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Development and multi-utility of an ANN model for an industrial gas turbine

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  • Fast, M.
  • Assadi, M.
  • De, S.

Abstract

Demonstration of different utilities for industrial use of an artificial neural network (ANN) model for a gas turbine has been reported in this paper. The ANN model was constructed with the multi-layer feed-forward network type and trained with operational data using back-propagation. The results showed that operational and performance parameters of the gas turbine, including identification of anti-icing mode, can be predicted with good accuracy for varying local ambient conditions. Different possible applications of this ANN model were also demonstrated. These include instantaneous gas turbine performance estimation through a graphical user interface and extrapolation beyond the range of training data.

Suggested Citation

  • Fast, M. & Assadi, M. & De, S., 2009. "Development and multi-utility of an ANN model for an industrial gas turbine," Applied Energy, Elsevier, vol. 86(1), pages 9-17, January.
    • Handle: RePEc:eee:appene:v:86:y:2009:i:1:p:9-17
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    References listed on IDEAS

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    1. De, S. & Kaiadi, M. & Fast, M. & Assadi, M., 2007. "Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden," Energy, Elsevier, vol. 32(11), pages 2099-2109.
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    8. Nikpey, H. & Assadi, M. & Breuhaus, P., 2013. "Development of an optimized artificial neural network model for combined heat and power micro gas turbines," Applied Energy, Elsevier, vol. 108(C), pages 137-148.
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    15. 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).
    16. Fang, Xiande & Dai, Qiumin & Yin, Yanxin & Xu, Yu, 2010. "A compact and accurate empirical model for turbine mass flow characteristics," Energy, Elsevier, vol. 35(12), pages 4819-4823.
    17. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    18. Rahmoune, Mohamed Ben & Hafaifa, Ahmed & Kouzou, Abdellah & Chen, XiaoQi & Chaibet, Ahmed, 2021. "Gas turbine monitoring using neural network dynamic nonlinear autoregressive with external exogenous input modelling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 179(C), pages 23-47.
    19. Xu, Xiandong & Li, Kang & Qi, Fengyu & Jia, Hongjie & Deng, Jing, 2017. "Identification of microturbine model for long-term dynamic analysis of distribution networks," Applied Energy, Elsevier, vol. 192(C), pages 305-314.
    20. Sipöcz, Nikolett & Tobiesen, Finn Andrew & Assadi, Mohsen, 2011. "The use of Artificial Neural Network models for CO2 capture plants," Applied Energy, Elsevier, vol. 88(7), pages 2368-2376, July.
    21. Asif Afzal & Saad Alshahrani & Abdulrahman Alrobaian & Abdulrajak Buradi & Sher Afghan Khan, 2021. "Power Plant Energy Predictions Based on Thermal Factors Using Ridge and Support Vector Regressor Algorithms," Energies, MDPI, vol. 14(21), pages 1-22, November.
    22. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).
    23. Fang, Xiande & Xu, Yu, 2011. "Development of an empirical model of turbine efficiency using the Taylor expansion and regression analysis," Energy, Elsevier, vol. 36(5), pages 2937-2942.

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