Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization

Autores
Giordano, Pablo César; Beccaria, Alejandro José; Goicoechea, Hector Casimiro; Olivieri, Alejandro Cesar
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The concentrations of glucose and total reducing sugars obtained by chemical hydrolysis of three different lignocellulosic feedstocks were maximized. Two response surface methodologies were applied to model the amount of sugars produced: (1) classical quadratic least-squares fit (QLS), and (2) artificial neural networks based on radial basis functions (RBF). The results obtained by applying RBF were more reliable and better statistical parameters were obtained. Depending on the type of biomass, different results wereobtained. Improvements in fit between 35% and 55% were obtained when comparing the coefficients of determination (R2) computed for both QLS and RBF methods. Coupling the obtained RBF models with particle swarm optimization to calculate the global desirability function, allowed to perform multiple response optimization. The predicted optimal conditions were confirmed by carrying out independent experiments.
Fil: Giordano, Pablo César. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigaciones en Catálisis y Petroquímica "Ing. José Miguel Parera". Universidad Nacional del Litoral. Instituto de Investigaciones en Catálisis y Petroquímica "Ing. José Miguel Parera"; Argentina
Fil: Beccaria, Alejandro José. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina
Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
Materia
Glucose
Modelling
Optimization
Artificial intelligence
Particle swarm optimization
Radial basis functions
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:11336/101885
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimizationGiordano, Pablo CésarBeccaria, Alejandro JoséGoicoechea, Hector CasimiroOlivieri, Alejandro CesarGlucoseModellingOptimizationArtificial intelligenceParticle swarm optimizationRadial basis functionshttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1The concentrations of glucose and total reducing sugars obtained by chemical hydrolysis of three different lignocellulosic feedstocks were maximized. Two response surface methodologies were applied to model the amount of sugars produced: (1) classical quadratic least-squares fit (QLS), and (2) artificial neural networks based on radial basis functions (RBF). The results obtained by applying RBF were more reliable and better statistical parameters were obtained. Depending on the type of biomass, different results wereobtained. Improvements in fit between 35% and 55% were obtained when comparing the coefficients of determination (R2) computed for both QLS and RBF methods. Coupling the obtained RBF models with particle swarm optimization to calculate the global desirability function, allowed to perform multiple response optimization. The predicted optimal conditions were confirmed by carrying out independent experiments.Fil: Giordano, Pablo César. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigaciones en Catálisis y Petroquímica "Ing. José Miguel Parera". Universidad Nacional del Litoral. Instituto de Investigaciones en Catálisis y Petroquímica "Ing. José Miguel Parera"; ArgentinaFil: Beccaria, Alejandro José. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; ArgentinaFil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; ArgentinaFil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; ArgentinaElsevier Science Sa2013-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/101885Giordano, Pablo César; Beccaria, Alejandro José; Goicoechea, Hector Casimiro; Olivieri, Alejandro Cesar; Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization; Elsevier Science Sa; Biochemical Engineering Journal; 80; 10-2013; 1-91369-703XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.bej.2013.09.004info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:18:26Zoai:ri.conicet.gov.ar:11336/101885instacron: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 15:18:26.723CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
title Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
spellingShingle Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
Giordano, Pablo César
Glucose
Modelling
Optimization
Artificial intelligence
Particle swarm optimization
Radial basis functions
title_short Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
title_full Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
title_fullStr Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
title_full_unstemmed Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
title_sort Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
dc.creator.none.fl_str_mv Giordano, Pablo César
Beccaria, Alejandro José
Goicoechea, Hector Casimiro
Olivieri, Alejandro Cesar
author Giordano, Pablo César
author_facet Giordano, Pablo César
Beccaria, Alejandro José
Goicoechea, Hector Casimiro
Olivieri, Alejandro Cesar
author_role author
author2 Beccaria, Alejandro José
Goicoechea, Hector Casimiro
Olivieri, Alejandro Cesar
author2_role author
author
author
dc.subject.none.fl_str_mv Glucose
Modelling
Optimization
Artificial intelligence
Particle swarm optimization
Radial basis functions
topic Glucose
Modelling
Optimization
Artificial intelligence
Particle swarm optimization
Radial basis functions
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The concentrations of glucose and total reducing sugars obtained by chemical hydrolysis of three different lignocellulosic feedstocks were maximized. Two response surface methodologies were applied to model the amount of sugars produced: (1) classical quadratic least-squares fit (QLS), and (2) artificial neural networks based on radial basis functions (RBF). The results obtained by applying RBF were more reliable and better statistical parameters were obtained. Depending on the type of biomass, different results wereobtained. Improvements in fit between 35% and 55% were obtained when comparing the coefficients of determination (R2) computed for both QLS and RBF methods. Coupling the obtained RBF models with particle swarm optimization to calculate the global desirability function, allowed to perform multiple response optimization. The predicted optimal conditions were confirmed by carrying out independent experiments.
Fil: Giordano, Pablo César. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigaciones en Catálisis y Petroquímica "Ing. José Miguel Parera". Universidad Nacional del Litoral. Instituto de Investigaciones en Catálisis y Petroquímica "Ing. José Miguel Parera"; Argentina
Fil: Beccaria, Alejandro José. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina
Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
description The concentrations of glucose and total reducing sugars obtained by chemical hydrolysis of three different lignocellulosic feedstocks were maximized. Two response surface methodologies were applied to model the amount of sugars produced: (1) classical quadratic least-squares fit (QLS), and (2) artificial neural networks based on radial basis functions (RBF). The results obtained by applying RBF were more reliable and better statistical parameters were obtained. Depending on the type of biomass, different results wereobtained. Improvements in fit between 35% and 55% were obtained when comparing the coefficients of determination (R2) computed for both QLS and RBF methods. Coupling the obtained RBF models with particle swarm optimization to calculate the global desirability function, allowed to perform multiple response optimization. The predicted optimal conditions were confirmed by carrying out independent experiments.
publishDate 2013
dc.date.none.fl_str_mv 2013-10
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/11336/101885
Giordano, Pablo César; Beccaria, Alejandro José; Goicoechea, Hector Casimiro; Olivieri, Alejandro Cesar; Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization; Elsevier Science Sa; Biochemical Engineering Journal; 80; 10-2013; 1-9
1369-703X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/101885
identifier_str_mv Giordano, Pablo César; Beccaria, Alejandro José; Goicoechea, Hector Casimiro; Olivieri, Alejandro Cesar; Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization; Elsevier Science Sa; Biochemical Engineering Journal; 80; 10-2013; 1-9
1369-703X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.bej.2013.09.004
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
dc.publisher.none.fl_str_mv Elsevier Science Sa
publisher.none.fl_str_mv Elsevier Science Sa
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
_version_ 1846083333568593920
score 13.22299

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