Applications of artificial olfaction and machine learning for detection of volatile gas
department or school: Applied Science, Faculty of
department or school: Mechanical Engineering, Department of
data provider: DSpace
degree campus: UBCO
creator: Barriault, Matthew
issued: 2021-06-01T07:00:00Z
issued: 2020
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Electronic noses are used in diverse applications where sensory information about chemical compounds in the air can provide valuable insight, just as humans use their sense of smell this way. In this thesis, the role of machine learning for data analysis in three main applications of electronic noses is examined. These applications are the regression of arbitrary binary mixtures, detection of natural gas leaks, and detection of THC in human breath. To emphasize the importance of machine learning in the overall workflow of an electronic nose, the data collection apparatus selected was a single metal oxide semiconducting sensor embedded in a coated microchannel to provide selectivity. This apparatus produces a time-series response as the target gas sample diffuses through the microchannel toward the sensor. A comparison of machine learning models and time-series data representation methods is examined for each application. Using this method, a 98.75% accuracy in classifying between methane, ethane, and binary mixtures is obtained, as well as a 12.0% mean relative error in regression estimates for arbitrary mixtures. For the natural gas detection application, simulated natural gas mixtures containing 1% and 3% ethane were discriminated from samples of pure methane with 86.7% and 93.3% accuracy, respectively, and concentrations of both methane and ethane were regressed with a maximum of a 19.3% mean relative error. Finally, samples of methanol were discriminated from samples of denatured THC dissolved in methanol with concentration 1.0 mg/mL. This classification was performed with 100% accuracy using a support vector machine.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/74834
note: Applications of Artificial Olfaction and Machine Learning for Detection of Volatile Gas by Matthew Barriault B.A.Sc., University of British Columbia, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) March 2020 © Matthew Barriault, 2020 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Applications of Artificial Olfaction and Machine Learning for Detection of Volatile Gas submitted by Matthew Barriault in partial fulfillment of the requirements of the degree of Master of Applied Science. Dr. Homayoun Najjaran, School of Engineering Supervisor Dr. Mina Hoorfar, School of Engineering Supervisor Dr. Abbas Milani, School of Engineering Supervisory Committee Member Dr. Zheng Liu, School of Engineering Supervisory Committee Member Dr. Richard Klukas, School of Engineering University Examiner iii Abstract Electronic noses are used in diverse applications where sensory information about chemical compounds in the air can provide valuable insight, just as humans use their sense of smell this way. In this thesis, the role of machine learning for data analysis in three main applications of electronic noses is examined. These applications are the regression of arbitrary binary mixtures, detection of natural gas leaks, and detection of THC in human breath. To emphasize the importance of machine learning in the overall workflow of an electronic nose, the data collection apparatus selected was a single metal oxide semiconducting sensor embedded in a coated microchannel to provide selectivity. This apparatus produces a time-series response as the target gas sample diffuses through the microchannel toward the sensor. A comparison of machine learning models and time-series data representation methods is examined for e…
has type: Thesis/Dissertation
date issued: 2020-09
is shown at: 10.14288/1.0392037
language: eng
degree discipline: Mechanical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/74834