Application of artificial neural networks for viscosity of crude oil-based nanofluids containing oxides nanoparticles

In this research, the effect of general performances of radial basis function (RBF) method of artificial neural networks (ANN) with laboratory data on NiO, WO3, TiO2, ZnO and FeO3 nanoparticles in different temperatures and mass fractions on viscosity of crude oil were studied. The morphology and nanoparticles stability were analyzed with the DLS and TEM analysis. The results showed that the average nanoparticles diameter ranged from 10 to 40 nm for different oxide nanoparticles. For learning RBF networks, the major method for calculating isotropic Gaussian basis functions span for RBF networks containing special algorithm were presented. The results declared RBF neural networks had an acceptable performance because of having strong academic basic and ability of filtering the noises. This method contain all the experimental data perfectly, and provides the possibility of using different mass fractions and temperatures and simulated charts for knowing the information about the viscosity. For TiO2, ZnO and FeO3 nanoparticles, adding small amounts of nanoparticles decreased the relative viscosity comparing to the base fluid viscosity. But for WO3 and NiO nanoparticles the viscosity of nanofluids was higher than base fluid with any mass fractions.