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

doi:10.4025/actascitechnol.v34i1.9656

Rafael Luan Sehn Canevesi, Elizeu Avelino Zanella Junior + Show 4 more

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https://doi.org/10.4025/actascitechnol.v34i1.9656

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Abstract

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.

Highlights

Ionic exchange is a highly employed process for the treatment of effluents with ionic species, the purification of pharmacological compounds, in which adsorption of ionic species occurs in a porous material and followed simultaneously by a desorption process of other ionic species in equivalent amounts, according to the equation: Acta Scientiarum
The evaluation of Law of Mass Action (LMA) and Artificial Neural Networks (ANNs) methodologies was undertaken by using equilibrium data of the binary systems SO42-NO3, SO42--Cl, NO3- Cl- and of the ternary system SO42--NO3--Cl, both at concentration 0.2 N and temperature 298 K, obtained by Smith and Woodburn (1978)
ANNs and the Law of Mass Action described with efficiency the binary equilibrium data which may be represented from average deviation (AAD) rates given in Tables 2 and 4, with close results obtained by MLA and ANNs

Summary

Introduction

Ionic exchange is a highly employed process for the treatment of effluents with ionic species, the purification of pharmacological compounds, in which adsorption of ionic species occurs in a porous material (such as artificial resins or zeolites) and followed simultaneously by a desorption process of other ionic species (already present in the exchanger) in equivalent amounts, according to the equation: Acta Scientiarum. Most industrial applications of the ion exchange process use fixed-bed column systems. According to Tamura (2004), the understanding and the prediction of ion exchange reactions are required for a better quantitative and efficient interpretation of ion exchange processes. Thermodynamic modeling of the ion exchange systems has a very important role in acquiring essential information for the project of ion exchange separation systems

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