Improved numerical inversion methods for the recovery of bivariate distributions of polymer properties from 2D probability generating function domains

Autores
Brandolin, Adriana; Balbueno, Ayslane Assini; Asteasuain, Mariano
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The 2D probability generating function technique is a powerfulmethod for modeling bivariate distributions of polymer properties. It isbased on the transformation of bivariate population balance equationsusing 2D probability generating functions (pgf) and a posteriori recovery of the distribution from the transform domain by numerical inversion. A key step of this method is the inversion of the pgf transforms. Available numerical inversion methods yield excellent results for pgf transforms of distributions with independent dimensions of similar orders of magnitude.However, numerical problems are found for 2D distributions in which the independent dimensions have very different range of values, such as the molecular weight distribution-branching distribution in branched polymers. In this work, two new 2D pgf inversion methods are developed,which regard the pgf as a complex variable. The superior accuracy ofthese new methods allows constructing a 2D inversion technique suitablefor any type of bivariate distribution.This enhances the capabilities of the 2D pgf modeling technique for simulation and optimization of polymer processes. An application example of the technique in a polymeric system of industrial interest is presented.
Fil: Brandolin, Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Balbueno, Ayslane Assini. Universidade Federal de Viçosa; Brasil
Fil: Asteasuain, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Materia
MODELING
POLYMERIZATION
BIVARIATE DISTRIBUTION
2D PROBABILITY GENERATING FUNCTION
Nivel de accesibilidad
acceso embargado
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/26932
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Improved numerical inversion methods for the recovery of bivariate distributions of polymer properties from 2D probability generating function domainsBrandolin, AdrianaBalbueno, Ayslane AssiniAsteasuain, MarianoMODELINGPOLYMERIZATIONBIVARIATE DISTRIBUTION2D PROBABILITY GENERATING FUNCTIONhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2The 2D probability generating function technique is a powerfulmethod for modeling bivariate distributions of polymer properties. It isbased on the transformation of bivariate population balance equationsusing 2D probability generating functions (pgf) and a posteriori recovery of the distribution from the transform domain by numerical inversion. A key step of this method is the inversion of the pgf transforms. Available numerical inversion methods yield excellent results for pgf transforms of distributions with independent dimensions of similar orders of magnitude.However, numerical problems are found for 2D distributions in which the independent dimensions have very different range of values, such as the molecular weight distribution-branching distribution in branched polymers. In this work, two new 2D pgf inversion methods are developed,which regard the pgf as a complex variable. The superior accuracy ofthese new methods allows constructing a 2D inversion technique suitablefor any type of bivariate distribution.This enhances the capabilities of the 2D pgf modeling technique for simulation and optimization of polymer processes. An application example of the technique in a polymeric system of industrial interest is presented.Fil: Brandolin, Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Balbueno, Ayslane Assini. Universidade Federal de Viçosa; BrasilFil: Asteasuain, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaElsevier2016-07-30info:eu-repo/date/embargoEnd/2018-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/26932Brandolin, Adriana; Balbueno, Ayslane Assini; Asteasuain, Mariano; Improved numerical inversion methods for the recovery of bivariate distributions of polymer properties from 2D probability generating function domains; Elsevier; Computers and Chemical Engineering; 94; 30-7-2016; 272-2860098-1354CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.compchemeng.2016.07.017info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S009813541630237Xinfo:eu-repo/semantics/embargoedAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:22:04Zoai:ri.conicet.gov.ar:11336/26932instacron: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 13:22:05.098CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Improved numerical inversion methods for the recovery of bivariate distributions of polymer properties from 2D probability generating function domains
title Improved numerical inversion methods for the recovery of bivariate distributions of polymer properties from 2D probability generating function domains
spellingShingle Improved numerical inversion methods for the recovery of bivariate distributions of polymer properties from 2D probability generating function domains
Brandolin, Adriana
MODELING
POLYMERIZATION
BIVARIATE DISTRIBUTION
2D PROBABILITY GENERATING FUNCTION
title_short Improved numerical inversion methods for the recovery of bivariate distributions of polymer properties from 2D probability generating function domains
title_full Improved numerical inversion methods for the recovery of bivariate distributions of polymer properties from 2D probability generating function domains
title_fullStr Improved numerical inversion methods for the recovery of bivariate distributions of polymer properties from 2D probability generating function domains
title_full_unstemmed Improved numerical inversion methods for the recovery of bivariate distributions of polymer properties from 2D probability generating function domains
title_sort Improved numerical inversion methods for the recovery of bivariate distributions of polymer properties from 2D probability generating function domains
dc.creator.none.fl_str_mv Brandolin, Adriana
Balbueno, Ayslane Assini
Asteasuain, Mariano
author Brandolin, Adriana
author_facet Brandolin, Adriana
Balbueno, Ayslane Assini
Asteasuain, Mariano
author_role author
author2 Balbueno, Ayslane Assini
Asteasuain, Mariano
author2_role author
author
dc.subject.none.fl_str_mv MODELING
POLYMERIZATION
BIVARIATE DISTRIBUTION
2D PROBABILITY GENERATING FUNCTION
topic MODELING
POLYMERIZATION
BIVARIATE DISTRIBUTION
2D PROBABILITY GENERATING FUNCTION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The 2D probability generating function technique is a powerfulmethod for modeling bivariate distributions of polymer properties. It isbased on the transformation of bivariate population balance equationsusing 2D probability generating functions (pgf) and a posteriori recovery of the distribution from the transform domain by numerical inversion. A key step of this method is the inversion of the pgf transforms. Available numerical inversion methods yield excellent results for pgf transforms of distributions with independent dimensions of similar orders of magnitude.However, numerical problems are found for 2D distributions in which the independent dimensions have very different range of values, such as the molecular weight distribution-branching distribution in branched polymers. In this work, two new 2D pgf inversion methods are developed,which regard the pgf as a complex variable. The superior accuracy ofthese new methods allows constructing a 2D inversion technique suitablefor any type of bivariate distribution.This enhances the capabilities of the 2D pgf modeling technique for simulation and optimization of polymer processes. An application example of the technique in a polymeric system of industrial interest is presented.
Fil: Brandolin, Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Balbueno, Ayslane Assini. Universidade Federal de Viçosa; Brasil
Fil: Asteasuain, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
description The 2D probability generating function technique is a powerfulmethod for modeling bivariate distributions of polymer properties. It isbased on the transformation of bivariate population balance equationsusing 2D probability generating functions (pgf) and a posteriori recovery of the distribution from the transform domain by numerical inversion. A key step of this method is the inversion of the pgf transforms. Available numerical inversion methods yield excellent results for pgf transforms of distributions with independent dimensions of similar orders of magnitude.However, numerical problems are found for 2D distributions in which the independent dimensions have very different range of values, such as the molecular weight distribution-branching distribution in branched polymers. In this work, two new 2D pgf inversion methods are developed,which regard the pgf as a complex variable. The superior accuracy ofthese new methods allows constructing a 2D inversion technique suitablefor any type of bivariate distribution.This enhances the capabilities of the 2D pgf modeling technique for simulation and optimization of polymer processes. An application example of the technique in a polymeric system of industrial interest is presented.
publishDate 2016
dc.date.none.fl_str_mv 2016-07-30
info:eu-repo/date/embargoEnd/2018-08-01
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/11336/26932
Brandolin, Adriana; Balbueno, Ayslane Assini; Asteasuain, Mariano; Improved numerical inversion methods for the recovery of bivariate distributions of polymer properties from 2D probability generating function domains; Elsevier; Computers and Chemical Engineering; 94; 30-7-2016; 272-286
0098-1354
CONICET Digital
CONICET
url http://hdl.handle.net/11336/26932
identifier_str_mv Brandolin, Adriana; Balbueno, Ayslane Assini; Asteasuain, Mariano; Improved numerical inversion methods for the recovery of bivariate distributions of polymer properties from 2D probability generating function domains; Elsevier; Computers and Chemical Engineering; 94; 30-7-2016; 272-286
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.2016.07.017
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S009813541630237X
dc.rights.none.fl_str_mv info:eu-repo/semantics/embargoedAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv embargoedAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
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
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score 12.48226

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