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Application d'un réseau neuronal pour la modélisation et le contrôle de la charge frigorifique d'un condenseur évaporatif.

Application of neural network for the modelling and control of evaporative condenser cooling load.

Auteurs : ABBASSI A., BAHAR L.

Type d'article : Article

Résumé

Artificial neural network, in comparison with PID controllers which have broad applications in the highly complex HVAC systems, has recently received more attention. The paper includes thermodynamic modelling of an evaporative condenser under steady state and transient state conditions for establishing control of thermal capacity, using artificial neural network. To train the system under dynamic condition, predictive neural network, capable of understanding dynamic behaviour and predicting the preset output is used. The principle operation of such neural networks is based on the reduction of gradients of errors existing between the predicted output and the actual output of the system. To control the system thermal capacity, neural controller based on training received from the reduction of gradients between the output controller and the ideal output, is used. Results obtained during the investigation indicate that the artificial neural network controller is a suitable substitute for PID controllers for thermal systems.

Détails

  • Titre original : Application of neural network for the modelling and control of evaporative condenser cooling load.
  • Identifiant de la fiche : 2006-0716
  • Langues : Anglais
  • Source : Applied Thermal Engineering - vol. 25 - n. 17-18
  • Date d'édition : 12/2005

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