Abstract
This study investigated the impact of both nanomaterials mixing ratio and temperature on the electrical conductivity of a GNP-Al2O3 hybrid nanofluid. The outcomes revealed 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 harnessed to forecast the electrical conductivity of the nanofluid based on the mixing ratio and temperature. The optimal configuration was determined to comprise four neurons within the hidden layer, resulting in a notably low root mean square error (RMSE) of 0.00696. The regression graph encompassing the training, validation, and test datasets, exhibited high correlation coefficients, underscoring the reliability of the ANN model. These observations offer valuable revelations into the performance of hybrid nanofluids and underscore the potential of using ANN for predicting their electrical conductivity.