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A study method on wet-membrane evaporative cooling process.

Author(s) : QIANG T., HUANG X., WU J., et al.

Summary

During evaporative cooling process, the actual area of the water-air interface can't be predicted because the process of water droplets or falling film is complex. So, it is difficult to calculate the value of heat transfer coefficient and mass transfer coefficient. In this paper, BP network is employed to study the complex process of evaporative cooling. The training data are employed to train the network. Examination data were employed to examine the network, and the results showed that the best linear regression slope of supply air dry-ball temperature is 0.983 and y-intercept is 0.525, while perfect fit slope is 1 and y-intercept is 0. Similarly, the best linear regression slope of supply air relative humidity is 1.01 and y-intercept is -0.811. This research has proved that artificial neural network is capable to predict performance of direct evaporative cooling process.

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Pages: ICR07-E1-340

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Details

  • Original title: A study method on wet-membrane evaporative cooling process.
  • Record ID : 2008-0792
  • Languages: English
  • Source: ICR 2007. Refrigeration Creates the Future. Proceedings of the 22nd IIR International Congress of Refrigeration.
  • Publication date: 2007/08/21

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