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OKIsItJustMe

(19,938 posts)
Tue Feb 7, 2017, 07:48 PM Feb 2017

Low-Cost Imaging System Detects Natural Gas Leaks in Real Time

http://www.osa.org/en-us/about_osa/newsroom/news_releases/2017/low-cost_imaging_system_detects_natural_gas_leaks/
6 February 2017

[center][font face=Serif][font size=5]Low-Cost Imaging System Detects Natural Gas Leaks in Real Time[/font]

[font size=4] Infrared device enables reliable monitoring under a range of environmental conditions[/font][/center]

[font size=3] WASHINGTON — Researchers have developed an infrared imaging system that could one day offer low-cost, real-time detection of methane gas leaks in pipelines and at oil and gas facilities. Leaks of methane, the primary component of natural gas, can be costly and dangerous while also contributing to climate change as a greenhouse gas.

“Despite methane gas being invisible to the eye, we have developed a method of color-coding this gas information and overlaying it onto a conventional camera image,” said Graham M. Gibson from the University of Glasgow, Scotland, who led the technical work. “This allows the user operating the camera to look around, identify things and see an overlay of where the gas is present.”

Gibson, along with the rest of the research team, worked with M Squared Lasers to develop the real-time infrared imaging system. In The Optical Society journal Optics Express, the researchers show that the system can acquire videos of methane gas leaking from a tube at about 0.2 liters per minute. The technology could also be expanded to other wavelengths or ranges of wavelengths, allowing the detection of a host of gases and chemicals.



The system performs hyperspectral imaging by projecting a series of infrared light patterns onto the scene using a laser wavelength that is absorbed by methane. These patterns are created with a laser and tiny device with hundreds of thousands of moving mirrors, known as a digital micromirror device. The system reconstructs an image showing areas where methane has absorbed the light by detecting the light that scatters off the scene and computationally comparing it to the original projected patterns.

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https://doi.org/10.1364/OE.25.002998
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