Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks

In this research work, the thermal conductivity and density of alumina/silica (Al2O3/SiO2) in water hybrid nanofluids at different temperatures and volume concentrations have been modeled using the artificial neural networks (ANN). The nanocolloid involved in the study was synthesized by the two-step method and characterized by XRD, TEM, SEM-EDX and zeta potential analysis. The properties of the synthesized nanofluid were measured at various volume concentrations (0.05%, 0.1% and 0.2%) and temperatures (20 to 60 degrees C). Established on the observational data and ANN, the optimum neural structure was suggested for predicting the thermal conductivity and density of the hybrid nanofluid as a function of temperature and solid volume concentrations. The results indicate that a neural network with 2 hidden layers and 10 neurons have the lowest error and a highest fitting coefficient of thermal conductivity, whereas in the case of density, the structure with 1 hidden layer consisting of 4 neurons proved to be the optimal structure. (C) 2018 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd.. All rights reserved.