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Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network

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  • Xie, Peiran
  • Gao, Mingming
  • Zhang, Hongfu
  • Niu, Yuguang
  • Wang, Xiaowen

Abstract

As environmental protection policies become more stringent, lower and lower NOx emission targets are required. Accurate NOx concentration prediction model plays an important role in low NOx emission control in power stations. This study aims to accurately predict the future sequence of NOx emission in the next horizon. Through the analysis on formation mechanism of NOx and the reaction mechanism of SCR reactor, a sequence to sequence dynamic prediction model is proposed, which can fit multivariable coupling, nonlinear and large delay systems. In particular, considering the different effects of multivariate on NOx, a new attention mechanism is necessary to be put forward. A large amount of historical data is used to fully train this dynamic prediction model. The results show that, the prediction accuracy of the NOx concentration and fluctuation trend based on this model is superior to comparison algorithms. Furthermore, some interesting features of this prediction model, such as error accumulation and bidirectional encoder, are also discussed in depth.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:190:y:2020:i:c:s0360544219321772
    DOI: 10.1016/j.energy.2019.116482
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Zhou, Hao & Cen, Kefa & Fan, Jianren, 2004. "Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks," Energy, Elsevier, vol. 29(1), pages 167-183.
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    Citations

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    Cited by:

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    6. Zhu, Yukun & Yu, Cong & Fan, Wei & Yu, Haiquan & Jin, Wei & Chen, Shuo & Liu, Xia, 2023. "A novel NOx emission prediction model for multimodal operational utility boilers considering local features and prior knowledge," Energy, Elsevier, vol. 280(C).
    7. Yu, Haoyang & Gao, Mingming & Zhang, Hongfu & Yue, Guangxi & Zhang, Zhen, 2023. "Data-driven optimization of pollutant emission and operational efficiency for circulating fluidized bed unit," Energy, Elsevier, vol. 281(C).
    8. Li, Ruilian & Zeng, Deliang & Li, Tingting & Ti, Baozhong & Hu, Yong, 2023. "Real-time prediction of SO2 emission concentration under wide range of variable loads by convolution-LSTM VE-transformer," Energy, Elsevier, vol. 269(C).
    9. Tang, Zhenhao & Wang, Shikui & Chai, Xiangying & Cao, Shengxian & Ouyang, Tinghui & Li, Yang, 2022. "Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction," Energy, Elsevier, vol. 256(C).
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