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Propriétés thermodynamiques des frigorigènes utilisant des réseaux neuronaux artificiels.

Thermodynamic properties of refrigerants using artificial neural networks.

Auteurs : MORA J. E., PEREZ C., GONZALEZ F. F., et al.

Type d'article : Article, Article de la RIF

Résumé

The application of Artificial Neural Networks (ANNs) for prediction of thermodynamic properties of refrigerants in vapor–liquid equilibrium is the scope of this article. It is very important to find new ways to calculate thermodynamic properties of new refrigerants to simplify equipment operation and design. ANNs are capable of learning the complex relationships between input and output data, therefore they can be a good replacement of the commonly used Equations of State (EoS) for thermodynamic properties prediction. In this work multilayer perceptron ANNs with back-propagation algorithm were employed to obtain accurate thermodynamic properties prediction models. No EoS were needed so far. ANNs show their ability to accurately predict properties of refrigerants opening a promissory way to process optimization and construction of intelligent devices, impacting in both cost and energy savings.

Documents disponibles

Format PDF

Pages : 9-16

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    20 €

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    Gratuit

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Détails

  • Titre original : Thermodynamic properties of refrigerants using artificial neural networks.
  • Identifiant de la fiche : 30012244
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 46
  • Date d'édition : 10/2014

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