Efficient and robust state estimation: Application to a copolymerization process

Autores
Tupaz Pantoja, Jhovany Alexander; Asteasuain, Mariano; Sanchez, Mabel Cristina
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Polymerization processes are highly non-linear systems that require strict control of their dynamic operation to be competitive. The unscented Kalman filter is a filtering strategy that has shown a rewarding performance for non-linear state estimation. Besides, filters based on robust statistics have been proposed to deal with the presence of outliers. However, reported robust filters have employed only the Huber M-estimator as the loss function of the estimation problem. This work presents a new state-estimation procedure based on the unscented transformation and robust statistics concepts. When outliers are present, estimates are more accurate than when using the conventional filter. In contrast to previous research, our methodology is also efficient when there are no outliers. The performances of different loss functions for solving the estimation problem are presented. The results show that redescending M-estimators outperform the Huber function. The behaviour of the technique is analyzed for a copolymerization process.
Fil: Tupaz Pantoja, Jhovany Alexander. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Asteasuain, Mariano. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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: Sanchez, Mabel Cristina. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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
POLYMERS
ROBUST STATISTICS
STATE ESTIMATION
UNSCENTED TRANSFORMATION
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:11336/161316
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Efficient and robust state estimation: Application to a copolymerization processTupaz Pantoja, Jhovany AlexanderAsteasuain, MarianoSanchez, Mabel CristinaPOLYMERSROBUST STATISTICSSTATE ESTIMATIONUNSCENTED TRANSFORMATIONhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Polymerization processes are highly non-linear systems that require strict control of their dynamic operation to be competitive. The unscented Kalman filter is a filtering strategy that has shown a rewarding performance for non-linear state estimation. Besides, filters based on robust statistics have been proposed to deal with the presence of outliers. However, reported robust filters have employed only the Huber M-estimator as the loss function of the estimation problem. This work presents a new state-estimation procedure based on the unscented transformation and robust statistics concepts. When outliers are present, estimates are more accurate than when using the conventional filter. In contrast to previous research, our methodology is also efficient when there are no outliers. The performances of different loss functions for solving the estimation problem are presented. The results show that redescending M-estimators outperform the Huber function. The behaviour of the technique is analyzed for a copolymerization process.Fil: Tupaz Pantoja, Jhovany Alexander. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Asteasuain, Mariano. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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: Sanchez, Mabel Cristina. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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; ArgentinaJohn Wiley & Sons Inc.2020-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/161316Tupaz Pantoja, Jhovany Alexander; Asteasuain, Mariano; Sanchez, Mabel Cristina; Efficient and robust state estimation: Application to a copolymerization process; John Wiley & Sons Inc.; The Canadian Journal Of Chemical Engineering; 99; S1; 11-2020; 1-290008-4034CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/full/10.1002/cjce.23976info:eu-repo/semantics/altIdentifier/doi/10.1002/cjce.23976info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:28:03Zoai:ri.conicet.gov.ar:11336/161316instacron: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:34982025-10-15 14:28:04.219CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Efficient and robust state estimation: Application to a copolymerization process
title Efficient and robust state estimation: Application to a copolymerization process
spellingShingle Efficient and robust state estimation: Application to a copolymerization process
Tupaz Pantoja, Jhovany Alexander
POLYMERS
ROBUST STATISTICS
STATE ESTIMATION
UNSCENTED TRANSFORMATION
title_short Efficient and robust state estimation: Application to a copolymerization process
title_full Efficient and robust state estimation: Application to a copolymerization process
title_fullStr Efficient and robust state estimation: Application to a copolymerization process
title_full_unstemmed Efficient and robust state estimation: Application to a copolymerization process
title_sort Efficient and robust state estimation: Application to a copolymerization process
dc.creator.none.fl_str_mv Tupaz Pantoja, Jhovany Alexander
Asteasuain, Mariano
Sanchez, Mabel Cristina
author Tupaz Pantoja, Jhovany Alexander
author_facet Tupaz Pantoja, Jhovany Alexander
Asteasuain, Mariano
Sanchez, Mabel Cristina
author_role author
author2 Asteasuain, Mariano
Sanchez, Mabel Cristina
author2_role author
author
dc.subject.none.fl_str_mv POLYMERS
ROBUST STATISTICS
STATE ESTIMATION
UNSCENTED TRANSFORMATION
topic POLYMERS
ROBUST STATISTICS
STATE ESTIMATION
UNSCENTED TRANSFORMATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Polymerization processes are highly non-linear systems that require strict control of their dynamic operation to be competitive. The unscented Kalman filter is a filtering strategy that has shown a rewarding performance for non-linear state estimation. Besides, filters based on robust statistics have been proposed to deal with the presence of outliers. However, reported robust filters have employed only the Huber M-estimator as the loss function of the estimation problem. This work presents a new state-estimation procedure based on the unscented transformation and robust statistics concepts. When outliers are present, estimates are more accurate than when using the conventional filter. In contrast to previous research, our methodology is also efficient when there are no outliers. The performances of different loss functions for solving the estimation problem are presented. The results show that redescending M-estimators outperform the Huber function. The behaviour of the technique is analyzed for a copolymerization process.
Fil: Tupaz Pantoja, Jhovany Alexander. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Asteasuain, Mariano. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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: Sanchez, Mabel Cristina. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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 Polymerization processes are highly non-linear systems that require strict control of their dynamic operation to be competitive. The unscented Kalman filter is a filtering strategy that has shown a rewarding performance for non-linear state estimation. Besides, filters based on robust statistics have been proposed to deal with the presence of outliers. However, reported robust filters have employed only the Huber M-estimator as the loss function of the estimation problem. This work presents a new state-estimation procedure based on the unscented transformation and robust statistics concepts. When outliers are present, estimates are more accurate than when using the conventional filter. In contrast to previous research, our methodology is also efficient when there are no outliers. The performances of different loss functions for solving the estimation problem are presented. The results show that redescending M-estimators outperform the Huber function. The behaviour of the technique is analyzed for a copolymerization process.
publishDate 2020
dc.date.none.fl_str_mv 2020-11
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/11336/161316
Tupaz Pantoja, Jhovany Alexander; Asteasuain, Mariano; Sanchez, Mabel Cristina; Efficient and robust state estimation: Application to a copolymerization process; John Wiley & Sons Inc.; The Canadian Journal Of Chemical Engineering; 99; S1; 11-2020; 1-29
0008-4034
CONICET Digital
CONICET
url http://hdl.handle.net/11336/161316
identifier_str_mv Tupaz Pantoja, Jhovany Alexander; Asteasuain, Mariano; Sanchez, Mabel Cristina; Efficient and robust state estimation: Application to a copolymerization process; John Wiley & Sons Inc.; The Canadian Journal Of Chemical Engineering; 99; S1; 11-2020; 1-29
0008-4034
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/full/10.1002/cjce.23976
info:eu-repo/semantics/altIdentifier/doi/10.1002/cjce.23976
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 John Wiley & Sons Inc.
publisher.none.fl_str_mv John Wiley & Sons Inc.
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 13.22299

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