Problem
Research questions and friction points this paper is trying to address.
combustion efficiency
flare stacks
regulatory standards
hydrocarbons
remote industrial sites
AI-Powered Flare Combustion Efficiency Estimation
🤖 AI Summary
为解决传统设备昂贵且维护复杂的问题，提出使用轻量级视觉-语言编码器和多层感知机从低成本热视频中预测燃烧效率的方法。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
lightweight vision-language encoder
compact multi-layer perceptron
thermal video footage
graphical user interface
real-time combustion efficiency
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