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Performance parameters of an ejector-absorption heat transformer

Author

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  • Sözen, Adnan
  • Arcaklioglu, Erol
  • Özalp, Mehmet
  • Yücesu, Serdar

Abstract

Ejector-absorption heat transformers (EAHTs) are attractive for increasing a solar-pond's temperature and for recovering low-level waste-heat. Thermodynamic analysis of the performance of an EAHT is complicated due to the associated complex differential equations and simulation programs. This paper proposes the use of artificial neural-networks (ANNs) as a new approach to determine the performance parameters, as functions of only the working temperatures of the EAHT, which is used to increase the solar pond's temperature under various working conditions. Thus, this study is helpful in predicting the performance of an EAHT where the temperatures are known. Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer-function were used in the network. The best approach was investigated for performance parameters with developed software using various algorithms. The best statistical coefficients of multiple determinations (R2-values) equal 0.99995, 0.99997 and 0.99995 for the coefficient of performance (COP), exergetic coefficient of performance (ECOP) and circulation ratio (F), respectively obtained by the LM algorithm with seven neurons. In the comparison of performances, results obtained via analytic equations and by means of the ANN, the COP, ECOP and F for all working situations differ by less than 1.05%, 0.7% and 3.07%, respectively. These accuracies are acceptable in the design of the EAHT. The ANN approach greatly reduces the time required by design engineers to find the optimum solution. Apart from reducing the time required, it is possible to find solutions that make solar-energy applications more viable and thus more attractive to potential users. Also, this approach has the advantages of high computational speed, low cost for feasibility, rapid turn-around, which is especially important during iterative design phases, and ease of design by operators with little technical experience.

Suggested Citation

  • Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Yücesu, Serdar, 2005. "Performance parameters of an ejector-absorption heat transformer," Applied Energy, Elsevier, vol. 80(3), pages 273-289, March.
    • Handle: RePEc:eee:appene:v:80:y:2005:i:3:p:273-289
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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. Sözen, Adnan & Ali Akçayol, M., 2004. "Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle," Applied Energy, Elsevier, vol. 79(3), pages 309-325, November.
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    1. Parham, Kiyan & Khamooshi, Mehrdad & Tematio, Daniel Boris Kenfack & Yari, Mortaza & Atikol, Uğur, 2014. "Absorption heat transformers – A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 430-452.
    2. Hernández, J.A. & Bassam, A. & Siqueiros, J. & Juárez-Romero, D., 2009. "Optimum operating conditions for a water purification process integrated to a heat transformer with energy recycling using neural network inverse," Renewable Energy, Elsevier, vol. 34(4), pages 1084-1091.
    3. Donnellan, Philip & Byrne, Edmond & Oliveira, Jorge & Cronin, Kevin, 2014. "First and second law multidimensional analysis of a triple absorption heat transformer (TAHT)," Applied Energy, Elsevier, vol. 113(C), pages 141-151.
    4. Donnellan, Philip & Cronin, Kevin & Byrne, Edmond, 2015. "Recycling waste heat energy using vapour absorption heat transformers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1290-1304.

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