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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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    5. 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.
    6. Wang, Chaoyang & Qiao, Yongqiang & Liu, Ming & Zhao, Yongliang & Yan, Junjie, 2020. "Enhancing peak shaving capability by optimizing reheat-steam temperature control of a double-reheat boiler," Applied Energy, Elsevier, vol. 260(C).
    7. 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).
    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).
    11. Vito Introna & Annalisa Santolamazza & Vittorio Cesarotti, 2024. "Integrating Industry 4.0 and 5.0 Innovations for Enhanced Energy Management Systems," Energies, MDPI, vol. 17(5), pages 1-16, March.

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