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Prediction of the NOx emissions from thermal power plant using long-short term memory neural network

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  • Yang, Guotian
  • Wang, Yingnan
  • Li, Xinli

Abstract

Coal combustion in thermal power plant is the main source of the NOx emission. An effective prediction model should be established for reducing NOx emission. This paper focuses on the application of long-short term memory (LSTM) neural network in modeling the relationship between operational parameters and NOx emission of a 660 MW boiler. Principal component analysis (PCA) method is used to eliminate the coupling between original variables, then the NOx emission model based on LSTM is established. The dropout strategy and Adam optimizer are adopted to improve the network performance. Compared with the least squares support vector machine (LSSVM), the proposed model has higher prediction accuracy, faster response speed, stronger generalization ability, and is more competitive in the modeling of NOx emission. The difference between the LSTM model and the traditional recurrent neural network (RNN) model is also compared. The results show that the performance of LSTM model is better than that of RNN model under the same model structure and parameters. In addition, a NOx prediction model for high-dimensional data is established and achieves good prediction performance. Thus, LSTM is capable to model the NOx emission for coal-fired boilers and is superior to other traditional modeling methods.

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  • 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).
    • Handle: RePEc:eee:energy:v:192:y:2020:i:c:s0360544219322923
    DOI: 10.1016/j.energy.2019.116597
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    References listed on IDEAS

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    Cited by:

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    12. 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).
    13. 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).
    14. Wang, Zhihong & Luo, Kangwei & Yu, Hongsen & Feng, Kai & Ding, Hang, 2024. "NOx Emission prediction of heavy-duty diesel vehicles based on Bayesian optimization -Gated Recurrent Unit algorithm," Energy, Elsevier, vol. 292(C).
    15. Hou, Guolian & Gong, Linjuan & Hu, Bo & Su, Huilin & Huang, Ting & Huang, Congzhi & Fan, Wei & Zhao, Yuanzhu, 2022. "Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit," Energy, Elsevier, vol. 239(PA).
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    17. Li, Xinli & Wang, Yingnan & Zhu, Yun & Yang, Guotian & Liu, He, 2021. "Temperature prediction of combustion level of ultra-supercritical unit through data mining and modelling," Energy, Elsevier, vol. 231(C).
    18. Fan, Yuchen & Liu, Xin & Zhang, Chaoqun & Li, Chi & Li, Xinying & Wang, Heyang, 2024. "Dynamic prediction of boiler NOx emission with graph convolutional gated recurrent unit model optimized by genetic algorithm," Energy, Elsevier, vol. 294(C).
    19. Kwak, Sanghyeok & Choi, Jaehong & Lee, Min Chul & Yoon, Youngbin, 2021. "Predicting instability frequency and amplitude using artificial neural network in a partially premixed combustor," Energy, Elsevier, vol. 230(C).
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    21. Nan Li & You Lv & Yong Hu, 2022. "Prediction of NOx Emissions from a Coal-Fired Boiler Based on Convolutional Neural Networks with a Channel Attention Mechanism," Energies, MDPI, vol. 16(1), pages 1-11, December.

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