---

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.

---

Research subject: Expandable Poly Styrene (EPS) has many applications. This polymer prepared by the radical polymerization. This material has many uses in packaging and insulation industries Some of the properties of this polymer like low mechanical strength caused its applications to be limited. By adding some materials, these properties can be improved. Styrene Butadiene Styrene (SBS) is from the materials that which by adding it to the EPS it can improve its quality.
Research approach: In this research, EPS having different percentages of SBS (0, 0.01, 0.02, 0.03) in different conversion percentages (0.6, 0.63, 0.66, 0.69) has been prepared. Different tests like Impact Test, Modular Melt Flow test, Vicat Softening Temperature test, Tensile at Break test, K-value test, Rochwell Hardness test and Elongation at Break test are done on the prepared polymer. Laboratory gained data has been simulated by Multi-Layer Perceptron (MLP) method of artificial neural networks (ANN) and the simulated data covers the laboratory data perfectly.
Main Results: Investigating the tests show that in constant percentages of SBS in EPS with increase in conversion percentage of EPS, the numerical amount of the tests increases except MFI test (low MFI number means better quality). Increase in SBS percentage in the EPS, increases the properties of polymer. In addition, the results of simulation show that the laboratory data covers the the simulated data perfectly. The data obtained from the results of this reasearch can be used for predicting the data for the points which has not been tested. Adding SBS in different weight percentages of poly styrene in different conversion percentages in order to increase the properties of poly styrene has been used for the first time in this research and the laboratory data results in points which has not been tested has been acquired by applications of ANN.