Soft Sensors for Monitoring Combustion Quality and Flue Gas Emissions in Power Station Boilers

Abstract

This research work includes a combination of Fisher’s Linear Discriminant (FLD) analysis by merging Radial Basis Function (RBF) Network and Back Propagation Algorithm (BPA) for monitoring the combustion conditions of a coal fired boiler. The CCD Camera is used to capture the two dimensional flame images. The features such as images, average intensity, area, brightness and orientation etc., of the flame are extracted after pre-processing the images. The FLD is applied to reduce the n-dimensional feature size to 2 dimensional feature size for faster learning of the RBF network. Also video processing has been done to extract three classes of images corresponding to different burning conditions of the flames. For various flame conditions, the corresponding temperatures and flue gas emissions are obtained using analyzers and sensors. The combustion quality indicates the air/fuel ratio which can be varied automatically. The proposed feed forward control scheme presents an alternative for the existing set-up for measuring SOx, NO_x, CO and CO₂ emissions that are detected from the samples collected at regular intervals of time in the laboratory or by using gas analyzers. Further training and testing of Parallel architecture of Radial Basis Function and Back Propagation Algorithm (PRBFBPA) with the data obtained has been done and the performance of the algorithms is presented.