Sequential Bayesian Experimental Design for Process Optimization with Stochastic Binary Outcomes

Autores
Luna, Martín Francisco; Martínez, Ernesto Carlos
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
For innovative products, the issue of reproducibly obtaining their desired end-use properties at industrial scale is the main problem to be addressed and solved in process development. Lacking a reliable first-principles process model, a Bayesian optimization algorithm is proposed. On this basis, a short of sequence of experimental runs for pinpointing operating conditions that maximize the probability of successfully complying with end-use product properties is defined. Bayesian optimization is able to take advantage of the full information provided by the sequence of experiments made using a probabilistic model (Gaussian process) of the probability of success based on a one-class classification method. The metric which is maximized to decide the conditions for the next experiment is designed around the expected improvement for a binary response. The proposed algorithm's performance is demonstrated using simulation data from a fed-batch reactor for emulsion polymerization of styrene.
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 Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Martínez, 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
BAYESIAN OPTIMIZATION
END-USE PRODUCT PROPERTIES
GAUSSIAN PROCESSES
ONE-CLASS CLASSIFICATION
SCALE-UP
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:11336/87044
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Sequential Bayesian Experimental Design for Process Optimization with Stochastic Binary OutcomesLuna, Martín FranciscoMartínez, Ernesto CarlosBAYESIAN OPTIMIZATIONEND-USE PRODUCT PROPERTIESGAUSSIAN PROCESSESONE-CLASS CLASSIFICATIONSCALE-UPhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2For innovative products, the issue of reproducibly obtaining their desired end-use properties at industrial scale is the main problem to be addressed and solved in process development. Lacking a reliable first-principles process model, a Bayesian optimization algorithm is proposed. On this basis, a short of sequence of experimental runs for pinpointing operating conditions that maximize the probability of successfully complying with end-use product properties is defined. Bayesian optimization is able to take advantage of the full information provided by the sequence of experiments made using a probabilistic model (Gaussian process) of the probability of success based on a one-class classification method. The metric which is maximized to decide the conditions for the next experiment is designed around the expected improvement for a binary response. The proposed algorithm's performance is demonstrated using simulation data from a fed-batch reactor for emulsion polymerization of styrene.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 Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Martínez, 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; ArgentinaElsevier B.V.2018-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/87044Luna, Martín Francisco; Martínez, Ernesto Carlos; Sequential Bayesian Experimental Design for Process Optimization with Stochastic Binary Outcomes; Elsevier B.V.; Computer Aided Chemical Engineering; 43; 1-2018; 943-9481570-7946CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/B978-0-444-64235-6.50166-2info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:49:31Zoai:ri.conicet.gov.ar:11336/87044instacron: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:34982025-11-12 09:49:32.179CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Sequential Bayesian Experimental Design for Process Optimization with Stochastic Binary Outcomes
title Sequential Bayesian Experimental Design for Process Optimization with Stochastic Binary Outcomes
spellingShingle Sequential Bayesian Experimental Design for Process Optimization with Stochastic Binary Outcomes
Luna, Martín Francisco
BAYESIAN OPTIMIZATION
END-USE PRODUCT PROPERTIES
GAUSSIAN PROCESSES
ONE-CLASS CLASSIFICATION
SCALE-UP
title_short Sequential Bayesian Experimental Design for Process Optimization with Stochastic Binary Outcomes
title_full Sequential Bayesian Experimental Design for Process Optimization with Stochastic Binary Outcomes
title_fullStr Sequential Bayesian Experimental Design for Process Optimization with Stochastic Binary Outcomes
title_full_unstemmed Sequential Bayesian Experimental Design for Process Optimization with Stochastic Binary Outcomes
title_sort Sequential Bayesian Experimental Design for Process Optimization with Stochastic Binary Outcomes
dc.creator.none.fl_str_mv Luna, Martín Francisco
Martínez, Ernesto Carlos
author Luna, Martín Francisco
author_facet Luna, Martín Francisco
Martínez, Ernesto Carlos
author_role author
author2 Martínez, Ernesto Carlos
author2_role author
dc.subject.none.fl_str_mv BAYESIAN OPTIMIZATION
END-USE PRODUCT PROPERTIES
GAUSSIAN PROCESSES
ONE-CLASS CLASSIFICATION
SCALE-UP
topic BAYESIAN OPTIMIZATION
END-USE PRODUCT PROPERTIES
GAUSSIAN PROCESSES
ONE-CLASS CLASSIFICATION
SCALE-UP
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv For innovative products, the issue of reproducibly obtaining their desired end-use properties at industrial scale is the main problem to be addressed and solved in process development. Lacking a reliable first-principles process model, a Bayesian optimization algorithm is proposed. On this basis, a short of sequence of experimental runs for pinpointing operating conditions that maximize the probability of successfully complying with end-use product properties is defined. Bayesian optimization is able to take advantage of the full information provided by the sequence of experiments made using a probabilistic model (Gaussian process) of the probability of success based on a one-class classification method. The metric which is maximized to decide the conditions for the next experiment is designed around the expected improvement for a binary response. The proposed algorithm's performance is demonstrated using simulation data from a fed-batch reactor for emulsion polymerization of styrene.
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 Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Martínez, 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 For innovative products, the issue of reproducibly obtaining their desired end-use properties at industrial scale is the main problem to be addressed and solved in process development. Lacking a reliable first-principles process model, a Bayesian optimization algorithm is proposed. On this basis, a short of sequence of experimental runs for pinpointing operating conditions that maximize the probability of successfully complying with end-use product properties is defined. Bayesian optimization is able to take advantage of the full information provided by the sequence of experiments made using a probabilistic model (Gaussian process) of the probability of success based on a one-class classification method. The metric which is maximized to decide the conditions for the next experiment is designed around the expected improvement for a binary response. The proposed algorithm's performance is demonstrated using simulation data from a fed-batch reactor for emulsion polymerization of styrene.
publishDate 2018
dc.date.none.fl_str_mv 2018-01
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/11336/87044
Luna, Martín Francisco; Martínez, Ernesto Carlos; Sequential Bayesian Experimental Design for Process Optimization with Stochastic Binary Outcomes; Elsevier B.V.; Computer Aided Chemical Engineering; 43; 1-2018; 943-948
1570-7946
CONICET Digital
CONICET
url http://hdl.handle.net/11336/87044
identifier_str_mv Luna, Martín Francisco; Martínez, Ernesto Carlos; Sequential Bayesian Experimental Design for Process Optimization with Stochastic Binary Outcomes; Elsevier B.V.; Computer Aided Chemical Engineering; 43; 1-2018; 943-948
1570-7946
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/B978-0-444-64235-6.50166-2
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 B.V.
publisher.none.fl_str_mv Elsevier B.V.
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 13.24909

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