Semester

Summer

Date of Graduation

2019

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Mechanical and Aerospace Engineering

Committee Chair

Derek Johnson

Committee Co-Chair

Andrew Nix

Committee Member

Andrew Nix

Committee Member

Marc Besch

Abstract

Natural gas is deployed as an alternative fuel due to its cost and post-combustion emissions. However, methane, the main component of natural gas, is a greenhouse gas with a global warming potential (GWP) of at least 28 over 100 years. Currently, natural gas and petroleum systems are the highest emitters of methane to the atmosphere. Using conventional methods, the detection of natural gas leaks is time consuming. Currently, natural gas production sites deploy the Environmental Protection Agency’s (EPA) Method 21 or optical gas imaging (OGI) for methane leak detection. Both methods require access to the natural gas site along with the time and workers necessary to conduct equipment leak checks. Industry and academia are seeking to develop and deploy mobile methane monitoring systems to geospatially identify methane emissions. There are a variety of sensor systems that can be combined to enable such monitoring but there may be implied limitations (implications) based on operating principle and sampling frequency. The goal of this research was to assess these implications and where applicable develop methods that could overcome limitations.

Using a vehicle mounted approach, two mobile methane detection systems were deployed in rural West Virginia (WV). Over the course of 90 days, a total of 43 trips were completed through Morgantown, WV and the surrounding area. During each trip, two systems were implemented simultaneously with different sampling frequencies and methane sampling methods. The slow system operated at 1 Hertz (Hz) with a closed-path methane analyzer, while the fast system operated at 10 Hz with an open-path methane analyzer. The effects of the sampling frequency and sampling method were observed for each system. The sampling frequency effects were examined with respect to geospatial limitations and wind speed limitations. The sampling method effects were compared between the systems using peak concentrations as the primary metric.

With the sample frequency effects, the closed-path methane analyzer required a signal reconstruction to report an accurate response in real time methane concentration. Methods of signal reconstruction, consisting of sequential inversion technique (SIT), inverse fast Fourier transform (IFFT), artificial neural network (ANN), and differential coefficients method (DCM), were investigated before the DCM and ANN were applied. A performance value was defined for improvement comparisons between the initial methane signal and the reconstructed signal. An application was created in MATLAB© to process the mobile methane detection data. After indicating the user defined parameters, the application created a MATLAB© workspace file and Google® Earth file consisting of a visual representation of the fast, slow, and reconstructed systems to elucidate the geospatial differences. The requirements for both the fast and slow systems were investigated with the intent of an operational mobile methane detection system. Suggested improvements and potential expansions of the mobile methane detection system were discussed.

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