Detection of outliers in a gas centrifuge experimental data
Main Author: | |
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Publication Date: | 2005 |
Other Authors: | , |
Format: | Article |
Language: | eng |
Source: | Brazilian Journal of Chemical Engineering |
Download full: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322005000300008 |
Summary: | Isotope separation with a gas centrifuge is a very complex process. Development and optimization of a gas centrifuge requires experimentation. These data contain experimental errors, and like other experimental data, there may be some gross errors, also known as outliers. The detection of outliers in gas centrifuge experimental data is quite complicated because there is not enough repetition for precise statistical determination and the physical equations may be applied only to control of the mass flow. Moreover, the concentrations are poorly predicted by phenomenological models. This paper presents the application of a three-layer feed-forward neural network to the detection of outliers in analysis of performed on a very extensive experiment. |
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Detection of outliers in a gas centrifuge experimental dataIsotope separationGas centrifugationUranium isotopesOutlier detectionNeural networkIsotope separation with a gas centrifuge is a very complex process. Development and optimization of a gas centrifuge requires experimentation. These data contain experimental errors, and like other experimental data, there may be some gross errors, also known as outliers. The detection of outliers in gas centrifuge experimental data is quite complicated because there is not enough repetition for precise statistical determination and the physical equations may be applied only to control of the mass flow. Moreover, the concentrations are poorly predicted by phenomenological models. This paper presents the application of a three-layer feed-forward neural network to the detection of outliers in analysis of performed on a very extensive experiment.Brazilian Society of Chemical Engineering2005-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322005000300008Brazilian Journal of Chemical Engineering v.22 n.3 2005reponame:Brazilian Journal of Chemical Engineeringinstname:Associação Brasileira de Engenharia Química (ABEQ)instacron:ABEQ10.1590/S0104-66322005000300008info:eu-repo/semantics/openAccessAndrade,M. C. V.Nascimento,C. A. O.Migliavacca,S. C. P.eng2005-09-28T00:00:00Zoai:scielo:S0104-66322005000300008Revistahttps://www.scielo.br/j/bjce/https://old.scielo.br/oai/scielo-oai.phprgiudici@usp.br||rgiudici@usp.br1678-43830104-6632opendoar:2005-09-28T00:00Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ)false |
dc.title.none.fl_str_mv |
Detection of outliers in a gas centrifuge experimental data |
title |
Detection of outliers in a gas centrifuge experimental data |
spellingShingle |
Detection of outliers in a gas centrifuge experimental data Andrade,M. C. V. Isotope separation Gas centrifugation Uranium isotopes Outlier detection Neural network |
title_short |
Detection of outliers in a gas centrifuge experimental data |
title_full |
Detection of outliers in a gas centrifuge experimental data |
title_fullStr |
Detection of outliers in a gas centrifuge experimental data |
title_full_unstemmed |
Detection of outliers in a gas centrifuge experimental data |
title_sort |
Detection of outliers in a gas centrifuge experimental data |
author |
Andrade,M. C. V. |
author_facet |
Andrade,M. C. V. Nascimento,C. A. O. Migliavacca,S. C. P. |
author_role |
author |
author2 |
Nascimento,C. A. O. Migliavacca,S. C. P. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Andrade,M. C. V. Nascimento,C. A. O. Migliavacca,S. C. P. |
dc.subject.por.fl_str_mv |
Isotope separation Gas centrifugation Uranium isotopes Outlier detection Neural network |
topic |
Isotope separation Gas centrifugation Uranium isotopes Outlier detection Neural network |
description |
Isotope separation with a gas centrifuge is a very complex process. Development and optimization of a gas centrifuge requires experimentation. These data contain experimental errors, and like other experimental data, there may be some gross errors, also known as outliers. The detection of outliers in gas centrifuge experimental data is quite complicated because there is not enough repetition for precise statistical determination and the physical equations may be applied only to control of the mass flow. Moreover, the concentrations are poorly predicted by phenomenological models. This paper presents the application of a three-layer feed-forward neural network to the detection of outliers in analysis of performed on a very extensive experiment. |
publishDate |
2005 |
dc.date.none.fl_str_mv |
2005-09-01 |
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=S0104-66322005000300008 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322005000300008 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0104-66322005000300008 |
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.22 n.3 2005 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 |
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1754213171906740224 |