Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action - doi: 10.4025/actascitechnol.v34i1.9656
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng por |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/9656 |
Resumo: | The Law of Mass Action generally models the equilibrium data from ion exchange processes. This methodology is rigorous in terms of thermodynamics and takes into consideration the non-idealities in the solid and aqueous phases. However, the artificial neural networks may also be employed in the phase equilibrium modeling. In this study, both methodologies were tested to describe the ion exchange equilibrium in the binary systems SO42--NO3-, SO42--Cl-, NO3-Cl- and in the ternary system SO42--Cl--NO3-, by AMBERLITE IRA 400 resin as ion exchanger. Datasets used in current study were generated by the application of the Law of Mass Action in the binary systems. Results showed that in the equilibrium modeling of binary systems both methodologies had a similar performance. However, in the prediction of the ternary system equilibrium, the Artificial Neural Networks were not efficient. Networks were also trained with the inclusion of ternary experimental data. The Law of Mass Action in the equilibrium modeling of the ternary system was more efficient than Artificial Neural Networks in all cases. |
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Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action - doi: 10.4025/actascitechnol.v34i1.9656artificial neural networkmass action lawion-exchangeEngenharia QuímicaThe Law of Mass Action generally models the equilibrium data from ion exchange processes. This methodology is rigorous in terms of thermodynamics and takes into consideration the non-idealities in the solid and aqueous phases. However, the artificial neural networks may also be employed in the phase equilibrium modeling. In this study, both methodologies were tested to describe the ion exchange equilibrium in the binary systems SO42--NO3-, SO42--Cl-, NO3-Cl- and in the ternary system SO42--Cl--NO3-, by AMBERLITE IRA 400 resin as ion exchanger. Datasets used in current study were generated by the application of the Law of Mass Action in the binary systems. Results showed that in the equilibrium modeling of binary systems both methodologies had a similar performance. However, in the prediction of the ternary system equilibrium, the Artificial Neural Networks were not efficient. Networks were also trained with the inclusion of ternary experimental data. The Law of Mass Action in the equilibrium modeling of the ternary system was more efficient than Artificial Neural Networks in all cases.Universidade Estadual De Maringá2011-07-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionRedes Neurais, Modelagem, troca iônicaapplication/pdfapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/965610.4025/actascitechnol.v34i1.9656Acta Scientiarum. Technology; Vol 34 No 1 (2012); 53-60Acta Scientiarum. Technology; v. 34 n. 1 (2012); 53-601806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMengporhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/9656/9656http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/9656/9656aCanevesi, Rafael Luan SehnZanella Junior, Elizeu AvelinoBarella, Rodrigo AugustoMartins, Tiago DiasMoreira, Marcos Flávio PintoSilva, Edson Antonio dainfo:eu-repo/semantics/openAccess2024-05-17T13:03:15Zoai:periodicos.uem.br/ojs:article/9656Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2024-05-17T13:03:15Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action - doi: 10.4025/actascitechnol.v34i1.9656 |
title |
Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action - doi: 10.4025/actascitechnol.v34i1.9656 |
spellingShingle |
Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action - doi: 10.4025/actascitechnol.v34i1.9656 Canevesi, Rafael Luan Sehn artificial neural network mass action law ion-exchange Engenharia Química |
title_short |
Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action - doi: 10.4025/actascitechnol.v34i1.9656 |
title_full |
Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action - doi: 10.4025/actascitechnol.v34i1.9656 |
title_fullStr |
Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action - doi: 10.4025/actascitechnol.v34i1.9656 |
title_full_unstemmed |
Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action - doi: 10.4025/actascitechnol.v34i1.9656 |
title_sort |
Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action - doi: 10.4025/actascitechnol.v34i1.9656 |
author |
Canevesi, Rafael Luan Sehn |
author_facet |
Canevesi, Rafael Luan Sehn Zanella Junior, Elizeu Avelino Barella, Rodrigo Augusto Martins, Tiago Dias Moreira, Marcos Flávio Pinto Silva, Edson Antonio da |
author_role |
author |
author2 |
Zanella Junior, Elizeu Avelino Barella, Rodrigo Augusto Martins, Tiago Dias Moreira, Marcos Flávio Pinto Silva, Edson Antonio da |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Canevesi, Rafael Luan Sehn Zanella Junior, Elizeu Avelino Barella, Rodrigo Augusto Martins, Tiago Dias Moreira, Marcos Flávio Pinto Silva, Edson Antonio da |
dc.subject.por.fl_str_mv |
artificial neural network mass action law ion-exchange Engenharia Química |
topic |
artificial neural network mass action law ion-exchange Engenharia Química |
description |
The Law of Mass Action generally models the equilibrium data from ion exchange processes. This methodology is rigorous in terms of thermodynamics and takes into consideration the non-idealities in the solid and aqueous phases. However, the artificial neural networks may also be employed in the phase equilibrium modeling. In this study, both methodologies were tested to describe the ion exchange equilibrium in the binary systems SO42--NO3-, SO42--Cl-, NO3-Cl- and in the ternary system SO42--Cl--NO3-, by AMBERLITE IRA 400 resin as ion exchanger. Datasets used in current study were generated by the application of the Law of Mass Action in the binary systems. Results showed that in the equilibrium modeling of binary systems both methodologies had a similar performance. However, in the prediction of the ternary system equilibrium, the Artificial Neural Networks were not efficient. Networks were also trained with the inclusion of ternary experimental data. The Law of Mass Action in the equilibrium modeling of the ternary system was more efficient than Artificial Neural Networks in all cases. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-07-08 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Redes Neurais, Modelagem, troca iônica |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/9656 10.4025/actascitechnol.v34i1.9656 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/9656 |
identifier_str_mv |
10.4025/actascitechnol.v34i1.9656 |
dc.language.iso.fl_str_mv |
eng por |
language |
eng por |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/9656/9656 http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/9656/9656a |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Technology; Vol 34 No 1 (2012); 53-60 Acta Scientiarum. Technology; v. 34 n. 1 (2012); 53-60 1806-2563 1807-8664 reponame:Acta scientiarum. Technology (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta scientiarum. Technology (Online) |
collection |
Acta scientiarum. Technology (Online) |
repository.name.fl_str_mv |
Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315333854527488 |