Dynamic Modeling and Parameter Estimation for Unit Operations in Lignocellulosic Bioethanol Production

Autores
Moreno, Marta Susana; Andersen, Federico Ezequiel; Diaz, Maria Soledad
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
During the past decades, intensive research has been pursued on the development of kinetic models to predict process behavior in ethanol production from lignocellulose. These models comprise a large number of parameters which have to be tuned with appropriate experimental data. Therefore, the parameter estimation problem plays an essential role. This work addresses the parameter estimation problem in models representing dilute acid hydrolysis, detoxification, and cofermentation operations in the biochemical production of ethanol from lignocellulosic biomass. The models are represented by sets of differential-algebraic equations (DAEs). Unlike previous approaches, these models account for the main process variables that affect the entire process, specially the final production of bioethanol. These detailed kinetic models, systematically tuned with experimental data, can be used in future studies within a model-based framework that allows performing realistic simulation and optimization aimed at bioethanol process design. A sensitivity analysis has been performed in order to identify the most sensitive parameters. The parameter estimation problem is solved with a simultaneous optimization approach in which the system of dynamic equations is converted into a set of algebraic ones through orthogonal collocation on finite elements. Thus, estimating the model parameters entails optimizing a weighted least squares objective function subject to the discretized algebraic constraints, resulting in a large-scale nonlinear programming problem (NLP). A good agreement with available experimental data has been obtained with estimated kinetic parameters in each model.
Fil: Moreno, Marta Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina. Universidad Nacional del Sur; Argentina
Fil: Andersen, Federico Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina. Universidad Nacional del Sur; Argentina
Fil: Diaz, Maria Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina. Universidad Nacional del Sur; Argentina
Materia
Parameter Estimation
Dynamic Optimization
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:11336/11924
Acceder
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spelling Dynamic Modeling and Parameter Estimation for Unit Operations in Lignocellulosic Bioethanol ProductionMoreno, Marta SusanaAndersen, Federico EzequielDiaz, Maria SoledadParameter EstimationDynamic OptimizationBioethanol Productionhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1https://purl.org/becyt/ford/2.9https://purl.org/becyt/ford/2During the past decades, intensive research has been pursued on the development of kinetic models to predict process behavior in ethanol production from lignocellulose. These models comprise a large number of parameters which have to be tuned with appropriate experimental data. Therefore, the parameter estimation problem plays an essential role. This work addresses the parameter estimation problem in models representing dilute acid hydrolysis, detoxification, and cofermentation operations in the biochemical production of ethanol from lignocellulosic biomass. The models are represented by sets of differential-algebraic equations (DAEs). Unlike previous approaches, these models account for the main process variables that affect the entire process, specially the final production of bioethanol. These detailed kinetic models, systematically tuned with experimental data, can be used in future studies within a model-based framework that allows performing realistic simulation and optimization aimed at bioethanol process design. A sensitivity analysis has been performed in order to identify the most sensitive parameters. The parameter estimation problem is solved with a simultaneous optimization approach in which the system of dynamic equations is converted into a set of algebraic ones through orthogonal collocation on finite elements. Thus, estimating the model parameters entails optimizing a weighted least squares objective function subject to the discretized algebraic constraints, resulting in a large-scale nonlinear programming problem (NLP). A good agreement with available experimental data has been obtained with estimated kinetic parameters in each model.Fil: Moreno, Marta Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina. Universidad Nacional del Sur; ArgentinaFil: Andersen, Federico Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina. Universidad Nacional del Sur; ArgentinaFil: Diaz, Maria Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina. Universidad Nacional del Sur; ArgentinaAmerican Chemical Society2013-03info: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/11336/11924Moreno, Marta Susana; Andersen, Federico Ezequiel; Diaz, Maria Soledad; Dynamic Modeling and Parameter Estimation for Unit Operations in Lignocellulosic Bioethanol Production; American Chemical Society; Industrial & Engineering Chemical Research; 52; 11; 3-2013; 4146-41600888-5885enginfo:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie302358einfo:eu-repo/semantics/altIdentifier/doi/10.1021/ie302358einfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:38:12Zoai:ri.conicet.gov.ar:11336/11924instacron: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 15:38:12.705CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Dynamic Modeling and Parameter Estimation for Unit Operations in Lignocellulosic Bioethanol Production
title Dynamic Modeling and Parameter Estimation for Unit Operations in Lignocellulosic Bioethanol Production
spellingShingle Dynamic Modeling and Parameter Estimation for Unit Operations in Lignocellulosic Bioethanol Production
Moreno, Marta Susana
Parameter Estimation
Dynamic Optimization
Bioethanol Production
title_short Dynamic Modeling and Parameter Estimation for Unit Operations in Lignocellulosic Bioethanol Production
title_full Dynamic Modeling and Parameter Estimation for Unit Operations in Lignocellulosic Bioethanol Production
title_fullStr Dynamic Modeling and Parameter Estimation for Unit Operations in Lignocellulosic Bioethanol Production
title_full_unstemmed Dynamic Modeling and Parameter Estimation for Unit Operations in Lignocellulosic Bioethanol Production
title_sort Dynamic Modeling and Parameter Estimation for Unit Operations in Lignocellulosic Bioethanol Production
dc.creator.none.fl_str_mv Moreno, Marta Susana
Andersen, Federico Ezequiel
Diaz, Maria Soledad
author Moreno, Marta Susana
author_facet Moreno, Marta Susana
Andersen, Federico Ezequiel
Diaz, Maria Soledad
author_role author
author2 Andersen, Federico Ezequiel
Diaz, Maria Soledad
author2_role author
author
dc.subject.none.fl_str_mv Parameter Estimation
Dynamic Optimization
Bioethanol Production
topic Parameter Estimation
Dynamic Optimization
Bioethanol Production
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/2.9
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv During the past decades, intensive research has been pursued on the development of kinetic models to predict process behavior in ethanol production from lignocellulose. These models comprise a large number of parameters which have to be tuned with appropriate experimental data. Therefore, the parameter estimation problem plays an essential role. This work addresses the parameter estimation problem in models representing dilute acid hydrolysis, detoxification, and cofermentation operations in the biochemical production of ethanol from lignocellulosic biomass. The models are represented by sets of differential-algebraic equations (DAEs). Unlike previous approaches, these models account for the main process variables that affect the entire process, specially the final production of bioethanol. These detailed kinetic models, systematically tuned with experimental data, can be used in future studies within a model-based framework that allows performing realistic simulation and optimization aimed at bioethanol process design. A sensitivity analysis has been performed in order to identify the most sensitive parameters. The parameter estimation problem is solved with a simultaneous optimization approach in which the system of dynamic equations is converted into a set of algebraic ones through orthogonal collocation on finite elements. Thus, estimating the model parameters entails optimizing a weighted least squares objective function subject to the discretized algebraic constraints, resulting in a large-scale nonlinear programming problem (NLP). A good agreement with available experimental data has been obtained with estimated kinetic parameters in each model.
Fil: Moreno, Marta Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina. Universidad Nacional del Sur; Argentina
Fil: Andersen, Federico Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina. Universidad Nacional del Sur; Argentina
Fil: Diaz, Maria Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina. Universidad Nacional del Sur; Argentina
description During the past decades, intensive research has been pursued on the development of kinetic models to predict process behavior in ethanol production from lignocellulose. These models comprise a large number of parameters which have to be tuned with appropriate experimental data. Therefore, the parameter estimation problem plays an essential role. This work addresses the parameter estimation problem in models representing dilute acid hydrolysis, detoxification, and cofermentation operations in the biochemical production of ethanol from lignocellulosic biomass. The models are represented by sets of differential-algebraic equations (DAEs). Unlike previous approaches, these models account for the main process variables that affect the entire process, specially the final production of bioethanol. These detailed kinetic models, systematically tuned with experimental data, can be used in future studies within a model-based framework that allows performing realistic simulation and optimization aimed at bioethanol process design. A sensitivity analysis has been performed in order to identify the most sensitive parameters. The parameter estimation problem is solved with a simultaneous optimization approach in which the system of dynamic equations is converted into a set of algebraic ones through orthogonal collocation on finite elements. Thus, estimating the model parameters entails optimizing a weighted least squares objective function subject to the discretized algebraic constraints, resulting in a large-scale nonlinear programming problem (NLP). A good agreement with available experimental data has been obtained with estimated kinetic parameters in each model.
publishDate 2013
dc.date.none.fl_str_mv 2013-03
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/11336/11924
Moreno, Marta Susana; Andersen, Federico Ezequiel; Diaz, Maria Soledad; Dynamic Modeling and Parameter Estimation for Unit Operations in Lignocellulosic Bioethanol Production; American Chemical Society; Industrial & Engineering Chemical Research; 52; 11; 3-2013; 4146-4160
0888-5885
url http://hdl.handle.net/11336/11924
identifier_str_mv Moreno, Marta Susana; Andersen, Federico Ezequiel; Diaz, Maria Soledad; Dynamic Modeling and Parameter Estimation for Unit Operations in Lignocellulosic Bioethanol Production; American Chemical Society; Industrial & Engineering Chemical Research; 52; 11; 3-2013; 4146-4160
0888-5885
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie302358e
info:eu-repo/semantics/altIdentifier/doi/10.1021/ie302358e
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 American Chemical Society
publisher.none.fl_str_mv American Chemical Society
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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