Bioethanol production optimization by direct numerical methods and evolutionary algorithms

Autores
Fernández Puchol, María Cecilia; Pantano, Maria Nadia; Groff, Maria Carla; Gil, Rocio Mariel; Scaglia, Gustavo Juan Eduardo
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper develops a dynamic optimization methodology based on direct numerical methods, for the bioethanol fed-batch production from glucose and fructose as a substrate. The mathematical model that governs the process consists of six differential equations and is highly nonlinear. The proposed strategy uses the Fourier trigonometric basis and normalized orthogonal polynomials for substrate feeding rate parameterization. Then, evolutionary algorithms and gradient methods are combined to search parameters that generate the best control action. This parameterization methodology requires a minimum number of parameters to optimize. Also, the continuous and differentiable nature of the optimal profile enables its direct implementation in the physical process, eliminating the necessity for filtering or smoothing it. In addition, they are ideal for bioprocesses, in which it is preferable to avoid abrupt changes in the operating modes of the process to promote cell growth. As a result, using only 3 parameters, a 3.5% increase in ethanol production was achieved, while the reference uses at least 10 parameters and provides a stepped feed profile. The simulations have yielded promising results, making this proposal an alternative with excellent potential for process optimization.
Fil: Fernández Puchol, María Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Fil: Pantano, Maria Nadia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Fil: Groff, Maria Carla. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Biotecnología; Argentina
Fil: Gil, Rocio Mariel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Fil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Materia
NONLINEAR SYSTEM
FOURIER SERIES
OPTIMAL CONTROL
EVOLUTIONARY ALGORITHMS
BIOETHANOL PRODUCTION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/240279
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/240279
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Bioethanol production optimization by direct numerical methods and evolutionary algorithmsFernández Puchol, María CeciliaPantano, Maria NadiaGroff, Maria CarlaGil, Rocio MarielScaglia, Gustavo Juan EduardoNONLINEAR SYSTEMFOURIER SERIESOPTIMAL CONTROLEVOLUTIONARY ALGORITHMSBIOETHANOL PRODUCTIONhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2This paper develops a dynamic optimization methodology based on direct numerical methods, for the bioethanol fed-batch production from glucose and fructose as a substrate. The mathematical model that governs the process consists of six differential equations and is highly nonlinear. The proposed strategy uses the Fourier trigonometric basis and normalized orthogonal polynomials for substrate feeding rate parameterization. Then, evolutionary algorithms and gradient methods are combined to search parameters that generate the best control action. This parameterization methodology requires a minimum number of parameters to optimize. Also, the continuous and differentiable nature of the optimal profile enables its direct implementation in the physical process, eliminating the necessity for filtering or smoothing it. In addition, they are ideal for bioprocesses, in which it is preferable to avoid abrupt changes in the operating modes of the process to promote cell growth. As a result, using only 3 parameters, a 3.5% increase in ethanol production was achieved, while the reference uses at least 10 parameters and provides a stepped feed profile. The simulations have yielded promising results, making this proposal an alternative with excellent potential for process optimization.Fil: Fernández Puchol, María Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Pantano, Maria Nadia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Groff, Maria Carla. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Biotecnología; ArgentinaFil: Gil, Rocio Mariel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaInstitute of Electrical and Electronics Engineers2024-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/240279Fernández Puchol, María Cecilia; Pantano, Maria Nadia; Groff, Maria Carla; Gil, Rocio Mariel; Scaglia, Gustavo Juan Eduardo; Bioethanol production optimization by direct numerical methods and evolutionary algorithms; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 22; 3; 2-2024; 259-2651548-0992CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://latamt.ieeer9.org/index.php/transactions/article/view/8307info:eu-repo/semantics/altIdentifier/doi/10.1109/TLA.2024.10431425info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:36:58Zoai:ri.conicet.gov.ar:11336/240279instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:36:58.996CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Bioethanol production optimization by direct numerical methods and evolutionary algorithms
title Bioethanol production optimization by direct numerical methods and evolutionary algorithms
spellingShingle Bioethanol production optimization by direct numerical methods and evolutionary algorithms
Fernández Puchol, María Cecilia
NONLINEAR SYSTEM
FOURIER SERIES
OPTIMAL CONTROL
EVOLUTIONARY ALGORITHMS
BIOETHANOL PRODUCTION
title_short Bioethanol production optimization by direct numerical methods and evolutionary algorithms
title_full Bioethanol production optimization by direct numerical methods and evolutionary algorithms
title_fullStr Bioethanol production optimization by direct numerical methods and evolutionary algorithms
title_full_unstemmed Bioethanol production optimization by direct numerical methods and evolutionary algorithms
title_sort Bioethanol production optimization by direct numerical methods and evolutionary algorithms
dc.creator.none.fl_str_mv Fernández Puchol, María Cecilia
Pantano, Maria Nadia
Groff, Maria Carla
Gil, Rocio Mariel
Scaglia, Gustavo Juan Eduardo
author Fernández Puchol, María Cecilia
author_facet Fernández Puchol, María Cecilia
Pantano, Maria Nadia
Groff, Maria Carla
Gil, Rocio Mariel
Scaglia, Gustavo Juan Eduardo
author_role author
author2 Pantano, Maria Nadia
Groff, Maria Carla
Gil, Rocio Mariel
Scaglia, Gustavo Juan Eduardo
author2_role author
author
author
author
dc.subject.none.fl_str_mv NONLINEAR SYSTEM
FOURIER SERIES
OPTIMAL CONTROL
EVOLUTIONARY ALGORITHMS
BIOETHANOL PRODUCTION
topic NONLINEAR SYSTEM
FOURIER SERIES
OPTIMAL CONTROL
EVOLUTIONARY ALGORITHMS
BIOETHANOL PRODUCTION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This paper develops a dynamic optimization methodology based on direct numerical methods, for the bioethanol fed-batch production from glucose and fructose as a substrate. The mathematical model that governs the process consists of six differential equations and is highly nonlinear. The proposed strategy uses the Fourier trigonometric basis and normalized orthogonal polynomials for substrate feeding rate parameterization. Then, evolutionary algorithms and gradient methods are combined to search parameters that generate the best control action. This parameterization methodology requires a minimum number of parameters to optimize. Also, the continuous and differentiable nature of the optimal profile enables its direct implementation in the physical process, eliminating the necessity for filtering or smoothing it. In addition, they are ideal for bioprocesses, in which it is preferable to avoid abrupt changes in the operating modes of the process to promote cell growth. As a result, using only 3 parameters, a 3.5% increase in ethanol production was achieved, while the reference uses at least 10 parameters and provides a stepped feed profile. The simulations have yielded promising results, making this proposal an alternative with excellent potential for process optimization.
Fil: Fernández Puchol, María Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Fil: Pantano, Maria Nadia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Fil: Groff, Maria Carla. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Biotecnología; Argentina
Fil: Gil, Rocio Mariel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Fil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
description This paper develops a dynamic optimization methodology based on direct numerical methods, for the bioethanol fed-batch production from glucose and fructose as a substrate. The mathematical model that governs the process consists of six differential equations and is highly nonlinear. The proposed strategy uses the Fourier trigonometric basis and normalized orthogonal polynomials for substrate feeding rate parameterization. Then, evolutionary algorithms and gradient methods are combined to search parameters that generate the best control action. This parameterization methodology requires a minimum number of parameters to optimize. Also, the continuous and differentiable nature of the optimal profile enables its direct implementation in the physical process, eliminating the necessity for filtering or smoothing it. In addition, they are ideal for bioprocesses, in which it is preferable to avoid abrupt changes in the operating modes of the process to promote cell growth. As a result, using only 3 parameters, a 3.5% increase in ethanol production was achieved, while the reference uses at least 10 parameters and provides a stepped feed profile. The simulations have yielded promising results, making this proposal an alternative with excellent potential for process optimization.
publishDate 2024
dc.date.none.fl_str_mv 2024-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/240279
Fernández Puchol, María Cecilia; Pantano, Maria Nadia; Groff, Maria Carla; Gil, Rocio Mariel; Scaglia, Gustavo Juan Eduardo; Bioethanol production optimization by direct numerical methods and evolutionary algorithms; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 22; 3; 2-2024; 259-265
1548-0992
CONICET Digital
CONICET
url http://hdl.handle.net/11336/240279
identifier_str_mv Fernández Puchol, María Cecilia; Pantano, Maria Nadia; Groff, Maria Carla; Gil, Rocio Mariel; Scaglia, Gustavo Juan Eduardo; Bioethanol production optimization by direct numerical methods and evolutionary algorithms; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 22; 3; 2-2024; 259-265
1548-0992
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://latamt.ieeer9.org/index.php/transactions/article/view/8307
info:eu-repo/semantics/altIdentifier/doi/10.1109/TLA.2024.10431425
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.22299

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