Prediction of the specific volume of polymeric systems using the artificial neural network-group contribution method

• The specific volumes of polymeric systems have been estimated using ANN–GCM method.
• The best network configuration consisted of 18 neurons in the hidden layer.
• The advantage of this technique is its high speed, simplicity and generalization.
• A wide comparison between this method and some previous works has been made.
• The AAD for train, validation, and test sets are 0.0403, 0.0439, and 0.0482, respectively.

In this work, the specific volumes of some polymeric 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 2865 data points of specific volume at several temperatures and pressures, corresponding to 25 different polymeric systems 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 specific volume of different polymeric systems with a good accuracy. The average relative deviations for train, validation, and test sets are 0.0403, 0.0439, and 0.0482, respectively. A wide comparison between our results and those of obtained from some previous methods show that this work can provide a simple procedure for prediction the specific volume of different polymeric systems in a better accord with experimental data up to high temperature, high pressure (HTHP) conditions