Prediction of thermophysical properties of mixed refrigerants using artificial neural network

ŞENCAN ŞAHİN A., Köse S. L., SELBAŞ R.

Energy Conversion and Management, vol.52, no.2, pp.958-974, 2011 (SCI-Expanded, Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 52 Issue: 2
  • Publication Date: 2011
  • Doi Number: 10.1016/j.enconman.2010.08.024
  • Journal Name: Energy Conversion and Management
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.958-974
  • Keywords: ANN, Density, Dynamic viscosity, Heat conduction coefficient, Kinematic viscosity, Refrigerant, Specific heat capacity, Thermal diffusivity
  • Isparta University of Applied Sciences Affiliated: No

Abstract

The determination of thermophysical properties of the refrigerants is very important for thermodynamic analysis of vapor compression refrigeration systems. In this paper, an artificial neural network (ANN) is proposed to determine properties as heat conduction coefficient, dynamic viscosity, kinematic viscosity, thermal diffusivity, density, specific heat capacity of refrigerants. Five alternative refrigerants are considered: R413A, R417A, R422A, R422D and R423A. The training and validation were performed with good accuracy. The thermophysical properties of the refrigerants are formulated using artificial neural network (ANN) methodology. Liquid and vapor thermophysical properties of refrigerants with new formulation obtained from ANN can be easily estimated. The method proposed offers more flexibility and therefore thermodynamic analysis of vapor compression refrigeration systems is fairly simplified. © 2010 Elsevier Ltd. All rights reserved.