Babarinde T O, Akinlabi S A, Madyira D M, Ekundayo F M, Adedeji P A
Department of Mechanical Engineering Science, University of Johannesburg, South Africa.
Department of Mechanical Engineering, Walter Sisulu University, Eastern Cape, South Africa.
Data Brief. 2020 Sep 14;32:106316. doi: 10.1016/j.dib.2020.106316. eCollection 2020 Oct.
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 ( ). 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.
本研究论文评估了R600a与基础润滑剂以及多壁碳纳米管（MWCNT）纳米润滑剂在稳态下的性能。它描述了测量数据参数所需的仪器及其不确定性，以及在蒸汽压缩制冷中用基础润滑剂制备和替换MWCNT纳米润滑剂浓度所涉及的步骤。在系统部件的入口和出口处收集温度数据。还在压缩机的出口和入口处记录压力数据。在环境温度为27°C时，每隔30分钟采集一次数据，共采集300分钟。该实验包括实验数据收集、自适应神经模糊推理系统（ANFIS）训练和测试数据集。解释了ANFIS模型在预测蒸汽压缩制冷系统中MWCNT纳米润滑剂效率方面的应用。ANFIS模型还提供统计输出量度，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（ ）。这些数据对于制冷领域高效节能材料专业的研究人员在蒸汽压缩制冷系统中用基础润滑剂替换MWCNT纳米润滑剂是有用且重要的。所呈现的数据可重新用于蒸汽压缩制冷系统的模拟和建模。
