Prediction of Surface Tension of Pure Hydrocarbons by An Artificial Neural Network System

摘要

In this study, a multilayer perceptron neural network with a back-propagation (BP) learning method containing three layers has been developed for prediction of surface tension of pure hydrocarbons. This model was developed using 211 experimental data on surface tension in the literature. Statistical criteria of R~2 and root mean square error (RMSE) for this network were 0.9997 and 0.0615, respectively. The accuracy and validity of this model were compared with the experimental data and results obtained from the Pazuki-Nikookar (PN) equation of state (EOS; Pazuki et al., 2007; Nikookar et at, 2008) as well as some semi-experimental correlations such as Pitzer (1995), Sastri and Rao (1999), and Brock and Bird (1955). Also, considering the results obtained by the artificial neural network system, a correlation is presented for estimation of the parachor constant and the results are compared with those obtained from equations of state.