scholarly journals Modeling and experimental data validation of vapor liquid equilibria (vle) for absorption and distillation

2021 ◽  
Vol 66 (4) ◽  
pp. 329-339
Author(s):  
M. Aslam Abdullah ◽  
◽  
Bandaru Kiran ◽  
Keyword(s):  
Experimental Data ◽  
Data Validation ◽  
Vapor Liquid Equilibria ◽  
Vapor Liquid

Vapor–liquid equilibria in dendrimer and hyperbranched polymer solutions: experimental data and modeling using UNIFAC-FV

2004 ◽  
Vol 221 (1-2) ◽  
pp. 83-96 ◽  
Author(s):  
Matthias Seiler ◽  
Jörn Rolker ◽  
LyudmilaV. Mokrushina ◽  
Holger Kautz ◽  
Holger Frey ◽  
...  
Keyword(s):  
Experimental Data ◽  
Hyperbranched Polymer ◽  
Polymer Solutions ◽  
Vapor Liquid Equilibria ◽  
Vapor Liquid

Isobaric Vapor-Liquid Equilibria of Hexamethyl Disiloxane + Ethyl Acetate System at Normal Pressure

2011 ◽  
Vol 396-398 ◽  
pp. 968-972
Author(s):  
Wen Lin Zhang ◽  
Nan Meng ◽  
Ru Yi Sun ◽  
Chun Li Li
Keyword(s):  
Experimental Data ◽  
Ethyl Acetate ◽  
Normal Pressure ◽  
Liquid Equilibrium ◽  
Vapor Liquid Equilibrium ◽  
Equilibrium Data ◽  
Vle Data ◽  
Vapor Liquid Equilibria ◽  
Ethyl Acetate System ◽  
Vapor Liquid

Vapor-liquid equilibrium data of hexamethyl disiloxane + ethyl acetate system at 101.3kPa were measured by using double circulating vapor-liquid equilibrium still. The thermodynamic consistency of the VLE data was examined by Herrington method. Experimental data was correlated by NRTL and UNIQUAC parameter models. Both of the models satisfactorily correlated with the VLE data. The result showed that the NRTL model was the most suitable one to represent experimental data satisfactorily and the system had a minimum temperature azeotrope at 350.31 K and the mole azeotropic composition was 0.0330.

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

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ahmad Azari ◽  
Saeid Atashrouz ◽  
Hamed Mirshekar
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Ann Model ◽  
Artificial Neural ◽  
Predicted Values ◽  
Artificial Neural Network Ann ◽  
Vapor Liquid Equilibria ◽  
Binary Refrigerant ◽  
Binary Refrigerant Mixtures ◽  
Vapor Liquid

Artificial neural network (ANN) technique has been applied for estimation of vapor-liquid equilibria (VLE) for eight binary refrigerant systems. The refrigerants include difluoromethane (R32), propane (R290), 1,1-difluoroethane (R152a), hexafluoroethane (R116), decafluorobutane (R610), 2,2-dichloro-1,1,1-trifluoroethane (R123), 1-chloro-1,2,2,2-tetrafluoroethane (R124), and 1,1,1,2-tetrafluoroethane (R134a). The related experimental data of open literature have been used to construct the model. Furthermore, some new experimental data (not applied in ANN training) have been used to examine the reliability of the model. The results confirm that there is a reasonable conformity between the predicted values and the experimental data. Additionally, the ability of the ANN model is examined by comparison with the conventional thermodynamic models. Moreover, the presented model is capable of predicting the azeotropic condition.

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