A Study on the Mathematic Model of Nox Emission in Coal-Fired Boiler with Opposed Combustion Pattern

Bao Ling Liu

doi:10.4028/www.scientific.net/AMR.403-408.8

Paper Titles

Preface, Organizing Committees and Sponsor

An Investigation of the Nano-Structure Titanum Dioxde Thin Films as a Function of Film Thickness
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A Study on the Mathematic Model of Nox Emission in Coal-Fired Boiler with Opposed Combustion Pattern
p.8

Comparison of Color, Texture and ICM Features in CBIR System
p.13

Effects of Holding Time and Temperature on Sr Content of Mg-Sr Master Alloys Produced by Metallothermic Reduction of SrO
p.20

GDA and ToF_SIMS of Plasma Carburizing Layer on Pure Titanium with Hydrogen-Free
p.24

Electrochemical Properties of FeNiMm Electrodes for Hydrogen Evolution in Alkaline Solutions by VIM Technique
p.28

Study on Fluid-Structure Interaction Performance of Shuttle Check Valves Based on ADINA
p.33

Electro-Magnetic Research of NEMS Beam and Cofferdam Structure with Porous Materials
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 403-408A Study on the Mathematic Model of Nox Emission in...

A Study on the Mathematic Model of Nox Emission in Coal-Fired Boiler with Opposed Combustion Pattern

Abstract:

The perfect mathematic model is critical to reseaech the NOx emission charateristic. In this paper, a new statistical learning algorithm SVM(support vector machine) was used to establish the model, based on the mechanism analysis of the NOx emission characteristics, and grid optimization method was applied to determine the model parameters. The model was tested on a 660MW power plant ,and the result indicated that SVM was a good tool for building NOx emission model and had better generalization ability and higher calculation speed comparing with BP modeling approaches.

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Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

8-12

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.403-408.8

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Online since:

November 2011

Authors:

Bao Ling Liu

Keywords:

Gridoptimization, NO_x Emission, Support Vector Machine (SVM)

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References

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