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

Abstract

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 La0.87Sr0.13CrO3, Indium Tin Oxide (In2O3 90 wt%, SnO2 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 CH4 in air, CH4 + NH3 in air, and simulated natural gas is less than 1.5% ppm when a two-temperature dataset is employed.

Comparison of Machine Learning Algorithms for Natural Gas Identification with Mixed Potential Electrochemical Sensor Arrays

“…This optimal temperature where identification accuracy is maximized occurs due to an optimum in sensitivity in the multi-electrode sensors where the temperature is high enough to readily catalyze the CH 4 oxidation reaction, but low enough that the presence of heavier hydrocarbon or NH 3 oxidation can be readily detected by the mixed potential difference created by the choice of electrode materials. 18 Learning curves for these algorithms are presented in Supplementary Information, Fig. S1.…”

“…A Hyrel System 30M was used for the printing of the substrate, Pt electrode, and Pt conducting leads for the sensing electrodes. Indium Tin Oxide (ITO), La 0.2 Sr 0.13 CrO 3 (LSC), and Au electrodes were added and fired according to the process outlined in Halley et al 18 A photograph of a completed sensor appear in Fig. 1.…”

“…Many metals and metal oxides have been tested as electrode materials for increasing the sensitivity and selectivity of gases. Indium tin oxide (ITO), Au, La 0.8 Sr 0.2 CrO 3 (LSC), and Pt have successfully been proven to detect methane, heavy hydrocarbons, ammonia, and hydrogen, [18][19][20][21][22][23] while yttria-stabilized zirconia is typically used as an electrolyte. 24 These materials were chosen as the four electrodes for the sensor array, and YSZ was chosen as the electrolyte for this study.…”

“…[35][36][37] Other surveys have compared different machine learning techniques z E-mail: lktsui@unm.edu ECS Sensors Plus, 2023 2 011402 applied to other types of gas sensors 38,39 and specific studies such as Tsitron et al 40 demonstrate the application of a single machine learning technique such as Bayesian decoding to MPES devices. We have previously demonstrated the effectiveness of artificial neural networks for gas mixture identification in the context of natural gas emissions 18 and automotive emissions. 21,22 Wang et al also showed that taking readings from an MPES device across a range of polarizations could accurately identify nine VOC species on a single sensor with random forest and gradient boosting methods.…”

Recent Developments in Sensor Technologies for Enabling the Hydrogen Economy

“…Methane has an existing pipeline network for efficient transportation, but this infrastructure also suffers from leaks that are estimated to result in economic losses worth over two billion dollars per year. 16,17 Additionally, methane is a greenhouse gas that is 30 times more potent than CO 2 . 18 This means the fugitive emissions of about 3.5% of methane during its extraction from natural gas and processing necessary for the production of hydrogen results in the release of more greenhouse gas than burning natural gas directly.…”