The goal of this project is to develop an autonomous, real-time methane leak detection technology, the Smart Methane Emission Detection System (SLED/M), which applies machine learning techniques to passive optical sensing modalities to mitigate emissions through early detection. Phase 1 leveraged previous Southwest Research Institute (SWRI) research and experience to develop the prototype methane detection system with integrated optical sensors and the embedded processing unit. Phase 2 focused on integration and field-testing of the prototype system along with a demonstration to the Department of Energy (DOE) within a representative controlled environment. Phase 3 focused on adapting the system developed under previous phases for use on a mobile aerial drone platform. Phase 4 is focused on quantifying detected methane and building a commercialization pathway. The system will target the following key features:
Table 1. Key Features of the Smart Methane Emission Detection System
Feature | Details |
Low False Alarm Rates |
Less than 2% (number of events incorrectly classified as leaks). |
Autonomous Detection |
No need for a human to be in the loop – the system acquires, processes, and makes autonomous decisions on whether or not a hazardous substance was observed, using machine learning algorithms with 96% precision. |
Near Real-Time Detection |
The time between acquiring data and obtaining an output from the system is only a few seconds. |
Quantification |
System provides a quantification estimation of detected methane leaks within 15% error, allowing for stratification of repairs and operational considerations |
Non-Intrusive, Passive Technology |
No need to retrofit existing equipment and facilities. The proposed technology is passive in nature, thus eliminating safety and operational restrictions. |
Target Platform |
Ability to deploy technology in a stationary platform for monitoring facilities such as refineries and pump stations. Ability to deploy technology to a mobile drone platform for monitoring distributed facilities such as refineries, pump stations, and storage. |
Extensibility |
Ability to nimbly integrate new detection techniques into the system for other types of target substances. |
SwRI, San Antonio, TX 78238
Falcon Inspection (camera in-kind contributor)
IRCameras (camera in-kind contributor)
FLIR Systems, Inc. (camera in-kind contributor)
Sierra-Olympic Tech. Inc. (camera in-kind contributor) – Phase 3 and 4
Heath Consultants (camera in-kind contributor) – Phase 4
While much has been accomplished in developing optical sensors for imaging methane leaks, limited work has been accomplished in developing methane emission detection technologies that meet five key critical criteria for effective methane emission mitigation:
This project focuses on these five key aspects and significantly advances the state-of-the-art in methane emission detection using optically based sensing technologies.
An autonomous, real-time methane leak detection system facilitates the early detection of emissions before they become a larger problem. Compressor station operators will be able to identify failing equipment in aging infrastructure and replace faulty components expediently, resulting in methane emissions being reduced significantly through early detection of non-compliant equipment. By adding the capability to estimate leak flow rates in conjunction with visual inspections, operators will be able to identify and stratify which components to replace first. This project produced the following outcomes and/or impacts:
The final report for this project can be viewed at https://www.osti.gov/.
$1,811,107
$453,057
NETL – Joseph B. Renk III (joseph.renk@netl.doe.gov or 412-386-6406)
SwRI – Heath Spidle (heath.spidle@swri.org or 210-522-6717)
Smart Methane Emission Detection System Development (Oct 2020)
Presented by Heath Spidle, Southwest Research Institute, Natural Gas Infrastructure Project Review Meeting, October 28, 2020