Studying experimental data related to change in viscosity of crude oil

In this research, general performance of Radial basis function (RBF) Artificial neural networks in experimental data on effect of the NiO, WO3, TiO2,ZnO and Fe2O3 nanoparticles in different temperatures and mass fractions on the viscosity of crude oil has been studied. The morphology and stability of the nanoparticles has been analyzed by DLS and TEM analysis, the results showed that the average diameter of the nanoparticles is from 10 to 30 nm which defers for different oxide nanoparticles. The general method for calculating the optimum span of the Isotropic Gaussian function with special algorithm for learning RBF networks, has been presented. This study's results declared that the RBF artificial neural networks, because of having strong academic basis and having the ability to filter the noises, has a good performance. With increase in temperature, the ratio of the viscosity of the nanofluids decreases compering to the viscosity of the basefluid. Also with increase in nanoparticles mass fraction the related viscosity increases boldly. For temperatures higher than 50°C, the related viscosity is less than the viscosity of the basefluid.