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Fin-and-tube condenser performance evaluation using neural networks.

Author(s) : ZHAO L. X., ZHANG C. L.

Type of article: Article, IJR article

Summary

The paper presents neural network approach to performance evaluation of the fin-and-tube air-cooled condensers which are widely used in air-conditioning and refrigeration systems. Inputs of the neural network include refrigerant and air-flow rates, refrigerant inlet temperature and saturated temperature, and entering air dry-bulb temperature. Outputs of the neural network consist of the heating capacity and the pressure drops on both refrigerant and air sides. The multi-input multi-output (MIMO) neural network is separated into multi-input single-output (MISO) neural networks for training. Afterwards, the trained MISO neural networks are combined into a MIMO neural network, which indicates that the number of training data sets is determined by the biggest MISO neural network not the whole MIMO network. Compared with a validated first-principle model, the standard deviations of neural network models are less than 1.9%, and all errors fall into plus or minus 5%.

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Pages: pp. 625-634

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Details

  • Original title: Fin-and-tube condenser performance evaluation using neural networks.
  • Record ID : 2010-0090
  • Languages: English
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 33 - n. 3
  • Publication date: 2010/05

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