Multiple response optimization of styrene–butadiene rubber emulsion polymerization

Autores
Martinez Delfa, Gerardo Esteban; Olivieri, Alejandro Cesar; Boschetti, Carlos Eugenio
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A multiple response optimization of styrene-butadiene rubber (SBR) emulsion batch polymerization is proposed. Several properties of latex and rubber were optimized to obtain a particular grade of SBR, namely 1712. Artificial neural networks (ANNs) were employed for the modelling of the following properties: solid content of latex, Mooney viscosity and polydispersity. The training was done by feeding the ANNs with experimental data obtained from a central composite design in which the concentration of some of the polymerization reagents (initiator, activator and chain transfer agent) was varied. The onedimensional desirability functionwas used for optimization, in order to obtain a single set of reaction conditions for the multiple responses. With optimum conditions, polymerization experiments were carried out and good agreementwas found between predicted and experimental values of the required properties.
Fil: Martinez Delfa, Gerardo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Petrobras Energia S.a.-planta Puerto Gr.san Martin; Argentina
Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
Fil: Boschetti, Carlos Eugenio. Petrobras Energia S.a.-planta Puerto Gr.san Martin; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina
Materia
SBR
Emulsion polymerization
Artificial neural networks
Multiple response optimization
Desirability function
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:11336/105022
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Multiple response optimization of styrene–butadiene rubber emulsion polymerizationMartinez Delfa, Gerardo EstebanOlivieri, Alejandro CesarBoschetti, Carlos EugenioSBREmulsion polymerizationArtificial neural networksMultiple response optimizationDesirability functionhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1A multiple response optimization of styrene-butadiene rubber (SBR) emulsion batch polymerization is proposed. Several properties of latex and rubber were optimized to obtain a particular grade of SBR, namely 1712. Artificial neural networks (ANNs) were employed for the modelling of the following properties: solid content of latex, Mooney viscosity and polydispersity. The training was done by feeding the ANNs with experimental data obtained from a central composite design in which the concentration of some of the polymerization reagents (initiator, activator and chain transfer agent) was varied. The onedimensional desirability functionwas used for optimization, in order to obtain a single set of reaction conditions for the multiple responses. With optimum conditions, polymerization experiments were carried out and good agreementwas found between predicted and experimental values of the required properties.Fil: Martinez Delfa, Gerardo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Petrobras Energia S.a.-planta Puerto Gr.san Martin; ArgentinaFil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; ArgentinaFil: Boschetti, Carlos Eugenio. Petrobras Energia S.a.-planta Puerto Gr.san Martin; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; ArgentinaPergamon-Elsevier Science Ltd2009-04info: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/11336/105022Martinez Delfa, Gerardo Esteban; Olivieri, Alejandro Cesar; Boschetti, Carlos Eugenio; Multiple response optimization of styrene–butadiene rubber emulsion polymerization; Pergamon-Elsevier Science Ltd; Computers and Chemical Engineering; 33; 4; 4-2009; 850-8560098-1354CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.compchemeng.2009.01.002info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0098135409000064info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:10:43Zoai:ri.conicet.gov.ar:11336/105022instacron: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 10:10:44.056CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Multiple response optimization of styrene–butadiene rubber emulsion polymerization
title Multiple response optimization of styrene–butadiene rubber emulsion polymerization
spellingShingle Multiple response optimization of styrene–butadiene rubber emulsion polymerization
Martinez Delfa, Gerardo Esteban
SBR
Emulsion polymerization
Artificial neural networks
Multiple response optimization
Desirability function
title_short Multiple response optimization of styrene–butadiene rubber emulsion polymerization
title_full Multiple response optimization of styrene–butadiene rubber emulsion polymerization
title_fullStr Multiple response optimization of styrene–butadiene rubber emulsion polymerization
title_full_unstemmed Multiple response optimization of styrene–butadiene rubber emulsion polymerization
title_sort Multiple response optimization of styrene–butadiene rubber emulsion polymerization
dc.creator.none.fl_str_mv Martinez Delfa, Gerardo Esteban
Olivieri, Alejandro Cesar
Boschetti, Carlos Eugenio
author Martinez Delfa, Gerardo Esteban
author_facet Martinez Delfa, Gerardo Esteban
Olivieri, Alejandro Cesar
Boschetti, Carlos Eugenio
author_role author
author2 Olivieri, Alejandro Cesar
Boschetti, Carlos Eugenio
author2_role author
author
dc.subject.none.fl_str_mv SBR
Emulsion polymerization
Artificial neural networks
Multiple response optimization
Desirability function
topic SBR
Emulsion polymerization
Artificial neural networks
Multiple response optimization
Desirability function
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv A multiple response optimization of styrene-butadiene rubber (SBR) emulsion batch polymerization is proposed. Several properties of latex and rubber were optimized to obtain a particular grade of SBR, namely 1712. Artificial neural networks (ANNs) were employed for the modelling of the following properties: solid content of latex, Mooney viscosity and polydispersity. The training was done by feeding the ANNs with experimental data obtained from a central composite design in which the concentration of some of the polymerization reagents (initiator, activator and chain transfer agent) was varied. The onedimensional desirability functionwas used for optimization, in order to obtain a single set of reaction conditions for the multiple responses. With optimum conditions, polymerization experiments were carried out and good agreementwas found between predicted and experimental values of the required properties.
Fil: Martinez Delfa, Gerardo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Petrobras Energia S.a.-planta Puerto Gr.san Martin; Argentina
Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
Fil: Boschetti, Carlos Eugenio. Petrobras Energia S.a.-planta Puerto Gr.san Martin; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina
description A multiple response optimization of styrene-butadiene rubber (SBR) emulsion batch polymerization is proposed. Several properties of latex and rubber were optimized to obtain a particular grade of SBR, namely 1712. Artificial neural networks (ANNs) were employed for the modelling of the following properties: solid content of latex, Mooney viscosity and polydispersity. The training was done by feeding the ANNs with experimental data obtained from a central composite design in which the concentration of some of the polymerization reagents (initiator, activator and chain transfer agent) was varied. The onedimensional desirability functionwas used for optimization, in order to obtain a single set of reaction conditions for the multiple responses. With optimum conditions, polymerization experiments were carried out and good agreementwas found between predicted and experimental values of the required properties.
publishDate 2009
dc.date.none.fl_str_mv 2009-04
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/11336/105022
Martinez Delfa, Gerardo Esteban; Olivieri, Alejandro Cesar; Boschetti, Carlos Eugenio; Multiple response optimization of styrene–butadiene rubber emulsion polymerization; Pergamon-Elsevier Science Ltd; Computers and Chemical Engineering; 33; 4; 4-2009; 850-856
0098-1354
CONICET Digital
CONICET
url http://hdl.handle.net/11336/105022
identifier_str_mv Martinez Delfa, Gerardo Esteban; Olivieri, Alejandro Cesar; Boschetti, Carlos Eugenio; Multiple response optimization of styrene–butadiene rubber emulsion polymerization; Pergamon-Elsevier Science Ltd; Computers and Chemical Engineering; 33; 4; 4-2009; 850-856
0098-1354
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/j.compchemeng.2009.01.002
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0098135409000064
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 Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
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 13.070432

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