AI predicts flare stack combustion efficiency using thermal video
AI-Powered Flare Combustion Efficiency Estimation
Artificial IntelligenceComputer Vision and Pattern Recognition
Summary
Burning hydrocarbons in flare stacks needs to be efficient to meet regulations and reduce pollution. The authors created an AI system that uses thermal videos, which are cheaper and easier to get than traditional instruments, to estimate how well the flare burns. Their system displays this information in real time with easy-to-understand visuals and needed very little maintenance over six months. This approach helps industries monitor flares safely and more affordably.
What this means in practice
- •For industrial site operators: Monitor flare combustion efficiency in real time using low-cost thermal video instead of expensive sensors, enabling easier regulatory compliance at remote or budget-limited locations.
- •For environmental monitoring teams: Access continuous combustion efficiency data through video overlays and trend summaries to better assess hydrocarbon emissions from flare stacks over time.
Authors
Afeefa Azam, Iyyakutti Iyappan Ganapathi, Fares Ossama Abdelhafez, Divya Velayudhan, Maregu Assefa Habtie, Hamad Karki, Khalid Yousef Al Awadhi, Naoufel Werghi
Abstract
Achieving high combustion efficiency in flare stacks is crucial for adhering to regulatory standards and controlling the release of hydrocarbons into the environment. Traditional instruments like gas analyzers and hyperspectral cameras are expensive, fragile, and require frequent calibration, which makes them impractical for remote or budget constrained industrial sites. We propose an innovative solution that combines a lightweight vision-language encoder with a compact multi-layer perceptron to predict combustion efficiency directly from low-cost thermal video footage. The fully trained model is integrated into an easy-to-deploy graphical user interface. This interface overlays predicted combustion efficiency values on each video frame, displays real-time trends in combustion efficiency, shows the distribution of combustion efficiency across all frames in the video, and allows users to export CSV reports. Over a six-month period, the system achieved 99% uptime and required less than 15 minutes of maintenance per week.