Artificial neural network model for desalination by sweeping gas membrane distillation

Sweeping gas membrane distillation process (SGMD) has been used for desalination and its performance index, defined as the product of the distillate flux and the salt rejection factor, has been modeled using artificial neural network (ANN) methodology. A feed-forward ANN has been developed for predi...

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Detalles Bibliográficos
Autores: Khayet Souhaimi, Mohamed, Cojocaru, C.
Tipo de recurso: artículo
Fecha de publicación:2013
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:español
OAI Identifier:oai:docta.ucm.es:20.500.14352/33586
Acceso en línea:https://hdl.handle.net/20.500.14352/33586
Access Level:acceso abierto
Palabra clave:536
Permeate Flux
Filtration
Simulation
Prediction
Optimization
Separation
Decline
RO
Termodinámica
2213 Termodinámica
id ES_abd2c4e523934c2fcee7f6028c75bd0e
oai_identifier_str oai:docta.ucm.es:20.500.14352/33586
network_acronym_str ES
network_name_str España
repository_id_str
spelling Artificial neural network model for desalination by sweeping gas membrane distillationKhayet Souhaimi, MohamedCojocaru, C.536Permeate FluxFiltrationSimulationPredictionOptimizationSeparationDeclineROTermodinámica2213 TermodinámicaSweeping gas membrane distillation process (SGMD) has been used for desalination and its performance index, defined as the product of the distillate flux and the salt rejection factor, has been modeled using artificial neural network (ANN) methodology. A feed-forward ANN has been developed for prediction of the performance index based on a set of 53 different experimental SGMD tests. A feed solution of 30 g/L sodium chloride was used in all experiments and the salt rejection factors were found to be greater than 99.4%. The individual and interaction effects of the input variables, namely the feed inlet temperature, the feed flow rate or the feed circulation velocity, and the air flow rate or the air circulation velocity, on the SGMD performance index have been investigated. The optimum point was determined by means of Monte Carlo simulation. The obtained optimal conditions were a feed inlet temperature of 69 degrees C, an air flow rate of 34.5 L/min (i.e. 2.02 m/s air circulation velocity) and a feed flow rate of 160 L/h (i.e. 0.155 m/s liquid circulation velocity). Under these operating conditions a performance index of 1.493 x 10(-3) kg/m(2).s has been achieved experimentally being the maximal SGMD performance index obtained inside the region of experimentation.Elsevier Science BVUniversidad Complutense de Madrid20132013-01-0220132013-01-02journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/33586reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Españolspaopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/335862026-06-02T12:44:21Z
dc.title.none.fl_str_mv Artificial neural network model for desalination by sweeping gas membrane distillation
title Artificial neural network model for desalination by sweeping gas membrane distillation
spellingShingle Artificial neural network model for desalination by sweeping gas membrane distillation
Khayet Souhaimi, Mohamed
536
Permeate Flux
Filtration
Simulation
Prediction
Optimization
Separation
Decline
RO
Termodinámica
2213 Termodinámica
title_short Artificial neural network model for desalination by sweeping gas membrane distillation
title_full Artificial neural network model for desalination by sweeping gas membrane distillation
title_fullStr Artificial neural network model for desalination by sweeping gas membrane distillation
title_full_unstemmed Artificial neural network model for desalination by sweeping gas membrane distillation
title_sort Artificial neural network model for desalination by sweeping gas membrane distillation
dc.creator.none.fl_str_mv Khayet Souhaimi, Mohamed
Cojocaru, C.
author Khayet Souhaimi, Mohamed
author_facet Khayet Souhaimi, Mohamed
Cojocaru, C.
author_role author
author2 Cojocaru, C.
author2_role author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 536
Permeate Flux
Filtration
Simulation
Prediction
Optimization
Separation
Decline
RO
Termodinámica
2213 Termodinámica
topic 536
Permeate Flux
Filtration
Simulation
Prediction
Optimization
Separation
Decline
RO
Termodinámica
2213 Termodinámica
description Sweeping gas membrane distillation process (SGMD) has been used for desalination and its performance index, defined as the product of the distillate flux and the salt rejection factor, has been modeled using artificial neural network (ANN) methodology. A feed-forward ANN has been developed for prediction of the performance index based on a set of 53 different experimental SGMD tests. A feed solution of 30 g/L sodium chloride was used in all experiments and the salt rejection factors were found to be greater than 99.4%. The individual and interaction effects of the input variables, namely the feed inlet temperature, the feed flow rate or the feed circulation velocity, and the air flow rate or the air circulation velocity, on the SGMD performance index have been investigated. The optimum point was determined by means of Monte Carlo simulation. The obtained optimal conditions were a feed inlet temperature of 69 degrees C, an air flow rate of 34.5 L/min (i.e. 2.02 m/s air circulation velocity) and a feed flow rate of 160 L/h (i.e. 0.155 m/s liquid circulation velocity). Under these operating conditions a performance index of 1.493 x 10(-3) kg/m(2).s has been achieved experimentally being the maximal SGMD performance index obtained inside the region of experimentation.
publishDate 2013
dc.date.none.fl_str_mv 2013
2013-01-02
2013
2013-01-02
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/33586
url https://hdl.handle.net/20.500.14352/33586
dc.language.none.fl_str_mv Español
spa
language_invalid_str_mv Español
language spa
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier Science BV
publisher.none.fl_str_mv Elsevier Science BV
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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