Deep-Learning-Based Intelligent Combustion System for Industry Boilers 

Industrial boilers face inefficiencies, unstable temperatures, and strict emissions rules, hindering safe operations. This project introduces an AI-driven system integrating multimodal sensing, CNN/CRNN for flame state recognition/prediction, LSTM for load forecasting, PSO-optimized fuzzy NN control, and distributed optimization for real-time air-fuel adjustments. A custom flue-gas analyzer ensures continuous O2/CO monitoring. Benefits include a 20% reduction in emission, lower coal use, stablilized clinker quality, and a 2.5-year payback period, enabling cleaner and more efficient boiler performance.