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Prévision de la masse volumique du liquide de cinq classes différentes de systèmes de frigorigène (HCFC, HFC, HFE, PFA et PFAA) utilisant la méthode de contribution par groupe de réseaux neuronaux artificiels.

Liquid density prediction of five different classes of refrigerant systems (HCFCs, HFCs, HFEs, PFAs and PFAAs) using the artificial neural network-group contribution method.

Auteurs : MOOSAVI M., SEDGHAMIZ E, ABARESHI M.

Type d'article : Article, Article de la RIF

Résumé

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.

Documents disponibles

Format PDF

Pages : 188-200

Disponible

  • Prix public

    20 €

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    Gratuit

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Détails

  • Titre original : Liquid density prediction of five different classes of refrigerant systems (HCFCs, HFCs, HFEs, PFAs and PFAAs) using the artificial neural network-group contribution method.
  • Identifiant de la fiche : 30012680
  • Langues : Anglais
  • Sujet : Alternatives aux HFC
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 48
  • Date d'édition : 12/2014

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