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Thermodynamic analyses of refrigerant mixtures using artificial neural networks

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  • ArcaklIoglu, Erol
  • Çavusoglu, Abdullah
  • Erisen, Ali

Abstract

The aim of this study is to make a contribution towards the efforts of reducing the use of CFCs by finding a drop-in replacement for pure refrigerants used in domestic and industrial appliances. The suggested solution is the use of HFC and HC based refrigerant mixtures. In this study, we investigate different possible ratios of these mixtures and their corresponding performances by using Artificial Neural-Networks (ANNs). We believe this dramatically reduces the times and efforts required to achieve these targets. Coefficients of Performances (COPs) and Total Irreversibilities (TIs) of refrigerants and their mixtures have been calculated for a vapor-compression refrigeration system with a liquid/suction line heat-exchanger. The constant cooling-load method is taken as a reference. The thermodynamic properties of refrigerants have been taken from REFPROP 6.01. To train the network, based on Scaled Conjugate Gradient (SCG), Pola-Ribiere Conjugate Gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function, we have used various ratios of 7 refrigerant mixtures of HFCs and HCs along with three CFCs (R12, R22, and R502). They were used as inputs while the COP and TI values, calculated as above, were the outputs. The network has yielded R2 values of 0.9999 and maximum errors for training and test data were found to be 2 and 3%, respectively.

Suggested Citation

  • ArcaklIoglu, Erol & Çavusoglu, Abdullah & Erisen, Ali, 2004. "Thermodynamic analyses of refrigerant mixtures using artificial neural networks," Applied Energy, Elsevier, vol. 78(2), pages 219-230, June.
    • Handle: RePEc:eee:appene:v:78:y:2004:i:2:p:219-230
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    References listed on IDEAS

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    1. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    2. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
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    1. Canakci, Mustafa & Erdil, Ahmet & Arcaklioglu, Erol, 2006. "Performance and exhaust emissions of a biodiesel engine," Applied Energy, Elsevier, vol. 83(6), pages 594-605, June.
    2. Comakli, K. & Simsek, F. & Comakli, O. & Sahin, B., 2009. "Determination of optimum working conditions R22 and R404A refrigerant mixtures in heat-pumps using Taguchi method," Applied Energy, Elsevier, vol. 86(11), pages 2451-2458, November.

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