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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/11924
Ver los metadatos del registro completo
id |
CONICETDig_beba3ec3aad545c8b304c65866701ae1 |
---|---|
oai_identifier_str |
oai:ri.conicet.gov.ar:11336/11924 |
network_acronym_str |
CONICETDig |
repository_id_str |
3498 |
network_name_str |
CONICET Digital (CONICET) |
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 Técnicas2025-09-29T10:40:23Zoai: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:34982025-09-29 10:40:24.23CONICET 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 |
_version_ |
1844614431673155584 |
score |
13.070432 |