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Smart Methane Emission Detection System Development
Project Number
DE-FE0029020
Last Reviewed Dated
Goal

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 focusing 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.

Near Real-Time Detection

The time between acquiring data and obtaining an output from the system is only a few seconds.

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.

Performer

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

Background

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 four key critical criteria for effective methane emission mitigation:

  • Autonomy (no need for a human to be in the loop)
  • High-reliability (low false alarm rates)
  • Real-time performance (ability to detect emissions within a few minutes once it starts)
  • Extensibility to multiple platforms (stationary, mobile, ground, aerial)

This project focuses on these four key aspects and significantly advances the state-of-the-art in methane emission detection using optically based sensing technologies.

Impact

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 is expected to produce the following outcomes and/or impacts:

  • Develop a system to reliably, accurately, and autonomously identify methane leaks at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions
  • Add a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility
  • Assist in the quantification process by providing a means of collecting temporal and spatial image data of a leak event
  • Reduce operational costs of emissions detection technologies by significantly minimizing the need for operator involvement
  • Provide a solution that is scalable, cost-effective, and non-intrusive
  • Reduce methane emissions through early real-time, autonomous detection of methane leaks
Accomplishments (most recent listed first)
  • The SLED/M system is going through several adaptations in order to enable it to detect and quantify fugitive methane emissions.
  • One (1) full week of data collection took place, recording methane emissions with known leak rates, and concentration profiles using the FLIR G300a and Heath Consultants EyeCGas 2.0, in conjunction with LwIR, LiDAR, Visible and methane concentration sensor for ground truth.
  • SLED/M Phase 3 Algorithm delivered.
  • The SLED/M model is going through several adaptations in order to enable it to detect methane from a drone platform.
  • The SLED/M pipeline is going through adaptations to enable enhanced real-time performance from an embedded platform.
  • Several data collection events took place using a DJI Matrice 600 as the drone platform and the FLIR G300a and the Sierra Olympic Ventus Optical Gas Imagers (OGIs).
  • Planning started for the integration and deployment of SLED/M onto a drone platform.
  • SLED/M Phase 2 Algorithm refined and delivered.
  • Field testing is being conducted.
  • Data has been collected for the final round of testing in order to establish the limits of the SLED/M detection capabilities.
  • The SLED/M system went through initial testing and successfully detected methane leaks from the live Mid-Wave Infrared (MWIR) camera stream in real-time.
  • The embedded detection algorithm (running on an NVIDIA Tegra TX1 chip) was successfully integrated with the real-time MWIR camera stream.
  • The algorithm passed all initial laboratory validation tests with target performance measures.
  • The embedded system was verified to drive the cameras at high framerate and execute the detection algorithm in real-time (~1Hz).
  • Sensor (cameras) setup, configuration, and calibration was completed
  • Initial test data were labeled for classifier training. An initial convolutional neural network (CNN) was configured for training.
  • The following equipment for the budget period was ordered and received: MWIR camera from IRCameras, MWIR camera from Falcon Inspection, and a thermal camera (SwRI in-kind).
  • Tests and data acquisition for budget period 1 are underway.
  • Feature extraction and analysis tasks were completed.
  • Development of initial leaks and non-leak events detection algorithm was completed.
Current Status

Various testing events using the FLIR G300a and the partner OGIs have been conducted over a one week period in which a variety of leak events (of various sizes and flow) rates were simulated. The SLED/M model is being overhauled to allow for rapid detection and subsequent quantification of a detected methane emission. The collected data has been curated and labeled, and the initial model investigation is ongoing. 

Project Start
Project End
DOE Contribution

$1,811,107

Performer Contribution

$453,057

Contact Information

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)

Additional Information

Smart Methane Emission Detection System Development (Oct 2020)
Presented by Heath Spidle, Southwest Research Institute, Natural Gas Infrastructure Project Review Meeting, October 28, 2020