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Solid State Mixed-Potential Electrochemical Sensors for Natural Gas Leak Detection and Quality Control
Project Number
DE-FE0031864
Last Reviewed Dated
Goal

The goal of this project is to develop low-cost sensing systems, based on mixed-potential electrochemical devices, to sense and quantify the presence of natural gas as an early warning system for pipeline leakages that contribute to loss of product and air pollution. The miniature solid state sensors are ideally suited to remote operation and widescale deployment on drones and autonomous vehicles. The project will include the development of sensing elements specifically suited for detection of natural gas components and contaminants. The performer will employ machine learning techniques to train sensing systems to quantify the concentration of natural gas species, distinguish between natural gas at different parts of the processing pipeline, and distinguish natural gas from natural and man-made interfering sources such as wetlands and agriculture. In collaboration with their subcontractor (SensorComm Technologies, Inc. (SCT), the research team will develop portable computing hardware to develop systems that can be deployed in the field with the aim of performing a field test at Colorado State University’s METEC facility.

Performer(s)

University of New Mexico - Albuquerque, NM 87131
Subrecipient:
SensorComm Technologies Inc. (SCT) - Albuquerque, NM 87131

Background

Natural gas is one of the largest energy resources in the United States with over 300,000 miles of pipeline used to transport the fuel where it is needed. The primary constituent gas of natural gas is methane (CH4), but other components that may be present at various stages of industrial processing and delivery include ethylene, ethane, butane, propane, CO2, and H2S. The emission of natural gas from leaking in pipeline and storage sites represents economic, safety, and environmental challenges to the industry. The loss of methane from infrastructure leaks is estimated to cost the industry ~ $2 billion USD a year. The Environmental Protection Agency (EPA) has estimated that the total emissions associated with the natural gas industry within the United States in 2015 accounts for 162.4 million metric tons of CO2 equivalent emitted into the atmosphere. There is need to develop sensor technologies that can be used to reliably provide early warning of emissions from natural gas industry to mitigate the economic and environmental costs of methane emissions, and the technologies that address this would revolutionize the way industry can tackle the problem of natural gas leakage at the infrastructure level.

Impact

At the conclusion of the project, the research team will have demonstrated a low-cost, widely deployable, portable sensor package that can discriminate between natural gas and other interferent sources and quantify the emission of methane from natural gas infrastructure. This will allow the industry to reduce the economic, safety, and environmental costs associated with the prevalence of methane emissions from their infrastructure by allowing them to deploy continuous monitoring systems and quickly address leaks in pipelines, storage tanks, and processing plants. Initial testing has indicated that the technology may be adaptable towards other types of emissions monitoring, including leaks of hydrogen and ammonia which are expected to make up part of the renewable fuel economy.

Accomplishments (most recent listed first)

The performer has completed the following milestones:

  • Development of a 2-electrode Mixed Potential Electrochemical Sensor (MPES) device.
  • Collection of a training dataset for artificial neural networks (ANNs)
  • Development of a 2-electrode sensor readout system.
  • Integration of a multi-electrode-pair device onto one substrate
  • Demonstrate wireless transmission capability of a multi-electrode MPES device.
  • Demonstration that ANNs can perform identification of wetlands methane, bovine methane, and 2 types of natural gas (pipeline and wet gas) with > 98% accuracy.
  • Demonstration that ANNs can perform quantification of three gases (CH4, C2H6, C2H6+C3H8, NH3, preliminary results with H2) with < 2.5% true concentration error.
  • Demonstrate ANNs from can run on portable hardware with 1s less processing time.
Current Status

The performer has completed the following milestones:

  • Development of a 2-electrode Mixed Potential Electrochemical Sensor (MPES) device.
  • Collection of a training dataset for artificial neural networks (ANNs)
  • Development of a 2-electrode sensor readout system.
  • Integration of a multi-electrode-pair device onto one substrate
  • Demonstrate wireless transmission capability of a multi-electrode MPES device.
  • Demonstration that ANNs can perform identification of wetlands methane, bovine methane, and 2 types of natural gas (pipeline and wet gas) with > 98% accuracy.
  • Demonstration that ANNs can perform quantification of three gases (CH4, C2H6, C2H6+C3H8, NH3, preliminary results with H2) with < 2.5% true concentration error.
  • Demonstrate ANNs from can run on portable hardware with 1s less processing time.

The performer has made progress towards the following milestones.

  • Development of field test plan: Selected a field test site (CSU METEC). In discussions with PM on available funds for field test in budget period 3. (50% completed)
  • Design and development of integrated UNM Sensor / SCT electronics complete sensor package (25% Completed)

 

Project Start
Project End
DOE Contribution

$1,498,217

Performer Contribution

$374,555

Contact Information

NETL – William Fincham (William.Fincham@netl.doe.gov or 304-285-4268)
UNM – Lok-Kun Tsui (lktsui@unm.edu or 505-925-5987)