title: Quantitative determination of ethanol in heated plumes by passive Fourier transform infrared remote sensing measurements.
abstract: Quantitative calibration models are developed for passive Fourier transform infrared (FT-IR) remote sensing measurements of open-air-generated vapors of ethanol. These experiments serve as a feasibility study for the use of passive FT-IR measurements in quantitative determinations of industrial stack emissions. A controlled-temperature plume generator is used to produce plumes of known concentrations of pure ethanol and mixtures of ethanol and methanol. Analyte plumes are generated over the path-averaged concentration range of 20-300 ppm-m and stack temperatures of 125, 150, 175, and 200 degrees C. A novel experimental setup is employed in which an ambient temperature polyvinyl chloride backdrop is placed behind the emission stack and used as a target for the passive IR measurements. An emission FT-IR spectrometer with telescope entrance optics is then employed to view the generated plumes against the backdrop. Signal processing techniques based on signal averaging and bandpass digital filtering are applied to both interferogram and single-beam spectral data obtained from these measurements, and the resulting filtered signals are used as inputs into the generation of multivariate partial least-squares (PLS) calibration models. Successful calibration models are obtained with both interferogram and spectral data, and neither analysis requires the collection of separate IR background data. For a set of validation data collected on a different day from the calibration measurements, standard errors of prediction of 30.6 and 32.2 ppm-m ethanol are obtained for the PLS models based on interferogram and spectral data, respectively.
authors.affiliation: Department of Chemistry & Optical Science and Technology Center, University of Iowa, Iowa City, IA 52242, USA.
authors.forename: Yusuf
authors.initials: Y
authors.lastname: Sulub
authors.forename: Gary W
authors.initials: GW
authors.lastname: Small