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首页> 外文期刊>Environmental Science & Technology >Are Optical Gas Imaging Technologies Effective For Methane Leak Detection?
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Are Optical Gas Imaging Technologies Effective For Methane Leak Detection?

机译：光学气体成像技术对甲烷泄漏检测有效吗？

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摘要

Concerns over mitigating methane leakage from the natural gas system have become ever more prominent in recent years. Recently, the U.S. Environmental Protection Agency proposed regulations requiring use of optical gas imaging (OGI) technologies to identify and repair leaks. In this work, we develop an open-source predictive model to accurately simulate the most common OGI technology, passive infrared (IR) imaging. The model accurately reproduces IR images of controlled methane release field experiments as well as reported minimum detection limits. We show that imaging distance is the most important parameter affecting IR detection effectiveness. In a simulated well-site, over 80% of emissions can be detected from an imaging distance of 10 m. Also, the presence of "superemitters" greatly enhance the effectiveness of IR leak detection. The minimum detectable limits of this technology can be used to selectively target "superemitters" thereby providing a method for approximate leak-rate quantification. In addition, model results show that imaging backdrop controls IR imaging effectiveness: land-based detection against sky or low-emissivity backgrounds have higher detection efficiency compared to aerial measurements. Finally； we show that minimum IR detection thresholds can be significantly lower for gas compositions that include a significant fraction nonmethane hydrocarbons.
机译：近年来，对减轻天然气系统中甲烷泄漏的关注变得越来越突出。最近，美国环境保护署提出了法规，要求使用光学气体成像（OGI）技术来识别和修复泄漏。在这项工作中，我们开发了一个开源预测模型来准确模拟最常见的OGI技术，即被动红外（IR）成像。该模型可精确再现受控甲烷释放​​场实验的IR图像以及报告的最低检测限。我们表明，成像距离是影响红外检测效率的最重要参数。在模拟的井场中，可以从10 m的成像距离检测到超过80％的排放。同样，“超级发射极”的存在大大提高了红外泄漏检测的效率。该技术的最低可检测极限可用于选择性地靶向“超级发射极”，从而提供一种近似泄漏率定量的方法。此外，模型结果表明，成像背景控制了红外成像的有效性：与空中测量相比，针对天空或低发射率背景的陆基探测具有更高的探测效率。最后，我们表明，对于包含大量非甲烷碳氢化合物的气体成分，最低的红外检测阈值可能会大大降低。

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  • 来源
    《Environmental Science & Technology》 |2017年第1期|718-724|共7页
  • 作者

    Arvind P. Ravikumar; Jingfin Wang; Adam R. Brandt;

  • 作者单位

    Department of Energy Resources Engineering, Stanford University, 367 Panama Street, Stanford, California 94305, United States;

    Department of Energy Resources Engineering, Stanford University, 367 Panama Street, Stanford, California 94305, United States;

    Department of Energy Resources Engineering, Stanford University, 367 Panama Street, Stanford, California 94305, United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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