Enhancing the Performance of Evolutionary Algorithm by Differential Evolution for Optimizing Distillation Sequence

Optimal synthesis of distillation sequence is a complex problem in chemical processes engineering, which involves process structure optimization and operation parameters optimization. The study of the synthesis of distillation sequence is a crucial step toward improving the efficiency of chemical pr...

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Bibliographic Details
Published in:Molecules (Basel, Switzerland) Switzerland), 2022-06, Vol.27 (12), p.3802
Main Authors: Hu, Zehua, Li, Peilong, Liu, Yefei
Format: Article
Language:English
Subjects:
Algorithms
Aspen Plus
Carbon dioxide
Chemical reactions
Distillation
distillation process synthesis
distillation sequence
Emissions
Emissions control
evolutionary algorithm
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Greenhouse effect
Greenhouse gases
Heuristic
Linear programming
Methods
Optimization
Population
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Summary:Optimal synthesis of distillation sequence is a complex problem in chemical processes engineering, which involves process structure optimization and operation parameters optimization. The study of the synthesis of distillation sequence is a crucial step toward improving the efficiency of chemical processes and reducing greenhouse gas emissions. This work introduced the concept of binary tree to encode the distillation sequence. The performance of the six evolutionary algorithms was evaluated by solving a 14-component distillation sequence synthesis problem. The best algorithm was used to optimize the operation parameters of a triple-column distillation process. The total annual cost and CO2 emissions were considered as the metrics to evaluate the performance of triple-column distillation processes. As a result, NSGA-II-DE was found to be the best one of the six tested evolutionary algorithms. Then, NSGA-II-DE was applied to the distillation sequence optimization to find the best operating parameters, which led to a significant reduction in CO2 emission and total annual costs.
ISSN:1420-3049
1420-3049
DOI:10.3390/molecules27123802