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Dynamic soft sensors for NOx emissions in a circulating fluidized bed boiler

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  • Liukkonen, Mika
  • Hälikkä, Eero
  • Hiltunen, Teri
  • Hiltunen, Yrjö

Abstract

Fuel flexibility and the ability to burn low-grade fuels are major advantages generally associated with fluidized bed combustion. The use of demanding heterogeneous fuels such as biomass, however, not only increases the need for monitoring the dynamics of the process but also complicates the development of new methods for diagnosing and monitoring it. Soft sensors can provide a tool for supporting or replacing potentially difficult and expensive measurements or, alternatively, for predicting the behavior of a process in the future. In this paper, we present an adaptive soft sensor that utilizes real plant data and makes it possible to estimate the nitrogen oxide content of flue gas in a CFB boiler fired by demolition wood. The main findings indicate on one hand that an adaptive approach is necessary to learn the dynamics of the process and on the other hand that both the linear and nonlinear soft sensors perform well in this case.

Suggested Citation

  • Liukkonen, Mika & Hälikkä, Eero & Hiltunen, Teri & Hiltunen, Yrjö, 2012. "Dynamic soft sensors for NOx emissions in a circulating fluidized bed boiler," Applied Energy, Elsevier, vol. 97(C), pages 483-490.
    • Handle: RePEc:eee:appene:v:97:y:2012:i:c:p:483-490
    DOI: 10.1016/j.apenergy.2012.01.074
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    References listed on IDEAS

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    6. Tan, Peng & Xia, Ji & Zhang, Cheng & Fang, Qingyan & Chen, Gang, 2016. "Modeling and reduction of NOX emissions for a 700 MW coal-fired boiler with the advanced machine learning method," Energy, Elsevier, vol. 94(C), pages 672-679.
    7. Han, Zhezhe & Tang, Xiaoyu & Xie, Yue & Liang, Ruiyu & Bao, Yongqiang, 2024. "Prediction of heavy-oil combustion emissions with a semi-supervised learning model considering variable operation conditions," Energy, Elsevier, vol. 288(C).
    8. Liukkonen, M. & Hiltunen, T., 2014. "Adaptive monitoring of emissions in energy boilers using self-organizing maps: An application to a biomass-fired CFB (circulating fluidized bed)," Energy, Elsevier, vol. 73(C), pages 443-452.
    9. Xie, Peiran & Gao, Mingming & Zhang, Hongfu & Niu, Yuguang & Wang, Xiaowen, 2020. "Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network," Energy, Elsevier, vol. 190(C).
    10. Dashti, Amir & Noushabadi, Abolfazl Sajadi & Asadi, Javad & Raji, Mojtaba & Chofreh, Abdoulmohammad Gholamzadeh & Klemeš, Jiří Jaromír & Mohammadi, Amir H., 2021. "Review of higher heating value of municipal solid waste based on analysis and smart modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    11. Ögren, Yngve & Tóth, Pál & Garami, Attila & Sepman, Alexey & Wiinikka, Henrik, 2018. "Development of a vision-based soft sensor for estimating equivalence ratio and major species concentration in entrained flow biomass gasification reactors," Applied Energy, Elsevier, vol. 226(C), pages 450-460.
    12. Guo, Zhihang & Wang, Qinhui & Fang, Mengxiang & Luo, Zhongyang & Cen, Kefa, 2014. "Thermodynamic and economic analysis of polygeneration system integrating atmospheric pressure coal pyrolysis technology with circulating fluidized bed power plant," Applied Energy, Elsevier, vol. 113(C), pages 1301-1314.

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