TWI541492B - Method and apparatus for siloxane measurements in a biogas - Google Patents

Description

Method and apparatus for measuring helium oxide in biogas [Cross-Reference to Related Applications]

The present application claims priority to U.S. Provisional Patent Application Serial No. 61/587,391, filed Jan. . This application is a continuation-in-part application of U.S. Application Serial No. 12/720,542, filed on Mar. 9, 2010, the U.S. Application Serial No. 12/720,542, filed on Sep. 28, 2009. Part of the continuation application, U.S. Application No. 12/567,981 is the continuation application of U.S. Application No. 12/119,244 (now U.S. Patent No. 7,595,887) filed on May 12, 2008, U.S. Application No. </ RTI> </ RTI> <RTIgt; </ RTI> <RTIgt; </ RTI> <RTIgt; </ RTI> <RTIgt; </ RTI> <RTIgt; </ RTI> <RTIgt; </ RTI> <RTIgt; </ RTI> <RTIgt; in.

The present invention relates generally to absorption spectrometers and, more particularly, to monitoring and measuring concentrations of oxoxane compounds in, for example, biofuels or biogas. The technique is also directed to determining the total concentration of all of the oxane compounds in the biofuel or biogas and/or the total concentration of all cerium-containing compounds.

Spectroscopy is the study of the interaction between electromagnetic radiation and a sample, such as one or more of gases, solids, and liquids. The way radiation interacts with a specific sample Depending on the nature of the sample (eg molecular composition). In general, when radiation passes through a sample, the molecules in the sample absorb radiation of a particular wavelength. The radiation of a particular wavelength absorbed is unique to each molecule in a particular sample. By identifying which wavelength of radiation is absorbed, it is possible to identify the particular molecule present in the sample.

Infrared spectroscopy is a specific field of spectroscopy in which, for example, a sample (eg, a gas, a solid, a liquid, or a combination thereof) is subjected to infrared electromagnetic energy to determine, for example, the type of molecules in a sample and the concentration of individual molecules. In general, infrared energy is characterized by electromagnetic energy having an energy wavelength between about 0.7 μm (frequency of 14,000 cm ^-1 ) and about 1000 μm (frequency of 10 cm ^-1 ). Infrared energy is directed through the sample and energy interacts with molecules in the sample. The energy passing through the sample is detected by a detector (eg, an electromagnetic detector). The detected signal is then used to determine, for example, the molecular composition of the sample and the concentration of a particular molecule in the sample.

One particular type of infrared spectrometer is a Fourier Transform Infrared (FTIR) spectrometer. It is used in a variety of industries such as air quality monitoring, explosion and biological reagent detection, semiconductor processing and chemical production. Different applications of FTIR spectrometers require different detection sensitivities to allow the user to distinguish between molecules present in the sample and to determine the concentration of different molecules. In some applications, it is necessary to identify the concentration of individual molecules in the sample in about one part per billion (ppb). As industrial applications require ever-increasing sensitivity, optimizing existing spectroscopy systems and employing new spectroscopy components allows the system to reproducibly and reliably resolve increasingly smaller molecular concentrations in a sample.

FTIR spectrometers can also be used to monitor, for example, the concentration of a compound in a gas. Biofuels, such as biogas, are used to power various devices, including turbine generators. Burn biogas to power the device. Biogas (eg, gas from animal waste, wastewater, or landfill) can include a variety of compounds, including oxoxane compounds. Generating an oxide (e.g. SiO ₂ (e.g. Silica or silica sand)) also combustion silicon alumoxane compound of biogas. SiO ₂ will coat turbine blades as well as turbine bearings, resulting in reduced turbine performance or even failure of the turbine. Higher levels of decane in the biogas accelerate the coating process. Biogas manufacturers typically use an activated carbon filter to trap the oxane, but when the filter is depleted, the oxane content increases.

The conventional method of monitoring the concentration of a oxoxane compound in a biogas is performed off-line by analyzing a sample taken from a biogas. For example, conventional techniques involve the separation and measurement of helioxane from background gases using GC/MS (ie, gas chromatography/mass spectrometry) techniques. To analyze the sample gas, the sample is taken for analysis and allowed to operate on a GC/MS system. Field samples are typically taken from the gas stream and introduced into stainless steel tanks or Tedlar sample bags, or collected using a methanol solvent impact gas sampler. This sample is then sent back to the analytical laboratory and analyzed; the results of the analysis usually remain unknown for several days. The sample has a tendency to condense the ingredients, making it difficult to assess the true composition of the sample. Samples taken in this manner also provide only a single hit for the analysis of the content, and thus may not represent the true composition of the sample. The GC/MS analysis of the sample will also take several hours to analyze the oxirane compound in the sample, at which time operator intervention may be too late. If a rise in the amount of decane has occurred, it may have missed the opportunity to perform any feasible help. Increasing the ability to monitor and measure the concentration of helium in biogas can extend turbine life. In addition, the ability to monitor and quickly detect and quantify the oxane and/or strontium content provides more time for feasible help/intervention.

Spectroscopy can be used to detect, identify, and/or quantify, for example, trace amounts of oxime and other ruthenium containing compounds in biogas (eg, identification of individual oxiranes in about 5 parts per billion (ppb) of sample biogas The concentration of the compound). It can detect and quantify trace amounts of cyclic oxiranes in biogas (such as D3-oxime, D4-oxime, D5-decane and D6-decane) and linear oxiranes (L2- Alkane, L3-oxime, L4-oxime, and L5-decane, and trimethyl silanol (TMS). It can usually be on the spot (for example, at a landfill, animal waste or wastewater) and immediately (for example, processing and analyzing the content of the sample biogas at a location without having to be in the laboratory at a later point in time) The sample is obtained and analyzed. The concentration of the oxane or hydrazine is measured. On-line continuous monitoring provides immediate sensing of the rise in helium oxide and/or total helium content and informs the operator or automatically shuts down the process to prevent the turbine from unnecessarily exposing to SiO ₂ .

Samples that include reagents (eg, compounds) that have substantially higher infrared absorption than other agents in the sample (eg, interference absorbers) can present problems in FTIR spectroscopy because FTIR relies on subjecting the sample to infrared energy. The interference absorber in the sample prevents effective detection and measurement of the concentration of other reagents to be detected having substantially lower infrared absorption in the sample. Biogas may include molecules such as oxoxane and other ruthenium containing compounds, hydrocarbon compounds such as methane or ethane, water or carbon dioxide. At certain wavelengths, a hydrocarbon compound in a biogas may have a relatively high infrared absorption (e.g., an absorption of D4 oxane at a wavelength of about 7.8 microns of about 0.001). The absorption of ethane at a wavelength of about 7.8 microns is about 0.055). Thus, the hydrocarbon can be an interference absorber. The oxoxane compound can have a relatively high infrared absorption in the wavelength range from about 8 microns to about 12 microns (e.g., the absorption of D4 decane at about 8.2 microns is about 0.075 and absorption at about 11 microns). Is 0.125). Thus, even in the presence of a hydrocarbon compound or other interfering absorber, the oxime in the sample biogas can be measured by taking spectral measurements in the wavelength range of interest (eg, from about 8 microns to about 12 microns). The concentration of the compound. The wavelength range of interest can be selected, within which the major components of the biogas (eg, H ₂ O, CO ₂ , CH ₄ ) do not have a large absorbance. The oxane and TMS compounds may have an absorbance that overlaps with other hydrocarbons in the wavelength range of interest. Multivariate analysis can be used to distinguish the contribution of the oxane compound to other hydrocarbons, as well as to assess the portion attributed strictly to the oxane compound.

In one aspect, a method for monitoring one or more cerium-containing compounds present in a biogas is provided, the method comprising providing a non-absorbent gas to a sample cell. The non-absorbent gas has substantially no infrared absorption in the specified wavelength range of interest. The method includes obtaining a first spectral measurement from a sample cell and providing a biogas to the sample cell. this method Also included is obtaining a second spectral measurement from the sample cell and generating a first absorption spectrum based on a ratio of the first spectral measurement to the second spectral measurement. The method further comprises generating at least one alternative absorption spectrum based on at least an individual absorption spectrum selected from each of a greater group of one or more groups of oxoxane compounds having a known concentration of a known oxoxane compound. The total concentration of one or more cerium-containing compounds in the biogas can be calculated based on the first absorption spectrum and at least one of the replacement absorption spectra.

In another aspect, a computer readable product is provided that is physically embodied on a non-transitory information carrier or machine readable storage device and operable on a digital signal processor of a biogas detection system. The computer readable product includes instructions operable to cause the digital signal processor to receive a first spectral measurement of the non-absorbent gas in the sample cell. The non-absorbent gas has substantially no infrared absorption in the specified wavelength range of interest. The computer readable product includes instructions operable to cause the digital signal processor to receive a second spectral measurement of the sample gas containing the biogas in the sample cell. The computer readable product also includes operable to cause the digital signal processor to generate a first absorption spectrum based on a ratio of the first spectral measurement to the second spectral measurement; and at least based on a known selected from a larger group having a known concentration The individual absorption spectra of each of a group of one or more ruthenium containing compounds of the oxoxane compound produce a set of instructions in place of the absorption spectrum. The computer readable product further includes instructions operable to cause the digital signal processor to perform a multiple regression analysis using the first absorption spectrum and the set of instead of the absorption spectrum to calculate a total concentration of one or more oxane compounds in the biogas.

In other examples, any of the above aspects can include one or more of the following features. In some embodiments, a correction factor is applied to the total concentration. The correction factor uses a factor to scale the total concentration. In some embodiments, the one or more cerium-containing compounds in the biogas comprise at least one oxoxane. The larger group of known ruthenium containing compounds may also include at least one oxoxane. The group of one or more cerium-containing compounds may also include at least one oxoxane.

In some embodiments, based on known bismuth-containing compounds and biogas One or more quinone-containing compounds of the panel are selected by spectral matching of one or more cerium-containing compounds. In some embodiments, at least one of the group of one or more cerium-containing compounds is present in the biogas. In some embodiments, at least one of the one or more cerium-containing compounds of the panel is absent from the biogas.

In some embodiments, the larger group of known antimony containing compounds comprises D3-decane, D4-decane, D5-decane, D6-decane, L2-decane, L3-oxime Alkanes, L4-decanes and L5-decanes. The group of one or more cerium-containing compounds may comprise from 3 to 5 cerium-containing compounds selected from the larger group of known cerium-containing compounds.

In some embodiments, the biogas comprises biogas. In this case, the group of one or more antimony-containing compounds may comprise one of: a) L2-decane, L3-decane and D4-decane; b) L2-decane, D3 - oxoxane and D4-oxime; or c) L2-oxime, D3-decane and D5 decane.

In some embodiments, the biogas comprises a digestion tank biogas. In this case, the group of one or more antimony-containing compounds comprises one of: a) D3-decane, D5-decane, and L3-decane; b) D4-decane, D5- Oxane and L3-oxime; or c) D3-oxane, D5-nonane and L2-decane.

In some embodiments, the instead of the absorption spectrum further comprises an individual absorption spectrum selected from each of a larger group of one or more hydrocarbon compounds having a known concentration of known hydrocarbon compounds. The larger group of known hydrocarbon compounds may comprise ethane, propane and butane. The replacement absorption spectrum can be a model based on at least an individual absorption spectrum of each of the one or more cerium-containing compounds of the panel and an individual absorption spectrum of each of the one or more hydrocarbon compounds of the panel.

In some embodiments, calculating the total concentration of one or more cerium-containing compounds in the biogas comprises performing a multiple regression analysis using the first absorption spectrum and instead of the absorption spectrum using a processor. Classical Least Square (CLS), Partial Least Squares (PLS), Inverse Least Squares (Inverse Least Squares; ILS) or Principal Component Analysis (PCA) to perform multiple regression analysis. The total concentration value can be determined such that the instead absorption spectrum is substantially similar to the first absorption spectrum. In some embodiments, the total concentration of one or more cerium-containing compounds in the biogas can be calculated on the spot and on the spot.

In some embodiments, the second spectral measurement can be obtained over a capture period of from about 10 seconds to about 20 seconds.

In some embodiments, the biogas system is obtained from animal waste, wastewater or landfill.

In another aspect, a system for monitoring one or more cerium-containing compounds in a biogas is provided. The system includes a source of a first radiation beam, an interferometer that receives a first radiation beam from the source and forms a second radiation beam that includes an interference signal, and a sample cell in optical communication with the interferometer. The system also includes a flow mechanism that establishes a first stream of non-absorbent gas that is substantially free of infrared absorption and a second stream of biogas that passes through the sample cell over a specified wavelength range of interest. The system includes a cooled detector that is in optical communication with the sample cell. The cooled detector is adapted to receive a first interference signal propagating through the non-absorbent gas in the sample cell and a second interference signal propagating through the sample gas in the sample cell. The system additionally includes a processor in electrical communication with the cooled detector. Having the processor configured to calculate a total concentration of one or more cerium-containing compounds in the biogas based on: 1) a first absorption spectrum based on a ratio of the first interference signal to the second interference signal; and 2) a set of substitutions An absorption spectrum that is based at least on individual absorption spectra of each of a small group of one or more cerium-containing compounds selected from a larger group of known cerium-containing compounds having known concentrations. The system further includes a housing in which the light source, interferometer, sample cell, cooled detector, and processor are disposed.

In some embodiments, the sample cell of the system includes a concave reflective field mirror surface at a first end of the sample cell. The sample cell can also include a substantially spherical concave reflective objective surface at the second end of the sample cell in face-to-face relationship with the field mirror surface. Objective lens The face has a cylindrical assembly that increases the coincidence of the focus in at least one of the planes to maximize the amount of delivery of the second radiation beam that propagates through the sample cell by multiple reflections on each of the field mirror surface and the objective lens surface.

In some embodiments, the set of substitutional absorption spectra is based on at least an individual absorption spectrum of each of the one or more cerium-containing compounds of the group and a group selected from a larger group of known hydrocarbon compounds having known concentrations A model of the individual absorption spectra of each of the one or more hydrocarbon compounds.

Other aspects and advantages of the invention will be apparent from the description and appended claims.

10‧‧‧ device

10'‧‧‧ device

14‧‧‧Light source

14'‧‧‧Light source

18‧‧‧Interferometer

18'‧‧ Interferometer

22‧‧‧sample cell/gas pool

22'‧‧‧ sample pool

26‧‧‧ gas samples

30‧‧‧Detector

30'‧‧‧Detector

34‧‧‧ Processor

38‧‧‧ display

38'‧‧‧First display

38"‧‧‧second display

42‧‧‧Shell

42'‧‧‧Shell

46‧‧‧ entrance to the sample pool

46'‧‧‧ gas inlet

50‧‧‧Export of the sample cell

50'‧‧‧ gas export

52‧‧‧ first mirror

54‧‧‧Parabolic mirror

54'‧‧‧Parabolic mirror

58‧‧‧First Folding Mirror

58'‧‧‧First Folding Mirror

62‧‧‧Second folding mirror

62'‧‧‧Second folding mirror

66‧‧‧Oval mirror

66'‧‧‧Oval Mirror

74‧‧‧ Objective surface

74'‧‧‧ Objective surface

78‧‧‧ Field mirror surface

78'‧‧‧ field mirror surface

82‧‧‧Mobile system

86‧‧‧Filter

90‧‧‧Flow sensor

94‧‧ ‧ heating elements as appropriate

98‧‧‧ Pressure Sensor

98'‧‧‧ Pressure Sensor

102‧‧‧ valve

106‧‧‧ pump

106'‧‧‧ pump

110‧‧‧ gas pipeline

118‧‧‧Port for connecting to external devices

122‧‧‧ top board

126‧‧‧ side panels

130‧‧‧floor

134‧‧‧Optical box

138‧‧‧ Valves for regulating airflow

142‧‧‧Accessories

146‧‧‧Power supply

150‧‧‧fan

154‧‧‧Connector

158‧‧‧Data Capture Module

162‧‧‧Mirror motion control module

166‧‧‧Single board computer

170‧‧‧Power distribution module

172‧‧‧ hard disk drive

174‧‧‧ Temperature Controller

The above advantages and other advantages of the present invention will be more readily understood from the <RTIgt; Throughout the drawings, the same reference characters generally refer to the same parts throughout the drawings. The drawings are not necessarily to scale and the emphasis is primarily to illustrate the principles of the invention.

1 depicts a block diagram of an exemplary detection system of the present invention for monitoring and/or detecting trace gases in a gas sample.

Figure 2 shows a schematic diagram of an exemplary optical configuration of the present invention.

Figure 3 shows a block diagram of an exemplary flow system of the present invention for introducing a sample into a sample cell.

Figure 4 is a graph of path length/NEA comparison pass times between optical surfaces of a sample cell of the present invention.

Figure 5 is a graph of concentration versus time for trace gases during the introduction of trace gases into an exemplary detection system of the present invention.

Figure 6 shows a time series of a series of measurements of the present invention.

Figure 7 depicts a plan view of an exemplary detection of the present invention for monitoring and/or detecting trace gases in a gas sample.

Figure 8 shows a plan view of some of the components of the present invention for monitoring and/or detecting exemplary detection of trace gases in a gas sample.

9 shows a flow chart depicting a method for monitoring a oxoxane compound in a biogas, in accordance with an illustrative embodiment of the invention.

Figure 10 shows a graphical representation of NIPALS decomposition of spectral information represented by matrix X (spectral measurement) and matrix Y (density data), in accordance with an illustrative embodiment of the invention.

Figure 11 shows an individual absorption spectrum for monitoring a oxoxane compound in a biogas, in accordance with an illustrative embodiment of the invention.

Figure 12 shows a flow chart depicting another illustrative method for monitoring a oxoxane compound in a biogas sample.

Figure 13 shows the results of the total decane concentration of the synthesized biogas sample over time.

Figure 14 shows the results of the total decane concentration of the digester gas sample over time.

1 shows a block diagram of an illustrative device 10 for monitoring and/or detecting trace gases in a gas sample. Device 10 can be used to detect trace amounts of substances such as sarin, tabun, soman, sulfur mustard, and VX nerve gas. Device 10 can also be used, for example, to detect the amount of decane in a biogas. In some embodiments, a vapor of a solid or liquid substance can be detected. Device 10 can be an absorption spectrometer and/or can be a Fourier Transform Infrared (FTIR) spectrometer. In the illustrated embodiment, device 10 includes a light source 14, an interferometer 18, a sample cell 22, a source of gas sample 26, a detector 30, a processor 34, a display 38, and a housing 42. In various embodiments, device 10 can be used to detect trace gases over a relatively short period of time with very few false positives or false negatives, if any.

In various embodiments, light source 14 can provide a radiation beam (eg, an infrared radiation beam). Light source 14 can be a laser or an incoherent light source. In one embodiment, the light source is a glowbar, which is an inert solid that is heated to about 1000 ° C to produce black body radiation. The glow rod can be formed from tantalum carbide and can be powered by electricity. The system can have a spectral range between about 600 cm ^-1 and about 5000 cm ^-1 . The resolution of the system can be 2 cm ^-1 and about 4 cm ^-1 . In one embodiment, the detection system can record a higher resolution spectrum of trace gases when detecting trace gases. Higher resolution spectra can help identify trace gases.

In various embodiments, the source 14 of radiation and the interferometer 18 can constitute a single instrument. In some embodiments, the interferometer 18 is a Michelson interferometer commonly known in the art. In one embodiment, the interferometer 18 is a BRIK interferometer commercially available from MKS Instruments, Inc. (Wilmington, MA). The BRIK interferometer may include a combiner that splits and merges incident radiation, a mobile corner that modulates radiation, a white light source that identifies a center burst, and a vertical cavity surface that monitors the velocity of the corners. Vertical Cavity Surface Emitting Laser (VCSEL). The BRIK interferometer is immune to tilt and lateral motion errors as well as thermal variations, thereby enhancing the durability of the interferometer.

In one embodiment, the interferometer 18 can be a module that includes a radiation source, a fixed mirror, a movable mirror, an optical module, and a detector module (eg, detector 30). The interferometer module can measure all optical frequencies generated by its source and transmitted through a sample (e.g., sample 26 contained within sample cell 22). The radiation is directed to an optical module (eg, a beam splitter) that splits the radiation into two beams, a first signal and a second signal. The movable mirror produces a variable path length difference between the two initially substantially identical electromagnetic energy beams. The movable mirror is usually moved or scanned at a constant speed. After the first signal travels a different distance from the second signal (in this embodiment, due to the movement of the movable mirror), the first and second signals may be reconstructed by the optical module to generate a radiation measurement signal, The intensity is modulated by the interference of two beams. This interference signal is passed through the sample and measured by the detector. The presence of different samples (such as solids, liquids, or gases) can modulate the intensity of the radiation detected by the detector. Thus, the output of the detector is a variable time-dependent signal that depends on the optical path difference established by the relative positions of the fixed mirror and the movable mirror and the modulation of the electromagnetic signal generated by the sample. This output signal can be described as an interferogram.

The interferogram can be expressed as a curve of the received energy intensity versus the position of the movable mirror. Those skilled in the art will refer to the interferogram as a signal as a function of time. The interferogram is a function of the variable optical path difference produced by the displacement of the movable mirror. Because the position of the movable mirror is typically and desirably scanned at a constant speed, those skilled in the art will refer to the interferogram as a "time domain" signal. An interferogram can be understood as the sum of the energy of all wavelengths emitted by the source and passing through the sample. Using the mathematical method of Fourier Transform (FT), a computer or processor can convert the interferogram into a characteristic spectrum that absorbs or transmits light through the sample. Because individual types of molecules absorb energy at specific wavelengths, it is possible to determine the molecules present in a sample based on the interferogram and corresponding spectra. In a similar manner, the amount of energy absorbed or transmitted through the sample by the sample can be used to determine the molecular concentration in the sample.

In various embodiments, the interferometer is not used to form an interference signal. An optical spectrum signal is recorded using an absorption spectrometer, and information about trace amounts of material is derived from signals transmitted through the sampling zone. For example, an absorption spectrum or a differential spectrum can be used.

In various embodiments, the sample cell 22 can be a folded path and/or a multi-pass absorption cell. The sample cell 22 can include an aluminum housing that encloses the optical component system. In some embodiments, the sample cell 22 is a folded-path optical analysis gas cell as described in U.S. Patent No. 5,440,143, the disclosure of which is incorporated herein in its entirety.

In various embodiments, the source of the gas sample 26 can be ambient air. The sample cell 22 or gas sampling system collects ambient air and introduces it into the sampling zone of the sample cell 22. A gas sample can be introduced into the sample cell 22 at a predetermined flow rate using a flow system including an inlet 46 and an outlet 50 of the sample cell 22.

In various embodiments, detector 30 can be an infrared detector. In some embodiments, detector 30 is a cooled detector. For example, the detector 30 can be a refrigerant cooled detector (such as a mercury cadmium telluride (MCT) detector), a Stirling cooled detector or a Peltier cooling detector. In one embodiment, the detector is a deuterated triglycine sulfate (DTGS) detector. In one embodiment, the detector is a 0.5 mm Stirling-cooled MCT detector with a cut-off point of 16 μm, which provides the sensitivity needed to detect trace gases. The relative responsiveness of the Stirling-cooled MCT detector (i.e., the responsiveness to wavelength) is at least 80% of the dominant wavelength range of interest (e.g., 8.3-12.5 μm). In addition, the D* value of the Stirling cooled MCT detector can be at least 3 x 10 ¹⁰ cm Hz ^1/2 W ^-1 . D* can be defined as the reciprocal of the detector noise equivalent power multiplied by the square root of the effective component area.

Processor 34 can receive signals from detector 30 and identify trace gases from their spectral fingerprints or provide a relative or absolute concentration of a particular material within the sample. Processor 34 can be, for example, a signal processing hardware and a quantitative analysis software executing on a personal computer. Processor 34 can include a processing unit and/or memory. Processor 34 can continuously capture and process the spectra while calculating the concentration of multiple gases within the sample. Processor 34 may transmit information such as the identity of trace gases, the spectrum of trace gases, and/or the concentration of trace gases to display 38. The processor 34 can store the spectral concentration time history of the graphical and tabular formats, as well as the measured spectral and spectral residuals, and these can also be displayed. Processor 34 may collect and store various other data for later processing or review. Display 38 can be a cathode ray tube display, a light emitting diode (LED) display, a flat screen display, or other suitable display known in the art.

In various embodiments, the housing 42 can be adapted to provide a detection system that is one or more of a portable, rugged, and lightweight type. The outer casing 42 can include a handle and/or a transport mechanism that can be easily secured to a pullell or handtruck. The outer casing 42 can be sufficiently strong to resist misalignment or breakage of the components during transport and/or dropping. In various embodiments, the weight of device 10 can be as light as 40 pounds. In one embodiment, device 10 is fully self-contained (e.g., includes all components necessary to collect samples, record spectra, process spectra, and display information related to the sample in housing 42).

FIG. 2 shows an illustrative embodiment of an optical configuration that can be used with device 10. From the light source Radiation of 14 (e.g., glow stick) is directed by first mirror 52 to interferometer 18 (e.g., including a potassium bromide beam splitter). The radiation beam is directed by a parabolic mirror 54 (PM) to the first folding mirror 58 and into the sample cell 22. The radiation beam exits the sample cell and is directed by a second folding mirror 62 to an elliptical mirror 66 (EM) which directs the radiation beam to the detector 30.

In a representative embodiment, parabolic mirror 54 has an effective focal length of about 105.0 mm, a female focal length of about 89.62 mm, and an eccentricity of about 74.2 mm. The parabolic mirror 54 can have a diameter of about 30.0 mm and a reflection angle of about 45°.

In one embodiment, the elliptical mirror 66 may have a major half-axis of about 112.5, a short semi-axis of about 56.09, and an ellipse with an angle of inclination of about 7.11. The elliptical mirror 66 may have a diameter of about 30.0 mm and a reflection angle (principal ray) of about 75°.

In various embodiments, the first folding mirror 58 can have a diameter of about 25 mm and the second folding mirror 62 can have a diameter of about 30 mm.

The mirrors and optics may comprise a gold coating, a silver coating or an aluminum coating. In one embodiment, the elliptical mirror and the parabolic mirror are coated with gold and the planar folded mirror is coated with silver.

In various embodiments, the sample cell can include an objective lens surface 74 and a field mirror surface 78. The objective lens surface 74 can be substantially spherical and concave. Field mirror surface 78 can be concave and positioned in face-to-face relationship with objective surface 74. The objective surface 74 can include at least one cylindrical component that increases the focus coincidence in at least one of the planes to maximize the amount of delivery of the radiation beam propagating between the surfaces 74 and 78. In one embodiment, the objective surface 74 can include a plurality of substantially spherical concave reflective objective surfaces, and each surface can include a cylindrical assembly that increases focus coincidence in at least one of the planes to maximize delivery of the radiation beam. The center of curvature of the objective lens surface can be positioned after the field mirror surface 78. By increasing the focus coincidence in at least one plane, distortion, astigmatism, sphere deviation, and coma aberration can be preferably controlled, and a higher throughput can be achieved. The addition of a cylindrical component can be used to reduce the effective radius of curvature in a plane such that light incident on the reflective surface is preferably close to the focus in the orthogonal plane. In one embodiment, the objective surface 74 has a stack thereon The cylindrical assembly provides different radii of curvature in two orthogonal planes. The objective surface 74 can have a contour that approximates the torus.

The total path length of the sample cell 22 can be between about 5 m and about 15 m, although longer and shorter path lengths can be used depending on the application. In a detailed embodiment, the total path length of the sample cell 22 is about 10.18 m, caused by the total number of passes between the objective lens surface 74 and the field mirror surface 78 about 48 times. The optics of the sample cell 22 can be optimized for a 0.5-mm detector and a 1 x arc collection angle. The detector optical magnification ratio can be about 8:1. The objective lens surface 74 and the field mirror surface 78 may have a gold coating having a nominal reflectance of between about 98.5% and between 800 and 1200 cm&lt;^1&gt; The internal volume of the sample cell can be between about 0.2 L and about 0.8 L, although larger and smaller volumes can be used depending on the application. In a detailed embodiment, the volume is about 0.45 L.

In one embodiment, the mirrors and optics for directing the radiation beam into and through the sample cell 22, focusing the radiation beam onto the entrance slit of the sample cell 22, and/or directing the radiation beam to the detector are The optimisation matches the optical characteristics of the sample cell, thereby maximizing the amount of radiation delivered and enhancing the sensitivity of the detection system.

For example, in one embodiment, an appropriately aligned optical configuration can have an efficiency of about 88.8%. As used herein, the efficiency can be the ratio of the number of rays striking the image square to the total number of rays emitted within the range of emission angles. In one embodiment, the positions of the folding mirrors 58 and 62 and the detector 30 can be adjustable, thereby allowing compensation for various mechanisms between the interferometer 18, the parabolic mirror 54, the sample cell 22, and the detector 30. Allowable error. In one embodiment, the following nominal (design) optical distance can be used to optimize delivery.

The detector is approximately 21.39 mm from the elliptical mirror (X1).

̇Oval mirror to folding mirror (X2) is about 132.86 mm.

̇ Folding mirror to sample cell (field mirror surface) (X3) about 70.00 mm.

The sample cell path length is approximately 10181.93 mm.

The sample cell to the folding mirror (X4) is approximately 70 mm.

̇ Folding mirror to parabolic mirror (X5) is about 35 mm.

FIG. 3 shows an illustrative embodiment of an exemplary flow system 82 for introducing a sample into a sample cell 22. The flow system 82 includes a filter 86 coupled by a gas line 110, a flow sensor 90, a heating element 94 as appropriate, a gas pool 22, a pressure sensor 98, a valve 102, and a pump 106. The arrow shows the direction of flow. One or more of the components of the flow system 82 may include a wetted part such as Teflon, stainless steel, and Kalrez to withstand the decontamination temperature and resist corrosion of CWA and TIC and to avoid oxygenation. The alkane condenses.

Filter 86 can be an in-line 2 μm stainless steel filter available from Mott Corporation (Farmington, CT). Flow sensor 90 can be a mass flow sensor that includes a stainless steel wetted component, such as a flow sensor available from McMillan Company (Georgetown, TX). Heating element 94 can be a line heater commercially available from Watlow Electric Manufacturing Company (St. Louis, MO). Pressure sensor 98 can be a Baratron pressure sensor commercially available from MKS Instruments (Wilmington, MA). Valve 102 can be stainless steel and include a Teflon O-ring, such as a valve available from Swagelok (Solon, OH). Gas line 110 can be a 3/8" diameter tube commercially available from Swagelok.

Pump 106 can be a "miniature" diaphragm pump with a heated head. A Dia-Vac B161 pump available from Air Dimensions, Inc. (Deerfield Beach, FL) can be used. In one embodiment, a small diaphragm pump commercially available from Hargraves Technology Corporation (Mooresville, NC) can be used. In an illustrative embodiment, pump 106 can be positioned downstream of sample cell 22 to draw air through the sample cell. In some embodiments, a pump that does not have a heated head can be used. In some embodiments, a pump is not required if the gas sample is pressurized. In this case, the delivery pressure of the gas sample is adapted to advance the sample through the gas pool. Thus, any leaks in the system can be pulled away from the analyzer rather than pushed into the analyzer to minimize the risk of contamination of the internal components of the analyzer. Additionally, unwanted products of unintended chemical reactions involving the elastomer of the pump can be prevented from entering the sample cell 22.

In various embodiments, the flow rate through the flow system 82 can be between 2 L/min and 10 L/min, although larger and smaller flow rates can be used depending on the application. In a real In the example, the flow rate is between 3 L/min and 6 L/min. The pressure of the sample can be about 1 atm, but depending on the application, it can maintain greater and lesser pressure. In some embodiments, the sample cell can be operated at a high pressure, such as up to 4 atm. The sample cell can operate at temperatures between about 10 ° C and about 40 ° C, although higher and lower temperatures can be maintained depending on the application. In one embodiment, the detection system can include a heating element to heat the sample to between about 40 ° C and about 180 ° C. In one embodiment, the temperature can be raised up to about 150 ° C to decontaminate the device.

In various embodiments, the sample cell path length can be between about 5 m and about 12 m. The spacing between the field mirror surface and the objective lens surface can be limited by the gas sampling flow rate. In one embodiment, a 5.11 meter sample cell having a 16 cm pitch and 32 passes can have an internal volume of about 0.2 L. In another embodiment, a 20.3 cm pitch having 32 passes can have a volume of about 0.4 L for the same number of passes. In yet another embodiment, the 25.4 cm pitch can have a volume of about 0.6 L. It can be determined that a sufficient "fresh" ambient gas supply flow rate is provided at least every 10 seconds, but a smaller sampling rate can be obtained. In various embodiments, the flow rate (e.g., between 2 L/min and 10 L/min) can be optimized to provide an optimum gas exchange rate. For example, in one embodiment, the gas exchange rate is at least 80% over a 20 second detection time interval. In one embodiment, the gas exchange rate is between about 80% and about 95% over a 10 second detection time interval.

The path length/NEA ratio can be used as a measure of the sensitivity of the quantitative detection system, where the path length is the total beam path length of the sample cell measured in meters, and the NEA is the noise measured in absorbance units (AU). Effective absorbance. If the sensitivity is limited by non-systematic errors in the detection system (also known as random noise, such as detectors and electronic noise), the detection limit can be inversely proportional to the path length/NEA ratio. For example, if the ratio is doubled, the detection limit (ppb or mg/m ³ ) of a particular sample will be halved. It is therefore an appropriate quantitative measure of sensitivity performance. This measure does not take into account the increased sensitivity attributed to advanced sampling techniques such as gas pressurization and cold trapping.

Taking into account the restrictive system noise such as detectors and digital noise, the path can be made The length/NEA ratio is optimized for various system configurations. Optimized parameters include flow rate, sample cell volume, optical path length, number of passes through the sample cell, optical configuration, specular reflectance, specular reflectance material, and detector used. For example, the best detector is a detector with the highest D* value and speed (lower response time) within the constraints of size, cost and lifetime.

For spectrometers that are subject to detector noise limitations, the sensitivity or path length/NEA ratio is proportional to the D* value. The detector bandwidth determines the maximum scan speed, which in turn determines the maximum number of data averaging that can be performed during the allowed measurement period. For systems subject to detector or electronic noise limitations, sensitivity typically increases with the square root of the average number of scans or, for example, the time at which such scans are performed. In one embodiment, the Stirling cooled detector can provide a path length/NEA sensitivity ratio of at least 1.5 x 10 ⁵ m/AU. DTGS detectors offer a cheap alternative because of their low cost and lifetime maintenance-free, but they can have lower D* values and are slower.

The path length/NEA value can be determined by optimizing the distance between the field mirror surface and the objective lens surface and the number of passes between such surfaces. Figure 4 shows a plot of path length/NEA as a function of specular reflection for various surface spacings, such as 6.3 吋 (16.0 cm), 8 吋 (20.3 cm), and 10 吋 (25.4 cm). As shown in Figure 4, the maximum path length / NEA value occurs at approximately 92 passes. However, at 92 passes, only 25% of the light was transmitted due to the reflection loss at the mirror surface. In a detailed embodiment, the transmittance of the sample cell is between about 50% and about 60%. At a mirror reflectance of 98.5%, 60% transmission corresponds to about 32 passes, which is indicated by a vertical line in FIG. The 50% transmission corresponds to about 48 passes. Table 1 shows an exemplary combination of parameters for providing a sampling system for detecting trace gases in a sample.

Table 1. Exemplary parameter combinations for providing a sampling system for detecting trace gases in a sample.

The path length/NEA ratio can be converted to a detection limit expressed in a concentration of mg/m ³ or billions of parts (ppb). One method for such conversion is to compare the expected peak absorbance metric to the expected NEA value. Device 10 can be used to detect trace amounts of materials such as decane, sarin, tabun, soman, sulfur mustard and VX nerve gas at concentrations below about 500 ppb. In various embodiments, the concentration can be between about 10 ppb and about 500 ppb, but higher and lower concentrations can be detected depending on the system and application. In some embodiments, depending on the species, the concentration can be between 5 ppb and about 50 ppb. For example, device 10 is capable of detecting traces of sarin at a concentration between about 8.6 ppb and about 30 ppb; traces of tower collapse at a concentration between about 12.9 ppb and about 39 ppb; concentration at about 7.3 ppb and about Traces of Somans between 22.8 ppb; trace amounts of sulfur mustard at a concentration between about 36.7 ppb and about 370.6 ppb; or traces of VX nerve gas at a concentration between about 12.9 ppb and about 43.9 ppb.

The gas update rate is a measure of the accumulation of fresh gas in the sample cell and can be combined with the path length/NEA ratio to produce a detection system designated as "X mg/m ³ (or ppb) gas Y detected in Z seconds" Response time. The detection system response time includes the measurement time and the calculation time (for example, about 5 seconds). Table 2 shows exemplary detection system response times for various reagents such as sarin, tabun, soman, sulfur mustard, and VX nerve gas.

Table 2. Exemplary detection system response times for trace gases measured using the detection system of the present invention. All response times are in seconds.

Figure 5 is a graph of concentration versus time for a trace amount of gas using a step profile input (e.g., trace gas entering the sample cell at the beginning of the measurement cycle). The measurement period "A" is the time at which data is collected and/or the interferogram is recorded. The calculation period "B" is when the interferogram is converted into a spectrum, and spectral analysis is performed to generate data that can be used to determine the alarm level and/or concentration value.

Figure 6 shows a time series of measurements. Reagent 1 entered the sample cell and was detected during measurement period 1. The interferogram is analyzed during the calculation period 1. Reagent 2 enters the sample cell during measurement period 1. If Reagent 2 is strong enough, it can be detected in the remainder of Measurement Period 1. If reagent 2 is undetectable, it is detected during a subsequent measurement period (eg, measurement period 2) and the interferogram is analyzed during a subsequent calculation period (eg, calculation period 2).

In one embodiment, the readings may be time separated by a fixed predetermined time interval. In various embodiments, the time interval can be between about 1 second and about 1 minute, although smaller or larger time intervals can be used depending on the application. In some embodiments, the time interval is about 5 seconds, about 10 seconds, or about 20 seconds. Therefore, the response time depends on this time interval and when the reagent can be detected by the detection system.

In various embodiments, the detection system can adapt one or more parameters based on external factors such as: detecting trace gases, threat levels, time of day, number of people affected by reagents in a room or building, specific amounts Test application or context, or a combination of the aforementioned factors. For example, in high threat situations, smaller time intervals can be used to minimize detection time and maximize detectability of trace reagents. In low-threat situations, larger time intervals can be used, thereby maintaining the probability of detecting system life and reducing false alarms (false positives or false negatives).

In addition, individual measurements that exceed the threshold level of a particular reagent can trigger the detection system to reduce the time interval so that additional measurements can be taken in a shorter time. In various embodiments, the first spectrum can be recorded at a first resolution or sensitivity. If contaminants are detected, the second spectrum can be recorded at a higher resolution or sensitivity, respectively. Additionally, the detector can have a standby mode in which it operates at a higher temperature, thereby reducing its sensitivity. When triggered by external factors, the temperature of the detector can be lowered to increase its sensitivity.

In various embodiments, the detection system can change the number of scans based on external factors or perceived threats. For example, the number of scans can be increased to increase the sensitivity of the detection system. In one embodiment, the detection system can operate at higher resolutions while recording such additional scans. In one embodiment, each scan may include an increased number of average or individual scans.

In various embodiments, the detection system digitizes only the low frequency regions of the spectrum (eg, below 1300 cm ^-1 ) so that the detection system can scan at a faster rate. Electronic filters or detector response can be used to remove higher frequency regions (eg, greater than 1300 cm ^-1 ) to prevent or minimize aliasing.

In some embodiments, the detection system can detect the presence of trace gases in a portion of the spectrum. The second portion of the spectrum can be analyzed to confirm the presence of trace gases and/or to determine the concentration level of trace gases.

In one embodiment, the detection system can be packaged as a compact self-contained multi-gas analyzer. For example, the detection system can be a diagnostic tool for recording, mapping, analyzing, and reporting air quality. Figures 7 and 8 show an exemplary detection system for monitoring air quality, such as trace gases of ambient air. Referring to Figure 7, the detection system includes a housing 42', a first display 38', a second display 38", a gas inlet 46', a gas outlet 50', and a port 118 for connection to an external device.

The outer casing 42' can be a three-dimensional rectangular box that includes a top plate 122, side panels 126, and a bottom panel 130 (shown in Figure 8). The top plate 122 can be hinged away from the side panel 126 so that the outer casing 42 can be opened for work. Work. The outer surface of the top plate 122 may include a first display 38' and a second display 38" connected thereto or embedded therein. The first display 38' may be, for example, a liquid crystal display (LCD) having a touch screen display. The first display 38 'A command to operate the detection system can be received and a graphical user interface (GUI) can be displayed. The second display 38" can be a light emitting diode (LED) display, for example having a series of LEDs that can be illuminated Indicates threat level, alarm status, and/or detects system health. For example, the second display 38" can include a first series of green, yellow, and red LEDs to indicate an alarm condition, and a second series of green, yellow, and red LEDs independently indicate a sensor normal state. In various embodiments, the housing 42' can define a hole for ingesting ambient air. The hole can be used to introduce a gas sample into the flow system for detection in the sample cell.

Figure 8 shows an internal view of the top plate 122 and the bottom plate 130 when the top plate 122 is hinged open. The bottom plate includes an inner chassis that includes an optics box 134 for receiving optical components. The optics box 134 can be formed from an aluminum housing (eg, 6061-T6). In one embodiment, the optics box 134 is a sealed box. As shown in FIG. 8, the optical device box 134 includes a light source 14', an interferometer 18', a sample cell 22', a detector 30', a parabolic mirror 54', a first folding mirror 58', and a second folding mirror 62. ', elliptical mirror 66', objective lens surface 74' and field mirror surface 78'. The optics housing 134 may also include a flow system including a valve 138 that regulates air flow, a pressure sensor 98', a pump 106', and a gas line 110 and fitting 142 for connection. Power supply 146 and fan 150 of various components may also be coupled to base plate 130. The detection system can operate in still air and the fan 150 maintains the internal temperature of the system. The bottom plate 130 also includes a connector 154 that engages the top plate 122.

As shown in Figure 8, the top plate 122 can include electronic components coupled thereto. For example, the top board 122 can include a data capture module 158 , a mirror motion control module 162 , a single board computer 166 , a power distribution module 170 , and a hard disk drive 172 . The data capture module 158 can include a preamplifier, an analog/digital converter, and a data capture board. The preamplifier amplifies the analog signal received from the detector 30'. An analog/digital converter can be used to convert analog signals into Digital signal. The data capture board can be a Netburner processor board available from Netburner (San Diego, CA). The single board computer 166 can be an off-the-shelf PC motherboard that runs Windows and presents the GUI to the user.

The power distribution module 170 can process and distribute power to other modules in the system and can implement normal and state sensors for monitoring the functionality of the detection system. For example, power distribution module 170 can distribute AC power to system power supply 146 and fan 150, and can control temperature controller 174, such as Love Controls, available from Dwyer Instruments, Inc. (Michigan City, IN). . The power distribution module 170 also monitors the sample cell pressure, the differential pressure across the air filter, the sample cell temperature, and the detector temperature, and the A/D converts the output and returns the results to the single board computer 166. The power distribution module 170 can also control the cooler motor of the Sterling cooled detector according to the command of the single board computer 166. The top plate 122 can also include a sample cell temperature transmitter.

Data processing can be performed using modules connected to the top plate 122, which enable real-time analysis of data. The spectral library can include spectral fingerprints of from about 300 to about 400 gases, but more gas can be added as the spectrum is recorded. Data processing can be performed in a standard computer-programmed language such as MATLAB or C++. The recorded spectra can be transferred to MATLAB for post-spectral processing to calculate gas concentrations, spectral residuals, and/or false alarm rates. In various embodiments, the detection system can operate with less than about six false alarms per year. False alarms can be generated by noise, anomalous spectral effects, analysis codes, model errors, spectral library errors, or unknown interferers.

The computer software can operate on a Java-based platform with graphical remote control capabilities. It can incorporate standard services, including user login, web-based GUI, alert triggering, and/or an Ethernet interface that can be located on the client computer remote from the detection system. The computer software can perform remote normal status and control diagnosis. In addition, port 118 can be used to connect the system to a stand-alone computer that can perform data processing and data analysis.

The outer casing 42' is designed to withstand a 50 G impact. In one embodiment, the outer casing 42' It may have a length of about 406 mm and a width of about 559 mm. The quality of the detection system can be approximately 20 kg. The outer casing 42' can be mounted to a wall, a movable cart or a cart, and can include a handle (not shown) for carrying it manually or using a mechanical lifting device. In one embodiment, the outer casing can be installed as part of an air handling system of a building. When the detector senses the presence of contaminants, remedial action can be taken to resolve the contaminants. For example, the alarm may be reverberated to evacuate the building, or the air flow in the air handling system may be increased to sweep the contaminants away from the common area or to dilute the trace gases to an acceptable level.

In various embodiments, if contamination occurs, the detection system can operate at high temperatures to decontaminate the system. The system can be configured to heat the sample cell and flow system to a temperature between about 150 ° C and about 200 ° C, while the remaining components, including the electronics and optical components, are maintained at a temperature below about 70 ° C. For example, components that are heated to about 150 ° C can be insulated from surrounding components to prevent damage to the electronics and realignment or damage of the optical components. The sample cell and flow system operate at high temperatures to accelerate the desorption of contaminants. In one embodiment, the detection system can operate while decontaminating the system so that the process of decontamination can be monitored. In one embodiment, the detection system is purged with nitrogen or ambient air during decontamination. The gas may include moisture (eg, relative humidity greater than or equal to about 30%). In various embodiments, the system can be decontaminated in less than about 2 hours and can be returned to work at any time.

In one embodiment, the concentration of contaminants in the detection system can be determined, and if the contaminant concentration exceeds the contamination value, at least the sample zone can be heated to a decontamination temperature to remove contaminants. The concentration of the contaminant can be monitored while heating the sample zone, and when the contaminant concentration reaches the decontamination value, the heating can be attenuated or stopped. The contamination value can be the concentration of the substance that inhibits the effectiveness of the detection system. The decontamination value can be the concentration of the substance that the detection system can operate without the effects of contaminants.

In various embodiments, the sample cell of the detection system can operate at high pressures. Although the path length/NEA ratio may not change, the sensitivity of the detection system may be enhanced by the presence of a larger amount of trace gas sample in a sample cell having the same path length. This in turn produces a larger absorption signal relative to the baseline. By increasing the flow rate while maintaining the sample The volume of the product pool is constant to increase the pressure.

The field mirror surface and the objective lens surface can be mounted in a fixed manner so that their position remains substantially constant as the pressure increases. For example, the field mirror surface and the objective lens surface can be mounted on a rod to hold the surfaces. Additionally, the sample cell can be substantially airtight. The objective lens surface and the field mirror surface in the sample cell can be immersed in the sample gas so that a positive pressure can be applied to the respective rear surfaces of the field mirror surface and the objective lens surface to prevent deformation under high pressure. In various embodiments, the pressure can be between 1 atm and about 10 atm. In one embodiment, the pressure is 4 atm.

In some embodiments, the signals at two different pressures can be measured and the ratio of such signals can be obtained. The signal is greater than the amplitude of the absorbance curve that removes baseline noise, enhances sensitivity, and/or increases trace gas relative to the baseline signal.

A first signal of a radiation beam propagating through a sample of ambient air in the sample cell at a first pressure is measured. The sample cell is pressurized to a second pressure with ambient air. A second signal that propagates a beam of radiation through a sample of ambient air in the sample cell at a second pressure is measured. The first signal and the second signal can be combined to determine a signal indicative of the presence of trace gases. For example, the signals can be combined to produce an absorption curve for trace gases. In one embodiment, the radiation beam can include an interference signal. The absorption curve of the trace gas can be determined from the interference signal. In one embodiment, the first pressure is about 1 atm and the second pressure is between about 1 atm and 10 atm. In a detailed embodiment, the first pressure is about 1 atm and the second pressure is about 4 atm.

In various embodiments, the first signal is used as a baseline signal for the second signal because the optical alignment of the sample cell remains substantially constant as the pressure is increased. In some embodiments, the baseline signal is measured and used as a baseline signal for the first signal and the second signal.

In various embodiments, the flow system can include a cold finger to capture a gaseous sample of interest by cooling the gaseous sample of interest below its saturation temperature. Many volatile materials condense at temperatures of -75 ° C or below -75 ° C. In one embodiment, A cryotrap is established in the gas outlet of the sample cell. After a specified period of time or collection period, one or more of the trapped gases can be rapidly vaporized or "flashed" back to the sample cell by heating them, and spectral measurements can be taken. This technique increases the target gas volume by approximately one or two orders of magnitude while maintaining the sample cell at atmospheric pressure. In one embodiment, continuous flow measurement is performed after a certain time interval (eg, about every 10 seconds) while flashing occurs over a longer time interval.

In various embodiments, the detection system can include a long pass filter. The noise attributed to the A/D converter can be of the same order of magnitude as the noise due to the detector. Incorporating a long pass filter blocks higher wavenumber regions and can increase sensitivity by reducing the dynamic range requirements of the digital converter by reducing the amplitude of the burst center. The dynamic range of a detector without an optical filter can be between about 600 cm ^-1 and about 5000 cm ^-1 . Because many target toxic substances are detectable below 1500 cm ^-1 , long pass filters can be used to eliminate spectra above 1500 cm ^-1 to increase sensitivity. For example, using a standard current growth pass filter with a cutoff point of about 1667 cm ^-1 , the path length / NEA ratio gain can be from about 20% to about 30%. In addition, the use of a long pass filter can increase the signal-to-noise ratio of the detection system by allowing the detector to operate at higher gains (eg, using the highest gain achievable with a particular detector). In various embodiments, a low sensitivity detector such as an MCT detector or a DTGS detector can be used to record the spectrum in the higher frequency region.

Biofuels can be used to power the turbine generator's engine. Such as a biological gas typically comprises a percentage level of CO ₂ and H ₂ O said hydrocarbon (e.g. CH ₄₎ material. Biogas also includes decane-containing hydrocarbons and oxane compounds. A cyclic oxirane (e.g., D3-oxime to D6-nonane) can be found in the biogas produced by the digestion tank. The biogas produced by the landfill may include linear helioxane (e.g., "linear" L2-oxime to L6-nonane), cyclic alkane, and/or trimethylstanol (TMS). The concentration of TMS and oxane compounds in the biogas can vary from parts per million (ppm) down to fractional parts per billion (ppb). When the TMS and the siloxane compound are oxidized in the turbine, SiO ₂ particles are produced together to promote excessive wear and tear. Therefore, continuous monitoring of the helium oxide of the biofuel treatment system enables early detection and measurement of TMS and helium oxide compounds. The system can use a stand-alone processor (such as processor 34 of Figure 1) to quantify the concentration of TMS and helium oxide (e.g., stand-alone FTIR, which detects helium in the biofuel range down to the ppb level. The content of impurities).

Figure 9 shows a flow chart depicting an illustrative method for monitoring a biogas containing hydrazine-containing compound, such as a decane. The method includes the step of providing a non-absorbent gas (e.g., nitrogen or helium) to a sample cell (e.g., sample cell 33 of Figures 1 and 3) (step 205). The non-absorbent gas is a gas that does not substantially have infrared absorption in a specified wavelength range of interest. The method also includes the step of obtaining a first spectral measurement (e.g., background instrument response) from the sample cell (step 210). Biogas is supplied to the sample cell (step 215). The biogas includes at least one antimony-containing compound (for example selected from the group consisting of TMS, L2-decane, L3-decane, L4-decane, L5-decane, D3-decane, D4-decane, a group consisting of D5-nonane or D6-decane. The method also includes obtaining a second spectral measurement from the sample cell (step 220). A first absorption spectrum is generated based on a ratio of a first spectral measurement of the non-absorbent gas to a second spectral measurement (eg, a measurement of the sample gas comprising the biogas provided to the sample cell) (step 225). A second absorption spectrum is generated based on at least a first individual absorption spectrum of at least one cerium-containing compound of known concentration in the biogas (step 230). The concentration of at least one cerium-containing compound (e.g., oxane or TMS) in the biogas is calculated by using the first absorption spectrum and the second absorption spectroscopy (step 235). The concentration of at least one ruthenium containing compound can be calculated after first removing all possible interferences/gas from the spectrum (eg, the first absorption spectrum) using, for example, CLS and/or other methods of direct spectral comparison. Alternatively, the interference/gas is not removed and used for the final spectral fit routine.

Non-absorbent gases and biogas can be supplied to the sample cell (steps 205 and 215) so that spectral measurements can be obtained (steps 210 and 220). Biogas can come from, for example, animal waste, wastewater or landfills. In general, the data acquisition period (for example, the time period for obtaining spectral measurements) Long, the lower the detection limit (for example, the lower the concentration of the detectable substance). Longer data capture periods allow for more accurate measurements (eg, larger signal-to-noise ratios). If, for example, the noise is random (eg, white noise), the signal-to-noise ratio will increase with the square root of the snap time. A second spectral measurement (e.g., step 220) can be obtained over a draw period of from about 10 seconds to about 90 seconds. In some embodiments, the second spectral measurement of the sample cell is taken in a wavelength range from about 8 microns to about 12 microns. The step of obtaining a second spectral measurement can include extracting an infrared signal from the sample cell (eg, obtaining a gas sample comprising the biogas).

The results of quantifying the concentration of TMS or a siloxane compound can be obtained immediately (eg, in seconds or minutes) and on the spot (eg, in a device in fluid communication with a source of biogas and not required to collect sample gas) The container or absorption medium) calculates the concentration of at least one ruthenium containing compound (e.g., step 235). Because the sample cell and processor (eg, processor 34 of FIG. 1) can be placed in fluid communication with the source of the biogas, analysis can be performed at/directly adjacent to the source without the need to obtain a sample and It is delivered outside the field for analysis (eg, like existing GC/MS methods). Depending on the final signal-to-noise ratio required to accurately quantify the amount of cerium-containing component present in the biogas mixture, the time to obtain and analyze the sample (eg, calculate the concentration of oxane, TMS, and/or all cerium-containing compounds) can be several seconds. Count to a few minutes. If, for example, the signal to noise ratio is not sufficient to accurately measure a particular concentration, the extraction time can be increased to further reduce noise (e.g., to increase the signal to noise ratio). In some embodiments, the turbine generator is turned off when the concentration of the at least one cerium-containing compound reaches a threshold.

In some embodiments, a processor (eg, processor 34 of FIG. 1) is used to calculate the concentration of at least one cerium-containing compound in the biogas (step 235). Chemometrics of combined spectroscopy (eg, FTIR spectroscopy) and mathematics (eg, multiple regression analysis) provide clear quantitative information on cerium-containing compounds in biogas. For example, the processor is configured to perform a multiple regression analysis using the first absorption spectrum and the second absorption spectrum to calculate a concentration of at least one cerium-containing compound in the biogas. Classical least squares (CLS), partial least squares (PLS), inverse least squares (ILS), principal component analysis (PCA), and/or other chemometric algorithms can be used to perform Multiple regression analysis.

The second absorption spectrum (eg, from step 230) can be based at least on the first individual absorption spectrum and one or more additional oxane compounds (eg, L2-oxirane, L3-decane, L4-oxirane, L5-矽) Oxyalkane, D3-decane, D4-oxime, D5-decane or D6-decane, sterols (such as trimethylstanol (TMS)), hydrocarbon compounds (including, for example, aromatics) Individual absorption spectra of hydrocarbons and chlorinated hydrocarbons, water or carbon dioxide are produced. The second absorption spectrum can be a model based on a known concentration of a oxoxane compound, TMS, hydrocarbon compound, water or carbon dioxide (eg, a model representing the individual absorption spectra of the various reagents in the biogas). In some embodiments, the second absorption spectrum is based at least on a first individual absorption spectrum (eg, a siloxane compound) and/or one or more additional oxane compounds, TMS, a hydrocarbon compound (eg, methane or ethane), A model of the individual absorption spectra of water or carbon dioxide.

In some embodiments, the concentration value of the at least one oxoxane compound is determined (eg, step 235) such that the second absorption spectrum is substantially similar to the first absorption spectrum (eg, the model absorption spectrum is compared to the measured absorption spectrum) Mathematical fit). For example, the concentration of at least one oxoxane compound can be calculated by providing at least one variable indicative of the concentration of at least one oxoxane compound and determining the value of the at least one variable (eg, concentration value) such that the second absorption spectrum Substantially similar to the first absorption spectrum (eg, mathematically fitting the second absorption spectrum to the first absorption spectrum).

For example, spectral measurements can be directly correlated to actual chemical components using a number of different types of quantitative analysis based on single variable and multivariate analysis techniques. The single variable method involves correlating the spectral peak height or the area under the spectral curve with the same characteristics of a known chemical quantity of matter in the biogas. In some embodiments, this can be done using, for example, least squares regression to develop a quantitative model that predicts the actual concentration of different species in the biogas. Another single variable method that can be used in alternative embodiments is the K matrix or classical least squares (CLS), which is based on an explicit linear additive model (e.g., Beer's law as described in Equation 1 below). CLS uses spectra in the regression of all chemical components in the spectral region Larger segment (or the entire spectrum).

CLS has the limitation that it requires that the concentration of all spectrally active ingredients is known and included in the calibration model before an appropriate prediction model can be developed, since for example, the unknown concentration will reduce the model accuracy. To avoid this limitation and other concurrency situations that arise when using a single variable model, multivariate techniques are generally more appropriate. In a multivariate approach, multiple linear regression (MLR) (also known as P matrix or inverse least squares (ILS)) is used to construct a model that uses only the concentration of the chemical component of interest (see, for example, H. Mark, Analytical Chemistry). , 58, 2814, 1986). This technique can be used to construct a model using only known concentrations without any unwanted effects; however, the model is limited in the number of wavelengths that can be used to describe each component.

Other multivariate techniques can be used in alternative embodiments that would use the ability of a larger region of the spectrum to represent the component of interest (such as the ability of the CLS model) and the ability to compete only with the component of interest (eg, an MLR model). The ability to combine. In one embodiment, principal component regression (PCR) is used (as described in Fredericks et al., Applied Spectroscopy, 39: 303, 1985). This method is based on the use of principal component analysis (PCA) followed by regression of the spectral decomposition of known concentration values for the PCA score matrix. In particular, in the case of PCR, the PCA is first derived from the X matrix that produces the score matrix T and the load matrix P. In the next step, several first score vectors are used in multiple linear regression using Y data. When the first few components of PCA do summarize most of the information about X associated with Y, PCR works almost as far as the partial least squares (PLS) for spectral data described below.

In another embodiment, PLS can be used to obtain actual concentration values of the damage component based on spectral data (see, for example, W. Lindberg, J. Persson and S. Wold, Analytical Chemistry , 55: 643, 1983; P Geladi and B Kowalski , Analytica Chemica Acta, 35: 1, 1986; and Haaland and Thomas, Analytical Chemistry, 60: 1193 and 1202, 1988). PLS is similar to PCR; however, in the case of PLS, both spectral information and concentration information are decomposed at the beginning of the method and the resulting scoring matrix is swapped between the two groups. This allows spectral information associated with concentration information to have higher weights within the model, thereby producing a more accurate model than PCR. The core of the PLS algorithm is through nonlinear iterative partial least squares (NIPALS) (see, for example, Wold, Perspectives in Probability and Statistics, J Gani (ed.) (Academic Press, London, pp. 520-540, 1975) or simple partial minimum Square (SIMPLS) (Jong, Chemom. Intell. Lab. Syst., 18: 251, 1993) The spectral decomposition step performed by the algorithm.

Further details of PCA, PCR, MLR and PLS analysis can be found in "Multi- and Megavariate Data Analysis, Part I, Basic Principles and Applications", Eriksson et al., Umetrics Academy, January 2006 and "Multi- and Megavariate Data Analysis," Part II, Advanced Applications and Method Extensions, Eriksson et al., Umetrics Academy, March 2006, the entire contents of which are incorporated herein by reference.

As noted above, various stoichiometric algorithms (eg, PCA, PCR, MLR, PLS) can be used to calculate the concentration of one or more cerium-containing compounds in the biogas. The chemometric algorithm method is used to fit the total absorption (eg, the spectrum measured by biogas-based spectral measurements) to the respective absorption of component materials (eg, oxane, TMS, methane) and provide groups The calculated concentration of the points. Bill's law is expressed as:

Where A _i ( ) for the substance i in the wave number ( Under the absorbance, a _i ( ) is the absorbency of the substance at the wavenumber, b is the path length and c _i is the concentration of the substance. Therefore, by measuring the absorbance of a substance at a known concentration, it is possible to determine the absorbance of a substance at a known concentration and a specified wavelength (e.g., wave number). The absorption spectrum can be generated by measuring the absorbance of a substance at a known concentration for a range of wavelengths.

If multiple species (eg, molecules) are present in the sample, Equation 1 can be modified to reflect the fact that the absorbance measurement of the sample (eg, the sample biogas in the sample cell) is the sum of the absorbances of all of the materials in the sample. For example, if the biogas includes one or more deuterium Compounds, hydrocarbon compounds (including aromatic hydrocarbons and chlorinated hydrocarbons), water and carbon dioxide, the absorbance measurement of the biogas sample is the sum of all absorbances of the substances in the biogas (for example, antimony compounds, hydrocarbon compounds, water) And the sum of carbon dioxide). Therefore, quantitative analysis can be used to predict the actual concentration of different cerium-containing compounds in the biogas.

A stoichiometry algorithm can be used to determine the concentration of a substance in a sample. For example, a stoichiometry algorithm can be used with Equation 1 and/or other equations to determine a concentration value such that the model spectrum (eg, the second absorption spectrum) is substantially similar to the measurement spectrum (eg, the first absorption spectrum) ( For example, by mathematically fitting the model spectrum to the measured spectrum after removing all interfering components.

In one embodiment, PLS is used to calculate the concentration of the oxane (and/or other compounds in the biogas). Figure 10 is a diagram showing the nonlinear iterative partial least squares (NIPALS) decomposition of spectral information represented by a matrix X containing spectral measurements and a matrix Y containing concentration information. The PLS component of the PLS model is traditionally calculated using the NIPALS algorithm (or other similar decomposition algorithm). The PLS associates two data matrices X and Y with each other by a linear multivariate model. In summary, the linear model specifies the relationship between the variable or back strain number y or a set of back strain numbers Y and a set of predicted variables X. For example, the return strain number y is the concentration, and the predicted variable X is the spectral measurement 1002a to 1002n. The numbers 1.0 and 0.45 in Y are calculated values corresponding to the concentration of the gas components in the spectrum. There are many variations on the NIPALS algorithm, which consists of matrix vector multiplications (eg, X'y). S and U are synthetic score matrices from spectral and component information, respectively. The numbers 0.39 and -0.37 in S are scalar (score) modifiers representing the base vector of the linear combination of the original spectral sets. These numbers are merely illustrative of the first column of numbers that fill S and U. In this example, the entire set of observed spectra is decomposed into two basis vectors, which is why there are two numbers. If the corresponding column in the PCx diagram is multiplied by this number, the original spectrum (eg, tiny noise) is generated. In other embodiments, the observed set of spectra can be decomposed into any number of basis vectors. PCx and PCy are synthetic principal components (or latent variables/feature vectors) from spectral and component information, respectively. PCx includes latent In the variables 1004a to 1004f. Other terms in the figure are for the number of spectra (n), the number of data points per spectrum (p), the number of components (m), and the number of final potential variables/feature vectors (f).

The first decomposition of the spectrum and concentration/component data yields the potential variables and scores of the X and Y matrices, and the score matrix (S) of the spectral information is exchanged with the score matrix (U) containing the concentration information. The potential variables of PCx and PCy are then subtracted from the X and Y matrices, respectively. These latest subtractive matrices are then used to calculate the next potential variable and score for each round until sufficient potential variables are found from PCx and PCy to represent the data. Before each round of decomposition, the new scoring matrix is swapped and the X and Y matrices of the subtraction are used to remove the new latent variables of PCx and PCy.

The number of final latent variables (or basis vectors) determined from the PLS decomposition (f) is highly correlated with the concentration information due to the exchanged scoring matrix, since the spectral matrix is correlated with the concentration information. Advantageously, the swap leaves a base vector in both sets of matrices, which are naturally related to each other. The PCx and PCy matrices contain highly correlated spectra with respect to changes in the components used to construct the model. The second set of matrices S and U contain actual scores representing the respective amounts of potential variable variations present in each spectrum. The S matrix values are used in the PLS model.

In one embodiment, the PLS method is used to predict the actual composition of the oxoxane compound in the biogas. For example, the PLS algorithm can be used to predict the chemical content of a biogas either directly or, for example, as a percentage of a compound present, such as a hydrazine containing compound, a hydrocarbon compound including aromatic hydrocarbons and chlorinated hydrocarbons, water or carbon dioxide.

In another embodiment, CLS can be used to calculate the concentration of oxoxane (and/or other compounds) based on model spectra and measurements. In some embodiments, the sample comprises two components and/or substances (s ₁ and s ₂ ) in the form of a mixture. The biogas may include two or more components/substances (for example, the biogas may include different substances including a ruthenium compound, a hydrocarbon compound, etc.), however, for the sake of clarity, the following examples assume two components.

If the sample includes two substances, the substances should vary at at least two wave numbers. In one embodiment, the absorbance of the two wave numbers can be modeled using CLS based on the relationship of absorbance at each wavelength. For example, the absorbance at the first wavelength is based on the absorbance of the first substance s ₁ at the first wavelength, the absorbance of the second substance s ₂ at the first wavelength, the path length (eg, as described above for Figures 2-4) The path length of the sample cell 22, the concentration of the first substance s _{1 ,} the concentration of the second substance s ₂ , and the residual error obtained by regression analysis of the first wavelength. Similarly, for example, the absorbance of the second wavelength is based on the absorbance of the first substance s ₁ at the second wavelength, the absorbance of the second substance s ₂ at the second wavelength, the path length, the first substance s ₁ and The relationship between the concentration of the second substance s ₂ and the residual error obtained by regression analysis of the second wavelength.

If the path length is constant, it is not necessary to consider the path length when measuring the absorbance at each wavelength. The fact that the absorbance of the first wavelength is based on the absorption coefficient of the first substance s ₁ at the first wavelength, the absorption coefficient of the second substance s ₂ at the first wavelength, the first substance s ₁ and the second substance s ₂ The relationship between the concentration and the residual error obtained from the regression analysis of the first wavelength. Similarly, the absorbance of the second wavelength is based on the absorption coefficient of the first substance s ₁ at the second wavelength, the absorption coefficient of the second substance s ₂ at the second wavelength, the first substance s ₁ and the second substance s ₂ The relationship between the concentration and the residual error obtained by regression analysis of the second wavelength.

Using the above relationship, the absorption coefficient for a certain wavelength can be determined by measuring the absorbance of the sample at a known concentration. These absorbance coefficients can then be used to measure/determine the unknown concentration of the substances s ₁ and s _{2 in} the sample. For example, the absorbance of a sample (eg, a measurement spectrum) can be measured at two wavelengths, respectively, to obtain an absorbance value for the number of wavelengths. Since the absorbance coefficient is known, it can be used together with the absorbance value to calculate the substance concentration.

As indicated above, the biogas may comprise more than two components/substances. In this case, the values of absorbance, absorption coefficient, and concentration can be modeled using the following matrix:

The "A matrix" is a matrix of spectral absorbance, the "K matrix" is a matrix indicating the absorbance coefficient, and the "C matrix" is a matrix indicating the concentration. The number of samples (spectrums) is represented by " n ", the number of wavelengths used for correction is represented by " p ", and the number of substances/components is represented by " m ". Equation 6 can be simplified and used to calculate the concentration of the material in the sample: C = A . K ^-1 Equation 3

Where K ^-1 is the reciprocal of the K matrix. The K matrix of Equation 2 can be solved by measuring the absorbance of a sample whose concentration of individual substances is known and using the following statement: K = A . C ^-1 Equation 4

The "C matrix" is known if the concentration of an individual substance (for example, a oxoxane compound, a hydrocarbon compound, water or carbon dioxide present in, for example, a biogas) is known. The "A matrix" can be constructed based on spectral measurements obtained using, for example, the detection system of FIG. 1 (eg, FTIR spectrometer). Therefore, the K matrix is obtained using Equation 4 using the A matrix and the reciprocal of the C matrix of known concentration.

After the K matrix is calculated from Equation 4, Equation 3 is used to calculate the concentration in the sample. The reciprocal of the K matrix (eg, calculated from Equation 4 using a sample of known concentration) is used to calculate the concentration of a substance (eg, a helium oxide in a biogas) in a sample of unknown individual concentration. Spectral measurements of samples (eg, sample biogas) can be obtained using a detection system (eg, the system of Figure 1). The A matrix representing the compilation of the absorbance of individual substances in the sample is based on spectral measurements. The inverse of the K matrix and the A matrix are used in Equation 3 to calculate the concentration of individual substances in the sample.

Figure 11 shows the graphical results of the CLS (ie, classical least squares) analysis of the gas composition of the wastewater digestion tank, which includes 920 ppb D4-oxane, 400 ppb D5-decane, 65% methane, 35% carbon dioxide. 1400 ppm ethane, 340 ppm propane and 65 ppm butane. The graph shows the absorbance value (y-axis) as a function of wavelength (i.e., wavenumber) (x-axis). Curve 300 represents the measured spectrum, curve 305 is the individual absorption spectrum of methane, curve 310 is the individual absorption spectrum of carbon dioxide, curve 315 is the individual absorption spectrum of ethane, curve 319 For the individual absorption spectra of propane, curve 320 is the individual absorption spectrum of butane, curve 325 is the individual absorption spectrum of D4-oxime, and curve 330 is the individual absorption spectrum of D5-nonane. For the sake of clarity, the model absorption spectrum representing the sum of the component spectra 305, 310, 315, 320, 325, and 330 is not shown, and the model absorption spectrum will cover the measurement spectrum 300 due to the relatively small residual value.

The spectral data such as that shown in Figure 11 can be used to calculate the concentration of the oxoxane compounds (e.g., D4-oxime and D5-decane). The measured/observed spectrum 300 can be used to fill in the values of the A matrix of Equations 7 and 10. The values of the K matrix and/or P matrix of Equations 7 and 10 can be filled using the individual absorption spectra 305, 310, 315, 320, 325, and 330 of the individual species of known concentration. Therefore, the measured A matrix and the calculated K matrix and/or P matrix are used to determine the value of the unknown concentration of the individual species in the measurement spectrum.

In another embodiment, ILS can be used to calculate the concentration of a substance in a sample. In CLS, the absorbance is a dependent variable. In ILS, the concentration becomes a dependent variable. For example, the concentration of the first substance s ₁ is based on a linear inverse coefficient (which is a function of the absorbance of the first substance s ₁ at the two wave numbers), the absorbance at the first wavelength and the second wavelength, and The relationship between the residual errors obtained by regression analysis of a substance s ₁ . When several substances are present in the sample, this concentration can be reduced to the following matrix: C = P . A + E _c Equation 5

In Equation 5, C is a matrix of concentrations, P is a matrix of linear inverse coefficients, A is a matrix of absorbances, and E is a matrix of residuals. As with CLS, the P matrix can be found using the known concentration of the sample. In this scenario, the residual error can be assumed to be zero because the ILS model can be recalculated until the residual error is close enough to zero (eg, by setting the indication error to be close enough to zero) and Equation 5 can be modified as follows: P = C . A ^-1 Equation 6

The value of the C matrix is obtained using the known concentration of the individual substance. The A matrix is based on spectral measurements taken from the sample at known concentrations (eg, using the detection system of Figure 1, such as FTIR spectroscopy) Build it. Thus, equation 6 can be used to calculate the P matrix based on the spectrum measured from a substance of known concentration within the sample.

The P matrix can then be used with Equation 5 to resolve the unknown concentration of material within the sample. In particular, spectral measurements can be obtained from samples having unknown concentrations of individual substances using a detection system such as the FTIR system (eg, the system of Figure 1). These spectral measurements can be used to fill in the absorbance values in the A matrix. A P matrix calculated using Equation 6 based on known concentrations can be used in Equation 5 to calculate the C matrix, thereby obtaining concentration values for individual species within the sample.

Incorporating any of the exemplary techniques described above, systems such as the system of Figure 1 above can be used to detect, quantify, and monitor ruthenium containing compounds (e.g., oxime) in biogas. The system can include, for example, a source of a first radiation beam (eg, source 14 of FIG. 1), an interferometer (such as interferometer 18 of FIG. 1), a sample cell (eg, pool 22 of FIG. 1), a flow mechanism (eg, FIG. 3) a flow system 82), a cooled detector (such as the detector 30 of FIG. 1), a processor (such as the processor 34 of FIG. 1), and a housing (such as the housing 42 of FIG. 1) in which the light source is disposed The interferometer, the sample cell, the cooled detector, and the processor. The interferometer receives the first radiation beam from the source and forms a second radiation beam comprising an interference signal (eg, an interferometer signal) (eg, the second beam reciprocates in the sample cell for a total of about 48 times, yielding about 10.18 meters Effective path length). The sample cell is in optical communication with the interferometer. The flow mechanism establishes a flow of non-absorbent gas (eg, a gas that has substantially no infrared absorption over a specified wavelength range of interest) through the sample cell and a second biogas flow (eg, pressurization introduced into the sample cell (eg, 3-5 psig) sample (eg 400 mL biogas), biogas retention time is about 5 seconds). A detector (eg, a cooled detector) optically communicates with the sample cell and receives a first interference signal propagating through the non-absorbable gas in the sample cell and a second interference signal propagating through the sample gas in the sample cell The sample gas contains biogas. The processor is in electrical communication with a detector (eg, a cooled detector, such as a Mercury-Cadmium-Telluride (MCT) detector cooled by a low temperature (eg, Stirling engine)) The concentration of at least one oxoxane compound. The processor uses chemometric techniques (eg The CLS and ILS techniques) calculate the concentration of at least one oxoxane compound in the biogas based on the first absorption spectrum and the second absorption spectrum. The first absorption spectrum is based on a ratio of the first interference signal of the detector to the second interference signal. The second absorption spectrum is based at least on the individual absorption spectra of at least one of the known concentrations of the oxoxane compound.

In some embodiments, a sample cell (eg, cell 22 of FIG. 1 having an optical configuration such as described above with respect to FIG. 2) includes a concave reflective field mirror surface at a first end of the sample cell (eg, FIG. 2 Field mirror surface 78) and a substantially spherical concave reflective objective surface (eg, objective lens surface 74 of FIG. 2) in a face-to-face relationship with the field mirror surface at the second end of the sample cell, the objective lens surface having a cylindrical component that increases at least The focus in one plane coincides to maximize the amount of delivery of the second radiation beam propagating through the sample cell through multiple reflections on each of the field mirror surface and the objective lens surface.

In one embodiment, a computer readable product physically embodied on an information carrier or machine readable storage device can be processed by a digital signal processor in a biogas detection system (eg, the system of FIG. 1) (eg, the processing of FIG. 1) The device 34) operates. The computer readable product includes a first spectral measurement (eg, from detector 30 of FIG. 1) operable to cause a digital signal processor to receive non-absorbent gas from a sampling cell (eg, cell 22 of FIG. 1) The instructions wherein the non-absorbent gas has substantially no infrared absorption over a specified wavelength range of interest. The computer product can also cause the digital signal processor to receive a second spectral measurement from the sample gas containing the biogas in the sampling cell and generate a first absorption spectrum based on a ratio of the first spectral measurement to the second spectral measurement (eg, Measuring the absorption spectrum). A second absorption spectrum (e.g., a model absorption spectrum) can be generated/provisioned based on at least a first individual absorption spectrum of at least one oxoxane compound at a known concentration. The computer product may also cause the processor to calculate the concentration of one or more oxoxane compounds using the above chemometric techniques (eg, performing multiple regression analysis and mathematically fitting the second absorption spectrum to the first absorption spectrum to calculate at least biogas) a concentration of a oxoxane compound).

As indicated above, the absorption spectrum based on the spectral measurement of the biogas in the sample cell and Individual spectra of individual components/substances in a biogas (eg, individual spectra of a biogas such as a oxane and a ruthenium containing compound, a hydrocarbon compound, water, or carbon dioxide) can be used to calculate the concentration of individual species in the sample. An absorption spectrum, such as the spectrum shown in Figure 11, can be used to generate a model based on a calibrated absorption spectrum (e.g., a second absorption spectrum as described above with respect to Figure 9), which represents a compilation of individual absorption spectra. (For example, depending on the analytical method used, based on concentration ranges and/or different spectral mixtures). In particular, an absorption spectrum based on the spectral measurement of an unknown biogas can be used to generate an A matrix as described above to calculate the concentration of the oxane using, for example, Equations 7 and 11. Individual spectra obtained based on measurements taken from substances of known concentration can be used to generate model spectra representing individual substances. Individual spectra of known concentrations can be used to calculate the P matrix or K matrix as described above in Equations 8 and 12. The model can include, for example, a P matrix or a K matrix (as determined, for example, by using a substance of known concentration) that can be used to calculate the concentration of the oxane using, for example, Equations 7 and 11. Figure 11 shows spectral data that can be used to quantify the concentration of oxoxane in a biogas, in accordance with an illustrative embodiment of the invention. Each absorbing material in the sample has a unique absorption contrast frequency distribution (ie, absorption spectrum). Using stoichiometric algorithms (such as multiple regression analysis), the components can be characterized and quantified so that individual substances of the siloxane compound can be detected, even in the presence of other interfering absorbers (such as hydrocarbon compounds such as methane or ethane). It can also be detected.

In general, the method for monitoring a cerium-containing compound in a biogas as described above with respect to Figure 9 involves generating a second absorption spectrum based on individual absorption spectra of all known cerium-containing compounds and hydrocarbon compounds having known concentrations (steps) 230). Thus, the second absorption spectrum exhibits all possible cyclic and linear oxane compounds and other ruthenium containing components that may be present in a given sample, such as TMS (eg, a speciation method). This method can be used for calculations by employing, for example, the CLS (Classical Least Squares) analysis described above with respect to Equations 2-4 or the ILS (ie, inverse least squares) analysis described above with respect to Equations 5-6. Step 235) The concentration of each substance in the sample containing a ruthenium compound such as oxime and/or TMS.

One disadvantage of using this method is the low level concentration in the sample (eg less than 0.02) Phenium-containing compounds of ppm-v) will not be detected. For example, if all known oxoxane compounds are used in the second absorption spectrum during modeling, but at least one oxane is not present in the actual sample or is similar to another component, this may be due to cross-correlation effects. Accurate determination of different species of oxoxane in the sample, especially at low concentrations. Another disadvantage of the seeding method is that it requires all unknown compounds that may be present at any time during the analysis as part of the second absorption spectrum. The resulting cross-correlation effect causes a total analysis of the respective substances that contribute to the concentration of the oxane and TMS by the noise injection, thereby reducing the ability to obtain chemical detection at low ppb levels.

In view of these shortcomings, another method for detecting/monitoring a cerium-containing compound in a biogas sample, such as a biogas or a digester gas, is provided. Instead of calculating the concentration of each cerium-containing compound present in the sample, this method calculates one or more total concentration values. The total concentration value may include, for example, a single value of the total concentration of oxoxane in the sample, a single value of the total concentration of other cerium-containing materials in the sample, and/or a single value for the total concentration of all cerium-containing materials. The single values can be determined based on the absorption spectra of a group of ruthenium containing compounds (including oxanes) and/or hydrocarbon compounds typically found in the biogas of interest, instead of using all of these as used in the seeding process. Know the compound. In particular, instead of using a known concentration of all cerium-containing compounds (including oxanes) and/or hydrocarbon compounds as used in the seeding method to fit a biogas sample based on, for example, CLS, this method uses only the selected one. The panel is known to contain ruthenium compounds and/or hydrocarbon compounds to perform a fitting analysis.

FIG. 12 shows a flow chart 300 depicting another illustrative method for monitoring a ruthenium containing compound in a biogas sample. The method includes the step of providing a non-absorbent gas (e.g., nitrogen or helium) to a sample cell (e.g., sample cell 33 of Figures 1 and 3) (step 305). The non-absorbent gas is a gas that does not substantially have infrared absorption in a specified wavelength range of interest. The method also includes the step of obtaining a first spectral measurement (e.g., background instrument response) from the sample cell (step 310). Biogas is supplied to the sample cell (step 315). The biogas includes at least one cerium-containing compound, such as at least one ceroxane compound (eg, from L2-decane, L3- A group consisting of a decane, an L4-oxirane, an L5-aluminoxane, a D3-oxane, a D4-oxime, a D5-decane or a D6-decane, the concentration of which is unknown. The method also includes obtaining a second spectral measurement from the sample cell (step 320). The first absorption spectrum is generated based on a ratio of the first spectral measurement of the non-absorbent gas to the second spectral measurement (eg, the measurement of the sample gas comprising the biogas provided to the sample cell) (step 325). In some embodiments, steps 305, 310, 315, 320, and 325 of flowchart 300 of FIG. 12 are substantially identical to steps 205, 210, 215, 220, and 225 of flowchart 200 of FIG. 9, respectively.

With continued reference to Figure 12, a set of alternate absorption spectra is determined (step 330). Although each absorption spectrum in the replacement set is based on individual absorption spectra of known ruthenium containing compounds, not all known ruthenium containing compounds are included in the replacement group and are used in this method, as described above with respect to Figure 9. The seeding method is contrasted. Here, an alternative method for generating a set of instead of the absorption spectrum is used. An alternative approach involves selecting only a small set of known ruthenium containing compounds (eg, decane) for the modeling phase. The group may be selected from, for example, D3-methoxyoxane, D4-decane, D5-decane, D6-decane, L2-decane, L3-decane, L4-decane, L5. a larger group of oxoxane and trimethylstanol (TMS). In the following, a small group of compounds selected from a larger group of known ruthenium containing compounds are referred to as substitution groups. In some embodiments, only 3 to 5 oxoxane compounds are selected from all known ruthenium containing compounds included in the replacement group. In some embodiments, the replacement group comprises a panel of a larger group of known hydrocarbon compounds including, for example, methane, ethane, butane, propane. In another example, the larger group of known hydrocarbons can include methane, toluene, ethanol, and methanol. In some embodiments, the replacement set comprises a small group of known oxoxane compounds and/or hydrocarbon compounds. In some embodiments, the replacement set comprises a panel of known oxoxane compounds, hydrocarbon compounds, and/or TMS. In general, the set of substitutional absorption spectra used in the model is based on individual absorption spectra of each of a small group of known ruthenium containing compounds (including oxime and TMS) and/or hydrocarbon compounds.

The choice of the ruthenium containing compound and/or hydrocarbon compound included in the replacement group may depend on the type of sample. For example, for landfill biogas, the replacement group may include a) L2-oxime Alkane, L3-decane and D4-oxime; b) L2-oxime, D3-oxane and D4-oxime; or c) L2-oxime, D3-oxime and D5 - oxime. In some embodiments, TMS is also added to the replacement group depending on the age of the landfill. For the digestion tank biogas, the substitution group may include a) D3-oxane, D5-oxime, and L3-decane; b) D4-oxane, D5-decane, and L3-decane; Or c) D3-oxane, D5-nonane and L2-decane.

Similar to the oxane compound, one or more hydrocarbon compounds in the group can be selected based on the type of biogas sample. For example, the selected hydrocarbon compound may depend on which hydrocarbon compound may be present in the biogas. For landfill biogas, the hydrocarbon compounds in the substituting group may include, but are not limited to, ethanol, methanol, toluene and/or freon, from about 10 ppm up to about 1% (or possibly higher). A hydrocarbon compound, up to 95% methane, and/or from about 5% up to about 50% CO ₂ . For digesting tank biogas, the hydrocarbon compound in the substituting group may include, but is not limited to, ethane, propane and/or butane, from about 100 ppm up to about 1% (or possibly higher) of hydrocarbon compounds, and/or About 10% up to about 50% CO ₂ .

For a particular sample, the choice of the ruthenium containing compound and/or hydrocarbon compound included in the replacement group can be based on, for example, whether the spectral peak of the selected replacement compound matches one or more of the spectral peaks of the compound in the biogas sample, experimentally. determine. For example, instead of a group of compounds, it can be determined by examining the gas composition using GC/MS. This eliminates the need for a pump if the biogas sample is pressurized. In one embodiment, the FTIR spectrometer operates at a scan rate of 10 to 20 seconds with an average scan of 36 to 72 times. This produces extremely low detection limits. The biogas sample can be fed to the gas cell of the FTIR spectrometer without heating the biogas sample. The gas pool can be heated, for example, at 40 °C. The spectrum can be measured at 35 ° C to 40 ° C. For lower detection limits, the spectrum of the sample can be collected using a gas path length of approximately 10.18 m at approximately 4 cm ^-1 resolution. Methane can be operated on an FTIR spectrometer and the methane spectrum observed in biogas can be used to correct 40% up to 100% span of each system. This produces good spectral subtraction results. For example, very low residuals and small spectral details can be inferred at low detection limits of oxane and/or total ruthenium concentrations, even in the presence of high methane content.

Even though the ruthenium containing compound or hydrocarbon compound selected for inclusion in the replacement group may be present in the biogas sample in some embodiments, this is not required. That is, the ruthenium-containing compound or hydrocarbon compound in the replacement group need not be present in the sample. In some embodiments, a library of alternate sets is maintained, each of which can be used for a particular type of biogas sample. For example, an alternative set comprising L2-dioxane, D3-decane, and D4-decane can be used to model any landfill biogas, regardless of when/where the biogas is collected.

After determining the first absorption spectrum (step 325) and determining the absorption spectrum of the replacement group (step 330), calculating the total concentration of all the oxane compounds in the biogas by using the first absorption spectrum and the absorption spectrum of the replacement group (steps) 335). The total concentration of all cerium-containing compounds can also be calculated. Using CLS, PLS, ILS, PCA, and/or other methods of direct spectral comparison, after removing interference/gas via modeling methods (eg, removing methane, CO2, certain interfering hydrocarbons, etc.), it can be determined that all of the samples are present A single number of total concentrations of oxoxane species and/or strontium containing materials. The total concentration can be determined to compare the absorption spectrum of the replacement set to the first absorption spectrum and to minimize the difference in spectral characteristics determined by the self-fitting routine as optimally as possible. For example, in the CLS analysis described above with reference to Equations 2-4, the dimensions of the A, K, and C matrices of the alternative method are reduced compared to the seeding method, taking into account the reduction in the number of compounds used in the replacement set. In some embodiments, to calculate the total concentration of oxane in the biogas sample, all of the synthetic concentration values in each of the C matrices in place of the oxane are added together to produce a total decane value. This total decane number can be present in ppm or in mg/m ³ form. In some embodiments, the total enthalpy value representing the total concentration of the cerium-containing compound in the biogas sample can be calculated. This can be achieved by first correcting the concentration of each substitute, and if the substitute contains a ruthenium molecule, by correcting the fraction of ruthenium present in the particular component. Subsequently, the concentrations of TMS substitutes, other ruthenium-containing substitutes, and all oxane substitutions in the C matrix are added together to produce a total enthalpy. In some embodiments, the concentrations of the cerium-containing components can be separately summed to produce a total enthalpy and the concentrations of the cerium-containing components are summed to produce a total decane number. In general, one of ordinary skill in the art can readily determine how to calculate a oxoxane compound in a sample using an alternative method based on the analytical techniques described above with reference to the seeding method (eg, CLS, PLS, ILS, or PCA). Total concentration and/or total concentration of cerium-containing compound.

The method 300 can also include the step of applying a correction factor to the total decane number and/or total enthalpy determined by the step 335 as appropriate (step 340), such as scaling the value by a factor. The correction factor can be determined based on the type of biogas being analyzed, the alkylene oxide compound used in the alternative group, the hydrocarbon compound used in the replacement group, or any combination thereof. For example, the correction factor can be calculated by comparing the biogas having a known concentration of a oxoxane compound to the concentration of decane determined for the same biogas at step 335. Based on this comparison, it can be determined whether the correction factor is needed, and if so, what the correction factor should be. In some embodiments, the same correction factor can be used to analyze other biogas after determining the correction factor for a certain type of biogas (such as biogas). In some embodiments, different correction factors are used for different replacement groups. In some embodiments, the correction factor is modified for each biogas sample due to, for example, a field experiment.

The replacement method has been shown by the dilution test to be well tracked compared to the seeding method. However, there are many advantages associated with alternative methods. One advantage is that the alternative method allows on-site process monitoring at different landfills without requiring changes or modifications. Another advantage is that the sample is not processed in any way (eg, by trapping the sample in a portable container or impinging a gas stream into the alcohol solution), thereby allowing more accurate quantification of the sample compared to processing the sample. Oxyalkane compound. Yet another advantage of the alternative method is that no cans and/or sample bags are required to obtain samples from the biogas stream. For example, the biogas stream can be directly connected via a sample line that does not absorb helium. Biogas can be delivered directly to the FTIR analyzer via a sample line.

In addition, the alternative method can reduce the total detection limit of the oxane and/or ruthenium containing compounds in the biogas by at least 10 times, such as 600 ppb to 60 compared to the seeding method of Figure 9. The total decane number of ppb. In some embodiments, the single digit detection limit can be achieved using, for example, FTIR analysis. It is readily apparent to those skilled in the art that FTIR analysis includes Fellgett and Jacquinot advantages (e.g., spectral throughput and sensitivity are naturally increased). These Fellgett and Jacquinot advantages combine with the use of highly sensitive detectors that are cooled by a refrigerant to produce high quality detection.

In some embodiments, an alternative method can be used to monitor the amount of oxane and/or total strontium present in the landfill or digester gas stream produced after the filtration system. This gas stream can be used to power turbines, boilers, automobiles, and/or household appliances, all of which can be damaged if the helium and/or helium content is not monitored and controlled. The helium oxide and/or helium content may need to be analyzed before the gas can enter the national pipeline for the compressed natural gas pipeline. In some embodiments, alternative methods can be implemented on the AIRGARD system or the MultiGas 2030 series of products, both available from MKS Instruments, Inc. of Andover, Massachusetts.

Figure 13 shows the results of a simulated biogas sample with a total oxirane concentration (ppm) over time, including 540 ppb of L2-oxirane, L3-oxime, L4-oxirane, D3-矽A mixture of oxane, D4-oxime, and D5-nonane in the balance of methane, which is also used in blending. The concentration of the decane can be determined using the CLS analysis method after applying the seeding method shown in Fig. 9 or using the alternative method of using the correction coefficient as shown in Fig. 12. Measurements can be made using a MKS MG 2030 FTIR spectrometer available from MKS Instruments Inc. of Andover, Massachusetts. The spectrometer has a 5.11 m gas cell heated at 40 ° C with a 20 second data average of 100 seconds. Figure 13 shows: 1) Total alkoxylated moieties 1310 as determined by the seeding method, according to which the second set of absorption spectra includes all known alumoxanes and/or hydrocarbon compounds; 2) use without correction factor Method for determining a total alkane graph 1320, according to which the absorption spectrum of the replacement group comprises only a small group of known oxoxane compounds and/or hydrocarbon compounds; and 3) the same alternative method as in Figure 1320 is used, but the correction is applied The total alumoxane figure 1330 is determined by the coefficient. For this synthetic biogas sample, the total expected The peak amount of oxyalkylene should be 3.24 ppm-v (i.e., 540 ppb times 6 for 6 oxiranes in the synthesis gas sample). The azide graph 1310 produced by the seeding process has a maximum concentration of undiluted cylinder values at 3.11 ppm-v. This deviates from the expected amount (3.24 ppm-v) by about 4%, which is within acceptable tolerances. As shown, the total alkane graph 1330 produced by the alternative method using the correction factor is as accurate as the azide graph 1310 obtained by seeding. For this particular example, an alternative method can begin to fail at a concentration of about 50 ppb-v total helium.

Figure 14 shows the results of the total decane concentration of the digester gas sample diluted with 100% methane over time. The digestive tank gas sample comprises a total of less than 200 ppb-v of a oxoxane compound, approximately 75% versus 25% of D4-oxime and D5-decane at about 60% methane and some ethane and propane and 40 % carbon. The concentration of decane can be determined using the CLS analysis method. Measurements can be made using the MKS AIRGARD system available from MKS Instruments Inc. of Andover, Massachusetts. The system has a 10.18 m gas cell heated at 40 ° C with an average of 20 seconds to 100 seconds. Figure 14 shows: 1) total alkane Figure 1410, which is based on the i) CO2 concentration and its variation in the addition of methane to the undiluted digestion tank gas, and ii) based on the digestive tank sample Determined by the dilution factor produced by the change in the total decane number estimated without diluting the total decane; 2) the total oxane number 1420 determined by the seeding method, according to which the second set of absorption spectra includes All major oxane compounds (three cyclic and three linear decane components) and the first group of hydrocarbon compounds; 3) total oxirane as determined by the seeding method. Figure 1430, according to which the second set of absorption spectra comprises All major oxane compounds (three cyclic and three linear decane components) and a second group of hydrocarbon compounds; 4) a total oxirane pattern 1440 determined using an alternative method, according to which the absorption spectrum of the replacement group includes a group of oxoxane compounds and the first group of hydrocarbon compounds used in Figure 1420; and 5) a total oxirane pattern 1450 as determined using an alternative method, according to which the absorption spectrum of the replacement group comprises a substituted group of oxoxane compounds And in Figure 1430 A second group of hydrocarbon compounds is used. As shown in FIG. 14 , each of the images 1440 measured by the alternative method and At 1450, the change in the concentration of the oxirane over time is compared to the reference Figure 1410. However, Figures 1420 and 1430, each determined using the seeding method, do not track changes in the concentration of the oxane in the digester gas over time as compared to the reference Figure 1410. Figures 1420 and 1430 are also more affected by the choices made in place of hydrocarbons than either of the two alternative methods represented by Figures 1440 and 1450. Thus, Figure 14 illustrates the use of a subgroup (i.e., group) of oxoxane compounds to determine the total decane concentration, particularly when the decane concentration is varied at low ppb-v.

The above systems and methods can be implemented in digital electronic circuits, in computer hardware, firmware, and/or software. It can be implemented as a computer program product (that is, a computer program physically embodied in an information carrier). It can be implemented, for example, in a machine readable storage device and/or in a propagated signal for execution by a data processing device or for controlling the operation of the data processing device. Embodiments may be, for example, a programmable processor, a computer, and/or multiple computers.

The computer program can be written in any form of stylized language, including compiled and/or interpreted language, and the computer program can be deployed in any form, including in a stand-alone program or in a subroutine, component, and/or suitable for use in a computing environment. Other unit forms. The computer program can be deployed to execute on one computer or on multiple computers at one location.

Method steps may be performed by one or more of the executable computer programs to perform the functions of the present invention by operating the input data and generating an output. The method steps can also be performed by dedicated logic circuitry and the apparatus can be implemented as dedicated logic circuitry. The circuit can be, for example, a field programmable gate array (FPGA) and/or an application-specific integrated circuit (ASIC). Modules, secondary, and software agents may refer to portions of computer programs, processors, special circuits, software, and/or hardware that implement functionalities.

Processors suitable for the execution of computer programs include, for example, general purpose and special purpose microprocessors and any one or more processors of any type of digital computer. Generally, a processor receives instructions and data from a read-only memory or a random access memory or both. The main components of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. In general, a computer can include operatively coupled to receive data from one or more mass storage devices (eg, magnetic disks, magneto-optical disks, or optical disks) for storing data and/or to transfer data to one or more A large-capacity storage device for storing data.

To provide interaction with the user, the above techniques can be implemented on a computer having a display device. The display device can be, for example, a cathode ray tube (CRT) and/or a liquid crystal display (LCD) monitor. The interaction with the user can be, for example, a display of information to the user and a keyboard and indicator device (eg, a mouse or trackball) through which the user can provide computer input (eg, interacting with user interface elements). Other types of devices can be used to provide interaction with the user. Other devices may, for example, provide feedback to the user in any form of sensation feedback (eg, visual feedback, audible feedback, or tactile feedback). The user's input can be received, for example, in any form, including acoustic, voice, and/or tactile input.

The above techniques can be implemented in a distributed computing system that includes backend components. The backend component can be, for example, a data server, an intermediate software component, and/or an application server. The above techniques can be implemented in a distributed computing system including front end components. The front end component can be, for example, a client computer having a graphical user interface (web browser) with which the user can interact with the exemplary implementation and/or other graphical user interface for the transfer device. The components of the system can be interconnected by any form of digital data communication or by a medium, such as a communication network. Examples of communication networks include area networks (LANs), wide area networks (WANs), the Internet, wired networks, and/or wireless networks.

The system can include a client and a server. The client and server are generally remote from each other and typically interact via a communication network. The relationship between the client and the server is generated by means of a computer program executing on a respective computer and having a client-server relationship with each other.

Although the present invention has been particularly shown and described with respect to the preferred embodiments of the present invention, it will be understood by those skilled in the art change.

10‧‧‧ device

14‧‧‧Light source

18‧‧‧Interferometer

22‧‧‧sample cell/gas pool

26‧‧‧ gas samples

30‧‧‧Detector

34‧‧‧ Processor

38‧‧‧ display

42‧‧‧Shell

46‧‧‧ entrance to the sample pool

50‧‧‧Export of the sample cell

Claims (43)

A method for monitoring one or more cerium-containing compounds present in a biogas, the method comprising: providing a non-absorbent gas to the sample cell, the non-absorbent gas having substantially no infrared absorption in a specified wavelength range of interest Obtaining a first spectral measurement from the sample cell; providing a biogas to the sample cell; obtaining a second spectral measurement from the sample cell; generating a first ratio based on the ratio of the first spectral measurement to the second spectral measurement Absorbing spectrum; generating at least one alternative absorption spectrum based on at least an individual absorption spectrum selected from a larger group of each of a group of one or more cerium-containing compounds having a known concentration of a known cerium-containing compound; The first absorption spectrum and the at least one alternative absorption spectrum calculate a total concentration of the one or more cerium-containing compounds in the biogas. The method of claim 1, wherein the one or more cerium-containing compounds in the biogas comprise at least one decane. The method of claim 1, wherein the larger group of known ruthenium containing compounds comprises at least one oxoxane. The method of claim 1, wherein the one or more cerium-containing compounds of the group comprise at least one oxoxane. The method of claim 1, wherein the total concentration comprises a total concentration of a oxoxane compound in the biogas, a total concentration of other cerium-containing compounds in the biogas, or a total concentration of all cerium-containing compounds in the biogas One of them. The method of claim 1, further comprising applying a correction factor to the total concentration, The correction factor adjusts the total concentration proportionally by a coefficient. The method of claim 1, further comprising selecting the one or more cerium-containing compounds of the group based on a spectral match of the known cerium-containing compound with the one or more cerium-containing compounds present in the biogas. The method of claim 1, wherein the larger group of known antimony-containing compounds comprises D3-decane, D4-decane, D5-decane, D6-decane, L2-decane, L3- Oxane, L4-oxirane and L5-decane. The method of claim 1, wherein the one or more cerium-containing compounds of the group comprise 3 to 5 cerium-containing compounds selected from the larger group of known cerium-containing compounds and cerium-containing components of trimethyl stanol (TMS) . The method of claim 1, wherein the biogas comprises a landfill gas. The method of claim 10, wherein the one or more cerium-containing compounds of the group comprise one of: a) L2-decane, L3-decane, and D4-decane; b) L2-decane , D3-oxime and D4-oxime; or c) L2-oxime, D3-oxime, and D5-nonane. The method of claim 11, wherein the one or more cerium-containing compounds of the group further comprise a cerium-containing component of trimethyl stanol (TMS). The method of claim 1, wherein the biogas comprises a digestion tank biogas. The method of claim 13, wherein the one or more cerium-containing compounds of the group comprise one of: a) D3-decane, D5-decane, and L3-decane; b) D4-decane , D5-oxime and L3-oxime; or c) D3-oxane, D5-nonane and L2-decane. The method of claim 1, wherein the at least one alternative absorption spectrum further comprises an individual absorption spectrum selected from each of a larger group of one or more hydrocarbon compounds having a known concentration of a known hydrocarbon compound. The method of claim 15, wherein the biogas comprises a digestion tank gas and the larger Groups of hydrocarbon compounds are known to comprise ethane, propane and butane. The method of claim 15, wherein the biogas comprises biogas and the larger group of known hydrocarbon compounds comprises toluene, methanol, and ethanol. The method of claim 15, wherein the at least one alternative absorption spectrum is based on at least the individual absorption spectrum of each of the one or more cerium-containing compounds of the panel and the one or more hydrocarbon compounds of the group. Individual absorption spectra of the model. The method of claim 1, wherein at least one of the one or more cerium-containing compounds of the group is present in the biogas. The method of claim 1, wherein at least one of the one or more cerium-containing compounds of the group is absent from the biogas. The method of claim 1, wherein the calculating comprises using the processor, performing the multiple regression analysis using the first absorption spectrum and the at least one instead of the absorption spectrum. The method of claim 21, further comprising performing the multiple regression analysis using classical least squares (CLS), partial least squares (PLS), inverse least squares (ILS), or principal component analysis (PCA). The method of claim 1, further comprising calculating the total concentration of the one or more cerium-containing compounds in the biogas on the spot and using the processor. The method of claim 1, further comprising obtaining the second spectral measurement over a capture period of from about 10 seconds to about 20 seconds. The method of claim 1, further comprising determining a total concentration value of the one or more cerium-containing compounds such that the at least one alternative absorption spectrum is substantially similar to the first absorption spectrum. The method of claim 1, further comprising providing the biogas with an automatic waste, waste water or landfill. The method of claim 1, wherein the total concentration is based on at least one of the first absorption spectrum, the at least one replacement absorption spectrum, and a baseline value or an offset value. The spectral characteristics of the one or more cerium-containing compounds in the biogas are calculated using a classical least squares (CLS) fitting routine. A system for monitoring one or more cerium-containing compounds in a biogas, the system comprising: a source of a first radiation beam; an interferometer receiving the first radiation beam from the source and forming an interference signal comprising a radiation beam; a sample cell in optical communication with the interferometer; a flow mechanism that establishes a first flow of non-absorbent gas having substantially no infrared absorption over a specified wavelength range of interest and passing through the sample cell a second stream of biogas; a cooled detector in optical communication with the sample cell, the cooled detector receiving: a first interference signal propagating through the non-absorbent gas in the sample cell; a second interference signal propagating through the sample gas in the sample cell, the sample gas comprising the biogas; a processor in electrical communication with the cooled detector, the processor configured to calculate the a total concentration of the one or more cerium-containing compounds in the biogas: a first absorption spectrum based on a ratio of the first interference signal to the second interference signal; and a set of substitution absorption spectra, At least based on an individual absorption spectrum selected from a larger group of known one or more groups of cerium-containing compounds having known concentrations; and an outer casing in which the light source, the interferometer, The sample cell, the cooled detector, and the processor. The system of claim 28, wherein the one or more of the biogas are combined The material comprises at least one oxoxane. The system of claim 28, wherein the sample cell comprises: a concave reflective field mirror surface at a first end of the sample cell; and a substantially spherical shape at a second end of the sample cell and in face-to-face relationship with the field mirror surface a concave reflective objective surface having a cylindrical assembly that increases a focus coincidence in at least one of the planes for propagation through the sample cell via multiple reflections on each of the field mirror surface and the objective lens surface The amount of delivery of the second radiation beam is maximized. The system of claim 28, wherein the set of substitutional absorption spectra is based on at least a model of: the individual absorption spectrum of each of the one or more cerium-containing compounds of the group and a selected one selected from the group consisting of a known concentration Individual absorption spectra for each of a small group of one or more hydrocarbon compounds of a hydrocarbon compound are known. A computer readable product physically embodied on a non-transitory information carrier or machine readable storage device and operative on a digital signal processor of a biogas detection system, the computer readable product comprising An instruction to cause the digital signal processor to: receive a first spectral measurement of a non-absorbent gas in the sample cell, the non-absorbable gas having substantially no infrared absorption over a specified wavelength range of interest; receiving the sample cell a second spectral measurement of the sample gas comprising the biogas, wherein the biogas comprises one or more oxoxane compounds; generating a first absorption spectrum based on the ratio of the first spectral measurement to the second spectral measurement; Generating a set of substitutional absorption spectra based on individual absorption spectra selected from a larger group of known one or more groups of ruthenium containing compounds having known concentrations; and using the first absorption spectrum and This group performs multiple regressions instead of absorption spectra Analysis to calculate the total concentration of the one or more oxoxane compounds in the biogas. A computer readable product of claim 32, wherein the digital processor is configured to determine the total concentration such that the set of alternate absorption spectra is substantially similar to the first absorption spectrum. A system for monitoring one or more cerium-containing compounds in a biogas, the system comprising: a source of a first radiation beam; an interferometer receiving the first radiation beam from the source and forming an interference signal comprising a radiation beam; a sample cell in optical communication with the interferometer; a detector in optical communication with the sample cell, the detector receiving: a first interference signal propagating through the non-absorbent gas in the sample cell The non-absorbent gas has substantially no infrared absorption in a specified wavelength range of interest; and a second interference signal propagating through the sample gas in the sample cell, the sample gas comprising biogas; and a detection a processor for electrical communication, the processor being configured to calculate a total concentration of the one or more cerium-containing compounds in the biogas based on: a first absorption spectrum based on the first interference signal and the second interference a ratio of signals; and a set of substitutional absorption spectra based at least on each of a small group of one or more cerium-containing compounds selected from a larger group of known cerium-containing compounds having known concentrations The absorption spectra of the individual. The system of claim 34, wherein the one or more cerium-containing compounds in the biogas comprise at least one oxoxane. The system of claim 34, wherein the sample pool comprises: a concave reflective field mirror surface at a first end of the sample cell; and a substantially spherical concave reflective objective surface at a second end of the sample cell and in face-to-face relationship with the field mirror surface, the objective lens surface having at least one The cylindrical assembly in which the focus coincides in the plane maximizes the amount of delivery of the second radiation beam propagating through the sample cell via multiple reflections on the field mirror surface and the objective lens surface. The system of claim 34, wherein the set of substitutional absorption spectra is based on at least a model of: the individual absorption spectrum of each of the one or more cerium-containing compounds of the group and a selected one selected from the group consisting of a known concentration Individual absorption spectra for each of a small group of one or more hydrocarbon compounds of a hydrocarbon compound are known. The system of claim 34, wherein the total concentration comprises a total concentration of a oxoxane compound in the biogas, a total concentration of other cerium-containing compounds in the biogas, or a total concentration of all cerium-containing compounds in the biogas One of them. The system of claim 34, wherein the processor is configured to apply a correction factor to the total concentration, wherein the correction factor scales the total concentration by a factor. The system of claim 34, wherein the set of substitutional absorption spectra further comprises an individual absorption spectrum selected from each of a larger group of one or more hydrocarbon compounds having a known concentration of a known hydrocarbon compound. The system of claim 34, wherein the set of substitutional absorption spectra is at least based on the individual absorption spectrum of each of the one or more cerium-containing compounds of the group and the individual of the one or more hydrocarbon compounds of the group A model of the absorption spectrum. The system of claim 34, wherein the processor is configured to perform a multiple regression analysis using the first absorption spectrum and the set instead of the absorption spectrum. The system of claim 34, wherein the processor is configured to use classical least squares (CLS) by at least one of based on the first absorption spectrum, the at least one replacement absorption spectrum, and a baseline value or an offset value The fitting routine presents the spectral characteristics of the one or more cerium-containing compounds in the biogas to calculate the total concentration.