Optimization of Time-Varying Temperature Profiles for Enhanced Beer Fermentation by Evolutive Algorithms

Autores
Ruarte, Pablo Javier; Pantano, Maria Nadia; Noriega, Marianela; Fernández, Cecilia; Serrano, Mario Emanuel; Scaglia, Gustavo Juan Eduardo
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Beer is one of the most popular alcoholic beverages globally, leading to continuous efforts to enhance its production methods. Raw materials and the production process are crucial in the brewing industry, with fermentation being a vital stage that significantly impacts beer quality. The aim of this study is to optimize the beer fermentation process by maximizing the ethanol concentration while minimizing species that adversely affect the organoleptic properties of beer. A novel optimization approach has been developed to derive an optimal, smooth, and continuous temperature profile that can be directly applied in real-world processes. This method integrates Fourier series and orthogonal polynomials for control action parameterization, in combination with evolutionary algorithms for parameter optimization. A key advantage of this methodology lies in its ability to handle a reduced parameter set efficiently, resulting in temperature profiles that are continuous and differentiable. This feature eliminates the need for post-smoothing and is particularly advantageous in biotechnological applications, where abrupt changes in temperature could negatively affect the viability of microorganisms. The optimized profiles not only enhancefermentation efficiency, but also improve the ethanol yield and reduce undesirable flavor compounds, providing a substantial improvement over current industrial practices. These advancements present significant potential for improving both the quality and consistency of beer production.
Fil: Ruarte, Pablo Javier. 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: 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: Noriega, Marianela. 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: Fernández, 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: Serrano, Mario Emanuel. 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: 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
BEER FERMENTATION
DYNAMIC MODEL
MULTIVARIABLE SYSTEMS
TEMPERATURE PROFILES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/256131
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Optimization of Time-Varying Temperature Profiles for Enhanced Beer Fermentation by Evolutive AlgorithmsRuarte, Pablo JavierPantano, Maria NadiaNoriega, MarianelaFernández, CeciliaSerrano, Mario EmanuelScaglia, Gustavo Juan EduardoBEER FERMENTATIONDYNAMIC MODELMULTIVARIABLE SYSTEMSTEMPERATURE PROFILEShttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Beer is one of the most popular alcoholic beverages globally, leading to continuous efforts to enhance its production methods. Raw materials and the production process are crucial in the brewing industry, with fermentation being a vital stage that significantly impacts beer quality. The aim of this study is to optimize the beer fermentation process by maximizing the ethanol concentration while minimizing species that adversely affect the organoleptic properties of beer. A novel optimization approach has been developed to derive an optimal, smooth, and continuous temperature profile that can be directly applied in real-world processes. This method integrates Fourier series and orthogonal polynomials for control action parameterization, in combination with evolutionary algorithms for parameter optimization. A key advantage of this methodology lies in its ability to handle a reduced parameter set efficiently, resulting in temperature profiles that are continuous and differentiable. This feature eliminates the need for post-smoothing and is particularly advantageous in biotechnological applications, where abrupt changes in temperature could negatively affect the viability of microorganisms. The optimized profiles not only enhancefermentation efficiency, but also improve the ethanol yield and reduce undesirable flavor compounds, providing a substantial improvement over current industrial practices. These advancements present significant potential for improving both the quality and consistency of beer production.Fil: Ruarte, Pablo Javier. 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: 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: Noriega, Marianela. 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: Fernández, 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: Serrano, Mario Emanuel. 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: 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; ArgentinaMDPI2024-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/256131Ruarte, Pablo Javier; Pantano, Maria Nadia; Noriega, Marianela; Fernández, Cecilia; Serrano, Mario Emanuel; et al.; Optimization of Time-Varying Temperature Profiles for Enhanced Beer Fermentation by Evolutive Algorithms; MDPI; Fermentation; 11; 1; 12-2024; 1-122311-5637CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2311-5637/11/1/2info:eu-repo/semantics/altIdentifier/doi/10.3390/fermentation11010002info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:19:39Zoai:ri.conicet.gov.ar:11336/256131instacron: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:34982025-10-22 11:19:39.785CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Optimization of Time-Varying Temperature Profiles for Enhanced Beer Fermentation by Evolutive Algorithms
title Optimization of Time-Varying Temperature Profiles for Enhanced Beer Fermentation by Evolutive Algorithms
spellingShingle Optimization of Time-Varying Temperature Profiles for Enhanced Beer Fermentation by Evolutive Algorithms
Ruarte, Pablo Javier
BEER FERMENTATION
DYNAMIC MODEL
MULTIVARIABLE SYSTEMS
TEMPERATURE PROFILES
title_short Optimization of Time-Varying Temperature Profiles for Enhanced Beer Fermentation by Evolutive Algorithms
title_full Optimization of Time-Varying Temperature Profiles for Enhanced Beer Fermentation by Evolutive Algorithms
title_fullStr Optimization of Time-Varying Temperature Profiles for Enhanced Beer Fermentation by Evolutive Algorithms
title_full_unstemmed Optimization of Time-Varying Temperature Profiles for Enhanced Beer Fermentation by Evolutive Algorithms
title_sort Optimization of Time-Varying Temperature Profiles for Enhanced Beer Fermentation by Evolutive Algorithms
dc.creator.none.fl_str_mv Ruarte, Pablo Javier
Pantano, Maria Nadia
Noriega, Marianela
Fernández, Cecilia
Serrano, Mario Emanuel
Scaglia, Gustavo Juan Eduardo
author Ruarte, Pablo Javier
author_facet Ruarte, Pablo Javier
Pantano, Maria Nadia
Noriega, Marianela
Fernández, Cecilia
Serrano, Mario Emanuel
Scaglia, Gustavo Juan Eduardo
author_role author
author2 Pantano, Maria Nadia
Noriega, Marianela
Fernández, Cecilia
Serrano, Mario Emanuel
Scaglia, Gustavo Juan Eduardo
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv BEER FERMENTATION
DYNAMIC MODEL
MULTIVARIABLE SYSTEMS
TEMPERATURE PROFILES
topic BEER FERMENTATION
DYNAMIC MODEL
MULTIVARIABLE SYSTEMS
TEMPERATURE PROFILES
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Beer is one of the most popular alcoholic beverages globally, leading to continuous efforts to enhance its production methods. Raw materials and the production process are crucial in the brewing industry, with fermentation being a vital stage that significantly impacts beer quality. The aim of this study is to optimize the beer fermentation process by maximizing the ethanol concentration while minimizing species that adversely affect the organoleptic properties of beer. A novel optimization approach has been developed to derive an optimal, smooth, and continuous temperature profile that can be directly applied in real-world processes. This method integrates Fourier series and orthogonal polynomials for control action parameterization, in combination with evolutionary algorithms for parameter optimization. A key advantage of this methodology lies in its ability to handle a reduced parameter set efficiently, resulting in temperature profiles that are continuous and differentiable. This feature eliminates the need for post-smoothing and is particularly advantageous in biotechnological applications, where abrupt changes in temperature could negatively affect the viability of microorganisms. The optimized profiles not only enhancefermentation efficiency, but also improve the ethanol yield and reduce undesirable flavor compounds, providing a substantial improvement over current industrial practices. These advancements present significant potential for improving both the quality and consistency of beer production.
Fil: Ruarte, Pablo Javier. 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: 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: Noriega, Marianela. 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: Fernández, 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: Serrano, Mario Emanuel. 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: 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 Beer is one of the most popular alcoholic beverages globally, leading to continuous efforts to enhance its production methods. Raw materials and the production process are crucial in the brewing industry, with fermentation being a vital stage that significantly impacts beer quality. The aim of this study is to optimize the beer fermentation process by maximizing the ethanol concentration while minimizing species that adversely affect the organoleptic properties of beer. A novel optimization approach has been developed to derive an optimal, smooth, and continuous temperature profile that can be directly applied in real-world processes. This method integrates Fourier series and orthogonal polynomials for control action parameterization, in combination with evolutionary algorithms for parameter optimization. A key advantage of this methodology lies in its ability to handle a reduced parameter set efficiently, resulting in temperature profiles that are continuous and differentiable. This feature eliminates the need for post-smoothing and is particularly advantageous in biotechnological applications, where abrupt changes in temperature could negatively affect the viability of microorganisms. The optimized profiles not only enhancefermentation efficiency, but also improve the ethanol yield and reduce undesirable flavor compounds, providing a substantial improvement over current industrial practices. These advancements present significant potential for improving both the quality and consistency of beer production.
publishDate 2024
dc.date.none.fl_str_mv 2024-12
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/11336/256131
Ruarte, Pablo Javier; Pantano, Maria Nadia; Noriega, Marianela; Fernández, Cecilia; Serrano, Mario Emanuel; et al.; Optimization of Time-Varying Temperature Profiles for Enhanced Beer Fermentation by Evolutive Algorithms; MDPI; Fermentation; 11; 1; 12-2024; 1-12
2311-5637
CONICET Digital
CONICET
url http://hdl.handle.net/11336/256131
identifier_str_mv Ruarte, Pablo Javier; Pantano, Maria Nadia; Noriega, Marianela; Fernández, Cecilia; Serrano, Mario Emanuel; et al.; Optimization of Time-Varying Temperature Profiles for Enhanced Beer Fermentation by Evolutive Algorithms; MDPI; Fermentation; 11; 1; 12-2024; 1-12
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/2311-5637/11/1/2
info:eu-repo/semantics/altIdentifier/doi/10.3390/fermentation11010002
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv 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 12.982451

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