Molecular thermodynamic modeling of surface tensions of some fatty acid esters and biodiesels

This work addresses the molecular thermodynamic and artificial neural network (ANN) modeling of surface tensions of several fatty acid esters and biodiesels. Two biodiesels were considered as pure fluid and the other as a binary mixture. The molecular thermodynamic model is based on the statistical mechanical expression according to Fowler-Kirkwood-Buff approximation. Regarding this, contributions to surface tension from the hard-chain repulsions, Lennard-Jones dispersion forces, and dipolar interactions were considered and assumed to be additive in the model development. The molecular thermodynamic model used three molecular parameters reflecting the hard-core diameter, dispersive energy and segment number as well as the liquid densities for which the values were predicted from perturbed Yukawa-chain equation of state. Further, the model used dipole moment as an adjustable parameter for the accurate calculation of surface tensions. The model could predict 149 surface tension data points for 9 FAEs and 3 biodiesels in 268.6–393 K range with the average absolute relative deviation (AARD) of 1.82%. The degree of accuracy of proposed model has also been compared with some empirical equations. Concerning ANN modeling, a network comprising two hidden layers and 9 neurons for each layer has been trained, according to the constructive approach. The result of the training was quite good, the AARD of the pure fluid dataset of 137 points was found to be 0.44%.