This work evaluated the steady state performance of R600a in the base lubricant and graphene nanolubricant. The measuring instruments required and their uncertainties were provided, step by step method and procedures for preparation of graphene nanolubricant concentration and substituting 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 outlet. The data was recorded for 3 h at 30 min interval at an ambient temperature of 27 °C. The experimental dataset, Artificial Neural Network (ANN) training and testing dataset are provided. The artificial intelligence approach of ANN model to predict the performance of graphene nanolubricant in domestic refrigerator is explained. Also, the ANN model prediction statistical performance metrics such as Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2) 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 in refrigerator systems. The data can be reuse for simulation and modelling vapour compression energy system.

This paper presents the coefficient of performance (COP), power consumption and the pull-down time of a domestic refrigerator using TiO₂ nanolubricant (0.4 and 0.6 g/L) in R600a refrigerant as a drop-in replacement for R134a refrigerant in a household refrigerator. The system was connected with type K thermocouples at the inlet and outlet of the major component (compressor, condenser, evaporator, expansion valve) of the system. The discharge and suction pressure are also determined by pressure gauges connected to the inlet and outlet of the compressor. The measurements output was used to determine the performance of the system using Refprop 9.0. The result showed that 0.4 g/L of TiO₂ nanolubricant has the highest COP, lowest power consumption and evaporator temperature compared to R134a in the system.

This research paper assessed the performance of R600a with the base lubricant and Multi-walled Carbon Nanotube (MWCNT) nanolubricant at steady state. It describes the instruments required for measurement of the data parameter and its uncertainties, steps involved in preparing and replacing the MWCNT nanolubricant concentration with base lubricant in vapour compression refrigeration. The system's temperature data was collected at the components inlets and outlets. Pressure data was also registered at the compressor outlet and inlet. The data was captured at 27 °C ambient temperature at an interval of 30 min for 300 min. The experiment includes the experimental data collection, Adaptive Neuro-Fuzzy Inference System (ANFIS) training and testing dataset. The use of ANFIS model is explained in predicting the efficiency of MWCNT nanolubricant in a vapour compression refrigerator system. The ANFIS model also provides statistical output measures such as Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), and determination coefficient (R2). The data is useful and important for replacing MWCNT nanolubricant with base lubricant in a vapour compression refrigeration system for researchers in the specialisation of energy-efficient materials in refrigeration. The data present can be reused for vapour compression refrigeration systems simulation and modelling.

This work investigated the possibility of improving liquefied petroleum gas (LPG) of propane and butane (60:40) mixture with graphene nanolubricant (GN) in a domestic refrigerator system. An artificial intelligence model with Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference (ANFIS) was also developed to predict the performance of the LPG with graphene nanolubricant in the system and validate it with experimental results. The graphene nanolubricant was investigated in 50–70 g charge of LPG refrigerant. Four Type K thermocouples were attached to the refrigerator components to track the temperature of the system. Two pressure gauges were also attached to the compressor to determine the pressure at suction and discharge of the domestic refrigerator. A digital watt-meter was used to measure the refrigerator’s compressor power consumption. The obtained result showed a higher COP with graphene nanolubricant concentrations. The power consumption and cooling capacity improved within the range of 16.3%–20.1% and 6.4%–19.6% respectively with graphene nanolubricant concentrations. Also, the ANN and ANFIS model predicts the COP of the refrigerator with a Root Mean Square Error (RMSE) value of 0.620 and 0.303 and Mean Absolute Percentage Error (MAPE) of 29.4% and 14.9% respectively.