A systematic approach for the design of optimal monitoring systems for large scale processes

Autores
Zumoffen, David Alejandro Ramon; Basualdo, Marta
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work a new concept for designing an efficient monitoring system for large scale chemical plants is presented. It is considered that the monitoring problem must be solved integrated with the optimal sensor location together with the plant-wide control structure design. The solution of these problems involves deciding among a great number of possible combinations between the input-output variables. It is done supported by the application of genetic algorithm (GA). The key new idea is to propose an adequate objective function, within the GA, that takes into account a fault detectability index based on combined statistics. Additionally, by using a specific penalty function, it is possible to drive the search to the less expensive structure, that is by using the lowest number of sensors. The well-known benchmark case of the Tennessee Eastman plant (TE) is chosen for testing this methodology and for discussion purposes. Since several authors have studied the TE case, the results obtained here can be rigorously compared with those already published. All of the previous works considered that every TE output variables were available for the abnormal events detection for designing the monitoring system.
Fil: Zumoffen, David Alejandro Ramon. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
Fil: Basualdo, Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
Materia
Optimal Monitoring System Design
Optimal Sensor Location
Detectability Index
Genetic Algorithm
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:11336/15200
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A systematic approach for the design of optimal monitoring systems for large scale processesZumoffen, David Alejandro RamonBasualdo, MartaOptimal Monitoring System DesignOptimal Sensor LocationDetectability IndexGenetic Algorithmhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2In this work a new concept for designing an efficient monitoring system for large scale chemical plants is presented. It is considered that the monitoring problem must be solved integrated with the optimal sensor location together with the plant-wide control structure design. The solution of these problems involves deciding among a great number of possible combinations between the input-output variables. It is done supported by the application of genetic algorithm (GA). The key new idea is to propose an adequate objective function, within the GA, that takes into account a fault detectability index based on combined statistics. Additionally, by using a specific penalty function, it is possible to drive the search to the less expensive structure, that is by using the lowest number of sensors. The well-known benchmark case of the Tennessee Eastman plant (TE) is chosen for testing this methodology and for discussion purposes. Since several authors have studied the TE case, the results obtained here can be rigorously compared with those already published. All of the previous works considered that every TE output variables were available for the abnormal events detection for designing the monitoring system.Fil: Zumoffen, David Alejandro Ramon. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; ArgentinaFil: Basualdo, Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; ArgentinaAmerican Chemical Society2010info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/15200Zumoffen, David Alejandro Ramon; Basualdo, Marta; A systematic approach for the design of optimal monitoring systems for large scale processes; American Chemical Society; Industrial & Engineering Chemical Research; 49; 4; -1-2010; 1749-17610888-58851520-5045enginfo:eu-repo/semantics/altIdentifier/doi/10.1021/ie9017836info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie9017836info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:37:55Zoai:ri.conicet.gov.ar:11336/15200instacron: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:34982025-11-12 09:37:56.203CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A systematic approach for the design of optimal monitoring systems for large scale processes
title A systematic approach for the design of optimal monitoring systems for large scale processes
spellingShingle A systematic approach for the design of optimal monitoring systems for large scale processes
Zumoffen, David Alejandro Ramon
Optimal Monitoring System Design
Optimal Sensor Location
Detectability Index
Genetic Algorithm
title_short A systematic approach for the design of optimal monitoring systems for large scale processes
title_full A systematic approach for the design of optimal monitoring systems for large scale processes
title_fullStr A systematic approach for the design of optimal monitoring systems for large scale processes
title_full_unstemmed A systematic approach for the design of optimal monitoring systems for large scale processes
title_sort A systematic approach for the design of optimal monitoring systems for large scale processes
dc.creator.none.fl_str_mv Zumoffen, David Alejandro Ramon
Basualdo, Marta
author Zumoffen, David Alejandro Ramon
author_facet Zumoffen, David Alejandro Ramon
Basualdo, Marta
author_role author
author2 Basualdo, Marta
author2_role author
dc.subject.none.fl_str_mv Optimal Monitoring System Design
Optimal Sensor Location
Detectability Index
Genetic Algorithm
topic Optimal Monitoring System Design
Optimal Sensor Location
Detectability Index
Genetic Algorithm
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this work a new concept for designing an efficient monitoring system for large scale chemical plants is presented. It is considered that the monitoring problem must be solved integrated with the optimal sensor location together with the plant-wide control structure design. The solution of these problems involves deciding among a great number of possible combinations between the input-output variables. It is done supported by the application of genetic algorithm (GA). The key new idea is to propose an adequate objective function, within the GA, that takes into account a fault detectability index based on combined statistics. Additionally, by using a specific penalty function, it is possible to drive the search to the less expensive structure, that is by using the lowest number of sensors. The well-known benchmark case of the Tennessee Eastman plant (TE) is chosen for testing this methodology and for discussion purposes. Since several authors have studied the TE case, the results obtained here can be rigorously compared with those already published. All of the previous works considered that every TE output variables were available for the abnormal events detection for designing the monitoring system.
Fil: Zumoffen, David Alejandro Ramon. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
Fil: Basualdo, Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
description In this work a new concept for designing an efficient monitoring system for large scale chemical plants is presented. It is considered that the monitoring problem must be solved integrated with the optimal sensor location together with the plant-wide control structure design. The solution of these problems involves deciding among a great number of possible combinations between the input-output variables. It is done supported by the application of genetic algorithm (GA). The key new idea is to propose an adequate objective function, within the GA, that takes into account a fault detectability index based on combined statistics. Additionally, by using a specific penalty function, it is possible to drive the search to the less expensive structure, that is by using the lowest number of sensors. The well-known benchmark case of the Tennessee Eastman plant (TE) is chosen for testing this methodology and for discussion purposes. Since several authors have studied the TE case, the results obtained here can be rigorously compared with those already published. All of the previous works considered that every TE output variables were available for the abnormal events detection for designing the monitoring system.
publishDate 2010
dc.date.none.fl_str_mv 2010
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/11336/15200
Zumoffen, David Alejandro Ramon; Basualdo, Marta; A systematic approach for the design of optimal monitoring systems for large scale processes; American Chemical Society; Industrial & Engineering Chemical Research; 49; 4; -1-2010; 1749-1761
0888-5885
1520-5045
url http://hdl.handle.net/11336/15200
identifier_str_mv Zumoffen, David Alejandro Ramon; Basualdo, Marta; A systematic approach for the design of optimal monitoring systems for large scale processes; American Chemical Society; Industrial & Engineering Chemical Research; 49; 4; -1-2010; 1749-1761
0888-5885
1520-5045
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1021/ie9017836
info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie9017836
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 American Chemical Society
publisher.none.fl_str_mv American Chemical Society
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 13.223955

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