Modeling of Osmotic Pressure of Aqueous Poly(Ethylene Glycol) Solutions Using the Artificial Neural Network and Free Volume Flory Huggins Model

In this work, the modified Flory-Huggins coupled with the free-volume concept and the artificial neural network models were used to obtain the osmotic pressure of aqueous poly(ethylene glycol) solutions. In the artificial neural network, the osmotic pressure of aqueous poly(ethylene glycol) solutions depends on temperature, molecular weight and the mole fractions of poly(ethylene glycol) in aqueous solution. The network topology is optimized and the (3-1-1) architecture is found using optimization of an objective function with batch back propagation (BBP) method for 134 experimental data points. The results obtained from the neural network in obtaining of the osmotic pressure of aqueous poly(ethylene glycol) were compared with those obtained from the free volume Flory-Huggins model (FV-FH). The results showed that the modified Flory-Huggins model and also the artificial neural network can accurately predict the osmotic pressure of aqueous poly(ethylene glycol) solutions but the accuracy of ANN is much better than the modified Flory-Huggins model.