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An artificial neural network approach to compressor performance prediction

Author

Listed:
  • Ghorbanian, K.
  • Gholamrezaei, M.

Abstract

The application of artificial neural network to compressor performance map prediction is investigated. Different types of artificial neural networks such as general regression neural network, rotated general regression neural network proposed by the authors, radial basis function network, and multilayer perceptron network are considered. Two different models are utilized in simulating the performance map. The results indicate that while the rotated general regression neural network has the least mean error and best agreement to the experimental data; it is however, limited to interpolation application. On the other hand, if one considers a tool for interpolation as well as extrapolation applications, multilayer perceptron network technique is the most powerful candidate. Further, the compressor efficiency based on the multilayer perceptron network technique is determined. Excellent agreement between the predictions and the experimental data is obtained.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:86:y:2009:i:7-8:p:1210-1221
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    References listed on IDEAS

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    1. 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.
    2. Joly, R. B. & Ogaji, S. O. T. & Singh, R. & Probert, S. D., 2004. "Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine," Applied Energy, Elsevier, vol. 78(4), pages 397-418, August.
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    Cited by:

    1. Zhang, Weihao & Zou, Zhengping & Ye, Jian, 2012. "Leading-edge redesign of a turbomachinery blade and its effect on aerodynamic performance," Applied Energy, Elsevier, vol. 93(C), pages 655-667.
    2. Finn, Joshua & Wagner, John & Bassily, Hany, 2010. "Monitoring strategies for a combined cycle electric power generator," Applied Energy, Elsevier, vol. 87(8), pages 2621-2627, August.
    3. repec:eee:appene:v:198:y:2017:i:c:p:122-144 is not listed on IDEAS
    4. repec:eee:energy:v:126:y:2017:i:c:p:217-230 is not listed on IDEAS
    5. repec:eee:energy:v:140:y:2017:i:p2:p:1398-1406 is not listed on IDEAS
    6. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    7. Lazrak, Amine & Leconte, Antoine & Chèze, David & Fraisse, Gilles & Papillon, Philippe & Souyri, Bernard, 2015. "Numerical and experimental results of a novel and generic methodology for energy performance evaluation of thermal systems using renewable energies," Applied Energy, Elsevier, vol. 158(C), pages 142-156.
    8. 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.
    9. 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.
    10. 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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