Optimization of fed-batch fermentation processes with bio-inspired algorithms
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , |
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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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 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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 instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
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 |
repository.mail.fl_str_mv |
|
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1799132586708041728 |