Abstract
This work evaluated the steady state performance of R600a in the base lubricant and graphene nanolubricant. The mea- suring instruments required and their uncertainties were provided, step by step method and procedures for prepara- tion of graphene nanolubricant concentration and substitut- ing it with the base lubricant in domestic refrigerator system are described. The system temperatures data was captured at the inlet and outlet of the system components. Also, the pressures data was recorded at the compressor inlet and out- let. The data was recorded for 3 h at 30 min interval at an ambient temperature of 27 °C. The experimental dataset, Ar- tificial Neural Network (ANN) training and testing dataset are provided. The artificial intelligence approach of ANN model to predict the performance of graphene nanolubricant in do- mestic refrigerator is explained. Also, the ANN model pre- diction statistical performance metrics such as Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R 2 ) are also provided. The data is useful to researchers in the field of refrigeration and energy efficiency materials, for replacing nanolubricant with the base lubricant.