Comparison between the artificial neural network system and SAFT equation in obtaining vapor pressure and liquid density of pure alcohols

Abstract

Vapor pressure and liquid density of 20 pure alcohols were correlated using an artificial neural network (ANN) system and statistical associating fluid theory (SAFT) equation of state. The SAFT equation has five adjustable parameters as temperature-independent segment diameter, square-well energy, number of segment per chain, association energy and association volume. These parameters can be obtained by a non-linear regression method using the experimental vapor pressure and liquid density data. In continue, the vapor pressure and liquid densities of pure alcohols were estimated by using an artificial neural network (ANN) system. In the neural network system, it is assumed that thermodynamic properties of pure alcohols depend on temperature, critical properties and acentric factor. The best network topology was obtained as (4-10-2). The weights connection and biases were obtained using batch back propagation (BBP) method for 611 experimental data points. The average absolute deviation percent (ADD%) for vapor pressure of pure alcohols for ANN system and SAFT equation of state are 3.593% and 3.378%, respectively. Also, the average absolute deviation percent (ADD%) for liquid density of pure alcohols for ANN system and SAFT equation of state are 0.792% and 1.367%, respectively. The results emphasized that the artificial neural network can more accurately predict thermophysical properties of pure alcohols than the SAFT equation of state.