DocumentCode :
603435
Title :
Support Vector Machines Applied to a Combustion Process
Author :
Torres, C.I. ; Hernandez, F. ; Trejo, A. ; Ronquillo, G.
Author_Institution :
Appl. Res. Manage., Centro de Ing. y Desarrollo Ind. (CIDESI), Queretaro, Mexico
fYear :
2012
fDate :
19-23 Nov. 2012
Firstpage :
176
Lastpage :
181
Abstract :
The following research aims to make the characterization of flames in the combustion process in an industrial boiler fossil fuel composed of one burner. The characterization of the flames is performed by analysis of electrical signals that are obtained through a flame detection sensor that measure the electromagnetic spectrum of the flame in the boiler as well as the acquisition of other variables involved in the combustion as are excess oxygen (%), flow, temperature and fuel density. After getting through a data acquisition system of the electromagnetic spectrum of the flame and the variables involved in combustion, it performs the signal processing of the spectrum by obtaining statistical moments and principal component analysis (PCA) to extract the most important characteristics. So get the patterns for training the support vector machine (SVM). After conducting the training of SVM was that the patterns obtained are suitable for proper classification of flames in a combustion process of a boiler, as the previously trained classifier has a high percentage of performance.
Keywords :
boilers; combustion; combustion equipment; electromagnetic waves; flames; fossil fuels; pattern classification; principal component analysis; sensors; signal processing; support vector machines; PCA; SVM; boiler; burner; classifier training; combustion process; data acquisition system; electrical signal analysis; electromagnetic spectrum; excess oxygen; flame characterization; flame detection sensor; flow; fuel density; industrial boiler fossil fuel; principal component analysis; signal processing; statistical moments; support vector machines; temperature; Combustion; electromagnetic radiation; principal components analysis; statistical moments; support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronics, Robotics and Automotive Mechanics Conference (CERMA), 2012 IEEE Ninth
Conference_Location :
Cuernavaca
Print_ISBN :
978-1-4673-5096-9
Type :
conf
DOI :
10.1109/CERMA.2012.36
Filename :
6524575
Link To Document :
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