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Article

A Deep Learning Model for NOx Emissions Prediction of a 660 MW Coal-Fired Boiler Considering Multiscale Dynamic Characteristics

1
School of Power Engineering, Jiangxi Electric Vocational & College, Nanchang 330032, China
2
School of Chinese Language and Literature, Nanjing XiaoZhuang University, Nanjing 211171, China
3
School of Electrical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(5), 533; https://doi.org/10.3390/atmos16050533
Submission received: 25 March 2025 / Revised: 15 April 2025 / Accepted: 28 April 2025 / Published: 30 April 2025
(This article belongs to the Section Air Quality)

Abstract

Coal-fired boilers significantly contribute to nitrogen oxides (NOx) emissions, posing critical environmental and health risks. Effective prediction of NOx emissions is essential for optimizing control measures and achieving stringent emission standards. This study applies a Multiscale Graph Convolutional Network (MSGNet) designed to capture multiscale dynamic relationships among operational parameters of a 660 MW coal-fired boiler. MSGNet employs Fast Fourier Transform (FFT) for automatic periodic pattern recognition, adaptive graph convolution for dynamic inter-variable relationships, and a multihead attention mechanism to assess temporal dependencies comprehensively. Compared with the existing state of the art, the proposed structure achieves a good performance of 2.176 mg/m3, 1.652 mg/m3, and 0.988 of RMSE, MAE, and R2. Experimental evaluations demonstrate that MSGNet achieves superior predictive performance compared with traditional methods such as LSTM, BiLSTM, and GRU. Results underscore MSGNet’s robust accuracy, stability, and generalization capability, highlighting its potential for advanced emission control and environmental management applications in thermal power generation.
Keywords: NOx emissions prediction; coal-fired boiler; multiscale dynamic characteristics; graph convolutional network; deep learning NOx emissions prediction; coal-fired boiler; multiscale dynamic characteristics; graph convolutional network; deep learning

Share and Cite

MDPI and ACS Style

Huang, J.; Ji, Y.; Yu, H. A Deep Learning Model for NOx Emissions Prediction of a 660 MW Coal-Fired Boiler Considering Multiscale Dynamic Characteristics. Atmosphere 2025, 16, 533. https://doi.org/10.3390/atmos16050533

AMA Style

Huang J, Ji Y, Yu H. A Deep Learning Model for NOx Emissions Prediction of a 660 MW Coal-Fired Boiler Considering Multiscale Dynamic Characteristics. Atmosphere. 2025; 16(5):533. https://doi.org/10.3390/atmos16050533

Chicago/Turabian Style

Huang, Jianrong, Yanlong Ji, and Haiquan Yu. 2025. "A Deep Learning Model for NOx Emissions Prediction of a 660 MW Coal-Fired Boiler Considering Multiscale Dynamic Characteristics" Atmosphere 16, no. 5: 533. https://doi.org/10.3390/atmos16050533

APA Style

Huang, J., Ji, Y., & Yu, H. (2025). A Deep Learning Model for NOx Emissions Prediction of a 660 MW Coal-Fired Boiler Considering Multiscale Dynamic Characteristics. Atmosphere, 16(5), 533. https://doi.org/10.3390/atmos16050533

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