Version 1
: Received: 27 August 2024 / Approved: 29 August 2024 / Online: 29 August 2024 (11:59:16 CEST)
How to cite:
Kiplimo, E.; Riddick, S. N.; Mbua, M.; Upreti, A.; Anand, A.; Zimmerle, D. J. Addressing Low-Cost Methane Sensor Calibration Shortcomings with Machine Learning. Preprints2024, 2024082111. https://doi.org/10.20944/preprints202408.2111.v1
Kiplimo, E.; Riddick, S. N.; Mbua, M.; Upreti, A.; Anand, A.; Zimmerle, D. J. Addressing Low-Cost Methane Sensor Calibration Shortcomings with Machine Learning. Preprints 2024, 2024082111. https://doi.org/10.20944/preprints202408.2111.v1
Kiplimo, E.; Riddick, S. N.; Mbua, M.; Upreti, A.; Anand, A.; Zimmerle, D. J. Addressing Low-Cost Methane Sensor Calibration Shortcomings with Machine Learning. Preprints2024, 2024082111. https://doi.org/10.20944/preprints202408.2111.v1
APA Style
Kiplimo, E., Riddick, S. N., Mbua, M., Upreti, A., Anand, A., & Zimmerle, D. J. (2024). Addressing Low-Cost Methane Sensor Calibration Shortcomings with Machine Learning. Preprints. https://doi.org/10.20944/preprints202408.2111.v1
Chicago/Turabian Style
Kiplimo, E., Abhinav Anand and Daniel J. Zimmerle. 2024 "Addressing Low-Cost Methane Sensor Calibration Shortcomings with Machine Learning" Preprints. https://doi.org/10.20944/preprints202408.2111.v1
Abstract
Quantifying methane emissions is essential for meeting near-term climate goals and typically done using methane concentrations measured downwind of the source. One major source of methane that is important to observe and promptly remediate is fugitive emissions from oil and gas productions sites but installing methane sensors at the thousands of sites within a production basin is expensive. In recent years, relatively inexpensive metal oxide sensors have been used to measure methane concentrations at production sites. Current methods used to calibrate metal oxide sensors have been shown to have significant shortcomings resulting in limited confidence in methane concentrations generated by these sensors. To address this, we investigate using machine learning (ML) to generate a model that converts metal oxide sensor output to methane mixing ratios. To generate test data, two metal oxide sensors, TGS2600 and TGS2611, were collocated with a trace methane analyzer downwind of controlled methane releases. Over the duration of the measurements, the trace gas analyzer’s average methane mixing ratio was 2.40 ppm with a maximum of 147.6 ppm. The average calculated methane mixing ratios for the TGS2600 and TGS2611 using the ML algorithm were 2.42 ppm and 2.40 ppm with maximum values of 105.9 ppm and 106.3 ppm, respectively. A comparison of histograms generated using the analyzer and metal oxide sensors mixing ratios show overlap coefficients of 0.95 and 0.94 for the TGS2600 and TGS2611, respectively. Overall, our results showed there was a good agreement between the ML derived metal oxide sensors’ mixing ratios and those generated using the more accurate trace gas analyzer. This suggests that the response of lower-cost sensors calibrated using ML could be used to generate mixing ratios with precision and accuracy comparable to higher price trace methane analyzers. This would improve confidence in low-cost sensors’ response, reduce the cost of sensor deploying, and allow for timely and accurate tracking of methane emissions.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
