Internet of things for flame monitoring power station boilers

Analysis of combustion quality of flame images of thermal and gas turbine power plants is of great importance. In the domain of image processing the detection, recognition and understanding is the foundation for identifying the combustion condition. Soft sensors are the state of the art. So the flame temperature based on subsequent combustion quality estimation is done using Back Propagation Algorithm (BPA) and Ant Colony Optimization (ACO). The basic idea utilizes the colour information from the flame images. The first step is to define a feature vector. The 9 feature elements, from the samples of 51 flame images are used to train and test the model. Experiments prove this method to be effective. The classification of flame images based on combustion quality is dependent on the flame temperature and colour. The solution includes the Internet of Things (IoT). The intelligent sensors are embedded in the computing system to monitor the combustion quality and flame temperature. This flexible and dispensable form of environment needs continuous monitoring, controlling and behavior analysis in power plants. The prototype implementation consists of Arduino UNO board, intelligent sensors with Arduino hardware support package. The implementation is tested for monitoring the combustion quality and its subsequent flame temperature to provide a feed control for combustion quality monitoring and to make the environment smart.