Hybrid Simulated Annealing for Optimal Cost Instrumentation in Chemical Plants

Autores
Hernández, José; Salto, Carolina; Minetti, Gabriela; Carnero, Mercedes; Sanchez, Mabel Cristina
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Process information is the foundation upon which many common tasks in chemical plants are based. To satisfy information requirements regarding its quality and availability, it is essential to locate an appropriate set of instruments or sensor network (SN) in the plant. The SN designer should decide whether to measure each process variable or not. These decisions are mathematically formulated in terms of binary variables. This results in a combinatorial optimization problem that usually involves many decision binary variables and exhibits multiple solutions locally or globally optimal.In this work, a metaheuristic based on simulated annealing hybridized with strategic oscillation, named HSA_SOTS, is proposed to solve the tackled problem. The performance of HSA_SOTS is evaluated considering several high scale designs with increasing complexities. The results of this metaheuristic outperform the ones presented in the literature.
Fil: Hernández, José. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; Argentina
Fil: Salto, Carolina. Universidad Nacional de la Pampa. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; Argentina
Fil: Minetti, Gabriela. Universidad Nacional de la Pampa. Facultad de Ingeniería; Argentina
Fil: Carnero, Mercedes. 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
14th International Conference on Chemical and Process Engineering
Bologna
Italia
Italian Association of Chemical Engineering
Materia
simulated annealing
sensor network design
optimization problem
quemical plants
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:11336/136219
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Hybrid Simulated Annealing for Optimal Cost Instrumentation in Chemical PlantsHernández, JoséSalto, CarolinaMinetti, GabrielaCarnero, MercedesSanchez, Mabel Cristinasimulated annealingsensor network designoptimization problemquemical plantshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Process information is the foundation upon which many common tasks in chemical plants are based. To satisfy information requirements regarding its quality and availability, it is essential to locate an appropriate set of instruments or sensor network (SN) in the plant. The SN designer should decide whether to measure each process variable or not. These decisions are mathematically formulated in terms of binary variables. This results in a combinatorial optimization problem that usually involves many decision binary variables and exhibits multiple solutions locally or globally optimal.In this work, a metaheuristic based on simulated annealing hybridized with strategic oscillation, named HSA_SOTS, is proposed to solve the tackled problem. The performance of HSA_SOTS is evaluated considering several high scale designs with increasing complexities. The results of this metaheuristic outperform the ones presented in the literature.Fil: Hernández, José. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; ArgentinaFil: Salto, Carolina. Universidad Nacional de la Pampa. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; ArgentinaFil: Minetti, Gabriela. Universidad Nacional de la Pampa. Facultad de Ingeniería; ArgentinaFil: Carnero, Mercedes. 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; Argentina14th International Conference on Chemical and Process EngineeringBolognaItaliaItalian Association of Chemical EngineeringItalian Association of Chemical Engineering2019info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectConferenciaJournalhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/136219Hybrid Simulated Annealing for Optimal Cost Instrumentation in Chemical Plants; 14th International Conference on Chemical and Process Engineering; Bologna; Italia; 2019; 1-62283-9216CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.cetjournal.it/index.php/cet/article/view/CET1974119info:eu-repo/semantics/altIdentifier/url/https://www.aidic.it/icheap14/programma/pro.htmlInternacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:39:29Zoai:ri.conicet.gov.ar:11336/136219instacron: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 09:39:29.631CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Hybrid Simulated Annealing for Optimal Cost Instrumentation in Chemical Plants
title Hybrid Simulated Annealing for Optimal Cost Instrumentation in Chemical Plants
spellingShingle Hybrid Simulated Annealing for Optimal Cost Instrumentation in Chemical Plants
Hernández, José
simulated annealing
sensor network design
optimization problem
quemical plants
title_short Hybrid Simulated Annealing for Optimal Cost Instrumentation in Chemical Plants
title_full Hybrid Simulated Annealing for Optimal Cost Instrumentation in Chemical Plants
title_fullStr Hybrid Simulated Annealing for Optimal Cost Instrumentation in Chemical Plants
title_full_unstemmed Hybrid Simulated Annealing for Optimal Cost Instrumentation in Chemical Plants
title_sort Hybrid Simulated Annealing for Optimal Cost Instrumentation in Chemical Plants
dc.creator.none.fl_str_mv Hernández, José
Salto, Carolina
Minetti, Gabriela
Carnero, Mercedes
Sanchez, Mabel Cristina
author Hernández, José
author_facet Hernández, José
Salto, Carolina
Minetti, Gabriela
Carnero, Mercedes
Sanchez, Mabel Cristina
author_role author
author2 Salto, Carolina
Minetti, Gabriela
Carnero, Mercedes
Sanchez, Mabel Cristina
author2_role author
author
author
author
dc.subject.none.fl_str_mv simulated annealing
sensor network design
optimization problem
quemical plants
topic simulated annealing
sensor network design
optimization problem
quemical plants
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Process information is the foundation upon which many common tasks in chemical plants are based. To satisfy information requirements regarding its quality and availability, it is essential to locate an appropriate set of instruments or sensor network (SN) in the plant. The SN designer should decide whether to measure each process variable or not. These decisions are mathematically formulated in terms of binary variables. This results in a combinatorial optimization problem that usually involves many decision binary variables and exhibits multiple solutions locally or globally optimal.In this work, a metaheuristic based on simulated annealing hybridized with strategic oscillation, named HSA_SOTS, is proposed to solve the tackled problem. The performance of HSA_SOTS is evaluated considering several high scale designs with increasing complexities. The results of this metaheuristic outperform the ones presented in the literature.
Fil: Hernández, José. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; Argentina
Fil: Salto, Carolina. Universidad Nacional de la Pampa. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; Argentina
Fil: Minetti, Gabriela. Universidad Nacional de la Pampa. Facultad de Ingeniería; Argentina
Fil: Carnero, Mercedes. 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
14th International Conference on Chemical and Process Engineering
Bologna
Italia
Italian Association of Chemical Engineering
description Process information is the foundation upon which many common tasks in chemical plants are based. To satisfy information requirements regarding its quality and availability, it is essential to locate an appropriate set of instruments or sensor network (SN) in the plant. The SN designer should decide whether to measure each process variable or not. These decisions are mathematically formulated in terms of binary variables. This results in a combinatorial optimization problem that usually involves many decision binary variables and exhibits multiple solutions locally or globally optimal.In this work, a metaheuristic based on simulated annealing hybridized with strategic oscillation, named HSA_SOTS, is proposed to solve the tackled problem. The performance of HSA_SOTS is evaluated considering several high scale designs with increasing complexities. The results of this metaheuristic outperform the ones presented in the literature.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Conferencia
Journal
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/136219
Hybrid Simulated Annealing for Optimal Cost Instrumentation in Chemical Plants; 14th International Conference on Chemical and Process Engineering; Bologna; Italia; 2019; 1-6
2283-9216
CONICET Digital
CONICET
url http://hdl.handle.net/11336/136219
identifier_str_mv Hybrid Simulated Annealing for Optimal Cost Instrumentation in Chemical Plants; 14th International Conference on Chemical and Process Engineering; Bologna; Italia; 2019; 1-6
2283-9216
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://www.cetjournal.it/index.php/cet/article/view/CET1974119
info:eu-repo/semantics/altIdentifier/url/https://www.aidic.it/icheap14/programma/pro.html
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.coverage.none.fl_str_mv Internacional
dc.publisher.none.fl_str_mv Italian Association of Chemical Engineering
publisher.none.fl_str_mv Italian Association of Chemical Engineering
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 13.070432

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