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Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant

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  • Fast, M.
  • Palmé, T.

Abstract

The objective of this study has been to create an online system for condition monitoring and diagnosis of a combined heat and power plant in Sweden. The system in question consisted of artificial neural network models, representing each main component of the combined heat and power plant, connected to a graphical user interface. The artificial neural network models were integrated on a power generation information manager server in the computer system of the combined heat and power plant, and the graphical user interface was made available on workstations connected to this server.

Suggested Citation

  • Fast, M. & Palmé, T., 2010. "Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant," Energy, Elsevier, vol. 35(2), pages 1114-1120.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:2:p:1114-1120
    DOI: 10.1016/j.energy.2009.06.005
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    4. Colorado, D. & Ali, M.E. & García-Valladares, O. & Hernández, J.A., 2011. "Heat transfer using a correlation by neural network for natural convection from vertical helical coil in oil and glycerol/water solution," Energy, Elsevier, vol. 36(2), pages 854-863.
    5. Kovačič, Miha & Šarler, Božidar, 2014. "Genetic programming prediction of the natural gas consumption in a steel plant," Energy, Elsevier, vol. 66(C), pages 273-284.
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    14. Lv, You & Liu, Jizhen & Yang, Tingting & Zeng, Deliang, 2013. "A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 319-329.
    15. Bekat, Tugce & Erdogan, Muharrem & Inal, Fikret & Genc, Ayten, 2012. "Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks," Energy, Elsevier, vol. 45(1), pages 882-887.
    16. BahooToroody, Ahmad & De Carlo, Filippo & Paltrinieri, Nicola & Tucci, Mario & Van Gelder, P.H.A.J.M., 2020. "Bayesian regression based condition monitoring approach for effective reliability prediction of random processes in autonomous energy supply operation," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    17. Kljajić, Miroslav & Gvozdenac, Dušan & Vukmirović, Srdjan, 2012. "Use of Neural Networks for modeling and predicting boiler's operating performance," Energy, Elsevier, vol. 45(1), pages 304-311.
    18. Damilola Elizabeth Babatunde & Ambrose Anozie & James Omoleye, 2020. "Artificial Neural Network and its Applications in the Energy Sector An Overview," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 250-264.
    19. Arslan, Oguz, 2011. "Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycle system-34," Energy, Elsevier, vol. 36(5), pages 2528-2534.
    20. 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.
    21. Xu, Maojun & Liu, Jinxin & Li, Ming & Geng, Jia & Wu, Yun & Song, Zhiping, 2022. "Improved hybrid modeling method with input and output self-tuning for gas turbine engine," Energy, Elsevier, vol. 238(PA).
    22. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    23. Teng, Sin Yong & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav & Stehlík, Petr, 2021. "Debottlenecking cogeneration systems under process variations: Multi-dimensional bottleneck tree analysis with neural network ensemble," Energy, Elsevier, vol. 215(PB).
    24. Mikulandrić, Robert & Lončar, Dražen & Cvetinović, Dejan & Spiridon, Gabriel, 2013. "Improvement of existing coal fired thermal power plants performance by control systems modifications," Energy, Elsevier, vol. 57(C), pages 55-65.
    25. 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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