Airborne passive Fourier transform infrared remote sensing of methanol vapor from industrial emissions

Abstract

Passive Fourier transform infrared (FT-IR) remote sensing measurements are applied to the detection of methanol vapor plumes released from a chemical manufacturing facility. With the spectrometer mounted in a downward-looking mode on a fixed-wing aircraft, overflights of the facility are made during the methanol release. Signal processing and pattern recognition methods are applied to the acquired data for the purpose of constructing an automated classification algorithm for the methanol detection. The analysis is based on the use of short, digitally filtered segments of the raw interferogram data collected by the spectrometer. The classifiers are trained with data collected on the ground by use of an experimental protocol designed to simulate background conditions observed from the air. Optimization of the digital filtering and interferogram segment parameters leads to successful classifiers based on 100 or 120 interferogram points. The optimal interferogram segment location is found to be 95-points displaced from the centerburst, and the best performing digital filters are centered on the methanol C-O stretching band at 1036 cm(-1) and have a passband full-width at half-maximum of 100 to 160 cm(-1). The best classifiers achieve classification errors of less than 1% and are observed to be resistant to possible interference effects from species such as ethanol and ozone. This work demonstrates the utility of airborne passive FT-IR remote sensing measurements of volatile organic compounds under complex background conditions such as those encountered while monitoring an operating industrial facility.

Pattern recognition methods for automated detection and quantification

“…The ANOVA of a full factorial experimental design revealed that segment-related factors, including segment length, segment starting point, and the interaction between the two segment factors, played the most significant roles with respect to classification performance on the monitoring set. The optimal choice of a 120-point segment starting from point 88 was consistent with previous applications of the interferogram-based data analysis methodology 75,76. For the detection of ammonia, the design of the IIR filters seemed to have relatively little effect on the classification performance.…”

Non-Luminous Flame Temperature Determination Method Based on CO2Radiation Intensity

“…Determining the non-luminous ame temperature is possible only in a 120 µm radiation range [5]. The open-path FTIR spectrometer is the most commonly used measuring instrument [6]. A special type of beroptic probe is utilized to monitor phenomena within the objects [7].…”

Signal processing and pattern recognition methods for the remote, airborne detection of radioisotopes by gamma-ray spectroscopy

“…This methodology has previously been effective in similar passive infrared remote sensing applications. 27,38,83,88 Aspects of this chapter will detail how the basic classifier is developed for the target analytes, with an emphasis on how this basic methodology can be transitioned to any number of radioisotopes. This chapter also includes field performance results, details the optimization procedures used, and describes the methodology used to assign a confidence level to detections in the field.…”

“…[30][31][32][33][34][35] Previous work done by this laboratory involves the pattern recognition of FT-IR interferograms collected during passive infrared remote sensing measurements. 30,[35][36][37][38] The work described in this dissertation focuses on the use of piecewise linear discriminant analysis (PLDA) or linear discriminant analysis (LDA). [39][40][41][42][43] These methods will be described in Chapter 3.…”

“…When dealing with gamma-ray detections, the further the detector is from the analyte the more suppressed the target signal will become due to the various gamma-ray interactions described in Chapter 2. 30,36,38,[60][61] Background or interfering signals can make these detections more difficult by further suppressing or interfering with the analyte signal. 62 Because of the inherent S/N difficulties, the spectra obtained for remote detection purposes frequently require preprocessing before they can be used effectively with pattern recognition algorithms.…”