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

Handle URI:
http://hdl.handle.net/10754/622175
Title:
Automated process flowsheet synthesis for membrane processes using genetic algorithm: role of crossover operators
Authors:
Shafiee, Alireza; Arab, Mobin; Lai, Zhiping ( 0000-0001-9555-6009 ) ; Liu, Zongwen; Abbas, Ali
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%.
KAUST Department:
Advanced Membranes and Porous Materials Research Center
Citation:
Shafiee A, Arab M, Lai Z, Liu Z, Abbas A (2016) Automated process flowsheet synthesis for membrane processes using genetic algorithm: role of crossover operators. 26th European Symposium on Computer Aided Process Engineering: 1201–1206. Available: http://dx.doi.org/10.1016/B978-0-444-63428-3.50205-8.
Publisher:
Elsevier BV
Journal:
26th European Symposium on Computer Aided Process Engineering
Issue Date:
25-Jun-2016
DOI:
10.1016/B978-0-444-63428-3.50205-8
Type:
Book Chapter
ISSN:
1570-7946
Sponsors:
This work is supported in part by a King Abdullah University of Science and Technology (KAUST) CRG Award.
Additional Links:
http://dx.doi.org/10.1016/B978-0-444-63428-3.50205-8
Appears in Collections:
Advanced Membranes and Porous Materials Research Center; Book Chapters

Full metadata record

DC FieldValue Language
dc.contributor.authorShafiee, Alirezaen
dc.contributor.authorArab, Mobinen
dc.contributor.authorLai, Zhipingen
dc.contributor.authorLiu, Zongwenen
dc.contributor.authorAbbas, Alien
dc.date.accessioned2017-01-02T08:42:36Z-
dc.date.available2017-01-02T08:42:36Z-
dc.date.issued2016-06-25en
dc.identifier.citationShafiee A, Arab M, Lai Z, Liu Z, Abbas A (2016) Automated process flowsheet synthesis for membrane processes using genetic algorithm: role of crossover operators. 26th European Symposium on Computer Aided Process Engineering: 1201–1206. Available: http://dx.doi.org/10.1016/B978-0-444-63428-3.50205-8.en
dc.identifier.issn1570-7946en
dc.identifier.doi10.1016/B978-0-444-63428-3.50205-8en
dc.identifier.urihttp://hdl.handle.net/10754/622175-
dc.description.abstractIn 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%.en
dc.description.sponsorshipThis work is supported in part by a King Abdullah University of Science and Technology (KAUST) CRG Award.en
dc.publisherElsevier BVen
dc.relation.urlhttp://dx.doi.org/10.1016/B978-0-444-63428-3.50205-8en
dc.subjectGenetic algorithmen
dc.subjectcrossover operatoren
dc.subjectcarbon captureen
dc.subjectprocess flowsheet synthesisen
dc.subjectgas separationen
dc.subjectmembraneen
dc.titleAutomated process flowsheet synthesis for membrane processes using genetic algorithm: role of crossover operatorsen
dc.typeBook Chapteren
dc.contributor.departmentAdvanced Membranes and Porous Materials Research Centeren
dc.identifier.journal26th European Symposium on Computer Aided Process Engineeringen
dc.contributor.institutionSchool of Chemical and Biomolecular Engineering, The University of Sydney, Sydney, Australiaen
kaust.authorLai, Zhipingen
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