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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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    4. Yuan, Shuai & Zhou, Zhi-jie & Li, Jun & Wang, Fu-chen, 2012. "Nitrogen conversion during rapid pyrolysis of coal and petroleum coke in a high-frequency furnace," Applied Energy, Elsevier, vol. 92(C), pages 854-859.
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    Citations

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    Cited by:

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    2. Lv, You & Lv, Xuguang & Fang, Fang & Yang, Tingting & Romero, Carlos E., 2020. "Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants," Energy, Elsevier, vol. 192(C).
    3. Hong, Feng & Long, Dongteng & Chen, Jiyu & Gao, Mingming, 2020. "Modeling for the bed temperature 2D-interval prediction of CFB boilers based on long-short term memory network," Energy, Elsevier, vol. 194(C).
    4. Yang, Dan & Peng, Xin & Ye, Zhencheng & Lu, Yusheng & Zhong, Weimin, 2021. "Domain adaptation network with uncertainty modeling and its application to the online energy consumption prediction of ethylene distillation processes," Applied Energy, Elsevier, vol. 303(C).
    5. 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).
    6. 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).
    7. 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).
    8. 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.
    9. Lv, You & Hong, Feng & Yang, Tingting & Fang, Fang & Liu, Jizhen, 2017. "A dynamic model for the bed temperature prediction of circulating fluidized bed boilers based on least squares support vector machine with real operational data," Energy, Elsevier, vol. 124(C), pages 284-294.
    10. 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.
    11. 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.
    12. 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.

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