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Hybrid model of steam boiler

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  • Rusinowski, Henryk
  • Stanek, Wojciech

Abstract

In the case of big energy boilers energy efficiency is usually determined with the application of the indirect method. Flue gas losses and unburnt combustible losses have a significant influence on the boiler's efficiency. To estimate these losses the knowledge of the operating parameters influence on the flue gases temperature and the content of combustible particles in the solid combustion products is necessary. A hybrid model of a boiler developed with the application of both analytical modelling and artificial intelligence is described. The analytical part of the model includes the balance equations. The empirical models express the dependence of the flue gas temperature and the mass fraction of the unburnt combustibles in solid combustion products on the operating parameters of a boiler. The empirical models have been worked out by means of neural and regression modelling.

Suggested Citation

  • Rusinowski, Henryk & Stanek, Wojciech, 2010. "Hybrid model of steam boiler," Energy, Elsevier, vol. 35(2), pages 1107-1113.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:2:p:1107-1113
    DOI: 10.1016/j.energy.2009.06.004
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    Citations

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    Cited by:

    1. Nikula, Riku-Pekka & Ruusunen, Mika & Leiviskä, Kauko, 2016. "Data-driven framework for boiler performance monitoring," Applied Energy, Elsevier, vol. 183(C), pages 1374-1388.
    2. Rostek, Kornel & Morytko, Łukasz & Jankowska, Anna, 2015. "Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks," Energy, Elsevier, vol. 89(C), pages 914-923.
    3. Colorado, D. & Ali, M.E. & García-Valladares, O. & Hernández, J.A., 2011. "Heat transfer using a correlation by neural network for natural convection from vertical helical coil in oil and glycerol/water solution," Energy, Elsevier, vol. 36(2), pages 854-863.
    4. Ali Chaibakhsh, 2013. "Modelling and long-term simulation of a heat recovery steam generator," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 19(2), pages 91-114.
    5. Szega, Marcin & Nowak, Grzegorz Tadeusz, 2015. "An optimization of redundant measurements location for thermal capacity of power unit steam boiler calculations using data reconciliation method," Energy, Elsevier, vol. 92(P1), pages 135-141.
    6. Usón, Sergio & Valero, Antonio, 2011. "Thermoeconomic diagnosis for improving the operation of energy intensive systems: Comparison of methods," Applied Energy, Elsevier, vol. 88(3), pages 699-711, March.
    7. Kljajić, Miroslav & Gvozdenac, Dušan & Vukmirović, Srdjan, 2012. "Use of Neural Networks for modeling and predicting boiler's operating performance," Energy, Elsevier, vol. 45(1), pages 304-311.
    8. Mikulandrić, Robert & Lončar, Dražen & Cvetinović, Dejan & Spiridon, Gabriel, 2013. "Improvement of existing coal fired thermal power plants performance by control systems modifications," Energy, Elsevier, vol. 57(C), pages 55-65.

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