Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks

Detalhes bibliográficos
Autor(a) principal: Migliavacca,S.C.P.
Data de Publicação: 2002
Outros Autores: Rodrigues,C., Nascimento,C.A.O.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Brazilian Journal of Chemical Engineering
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322002000300005
Resumo: Neural networks are an attractive alternative for modeling complex problems with too many difficulties to be solved by a phenomenological model. A feed-forward neural network was used to model a gas-centrifugal separation of uranium isotopes. The prediction showed good agreement with the experimental data. An optimization study was carried out. The optimal operational condition was tested by a new experiment and a difference of less than 1% was found.
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spelling Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networksneural networksisotope separationgas centrifugationoptimizationuranium isotopesmodelingNeural networks are an attractive alternative for modeling complex problems with too many difficulties to be solved by a phenomenological model. A feed-forward neural network was used to model a gas-centrifugal separation of uranium isotopes. The prediction showed good agreement with the experimental data. An optimization study was carried out. The optimal operational condition was tested by a new experiment and a difference of less than 1% was found.Brazilian Society of Chemical Engineering2002-07-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322002000300005Brazilian Journal of Chemical Engineering v.19 n.3 2002reponame:Brazilian Journal of Chemical Engineeringinstname:Associação Brasileira de Engenharia Química (ABEQ)instacron:ABEQ10.1590/S0104-66322002000300005info:eu-repo/semantics/openAccessMigliavacca,S.C.P.Rodrigues,C.Nascimento,C.A.O.eng2003-01-21T00:00:00Zoai:scielo:S0104-66322002000300005Revistahttps://www.scielo.br/j/bjce/https://old.scielo.br/oai/scielo-oai.phprgiudici@usp.br||rgiudici@usp.br1678-43830104-6632opendoar:2003-01-21T00:00Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ)false
dc.title.none.fl_str_mv Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks
title Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks
spellingShingle Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks
Migliavacca,S.C.P.
neural networks
isotope separation
gas centrifugation
optimization
uranium isotopes
modeling
title_short Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks
title_full Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks
title_fullStr Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks
title_full_unstemmed Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks
title_sort Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks
author Migliavacca,S.C.P.
author_facet Migliavacca,S.C.P.
Rodrigues,C.
Nascimento,C.A.O.
author_role author
author2 Rodrigues,C.
Nascimento,C.A.O.
author2_role author
author
dc.contributor.author.fl_str_mv Migliavacca,S.C.P.
Rodrigues,C.
Nascimento,C.A.O.
dc.subject.por.fl_str_mv neural networks
isotope separation
gas centrifugation
optimization
uranium isotopes
modeling
topic neural networks
isotope separation
gas centrifugation
optimization
uranium isotopes
modeling
description Neural networks are an attractive alternative for modeling complex problems with too many difficulties to be solved by a phenomenological model. A feed-forward neural network was used to model a gas-centrifugal separation of uranium isotopes. The prediction showed good agreement with the experimental data. An optimization study was carried out. The optimal operational condition was tested by a new experiment and a difference of less than 1% was found.
publishDate 2002
dc.date.none.fl_str_mv 2002-07-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322002000300005
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322002000300005
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0104-66322002000300005
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Brazilian Society of Chemical Engineering
publisher.none.fl_str_mv Brazilian Society of Chemical Engineering
dc.source.none.fl_str_mv Brazilian Journal of Chemical Engineering v.19 n.3 2002
reponame:Brazilian Journal of Chemical Engineering
instname:Associação Brasileira de Engenharia Química (ABEQ)
instacron:ABEQ
instname_str Associação Brasileira de Engenharia Química (ABEQ)
instacron_str ABEQ
institution ABEQ
reponame_str Brazilian Journal of Chemical Engineering
collection Brazilian Journal of Chemical Engineering
repository.name.fl_str_mv Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ)
repository.mail.fl_str_mv rgiudici@usp.br||rgiudici@usp.br
_version_ 1754213171157008384

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