Neural network model for the on-line monitoring of a crystallization process
Autor(a) principal: | |
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Data de Publicação: | 2001 |
Outros Autores: | , |
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=S0104-66322001000300006 |
Resumo: | This paper presents the results of the application of a recently developed technique, based on Neural Networks (NN), in the recognition of angular distribution patterns of light scattered by particles in suspension, for the purpose of estimating concentration and crystal size distribution (CSD) in a precipitation process based on the addition of antisolvent (a model system consisting of sodium chloride, water and ethanol). In the first step, in NN model was fitted, using particles with different size distributions and concentrations. Then the model was used to monitor the process, thus enabling a fast and reliable estimation of supersaturation and CSD. Such information, which is difficult to obtain by any other means, can be used in the study of fundamental aspects of crystallization and precipitation processes. |
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Brazilian Journal of Chemical Engineering |
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Neural network model for the on-line monitoring of a crystallization processParticle size distributionLaser diffractioncrystallizationNeural Networks modelingThis paper presents the results of the application of a recently developed technique, based on Neural Networks (NN), in the recognition of angular distribution patterns of light scattered by particles in suspension, for the purpose of estimating concentration and crystal size distribution (CSD) in a precipitation process based on the addition of antisolvent (a model system consisting of sodium chloride, water and ethanol). In the first step, in NN model was fitted, using particles with different size distributions and concentrations. Then the model was used to monitor the process, thus enabling a fast and reliable estimation of supersaturation and CSD. Such information, which is difficult to obtain by any other means, can be used in the study of fundamental aspects of crystallization and precipitation processes.Brazilian Society of Chemical Engineering2001-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322001000300006Brazilian Journal of Chemical Engineering v.18 n.3 2001reponame:Brazilian Journal of Chemical Engineeringinstname:Associação Brasileira de Engenharia Química (ABEQ)instacron:ABEQ10.1590/S0104-66322001000300006info:eu-repo/semantics/openAccessGuardani,R.Onimaru,R.S.Crespo,F.C.A.eng2001-10-11T00:00:00Zoai:scielo:S0104-66322001000300006Revistahttps://www.scielo.br/j/bjce/https://old.scielo.br/oai/scielo-oai.phprgiudici@usp.br||rgiudici@usp.br1678-43830104-6632opendoar:2001-10-11T00:00Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ)false |
dc.title.none.fl_str_mv |
Neural network model for the on-line monitoring of a crystallization process |
title |
Neural network model for the on-line monitoring of a crystallization process |
spellingShingle |
Neural network model for the on-line monitoring of a crystallization process Guardani,R. Particle size distribution Laser diffraction crystallization Neural Networks modeling |
title_short |
Neural network model for the on-line monitoring of a crystallization process |
title_full |
Neural network model for the on-line monitoring of a crystallization process |
title_fullStr |
Neural network model for the on-line monitoring of a crystallization process |
title_full_unstemmed |
Neural network model for the on-line monitoring of a crystallization process |
title_sort |
Neural network model for the on-line monitoring of a crystallization process |
author |
Guardani,R. |
author_facet |
Guardani,R. Onimaru,R.S. Crespo,F.C.A. |
author_role |
author |
author2 |
Onimaru,R.S. Crespo,F.C.A. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Guardani,R. Onimaru,R.S. Crespo,F.C.A. |
dc.subject.por.fl_str_mv |
Particle size distribution Laser diffraction crystallization Neural Networks modeling |
topic |
Particle size distribution Laser diffraction crystallization Neural Networks modeling |
description |
This paper presents the results of the application of a recently developed technique, based on Neural Networks (NN), in the recognition of angular distribution patterns of light scattered by particles in suspension, for the purpose of estimating concentration and crystal size distribution (CSD) in a precipitation process based on the addition of antisolvent (a model system consisting of sodium chloride, water and ethanol). In the first step, in NN model was fitted, using particles with different size distributions and concentrations. Then the model was used to monitor the process, thus enabling a fast and reliable estimation of supersaturation and CSD. Such information, which is difficult to obtain by any other means, can be used in the study of fundamental aspects of crystallization and precipitation processes. |
publishDate |
2001 |
dc.date.none.fl_str_mv |
2001-09-01 |
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=S0104-66322001000300006 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322001000300006 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0104-66322001000300006 |
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.18 n.3 2001 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_ |
1754213171096190976 |