|Table of Contents|

[1] Dong Ze, Ma Ning, Li Changqing, et al. NOx emission model for coal-fired boilersusing partial least squares and extreme learning machine [J]. Journal of Southeast University (English Edition), 2019, 35 (2): 179-184. [doi:10.3969/j.issn.1003-7985.2019.02.006]

NOx emission model for coal-fired boilersusing partial least squares and extreme learning machine()

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

2019 2
Research Field:
Energy and Power Engineering
Publishing date:


NOx emission model for coal-fired boilersusing partial least squares and extreme learning machine
Dong Ze1 2 Ma Ning1 2 Li Changqing1
1Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China
2School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
NOx emission partial least squares extreme learning machine coal-fired boiler
To implement a real-time reduction in NOx, a rapid and accurate model is required. A PLS-ELM model based on the combination of partial least squares(PLS)and the extreme learning machine(ELM)for the establishment of the NOx emission model of utility boilers is proposed. First, the initial input variables of the NOx emission model are determined according to the mechanism analysis. Then, the initial input data is extracted by PLS. Finally, the extracted information is used as the input of the ELM model. A large amount of real data was obtained from the distributed control system(DCS)historical database of a 1 000 MW power plant boiler to train and validate the PLS-ELM model. The modeling performance of the PLS-ELM was compared with that of the back propagation(BP)neural network, support vector machine(SVM)and ELM models. The mean relative errors(MRE)of the PLS-ELM model were 1.58% for the training dataset and 1.69% for the testing dataset. The prediction precision of the PLS-ELM model is higher than those of the BP, SVM and ELM models. The consumption time of the PLS-ELM model is also shorter than that of the BP, SVM and ELM models.


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Biography: Dong Ze(1970—), male, doctor, professor, dongze33@126.com.
Foundation items: The National Natural Science Foundation of China(No.71471060), Natural Science Foundation of Hebei Province(No.E2018502111).
Citation: Dong Ze, Ma Ning, Li Changqing.NOx emission model for coal-fired boilers using partial least squares and extreme learning machine[J].Journal of Southeast University(English Edition), 2019, 35(2):179-184.DOI:10.3969/j.issn.1003-7985.2019.02.006.
Last Update: 2019-06-20