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 (i) environmental, (ii) combustion, and (iii) petrochemical applications.

Mid-infrared laser-based sensors were designed for trace detection of benzene, toluene, ethylbenzene, and xylene isomers at ambient conditions. The sensors were based on a distributed feedback inter-band cascade laser emitting near 3.3 μm and off-axis cavity-enhanced absorption spectroscopy aimed at achieving unprecedented detection limits. Deep neural networks were employed to differentiate the broadband similar-shaped absorbance spectra of these species. Benzene sensing was further enhanced by taking advantage of the 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 and selective detection of benzene. In addition, cepstral analysis creates a modified form of the time-domain molecular free-induction-decay signal to temporally separate optical and molecular responses. A sensor was developed using wavelength tuning near 3.3 μm and cepstral analysis 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.

Well and reservoir management requires highly accurate water-cut measurements. Existing sensors need frequent calibration especially with changing well composition. Here, a novel near-infrared laser-based sensor is developed for water-cut sensing in oil-water flow. An algorithm was developed to calculate the gradient in the composite absorbance spectra and eliminate the effect of interfering oil species. The calibration-free property of the sensor was validated through water-cut measurements in various oils (e.g., glycol, gasoline, and diesel). 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.

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