Dynamic Optimization of Xylitol Production Using Legendre-Based Control Parameterization

Autores
Gutiérrez, Eugenia; Noriega, Marianela; Fernández Puchol, María Cecilia; Pantano, Maria Nadia; Rodriguez Aguilar, Leandro Pedro Faustino; Scaglia, Gustavo Juan Eduardo
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper presents an improved methodology for optimizing the fed-batch fermentation process of xylitol production, aiming to maximize the final concentration in a bioreactor co-fed with xylose and glucose. Xylitol is a valuable sugar alcohol widely used in the food and pharmaceutical industries, and its microbial production requires precise control over substrate feeding strategies. The proposed technique employs Legendre polynomials to parameterize two control actions (the feeding rates of glucose and xylose), and it uses a hybrid optimization algorithm combining Monte Carlo sampling with genetic algorithms for coefficient selection. Unlike traditional optimization approaches based on piecewise parameterization, which produce discontinuous control profiles and require post-processing, this method generates smooth profiles directly applicable to real systems. Additionally, it significantly reduces mathematical complexity compared to strategiesthat combine Fourier series with orthonormal polynomials while maintaining similar optimization results. The methodology achieves good results in xylitol production using only eight parameters, compared to at least twenty in other approaches. This dimensionality reduction improves the robustness of the optimization by decreasing the likelihood of convergence to local optima while also reducing the computational cost and enhancing feasibility for implementation. The results highlight the potential of this strategy as a practical and efficient tool for optimizing nonlinear multivariable bioprocesses.
Fil: Gutiérrez, Eugenia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Fil: Noriega, Marianela. 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: Fernández Puchol, María Cecilia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Fil: Pantano, Maria Nadia. 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: Rodriguez Aguilar, Leandro Pedro Faustino. 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: 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
BIOPROCESSES
LEGENDRE POLYNOMIALS
OPTIMIZATION
EVOLUTIONARY ALGORITHMS
NON-LINEAR SYSTEMS
GENETIC ALGORITHM
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:11336/280243
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/280243
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Dynamic Optimization of Xylitol Production Using Legendre-Based Control ParameterizationGutiérrez, EugeniaNoriega, MarianelaFernández Puchol, María CeciliaPantano, Maria NadiaRodriguez Aguilar, Leandro Pedro FaustinoScaglia, Gustavo Juan EduardoBIOPROCESSESLEGENDRE POLYNOMIALSOPTIMIZATIONEVOLUTIONARY ALGORITHMSNON-LINEAR SYSTEMSGENETIC ALGORITHMhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2This paper presents an improved methodology for optimizing the fed-batch fermentation process of xylitol production, aiming to maximize the final concentration in a bioreactor co-fed with xylose and glucose. Xylitol is a valuable sugar alcohol widely used in the food and pharmaceutical industries, and its microbial production requires precise control over substrate feeding strategies. The proposed technique employs Legendre polynomials to parameterize two control actions (the feeding rates of glucose and xylose), and it uses a hybrid optimization algorithm combining Monte Carlo sampling with genetic algorithms for coefficient selection. Unlike traditional optimization approaches based on piecewise parameterization, which produce discontinuous control profiles and require post-processing, this method generates smooth profiles directly applicable to real systems. Additionally, it significantly reduces mathematical complexity compared to strategiesthat combine Fourier series with orthonormal polynomials while maintaining similar optimization results. The methodology achieves good results in xylitol production using only eight parameters, compared to at least twenty in other approaches. This dimensionality reduction improves the robustness of the optimization by decreasing the likelihood of convergence to local optima while also reducing the computational cost and enhancing feasibility for implementation. The results highlight the potential of this strategy as a practical and efficient tool for optimizing nonlinear multivariable bioprocesses.Fil: Gutiérrez, Eugenia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Noriega, Marianela. 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: Fernández Puchol, María Cecilia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Pantano, Maria Nadia. 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: Rodriguez Aguilar, Leandro Pedro Faustino. 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: 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; ArgentinaMDPI2025-05info: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/11336/280243Gutiérrez, Eugenia; Noriega, Marianela; Fernández Puchol, María Cecilia; Pantano, Maria Nadia; Rodriguez Aguilar, Leandro Pedro Faustino; et al.; Dynamic Optimization of Xylitol Production Using Legendre-Based Control Parameterization; MDPI; Fermentation; 11; 6; 5-2025; 1-132311-5637CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2311-5637/11/6/308info:eu-repo/semantics/altIdentifier/doi/10.3390/fermentation11060308info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-02-26T10:01:23Zoai:ri.conicet.gov.ar:11336/280243instacron: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:34982026-02-26 10:01:24.02CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Dynamic Optimization of Xylitol Production Using Legendre-Based Control Parameterization
title Dynamic Optimization of Xylitol Production Using Legendre-Based Control Parameterization
spellingShingle Dynamic Optimization of Xylitol Production Using Legendre-Based Control Parameterization
Gutiérrez, Eugenia
BIOPROCESSES
LEGENDRE POLYNOMIALS
OPTIMIZATION
EVOLUTIONARY ALGORITHMS
NON-LINEAR SYSTEMS
GENETIC ALGORITHM
title_short Dynamic Optimization of Xylitol Production Using Legendre-Based Control Parameterization
title_full Dynamic Optimization of Xylitol Production Using Legendre-Based Control Parameterization
title_fullStr Dynamic Optimization of Xylitol Production Using Legendre-Based Control Parameterization
title_full_unstemmed Dynamic Optimization of Xylitol Production Using Legendre-Based Control Parameterization
title_sort Dynamic Optimization of Xylitol Production Using Legendre-Based Control Parameterization
dc.creator.none.fl_str_mv Gutiérrez, Eugenia
Noriega, Marianela
Fernández Puchol, María Cecilia
Pantano, Maria Nadia
Rodriguez Aguilar, Leandro Pedro Faustino
Scaglia, Gustavo Juan Eduardo
author Gutiérrez, Eugenia
author_facet Gutiérrez, Eugenia
Noriega, Marianela
Fernández Puchol, María Cecilia
Pantano, Maria Nadia
Rodriguez Aguilar, Leandro Pedro Faustino
Scaglia, Gustavo Juan Eduardo
author_role author
author2 Noriega, Marianela
Fernández Puchol, María Cecilia
Pantano, Maria Nadia
Rodriguez Aguilar, Leandro Pedro Faustino
Scaglia, Gustavo Juan Eduardo
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv BIOPROCESSES
LEGENDRE POLYNOMIALS
OPTIMIZATION
EVOLUTIONARY ALGORITHMS
NON-LINEAR SYSTEMS
GENETIC ALGORITHM
topic BIOPROCESSES
LEGENDRE POLYNOMIALS
OPTIMIZATION
EVOLUTIONARY ALGORITHMS
NON-LINEAR SYSTEMS
GENETIC ALGORITHM
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 presents an improved methodology for optimizing the fed-batch fermentation process of xylitol production, aiming to maximize the final concentration in a bioreactor co-fed with xylose and glucose. Xylitol is a valuable sugar alcohol widely used in the food and pharmaceutical industries, and its microbial production requires precise control over substrate feeding strategies. The proposed technique employs Legendre polynomials to parameterize two control actions (the feeding rates of glucose and xylose), and it uses a hybrid optimization algorithm combining Monte Carlo sampling with genetic algorithms for coefficient selection. Unlike traditional optimization approaches based on piecewise parameterization, which produce discontinuous control profiles and require post-processing, this method generates smooth profiles directly applicable to real systems. Additionally, it significantly reduces mathematical complexity compared to strategiesthat combine Fourier series with orthonormal polynomials while maintaining similar optimization results. The methodology achieves good results in xylitol production using only eight parameters, compared to at least twenty in other approaches. This dimensionality reduction improves the robustness of the optimization by decreasing the likelihood of convergence to local optima while also reducing the computational cost and enhancing feasibility for implementation. The results highlight the potential of this strategy as a practical and efficient tool for optimizing nonlinear multivariable bioprocesses.
Fil: Gutiérrez, Eugenia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Fil: Noriega, Marianela. 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: Fernández Puchol, María Cecilia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Fil: Pantano, Maria Nadia. 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: Rodriguez Aguilar, Leandro Pedro Faustino. 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: 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 presents an improved methodology for optimizing the fed-batch fermentation process of xylitol production, aiming to maximize the final concentration in a bioreactor co-fed with xylose and glucose. Xylitol is a valuable sugar alcohol widely used in the food and pharmaceutical industries, and its microbial production requires precise control over substrate feeding strategies. The proposed technique employs Legendre polynomials to parameterize two control actions (the feeding rates of glucose and xylose), and it uses a hybrid optimization algorithm combining Monte Carlo sampling with genetic algorithms for coefficient selection. Unlike traditional optimization approaches based on piecewise parameterization, which produce discontinuous control profiles and require post-processing, this method generates smooth profiles directly applicable to real systems. Additionally, it significantly reduces mathematical complexity compared to strategiesthat combine Fourier series with orthonormal polynomials while maintaining similar optimization results. The methodology achieves good results in xylitol production using only eight parameters, compared to at least twenty in other approaches. This dimensionality reduction improves the robustness of the optimization by decreasing the likelihood of convergence to local optima while also reducing the computational cost and enhancing feasibility for implementation. The results highlight the potential of this strategy as a practical and efficient tool for optimizing nonlinear multivariable bioprocesses.
publishDate 2025
dc.date.none.fl_str_mv 2025-05
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/11336/280243
Gutiérrez, Eugenia; Noriega, Marianela; Fernández Puchol, María Cecilia; Pantano, Maria Nadia; Rodriguez Aguilar, Leandro Pedro Faustino; et al.; Dynamic Optimization of Xylitol Production Using Legendre-Based Control Parameterization; MDPI; Fermentation; 11; 6; 5-2025; 1-13
2311-5637
CONICET Digital
CONICET
url http://hdl.handle.net/11336/280243
identifier_str_mv Gutiérrez, Eugenia; Noriega, Marianela; Fernández Puchol, María Cecilia; Pantano, Maria Nadia; Rodriguez Aguilar, Leandro Pedro Faustino; et al.; Dynamic Optimization of Xylitol Production Using Legendre-Based Control Parameterization; MDPI; Fermentation; 11; 6; 5-2025; 1-13
2311-5637
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://www.mdpi.com/2311-5637/11/6/308
info:eu-repo/semantics/altIdentifier/doi/10.3390/fermentation11060308
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 MDPI
publisher.none.fl_str_mv MDPI
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 13.176822

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