Impact of Green House Gases from Thermal Power Plants

Abstract

Scrutiny of combustion quality and its equivalent NO_x emissions from flame images in thermal and gas turbine power plants is of immense significance in the realm of climate change. A remote monitoring scheme using image processing, Artificial Intelligence (AI) and Internet of Things (IoT) to efficiently minimize the flue gas emissions can be carried out. The principal goal is in detection, recognition and understanding of combustion conditions in power plants ensuring low green house or flue gas emissions which contribute to climate change. In this work, smart sensors using feed forward neural network with Ant Colony Optimisation (ACO) and Particle Swarm Optimization (PSO) are used for estimation of various flue emissions. This scheme uses the information from the colour of the flame images in the combustion chamber at power plants, which is the foundation for obtaining high combustion quality and low flue gas emissions. The initial gait is to describe a facet vector for each flame image including 10 feature elements. Image Enhancement is done to obtain distinctive attributes from the captured images. The perception of object (flame feature) recognition and classification of the flame image is conceded out to measure the combustion quality and flue gas emissions from the flame colour. The samples including some flame images, parts of which are used to train and test the model. Finally, the entire samples are recognized and classified. Experiments prove that flame image classification to be an effective monitoring scheme for reducing the flue gas emissions.