Improving the R600a vapour compression refrigeration system’s energy efficiency employing graphene oxide nanoparticles to retrofit R134a refrigerator using fuzzy logic prediction
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Publisher
AIP Publishing
Link
http://aip.scitation.org/doi/pdf/10.1063/5.0110972
Reference10 articles.
1. T.O. Babarinde, S.A. Akinlabi, D.M. Madyira, F.M. Ekundayo, P.A. Adedeji, Dataset and ANN model prediction of performance of graphene nanolubricant with R600a in domestic refrigerator system, Data, Vol. 32, 2020, 106098. https://doi.org/10.1016/j.dib.2020.106098
2. Enhancing the energy efficiency of vapour compression refrigerator system using R600a with graphene nanolubricant
3. A. Senthilkumar, E.P. Abhijith, C. Ahammed Ansar Jawhar, Jamshid, Experimental investigation of Al2o3/Sio2 hybrid nanolubriant in R600a vapour compression refrigeration system, materialsoday: PROCEEDINGS, Vol. 45(7), (2021), Pages 5921–5924. https://doi.org/10.1016/j.matpr.2020.08.779
4. Dataset of experimental and adaptive neuro-fuzzy inference system (ANFIS) model prediction of R600a/MWCNT nanolubricant in a vapour compression system
5. Jatinder Gill, Jagdev Singh, Energetic and exergetic performance analysis of the vapor compression refrigeration system using adaptive neuro-fuzzy inference system approach, Vol. 88, (2017), Pages 246–260.
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