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Development And Evaluation Of Machine Learning Models For Fugitive Methane Detection And Intensity Prediction, Jacquan Pollard
Development And Evaluation Of Machine Learning Models For Fugitive Methane Detection And Intensity Prediction, Jacquan Pollard
All Theses
The environmental impacts of global warming driven by fugitive methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4 emissions. This study evaluated the performance of data-driven machine learning (ML) models using support vector machines (SVM) to detect the presence of trace CH4 emissions and the corresponding intensity amongst various meteorological conditions. The author used simulation data comprising various meteorological parameters such as temperature, relative humidity, wind speed, water vapor, pressure, precipitation rate, and a parameter possessing trace concentrations of CH4 emissions. The novelty of the SVM models developed in …
Evaluating The Effect Of Palmitic Acid Concentration On Growth And Inhibition Of Syntrophomonas Sp., Clancy Kerr
Evaluating The Effect Of Palmitic Acid Concentration On Growth And Inhibition Of Syntrophomonas Sp., Clancy Kerr
All Theses
Various industries produce fats, oils, and greases (FOG). These hydrophobic compounds cause issues in municipal wastewater collection and treatment systems. Anaerobic co-digestion is an effective way to treat FOG and presents an opportunity to produce more significant quantities of biogas. However, many microorganisms are sensitive to the degradation products like long-chain fatty acids (LCFAs). Palmitic acid is the most prevalent LCFA in anaerobic co-digestion of FOG and wastewater. Sytrophomonas is a β-oxidizer able to break down palmitic acid. Palmitic acid is known to feed and inhibit Syntrophomonas growth and methanogenesis. The objectives of this investigation are: To determine if Syntrophomonas …