Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action
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| Natura: | Artículo científico |
| Lingua: | en |
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Universidade Estadual de Maringá
2012
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| _version_ | 1876467654007455744 |
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| author | Rafael Luan Sehn Canevesi |
| author_facet | Rafael Luan Sehn Canevesi |
| contents | Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action Rafael Luan Sehn Canevesi Elizeu Avelino Zanella Junior Rodrigo Augusto Barella Tiago Dias Martins Marcos Flávio Pinto Moreira Edson Antonio da Silva Ingeniería ion exchange mass action law Artificial neural network 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 SO4 2--NO3 -, SO4 2--Cl-, NO3-Cl- and in the ternary system SO4 2--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. 2012 artículo científico 1806-2563 https://www.redalyc.org/articulo.oa?id=303226534009 en http://www.redalyc.org/revista.oa?id=3032 Acta Scientiarum. Technology application/pdf Universidade Estadual de Maringá Acta Scientiarum. Technology (Brasil) Num.1 Vol.34 |
| format | Artículo científico |
| id | redalyc_303226534009 |
| institution | Redalyc |
| language | en |
| publishDate | 2012 |
| publisher | Universidade Estadual de Maringá |
| spellingShingle | Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action Rafael Luan Sehn Canevesi Ingeniería ion exchange mass action law Artificial neural network Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action Rafael Luan Sehn Canevesi Elizeu Avelino Zanella Junior Rodrigo Augusto Barella Tiago Dias Martins Marcos Flávio Pinto Moreira Edson Antonio da Silva Ingeniería ion exchange mass action law Artificial neural network 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 SO4 2--NO3 -, SO4 2--Cl-, NO3-Cl- and in the ternary system SO4 2--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. 2012 artículo científico 1806-2563 https://www.redalyc.org/articulo.oa?id=303226534009 en http://www.redalyc.org/revista.oa?id=3032 Acta Scientiarum. Technology application/pdf Universidade Estadual de Maringá Acta Scientiarum. Technology (Brasil) Num.1 Vol.34 |
| title | Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action |
| topic | Ingeniería ion exchange mass action law Artificial neural network |
| url | https://www.redalyc.org/articulo.oa?id=303226534009 |