Nanotechnology, with the purpose of improving the thermal conductivity of fluids, suggests possible disruptive methods. Therefore, the main goal of this paper was focused on decreasing the operating expenses and costs of industrial systems by means of adding CuO nanoparticles to the working fluid. The influences of adding nanoparticles to water flowing through a horizontal tube were patently mirrored as a heat transfer augmentation. So, nanofluids with 0, 0.03%, 0.1%, 0.3%, 0.5%, and 0.7% nanoparticle volume fractions were prepared in this regard. Heat transfer coefficient and pressure drop were measured at the Reynolds number of 6200 to 14200. The results indicated that the maximum heat transfer and the pressure drop for the nanofluid were 2.8 and 1.4 times more than those for pure water. Also, considering the same heat flux for nanofluid and pure water, it was indicated that the working fluid consumption and system size were reduced up to 37.6% and 55.7%, respectively. An artificial neural network (ANN) with one hidden layer and eight neurons was designed in order to predict the Nusselt number. The transfer function and training algorithm were respectively the Tansig and the LevenbergMarquardt. The ANN outputs showed a maximum of 1% deviation; the R-squared, the mean squared error (MSE), and the average absolute relative deviation (%AARD) were also 0.9966, 3.1384, and 0.0236, respectively.
