P–V–T properties of polymer melts based on equation of state and neural network

Abstract

An accurate and efficient analytical equation of state (EOS) and artificial neural network (ANN) methods are developed for the prediction of volumetric properties of polymer melts. To apply EOS, the second virial coefficients B2(T), effective van der Waals co-volume, b(T) and correction factor, α(T) were determined. The second virial coefficient was calculated from a two-parameter corresponding states correlation, which is constructed with two constants as scaling parameters, i.e., temperature (Tf) and density at melting (ρf) point. The new correlations were used to predict the specific volumes of polypropylene glycol (PPG), polyethylene glycol (PEG), polypropylene (PP), polyvinylchloride (PVC), poly(1-butene)(PB1), poly (ϵ-caprolactone) (PCL), polyethylene (PE) and polyvinylmethylether (PVME) at compressed state in the temperature range of 298.15–634.6K. The obtained results show that the two models have good agreement with the experimental data with absolute average deviation of 0.28% and 0.39% for ANN and EOS, respectively. The Comparison of the results with ISM model shows that the proposed models represent an efficient method and are more accurate.