Target Analyte Detection and Quantification in Sample Gases With Complex Background Compositions

• US 20160132617A1
• Filed: 11/11/2014
• Published: 05/12/2016
• Est. Priority Date: 11/11/2014
• Status: Active Grant

• Current Assignee: Endress+Hauser Optical Analysis Incorporated
• Original Assignee: Spectrasensors, Inc. (Endress+Hauser AG)
• Inventors: Tedesco, James, Liu, Xiang, Feitisch, Alfred, Helbley, Keith Benjamin
• Granted Patent: US 10,643,008 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01J 1/0433 using notch filters
• CPC Class Codes: G01J 3/0294 Multi-channel spectroscopy
• CPC Class Codes: G01J 3/28 Investigating the spectrum using colour filters G01J3/51
• CPC Class Codes: G01J 3/42 Absorption spectrometry; Double beam spectrometry; Flicker spectrometry; Reflection spectrometry beam switching arrangements G01J3/08
• CPC Class Codes: G01J 3/44 Raman spectrometry; Scattering spectrometry ; Fluorescence spectrometry
• CPC Class Codes: G01N 2021/3545 Disposition for compensating effect of interfering gases
• CPC Class Codes: G01N 2021/399 Diode laser
• CPC Class Codes: G01N 21/3504 for analysing gases, e.g. multi-gas analysis
• CPC Class Codes: G01N 21/39 using tunable lasers
• CPC Class Codes: G01N 21/65 Raman scattering
• CPC Class Codes: G01N 2201/0612 Laser diodes
• CPC Class Codes: G01N 2201/129 Using chemometrical methods
• CPC Class Codes: G06F 17/10 Complex mathematical operations function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30

Abstract

Background composition concentration data representative of an actual background composition of a sample gas can be used to model absorption spectroscopy measurement data obtained for a gas sample and to correct an analysis of the absorption spectroscopy data (e.g. for structural interference and collisional broadening) based on the modeling.

Claims

1. A method comprising:

modeling absorption spectroscopy measurement data obtained for a gas sample as one or more functions of a background composition of the gas sample, the modeling comprising generating mathematical representations of structural interference and/or collisional broadening effects based on background composition concentration data representative of an actual background composition of the gas sample;

applying one or more multivariate regression analysis algorithms to correct, based on the modeling, an analysis of the absorption spectroscopy data, the absorption spectroscopy data being obtained by a second analysis approach comprising absorption spectroscopy; and

determining a target analyte concentration in the gas sample based on the corrected analysis of the absorption spectroscopy data obtained by the second analysis approach.

2. The method of claim 1, wherein the actual background composition comprises a plurality of chemical species whose concentrations in the gas sample vary sufficiently between over time to cause variation in the determined target analyte concentration if the one or more multivariate regression analysis algorithms are not applied to correct the analysis of the absorption spectroscopy data obtained by the second analysis approach.

3. The method of claim 1, wherein the background composition concentration data comprises data obtained by a first analysis approach, the first analysis approach comprising at least one of Raman spectroscopy, gas chromatography, gathering of concentration and/or other physical data from a distributed control system or a supervisory control and data acquisition system, nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy, direct absorption spectroscopy, emission spectroscopy, and nondispersive infrared spectroscopy.

4. The method of claim 1, wherein the background composition concentration data comprises Raman spectral data obtained by a first analysis approach comprising Raman spectroscopy.

5. The method of claim 4, wherein the Raman spectroscopy is performed on a same gas sample as the second analysis approach.

6. The method of claim 5, wherein the absorption spectroscopy data are obtained substantially simultaneously to the Raman spectral data.

7. The method of claim 5, wherein the absorption spectroscopy data are obtained sequentially with the Raman spectral data.

8. The method of claim 4, wherein the Raman spectral data are obtained in situ on a gas within a gas source, and wherein the gas sample for use in the second analysis approach is extracted from the gas source substantially concurrently with the obtaining of the Raman spectral data.

9. The method of claim 4, further comprising determining a temperature and/or a pressure of the gas sample based on the Raman spectral data.

10. The method of claim 4, wherein the Raman spectral data comprises measurements over wavenumbers indicative of one or more of:

hydrocarbon species, hydrogen gas (H₂), nitrogen gas (N₂), oxygen gas (O₂), chlorine gas (Cl₂), carbon dioxide (CO₂), carbon monoxide (CO), ammonia (NH₃), hydrogen-sulfide (H₂S), sulfur compounds, fluoro-carbons, chloro-carbons, hydro-fluoro-carbons, hydro-fluoro-chloro-carbons, and hydro-chloro-carbons.

11. The method of claim 1, wherein the sample gas comprises hydrocarbon gases.

12. The method of claim 1, wherein the one or more target analyte comprises at least one of acids, bases, hydrogen sulfide (H₂S), other sulfur compounds, hydrogen chloride (HCl), hydrogen fluoride (HF), hydrogen bromide (HBr), other acids, water or water vapor (H₂O), hydrogen cyanide (HCN), ammonia (NH₃), arsine (AsH₃), phosphine (PH₃), oxygen (O₂) carbon monoxide (CO), carbon dioxide (CO₂), methane (CH₄), ethane (C₂H₆), ethene (C₂H₄), ethylene (C₂H₂), other hydrocarbons, hydro-fluoro-carbons, hydro-chloro-carbons, and hydro-fluoro-chloro-carbons.

13. The method of claim 1, wherein the second analysis approach comprises at least one of tunable diode laser absorption spectroscopy, tunable semiconductor laser absorption spectroscopy, and infrared spectroscopy using filter-based and/or grating-based light sources.

14. A system comprising:

computer hardware configured to perform operations comprising;

modeling absorption spectroscopy measurement data obtained for a gas sample as one or more functions of a background composition of the gas sample, the modeling comprising generating mathematical representations of structural interference and/or collisional broadening effects based on background composition concentration data representative of an actual background composition of the gas sample;

applying one or more multivariate regression analysis algorithms to correct, based on the modeling, an analysis of the absorption spectroscopy data, the absorption spectroscopy data being obtained by a second analysis approach comprising absorption spectroscopy; and

determining a target analyte concentration in the gas sample based on the corrected analysis of the absorption spectroscopy data obtained by the second analysis approach.

15. The system of claim 14, wherein the actual background composition comprises a plurality of chemical species whose concentrations in the gas sample vary sufficiently between over time to cause variation in the determined target analyte concentration if the one or more multivariate regression analysis algorithms are not applied to correct the analysis of the absorption spectroscopy data obtained by the second analysis approach.

16. The system of claim 14, wherein the background composition concentration data comprises data obtained by a first analysis approach, the first analysis approach comprising at least one of Raman spectroscopy, gas chromatography, gathering of concentration and/or other physical data from a distributed control system or a supervisory control and data acquisition system, nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy, direct absorption spectroscopy, emission spectroscopy, and nondispersive infrared spectroscopy.

18. The system of claim 14, further comprising a Raman spectroscopy subsystem, and further comprising an absorption spectroscopy subsystem for executing the second analysis approach.

19. The system of claim 18, wherein the Raman spectroscopy subsystem and the absorption spectroscopy subsystem are configured to obtain Raman spectral data and the absorption spectroscopy data on a same gas sample.

20. The system of claim 18, wherein the Raman spectroscopy subsystem comprises an in situ probe configured to obtain the Raman spectral data from within a gas source, and wherein the system includes a gas sampling system for extracting the gas sample from the gas source substantially concurrently with the obtaining of the Raman spectral data by the probe.

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29. A computer program product comprising a computer-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations comprising:

modeling absorption spectroscopy measurement data obtained for a gas sample as one or more functions of a background composition of the gas sample, the modeling comprising generating mathematical representations of structural interference and/or collisional broadening effects based on background composition concentration data representative of an actual background composition of the gas sample;

applying one or more multivariate regression analysis algorithms to correct, based on the modeling, an analysis of the absorption spectroscopy data, the absorption spectroscopy data being obtained by a second analysis approach comprising absorption spectroscopy; and

determining a target analyte concentration in the gas sample based on the corrected analysis of the absorption spectroscopy data obtained by the second analysis approach.