Prediction of hydrocarbon densities using an artificial neural network-group contribution method up to high temperatures and pressures

Abstract

In this work, the densities of hydrocarbon systems have been estimated using a combined method that includes an artificial neural network (ANN) and a simple group contribution method (GCM). A total of 2891 data points of density at several temperatures and pressures, corresponding to 40 different hydrocarbons including short- and long-chain alkanes ranging from CH4 to n-C40H82, and also several cycloalkanes, highly branched alkanes and aromatic hydrocarbons have been used to train, validate and test the model. This study shows that the ANN-GCM model represent an excellent alternative for the estimation of the density of hydrocarbons with a good accuracy. A wide comparison between our results and those of obtained from some previous methods shows that this work can provide a simple procedure for prediction the density of different classes of hydrocarbons in a better accord with experimental data up to high temperature, high pressure (HTHP) conditions. © 2013 Elsevier B.V.