Artificial Neural Network Modelling of Electrical Conductivity in GNP-Al₂O₃ Hybrid Nanofluids

Abstract

This study investigated the effects of mixing ratio and temperature on the electrical conductivity of a GNP-Al2O3 hybrid nanofluid. The results showed that an increase in the mixing ratio reduced the electrical conductivity ratio of the nanofluid, while an increase in temperature improved the electrical conductivity ratio. Additionally, an Artificial Neural Network (ANN) was used to predict the electrical conductivity of the nanofluid based on the mixing ratio and temperature. The optimal number of neurons in the hidden layer was found to be four neurons, with a low root mean square error (RMSE) value of 0.00696. The regression plot for the training, validation, and test data exhibited high correlation coefficients, indicating the reliability of the ANN model. These findings provide valuable insights into the behaviour of hybrid nanofluids and highlight the potential of using ANN for predicting their electrical conductivity.