Thermodynamics Research Center / ThermoML | Fluid Phase Equilibria

ABSTRACT

The evaluation of deep eutectic solvents (DESs) as a new generation of solvents for various practical application requires an insight of the main physical, chemical, and thermodynamic properties. In this study,the experimental measurements of the electrical conductivity of two classes of DESs based on ammoniumand phosphonium salts at different compositions and temperatures were reported. The results revealed that electrical conductivity of DESs has temperature-dependency. In addition, molar conductivities ofammonium and phosphonium salts in DESs were obtained using DESs experimental values of electricalconductivities. The feasibility of using an artificial neural network (ANN) model to predict the electricalconductivity of ammonium and phosphonium based DESs at different temperatures and compositionswas also examined. A feed-forward back propagation neural network with 8 hidden neurons was successfully developed and trained with the measured electrical conductivity data. The results indicated thatamong the different networks tested, the network with 8 hidden neurons had the best prediction per-formance and gave the smallest value of Normalized Mean Square Error (NMSE) (0.0010) and acceptablevalues of Index of Agreement (IA) (0.9999) and Regression Coefficient (R2) (0.9988). The comparison ofthe predicted electrical conductivity of DESs by the proposed model with those obtained by experimentsconfirmed the reliability of the ANN model with an average absolute relative deviation (AARD%) of 4.40%.