Combined Mixed Potential Electrochemical Sensors and Artificial Neural Networks for the Quantificationand Identification of Methane in Natural Gas Emissions Monitoring

Halley, Sleight; Tsui, Lok-kun; Garzon, Fernando

doi:10.1149/1945-7111/ac2465

Combined Mixed Potential Electrochemical Sensors and Artificial Neural Networks for the Quantificationand Identification of Methane in Natural Gas Emissions Monitoring

Journal Article · Thu Sep 16 00:00:00 EDT 2021 · Journal of the Electrochemical Society

DOI:https://doi.org/10.1149/1945-7111/ac2465· OSTI ID:1835397

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Sensors capable of quantifying methane concentration and discriminating between possible sources are needed for natural gas leak detection where multiple spatially overlapping sources including wetlands and agriculture may be present. We report on the fabrication by an additive manufacturing process of a four electrode La _0.87 Sr _0.13 CrO ₃ , Indium Tin Oxide (In ₂ O ₃ 90 wt%, SnO ₂ 10 wt%), Au, Pt mixed potential electrochemical sensor using yttria-stabilized zirconia (YSZ) as a solid electrolyte to natural gas detection. Artificial neural networks (ANNs) are used to automatically decode the possible source and concentration of methane. The ANNs trained on sensor data are capable of correctly discriminating between three sources of methane emissions from simulated mixtures of emissions from cattle, wetlands, or natural gas with >98% accuracy. Quantification error for methane in mixtures of CH ₄ in air, CH ₄ + NH3 in air, and simulated natural gas is less than 1.5% ppm when a two-temperature dataset is employed.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: NONE; FE0031864

OSTI ID:: 1835397

Alternate ID(s):: OSTI ID: 1820353

Journal Information:: Journal of the Electrochemical Society, Journal Name: Journal of the Electrochemical Society Journal Issue: 9 Vol. 168; ISSN 0013-4651

Publisher:: The Electrochemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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