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Modélisation et optimisation à objectifs multiples d’un refroidisseur évaporatif indirect à écoulements croisés à cycle M en utilisant le réseau neuronal de type GMDH.

Modeling and multi-objective optimization of an M-cycle cross-flow indirect evaporative cooler using the GMDH type neural network.

Auteurs : SOHANI A., SAYYAADI H., HOSEINPOORI S.

Type d'article : Article, Article de la RIF

Résumé

A model was presented to determine product air properties of dew-point indirect evaporative coolers with cross flow heat exchanger, M-cycle CrFIEC. In this regard, the most powerful statistical method known as the group method of data handling-type neural network (GMDH) was employed. Then the developed GMDH model was implemented for multi-objective optimization of a prototype CrFIEC and the average annual values of coefficient of performance (COP) and cooling capacity (CC) were maximized, simultaneously, while working to air ratio (WAR) and inlet air velocity were decision variables of optimization. Accordingly, features of the proposed system were optimized at twelve diverse climates of the world based on Koppen–Geiger's classification. Results implied that the optimized inlet air velocity for all climates varied between 1.796 and 1.957?m.s-1, while the optimum WAR was 0.318 for “A” class cities. Moreover, the mean values of the COP and CC were improved 8.1% and 6.9%, respectively.

Documents disponibles

Format PDF

Pages : 186-204

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 : Modeling and multi-objective optimization of an M-cycle cross-flow indirect evaporative cooler using the GMDH type neural network.
  • Identifiant de la fiche : 30018481
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 69
  • Date d'édition : 09/2016

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