Artificial Neural Network Prediction of Tribological Properties of Nano Lubricants with Eco-Friendly Refrigerant

With the emergence of the Montreal and Kyoto Protocols, there's been a prohibition on refrigerants in vapor compression systems that harm the ozone layer and contribute to global warming. While alternative refrigerants have been suggested, they tend to be more energy-intensive than the conventional ones they replace. A promising approach to mitigate this increased energy consumption lies in the use of innovative lubricants that can enhance system efficiency. Nano-lubricants have recently gained attention for their superior performance compared to traditional lubricants in refrigeration systems. However, formulating nano-lubricants and determining their composition with the desired thermophysical and tribological properties pose significant challenges. The development of nano-lubricants requires extensive experimentation, and predicting the properties of these lubricants, especially those that offer optimal performance, demands even more rigorous experimentation. Recently, Artificial Neural Networks (ANNs) have been employed to forecast system performance based on existing experimental data. In this study, we leverage literature data on nano-lubricants of varying compositions to design an ANN architecture. The model is then trained, assessed, and validated for predicting the properties of nano-lubricants with unknown compositions. The ANN's predictions are subsequently compared with actual results to gauge its accuracy and reliability.