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dc.description.abstractThe quality of combustion in power station boiler plays an important role in minimizing the flue gas emissions. In this research work, various intelligent schemes are proposed to infer the flue gas emissions by monitoring the flame colour at the furnace of the boiler. Limited work is reported in the literature regarding the flue gas emissions. Different schemes or algorithms have been developed by various researchers to infer the flue gas emissions like CO, CO2, and SOx etc. As a part of the present research work, integrated intelligent schemes are proposed to infer combustion quality as well as flue gas emissions based on the colour of the furnace flame. The work attempted involves capturing the flame video using an infrared camera. The flame video is then split up into the frames for further analysis. The Cannon video splitter is used for progressive extraction of the flame images from the video. The various features like average intensity, orientation, area of the high temperature flame, centroid, standard deviation, median, mode etc., were extracted. The conventional classification and clustering techniques include the Euclidean distance classifier (L2 norm classifier) Bayesian classifier and K-means clustering. The major contribution of this research work is to develop an indigenous online intelligent scheme for inferring the process parameters and gas emissions in the centralized control room directly from the combustion chamber of a boiler. Hence this method offers an economical intelligent feed forward scheme to minimize the flue gas emissions based on the colour of the furnace flame. Moreover in the existing setup, measurements of various flue gases are inferred from the samples that are collected periodically or by using gas analyzers (expensive and difficult to maintain). The proposed algorithm can be integrated with the Distributed Control System (DCS) that is used for automation of the power plant. The inferred parameters can be displayed in the centralized control room a (cost effective solution). newlineen_US
dc.format.extentxlii, 250en_US
dc.titleImage processing for flame monitoring in power station boilersen_US
dc.creator.researcherSujatha Ken_US
dc.subject.keywordImage processing, flame monitoring, power station boilers, K-means clustering, Distributed Control Systemen_US
dc.contributor.guidePappa, N.en_US
dc.publisher.universityAnna Universityen_US
dc.publisher.institutionFaculty of Information and Communication Engineeringen_US, August 2011en_US
dc.format.dimensions23.5 cm x 15 cmen_US
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificates.pdf617.76 kBAdobe PDFView/Open
03_abstract.pdf19.59 kBAdobe PDFView/Open
04_acknowledgement.pdf15.37 kBAdobe PDFView/Open
05_contents.pdf101.08 kBAdobe PDFView/Open
06_chapter 1.pdf581.42 kBAdobe PDFView/Open
07_chapter 2.pdf45.52 kBAdobe PDFView/Open
08_chapter 3.pdf789.16 kBAdobe PDFView/Open
09_chapter 4.pdf475.61 kBAdobe PDFView/Open
10_chapter 5.pdf230.76 kBAdobe PDFView/Open
11_chapter 6.pdf1.36 MBAdobe PDFView/Open
12_chapter 7.pdf2.4 MBAdobe PDFView/Open
13_chapter 8.pdf18.16 kBAdobe PDFView/Open
14_references.pdf36.91 kBAdobe PDFView/Open
15_publications.pdf16.8 kBAdobe PDFView/Open
16_vitae.pdf12.89 kBAdobe PDFView/Open

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