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Artificial neural networking and fuzzy logic exergy controlling model of combined heat and power system in thermal power plant

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  • Strušnik, Dušan
  • Avsec, Jurij

Abstract

This paper presents entropy generation minimisation model of combined heat and power system. The turbine control valves and heater throttle valves were analysed. The high-pressure control valves regulate the mass flow rate of steam into the turbine, whereas the intermediate-pressure and low-pressure control valves the steam pressure of the turbine extracts 3 and 5. The steam of the turbine extracts 3 and 5 is used for the city-wide heating system purposes by means of the peak and basic heaters. The quantity of the extracted steam used for the city-wide heating system is additionally controlled by the throttles regulating the extracted steam into the basic or peak heater. This results in a double throttling of the extracted steam of the turbine, double generated entropy and a double loss of work. If adequate pressure of the extracted steam of the turbines is maintained by means of the turbine control valves the two heaters for the heating system could operate with the throttles open. As a result, the generated entropy of the throttles of the steam admitted to the heater could be avoided and the amount of generated entropy of the turbine control valves reduced.

Suggested Citation

  • Strušnik, Dušan & Avsec, Jurij, 2015. "Artificial neural networking and fuzzy logic exergy controlling model of combined heat and power system in thermal power plant," Energy, Elsevier, vol. 80(C), pages 318-330.
    • Handle: RePEc:eee:energy:v:80:y:2015:i:c:p:318-330
    DOI: 10.1016/j.energy.2014.11.074
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    Cited by:

    1. Şöhret, Yasin & Dinç, Ali & Karakoç, T. Hikmet, 2015. "Exergy analysis of a turbofan engine for an unmanned aerial vehicle during a surveillance mission," Energy, Elsevier, vol. 93(P1), pages 716-729.
    2. Strušnik, Dušan & Marčič, Milan & Golob, Marjan & Hribernik, Aleš & Živić, Marija & Avsec, Jurij, 2016. "Energy efficiency analysis of steam ejector and electric vacuum pump for a turbine condenser air extraction system based on supervised machine learning modelling," Applied Energy, Elsevier, vol. 173(C), pages 386-405.
    3. Vazquez, Luis & Blanco, Jesús María & Ramis, Rolando & Peña, Francisco & Diaz, David, 2015. "Robust methodology for steady state measurements estimation based framework for a reliable long term thermal power plant operation performance monitoring," Energy, Elsevier, vol. 93(P1), pages 923-944.
    4. Cao, Li-hua & Yu, Jing-wen & Li, Yong, 2016. "Study on the determination method of the normal value of relative internal efficiency of the last stage group of steam turbine," Energy, Elsevier, vol. 98(C), pages 101-107.
    5. Strušnik, Dušan & Brandl, Daniel & Schober, Helmut & Ferčec, Janko & Avsec, Jurij, 2020. "A simulation model of the application of the solar STAF panel heat transfer and noise reduction with and without a transparent plate: A renewable energy review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    6. Zhang, Guojie & Wang, Xiaogang & Jin, Zunlong & Dykas, Sławomir & Smołka, Krystian, 2023. "Numerical study of the loss and power prediction based on a modified non-equilibrium condensation model in a 200 MW industrial-scale steam turbine under different operation conditions," Energy, Elsevier, vol. 275(C).
    7. Jun Wang & Baocang Ding & Ping Wang, 2022. "Modeling and Finite-Horizon MPC for a Boiler-Turbine System Using Minimal Realization State-Space Model," Energies, MDPI, vol. 15(21), pages 1-20, October.
    8. Sangi, Roozbeh & Müller, Dirk, 2019. "Application of the second law of thermodynamics to control: A review," Energy, Elsevier, vol. 174(C), pages 938-953.
    9. Ahn, Jonghoon & Chung, Dae Hun & Cho, Soolyeon, 2018. "Energy cost analysis of an intelligent building network adopting heat trading concept in a district heating model," Energy, Elsevier, vol. 151(C), pages 11-25.

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