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dc.contributor.advisorSiddique, Zahed
dc.contributor.advisorRaman, Shivakumar
dc.contributor.authorHovorka, Abigail
dc.date.accessioned2020-08-05T21:08:08Z
dc.date.available2020-08-05T21:08:08Z
dc.date.issued2020-07
dc.identifier.urihttps://hdl.handle.net/11244/325365
dc.description.abstractGreenhouse gases have become an increasingly significant issue in the last few decades. As a response, many organizations have sought to tighten their regulations on their operations to reduce their contributions to greenhouse gases. The International Organization for Standardization has a standard 15848-1 that classifies industrial valves for the oil and gas industry. They too have aimed to tighten their regulations, including this specific standard. However, the current requirements from ISO 15848-1 has made it extremely difficult for manufacturers to get any industrial valves and seals passed. This begs the question, are the new tightness classifications for the ISO standard appropriately relating the test gas, Helium, to the allowable Methane leakage concentration? And with that, is Helium even the best option for a testing gas? To investigate this, a series of experiments were conducted to collect Helium and Argon leak rate data under many temperature and pressure conditions. With this data, a Helium/Argon leak rate ratio model was created with machine learning techniques. Using this model, an Ar/CH₄ multiplier, and diffusion modeling, the ISO 15848-1 tightness classes can be assessed for their accuracy. A disconnect between the ISO 15484-1 Helium and Methane requirements has been identified and there is a call to reconsider the Helium requirements. In addition, a suggestion to investigate Argon as an alternative leakage test gas is also raised. Keywords: fugitive emissions, methane leak testing, ISO 15848en_US
dc.languageen_USen_US
dc.subjectfugitive emissionsen_US
dc.subjectmethane leak testingen_US
dc.subjectISO 15848en_US
dc.titlePredictive Modeling in Fugitive Emissions Testingen_US
dc.contributor.committeeMemberBarker, Kash
dc.contributor.committeeMemberHuebner, Pedro
dc.contributor.committeeMemberMadhavan, Raghu
dc.date.manuscript2020-07
dc.thesis.degreeMaster of Scienceen_US
ou.groupGallogly College of Engineering::School of Industrial and Systems Engineeringen_US
shareok.orcid0000-0002-3986-3634en_US


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