Mass exchange networks synthesis using line-up competition algorithm[J]. Computers and Applied Chemistry, 2010, 27(12): 1617-1620.
15	都健, 高志辉, 陈理, 等. 采用浓度差同步优化的质量交换网络设计[J]. 化工学报, 2007, 58(7): 1768-1775.
	Du J, Gao Z H, Chen L, et al. Mass exchange network design using simultaneous optimization of composition differences[J]. Journal of Chemical Industry and Engineering (China), 2007, 58(7): 1768-1775.
16	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.
	Hou C, Luo M S, Xu W X. An integral function to optimize the mass exchange network based on superstructure model[J]. Chemical Reaction Engineering and Technology, 2020, 36(2): 108-116.
17	Liu L L, Du J, Yang F L. Combined mass and heat exchange network synthesis based on stage-wise superstructure model[J]. Chinese Journal of Chemical Engineering, 2015, 23(9): 1502-1508.
18	Xu Y, Kayange H A, Cui G M. A nodes-based non-structural model considering a series structure for heat exchanger network synthesis[J]. Processes, 2020, 8(6): 695.
19	Xiao Y, Cui G M. A novel random walk algorithm with compulsive evolution for heat exchanger network synthesis[J]. Applied Thermal Engineering, 2017, 115: 1118-1127.
20	韩正恒, 崔国民, 赵倩倩, 等. RWCE算法中采用单元重构策略激励换热网络结构优化[J]. 化工学报, 2021, 72(6): 3316-3327.
	Han Z H, Cui G M, Zhao Q Q, et al. Impelling structural optimization of heat exchanger network by unitreconfiguration strategy in RWCE algorithm[J]. CIESC Journal, 2021, 72(6): 3316-3327.
21	周志强, 崔国民, 杨岭, 等. 一种基于并行计算的混合算法优化有分流换热网络[J]. 化工学报, 2022, 73(2): 801-813.
	Zhou Z Q, Cui G M, Yang L, et al. A hybrid algorithm based on parallel computing for heat exchanger network optimization with stream splits[J]. CIESC Journal, 2022, 73(2): 801-813.
22	韦根原, 冯新强. 热工过程参数的改进逐维黄金分割法辨识[J]. 热力发电, 2015, 44(10): 68-71.
	Wei G Y, Feng X Q. Identification of thermal process parameters by improved dimension-by-dimension golden section method[J]. Thermal Power Generation, 2015, 44(10): 68-71.
23	Fraser D M, Shenoy U V. A new method for sizing mass exchange units without the singularity of the Kremser equation[J]. Computers & Chemical Engineering, 2004, 28(11): 2331-2335.
24	Isafiade A J. Interval based MINLP superstructure synthesis of heat and mass exchange networks[D]. Cape Town: University of Cape Town, 2008.
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	Chen C L, Hung P S. Simultaneous synthesis of mass exchange networks for waste minimization[J]. Computers & Chemical Engineering, 2005, 29(7): 1561-1576.
27	王江峰, 沈静珠, 李有润, 等. 多目标模糊评价遗传算法综合质量交换网络[J]. 高校化学工程学报, 2002, 16(5): 549-554.
	Wang J F, Shen J Z, Li Y R, et al. Mass exchange networks synthesis using multi-objective genetic algorithm based on the fuzzy reasoning[J]. Journal of Chemical Engineering of Chinese Universities, 2002, 16(5): 549-554.
28	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.
	Gao Z H. Study on synthesis of mass exchange network targeting minimum cost[D]. Dalian: Dalian University of Technology, 2007.
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.

流股	最大流量/(kg·s^-1)	入口浓度/(kg H₂S·kg^-1)	目标浓度/(kg H₂S·kg^-1)	m	b	C₀
R1	0.9000	0.07000	0.00030
R2	0.1000	0.05100	0.00010
S1	2.3000	0.00060	0.03100	1.45	0	117360
S2	∞	0.00020	0.00350	0.26	0	176040

流股	最大流量/(kg·s^-1)	入口浓度/(kg H₂S·kg^-1)	目标浓度/(kg H₂S·kg^-1)	m	b	C₀
R1	0.9000	0.07000	0.00030
R2	0.1000	0.05100	0.00010
S1	2.3000	0.00060	0.03100	1.45	0	117360
S2	∞	0.00020	0.00350	0.26	0	176040

文献	单元数	总塔板数	TAC/(USD·a^-1)
[24]	4	—	530471
[25]	5	—	469968
[7]	5	—	431613
[26]	4	25	429700
[27]	4	21	422293
[28]	4	24	420545
[15]	4	20	412500
[16]	4	22	411166
[13]	6	19	410971
[14]	6	19	410565
本文	4	21	407308

文献	单元数	总塔板数	TAC/(USD·a^-1)
[24]	4	—	530471
[25]	5	—	469968
[7]	5	—	431613
[26]	4	25	429700
[27]	4	21	422293
[28]	4	24	420545
[15]	4	20	412500
[16]	4	22	411166
[13]	6	19	410971
[14]	6	19	410565
本文	4	21	407308

流股	最大流量/(kg·s^-1)	入口浓度/(kg NH₃·kg^-1)	目标浓度/(kg NH₃·kg^-1)	m	b	C₀
R1	2.0000	0.00500	0.00100
R2	4.0000	0.00500	0.00250
R3	3.5000	0.01100	0.00250
R4	1.5000	0.01000	0.00500
R5	0.5000	0.00800	0.00250
S1	1.8000	0.00170	0.00710	1.2	0	0
S2	1.0000	0.00250	0.00850	1	0	0
S3	∞	0.00000	0.01700	0.5	0	0.001