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Thermodynamic analysis of absorption systems using artificial neural network

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  • Şencan, Arzu
  • Yakut, Kemal A.
  • Kalogirou, Soteris A.

Abstract

Thermodynamic analysis of absorption systems is a very complex process, mainly because of the limited experimental data and analytical functions required for calculating the thermodynamic properties of fluid pairs, which usually involves the solution of complex differential equations. In order to simplify this complex process, Artificial Neural Networks (ANNs) are used. In this study, ANNs are used as a new approach for the determination of the thermodynamic properties of LiBr–water and LiCl–water solutions which have been the most widely used in the absorption heat pump systems. Instead of complex differential equations and limited experimental data, faster and simpler solutions were obtained by using equations derived from the ANN model. It was found that the coefficient of multiple determination (R2-value) between the actual and ANN predicted data is equal to about 0.999 for the enthalpy of both LiBr–water and LiCl–water solutions. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable limits. In addition, the coefficient of performance (COP) of absorption systems operating under different conditions with LiBr–water and LiCl–water solutions is calculated. The use of the derived equations, which can be employed with any programming language or spreadsheet program for the estimation of the enthalpy of the solutions, as described in this paper, may make the use of dedicated ANN software unnecessary.

Suggested Citation

  • Şencan, Arzu & Yakut, Kemal A. & Kalogirou, Soteris A., 2006. "Thermodynamic analysis of absorption systems using artificial neural network," Renewable Energy, Elsevier, vol. 31(1), pages 29-43.
    • Handle: RePEc:eee:renene:v:31:y:2006:i:1:p:29-43
    DOI: 10.1016/j.renene.2005.03.011
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    References listed on IDEAS

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    1. Kalogirou, Soteris A., 2004. "Optimization of solar systems using artificial neural-networks and genetic algorithms," Applied Energy, Elsevier, vol. 77(4), pages 383-405, April.
    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.
    3. Kalogirou, Soteris A., 2000. "Long-term performance prediction of forced circulation solar domestic water heating systems using artificial neural networks," Applied Energy, Elsevier, vol. 66(1), pages 63-74, May.
    4. Kalogirou, Soteris A & Panteliou, Sofia & Dentsoras, Argiris, 1999. "Artificial neural networks used for the performance prediction of a thermosiphon solar water heater," Renewable Energy, Elsevier, vol. 18(1), pages 87-99.
    5. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
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    Cited by:

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    2. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
    3. Souliotis, M. & Kalogirou, S. & Tripanagnostopoulos, Y., 2009. "Modelling of an ICS solar water heater using artificial neural networks and TRNSYS," Renewable Energy, Elsevier, vol. 34(5), pages 1333-1339.
    4. Labus, J. & Hernández, J.A. & Bruno, J.C. & Coronas, A., 2012. "Inverse neural network based control strategy for absorption chillers," Renewable Energy, Elsevier, vol. 39(1), pages 471-482.
    5. Lazrak, Amine & Boudehenn, François & Bonnot, Sylvain & Fraisse, Gilles & Leconte, Antoine & Papillon, Philippe & Souyri, Bernard, 2016. "Development of a dynamic artificial neural network model of an absorption chiller and its experimental validation," Renewable Energy, Elsevier, vol. 86(C), pages 1009-1022.
    6. Álvarez, María E. & Hernández, José A. & Bourouis, Mahmoud, 2016. "Modelling the performance parameters of a horizontal falling film absorber with aqueous (lithium, potassium, sodium) nitrate solution using artificial neural networks," Energy, Elsevier, vol. 102(C), pages 313-323.
    7. Colorado, D. & Hernández, J.A. & Rivera, W. & Martínez, H. & Juárez, D., 2011. "Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse," Applied Energy, Elsevier, vol. 88(4), pages 1281-1290, April.
    8. Arslan, Oguz, 2011. "Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycle system-34," Energy, Elsevier, vol. 36(5), pages 2528-2534.
    9. Alosaimy, A.S. & Hamed, Ahmed M., 2011. "Theoretical and experimental investigation on the application of solar water heater coupled with air humidifier for regeneration of liquid desiccant," Energy, Elsevier, vol. 36(7), pages 3992-4001.

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