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Prediction of NOx Emissions from a Coal-Fired Boiler Based on Convolutional Neural Networks with a Channel Attention Mechanism

Author

Listed:
  • Nan Li

    (School of Information and Electrical Engineering, Lu Dong University, Yantai 264025, China)

  • You Lv

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Yong Hu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

Abstract

This paper presents a small and efficient model for predicting NOx emissions from coal-fired boilers. The raw data collected are processed by the min–max scale method and converted into a multivariate time series. The overall model’s architecture is mainly based on building blocks consisting of separable convolutional neural networks and efficient channel attention (ECA) modules. The experimental results show that the model can learn good representations from sufficient data covering different operation conditions. These results also suggest that ECA modules can improve the model’s performance. The comparative study shows our model’s strong performance compared to other NOx prediction models. Then, we demonstrate the effectiveness of the model proposed in this paper in terms of predicting NOx emissions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:76-:d:1010410
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    References listed on IDEAS

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    1. 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.
    2. Tan, Peng & He, Biao & Zhang, Cheng & Rao, Debei & Li, Shengnan & Fang, Qingyan & Chen, Gang, 2019. "Dynamic modeling of NOX emission in a 660 MW coal-fired boiler with long short-term memory," Energy, Elsevier, vol. 176(C), pages 429-436.
    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. 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).
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    Cited by:

    1. Youngjin Seol & Seunghyun Lee & Jiho Lee & Chang-Wan Kim & Hyun Su Bak & Youngchul Byun & Janghyeok Yoon, 2024. "An Interpretable Time Series Forecasting Model for Predicting NOx Emission Concentration in Ferroalloy Electric Arc Furnace Plants," Mathematics, MDPI, vol. 12(6), pages 1-22, March.

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