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Prediction on the viscosity and thermal conductivity of HFC/HFO refrigerants with artificial neural network models.

Prévision de la viscosité et la conductivité thermique des frigorigènes HFC/HFO avec des modèles de réseaux neuronaux artificiels.

Auteurs : WANG X., LI Y., WRIGHT E., GAO N., CHEN G.

Type d'article : Article de la RIF

Résumé

Accurate prediction models for the viscosity and thermal conductivity of refrigerants are of great importance and have drawn wide attention from scholars. Considering the great advantage of artificial neural network (ANN) models in solving non-linear problems, two fully connected feed-forward ANN models were proposed to predict the viscosity and thermal conductivity of the HFC/HFO refrigerants in this paper. The reduced pressure (pr), reduced temperature (Tr), mole mass (M) and acentric factor (ω) of the refrigerants were selected as the input variables for both ANN models. Regarding the ANN model for viscosity, the neural number of the hidden layer was optimized to be 9 by trial-and-error method. The prediction results coincided with the experimental data very well. The correlation coefficient and the average absolute deviation (AAD) of regression were 0.9998 and 1.21%, respectively. The prediction of thermal conductivity also showed a good agreement with the experimental data, and the AAD of the model was 1.00%. The paper is expected to provide valuable methods to predict the viscosity and thermal conductivity of HFC/HFO refrigerants.

Documents disponibles

Format PDF

Pages : 316-325

Disponible

  • Prix public

    15 €

  • Prix membre*

    Gratuit

* meilleur tarif applicable selon le type d'adhésion (voir le détail des avantages des adhésions individuelles et collectives)

Détails

  • Titre original : Prediction on the viscosity and thermal conductivity of HFC/HFO refrigerants with artificial neural network models.
  • Identifiant de la fiche : 30027805
  • Langues : Anglais
  • Sujet : Alternatives aux HFC
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 119
  • Date d'édition : 11/2020
  • DOI : http://dx.doi.org/10.1016/j.ijrefrig.2020.07.006
  • Disponible à la bibliothèque de l'IIF

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