Partager

Document IIF

Modèles de réseau neuronal artificiel pour représenter le débit massique de R22, R407C et R410A à travers des détendeurs électroniques.

Artificial neural network models for depicting mass flow rate of R22, R407C and R410A through electronic expansion valves.

Auteurs : SALEH B., ALY A. A.

Type d'article : Article, Article de la RIF

Résumé

The utilization of electronic expansion valves (EEVs) in refrigeration and air conditioning systems is increased for energy saving and comfort environmental. However, experimental data and refrigerant mass flow models through EEVs are very limited in open literature. In this study, a new technique using artificial neural network (ANN) is applied to depict the mass flow rates of R22 and its alternatives R407C and R410A flowing through EEVs based on the error back propagation learning algorithm. Two strategies are followed; the first is to construct individual ANN models for each refrigerant, and the second is to construct a generalized ANN model for the three investigated refrigerants. The experimental results from open literature are used to construct the ANN models. The ANN models results showed a good agreement with the corresponding experimental data. For individual models, the relative deviations for R22, R407C, and R410A are within ±0.7%, ±1.1%, and ±0.006%, respectively. While for generalized model, the relative deviations are within ±2.5%. Also the generalized model was tested out of its construction range in a predictive mode and it was found to be a reliable tool to estimate the refrigerants mass flow rates.

Documents disponibles

Format PDF

Pages : 113-124

Disponible

  • Prix public

    20 €

  • Prix membre*

    Gratuit

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

Détails

  • Titre original : Artificial neural network models for depicting mass flow rate of R22, R407C and R410A through electronic expansion valves.
  • Identifiant de la fiche : 30016731
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 63
  • Date d'édition : 03/2016

Liens


Voir d'autres articles du même numéro (19)
Voir la source

Indexation