• HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
Saturday, February 27, 2021
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
  • HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News Chemistry

Sensors driven by machine learning sniff-out gas leaks fast

Bioengineer by Bioengineer
October 29, 2020
in Chemistry
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

ALFaLDS works on large oil& gas infrastructure, can help cut methane emissions by 90%

IMAGE

Credit: Los Alamos National Laboratory

LOS ALAMOS, N.M., October 28, 2020–A new study confirms the success of a natural-gas leak-detection tool pioneered by Los Alamos National Laboratory scientists that uses sensors and machine learning to locate leak points at oil and gas fields, promising new automatic, affordable sampling across vast natural gas infrastructure.

“Our automated leak location system finds gas leaks fast, including small ones from failing infrastructure, and lowers cost as current methods to fix gas leaks are labor intensive, expensive and slow,” said Manvendra Dubey, the lead Los Alamos National Laboratory scientist and coauthor of the new study. “Our sensors outperformed competing techniques in sensitivity to detecting methane and ethane. In addition, our neural network can be coupled to any sensor, which makes our tool very powerful and will enable market penetration.”

The Autonomous, Low-cost, Fast Leak Detection System (ALFaLDS) was developed to discover accidental releases of methane, a potent greenhouse gas, and won a 2019 R&D 100 award. ALFaLDS detects, locates and quantifies a natural gas leak based on real-time methane and ethane (in natural gas) and atmospheric wind measurements that are analyzed by a machine-learning code trained to locate leaks. The code is trained using Los Alamos National Laboratory’s high resolution plume dispersion models and the training is finessed on-site by controlled releases.

Test results using blind releases at an oil and gas well-pad facility at Colorado State University in Fort Collins, Colorado, demonstrated that the ALFaLDS locates the engineered methane leaks precisely and quantifies their size. This novel capability for locating leaks with high skill, speed and accuracy at lower cost promises new automatic, affordable sampling of fugitive gas leaks at well pads and oil and gas fields, the paper in the journal Atmospheric Environment: X concludes.

ALFaLDS’s success in locating and quantifying fugitive methane leaks at natural gas facilities could lead to a 90 percent reduction in methane emissions if implemented by the industry.

ALFaLDS used a small sensor, which makes it ideal for deployment on cars and drones. The Los Alamos team is developing the sensors that were integrated with a mini 3D sonic anemometer and the powerful machine-learning code in these studies.

However, the code is autonomous and can read data from any gas and wind sensors to help find leaks fast and minimize fugitive emissions from the vast network of natural gas extraction, production and consumption.

With this integration, ALFaLDS offers a revolutionary approach for oil and gas service providers in leak detection, to non-profit organizations surveying the issue, and to national laboratories and academia researching natural gas production.

###

Paper: “Neural networks to locate and quantify fugitive natural gas leaks for a MIR detection system,” Bryan Travis, Manvendra Dubey, and Jeremy Sauer, Atmospheric Environment: X, 2020, https://doi.org/10.1016/j.aeaoa.2020.100092

Funding: ALFaLDS was sponsored by the Department of Energy Advanced Research Projects Agency-Energy. Aeris Technologies and Rice University collaborated on the initial project that won the 2019 R&D 100 award from “R&D Magazine.”

About Los Alamos National Laboratory

Los Alamos National Laboratory, a multidisciplinary research institution engaged in strategic science on behalf of national security, is managed by Triad, a public service oriented, national security science organization equally owned by its three founding members: Battelle Memorial Institute (Battelle), the Texas A&M University System (TAMUS), and the Regents of the University of California (UC) for the Department of Energy’s National Nuclear Security Administration.

Los Alamos enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.

LA-UR-20-28626

Media Contact
Charles Poling
[email protected]

Original Source

https://www.lanl.gov/discover/news-release-archive/2020/October/1028-gas-leak-sensor.php

Related Journal Article

http://dx.doi.org/10.1016/j.aeaoa.2020.100092

Tags: Atmospheric ScienceChemistry/Physics/Materials SciencesTechnology/Engineering/Computer Science
Share12Tweet7Share2ShareShareShare1

Related Posts

IMAGE

C-Path and Global Partners launch Ataxia Consortium

February 26, 2021
IMAGE

Quantum quirk yields giant magnetic effect, where none should exist

February 26, 2021

Meteorites remember conditions of stellar explosions

February 26, 2021

How photoblueing disturbs microscopy

February 26, 2021

Leave a Reply Cancel reply

Your email address will not be published.

This site uses Akismet to reduce spam. Learn how your comment data is processed.

POPULAR NEWS

  • IMAGE

    Terahertz accelerates beyond 5G towards 6G

    638 shares
    Share 255 Tweet 160
  • People living with HIV face premature heart disease and barriers to care

    82 shares
    Share 33 Tweet 21
  • Global analysis suggests COVID-19 is seasonal

    38 shares
    Share 15 Tweet 10
  • HIV: an innovative therapeutic breakthrough to optimize the immune system

    35 shares
    Share 14 Tweet 9

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Tags

Technology/Engineering/Computer ScienceMedicine/HealthcancerInfectious/Emerging DiseasesEcology/EnvironmentMaterialsCell BiologyClimate ChangeBiologyGeneticsPublic HealthChemistry/Physics/Materials Sciences

Recent Posts

  • Predicts the onset of Alzheimer’s Disease (AD) using deep learning-based Splice-AI
  • When foams collapse (and when they don’t)
  • UTA researcher explores effects of trauma at the cellular, tissue levels of the brain
  • Picture books can boost physical activity for youth with autism
  • Contact Us

© 2019 Bioengineer.org - Biotechnology news by Science Magazine - Scienmag.

No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

© 2019 Bioengineer.org - Biotechnology news by Science Magazine - Scienmag.

Welcome Back!

Login to your account below

Forgotten Password?

Create New Account!

Fill the forms below to register

All fields are required. Log In

Retrieve your password

Please enter your username or email address to reset your password.

Log In