Viscosity correlation for ethane in the form of multilayer feedforward neural networks
Giancarlo Scalabrin, Lorenzo Piazza, Velisa Vesovic
A multilayer feedforward neural network (MLFN) technique is proposed for developing viscosity correlations. The accuracy and usefulness of the method is demonstrated by developing the viscosity correlation for ethane as a function of temperature and density and comparing its predictive power to that of the traditional ethane correlation. The overall average absolute deviation (AAD), based on the primary data set, is the same for both correlations, while the bias of the MLFN correlation is much lower.