A Combined Mixed Potential Sensor, Machine Learning, and Iot Platform for Methane Emissions Detection and Flare Monitoring
Authors: Tsui Lok-kun , Halley Sleight , Garzon Fernando H. , Smith James , Ian Robert , Agi Kamil 
Published: December 2024

Methane’s global warming potential is 28x higher than that of CO2 on a 100 year basis.[1] The US has over 300,000 miles of pipeline transporting natural gas, and recent estimates put the cost of emissions in terms of lost product and societal impacts on the order of billions of dollars.[2] In the natural gas industry, excess gas which cannot be economically used for production is destroyed by burning in flares. A recent aerial study found that the methane destruction efficiency was only 91%, a result of inefficient flaring instruments or unlit flares.[3] Continuous monitoring solutions using networked Internet of Things (IoT) sensor platforms are a promising solution to give natural gas infrastructure operators real time insight into leaks, methane destruction efficiency, and warn of events like flameouts.

Solid-state mixed potential electrochemical sensors (MPES) are an attractive option as a sensor technology due to their low cost, robustness in harsh exhaust gas conditions, and ability to modify selectivity by the electrode composition and temperature.[4] They use the difference in catalytic activity between two dissimilar electrodes in the presence of an oxidation and reduction reaction to generate a voltage signal. We have developed a sensor platform which pairs this sensor with artificial neural networks for mixture identification and quantification and an IoT platform for data acquisition, edge processing, and transmission. We have achieved 5-5000 ppm sensitivity to CH4 in natural gas mixtures, carried out field testing of this platform for surface and underground leaks,[5] and demonstrated the ability to detect precursors to flameout in simulated methane combustion flares.

This work was sponsored by DOE SBIR Office of Science Award DESC0023770 and Office of Fossil Energy and Carbon Management Award FE0031864.

[1] M. Saunois, et al., The Global Methane Budget 2000–2017, Earth Syst. Sci. Data 12 (2020) 1561–1623. https://doi.org/10.5194/essd-12-1561-2020.

[2] E.D. Sherwin, J.S. Rutherford, Z. Zhang, Y. Chen, E.B. Wetherley, P.V. Yakovlev, E.S.F. Berman, B.B. Jones, D.H. Cusworth, A.K. Thorpe, A.K. Ayasse, R.M. Duren, A.R. Brandt, US oil and gas system emissions from nearly one million aerial site measurements, Nature 627 (2024) 328–334. https://doi.org/10.1038/s41586-024-07117-5.

[3] G. Plant, E.A. Kort, A.R. Brandt, Y. Chen, G. Fordice, A.M. Gorchov Negron, S. Schwietzke, M. Smith, D. Zavala-Araiza, Inefficient and unlit natural gas flares both emit large quantities of methane, Science 377 (2022) 1566–1571. https://doi.org/10.1126/science.abq0385.

[4] S. Halley, K.P. Ramaiyan, L. Tsui, F. Garzon, A review of zirconia oxygen, NOx, and mixed potential gas sensors – History and current trends, Sens. Actuators B Chem. 370 (2022) 132363. https://doi.org/10.1016/j.snb.2022.132363.

[5] S. Halley, K. Ramaiyan, J. Smith, R. Ian, K. Agi, F. Garzon, L. Tsui, Field Testing of a Mixed Potential IoT Sensor Platform for Methane Quantification, ECS Sens. Plus 3 (2024) 011402. https://doi.org/10.1149/2754-2726/ad23df.