Prediction Model for the Boiler NOx Emission with Material Properties Based on the Artificial Neural Network

Bao Min Sun; Ding Hui Wang; Bin Yang; Shou Heng Zhang; Ling Yu Kong

doi:10.4028/www.scientific.net/AMR.676.40

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 676Prediction Model for the Boiler NOx Emission with...

Prediction Model for the Boiler NOx Emission with Material Properties Based on the Artificial Neural Network

Abstract:

This paper establishes the prediction model for the NOx emission with Material Properties based on the artificial neural network,and predicts the NOx emission before and after the borler’s combustion reform .First, this paper analyzes the NOx formation mechanism. Then,this paper establishes the prediction model for the NOx emission with Material Properties based on the artificial neural network,which uses the main factors of influencing NOx formation as input variable. At last , this paper trains new samples again,and predicts the boiler NOx emission after the boiler’s low NOx combustion reform.This paper demonstrates that the model is effective for predicting boiler NOx emission before and after the boiler’s low NOx combustion reform.

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Periodical:

Advanced Materials Research (Volume 676)

Pages:

40-45

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.676.40

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Online since:

March 2013

Authors:

Bao Min Sun, Ding Hui Wang, Bin Yang, Shou Heng Zhang, Ling Yu Kong

Keywords:

Artificial Neural Network (ANN), Borler, Combustion Reform, Emission Reduction, Energy Conservation, NO_x Emission

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References

[1] Gao Xiaotao,Gao Suiqiang,Zhang Enxian. Combustion optimization to reduce the Boiler NOx emissions study. Boiler technology. Section 34 (3).

Google Scholar

[2] Zhu Baosan. Coal-fired boiler air pollutant purification technical manuals [M].Beijing: China Electric Power Press, 2006:227-235.

Google Scholar

[3] Cen Kefa,Yao Qiang,Luo Zhongyan. Combustion Theory and contamination control[M]．Beijing: Machinery Industry Press,2004:447-452.

Google Scholar

[4] Fan Guiquangui. Boiler Principle[M].Beijing: China Electric Power Press, 2008.

Google Scholar

[5] Cen Kefa,Zhou Hao,Chi Zuohe. Large boiler safety and optimal operation of technology[M].Beijing: China Electric Power Press, 2003:571-589.

Google Scholar

[6] Wang Xuedong,Luantao,Cheng Ling.Experimental Study of the Effect of Boiler Combustion Control on NOx Emission and Boiler Efficiency[J]. Journal of Power Engineering, 2008, 28(1):19-23.

Google Scholar

[7] Han Liqun. Artificial neural network theory, design and application[M].Beijing: Chemical Industry Press.2002.

Google Scholar