Babarinde T O, Akinlabi S A, Madyira D M, Ekundayo F M, Adedeji P A
Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa.
Department of Mechanical Engineering, Walter Sisulu University, South Africa.
Data Brief. 2020 Jul 30;32:106098. doi: 10.1016/j.dib.2020.106098. eCollection 2020 Oct.
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 (R) 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.
这项工作评估了R600a在基础润滑剂和石墨烯纳米润滑剂中的稳态性能。提供了所需的测量仪器及其不确定度，描述了制备石墨烯纳米润滑剂浓度并用其替代家用冰箱系统中基础润滑剂的逐步方法和程序。在系统部件的入口和出口采集系统温度数据。此外，在压缩机入口和出口记录压力数据。在环境温度为27°C的条件下，每隔30分钟记录3小时的数据。提供了实验数据集、人工神经网络（ANN）训练和测试数据集。解释了ANN模型预测家用冰箱中石墨烯纳米润滑剂性能的人工智能方法。还提供了ANN模型预测的统计性能指标，如均方根误差（RMSE）、平均绝对偏差（MAD）、平均绝对百分比误差（MAPE）和决定系数（R）。这些数据对制冷和能源效率材料领域的研究人员有用，可用于在冰箱系统中用基础润滑剂替代纳米润滑剂。这些数据可重新用于模拟和建模蒸汽压缩能量系统。
