Automated process flowsheet synthesis for membrane processes using genetic algorithm: role of crossover operators

Alireza Shafiee, Mobin Arab, Zhiping Lai, Zongwen Liu, Ali Abbas

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

In optimization-based process flowsheet synthesis, optimization methods, including genetic algorithms (GA), are used as advantageous tools to select a high performance flowsheet by ‘screening’ large numbers of possible flowsheets. In this study, we expand the role of GA to include flowsheet generation through proposing a modified Greedysub tour crossover operator. Performance of the proposed crossover operator is compared with four other commonly used operators. The proposed GA optimizationbased process synthesis method is applied to generate the optimum process flowsheet for a multicomponent membrane-based CO2 capture process. Within defined constraints and using the random-point crossover, CO2 purity of 0.827 (equivalent to 0.986 on dry basis) is achieved which results in improvement (3.4%) over the simplest crossover operator applied. In addition, the least variability in the converged flowsheet and CO2 purity is observed for random-point crossover operator, which approximately implies closeness of the solution to the global optimum, and hence the consistency of the algorithm. The proposed crossover operator is found to improve the convergence speed of the algorithm by 77.6%.
Original languageEnglish (US)
Title of host publication26th European Symposium on Computer Aided Process Engineering
PublisherElsevier BV
Pages1201-1206
Number of pages6
ISBN (Print)9780444634283
DOIs
StatePublished - Jun 25 2016

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

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