Liquid density prediction of five different classes of refrigerant systems (HCFCs,HFCs, HFEs,PFAs and PFAAs) using the artificial neural network-group contribution method |
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| Affiliation: | 1. Department of Chemistry, University of Isfahan, Isfahan 81746-73441, Iran;2. Department of Chemistry, Payame Noor University, 19395-3697 Tehran, Iran;1. Ural Federal University named after the first President of Russia B.N.Yeltsin, 19, Mira St., Ekaterinburg 620002, Russia;2. Design Office CHKZ-YUGSON, 8, Avtomatiki St., Ekaterinburg 620049, Russia;1. Odessa National Academy of Food Technologies, Department of Engineering Thermophysics, Kanatna Street 112, 65039 Odessa, Ukraine;2. Physical Chemistry Department, Faculty of Applied Sciences, University of Brussels (ULB), CP165/62, av. F.D. Roosevelt 50, B-1050 Brussels, Belgium;1. Department of Chemical Engineering & Biotechnology, Ariel University, 40700 Ariel, Israel;2. Petrovietnam Manpower Training College, No.43 30/4 Street, Ward 9, Vung Tau City, Vietnam |
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| Abstract: | In this work, the densities of 48 refrigerant systems from 5 different categories including hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), hydrofluoroethers (HFEs), perfluoroalkanes (PFAs), and perfluoroalkylalkanes (PFAAs) have been studied using a combined method that includes an artificial neural network (ANN) and a simple group contribution method (GCM). A total of 3825 data points of liquid density at several temperatures and pressures have been used to train, validate and test the model. This study shows that the ANN-GCM model represents an excellent alternative to estimate the density of different refrigerant systems with a good accuracy. The average absolute deviations for train, validation, and test sets are 0.18, 0.26, and 0.28, respectively. A comparison between our results and those obtained from some previous methods shows that as well as generality, this model can predict the density of different refrigerants in a better accord with experimental data up to high temperature, high pressure (HTHP) conditions. |
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| Keywords: | Refrigerant system Liquid density Artificial neural network Group contribution method Systèmes de frigorigènes Masse volumique du liquide Réseau neuronal artificiel Méthode de contribution de groupe |
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