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Formulation based on artificial neural network of thermodynamic properties of ozone friendly refrigerant/absorbent couples.

Author(s) : SÖZEN A., ARCAKLIOGLU E., ÖZALP M.

Type of article: Article

Summary

This article presents a new approach based on artificial neural networks (ANNs) to determine the properties of liquid and two-phase boiling and condensing of two alternative refrigerant/absorbent couples (methanol/LiBr and methanol/LiCl). These couples do not cause ozone depletion. ANNs are able to learn the key information patterns within multidimensional information domain. ANNs operate such as a 'black box' model, requiring no detailed information about the system. On the other hand, they learn the relationship between the input and the output. In order to train the neural network, limited experimental measurements were used as training data and test data. The paper shows that values predicted with ANN can be used to define the thermodynamic properties instead of approximate and complex analytic equations.

Details

  • Original title: Formulation based on artificial neural network of thermodynamic properties of ozone friendly refrigerant/absorbent couples.
  • Record ID : 2006-0207
  • Languages: English
  • Source: Applied Thermal Engineering - vol. 25 - n. 11-12
  • Publication date: 2005/08

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