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Formulation basée sur le réseau neuronal artificiel des propriétés thermodynamiques des couples de frigorigènes et des couples absorbants respectueux de la couche d'ozone.

Formulation based on artificial neural network of thermodynamic properties of ozone friendly refrigerant/absorbent couples.

Auteurs : SÖZEN A., ARCAKLIOGLU E., ÖZALP M.

Type d'article : Article

Résumé

This article presents a new approach based on artificial neural networks (ANNs) to determine the properties of liquid and two-phase boiling and condensing of two alternative refrigerant/absorbent couples (methanol/LiBr and methanol/LiCl). These couples do not cause ozone depletion. ANNs are able to learn the key information patterns within multidimensional information domain. ANNs operate such as a 'black box' model, requiring no detailed information about the system. On the other hand, they learn the relationship between the input and the output. In order to train the neural network, limited experimental measurements were used as training data and test data. The paper shows that values predicted with ANN can be used to define the thermodynamic properties instead of approximate and complex analytic equations.

Détails

  • Titre original : Formulation based on artificial neural network of thermodynamic properties of ozone friendly refrigerant/absorbent couples.
  • Identifiant de la fiche : 2006-0207
  • Langues : Anglais
  • Source : Applied Thermal Engineering - vol. 25 - n. 11-12
  • Date d'édition : 08/2005

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