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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/225892
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:43:54Zoai: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:34982025-09-29 09:43:54.435CONICET 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 |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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13.070432 |