|Table of Contents|

[1] Ma Ning, Liu Lei, Yang Zhenyong, Yan Laiqing, et al. Dynamic model for predicting nitrogen oxide concentrationat outlet of selective catalytic reduction denitrificationsystem based on kernel extreme learning machine [J]. Journal of Southeast University (English Edition), 2022, 38 (4): 383-391. [doi:10.3969/j.issn.1003-7985.2022.04.007]

Dynamic model for predicting nitrogen oxide concentrationat outlet of selective catalytic reduction denitrificationsystem based on kernel extreme learning machine()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

2022 4
Research Field:
Energy and Power Engineering
Publishing date:


Dynamic model for predicting nitrogen oxide concentrationat outlet of selective catalytic reduction denitrificationsystem based on kernel extreme learning machine
Ma Ning1 Liu Lei1 Yang Zhenyong1 Yan Laiqing2 Dong Ze3
1North China Electric Power Research Institute Co., Ltd., Beijing 100045, China
2School of Electric Power, Civil Engineering and Architecture, Shanxi University, Taiyuan 030006, China
3Hebei Technology Innovation Center of Simulation and Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China
selective catalytic reduction nitrogen oxides principal component analysis kernel extreme learning machine dynamic model
To solve the increasing model complexity due to several input variables and large correlations under variable load conditions, a dynamic modeling method combining a kernel extreme learning machine(KELM)and principal component analysis(PCA)was proposed and applied to the prediction of nitrogen oxide(NOx)concentration at the outlet of a selective catalytic reduction(SCR)denitrification system. First, PCA is applied to the feature information extraction of input data, and the current and previous sequence values of the extracted information are used as the inputs of the KELM model to reflect the dynamic characteristics of the NOx concentration at the SCR outlet. Then, the model takes the historical data of the NOx concentration at the SCR outlet as the model input to improve its accuracy. Finally, an optimization algorithm is used to determine the optimal parameters of the model. Compared with the Gaussian process regression, long short-term memory, and convolutional neural network models, the prediction errors are reduced by approximately 78.4%, 67.6%, and 59.3%, respectively. The results indicate that the proposed dynamic model structure is reliable and can accurately predict NOx concentrations at the outlet of the SCR system.


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Biography: Ma Ning(1992—), male, doctor, engineer, maningncepu@163.com.
Foundation items: The National Natural Science Foundation of China(No.71471060), the Natural Science Foundation of Hebei Province(No. E2018502111).
Citation: Ma Ning, Liu Lei, Yang Zhenyong, et al.Dynamic model for predicting nitrogen oxide concentration at outlet of selective catalytic reduction denitrification system based on kernel extreme learning machine[J].Journal of Southeast University(English Edition), 2022, 38(4):383-391.DOI:10.3969/j.issn.1003-7985.2022.04.007.
Last Update: 2022-12-20