Monitoring of SO 2 emissions in power plants using Internet of Things

TLDR: Virtual sensor utilizing single layer perceptron up skilled by Back Propagation Algorithm and Ant Colony Optimization for blaze examination incorporates the Industrial Internet of Things (IIoT) where the intelligent sensors form the embedded computing system to screen the changeability in parameters relating to the flame colour.

Abstract: Combustion quality in power stations plays an important role in minimizing the flue gas emissions. Complete combustion occurs when all the energy in the fuel being burnt is extracted and none of the carbon and hydrogen compounds are left unburnt. Complete combustion will occur when proper amounts of fuel and air are mixed in correct proportion under the appropriate conditions of temperature. The Monitoring process is accomplished by capturing the image of the flame with a camera and processing the image in a laptop with MATLAB code. Estimation of Sulphur Dioxide (SO2) emissions from combustion images in thermic and gas turbine control plants is of immense significance in the field of image processing and the prime aim is the presentation, distinguishing proof and understanding of the start conditions. In this effort, virtual sensor utilizing single layer perceptron up skilled by Back Propagation Algorithm (BPA) and Ant Colony Optimization (ACO) for blaze examination. The arrangement incorporates the Industrial Internet of Things (IIoT) where the intelligent sensors form the embedded computing system to screen the changeability in parameters relating to the flame colour.