基于多策略集成优化算法的己烷油精馏过程3E多目标优化

doi:10.16085/j.issn.1000-6613.2021-1594

摘要/Abstract

摘要：

己烷油精馏工序是溶剂油生产过程的重要环节，但其伴随着高能耗和高排放。因此，己烷油精馏工序的能量、经济和环境（3E）多目标优化对于溶剂油工业的可持续发展具有重要意义。针对传统非支配排序遗传算法（NSGA-Ⅱ）收敛速度慢、易陷入局部最优等问题，本文提出了一种改进的基于多策略集成的多目标遗传算法（MENSGA-Ⅱ）。该算法发展了一种基于邻域的引导策略，以增强算法的搜索能力进而加快收敛速度；同时引入随机极限游走策略以维持算法所获得解集的分布性。将MENSGA-Ⅱ应用于典型的测试函数和实际的己烷油精馏过程，结果表明，该算法在鲁棒性、收敛速度和解集分布性上具有优越性。与实际运行工况相比，典型优化工况下精馏系统年度毛利润可提升4.99×10⁵USD/a，能源消耗和CO₂排放可分别减少5.09×10²kW/a和4.82×10²kg/a。

关键词: 多目标优化, 己烷油精馏, 非支配排序遗传算法（NSGA-Ⅱ）, 多策略

Abstract:

Hexane oil distillation is an important process in solvent oil production accompanied by high energy consumption and high emission. Therefore, the energetic, economic and environmental(3E) multi-objective optimization of hexane oil distillation process is of great significance for the sustainable development of solvent oil industry. Regarding slow convergence and easiness of trapping in local optimum for the conventional non-dominated sorting genetic algorithm (NSGA-Ⅱ), a novel multi-strategy ensemble non-dominated sorting genetic algorithm (MENSGA-Ⅱ) was proposed. In this algorithm, a guidance strategy based on the global and local optimal of individual neighborhood, was developed to accelerate the convergence speed of the algorithm. At the same time, the random limit walk strategy was introduced to improve the convergence and distribution of the solution set obtained. The MENSGA-Ⅱ was applied to the typical test functions and the actual hexane oil distillation process. The results showed that the algorithm has advantages in stability, convergence speed and uniformity of the Pareto front. Compared with the actual operating condition, the annual gross profit of the distillation system under typical optimized conditions could be increased by 4.99×10⁵USD/a, with energy consumption and CO₂ emissions reduced by 5.09×10²kW/a and 4.82×10²kg/a, respectively.

Key words: multi-objective optimization, hexane oil distillation, non-dominated sorting genetic algorithm (NSGA-Ⅱ), multi-strategy

中图分类号:

TK91

代敏, 杨福胜, 张早校, 刘桂莲, 冯霄. 基于多策略集成优化算法的己烷油精馏过程3E多目标优化[J]. 化工进展, 2022, 41(6): 2852-2863.

DAI Min, YANG Fusheng, ZHANG Zaoxiao, LIU Guilian, FENG Xiao. 3E Multi-objective optimization of hexane oil distillation process based on multi-strategy ensemble optimization algorithm[J]. Chemical Industry and Engineering Progress, 2022, 41(6): 2852-2863.

图/表 16

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测试函数	目标维数	决策变量维数	特性
ZDT1	2	30	凸
ZDT2	2	30	凹
ZDT3	2	30	凸，不连续
ZDT4	2	10	凸，多模态
Viennet4	3	2	混合，多模态
DTLZ7	3	22	凹，不连续
UF9	3	30	平面
UF10	3	30	抛物线

测试函数	目标维数	决策变量维数	特性
ZDT1	2	30	凸
ZDT2	2	30	凹
ZDT3	2	30	凸，不连续
ZDT4	2	10	凸，多模态
Viennet4	3	2	混合，多模态
DTLZ7	3	22	凹，不连续
UF9	3	30	平面
UF10	3	30	抛物线

算法	参数设置
MENSGA-Ⅱ	交叉概率为1，突变概率为1/n，交叉分布指数为20，突变分布指数为20
NSGA-Ⅱ	交叉概率为1，突变概率为1/n，交叉分布指数为20，突变分布指数为20
e-MOEA	ε=0.06
MOEA/D	分解方法为基于惩罚的边界交集法

算法	参数设置
MENSGA-Ⅱ	交叉概率为1，突变概率为1/n，交叉分布指数为20，突变分布指数为20
NSGA-Ⅱ	交叉概率为1，突变概率为1/n，交叉分布指数为20，突变分布指数为20
e-MOEA	ε=0.06
MOEA/D	分解方法为基于惩罚的边界交集法

算法	平均排名
MENSGA-Ⅱ	1
NSGA-Ⅱ	2.75
MOEA/D	3.75
e-MOEA	2.87