Quantitative Natural Gas Discrimination For Pipeline Leak Detection Through Time-Series Analysis of an MOS Sensor Response
| dc.contributor.author | Barriault, Matthew | |
| dc.contributor.author | Montazeri, Mahyar Mohaghegh | |
| dc.contributor.author | O'Brien, Allen | |
| dc.contributor.author | Najjaran, Homayoun | |
| dc.contributor.author | Hoorfar, Mina | |
| dc.date.accessioned | 2018-11-08T16:29:29Z | |
| dc.date.available | 2018-11-08T16:29:29Z | |
| dc.date.issued | May-18 | |
| dc.description | Paper presented at 2018 Canadian Society of Mechanical Engineers International Congress, 27-30 May 2018. | en_US |
| dc.description.abstract | In order to detect natural gas pipeline leaks, ethane in the natural gas must be discriminated from background methane emissions. Our gas detection apparatus is well-suited for this application due to its flexibility and low cost. We present a comparison of machine learning models for quantitative estimation of concentrations of both methane and ethane in a target gas sample, using a response over time from a single sensor in our apparatus. We also demonstrate that the use of synthetic data is very effective for training a model to discriminate between methane and ethane. | en_US |
| dc.identifier | CSME189 | |
| dc.identifier.isbn | 978-1-77355-023-7 | |
| dc.identifier.uri | http://hdl.handle.net/10315/35334 | |
| dc.identifier.uri | http://dx.doi.org/10.25071/10315/35334 | |
| dc.language.iso | en | en_US |
| dc.publisher | CSME-SCGM | en_US |
| dc.rights | The copyright for the paper content remains with the author | |
| dc.subject | Microtechnology and Nanotechnology | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Gas detection | en_US |
| dc.subject | Microfluidic | en_US |
| dc.subject | Diffusion simulation | en_US |
| dc.subject | Pipeline leak detection | en_US |
| dc.title | Quantitative Natural Gas Discrimination For Pipeline Leak Detection Through Time-Series Analysis of an MOS Sensor Response | en_US |
| dc.type | Article | en_US |