Monitoring Power Station Boilers Using ANN and Image Processing

Abstract

This project deals with the monitoring the combustion quality of the power station boilers using Artificial Intelligence for improvement in the combustion quality in the power station boiler. The colour of the flame indicates whether the combustion taking place is complete, partial or incomplete. When complete combustion takes place the flue gases released are within the permissible limits otherwise its level is high which is out of limit. By analyzing the flame color which is captured using infrared camera and displayed on CCTV the quality of combustion is estimated. If combustion is partial or incomplete the flue gases released will create air pollution. So this work includes enhancement in the quality of combustion, saving of energy as well as check on the pollution level. The features are extracted from the flame images such as average intensity, area, brightness and orientation are obtained after preprocessing. Three classes of images corresponding to different burning conditions are taken from continuous video. Further training, testing and validation with the data collected have been carried out and performance of the various intelligent algorithms is presented.

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

“…Precision is defined as the ratio of number of all the images retrieved to number of all the relevant images retrieved. Recall is defined as the ratio of number of all the relevant images retrieved to the number of all the images in each group (Sujatha, 2013).…”