Model predictive control to ensure high quality hydrogen production for fuel cells

Autores
Rullo, Pablo Gabriel; Nieto Degliuomini, Lucas; García, Maximiliano Pablo; Basualdo, Marta Susana
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work, a conventional plant wide control of a hydrogen production process from bioethanol is analyzed. The objective is to determine if the carbon monoxide (CO), in the produced hydrogen, exceeds the Proton Exchange Membrane Fuel Cell quality requirement of 10 ppm. Commercial sensors that meet those process conditions at high temperature are not easily available. Then, the development of two soft sensors, based on neural network, for online estimation of CO concentration in the H2 stream is presented. Higher CO concentration than allowed is detected in the fuel cell feeding. Strong interaction effects among the control loops around the last reactor, are found. Based on this, two model predictive control technologies are tested and compared in this interacted zone, in order to improve the disturbance rejection and satisfy the H2 expected quality. An exigent disturbance profile was used for simulating dynamically the complete process behavior.
Fil: Rullo, Pablo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Nieto Degliuomini, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: García, Maximiliano Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Basualdo, Marta Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina. Universidad Tecnológica Nacional; Argentina
Materia
Model Predictive Control
Bioethanol Processor System
Co Soft Sensor
Pem Quality Hydrogen Production
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/32983
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/32983
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Model predictive control to ensure high quality hydrogen production for fuel cellsRullo, Pablo GabrielNieto Degliuomini, LucasGarcía, Maximiliano PabloBasualdo, Marta SusanaModel Predictive ControlBioethanol Processor SystemCo Soft SensorPem Quality Hydrogen Productionhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2In this work, a conventional plant wide control of a hydrogen production process from bioethanol is analyzed. The objective is to determine if the carbon monoxide (CO), in the produced hydrogen, exceeds the Proton Exchange Membrane Fuel Cell quality requirement of 10 ppm. Commercial sensors that meet those process conditions at high temperature are not easily available. Then, the development of two soft sensors, based on neural network, for online estimation of CO concentration in the H2 stream is presented. Higher CO concentration than allowed is detected in the fuel cell feeding. Strong interaction effects among the control loops around the last reactor, are found. Based on this, two model predictive control technologies are tested and compared in this interacted zone, in order to improve the disturbance rejection and satisfy the H2 expected quality. An exigent disturbance profile was used for simulating dynamically the complete process behavior.Fil: Rullo, Pablo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Nieto Degliuomini, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: García, Maximiliano Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Basualdo, Marta Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina. Universidad Tecnológica Nacional; ArgentinaElsevier2014-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/32983Basualdo, Marta Susana; García, Maximiliano Pablo; Nieto Degliuomini, Lucas; Rullo, Pablo Gabriel; Model predictive control to ensure high quality hydrogen production for fuel cells; Elsevier; International Journal of Hydrogen Energy; 39; 16; 1-2014; 8635-86490360-3199CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.ijhydene.2013.12.069info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0360319913030048info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:36:32Zoai:ri.conicet.gov.ar:11336/32983instacron: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 09:36:32.761CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Model predictive control to ensure high quality hydrogen production for fuel cells
title Model predictive control to ensure high quality hydrogen production for fuel cells
spellingShingle Model predictive control to ensure high quality hydrogen production for fuel cells
Rullo, Pablo Gabriel
Model Predictive Control
Bioethanol Processor System
Co Soft Sensor
Pem Quality Hydrogen Production
title_short Model predictive control to ensure high quality hydrogen production for fuel cells
title_full Model predictive control to ensure high quality hydrogen production for fuel cells
title_fullStr Model predictive control to ensure high quality hydrogen production for fuel cells
title_full_unstemmed Model predictive control to ensure high quality hydrogen production for fuel cells
title_sort Model predictive control to ensure high quality hydrogen production for fuel cells
dc.creator.none.fl_str_mv Rullo, Pablo Gabriel
Nieto Degliuomini, Lucas
García, Maximiliano Pablo
Basualdo, Marta Susana
author Rullo, Pablo Gabriel
author_facet Rullo, Pablo Gabriel
Nieto Degliuomini, Lucas
García, Maximiliano Pablo
Basualdo, Marta Susana
author_role author
author2 Nieto Degliuomini, Lucas
García, Maximiliano Pablo
Basualdo, Marta Susana
author2_role author
author
author
dc.subject.none.fl_str_mv Model Predictive Control
Bioethanol Processor System
Co Soft Sensor
Pem Quality Hydrogen Production
topic Model Predictive Control
Bioethanol Processor System
Co Soft Sensor
Pem Quality Hydrogen Production
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, a conventional plant wide control of a hydrogen production process from bioethanol is analyzed. The objective is to determine if the carbon monoxide (CO), in the produced hydrogen, exceeds the Proton Exchange Membrane Fuel Cell quality requirement of 10 ppm. Commercial sensors that meet those process conditions at high temperature are not easily available. Then, the development of two soft sensors, based on neural network, for online estimation of CO concentration in the H2 stream is presented. Higher CO concentration than allowed is detected in the fuel cell feeding. Strong interaction effects among the control loops around the last reactor, are found. Based on this, two model predictive control technologies are tested and compared in this interacted zone, in order to improve the disturbance rejection and satisfy the H2 expected quality. An exigent disturbance profile was used for simulating dynamically the complete process behavior.
Fil: Rullo, Pablo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Nieto Degliuomini, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: García, Maximiliano Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Basualdo, Marta Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina. Universidad Tecnológica Nacional; Argentina
description In this work, a conventional plant wide control of a hydrogen production process from bioethanol is analyzed. The objective is to determine if the carbon monoxide (CO), in the produced hydrogen, exceeds the Proton Exchange Membrane Fuel Cell quality requirement of 10 ppm. Commercial sensors that meet those process conditions at high temperature are not easily available. Then, the development of two soft sensors, based on neural network, for online estimation of CO concentration in the H2 stream is presented. Higher CO concentration than allowed is detected in the fuel cell feeding. Strong interaction effects among the control loops around the last reactor, are found. Based on this, two model predictive control technologies are tested and compared in this interacted zone, in order to improve the disturbance rejection and satisfy the H2 expected quality. An exigent disturbance profile was used for simulating dynamically the complete process behavior.
publishDate 2014
dc.date.none.fl_str_mv 2014-01
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/11336/32983
Basualdo, Marta Susana; García, Maximiliano Pablo; Nieto Degliuomini, Lucas; Rullo, Pablo Gabriel; Model predictive control to ensure high quality hydrogen production for fuel cells; Elsevier; International Journal of Hydrogen Energy; 39; 16; 1-2014; 8635-8649
0360-3199
CONICET Digital
CONICET
url http://hdl.handle.net/11336/32983
identifier_str_mv Basualdo, Marta Susana; García, Maximiliano Pablo; Nieto Degliuomini, Lucas; Rullo, Pablo Gabriel; Model predictive control to ensure high quality hydrogen production for fuel cells; Elsevier; International Journal of Hydrogen Energy; 39; 16; 1-2014; 8635-8649
0360-3199
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ijhydene.2013.12.069
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0360319913030048
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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 12.976206

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