Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics

Autores
Carnero, Mercedes del Carmen; Hernandez, Jose Luis; Sanchez, Mabel Cristina
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work the optimal design of sensor networks for chemical plants is addressed using stochastic optimization strategies. The problem consists in selecting the type, number and location of new sensors that provide the required quantity and quality of process information. Ad-hoc strategies based on Tabu Search, Scatter Search and Population Based Incremental Learning Algorithms are proposed. Regarding Tabu Search, the intensification and diversification capabilities of the technique are enhanced using Path Relinking. The strategies are applied for solving minimum cost design problems subject to quality constraints on variable estimates, and their performances are compared.
Fil: Carnero, Mercedes del Carmen. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; Argentina
Fil: Hernandez, Jose Luis. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; Argentina
Fil: Sanchez, Mabel Cristina. 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
SENSOR LOCATION
STOCHASTIC OPTIMIZATION
TABU SEARCH
SCATTER SEARCH
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:11336/42342
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Design of Sensor Networks for Chemical Plants Based on Meta-HeuristicsCarnero, Mercedes del CarmenHernandez, Jose LuisSanchez, Mabel CristinaSENSOR LOCATIONSTOCHASTIC OPTIMIZATIONTABU SEARCHSCATTER SEARCHhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2In this work the optimal design of sensor networks for chemical plants is addressed using stochastic optimization strategies. The problem consists in selecting the type, number and location of new sensors that provide the required quantity and quality of process information. Ad-hoc strategies based on Tabu Search, Scatter Search and Population Based Incremental Learning Algorithms are proposed. Regarding Tabu Search, the intensification and diversification capabilities of the technique are enhanced using Path Relinking. The strategies are applied for solving minimum cost design problems subject to quality constraints on variable estimates, and their performances are compared.Fil: Carnero, Mercedes del Carmen. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; ArgentinaFil: Hernandez, Jose Luis. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; ArgentinaFil: Sanchez, Mabel Cristina. 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; ArgentinaMolecular Diversity Preservation International2009-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/42342Carnero, Mercedes del Carmen; Hernandez, Jose Luis; Sanchez, Mabel Cristina; Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics; Molecular Diversity Preservation International; Algorithms; 2; 1; 2-2009; 259-2811999-4893CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://mdpi.com/1999-4893/2/1/259info:eu-repo/semantics/altIdentifier/doi/10.3390/a2010259info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:41:19Zoai:ri.conicet.gov.ar:11336/42342instacron: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 09:41:20.377CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics
title Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics
spellingShingle Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics
Carnero, Mercedes del Carmen
SENSOR LOCATION
STOCHASTIC OPTIMIZATION
TABU SEARCH
SCATTER SEARCH
title_short Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics
title_full Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics
title_fullStr Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics
title_full_unstemmed Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics
title_sort Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics
dc.creator.none.fl_str_mv Carnero, Mercedes del Carmen
Hernandez, Jose Luis
Sanchez, Mabel Cristina
author Carnero, Mercedes del Carmen
author_facet Carnero, Mercedes del Carmen
Hernandez, Jose Luis
Sanchez, Mabel Cristina
author_role author
author2 Hernandez, Jose Luis
Sanchez, Mabel Cristina
author2_role author
author
dc.subject.none.fl_str_mv SENSOR LOCATION
STOCHASTIC OPTIMIZATION
TABU SEARCH
SCATTER SEARCH
topic SENSOR LOCATION
STOCHASTIC OPTIMIZATION
TABU SEARCH
SCATTER SEARCH
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this work the optimal design of sensor networks for chemical plants is addressed using stochastic optimization strategies. The problem consists in selecting the type, number and location of new sensors that provide the required quantity and quality of process information. Ad-hoc strategies based on Tabu Search, Scatter Search and Population Based Incremental Learning Algorithms are proposed. Regarding Tabu Search, the intensification and diversification capabilities of the technique are enhanced using Path Relinking. The strategies are applied for solving minimum cost design problems subject to quality constraints on variable estimates, and their performances are compared.
Fil: Carnero, Mercedes del Carmen. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; Argentina
Fil: Hernandez, Jose Luis. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; Argentina
Fil: Sanchez, Mabel Cristina. 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 In this work the optimal design of sensor networks for chemical plants is addressed using stochastic optimization strategies. The problem consists in selecting the type, number and location of new sensors that provide the required quantity and quality of process information. Ad-hoc strategies based on Tabu Search, Scatter Search and Population Based Incremental Learning Algorithms are proposed. Regarding Tabu Search, the intensification and diversification capabilities of the technique are enhanced using Path Relinking. The strategies are applied for solving minimum cost design problems subject to quality constraints on variable estimates, and their performances are compared.
publishDate 2009
dc.date.none.fl_str_mv 2009-02
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/11336/42342
Carnero, Mercedes del Carmen; Hernandez, Jose Luis; Sanchez, Mabel Cristina; Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics; Molecular Diversity Preservation International; Algorithms; 2; 1; 2-2009; 259-281
1999-4893
CONICET Digital
CONICET
url http://hdl.handle.net/11336/42342
identifier_str_mv Carnero, Mercedes del Carmen; Hernandez, Jose Luis; Sanchez, Mabel Cristina; Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics; Molecular Diversity Preservation International; Algorithms; 2; 1; 2-2009; 259-281
1999-4893
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://mdpi.com/1999-4893/2/1/259
info:eu-repo/semantics/altIdentifier/doi/10.3390/a2010259
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 Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
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
_version_ 1844613305848561664
score 13.069144

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