Prediction of vapor–liquid equilibrium for binary mixtures containing R1234yf or R1234ze (E)

•A CSA-LSSVM model was developed for prediction of phase behavior of binary mixtures containing R1234yf or R1234ze (E).•The performance of the developed model is evaluated by using statistical quality measure approaches.•The outcomes of the developed model are compared with PR EoS, PC-SAFT model.•Th...

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Veröffentlicht in:International journal of refrigeration 2018-04, Vol.88, p.239-247
Hauptverfasser: Barati-Harooni, Ali, Najafi-Marghmaleki, Adel
Format: Artikel
Sprache:eng
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Zusammenfassung:•A CSA-LSSVM model was developed for prediction of phase behavior of binary mixtures containing R1234yf or R1234ze (E).•The performance of the developed model is evaluated by using statistical quality measure approaches.•The outcomes of the developed model are compared with PR EoS, PC-SAFT model.•The performance of the developed model is better than the PR EoS and PC-SAFT model. The use of pure low global warming potential (GWP) refrigerants can be replaced with mixture of carbon dioxide or other refrigerants with low GWP refrigerants due to their better features such as higher cycle efficiency. Hence, developing simple and accurate models for estimation of vapor–liquid equilibrium (VLE) of these mixtures is of great importance. In this work, an intelligent model named CSA-LSSVM was implemented for prediction of VLE data in the binary mixture of CO2 or a hydrofluorocarbon (HFC) or hydrocarbon (HC) refrigerant with two low GWP refrigerants (R1234yf and R1234ze (E)). The precision and reliability of the developed model was evaluated by using various approaches. Results show that the implemented model is accurate and effective for prediction of experimental VLE data. To further validate the precision and effectiveness of the implemented model, its predictions were compared with predictions of PC-SAFT and Peng Robinson (PR) equation of states (EoSs). The PR EoS was combined with van der Waals type mixing rules. The outcomes of comparison also show that the developed CSA-LSSVM model provide better predictions with lower errors compared to PC-SAFT model and PR EoS.
ISSN:0140-7007
1879-2081
DOI:10.1016/j.ijrefrig.2018.01.008