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Data-driven framework for boiler performance monitoring

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  • Nikula, Riku-Pekka
  • Ruusunen, Mika
  • Leiviskä, Kauko

Abstract

The energy industry is striving for cost-efficient production with low emissions. The efficiency of energy conversion processes is one of the most important factors affecting the path to such a goal. This paper provides a framework for the monitoring of steam boiler operation in power stations. The framework is based on the use of historical process data as a reference for real-time operation. The actual boiler efficiency is monitored together with its expected efficiency, which is an estimate of the highest historical efficiency in the corresponding process state, based on a data-driven model. In the presented approach, the process state is defined on the basis of variables that have the strongest correlation with boiler efficiency according to information-theoretic variable ranking. Boiler performance is monitored using a statistical process control chart for the difference between the expected and actual efficiencies. The framework was tested using data from a circulating fluidised bed boiler and from a corner-fired boiler. The results revealed that the strongest correlations between process variables and boiler efficiency are substantially consistent in both cases. Moreover, the framework provides a novel measure for boiler performance enhancement.

Suggested Citation

  • Nikula, Riku-Pekka & Ruusunen, Mika & Leiviskä, Kauko, 2016. "Data-driven framework for boiler performance monitoring," Applied Energy, Elsevier, vol. 183(C), pages 1374-1388.
    • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:1374-1388
    DOI: 10.1016/j.apenergy.2016.09.072
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    Cited by:

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    4. Hong, Feng & Wang, Rui & Song, Jie & Gao, Mingming & Liu, Jizhen & Long, Dongteng, 2022. "A performance evaluation framework for deep peak shaving of the CFB boiler unit based on the DBN-LSSVM algorithm," Energy, Elsevier, vol. 238(PA).
    5. Dong, Zhe & Liu, Miao & Guo, Zhiwu & Huang, Xiaojin & Zhang, Yajun & Zhang, Zuoyi, 2019. "Adaptive state-observer for monitoring flexible nuclear reactors," Energy, Elsevier, vol. 171(C), pages 893-909.
    6. 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).
    7. Guillermo Valencia Ochoa & Jhan Piero Rojas & Juan Campos Avella, 2019. "Energy Optimization of Industrial Steam Boiler using Energy Performance Indicator," International Journal of Energy Economics and Policy, Econjournals, vol. 9(6), pages 109-117.
    8. Sunil, P.U. & Barve, Jayesh & Nataraj, P.S.V., 2017. "Mathematical modeling, simulation and validation of a boiler drum: Some investigations," Energy, Elsevier, vol. 126(C), pages 312-325.
    9. Miriam Benedetti & Francesca Bonfà & Vito Introna & Annalisa Santolamazza & Stefano Ubertini, 2019. "Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications," Energies, MDPI, vol. 12(20), pages 1-28, October.
    10. Jia, Xiongjie & Sang, Yichen & Li, Yanjun & Du, Wei & Zhang, Guolei, 2022. "Short-term forecasting for supercharged boiler safety performance based on advanced data-driven modelling framework," Energy, Elsevier, vol. 239(PE).

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