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Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks

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  • Rostek, Kornel
  • Morytko, Łukasz
  • Jankowska, Anna

Abstract

Leaks in fluidized-bed boilers are typically characterized by slow escalation. Early detection and prediction of such faults is an important task that has not been solved in practice yet. The paper reports a series of research and development works related to achieving early detection and prediction of leaks in fluidized-bed boilers using ANN (artificial neural networks). The obtained results were used in pilot implementation of a diagnostics and prediction system covering six blocks of a professional power plant. The diagnostics and prediction task is divided into two stages: early fault detection by virtual sensors and leak isolation using classifiers of fault state. Models of process variables were created by employing a novel two-stage structure of ANN. The resulting efficiency of leak detection is presented. Also provided is an example of 12 faults of a fluidized-bed boiler, achieving detection of 11 faults with at least two days advance prediction of a boiler shutdown. These results are compared with detections obtained by the authors previously with the use of neuro-fuzzy models. Then, the paper reports the ability to distinguish between three classes of leaks by the developed classifier of the fault state. Further possible improvements of this fault classification system are discussed.

Suggested Citation

  • Rostek, Kornel & Morytko, Łukasz & Jankowska, Anna, 2015. "Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks," Energy, Elsevier, vol. 89(C), pages 914-923.
    • Handle: RePEc:eee:energy:v:89:y:2015:i:c:p:914-923
    DOI: 10.1016/j.energy.2015.06.042
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    References listed on IDEAS

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    1. Smrekar, J. & Assadi, M. & Fast, M. & Kuštrin, I. & De, S., 2009. "Development of artificial neural network model for a coal-fired boiler using real plant data," Energy, Elsevier, vol. 34(2), pages 144-152.
    2. 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.
    3. Rusinowski, Henryk & Stanek, Wojciech, 2010. "Hybrid model of steam boiler," Energy, Elsevier, vol. 35(2), pages 1107-1113.
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    9. Jungwon Yu & Jaeyel Jang & Jaeyeong Yoo & June Ho Park & Sungshin Kim, 2018. "A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant," Energies, MDPI, vol. 11(5), pages 1-19, May.
    10. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    11. Fan, He & Zhang, Yu-fei & Su, Zhi-gang & Wang, Ben, 2017. "A dynamic mathematical model of an ultra-supercritical coal fired once-through boiler-turbine unit," Applied Energy, Elsevier, vol. 189(C), pages 654-666.
    12. Cao, Li-hua & Yu, Jing-wen & Li, Yong, 2016. "Study on the determination method of the normal value of relative internal efficiency of the last stage group of steam turbine," Energy, Elsevier, vol. 98(C), pages 101-107.
    13. Bo Gao & Chunsheng Wang & Yukun Hu & C. K. Tan & Paul Alun Roach & Liz Varga, 2018. "Function Value-Based Multi-Objective Optimisation of Reheating Furnace Operations Using Hooke-Jeeves Algorithm," Energies, MDPI, vol. 11(9), pages 1-18, September.
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