Optimization of fed-batch fermentation processes with bio-inspired algorithms

Detalhes bibliográficos
Autor(a) principal: Rocha, Miguel
Data de Publicação: 2014
Outros Autores: Mendes, Rui, Rocha, Orlando, Rocha, I., Ferreira, Eugénio C.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/27513
Resumo: The optimization of the feeding trajectories in fed-batch fermentation processes is a complex problem that has gained attention given its significant economical impact. A number of bio-inspired algorithms have approached this task with considerable success, but systematic and statistically significant comparisons of the different alternatives are still lacking. In this paper, the performance of different metaheuristics, such as Evolutionary Algorithms (EAs), Differential Evolution (DE) and Particle Swarm Optimization (PSO) is compared, resorting to several case studies taken from literature and conducting a thorough statistical validation of the results. DE obtains the best overall performance, showing a consistent ability to find good solutions and presenting a good convergence speed, with the DE/rand variants being the ones with the best performance. A freely available computational application, OptFerm, is described that provides an interface allowing users to apply the proposed methods to their own models and data.
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spelling Optimization of fed-batch fermentation processes with bio-inspired algorithmsFed-batch fermentationDifferential EvolutionEvolutionary algorithmsParticle Swarm OptimizationFeeding trajectory optimizationScience & TechnologyThe optimization of the feeding trajectories in fed-batch fermentation processes is a complex problem that has gained attention given its significant economical impact. A number of bio-inspired algorithms have approached this task with considerable success, but systematic and statistically significant comparisons of the different alternatives are still lacking. In this paper, the performance of different metaheuristics, such as Evolutionary Algorithms (EAs), Differential Evolution (DE) and Particle Swarm Optimization (PSO) is compared, resorting to several case studies taken from literature and conducting a thorough statistical validation of the results. DE obtains the best overall performance, showing a consistent ability to find good solutions and presenting a good convergence speed, with the DE/rand variants being the ones with the best performance. A freely available computational application, OptFerm, is described that provides an interface allowing users to apply the proposed methods to their own models and data.The work is partially funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects Ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEst-OE/ES/UI0752/2011.ElsevierPergamon Press Ltd.Universidade do MinhoRocha, MiguelMendes, RuiRocha, OrlandoRocha, I.Ferreira, Eugénio C.20142014-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/27513eng0957-417410.1016/j.eswa.2013.09.017info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:21:11Zoai:repositorium.sdum.uminho.pt:1822/27513Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:14:23.499542Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Optimization of fed-batch fermentation processes with bio-inspired algorithms
title Optimization of fed-batch fermentation processes with bio-inspired algorithms
spellingShingle Optimization of fed-batch fermentation processes with bio-inspired algorithms
Rocha, Miguel
Fed-batch fermentation
Differential Evolution
Evolutionary algorithms
Particle Swarm Optimization
Feeding trajectory optimization
Science & Technology
title_short Optimization of fed-batch fermentation processes with bio-inspired algorithms
title_full Optimization of fed-batch fermentation processes with bio-inspired algorithms
title_fullStr Optimization of fed-batch fermentation processes with bio-inspired algorithms
title_full_unstemmed Optimization of fed-batch fermentation processes with bio-inspired algorithms
title_sort Optimization of fed-batch fermentation processes with bio-inspired algorithms
author Rocha, Miguel
author_facet Rocha, Miguel
Mendes, Rui
Rocha, Orlando
Rocha, I.
Ferreira, Eugénio C.
author_role author
author2 Mendes, Rui
Rocha, Orlando
Rocha, I.
Ferreira, Eugénio C.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Rocha, Miguel
Mendes, Rui
Rocha, Orlando
Rocha, I.
Ferreira, Eugénio C.
dc.subject.por.fl_str_mv Fed-batch fermentation
Differential Evolution
Evolutionary algorithms
Particle Swarm Optimization
Feeding trajectory optimization
Science & Technology
topic Fed-batch fermentation
Differential Evolution
Evolutionary algorithms
Particle Swarm Optimization
Feeding trajectory optimization
Science & Technology
description The optimization of the feeding trajectories in fed-batch fermentation processes is a complex problem that has gained attention given its significant economical impact. A number of bio-inspired algorithms have approached this task with considerable success, but systematic and statistically significant comparisons of the different alternatives are still lacking. In this paper, the performance of different metaheuristics, such as Evolutionary Algorithms (EAs), Differential Evolution (DE) and Particle Swarm Optimization (PSO) is compared, resorting to several case studies taken from literature and conducting a thorough statistical validation of the results. DE obtains the best overall performance, showing a consistent ability to find good solutions and presenting a good convergence speed, with the DE/rand variants being the ones with the best performance. A freely available computational application, OptFerm, is described that provides an interface allowing users to apply the proposed methods to their own models and data.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/27513
url https://hdl.handle.net/1822/27513
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0957-4174
10.1016/j.eswa.2013.09.017
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dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
Pergamon Press Ltd.
publisher.none.fl_str_mv Elsevier
Pergamon Press Ltd.
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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