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Neural-network based analysis and prediction of a compressor's characteristic performance map

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  • Yu, Youhong
  • Chen, Lingen
  • Sun, Fengrui
  • Wu, Chih

Abstract

The difficulties, due to a lack of information about stage-by-stage axial-compressor performance, are analyzed. To overcome these issues, a three-layer back-propagation neural-network applied Levenberg-Marquardt algorithm is presented and discussed. The experimental data provided by manufacturers are used for the neural-network training. Through twice training, the compressor's performance map can be predicted. The results can be used for the development of an off-design model or overall dynamic simulation of the behaviour of a gas-turbine power-plant.

Suggested Citation

  • Yu, Youhong & Chen, Lingen & Sun, Fengrui & Wu, Chih, 2007. "Neural-network based analysis and prediction of a compressor's characteristic performance map," Applied Energy, Elsevier, vol. 84(1), pages 48-55, January.
    • Handle: RePEc:eee:appene:v:84:y:2007:i:1:p:48-55
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    Cited by:

    1. Tong, Yongjing & Duan, Liqiang & Pang, Liping, 2021. "Off-design performance analysis of a new 300 MW supercritical CO2 coal-fired boiler," Energy, Elsevier, vol. 216(C).
    2. Tahan, Mohammadreza & Tsoutsanis, Elias & Muhammad, Masdi & Abdul Karim, Z.A., 2017. "Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review," Applied Energy, Elsevier, vol. 198(C), pages 122-144.
    3. Li, Zhihui & Liu, Yanming, 2017. "Blade-end treatment for axial compressors based on optimization method," Energy, Elsevier, vol. 126(C), pages 217-230.
    4. Likun Ren & Haiqin Qin & Zhenbo Xie & Jing Xie & Bianjiang Li, 2022. "A Thermodynamics-Oriented and Neural Network-Based Hybrid Model for Military Turbofan Engines," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    5. Yazar, Isil & Yavuz, Hasan Serhan & Yavuz, Arzu Altin, 2017. "Comparison of various regression models for predicting compressor and turbine performance parameters," Energy, Elsevier, vol. 140(P2), pages 1398-1406.
    6. Chaczykowski, M. & Osiadacz, A.J. & Uilhoorn, F.E., 2011. "Exergy-based analysis of gas transmission system with application to Yamal-Europe pipeline," Applied Energy, Elsevier, vol. 88(6), pages 2219-2230, June.
    7. Safiyullah, F. & Sulaiman, S.A. & Naz, M.Y. & Jasmani, M.S. & Ghazali, S.M.A., 2018. "Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming," Energy, Elsevier, vol. 158(C), pages 485-494.
    8. Balerna, Camillo & Lanzetti, Nicolas & Salazar, Mauro & Cerofolini, Alberto & Onder, Christopher, 2020. "Optimal low-level control strategies for a high-performance hybrid electric power unit," Applied Energy, Elsevier, vol. 276(C).
    9. Cortés, O. & Urquiza, G. & Hernández, J.A., 2009. "Optimization of operating conditions for compressor performance by means of neural network inverse," Applied Energy, Elsevier, vol. 86(11), pages 2487-2493, November.
    10. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2014. "A component map tuning method for performance prediction and diagnostics of gas turbine compressors," Applied Energy, Elsevier, vol. 135(C), pages 572-585.
    11. Ghorbanian, K. & Gholamrezaei, M., 2009. "An artificial neural network approach to compressor performance prediction," Applied Energy, Elsevier, vol. 86(7-8), pages 1210-1221, July.
    12. Guan, Cong & Theotokatos, Gerasimos & Zhou, Peilin & Chen, Hui, 2014. "Computational investigation of a large containership propulsion engine operation at slow steaming conditions," Applied Energy, Elsevier, vol. 130(C), pages 370-383.

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