Derivation of empirical equations for thermodynamic properties of a ozone safe refrigerant (R404a) using artificial neural network


SÖZEN A. , ARCAKLIOĞLU E., MENLİK T.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.37, sa.2, ss.1158-1168, 2010 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37 Konu: 2
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1016/j.eswa.2009.06.016
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Sayfa Sayıları: ss.1158-1168

Özet

This study, deals with the potential application of the artificial neural networks (ANNs) to represent PVTx (pressure-specific volume-temperature-vapor quality) data in the range of temperature of 173-498 K and pressure of 10-3600 kPa. Generally, numerical equations of thermodynamic properties are used in the computer simulation analysis instead of analytical differential equations. And also analytical computer codes usually require a large amount of computer power and need a considerable amount of time to give accurate predictions Instead of complex rules and mathematical routines, this study proposes an alternative approach based on ANN to determine the thermodynamic properties of an environmentally friendly refrigerant (R404a) for both saturated liquid-vapor region (wet vapor) and superheated vapor region as numerical equations. Therefore, reducing the risk of experimental uncertainties and also removing the need for complex analytic equations requiring long computational time and effort. R-2 values which are errors known as absolute fraction of variance - in wet vapor region are 0.999401, 0 999982 and 0.999993 for specific volume. enthalpy and entropy for training data, respectively. For testing data, these values are 0.998808. 0.999988, and 0 999993 Similarly, for superheated vapor region, they are 0.999967, 0.999999 and 0.999999 for training data, 0.999978, 0.999997 and 0.999999 for testing data. As seen from the results of mathematical modeling, the calculated thermodynamic properties are obviously within acceptable uncertainties. (C) 2009 Elsevier Ltd. All rights reserved.