2015
DOI: 10.1016/j.applthermaleng.2015.02.061
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Statistical analysis of the energy performance of a refrigeration system working with R1234yf using artificial neural networks

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Cited by 26 publications
(3 citation statements)
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“…Therefore, it is expected that environmentally friendly halocarbons, hydrocarbons, natural refrigerants (R717, R744) and HFC/ HFO mixtures will be increasingly adopted [228]. Further research should be considered for potential substitutes: for example R1234yf [229] can be a valuable for R134a and has already been investigated for ejector expansion refrigeration system [20, 230-232] and other refrigeration systems [323][324][325][326]. Future studies should also consider refrigerant blends [233].…”
Section: Working Fluidsmentioning
confidence: 99%
“…Therefore, it is expected that environmentally friendly halocarbons, hydrocarbons, natural refrigerants (R717, R744) and HFC/ HFO mixtures will be increasingly adopted [228]. Further research should be considered for potential substitutes: for example R1234yf [229] can be a valuable for R134a and has already been investigated for ejector expansion refrigeration system [20, 230-232] and other refrigeration systems [323][324][325][326]. Future studies should also consider refrigerant blends [233].…”
Section: Working Fluidsmentioning
confidence: 99%
“…Ran and Xu et al [12] were conducted for the first time to evaluate the feasibility of R1234yf in replacing R134a from the perspective of supercritical heat transfer performance. Belman-Flores [13] applied artificial neural network method to analyze the energy performance of refrigeration system with R1234yf.…”
Section: Introductionmentioning
confidence: 99%
“…Further to this study, they proposed a new tool that uses ANN to build energy maps for a vapor compression system working with R1234yf. From these maps, it is possible to identify the best performance zones [18]; in a later study, they built 3D plots for visualization of the energy performance and its variability when the input operating parameters change [19]. Cao et al [20] developed an ANN model for an electronic expansion valve using the refrigerant pressures at the inlet and at the outlet, the inlet subcooling and the refrigerant mass flow rate as output.…”
Section: Introductionmentioning
confidence: 99%