Research Article

Prediction the Vapor-Liquid Equilibria of CO2-Containing Binary Refrigerant Mixtures Using Artificial Neural Networks

Table 1

Experimental data range used for development of the ANN model.

Mixture (K) (MPa) Reference

R744 (1)-R32 (2)283.121.106–4.5080-10-156 [4]
293.111.472–5.7380-10-1
303.131.911–7.2080-10-1
305.152.032–6.9050–0.9280–0.905
313.32.486–7.3180–0.8220–0.788
323.343.157–7.4640–0.6560–0.625
333.333.954–7.1910–0.4650–0.433
343.234.879–6.5890–0.2280–0.217

R744 (1)-R152a (2)258.440.144–2.2940-10-167 [8]
278.250.311–3.9770-10-1
298.80.604–6.5020-10-1
308.370.812–7.2000–0.9590–0.9355
323.31.185–7.6480–0.85220–0.808
343.21.891–7.410–0.67100–0.5941
253.150.244–1.9640-10-1

R744 (1)-R290 (2)263.150.344–2.6410-10-1124 [3]
273.150.473–3.4780-10-1
283.150.636–4.4970-10-1
293.150.836–5.7230-10-1
303.151.079–7.2060-10-1
313.151.369–3.6500–0.5840–0.286
323.151.714–6.2810–0.6360–0.559

R744 (1)-R116 (2)253.292.043-1.0510.0505–10.0284–175 [6]
273.273.576-1.8430.0385–10.0281–1
283.244.677-2.3820.0685–10.0592–1
291.225.550-2.9060.0244–10.0212–1
294.225.923–6.1550.0145–0.11520.0132–0.1154
296.726.621-6.4480.0096–0.07170.0090–0.0705

R744 (1)-R610 (2)263.150.1832–2.39970.5736–0.98300.0300–0.890083 [7]
2830.4589–3.99010.6253–0.97550.0636–0.9880
303.120.6544–6.61650.4800–0.96950.0593–0.9473
308.190.5021–6.86280.2418–0.93950.0218–0.9119
323.20.9625–6.73760.3719–0.83230.0565–0.7737
338.21.3940–6.37100.3448–0.70490.0713–0.6633
352.981.7097–5.53390.2492–0.56140.0638–0.5145

R744 (1)-R123 (2)313.150.873–7.1890.8258–0.97880.1408–0.920918 [2]
323.152.094–8.0740.8975–0.96110.3073–0.9015
333.151.083–7.9040.7352–0.94260.1219–0.8146

R744 (1)-R124 (2)313.150.594–7.2560–0.94840–0.909622 [2]
323.150.776–7.7450–0.88780–0.8679
333.151.045–7.6820–0.82310–0.7829

R744 (1)-R134a (2)252.950.131–1.6850–0.9830–0.86729 [5]
272.750.288–2.0330–0.9160–0.606
292.950.566–2.0480–0.7550–0.354
329.61.991–7.3690.2412–0.76400.0821–0.7450
339.12.305–7.0980.1781–0.66120.0666–0.6266
3543.250–6.0430.1733–0.45600.0826–0.3920