Enhancement of R600a vapour compression refrigeration system with MWCNT/TiO2 hybrid nano lubricants for net zero emissions building. (March 2023)

• Record Type: Journal Article
• Authors: Senthilkumar, A.; Prabhu, L.; Sathish, T.; Saravanan, R.; Casmir Jeyaseelan, G.Antony; Ağbulut, Ümit; Mahmoud, Z.; Shaik, Saboor; Ahamed Saleel, C
• Is Part Of: Sustainable energy technologies and assessments. Volume 56(2023)
• Publication Date: 2023-03
• Subjects: MWCNT/TiO2 hybrid Nano lubricants -- ANFIS prediction -- ANN model. NZEB -- COP -- R600a -- Energy conservation; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2023.103055
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: Net zero emissions building is widely investigated with great environmental care. In the case of refrigeration selection for net zero emissions building (NZEB), the ozone depletion potential is the primary criterion to choose the refrigerant. For achieving the NZEB, the R600a was preferred as it possesses the potential for global warming lower and zero potential for ozone depletion. This paper aims to amplify the coefficient of performance by utilizing MWCNT/TiO2 hybrid Nano lubricants in the R600a vapour compression refrigeration system. As numerous factors and equations are involved in the study and prediction of the Coefficient of Performance in vapour compression refrigeration systems which is comparatively complex and takes more time for promoting the development of precise prediction and results. Artificial neural networks (ANN) and adaptive neuro-fuzzy interface systems (ANFIS) are the two techniques mainly concentrated in this study which were not properly implemented previously. By using the ANFIS technique enhanced cooling effect of 200 W with a 50 % increment was obtained with 0.4 g/L of MWCNT/TiO2 hybrid nano lubricants which is better in comparison with ANN and experimental results. The minimum energy utilization of 90 W was obtained with the ANFIS technique. This method also predicted the enhanced COP of 3.7 with a 32 % increase in comparison to the ANN prediction method. When compared to the ANN prediction model, the ANFIS model's estimated least training error value. The results indicate that when compared to ANN prediction the ANFIS predicted values produced results that were more accurate and were the proper approach for predicting COP parameters and consumed 35 % less energy.