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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/161316
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:38:50Zoai: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:34982025-09-29 09:38:50.633CONICET 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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1844613228140691456 |
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13.070432 |