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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/42342
Ver los metadatos del registro completo
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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 Técnicas2025-09-03T09:48:17Zoai: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:34982025-09-03 09:48:17.565CONICET 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 |
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1842268915767443456 |
score |
13.13397 |