Model-based run-to-run optimization under uncertainty of biodiesel production

Autores
Luna, Martín Francisco; Martinez, Ernesto Carlos
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A significant source of uncertainty in biodiesel production is the variability of feed composition since the percentage and type of triglycerides varies considerably across different raw materials. Also, due to the complexity of both transesterification and saponification kinetics, first-principles models of biodiesel production typically have built-in errors (structural and parametric uncertainty) which give rise to the need for obtaining relevant data through experimental design in modeling for optimization. A run-to-run optimization strategy which integrates tendency models with Bayesian active learning is proposed. Parameter distributions in a probabilistic model of process performance are re-estimated using data from experiments designed for maximizing information and performance. Results obtained highlight that Bayesian optimal design of experiments using a probabilistic tendency model is effective in achieving the maximum ester content and yield in biodiesel production even though significant uncertainty in feed composition and modeling errors are present.
Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Reg.santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Materia
Biodiesel
Modeling for Optimization
Tendency Models
Uncertainty
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:11336/16300
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Model-based run-to-run optimization under uncertainty of biodiesel productionLuna, Martín FranciscoMartinez, Ernesto CarlosBiodieselModeling for OptimizationTendency ModelsUncertaintyhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2A significant source of uncertainty in biodiesel production is the variability of feed composition since the percentage and type of triglycerides varies considerably across different raw materials. Also, due to the complexity of both transesterification and saponification kinetics, first-principles models of biodiesel production typically have built-in errors (structural and parametric uncertainty) which give rise to the need for obtaining relevant data through experimental design in modeling for optimization. A run-to-run optimization strategy which integrates tendency models with Bayesian active learning is proposed. Parameter distributions in a probabilistic model of process performance are re-estimated using data from experiments designed for maximizing information and performance. Results obtained highlight that Bayesian optimal design of experiments using a probabilistic tendency model is effective in achieving the maximum ester content and yield in biodiesel production even though significant uncertainty in feed composition and modeling errors are present.Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Reg.santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaElsevier2013-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/16300Luna, Martín Francisco; Martinez, Ernesto Carlos; Model-based run-to-run optimization under uncertainty of biodiesel production; Elsevier; Computer Aided Chemical Engineering; 32; 6-2013; 103-1081570-7946enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/B978-0-444-63234-0.50018-Xinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:35:25Zoai:ri.conicet.gov.ar:11336/16300instacron: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 09:35:26.133CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Model-based run-to-run optimization under uncertainty of biodiesel production
title Model-based run-to-run optimization under uncertainty of biodiesel production
spellingShingle Model-based run-to-run optimization under uncertainty of biodiesel production
Luna, Martín Francisco
Biodiesel
Modeling for Optimization
Tendency Models
Uncertainty
title_short Model-based run-to-run optimization under uncertainty of biodiesel production
title_full Model-based run-to-run optimization under uncertainty of biodiesel production
title_fullStr Model-based run-to-run optimization under uncertainty of biodiesel production
title_full_unstemmed Model-based run-to-run optimization under uncertainty of biodiesel production
title_sort Model-based run-to-run optimization under uncertainty of biodiesel production
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 Biodiesel
Modeling for Optimization
Tendency Models
Uncertainty
topic Biodiesel
Modeling for Optimization
Tendency Models
Uncertainty
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv A significant source of uncertainty in biodiesel production is the variability of feed composition since the percentage and type of triglycerides varies considerably across different raw materials. Also, due to the complexity of both transesterification and saponification kinetics, first-principles models of biodiesel production typically have built-in errors (structural and parametric uncertainty) which give rise to the need for obtaining relevant data through experimental design in modeling for optimization. A run-to-run optimization strategy which integrates tendency models with Bayesian active learning is proposed. Parameter distributions in a probabilistic model of process performance are re-estimated using data from experiments designed for maximizing information and performance. Results obtained highlight that Bayesian optimal design of experiments using a probabilistic tendency model is effective in achieving the maximum ester content and yield in biodiesel production even though significant uncertainty in feed composition and modeling errors are present.
Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Reg.santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
description A significant source of uncertainty in biodiesel production is the variability of feed composition since the percentage and type of triglycerides varies considerably across different raw materials. Also, due to the complexity of both transesterification and saponification kinetics, first-principles models of biodiesel production typically have built-in errors (structural and parametric uncertainty) which give rise to the need for obtaining relevant data through experimental design in modeling for optimization. A run-to-run optimization strategy which integrates tendency models with Bayesian active learning is proposed. Parameter distributions in a probabilistic model of process performance are re-estimated using data from experiments designed for maximizing information and performance. Results obtained highlight that Bayesian optimal design of experiments using a probabilistic tendency model is effective in achieving the maximum ester content and yield in biodiesel production even though significant uncertainty in feed composition and modeling errors are present.
publishDate 2013
dc.date.none.fl_str_mv 2013-06
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/11336/16300
Luna, Martín Francisco; Martinez, Ernesto Carlos; Model-based run-to-run optimization under uncertainty of biodiesel production; Elsevier; Computer Aided Chemical Engineering; 32; 6-2013; 103-108
1570-7946
url http://hdl.handle.net/11336/16300
identifier_str_mv Luna, Martín Francisco; Martinez, Ernesto Carlos; Model-based run-to-run optimization under uncertainty of biodiesel production; Elsevier; Computer Aided Chemical Engineering; 32; 6-2013; 103-108
1570-7946
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/B978-0-444-63234-0.50018-X
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 Elsevier
publisher.none.fl_str_mv Elsevier
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_ 1844613103563571200
score 13.070432

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