NDLI: Temperature and combustion quality control in power station boilers using artificial neural networks

Content Provider	ACM Digital Library
Author	Pappa, N. Sujatha, K.
Abstract	The classification is an important domain in boiler flame image processing and is a preliminary step toward detection, recognition and understanding of combustion condition. In this paper, Back Propagation Algorithm (BPA) is introduced for boiler flame image classification. Firstly, we define a feature vector for each flame image including 7 feature elements, which are the brightness of flame, the area of the high temperature flame, the brightness of high temperature flame, the rate of area of the high temperature flame, the flame centroid respectively. The classification and recognition of the flame image is done to measure the temperature from the flame colour and the flue gas emissions from the flame colour. The samples including 102 flame images, parts of which are used to train and test the model. Finally, the entire samples are recognized and classified. Experiments prove this method is effective for classification of boiler flame images.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781450301947
DOI	10.1145/1858378.1858382
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-09-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Temperature identification Image processing Combustion quality Features Back propagation algorithm Flame colour Fisher's linear discriminant analysis
Content Type	Text
Resource Type	Article

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Temperature and combustion quality control in power station boilers using artificial neural networks