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Dynamic prediction of pollutants emission from circulating fluidized bed unit based on CNN-GRU-MHA
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Yongquan WANG1, 2, Mingming GAO1, 2, Weihua WANG1, 2, Pengxin ZHANG1, 2, Yongqiang CHENG2
Thermal Power Generation | 2025, 54(7) : 33 - 42
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Thermal Power Generation | 2025, 54(7): 33-42
Special topic on “ultra supercritical circulating fluidized bed power generation technology”
Dynamic prediction of pollutants emission from circulating fluidized bed unit based on CNN-GRU-MHA
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Yongquan WANG1, 2, Mingming GAO1, 2, Weihua WANG1, 2, Pengxin ZHANG1, 2, Yongqiang CHENG2
Affiliations
  • 1.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, China
  • 2.School of Control and Computer Engineering (North China Electric Power University), Beijing 102206, China
Published: 2025-07-25 doi: 10.19666/j.rlfd.202408194
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The accurate prediction of SO2 and NOx emission mass concentrations can effectively guide the control of pollutants emissions, which is of great significance for the environmental protection operation of circulating fluidized bed (CFB) units. A 330 MW CFB unit is taken as the research object, and the Pearson coefficient is used to realize the screening of input variables, and the interquartile range (IQR) method is applied to screen the outliers and replace them with the normalization at the same time, to complete the data preprocessing. Subsequently, the features of input variables are extracted by convolutional neural network (CNN), and by entering into the gate-recurrent unit (GRU) the time-series features are processed. The multi-head self-attention (MHA) mechanism is introduced to capture the important relationships between features, and the model output is obtained after training. Finally, the results of the test set are evaluated using the mean absolute error (MAE), mean absolute percentage error (MAPE), and the coefficient of determination (R2). The results show that the model is able to predict the pollutants mass concentration in CFBs more accurately and achieve good prediction results, and the superior performance of the model is proved by the comparison of ablation experiments with the model. The proposed CNN-GRU-MHA model can realize the monitoring and optimization guidance of pollutants emissions CFB units, so that the power plant can adjust the operation parameters in time to ensure that the pollutants emissions meet the standards.

circulating fluidized bed  /  pollutant emission prediction  /  deep learning  /  data-driven
Yongquan WANG, Mingming GAO, Weihua WANG, Pengxin ZHANG, Yongqiang CHENG. Dynamic prediction of pollutants emission from circulating fluidized bed unit based on CNN-GRU-MHA[J]. Thermal Power Generation, 2025 , 54 (7) : 33 -42 . DOI: 10.19666/j.rlfd.202408194
  • National Key Research and Development Program(2022YFB4100304)
Year 2025 volume 54 Issue 7
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Article Info
doi: 10.19666/j.rlfd.202408194
  • Online Date:2026-03-06
  • Published:2025-07-25
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  • Revised:2024-12-24
Funding
National Key Research and Development Program(2022YFB4100304)
Affiliations
    1.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, China
    2.School of Control and Computer Engineering (North China Electric Power University), Beijing 102206, China
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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