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Artificial neural networks for analysis of process states in fluidized bed combustion

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  • Liukkonen, M.
  • Heikkinen, M.
  • Hiltunen, T.
  • Hälikkä, E.
  • Kuivalainen, R.
  • Hiltunen, Y.

Abstract

There are several challenges confronting energy production nowadays, such as increasing the efficiency of combustion processes and at the same time reducing harmful emissions. The latter, however, often necessitates process improvement, which requires knowledge of the behavior of the process. It is therefore important to develop and implement novel methods for process diagnostics that can respond to the challenges of modern-day energy plants. In this study the formation of nitrogen oxides (NOx) in a circulating fluidized bed (CFB) boiler is modeled by using artificial neural networks (ANN). In the approach used, the process data are first arranged using self-organizing maps (SOM) and k-means clustering to create subsets representing the separate process states in the boiler, including load increase and load decrease situations and conditions of high or low boiler load. After the determination of these process states, variable selection based on multilayer perceptrons (MLP) is performed to obtain information on the factors affecting the formation of NOx in those states. The results show that this approach provides a useful way of monitoring a combustion process.

Suggested Citation

  • Liukkonen, M. & Heikkinen, M. & Hiltunen, T. & Hälikkä, E. & Kuivalainen, R. & Hiltunen, Y., 2011. "Artificial neural networks for analysis of process states in fluidized bed combustion," Energy, Elsevier, vol. 36(1), pages 339-347.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:1:p:339-347
    DOI: 10.1016/j.energy.2010.10.033
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    Cited by:

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    2. Zhao, Yi & Wang, Shuqin & Shen, Yanmei & Lu, Xiaojuan, 2013. "Effects of nano-TiO2 on combustion and desulfurization," Energy, Elsevier, vol. 56(C), pages 25-30.
    3. 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.
    4. 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.
    5. Rossi, Francesco & Velázquez, David, 2015. "A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant," Energy, Elsevier, vol. 89(C), pages 528-544.
    6. Wen, Xiaoqiang & Li, Kaichuang & Wang, Jianguo, 2023. "NOx emission predicting for coal-fired boilers based on ensemble learning methods and optimized base learners," Energy, Elsevier, vol. 264(C).
    7. Yun Chen & Chengwei Liang & Dengcheng Liu & Qingren Niu & Xinke Miao & Guangyu Dong & Liguang Li & Shanbin Liao & Xiaoci Ni & Xiaobo Huang, 2022. "Embedding-Graph-Neural-Network for Transient NOx Emissions Prediction," Energies, MDPI, vol. 16(1), pages 1-20, December.
    8. Xu, Yingjie & Mao, Chengbin & Huang, Yuangong & Shen, Xi & Xu, Xiaoxiao & Chen, Guangming, 2021. "Performance evaluation and multi-objective optimization of a low-temperature CO2 heat pump water heater based on artificial neural network and new economic analysis," Energy, Elsevier, vol. 216(C).
    9. Ma, Yunpeng & Niu, Peifeng & Yan, Shanshan & Li, Guoqiang, 2018. "A modified online sequential extreme learning machine for building circulation fluidized bed boiler's NOx emission model," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 214-226.
    10. Lv, You & Liu, Jizhen & Yang, Tingting & Zeng, Deliang, 2013. "A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 319-329.
    11. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
    12. Bekat, Tugce & Erdogan, Muharrem & Inal, Fikret & Genc, Ayten, 2012. "Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks," Energy, Elsevier, vol. 45(1), pages 882-887.
    13. 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.
    14. Yang, Guotian & Wang, Yingnan & Li, Xinli, 2020. "Prediction of the NOx emissions from thermal power plant using long-short term memory neural network," Energy, Elsevier, vol. 192(C).
    15. Mikulandrić, Robert & Lončar, Dražen & Cvetinović, Dejan & Spiridon, Gabriel, 2013. "Improvement of existing coal fired thermal power plants performance by control systems modifications," Energy, Elsevier, vol. 57(C), pages 55-65.
    16. Wang, Chunlin & Liu, Yang & Zheng, Song & Jiang, Aipeng, 2018. "Optimizing combustion of coal fired boilers for reducing NOx emission using Gaussian Process," Energy, Elsevier, vol. 153(C), pages 149-158.

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