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Artificial neural networks for modelling the starting-up of a solar steam-generator

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  • Kalogirou, Soteris A.
  • Neocleous, Constantinos C.
  • Schizas, Christos N.

Abstract

An experimental solar steam generator, consisting of a parabolic trough collector, a high-pressure steam circuit, and a suitable flash vessel has been constructed and tested in order to establish the thermodynamic performance during heat-up. The heat-up energy requirement has a marked effect on the system's performance because solar energy collected during the heating-up period is lost at night due to the diurnal cycle. This depends mostly on the dimensions and the inventory of the flash vessel, and the prevailing environmental conditions. Experimental data were obtained and used to train an artificial neural network in order to implement a mapping between easily measurable features (environmental conditions, water content and vessel dimensions) and the system temperatures. Such mapping may be useful to system designers when seeking to find the optimal vessel-dimensions. The trained network predicted very well the response of the system, as indicated by the statistical R-squared value of 0.999 obtained and a maximum deviation between predicted and actual values being less than 3.9%. This degree of accuracy is acceptable in the design of such systems. The results are important, because the system was tested during its heat-up cycle, under transient conditions, which is quite difficult to model analytically.

Suggested Citation

  • Kalogirou, Soteris A. & Neocleous, Constantinos C. & Schizas, Christos N., 1998. "Artificial neural networks for modelling the starting-up of a solar steam-generator," Applied Energy, Elsevier, vol. 60(2), pages 89-100, June.
    • Handle: RePEc:eee:appene:v:60:y:1998:i:2:p:89-100
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    Cited by:

    1. Kalogirou, S.A. & Mathioulakis, E. & Belessiotis, V., 2014. "Artificial neural networks for the performance prediction of large solar systems," Renewable Energy, Elsevier, vol. 63(C), pages 90-97.
    2. Vakili, Masoud & Yahyaei, Masood & Ramsay, James & Aghajannezhad, Pouria & Paknezhad, Behnaz, 2021. "Adaptive neuro-fuzzy inference system modeling to predict the performance of graphene nanoplatelets nanofluid-based direct absorption solar collector based on experimental study," Renewable Energy, Elsevier, vol. 163(C), pages 807-824.
    3. Ferruzza, Davide & Kærn, Martin Ryhl & Haglind, Fredrik, 2020. "A method to account for transient performance requirements in the design of steam generators for concentrated solar power applications," Applied Energy, Elsevier, vol. 269(C).
    4. Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
    5. Zhijian Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine," Energies, MDPI, vol. 8(8), pages 1-21, August.
    6. 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.
    7. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.

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