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A mathematical model of an existing gas-steam combined heat and power plant for thermal diagnostic systems

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  • Plis, Marcin
  • Rusinowski, Henryk

Abstract

Systems of thermal diagnostic of an operation require computational tools, including mathematical models. These models should have a simple structure and short computing time. Therefore, often analytical-empirical models are used which encompass both mass and energy balances as well as additional empirical functions whose coefficients are estimated on the basis of measurement results. As a result, changing technical conditions of modelled machines are taken into account. This paper presents the simulation model of an existing gas-steam combined heat and power plant, which has a modular design and is divided into three partial models of: the gas turbine unit with rated power of 125.4 MW, the double-pressure heat recovery steam generator and the steam-water cycle with steam turbine rated 65 MW. The simulation models allow to calculate non-measured operating parameters and energy assessment indicators. They also have the capability of adapting to the changing technical conditions of the modelled machines. The developed models were validated by the use of measurements. Model predictive quality was verified with the determination factor and root mean square error. The models were also used to simulate the behavior of the analyzed gas-steam CHP plant under different operating conditions. Exemplary calculations have been presented.

Suggested Citation

  • Plis, Marcin & Rusinowski, Henryk, 2018. "A mathematical model of an existing gas-steam combined heat and power plant for thermal diagnostic systems," Energy, Elsevier, vol. 156(C), pages 606-619.
    • Handle: RePEc:eee:energy:v:156:y:2018:i:c:p:606-619
    DOI: 10.1016/j.energy.2018.05.113
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    Cited by:

    1. Ziółkowski, Paweł & Badur, Janusz & Ziółkowski, Piotr Józef, 2019. "An energetic analysis of a gas turbine with regenerative heating using turbine extraction at intermediate pressure - Brayton cycle advanced according to Szewalski's idea," Energy, Elsevier, vol. 185(C), pages 763-786.
    2. Paweł Ziółkowski & Marta Drosińska-Komor & Jerzy Głuch & Łukasz Breńkacz, 2023. "Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence," Energies, MDPI, vol. 16(17), pages 1-28, August.
    3. Hofmann, René & Linzner, Paul & Walter, Heimo & Will, Thomas, 2018. "New approximation algorithms for the state functions of water and steam for the application of transient processes and fast on-line applications," Energy, Elsevier, vol. 164(C), pages 1079-1096.
    4. Angjun Xie & Gang Xu & Chunming Nie & Heng Chen & Tailaiti Tuerhong, 2024. "Operation Data Analysis and Performance Optimization of the Air-Cooled System in a Coal-Fired Power Plant Based on Machine Learning Algorithms," Energies, MDPI, vol. 17(22), pages 1-23, November.
    5. Ivan Lorencin & Nikola Anđelić & Vedran Mrzljak & Zlatan Car, 2019. "Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation," Energies, MDPI, vol. 12(22), pages 1-26, November.
    6. Kalina, Jacek & Świerzewski, Mateusz, 2019. "Identification of ORC unit operation in biomass-fired cogeneration system," Renewable Energy, Elsevier, vol. 142(C), pages 400-414.
    7. Park, Yeseul & Choi, Minsung & Kim, Kibeom & Li, Xinzhuo & Jung, Chanho & Na, Sangkyung & Choi, Gyungmin, 2020. "Prediction of operating characteristics for industrial gas turbine combustor using an optimized artificial neural network," Energy, Elsevier, vol. 213(C).
    8. Chen, Yu-Zhi & Li, Yi-Guang & Newby, Mike A., 2019. "Performance simulation of a parallel dual-pressure once-through steam generator," Energy, Elsevier, vol. 173(C), pages 16-27.
    9. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    10. Chen, Yu-Zhi & Tsoutsanis, Elias & Xiang, Heng-Chao & Li, Yi-Guang & Zhao, Jun-Jie, 2022. "A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions," Applied Energy, Elsevier, vol. 317(C).
    11. Chen, Yu-Zhi & Zhao, Xu-Dong & Xiang, Heng-Chao & Tsoutsanis, Elias, 2021. "A sequential model-based approach for gas turbine performance diagnostics," Energy, Elsevier, vol. 220(C).
    12. Bartnik, Ryszard & Hnydiuk-Stefan, Anna & Buryn, Zbigniew, 2020. "Thermodynamic and economic analysis of a gas turbine set coupled with a turboexpander in a hierarchical gas-gas system," Energy, Elsevier, vol. 190(C).
    13. Witanowski, Łukasz & Klonowicz, Piotr & Lampart, Piotr & Ziółkowski, Paweł, 2023. "Multi-objective optimization of the ORC axial turbine for a waste heat recovery system working in two modes: cogeneration and condensation," Energy, Elsevier, vol. 264(C).
    14. Jamil, Ahmad & Javed, Adeel & Wajid, Abdul & Zeb, Muhammad Omar & Ali, Majid & Khoja, Asif Hussain & Imran, Muhammad, 2021. "Multiparametric optimization for reduced condenser cooling water consumption in a degraded combined cycle gas turbine power plant from a water-energy nexus perspective," Applied Energy, Elsevier, vol. 304(C).

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