Convolutional Neural Network-Based Classification of Industrial Air Pollutant Sources from Stack Emission Images

Abstract

Accurate identification of industrial air pollutant sources is essential for effective emission control, environmental monitoring, and regulatory compliance in industrial regions. Conventional source identification techniques often rely on manual inspection and sensor-based measurements, which may be time-consuming and limited in handling complex emission patterns. This study presents a convolutional neural network (CNN)-based classification framework for identifying industrial air pollutant sources using stack emission images. The proposed approach integrates deep learning image analysis with environmental monitoring techniques to automatically classify emission characteristics from industrial exhaust stacks under varying atmospheric and operational conditions. A comprehensive image dataset containing emission plume patterns from multiple industrial sources was collected and preprocessed to train and validate the CNN model. Key image features related to plume texture, color distribution, opacity, dispersion behavior, and emission intensity were extracted through automated feature learning mechanisms. The developed CNN architecture was designed to distinguish between different categories of industrial pollutant sources, including thermal power plants, chemical manufacturing facilities, cement industries, and metallurgical operations. Performance evaluation demonstrated high classification accuracy, robust feature extraction capability, and improved adaptability compared to conventional image processing and machine learning approaches. The model also exhibited strong resistance to environmental variability such as lighting changes, atmospheric interference, and image noise during real-time monitoring conditions. Comparative analysis revealed significant improvements in source identification efficiency, operational responsiveness, and automated environmental surveillance capability.