U.S. President Joe Biden joined European Union leaders at the United Nations Climate Summit in Glasgow this month in pledging to reduce global methane emissions by 30 percent over the next decade.

This is a heavy lift, but researchers at The University of New Mexico are working to help make it happen. The UNM Center for Micro-Engineered Materials recently received additional funding from the Department of Energy (DOE) to continue research development of sensor systems for natural gas leak monitoring and detection. Oil and gas infrastructure accounts for 30 percent of methane emissions in the US.

More than 300,000 miles of pipeline transport natural gas across the U.S., and leaks cost billions per year while contributing to global methane emissions. The infrastructure crisscrosses portions of the country that have other sources of methane too, like livestock and coastal wetlands. Pinpointing exactly where the methane is coming from, whether it’s from a pipeline leak, and how much methane is being emitted is a science being perfected at UNM.

Lok-kun Tsui
Fernando Garzon

The $1.5 million DOE-funded project at UNM aims at developing low-cost sensors to detect natural gas pipeline leaks. It’s being led by Research Associate Professor Lok-kun Tsui and Distinguished Professor Fernando Garzon, both from Department of Chemical and Biological Engineering. The two are developing low-cost sensing systems, based on mixed-potential electrochemical devices. The sensors can quantify the presence of natural gas and act as an early warning system for pipeline leakages that contribute to loss of product and air pollution. UNM has partnered with SensorComm Technologies for this project to develop portable data acquisition and transmission technology needed for a sensor system.

“We have also trained machine learning algorithms to both quantify methane concentrations and identify mixtures containing methane,” Tsui explained. “This allows us to distinguish methane emissions originating at natural gas infrastructure from sources such as wetlands and agriculture.”

The Internet of Things (IoT) methane sensor system uses:

  • Additive manufacturing for rapid prototyping and low-cost production of sensor devices.
  • Machine learning techniques for quantification of methane concentration and identification of the source of these methane leaks.
  • IoT-based portable data acquisition and transmission technology to facilitate deployment of the sensor systems in remote areas.

The low-cost, real-time monitoring results in early warnings of leaks so they can be quickly repaired. The sensors’ components are selected so that additive manufacturing can be used for prototyping but remain compatible with industrial scale ceramic sensor manufacturing techniques for mass production.

The researchers’ next steps are to conduct more tests at low concentration limits, integrate the sensor system in a portable form, and carry out a field test to show operation outside the laboratory.

“The sensors are robust. Optical sensors currently being used for methane detection today need to be kept clean and may have issues with certain gases sticking to them,” Tsui said. “Our sensors are descended from automotive exhaust sensors we worked on a few years ago, so they can withstand harsh environments and exposure to sticky gases like ammonia. This low maintenance aspect makes them more suitable for long term monitoring in the field.”

Work on developing the innovative IoT methane sensor system began in 2020 when the DOE Office of Fossil Energy and Carbon Management approved an initial year of funding. Positive progress led to approval of an additional year, with the possibility of a third year if research advancement continues.