Investigation the effects of different nanoparticles on density and specific heat: Prediction using MLP artificial neural network and response surface methodology

Increasing energy efficiency is of particular importance and therefore the use of nanofluids has been considered due to their thermophysical properties. in this study Response surface methodology (RSM) and MLP artificial neural network (ANN) models are applied for the determination of the density and specific heat capacity. Results indicate that the maximum residual value for the RSM model of density was ±1.5 and for the RSM model of specific heat was + 0.008 and − 0.006. The optimum MLP structure for density is created with 5 neurons in the first layer and 2 neurons in the second layer. For the MLP structure of specific heat, 4 neurons in the first layer and 5 neurons in the second layer are considered. The best MLP structures have MSE, MAE and R2 equal to 0.324113, 0.395038 and 0.9985 for density and 1.89E-05, 3.24E-03 and 0.9974 for thermal conductivity, respectively. The maximum influence of volume fraction of nanoparticles (φ) on the density belongs to TiN50–EG nanofluid and the minimum one belongs to Si3N420–EG nanofluid. TiN50–EG and TiN20–EG nanofluids have the minimum effect on the specific heat.