Viscosity prediction by computational method and artificial neural network approach: The case of six refrigerants

There are some computational models for fluids viscosity calculation. However, each of these models is reliable in confined density. In this comparative study two methods are evaluated for viscosity prediction in all range of density. We determine the effectiveness of each of the models and we demonstrate the strengths and weaknesses of them. Viscosity of the six refrigerants is calculated by some computational models based on Chapman?Enskog and Rainwater?Friend theories. Then a feed forward artificial neural network (ANN) with multilayer perceptrons is used to viscosity prediction and finally two methods (computational models and artificial neural network) are comparing. It is concluded that there is no opinion by computational methods to calculate viscosity from low to high density. The results show that prediction accuracy of computational models in low and moderate densities is good as ANN method. However artificial neural network has very good accuracy in high densities while computational method is defeated when the density is more than 8.