有分流棋盘模型应用于不相容多组分质量交换网络优化

doi:10.16085/j.issn.1000-6613.2023-0880

摘要/Abstract

摘要：

不相容多组分质量交换网络是质量交换网络综合中的难题之一。传统的分级超结构模型在模拟不相容多组分质量交换网络时，需要进行迭代计算，这使得优化过程变得复杂，并且难以兼顾计算效率。因此，本文提出将有分流棋盘模型用于优化不相容多组分质量交换网络问题，并采用基于种群识别策略的强制进化随机游走（RWCE）算法进行求解。有分流棋盘模型简化了模拟时的计算复杂度，且种群识别策略能够解决种群中个体“团聚”现象，从而全面提升了搜索全局最优解的计算效率。焦炉气脱硫算例的验证结果显示：有分流棋盘模型和基于种群识别策略的RWCE算法能够高效地解决不相容多组分质量交换网络问题，并且在优化平均时长和年综合费用方面取得了更为显著的成果。

关键词: 过程系统, 模型, 质量交换网络, 算法, 种群识别策略

Abstract:

The incompatible multi-component mass exchange network is one of the difficult problems in mass exchange network synthesis. When simulating incompatible multi-component mass exchange networks, the traditional stage-wise superstructure model requires iterative calculation, resulting in complex optimization processes and compromised computational efficiency. To solve these problem, the chessboard model is applied to the incompatible multi-component mass exchange network, and the random walking algorithm with compulsive evolution (RWCE) based on population identification strategy is employed. The chessboard model simplifies the computational complexity of the simulation, and the population identification strategy solves the "agglomeration" phenomenon of individuals in the population. This approach comprehensively improves the computational efficiency of searching for the global optimal solution. A published example for the coke oven gas sweetening problem shows that the chessboard model and the RWCE based on population identification strategy can efficiently handle incompatible multi-component mass exchange networks, achieving faster average optimization time and minimizing the total annual cost.

Key words: process systems, model, mass exchange network, algorithm, population identification strategy

中图分类号:

TQ021.8

易智康, 刘思琪, 崔国民, 段欢欢, 肖媛. 有分流棋盘模型应用于不相容多组分质量交换网络优化[J]. 化工进展, 2024, 43(6): 2986-2995.

YI Zhikang, LIU Siqi, CUI Guomin, DUAN Huanhuan, XIAO Yuan. A chessboard model for incompatible multi-component mass exchange network optimization[J]. Chemical Industry and Engineering Progress, 2024, 43(6): 2986-2995.

图/表 10

图1 有分流棋盘模型

图2 有分流棋盘模型的不相容多组分质量交换网络模拟计算过程

图3 个体数Nk =50时种群中结构重复的个体数量

图4 基于种群识别策略的RWCE算法流程图

图5 焦炉气脱硫的流程图

表1 算例焦炉气脱硫中的流股数据

富流股	G_i /kg·s^-1	$Y H 2 S i n$ /kg·kg^-1	$Y H 2 S o u t$ /kg·kg^-1	$Y C O 2 i n$ /kg·kg^-1	$Y C O 2 o u t$ /kg·kg^-1
R₁	0.9	0.07	0.0003	0.06	0.005
R₂	0.1	0.051	0.0001	0.115	0.005
贫流股	L_i /kg·s^-1	$X H 2 S i n$ /kg·kg^-1	$X H 2 S o u t$ /kg·kg^-1	$X C O 2 i n$ /kg·kg^-1	$X C O 2 o u t$ /kg·kg^-1	年运营成本 /USD·kg^-1
S₁	2.3	0.0006	0.031	0	0.171	117360
S₂	∞	0.0002	0.0035	0	0.103	176040

表1 算例焦炉气脱硫中的流股数据

富流股	G_i /kg·s^-1	$Y H 2 S i n$ /kg·kg^-1	$Y H 2 S o u t$ /kg·kg^-1	$Y C O 2 i n$ /kg·kg^-1	$Y C O 2 o u t$ /kg·kg^-1
R₁	0.9	0.07	0.0003	0.06	0.005
R₂	0.1	0.051	0.0001	0.115	0.005
贫流股	L_i /kg·s^-1	$X H 2 S i n$ /kg·kg^-1	$X H 2 S o u t$ /kg·kg^-1	$X C O 2 i n$ /kg·kg^-1	$X C O 2 o u t$ /kg·kg^-1	年运营成本 /USD·kg^-1
S₁	2.3	0.0006	0.031	0	0.171	117360
S₂	∞	0.0002	0.0035	0	0.103	176040

表2 RWCE算法参数

N_k	ΔL_L	ΔL_N	ΔL_SPR	ΔL_SPS	L_min	SPR_min	SPS_min	θ	N_max	η
100	0.05	0.99	0.01	0.01	0.02	0.01	0.01	0.009	0.2	0.001

图6 使用有分流棋盘模型和基于种群识别策略RWCE算法优化焦炉气脱硫的最优结构

表3 算例焦炉气净化的结果对比

参考文献	L₁/kg·s^-1	L₂/kg·s^-1	塔板总数/个	单元数/个	分流数(富/贫)/个	TAC/USD·a^-1
Liu等^[24]	2.2300	0.4600	20	4	0/2	433730
Chen等^[9]	2.1950	0.3310	25	4	0/2	429700
王江峰等^[25]	2.2352	0.3657	21	4	0/2	422293
Liu等^[6]	2.1920	0.3700	20	4	0/2	413450
Tao等^[23]	2.1909	0.3692	20	4	0/2	413160
本文	2.1892	0.3454	20	6	0/2	408773

图7 基于种群识别策略的RWCE算法以及基础RWCE算法用于焦炉气脱硫的优化过程

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