Prediction dielectric constant of different ternary liquid mixtures at various temperatures and compositions using artificial neural networks

Abstract

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction dielectric constant of different ternary liquid mixtures at various temperatures (−10°C ≤ t ≤ 80°C) and over the complete composition range (0 ≤ x 1, x 2, x 3 ≤ 1). A three-layered feed forward ANN with architecture 7-16-1 was generated using seven parameters as inputs and its output is dielectric constant of media. It was found that properly selected and trained neural network could fairly represent the dependence of dielectric constant of different ternary liquid mixtures on temperature and composition. For the evaluation of the predictive power of the generated ANN, an optimized network was applied for predicting the dielectric constant in the prediction set, which were not used in the modeling procedure. Squared correlation coefficient (R 2) and root mean square error for prediction set are 0.9997 and 0.2060, respectively. The mean percent deviation (MPD) for the property in the prediction set is 0.8892%. The results show nonlinear dependence of dielectric constant of ternary mixed solvent systems on temperature and composition is significant.