Automated detection of sulfur dioxide in stack emissions by passive Fourier transform infrared spectrometry

Abstract

Passive Fourier transform infrared (FT-IR) spectrometry is used in the automated qualitative determination of sulfur dioxide (SO 2) in a stack monitoring application. Digital filtering and pattern recognition techniques are optimized and applied to short sections of interferograms in a methodology developed to minimize effects of background variation. Two data sets are investigated that were collected with four similarly configured FT-IR emission spectrometers positioned to monitor stack releases of SO 2 against low-angle sky backgrounds. In the two data sets, 98.2% of 39,058 and 99.58% of 386,260 FT-IR interferograms collected are correctly classified into analyte-active or analyte-inactive categories, respectively, representing the presence or absence of SO 2 in the field-of-view of the spectrometer. This work demonstrates the validity of the methodology with data collected from stack emissions, and shows that the methodology allows training and subsequent prediction of data sets composed of data collected with multiple spectrometers.