Abstract
The law of mass action (LMA) is generally used to describe the equilibrium of ion exchange processes. This is a rigorous methodology in terms of thermodynamics and considers the non-idealities in the solid and solution phases. However, artificial neural networks (ANNs) can also be effectively used in phase equilibrium modeling. In the current study, ANNs were used to describe the ion exchange equilibrium in the binary systems \(\hbox {Cu}^{2+}{-}\hbox {Na}^+\), \(\hbox {Zn}^{2+}{-}\hbox {Na}^+\) and \(\hbox {Zn}^{2+}{-}\hbox {Cu}^{2+}\) and in the ternary system \(\hbox {Cu}^{2+}{-}\hbox {Na}^{+}{-}\hbox {Zn}^{2+}\), using the resin Amberlite IR 120 as ion exchanger. The datasets used in the training stage of the ANNs in this study were generated by the application of the LMA in the binary systems. Results showed that, in the equilibrium modeling of the binary systems and in the prediction of the ternary system, the two methodologies had similar performance and can be used to describe binary and ternary equilibrium.
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Raizer, E., Reinehr, T.O., Awadallak, J.A. et al. Prediction of ion exchange equilibrium of \(\hbox {Cu}^{2+}{-}\hbox {Na}^{+}{-}\hbox {Zn}^{2+}\) ternary system using artificial neural networks. Adsorption 21, 17–23 (2015). https://doi.org/10.1007/s10450-014-9645-5
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DOI: https://doi.org/10.1007/s10450-014-9645-5