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UBC Theses and Dissertations
Feasibility assessment and development of knowledge, technology, and tools for monitoring natural gas odorants Ghazi Mirsaeed, Seyed Mahan
Abstract
A natural gas (NG) odorization system requires a continuous monitoring system in addition to the proper injection setup. As other sulfur based compounds such as hydrogen sulfide can also coexist in the environment, one of the challenges associated with monitoring the odorants is the selectivity. In this thesis, first, microfluidic gas sensors are modified to enhance their selectivity. In our first study, the effect of microchannel geometry modification (reduction of width) is studied through a computational parametric approach. The results show an average improvement of 93.44% and 60.1% in selectivity toward polar and nonpolar VOCs, respectively. In the next step, the surface of the microchannel is modified with graphene quantum dots, which has a two-fold effect: (i) increasing the surface area, and (ii) adding functional groups. The experimental results of this step show an average improvement of 98.72% and 86.61% in the sensorβs selectivity toward polar and nonpolar VOCs, respectively. In our second study, optimized cylindrical microfeatures are embedded into the microchannel. The microchannel is coated with graphene oxide, to increase the surface to volume ratio by introducing nanofeatures to the surfaces. The changes in the sensor response are compared to a plain microfluidic gas sensor, the results show an average of 64.42% and 120.91% improvement in the selectivity of the sensor with microfeatures and both nano- and microfeatures, respectively (toward VOCs). In the last part of this project, a portable device integrated with an array of five different sensors and a machine learning model are developed for the selective detection and prediction of NG odorant: a mixture of tert-butyl mercaptan and methyl ethyl sulfide for a concentration range of 1 ppm to 10 ppm. The device shows the sensitivity and selectivity indicator of 0.36 (1 β πππ), and 28.59 toward odorant respectively. In the next step, we achieved a 98.75% classification accuracy between NG odorant and hydrogen sulfide. The overall Mean Square Error (MSE) and π Β² error of the regression model of 0.50 and 95.16% have also been achieved. These results indicate that the developed device and the prediction model have promising applications for selective monitoring of the NG odorant.
Item Metadata
Title |
Feasibility assessment and development of knowledge, technology, and tools for monitoring natural gas odorants
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Creator | |
Supervisor | |
Publisher |
University of British Columbia
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Date Issued |
2021
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Description |
A natural gas (NG) odorization system requires a continuous monitoring system in addition to the
proper injection setup. As other sulfur based compounds such as hydrogen sulfide can also coexist in the environment, one of the challenges associated with monitoring the odorants is the
selectivity. In this thesis, first, microfluidic gas sensors are modified to enhance their selectivity.
In our first study, the effect of microchannel geometry modification (reduction of width) is studied
through a computational parametric approach. The results show an average improvement of
93.44% and 60.1% in selectivity toward polar and nonpolar VOCs, respectively. In the next step,
the surface of the microchannel is modified with graphene quantum dots, which has a two-fold
effect: (i) increasing the surface area, and (ii) adding functional groups. The experimental results
of this step show an average improvement of 98.72% and 86.61% in the sensorβs selectivity
toward polar and nonpolar VOCs, respectively. In our second study, optimized cylindrical
microfeatures are embedded into the microchannel. The microchannel is coated with graphene
oxide, to increase the surface to volume ratio by introducing nanofeatures to the surfaces. The
changes in the sensor response are compared to a plain microfluidic gas sensor, the results show
an average of 64.42% and 120.91% improvement in the selectivity of the sensor with
microfeatures and both nano- and microfeatures, respectively (toward VOCs). In the last part of
this project, a portable device integrated with an array of five different sensors and a machine
learning model are developed for the selective detection and prediction of NG odorant: a mixture
of tert-butyl mercaptan and methyl ethyl sulfide for a concentration range of 1 ppm to 10 ppm.
The device shows the sensitivity and selectivity indicator of 0.36 (1 β πππ), and 28.59 toward
odorant respectively. In the next step, we achieved a 98.75% classification accuracy between NG
odorant and hydrogen sulfide. The overall Mean Square Error (MSE) and π
Β² error of the
regression model of 0.50 and 95.16% have also been achieved. These results indicate that the
developed device and the prediction model have promising applications for selective monitoring
of the NG odorant.
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Genre | |
Type | |
Language |
eng
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Date Available |
2021-08-31
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0401827
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2021-09
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International