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Addressing low-cost methane sensor calibration shortcomings with machine learning

dc.contributor.authorKiplimo, Elijah, author
dc.contributor.authorRainwater, Bryan, advisor
dc.contributor.authorZimmerle, Daniel J., advisor
dc.contributor.authorBradley, Thomas, committee member
dc.contributor.authorReza, Nazemi, committee member
dc.contributor.authorRiddick, Stuart, committee member
dc.date.accessioned2025-06-02T15:20:08Z
dc.date.available2025-06-02T15:20:08Z
dc.date.issued2025
dc.description.abstractQuantifying methane emissions is essential for meeting near-term climate goals and is typically done using methane concentrations measured downwind of the source. One major source of methane important to observe and remediate is fugitive emissions from oil and gas productions sites; however, installing methane sensors at thousands of sites within a production basin can be prohibitively 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 a machine learning (ML) model to convert metal oxide sensor output to methane mixing ratios. To generate data to train this model, two metal oxide sensors, TGS2600 and TGS2611, were collocated with a trace methane analyzer downwind of controlled methane releases. 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 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 higher precision and accuracy, thereby reducing the cost of sensor deployments, and allowing for timely and accurate tracking of methane emissions.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierKiplimo_colostate_0053N_18923.pdf
dc.identifier.urihttps://hdl.handle.net/10217/240977
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectmachine learning
dc.subjectmethane
dc.subjectsensor
dc.subjectmetal-oxide
dc.subjectcalibration
dc.subjectquantification
dc.titleAddressing low-cost methane sensor calibration shortcomings with machine learning
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineSystems Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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