Detecting stationary gain changes in large process systems

Autores
Bustos, Germán Andrés; González, Alejandro Hernán; Marchetti, Jacinto Luis
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Stationary process gains are critical model parameters to determine targets in commercial MPC technologies. Consequently, important savings can be reached by acceding to an early prevention method capable of detecting whether the actual process moves away from the modeled dynamics or not, particularly by indicating when the process gains are not more represented by those included in the model identified during commissioning stages. In this first approach, a subspace identification method is used under open loop process condition to develop a process gain-matrix estimator. The main reason for using the subspace identification method is that it works directly with raw data and that the development is intended for monitoring future applications under multivariable closed-loop optimizing control where the transient regime is a frequent scenario. The objective of this paper is to present a method capable of detecting those gains of a multivariable model that start moving away from the original values. The anticipated knowledge of these events could provide a warning to process engineers and prevent from targeting process conditions with wrong gain estimations. The regular follow-up of the gain matrix should also help to localize those dynamics needing an updating identification.
Fil: Bustos, Germán Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Marchetti, Jacinto Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Materia
Steady-state gains
Subspace identification
Multivariable Processes
LP-MPC
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:11336/8861
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/8861
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Detecting stationary gain changes in large process systemsBustos, Germán AndrésGonzález, Alejandro HernánMarchetti, Jacinto LuisSteady-state gainsSubspace identificationMultivariable ProcessesLP-MPChttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2https://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Stationary process gains are critical model parameters to determine targets in commercial MPC technologies. Consequently, important savings can be reached by acceding to an early prevention method capable of detecting whether the actual process moves away from the modeled dynamics or not, particularly by indicating when the process gains are not more represented by those included in the model identified during commissioning stages. In this first approach, a subspace identification method is used under open loop process condition to develop a process gain-matrix estimator. The main reason for using the subspace identification method is that it works directly with raw data and that the development is intended for monitoring future applications under multivariable closed-loop optimizing control where the transient regime is a frequent scenario. The objective of this paper is to present a method capable of detecting those gains of a multivariable model that start moving away from the original values. The anticipated knowledge of these events could provide a warning to process engineers and prevent from targeting process conditions with wrong gain estimations. The regular follow-up of the gain matrix should also help to localize those dynamics needing an updating identification.Fil: Bustos, Germán Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Marchetti, Jacinto Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaTaylor & Francis2013-11info: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/11336/8861Bustos, Germán Andrés; González, Alejandro Hernán; Marchetti, Jacinto Luis; Detecting stationary gain changes in large process systems; Taylor & Francis; Chemical Engineering Communications; 201; 5; 11-2013; 688-7080098-6445enginfo:eu-repo/semantics/altIdentifier/url/http://www.tandfonline.com/doi/abs/10.1080/00986445.2013.785945?journalCode=gcec20#previewinfo:eu-repo/semantics/altIdentifier/doi/10.1080/00986445.2013.785945info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:59:38Zoai:ri.conicet.gov.ar:11336/8861instacron: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 11:59:38.342CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Detecting stationary gain changes in large process systems
title Detecting stationary gain changes in large process systems
spellingShingle Detecting stationary gain changes in large process systems
Bustos, Germán Andrés
Steady-state gains
Subspace identification
Multivariable Processes
LP-MPC
title_short Detecting stationary gain changes in large process systems
title_full Detecting stationary gain changes in large process systems
title_fullStr Detecting stationary gain changes in large process systems
title_full_unstemmed Detecting stationary gain changes in large process systems
title_sort Detecting stationary gain changes in large process systems
dc.creator.none.fl_str_mv Bustos, Germán Andrés
González, Alejandro Hernán
Marchetti, Jacinto Luis
author Bustos, Germán Andrés
author_facet Bustos, Germán Andrés
González, Alejandro Hernán
Marchetti, Jacinto Luis
author_role author
author2 González, Alejandro Hernán
Marchetti, Jacinto Luis
author2_role author
author
dc.subject.none.fl_str_mv Steady-state gains
Subspace identification
Multivariable Processes
LP-MPC
topic Steady-state gains
Subspace identification
Multivariable Processes
LP-MPC
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Stationary process gains are critical model parameters to determine targets in commercial MPC technologies. Consequently, important savings can be reached by acceding to an early prevention method capable of detecting whether the actual process moves away from the modeled dynamics or not, particularly by indicating when the process gains are not more represented by those included in the model identified during commissioning stages. In this first approach, a subspace identification method is used under open loop process condition to develop a process gain-matrix estimator. The main reason for using the subspace identification method is that it works directly with raw data and that the development is intended for monitoring future applications under multivariable closed-loop optimizing control where the transient regime is a frequent scenario. The objective of this paper is to present a method capable of detecting those gains of a multivariable model that start moving away from the original values. The anticipated knowledge of these events could provide a warning to process engineers and prevent from targeting process conditions with wrong gain estimations. The regular follow-up of the gain matrix should also help to localize those dynamics needing an updating identification.
Fil: Bustos, Germán Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Marchetti, Jacinto Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
description Stationary process gains are critical model parameters to determine targets in commercial MPC technologies. Consequently, important savings can be reached by acceding to an early prevention method capable of detecting whether the actual process moves away from the modeled dynamics or not, particularly by indicating when the process gains are not more represented by those included in the model identified during commissioning stages. In this first approach, a subspace identification method is used under open loop process condition to develop a process gain-matrix estimator. The main reason for using the subspace identification method is that it works directly with raw data and that the development is intended for monitoring future applications under multivariable closed-loop optimizing control where the transient regime is a frequent scenario. The objective of this paper is to present a method capable of detecting those gains of a multivariable model that start moving away from the original values. The anticipated knowledge of these events could provide a warning to process engineers and prevent from targeting process conditions with wrong gain estimations. The regular follow-up of the gain matrix should also help to localize those dynamics needing an updating identification.
publishDate 2013
dc.date.none.fl_str_mv 2013-11
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/11336/8861
Bustos, Germán Andrés; González, Alejandro Hernán; Marchetti, Jacinto Luis; Detecting stationary gain changes in large process systems; Taylor & Francis; Chemical Engineering Communications; 201; 5; 11-2013; 688-708
0098-6445
url http://hdl.handle.net/11336/8861
identifier_str_mv Bustos, Germán Andrés; González, Alejandro Hernán; Marchetti, Jacinto Luis; Detecting stationary gain changes in large process systems; Taylor & Francis; Chemical Engineering Communications; 201; 5; 11-2013; 688-708
0098-6445
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.tandfonline.com/doi/abs/10.1080/00986445.2013.785945?journalCode=gcec20#preview
info:eu-repo/semantics/altIdentifier/doi/10.1080/00986445.2013.785945
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
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
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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