An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process

Autores
Sanseverinatti, Carlos Ignacio; Perdomo, Mariano Miguel; Clementi, Luis Alberto; Vega, Jorge Ruben
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Soft sensors (SS) are of importance in monitoring polymerization processes because numerous production and quality variables cannot be measured online. Adaptive SSs are of interest to maintain accurate estimations under disturbances and changes in operating points. This study proposes an adaptive SS to online estimate the mass conversion in the emulsion copolymerization required for the production of Styrene-Butadiene rubber (SBR). The SS includes a bias term calculated from sporadic laboratory measurements. Typically, the bias is updated every time a new laboratory report becomes available, but this strategy leads to unnecessarily frequent bias updates. The SS includes a statistic-based tool to avoid unnecessary bias updates and reduce the variability of the bias with respect to classical approaches. A control chart (CC) for individual determinations combined with an algorithmic Cusum is used to monitor the statistical stability of the average prediction error. The adaptive SS enables a bias update only when a loss of said statistical stability is detected. Several bias update methods are tested on a simulated industrial train of reactors for the latex production in the SBR process. The best results are obtained by combining the proposed CC-based approach with a previously developed Bayesian bias update strategy.
Fil: Sanseverinatti, Carlos Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Perdomo, Mariano Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Clementi, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Materia
BIAS UPDATING
CONTROL CHARTS
MASS CONVERSION
STATISTICAL STABILITY
STYRENE-BUTADIENE RUBBER
VARIABLE ESTIMATION
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:11336/225892
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR ProcessSanseverinatti, Carlos IgnacioPerdomo, Mariano MiguelClementi, Luis AlbertoVega, Jorge RubenBIAS UPDATINGCONTROL CHARTSMASS CONVERSIONSTATISTICAL STABILITYSTYRENE-BUTADIENE RUBBERVARIABLE ESTIMATIONhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Soft sensors (SS) are of importance in monitoring polymerization processes because numerous production and quality variables cannot be measured online. Adaptive SSs are of interest to maintain accurate estimations under disturbances and changes in operating points. This study proposes an adaptive SS to online estimate the mass conversion in the emulsion copolymerization required for the production of Styrene-Butadiene rubber (SBR). The SS includes a bias term calculated from sporadic laboratory measurements. Typically, the bias is updated every time a new laboratory report becomes available, but this strategy leads to unnecessarily frequent bias updates. The SS includes a statistic-based tool to avoid unnecessary bias updates and reduce the variability of the bias with respect to classical approaches. A control chart (CC) for individual determinations combined with an algorithmic Cusum is used to monitor the statistical stability of the average prediction error. The adaptive SS enables a bias update only when a loss of said statistical stability is detected. Several bias update methods are tested on a simulated industrial train of reactors for the latex production in the SBR process. The best results are obtained by combining the proposed CC-based approach with a previously developed Bayesian bias update strategy.Fil: Sanseverinatti, Carlos Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Perdomo, Mariano Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Clementi, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaWiley VCH Verlag2023-05info: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/11336/225892Sanseverinatti, Carlos Ignacio; Perdomo, Mariano Miguel; Clementi, Luis Alberto; Vega, Jorge Ruben; An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process; Wiley VCH Verlag; Macromolecular Reaction Engineering; 17; 5; 5-2023; 1-291862-832XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/10.1002/mren.202300025info:eu-repo/semantics/altIdentifier/doi/10.1002/mren.202300025info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:12:37Zoai:ri.conicet.gov.ar:11336/225892instacron: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:34982025-10-22 11:12:37.295CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process
title An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process
spellingShingle An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process
Sanseverinatti, Carlos Ignacio
BIAS UPDATING
CONTROL CHARTS
MASS CONVERSION
STATISTICAL STABILITY
STYRENE-BUTADIENE RUBBER
VARIABLE ESTIMATION
title_short An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process
title_full An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process
title_fullStr An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process
title_full_unstemmed An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process
title_sort An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process
dc.creator.none.fl_str_mv Sanseverinatti, Carlos Ignacio
Perdomo, Mariano Miguel
Clementi, Luis Alberto
Vega, Jorge Ruben
author Sanseverinatti, Carlos Ignacio
author_facet Sanseverinatti, Carlos Ignacio
Perdomo, Mariano Miguel
Clementi, Luis Alberto
Vega, Jorge Ruben
author_role author
author2 Perdomo, Mariano Miguel
Clementi, Luis Alberto
Vega, Jorge Ruben
author2_role author
author
author
dc.subject.none.fl_str_mv BIAS UPDATING
CONTROL CHARTS
MASS CONVERSION
STATISTICAL STABILITY
STYRENE-BUTADIENE RUBBER
VARIABLE ESTIMATION
topic BIAS UPDATING
CONTROL CHARTS
MASS CONVERSION
STATISTICAL STABILITY
STYRENE-BUTADIENE RUBBER
VARIABLE ESTIMATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Soft sensors (SS) are of importance in monitoring polymerization processes because numerous production and quality variables cannot be measured online. Adaptive SSs are of interest to maintain accurate estimations under disturbances and changes in operating points. This study proposes an adaptive SS to online estimate the mass conversion in the emulsion copolymerization required for the production of Styrene-Butadiene rubber (SBR). The SS includes a bias term calculated from sporadic laboratory measurements. Typically, the bias is updated every time a new laboratory report becomes available, but this strategy leads to unnecessarily frequent bias updates. The SS includes a statistic-based tool to avoid unnecessary bias updates and reduce the variability of the bias with respect to classical approaches. A control chart (CC) for individual determinations combined with an algorithmic Cusum is used to monitor the statistical stability of the average prediction error. The adaptive SS enables a bias update only when a loss of said statistical stability is detected. Several bias update methods are tested on a simulated industrial train of reactors for the latex production in the SBR process. The best results are obtained by combining the proposed CC-based approach with a previously developed Bayesian bias update strategy.
Fil: Sanseverinatti, Carlos Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Perdomo, Mariano Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Clementi, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
description Soft sensors (SS) are of importance in monitoring polymerization processes because numerous production and quality variables cannot be measured online. Adaptive SSs are of interest to maintain accurate estimations under disturbances and changes in operating points. This study proposes an adaptive SS to online estimate the mass conversion in the emulsion copolymerization required for the production of Styrene-Butadiene rubber (SBR). The SS includes a bias term calculated from sporadic laboratory measurements. Typically, the bias is updated every time a new laboratory report becomes available, but this strategy leads to unnecessarily frequent bias updates. The SS includes a statistic-based tool to avoid unnecessary bias updates and reduce the variability of the bias with respect to classical approaches. A control chart (CC) for individual determinations combined with an algorithmic Cusum is used to monitor the statistical stability of the average prediction error. The adaptive SS enables a bias update only when a loss of said statistical stability is detected. Several bias update methods are tested on a simulated industrial train of reactors for the latex production in the SBR process. The best results are obtained by combining the proposed CC-based approach with a previously developed Bayesian bias update strategy.
publishDate 2023
dc.date.none.fl_str_mv 2023-05
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/11336/225892
Sanseverinatti, Carlos Ignacio; Perdomo, Mariano Miguel; Clementi, Luis Alberto; Vega, Jorge Ruben; An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process; Wiley VCH Verlag; Macromolecular Reaction Engineering; 17; 5; 5-2023; 1-29
1862-832X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/225892
identifier_str_mv Sanseverinatti, Carlos Ignacio; Perdomo, Mariano Miguel; Clementi, Luis Alberto; Vega, Jorge Ruben; An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process; Wiley VCH Verlag; Macromolecular Reaction Engineering; 17; 5; 5-2023; 1-29
1862-832X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/10.1002/mren.202300025
info:eu-repo/semantics/altIdentifier/doi/10.1002/mren.202300025
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 Wiley VCH Verlag
publisher.none.fl_str_mv Wiley VCH Verlag
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 12.982451

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