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A Novel Neural Network Based Modeling for Control of NOx Emission in Power Plant

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Abstract:

A novel neural network based modeling for non-linear model identification technique is proposed. It combines a nonlinear steady state model with a linear one, to describe the disturbance and dynamics in the coal-fired power plant. The modeling and training algorithm is used to develop a model of nitrogen oxides (NOx) emitted from the process where one-step ahead optimal prediction formula are developed. Two cases show that the resulting model provides a better prediction of NOx and fitting capabilities.

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Periodical:

Applied Mechanics and Materials (Volume 643)

Pages:

385-390

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.643.385

Citation:

Cite this paper

Online since:

September 2014

Authors:

Ping Kang Li*, Rui Pan, Chen Chen

Keywords:

Modeling, Neural Network (NN), NOx Emission, Training Algorithm

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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References

[1] Zhou H, Cen K, Fan J. Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks [J]. Energy, 2004, 29(1): 167-183.

DOI: 10.1016/j.energy.2003.08.004

Google Scholar

[2] Zheng L G, Zhou H, Cen K F, et al. A comparative study of optimization algorithms for low NOx combustion modification at a coal-fired utility boiler [J]. Expert Systems with Applications, 2009, 36(2): 2780-2793.

DOI: 10.1016/j.eswa.2008.01.088

Google Scholar

[3] Iliyas S A, Elshafei M, Habib M A, et al. RBF neural network inferential sensor for process emission monitoring [J]. Control Engineering Practice, 2013, 21(7): 962-970.

DOI: 10.1016/j.conengprac.2013.01.007

Google Scholar

[4] Song Z, Kusiak A. Optimization of temporal processes: a model predictive control approach [J]. Evolutionary Computation, IEEE Transactions on, 2009, 13(1): 169-179.

DOI: 10.1109/tevc.2008.920680

Google Scholar

[5] Ting-Ting Yang, Ji-Zhen Liu, De-liang Zeng, Xie Xie. Application of data mining in boiler combustion optimization [C]. 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE): 2010, 225-228.

DOI: 10.1109/iccae.2010.5451473

Google Scholar

[6] Stephen Piche, Paul Sabiston. A Disturbance Rejection Based Neural Network Algorithm for Control of Air Pollution Emissions. Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2005, 2937-2941.

DOI: 10.1109/ijcnn.2005.1556392

Google Scholar

[7] Pavilion Technologies, Inc., Austin, TX(US). Method and apparatus for approximating gains in dynamic and steady-state processes for prediction, control, and optimization: US 7, 418, 301B2 [P]. (2008).

Google Scholar