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A multi-band integrated virtual calibration-inversion method for open path FTIR spectrometry

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Języki publikacji
EN
Abstrakty
EN
This paper addresses problems arising from in situ measurement of gas content and temperature. Such measurements can be considered indirect. Transmittance or natural radiation of a gas is measured directly. The latter method (spectral radiation measurement) is often called spectral remote sensing. Its primary uses are in astronomy and in the measurement of atmospheric composition. In industrial processes, in situ spectroscopic measurements in the plant are often made with an open path Fourier Transform Infrared (FTIR) spectrometer. The main difficulty in this approach is related to the calibration process, which often cannot be carried out in the manner used in the laboratory. Spectral information can be obtained from open path spectroscopic measurements using mathematical modeling, and by solving the inverse problem. Determination of gas content based on spectral measurements requires comparison of the measured and modeled spectra. This paper proposes a method for the simultaneous use of multiple lines to determine the gas content. The integrated absorptions of many spectral lines permits calculation of the average band absorption. An inverse model based on neural networks is used to determine gas content based on mid-infrared spectra at variable temperatures.
Słowa kluczowe
Rocznik
Strony
287--298
Opis fizyczny
Bibliogr. 27 poz., rys., wykr.
Twórcy
  • Lublin University of Technology, Institute of Electronics and Information Technology, Nadbystrzycka 38A, 20-618 Lublin, Poland
Bibliografia
  • [1] Griffiths P.R., Shao L., Leytem A. B. (2009): Completely automated open-path FTIR spectrometry, Anal Bioanal Chem, 393, 45-50.
  • [2] Kastek M., Piątkowski T., Trzaskawka P. (2011): Infrared imaging Fourier transform spectrometer as the stand-off gas detection system, Metrology and Measurement Systems, 18(4), 607-620,
  • [3] Smith T.E.L., Woodster M.J,, Tattaris M., Griffith D.W.T.(2010): Absolute accuracy and sensitivity analysis of OP-FTIR retrievals of CO2, CH4 and CO over concentrations representative of clean air and polluted plumes, Atmospheric Measurement Techniques, 4, 97-116.
  • [4] Wojtas J., Czyżewski A., Stacewicz T., Bielecki Z.(2006): Sensitive detection of NO2 with cavity enhanced spectroscopy, Optica Applicata 36(4), 461-465.
  • [5] Cuadros-Rodrigues L., Gamiz-Gracia L., Almansa-Lopez E.(2001): Calibration in chemical measurement process: I. A metrological approach, Trends in analytical chemistry, 20(4), 195-206.
  • [6] Lorber A., Kowalski B.R.(1988): Estimation of prediction terror for multivariate calibration, Journal of Chemometrics, 2(2), 93-109.
  • [7] Haaland D.M.(2000): Synthetic multivariate models to accommodate unmodeled interfering spectral components during quantitative spectral analyses, Applied Spectroscopy, 54(2), 246-254.
  • [8] Shao L., Roske C.W., Griffiths P.R.(2010): Detection of chemical agents in the atmosphere by open-path FTIR spectroscopy under condition of background interference: II. Fog and rain, Anal Bioanal Chem, 397, 1521-1528.
  • [9] Shao L., Roske C.W., Griffiths P.R.(2010): Detection of chemical agents in the atmosphere by open-path FTIR spectroscopy under condition of background interference: I. High-frequency flashes, Anal Bioanal Chem, 397, 1511-1513.
  • [10] Barman I., Dingari N.C., Singh G.P., Soares J.S., Dasari R.R., Smulko J.M.(2012): Investigation of Noise- Induced Instabilities in Quantitative Biological Spectroscopy and Its Implications for Noninvasive Glucose Monitoring, Analytical Chemistry, 84(19), 8149-8156.
  • [11] Johnson T.J. at all(2010): An infrared spectral database for detection of gases emitted by biomass burning, Vibrational Spectroscopy, 53, 97-102.
  • [12] Rothman L. S., at all(2005): The HITRAN 2004 molecular spectroscopic database, Journal of Quantitative Spectroscopy and Radiative Transfer, 96, 139-204.
  • [13] Szczuczyński D.K., Mroczka J.(2009): Comparing the quality of solutions of inverse problem in nephelometric and turbidimetric measurements, Optica Applicata 39(3), 521-531.
  • [14] Mroczka, J., Szczuczyński, D. (2012). Simulation research on improved regularized solution of inverse problem in spectral extinction measurements, Applied Optics, 51(11), 1715-1723.
  • [15] Smith T.E.L., Woodster M.J., Tattaris M(2010).: Open-Path FTIR spectroscopy of CO2, CH4 & CO: Experimental accuracy evaluation for ambient to highly polluted concentrations, Proceedings of the Remote Sensing and Photogrammetry Society Conference, Remote Sensing and the Carbon Cycle, Burlington House, London, 1-4.
  • [16] Granada E., Equia P., Vilan J.A., Comesana J.A., Comesana R.(2012): FTIR quantitative analysis technique for gases. Application in a biomass thermochemical process, Renewable Energy 41, 416-421.
  • [17] Sharpe S.W., Johnson T.J., Sams R.L., Chu P.M., Rhoderick G.C., Johnson P.A.(2004): Gas-Phase Databases for Quantitative Infrared Spectroscopy, Applied Spectroscopy, 58(12), 1452-1461.
  • [18] Brulls M., Folestad S., Sparen A., Rasmuson A., Salomonsson J.(2007): Applying spectral peak area analysis in near-infrared spectroscopy moisture assays, Journal of Pharmaceutical and Biomedical Analysis, 44, 127-136.
  • [19] Mroczka J., Szczuczyński D.(2009): Inverse problems formulated in terms of first-kind Fredholm integral equations in indirect measurements, Metrology and Measurement Systems, 16(3), 333-357.
  • [20] Mroczka J., Szczuczyński D.(2010): Improved regularized solution of the inverse problem in turbidimetric measurements, Applied Optics, 49(24), 4591-4603.
  • [21] Morawski R.Z.(2006): Spectrophotometric applications of digital signal processing, Measurement Science and Technology, 17, R117-R144.
  • [22] Sepman A.V., Den Blanken R., Scheppers R., De Goey L.P.H.(2009): Quantitative Fourier Transform Infrared Diagnostics of the Gas-Phase Composition Using the Hitran Database and the Equivalent Width of the Spectral Features, Applied Spectroscopy, 63(11), 1211-1222.
  • [23] Adler F., Maslowski P., Foltynowicz A., Cossel K., Briles T., Hartl I., Ye J.: Mid-infrared Fourier transform spectroscopy with a broadband frequency comb, Optics Express, 18(21), 21861-21872.
  • [24] Cubillas A.M., Lazaro J.M., Conde O.M., Petrovich M.N., Lopez-Higuera J.M.(2009): Multi-Line Fit Model for the Detection of Methane at v2+2v3 Band using Hollow-Core Photocic Bandgap Fibres, Sensors,9, 490-502.
  • [25] Karpf A., Rao G.N.(2009): Enhanced sensitivity for the detection of trace gases using multiple line integrated absorption spectroscopy, Applied Optics, 48(27), 5061-5066.
  • [26] U. S. Environmental Protection Agency: Additional Fourier Transform Infrared (FTIR) Spectra (January 2013) http://www.epa.gov/ttn/emc/ftir/addcas.html.
  • [27] Wójcik W., Cięszczyk S., Golec T.( 2010): Narrow-band spectra models for diagnostic of gases produced during the biomass production, Environmental Engineering III (ed. L. Pawłowski, M. Dudzińska, A. Pawłowski), CRC Press, 597-601.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c680962a-8289-47eb-b43e-473562a75000
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