Run-to-run optimization of biodiesel production using probabilistic tendency models: A simulation study

Autores
Luna, Martín Francisco; Martinez, Ernesto Carlos
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Variability of the composition and properties of raw materials used for biodiesel production may cause a loss of productivity, since the same operating conditions give rise to different yields for alternative feedstock sources. The capability to re-optimize the process when the raw materials change may lead to a significant improvement in productivity. For yield optimization, first-principles models of a biodiesel reactor have limited prediction capabilities due to the complex kinetics involving transesterifications and saponifications reactions, which demands active learning of relevant data through optimal design of experiments. In this work, a Bayesian approach for integrating experimentation with imperfect models is proposed to optimize biodiesel production on a run-to-run basis. Parameter distributions in a probabilistic tendency model for the transesterification of triglycerides are re-estimated using data from a sequence of experiments designed to guide policy improvement. Global sensitivity analysis is used to formulate the optimal sampling strategy in each dynamic experiment as an optimization problem. Results obtained highlight that, even when there are significant errors in the tendency model structure and reduced information content in samples, a significant increase in biodiesel production can be achieved after a handful of runs.
Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina
Fil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina
Materia
Modeling for Optimization
Biodiesel Production
Tendency Modeling
Optimal Experimental Design
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:11336/6892
Acceder
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spelling Run-to-run optimization of biodiesel production using probabilistic tendency models: A simulation studyLuna, Martín FranciscoMartinez, Ernesto CarlosModeling for OptimizationBiodiesel ProductionTendency ModelingOptimal Experimental Designhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Variability of the composition and properties of raw materials used for biodiesel production may cause a loss of productivity, since the same operating conditions give rise to different yields for alternative feedstock sources. The capability to re-optimize the process when the raw materials change may lead to a significant improvement in productivity. For yield optimization, first-principles models of a biodiesel reactor have limited prediction capabilities due to the complex kinetics involving transesterifications and saponifications reactions, which demands active learning of relevant data through optimal design of experiments. In this work, a Bayesian approach for integrating experimentation with imperfect models is proposed to optimize biodiesel production on a run-to-run basis. Parameter distributions in a probabilistic tendency model for the transesterification of triglycerides are re-estimated using data from a sequence of experiments designed to guide policy improvement. Global sensitivity analysis is used to formulate the optimal sampling strategy in each dynamic experiment as an optimization problem. Results obtained highlight that, even when there are significant errors in the tendency model structure and reduced information content in samples, a significant increase in biodiesel production can be achieved after a handful of runs.Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); ArgentinaFil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); ArgentinaWiley2015-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/6892Luna, Martín Francisco; Martinez, Ernesto Carlos; Run-to-run optimization of biodiesel production using probabilistic tendency models: A simulation study; Wiley; The Canadian Journal Of Chemical Engineering; 93; 9; 9-2015; 1613-16230008-4034enginfo:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/cjce.22249/abstractinfo:eu-repo/semantics/altIdentifier/doi/10.1002/cjce.22249info:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:22:19Zoai:ri.conicet.gov.ar:11336/6892instacron: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:34982025-09-10 13:22:19.283CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Run-to-run optimization of biodiesel production using probabilistic tendency models: A simulation study
title Run-to-run optimization of biodiesel production using probabilistic tendency models: A simulation study
spellingShingle Run-to-run optimization of biodiesel production using probabilistic tendency models: A simulation study
Luna, Martín Francisco
Modeling for Optimization
Biodiesel Production
Tendency Modeling
Optimal Experimental Design
title_short Run-to-run optimization of biodiesel production using probabilistic tendency models: A simulation study
title_full Run-to-run optimization of biodiesel production using probabilistic tendency models: A simulation study
title_fullStr Run-to-run optimization of biodiesel production using probabilistic tendency models: A simulation study
title_full_unstemmed Run-to-run optimization of biodiesel production using probabilistic tendency models: A simulation study
title_sort Run-to-run optimization of biodiesel production using probabilistic tendency models: A simulation study
dc.creator.none.fl_str_mv Luna, Martín Francisco
Martinez, Ernesto Carlos
author Luna, Martín Francisco
author_facet Luna, Martín Francisco
Martinez, Ernesto Carlos
author_role author
author2 Martinez, Ernesto Carlos
author2_role author
dc.subject.none.fl_str_mv Modeling for Optimization
Biodiesel Production
Tendency Modeling
Optimal Experimental Design
topic Modeling for Optimization
Biodiesel Production
Tendency Modeling
Optimal Experimental Design
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Variability of the composition and properties of raw materials used for biodiesel production may cause a loss of productivity, since the same operating conditions give rise to different yields for alternative feedstock sources. The capability to re-optimize the process when the raw materials change may lead to a significant improvement in productivity. For yield optimization, first-principles models of a biodiesel reactor have limited prediction capabilities due to the complex kinetics involving transesterifications and saponifications reactions, which demands active learning of relevant data through optimal design of experiments. In this work, a Bayesian approach for integrating experimentation with imperfect models is proposed to optimize biodiesel production on a run-to-run basis. Parameter distributions in a probabilistic tendency model for the transesterification of triglycerides are re-estimated using data from a sequence of experiments designed to guide policy improvement. Global sensitivity analysis is used to formulate the optimal sampling strategy in each dynamic experiment as an optimization problem. Results obtained highlight that, even when there are significant errors in the tendency model structure and reduced information content in samples, a significant increase in biodiesel production can be achieved after a handful of runs.
Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina
Fil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina
description Variability of the composition and properties of raw materials used for biodiesel production may cause a loss of productivity, since the same operating conditions give rise to different yields for alternative feedstock sources. The capability to re-optimize the process when the raw materials change may lead to a significant improvement in productivity. For yield optimization, first-principles models of a biodiesel reactor have limited prediction capabilities due to the complex kinetics involving transesterifications and saponifications reactions, which demands active learning of relevant data through optimal design of experiments. In this work, a Bayesian approach for integrating experimentation with imperfect models is proposed to optimize biodiesel production on a run-to-run basis. Parameter distributions in a probabilistic tendency model for the transesterification of triglycerides are re-estimated using data from a sequence of experiments designed to guide policy improvement. Global sensitivity analysis is used to formulate the optimal sampling strategy in each dynamic experiment as an optimization problem. Results obtained highlight that, even when there are significant errors in the tendency model structure and reduced information content in samples, a significant increase in biodiesel production can be achieved after a handful of runs.
publishDate 2015
dc.date.none.fl_str_mv 2015-09
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/11336/6892
Luna, Martín Francisco; Martinez, Ernesto Carlos; Run-to-run optimization of biodiesel production using probabilistic tendency models: A simulation study; Wiley; The Canadian Journal Of Chemical Engineering; 93; 9; 9-2015; 1613-1623
0008-4034
url http://hdl.handle.net/11336/6892
identifier_str_mv Luna, Martín Francisco; Martinez, Ernesto Carlos; Run-to-run optimization of biodiesel production using probabilistic tendency models: A simulation study; Wiley; The Canadian Journal Of Chemical Engineering; 93; 9; 9-2015; 1613-1623
0008-4034
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/cjce.22249/abstract
info:eu-repo/semantics/altIdentifier/doi/10.1002/cjce.22249
info:eu-repo/semantics/altIdentifier/doi/
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
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
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 12.48226

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