Laser absorption spectroscopy has been a valuable technique for sensitive, non-intrusive, in-situ detection of gaseous and liquid phase target species. The infrared spectral region is specifically attractive as it provides opportunities for selective sensing of a multitude of species in various applications. This thesis explores techniques for interference-free sensing in the infrared region for environmental, combustion, and petrochemical applications.
A mid-infrared laser-based sensor was designed to detect trace amounts of benzene using off-axis cavity-enhanced absorption spectroscopy and a multidimensional linear regression algorithm. This sensor achieved unprecedented detection limits, making it ideal for environmental and occupational pollution monitoring. Moreover, wavelength tuning and deep neural networks were employed to differentiate between the broadband similar-shaped absorbance spectra of benzene, toluene, ethylbenzene, and xylene isomers.
Benzene sensing was enhanced by recent advancement in semiconductor laser technology, which enabled access to the long wavelength mid-infrared region through commercial distributed feedback quantum cascade lasers. The strongest benzene absorbance band in the infrared is near 14.84 μm, and thus was probed for sensitive benzene detection. Wavelength tuning with multidimensional linear regression were employed to selectively measure benzene, carbon dioxide, and acetylene.
Cepstral analysis and wavelength tuning were used to develop a selective sensor for fugitive methane emissions. The sensor was proved to be insensitive to baseline laser intensity imperfections and spectral interference from other present species.
In combustion studies, it is desirable to have a diagnostic technique that can detect multiple species simultaneously with high sensitivity, selectivity, and fast time response to validate and improve chemical kinetic mechanisms. A mid-infrared laser sensor was developed for selective and sensitive benzene, toluene, ethylbenzene, and xylenes detection in high-temperature shock tube experiments using deep neural networks. The laser was tuned near 3.3 μm, and an off-axis cavity-enhanced absorption spectroscopy setup was used to enable trace detection.
Finally, a novel near-infrared laser-based sensor was developed for water-cut sensing in oil-water flow. The sensor was shown to be immune to the presence of salt and sand in the flow and to temperature variations over 25-60°C. This technique has significant advantages for well and reservoir management, where highly accurate water-cut measurements are required.
|Date of Award||Apr 2023|
|Original language||English (US)|
- Physical Sciences and Engineering
|Supervisor||Aamir Farooq (Supervisor)|
- Laser Absorption Spectroscopy
- Deep Neural Networks
- Shock Tube