Nanofluids are a class of fluids that contain a small number of nanoparticles, which have unique thermal and physical properties that make them suitable for various industrial and biomedical applications. However, the quality of nanofluids is often affected by factors such as temperature, concentration, and stability, which can affect their performance. This study aimed to develop an AI-based method for assessing the massic temperature quality of nanofluids, which can be used to optimize their performance and ensure their stability. The study used a dataset of massic temperature measurements of nanofluids, which were collected from experiments. The dataset was then preprocessed and used to train a machine learning model, which was able to predict the massic temperature of nanofluids based on their concentration and stability. The results showed that the AI-based method was able to accurately predict the massic temperature of nanofluids, with a mean absolute error of less than 1%. The study also investigated the effect of different factors on the massic temperature of nanofluids, such as the type of nanoparticle, the size of the nanoparticle, and the method of preparation. The results showed that these factors have a significant impact on the massic temperature of nanofluids and that the AI-based method can be used to optimize the performance of nanofluids by adjusting these factors. The study utilizes a Mean Absolute Error (MAE) to ensure better consistency between predicted and observed values. The results indicate that the heat capacity of the nanofluids improved by 57%.

Keywords: Specific heat capacity, Nanofluid, Artificial neural network, Preparation parameters, Thermal conductivity