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Energy efficiency analysis of steam ejector and electric vacuum pump for a turbine condenser air extraction system based on supervised machine learning modelling

Author

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  • Strušnik, Dušan
  • Marčič, Milan
  • Golob, Marjan
  • Hribernik, Aleš
  • Živić, Marija
  • Avsec, Jurij

Abstract

This paper compares the vapour ejector and electric vacuum pump power consumptions with machine learning algorithms by using real process data and presents some novelty guideline for the selection of an appropriate condenser vacuum pump system of a steam turbine power plant. The machine learning algorithms are made by using the supervised machine learning methods such as artificial neural network model and local linear neuro-fuzzy models. The proposed non-linear models are designed by using a wide range of real process operation data sets from the CHP system in the thermal power plant. The novelty guideline for the selection of an appropriate condenser vacuum pumps system is expressed in the comparative analysis of the energy consumption and use of specific energy capable of work. Furthermore, the novelty is expressed in the economic efficiency analysis of the investment taking into consideration the operating costs of the vacuum pump systems and may serve as basic guidelines for the selection of an appropriate condenser vacuum pump system of a steam turbine.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:173:y:2016:i:c:p:386-405
    DOI: 10.1016/j.apenergy.2016.04.047
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    Cited by:

    1. Miladi, Rihab & Frikha, Nader & Gabsi, Slimane, 2021. "Modeling and energy analysis of a solar thermal vacuum membrane distillation coupled with a liquid ring vacuum pump," Renewable Energy, Elsevier, vol. 164(C), pages 1395-1407.
    2. Hundi, Prabhas & Shahsavari, Rouzbeh, 2020. "Comparative studies among machine learning models for performance estimation and health monitoring of thermal power plants," Applied Energy, Elsevier, vol. 265(C).
    3. Yang, Yan & Zhu, Xiaowei & Yan, Yuying & Ding, Hongbing & Wen, Chuang, 2019. "Performance of supersonic steam ejectors considering the nonequilibrium condensation phenomenon for efficient energy utilisation," Applied Energy, Elsevier, vol. 242(C), pages 157-167.
    4. Wen, Chuang & Gong, Liang & Ding, Hongbing & Yang, Yan, 2020. "Steam ejector performance considering phase transition for multi-effect distillation with thermal vapour compression (MED-TVC) desalination system," Applied Energy, Elsevier, vol. 279(C).
    5. Wen, Chuang & Rogie, Brice & Kærn, Martin Ryhl & Rothuizen, Erasmus, 2020. "A first study of the potential of integrating an ejector in hydrogen fuelling stations for fuelling high pressure hydrogen vehicles," Applied Energy, Elsevier, vol. 260(C).
    6. Yang, Yan & Karvounis, Nikolas & Walther, Jens Honore & Ding, Hongbing & Wen, Chuang, 2021. "Effect of area ratio of the primary nozzle on steam ejector performance considering nonequilibrium condensations," Energy, Elsevier, vol. 237(C).
    7. Zhang, Guojie & Wang, Xiaogang & Wiśniewski, Piotr & Chen, Jiaheng & Qin, Xiang & Dykas, Sławomir, 2023. "Effect of NaCl presence caused by salting out on the heterogeneous-homogeneous coupling non-equilibrium condensation flow in a steam turbine cascade," Energy, Elsevier, vol. 263(PE).
    8. Zhang, Shaozhi & Luo, Jielin & Wang, Qin & Chen, Guangming, 2018. "Step utilization of energy with ejector in a heat driven freeze drying system," Energy, Elsevier, vol. 164(C), pages 734-744.
    9. Cao, Yue & Hu, Hui & Chen, Ranjing & He, Tianyu & Si, Fengqi, 2023. "Comparative analysis on thermodynamic performance of combined heat and power system employing steam ejector as cascaded heat sink," Energy, Elsevier, vol. 275(C).

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