[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]
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NOx emission model for coal-fired boilersusing partial least squares and extreme learning machine(
)
基于偏最小二乘和超限学习机结合的电站锅炉NOx排放建模
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 35
- Issue:
- 2019 2
- Page:
- 179-184
- Research Field:
- Energy and Power Engineering
- Publishing date:
- 2019-06-30
Info
- Title:
- NOx emission model for coal-fired boilersusing partial least squares and extreme learning machine
- 基于偏最小二乘和超限学习机结合的电站锅炉NOx排放建模
- Author(s):
- 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
- PACS:
- TK22
- DOI:
- 10.3969/j.issn.1003-7985.2019.02.006
- Abstract:
- 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.
- 为了降低NOx排放量, 需要建立一个实时准确的NOx排放模型.提出了基于偏最小二乘(PLS)和超限学习机(ELM)相结合的PLS-ELM模型用于建立电站锅炉NOx排放模型.首先, 根据机理分析确定NOx排放模型的初始输入变量, 然后, 利用PLS对初始输入数据进行特征提取, 最后, 将提取后的信息作为ELM模型的输入.利用某1 000 MW电站锅炉分散控制系统(DCS)历史数据库中的现场运行数据对PLS-ELM模型进行训练和验证, 并将模型的性能与BP神经网络、SVM和ELM模型进行了对比.PLS-ELM模型对训练数据集和测试数据集的平均相对误差(MRE)分别为1.58%和1.69%.仿真结果表明:PLS-ELM模型的预测精度和模型的耗时均优于BP、SVM和ELM模型.
References:
[1] Molina A, Eddings E G, Pershing D W, et al. Char nitrogen conversion: Implications to emissions from coal-fired utility boilers[J]. Progress in Energy and Combustion Science, 2000, 26(4/5/6): 507-531. DOI:10.1016/s0360-1285(00)00010-1.
[2] Beloševi S V, Tomanovi I, Crnomarkovic N, et al. Modling and optimization of processes for clean and efficient pulverized coal combustion in utility boilers [J]. Thermal Science, 2016, 20(suppl.1): 183-196. DOI:10.2298/tsci150604223b.
[3] Luo Z X, Wang F, Zhou H C, et al. Principles of optimization of combustion by radiant energy signal and its application in a 660 MWe down- and coal-fired boiler[J]. Korean Journal of Chemical Engineering, 2011, 28(12): 2336-2343. DOI:10.1007/s11814-011-0098-1.
[4] Wei Z B, Li X L, Xu L J, et al. Comparative study of computational intelligence approaches for NOxx reduction of coal-fired boiler[J]. Energy, 2013, 55: 683-692. DOI:10.1016/j.energy.2013.04.007.
[5] Tan P, Zhang C, Xia J, et al. NOxx emission model for coal-fired boilers using principle component analysis and support vector regression [J]. Journal of Chemical Engineering of Japan, 2016, 49(2):211-216. DOI:10.1252/jcej.15we066.
[6] Wang L P. Support vector machines: Theory and applications [M] //Studies in Fuzziness and Soft Computing. New York: Springer-Verlag, 2005.
[7] Ilamathi P, Selladurai V, Balamurugan K, et al. ANN-GA approach for predictive modeling and optimization of NOxx emission in a tangentially fired boiler[J]. Clean Technologies and Environmental Policy, 2013, 15(1): 125-131. DOI:10.1007/s10098-012-0490-5.
[8] Lopes C, Perdigão F. Event detection by HMM, SVM and ANN: A comparative study[C]// Lecture Notes in Computer Science. Berlin, Heidelberg: Springer-Verlag, 2008. DOI:10.1007/978-3-540-85980-2_1.
[9] Li G Q, Niu P F, Duan X L, et al. Fast learning network: A novel artificial neural network with a fast learning speed[J]. Neural Computing and Applications, 2014, 24(7/8): 1683-1695. DOI:10.1007/s00521-013-1398-7.
[10] Singh K P, Ojha P, Malik A, et al. Partial least squares and artificial neural networks modeling for predicting chlorophenol removal from aqueous solution[J]. Chemometrics and Intelligent Laboratory Systems, 2009, 99(2): 150-160. DOI:10.1016/j.chemolab.2009.09.004.
[11] Ronen D, Sanders C F W, Tan H S, et al. Predictive dynamic modeling of key process variables in granulation processes using partial least squares approach[J]. Industrial & Engineering Chemistry Research, 2011, 50(3): 1419-1426. DOI:10.1021/ie100836w.
[12] Huang Z Y, Yu Y L, Gu J, et al. An efficient method for traffic sign recognition based on extreme learning machine[J]. IEEE Transactions on Cybernetics, 2017, 47(4): 920-933. DOI:10.1109/tcyb.2016.2533424.
[13] Baffi G, Martin E B, Morris A J. Non-linear projection to latent structures revisited(the neural network PLS algorithm)[J]. Computers & Chemical Engineering, 1999, 23(9): 1293-1307. DOI:10.1016/s0098-1354(99)00291-4.
[14] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1/2/3): 489-501. DOI:10.1016/j.neucom.2005.12.126.
[15] Smrekar J, Potoˇ/cnik P, Senegaˇ/cnik A. Multi-step-ahead prediction of NOx emissions for a coal-based boiler[J]. Applied Energy, 2013, 106: 89-99. DOI:10.1016/j.apenergy.2012.10.056.
Memo
- Memo:
-
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.