发表机构
Khalifa University of Science and Technology; ADNOC（哈利法科学技术大学; 阿布扎比国家石油公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种结合轻量级视觉-语言编码器与多层感知器的AI方案，从低成本热视频直接预测火炬燃烧效率，集成于易部署界面，实现99%正常运行时间。
AI 中文摘要
在火炬烟囱中实现高燃烧效率对于遵守监管标准和控制碳氢化合物释放到环境中至关重要。传统仪器如气体分析仪和高光谱相机价格昂贵、易碎且需要频繁校准，这使得它们对于偏远或预算受限的工业现场不切实际。我们提出了一种创新解决方案，将轻量级视觉-语言编码器与紧凑的多层感知器相结合，直接从低成本热视频片段中预测燃烧效率。完全训练好的模型被集成到一个易于部署的图形用户界面中。该界面在每个视频帧上叠加预测的燃烧效率值，显示燃烧效率的实时趋势，展示视频中所有帧的燃烧效率分布，并允许用户导出CSV报告。在六个月的期间内，该系统实现了99%的正常运行时间，并且每周维护时间少于15分钟。
英文摘要
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.
CommentsAccepted at the 4th International Conference on Machine Learning and Data Engineering (ICMLDE 2025). 5 pages
