Estimation of Binary Infinite Dilute Diffusion Coefficient Using Artificial Neural Network

In this study, the use of the three-layer feed forward neural network has been investigated for estimating of infinite dilute diffusion coefficient ( ) of supercritical fluid (SCF), liquid and gas binary systems. Infinite dilute diffusion coefficient was spotted as a function of critical temperature, critical pressure, critical volume, normal boiling point, molecular volume in normal boiling point, molecule diameter, Lennard-Jones’s (LJ) energy parameter, temperature and pressure. For each set of SCF, liquid and gas systems a three-layer network has been applied with training algorithm of Levenberg-Marquard (LM). The obtained results of models have shown good accuracy of artificial neural network (ANN) for estimating infinite dilute diffusion coefficient of SCF, liquid and gas binary systems with mean relative error (MRE) of 2.88 % for 231 systems containing 4078 data points (mean relative error for ANN model in SCF, liquid and gas binary systems are 3.00, 2.99 and 1.21 %, respectively).