The goal of this project is to develop low-cost sensing systems, based on mixed-potential electrochemical devices, to sense, identify 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 project includes the development of sensing elements specifically suited for detection of natural gas components and contaminants. Machine learning techniques are employed 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. 

University of New Mexico (UNM) - Albuquerque, NM 87131

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.

This project will demonstrate a low-cost, widely deployable, portable sensor package that can discriminate between natural gas and other interferent sources (i.e., selective) 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.

• Developed a 2-electrode Mixed Potential Electrochemical Sensor (MPES) device.
• Collected a training dataset for artificial neural networks (ANNs)
• Developed a 2-electrode sensor readout system.
• Integrated a multi-electrode-pair device onto one substrate
• Demonstrated 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.
• Demonstrated < 40 ppm limit of detection for laboratory measurements of simulated natural gas.
• • Demonstrated ANNs from can run on portable hardware with 1s less processing time.
• Demonstrated proof of concept data exchange between data collection system and portable ANN hardware.
• Developed a portable version of the sensor with an integrated heater that does not require the use of a furnace. Reduced power requirements for heating from >1kW to 10 W.
• Developed test electronics for signal measurement, data recording and remote transmission without need for bank of digital multimeters or PC.

Field tests were completed at CSU METEC in August and October of 2022. The first field test resulted in a successful demonstration of the integration between SCT’s IoT system and UNM’s Sensor devices in a relevant environment. Emissions from a 2.5 ft. underground buried pipeline were detected at 10-40 standard liters per minute (SLPM) leak rate. The first field test also gave valuable feedback on needed improvements of both UNM and SCT devices. These included faster gas sampling response time, reliable network connectivity, data acquisition and filtering, integrated heating control, and the addition of an air purge into the test system. These changes were integrated into a field test package examined sensor response as a function of leak rate, lateral positioning, and vertical positioning from a simulated underground pipeline. A machine learning model trained on laboratory data to quantify the concentration of methane was applied. Efforts in the next quarter will focus on transitioning our sensor technology to manufacturing methods more suitable for scale-up.

$1,998,217

$499,555

NETL – Joseph Renk (Joseph.Renk@netl.doe.gov or 412-386-6406)
UNM – Lok-Kun Tsui (lktsui@unm.edu or 505-925-5987)