Article: Automation of combustion monitoring in boilers using discriminant radial basis network Journal: International Journal of Artificial Intelligence and Soft Computing (IJAISC) 2013 Vol.3 No.3 pp.257 - 275 Abstract: This research work aims at monitoring and control of the combustion quality in a power station coal fired boiler using a combination of Fisher's linear discriminant (FLD) analysis and radial basis network (RBN). The flame video is acquired with CCD camera. The features of the flame images like average intensity, area of the flame, brightness of the flame, orientation of the flame, etc. are extracted from the preprocessed images. The FLD is applied to reduce the n-dimensional feature size to two-dimensional feature size for faster learning by the RBN. The results of the proposed technique are compared with the conventional Euclidean distance classifier (EDC), which is also used to find the distance between the three groups of images. Three groups of images corresponding to different combustion conditions of the flames have been extracted from a continuous video. The corresponding temperatures and the carbon monoxide (CO) in the flue gas have been obtained through measurements. Training and testing of Fisher's linear discriminant radial basis network (FLDRBN) with the data collected have been done and the performances of the various algorithms are evaluated. Inderscience Publishers - linking academia, business and industry through research

Title: Automation of combustion monitoring in boilers using discriminant radial basis network

Authors: K. Sujatha; N. Pappa; U. Siddharth Nambi; K. Senthil Kumar; C.R. Raja Dinakaran

Addresses: Department of EEE/E&I, Dr. M.G.R. Educational and Research Institute, Chennai-95, India ' Department of Instrumentation Engineering, M.I.T., Chromepet, Chennai-44, India ' Department of EEE/E&I, Dr. M.G.R. Educational and Research Institute, Chennai-95, India ' Department of EEE/E&I, Dr. M.G.R. Educational and Research Institute, Chennai-95, India ' Shriram Value Services Pvt Ltd., T. Nagar, Chennai, India

Abstract: This research work aims at monitoring and control of the combustion quality in a power station coal fired boiler using a combination of Fisher's linear discriminant (FLD) analysis and radial basis network (RBN). The flame video is acquired with CCD camera. The features of the flame images like average intensity, area of the flame, brightness of the flame, orientation of the flame, etc. are extracted from the preprocessed images. The FLD is applied to reduce the n-dimensional feature size to two-dimensional feature size for faster learning by the RBN. The results of the proposed technique are compared with the conventional Euclidean distance classifier (EDC), which is also used to find the distance between the three groups of images. Three groups of images corresponding to different combustion conditions of the flames have been extracted from a continuous video. The corresponding temperatures and the carbon monoxide (CO) in the flue gas have been obtained through measurements. Training and testing of Fisher's linear discriminant radial basis network (FLDRBN) with the data collected have been done and the performances of the various algorithms are evaluated.

Keywords: flame images; radial basis networks; RBN; neural networks; linear discriminant analysis; LDA; Euclidean distance classifier; EDC; temperature monitoring; combustion quality; Image J; quality monitoring; quality control; combustion quality; power stations; coal fired boilers; feature extraction; carbon monoxide; flue gas; flame video.

DOI: 10.1504/IJAISC.2013.053406

International Journal of Artificial Intelligence and Soft Computing, 2013 Vol.3 No.3, pp.257 - 275

Accepted: 08 Jan 2013
Published online: 12 Jul 2014 *

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Title: Automation of combustion monitoring in boilers using discriminant radial basis network

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