Patent Application
20230417656
The present disclosure relates to measuring chemical constituents and associated properties of hydrocarbon fuel mixtures, and further relates to tunable diode laser absorption spectrometry gas analyzers having improved chemometric models.
Whenever fuel gas (natural gas, coal syngas, biogas, etc.) is generated, transferred or used, knowledge of the fuel gas' level of contamination, heating value, relative density, compressibility, theoretical hydrocarbon liquid content, and Wobbe index are typically required. Measurement of various target components, such as gas contaminants (e.g. H2S, H2O, O2, CO2) is critical for preventing infrastructure damage due to corrosion or chemical reactivity. Natural gas producers must clean extracted natural gas to remove contaminants and then verify any residual contaminant levels before the fuel gas is introduced into a pipeline. Desulfurizer beds in fuel reformers need periodic replacement or regeneration to prevent H2S breakthrough into the reformed fuel product, and so require frequent contaminant level monitoring. Accurate measurement and monitoring of key gas parameters, including heating value, relative density, compressibility, theoretical hydrocarbon liquid content, and Wobbe index, are critical for pricing the fuel, optimizing burner conditions, and determining combustion efficiency.
Laser absorption spectrometers draw a sample of fuel gas from a gas stream to measure the total light absorption spectrum associated with the gas sample. The laser absorption spectrometer is calibrated to measure light absorption of the various contaminants within the gas sample, however, background gases (such as non-contaminants methane and propane, or other dominant constituents of natural gas, coal syngas and biogas), can affect the light absorption measurement of the contaminants. Empirical models, multivariate fitting routines, or more generally post-processing of the measured light absorption of the contaminants are used to determine the amount of the various contaminants present in the gas mixture. A multitude of input parameters and iterative calibration processes are implemented within the post-processing methods to yield an empirical approximation of the various contaminants present in the gas mixture. Such approximations are accurate where the input parameters are known, however approximating the contaminants is difficult because the gas concentration of background gasses are often variable, leading to inaccuracies in the post-processing and tedious post-calibration procedures.
Therefore, there exists a need in the art to improve systems and methods of measuring gas contaminants using laser absorption spectrometry.
In one aspect, method for determining target components in a sample gas mixture is described. The method includes obtaining measured gas concentration data for each of one or more background gases present in the sample gas mixture from a concentration measurement instrument, the gas concentration measurement instrument configured to measure gas concentrations of the one or more background gases; obtaining measured light absorption data of each of one or more gaseous target components from a laser spectroscopy instrument indicative of an amount of absorption of light by the sample gas mixture at a frequency of each of the one or more gaseous target components, the laser spectroscopy instrument configured to measure light absorption of the one or more gaseous target components within the sample gas, each of the one or more gaseous target components having an absorption spectrum at the frequency of light or at a combination of frequencies of frequencies of light; setting gas concentration fit coefficients to a chemometric model for the measured gas concentration data for each of one or more background; wherein the chemometric model employs a broadband offset basis to a final basis set spectrum for the one or more gaseous target components; applying an iterative mathematical model to the measured light absorption data by iteratively adjusting target component fit coefficients until the measured light absorption data matches the chemometric model; and, determining a calculated amount of the one or more target components within the sample gas mixture by applying the gas concentration fit coefficients and the target component fit coefficients to the measured light absorption data and to the measured gas concentration data.
In another aspect, a method for determining target components in a sample gas mixture is described. The method includes obtaining measured gas concentration data for each of one or more background gases present in the sample gas mixture from a concentration measurement instrument, the gas concentration measurement instrument configured to measure gas concentrations of the one or more background gases; obtaining measured light absorption data of each of one or more gaseous target components from a laser spectroscopy instrument indicative of an amount of absorption of light by the sample gas mixture at a frequency of each of the one or more gaseous target components, the laser spectroscopy instrument configured to measure light absorption of the one or more gaseous target components within the sample gas, each of the one or more gaseous target components having an absorption spectrum at the frequency of light or at a combination of frequencies of frequencies of light; applying an iterative mathematical model to the measured light absorption data by iteratively adjusting gas concentration fit coefficients and target component fit coefficients until the measured light absorption data matches the chemometric model; wherein the chemometric model employs a broadband offset basis to a final basis set spectrum for the one or more gaseous target components; applying a correction function to the iterative mathematical model, wherein input parameters of the correction function are the measured gas concentration data and one or more of the iterated gas concentration fit coefficients and target component fit coefficients from the iterative mathematical model; and, determining a calculated amount of the one or more target components within the sample gas mixture by applying the fit coefficient to the measured light absorption data and to the measured gas concentration data
The reference symbols used in the drawings, and their meanings, are listed in summary form in the list of reference symbols. In principle, identical parts are provided with the same reference symbols in the figures.
In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings.
As used herein, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. The terms “optional” or “optionally” means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where the event occurs and instances where it does not.
With reference to
Choice of wavelength range of the lasers depends upon the chemical species to be detected, avoiding where possible interfering absorptions from different species. Multiple laser diodes may be available for providing absorption measurements over several different ranges. Some embodiments utilize two lasers; for example, laser 10 operating near 1.58 μm and 1.27 μm, but other choices are possible. The spectral range over which each laser diode may be tuned is at least 20 GHz and preferably 60-80 GHz. Sensor data is collected and analyzed by a computer system 23, which in accord with the present disclosure employs chemometric fitting routines and calculations of heating value, relative density, compressibility, theoretical hydrocarbon liquid content, Wobbe index, and contaminant concentrations for the fuel gas stream.
The processor 120 and memory 130 are configured to perform mathematical algorithms, mathematical modeling, iterative modeling, regression modeling, linear regression modeling, line fitting and the like using commercially available or proprietary software or such as MATLAB by MathWorks ©. The memory 130 is configured to store such software, as well as chemometric models and final basis set spectra as explained in further detail below. The processor 120 is configured to receive data from the off-axis ICOS instrument 10 (or more generally a laser spectroscopy instrument) and the gas concentration measurement instrument 110 and input said data into the chemometric models, which can provide as output modeled values for said measured data.
In some embodiments, the off-axis ICOS instrument 10 is a laser spectroscopy device. In some embodiments, the gas concentration measurement instrument 110 is selected from a group consisting of a gas chromatography instrument, a Fourier-transform infrared spectroscopy instrument, a gas concentration sensor, a metal oxide gas sensor, an electrochemical gas sensor, a dynamic zirconia dioxide sensor and a catalytic gas sensor. The gas concentration measurement instrument 110 is configured to measure or determine gas concentrations of background gasses (such as non-contaminants methane and propane, or more generally other dominant constituents of natural gas, coal syngas and biogas).
As used herein, the term “chemometric model” shall mean a mathematical model stored in memory 130 and executed by the processor 120. The chemometric model employs a broadband offset basis to a final basis set spectrum for the one or more gaseous target components, which include spectra of target components (i.e. contaminants) found in gas streams and the background (base or constituent) components of the gas streams. The spectra (modeled wavelengths) can be modeled for variations in concentrations of any of the target components or background components, and a multitude of static parameters, variable parameters, gain factors and fit coefficients may be applied to the chemometric model such that the processor 120 yields a resultant measurement.
To facilitate line fitting mathematical processes using software (hereinafter referred to as “line fitting”) of the measured spectrum, the stored basis sets for use with the chemometric modeling can be stored in memory 130 (as shown in
The methane (CH4) spectrum (or any of the background gases) is included in a basis set spectra as shown in
Thus, where known or certified concentrations of background gases are properly modeled, the final basis set spectrum can be modeled by the broadband offset basis as previously described, and included in the basis set spectra (as shown in
A chemometric data analysis strategy like that described in Linh D. Le et al., “Development of a Rapid On-Line Acetylene Sensor for Industrial Hydrogenation Reactor Optimization Using Off-Axis Integrated Cavity Output Spectroscopy”, Applied Spectroscopy 62(1), pp. 59-62 (2008) is one known way to quantify the respective constituents. Background, non-contaminant gases are strong broadband absorbers and measurement of the contaminant gasses can be influenced by the background gas leading to inaccurate results. For example, a measurement of target component H2S at 0, 10, or 20 ppm in a background gases of CH4 and C3H8 will produce varied measurements. This is due to collisional broadening from the background gases present in the gaseous mixture. The broadening is different because natural gas has different gases than in the certified inert nitrogen background the model is based on. Generally, broadening denotes enlargement of the recorded spectrum stored in memory 130.
Mixture concentrations of the background gases must therefore be incorporated into the empirical model. Even if the background gases are properly fit by iterative approximation; if the broadening is not correctly accounted for the model will not match the measurement accurately. Fitting algorithms can be implemented to minimize measurement errors from interfering absorptions of background gases. The fit algorithm also includes a fit of the baseline to compensate for any low-frequency perturbation, like a change in the laser intensity or change in the cell transmission. Knowing the actual broadband absorber composition allows for the inclusion of the theoretical absorption line in the model or adjust the calculated concentration value to correct for the change in background gas. One or more fit coefficients for the concentrations or light absorption of background gases or target components can therefore be applied to the chemometric model.
Embodiments of the present disclosure utilize instrumentation (such as the gas concentration measurement instrument 110 of
As previously described, the off-axis ICOS instrument 10 employs chemometric fitting routines and empirically contaminant concentrations for the fuel gas stream 101 from the final basis set spectrum included in memory 130. The chemometric model employs a broadband offset basis to the final basis set spectrum for measurement of target components (e.g. H2S, H2O, O2, and CO2) as described in U.S. Pat. No. 6,795,190. Thus, where known or certified concentrations of background gases are properly modeled, the final basis set spectrum can be determined by modeling or by tabulation tables. However, where variable concentrations of background gases are present, the model must be adjusted by the fit coefficients, or the concentrations must be measured and fit into the model. Stated differently, the offset of the final basis set spectrum of the measurement can be empirically calculated as previously described when the background gas concentrations are known or by using certified gas concentrations, but variations in background gas concentrations increase model complexity and can introduce error into the model for measuring wavelengths without proper adjustment of the model and in particular the broadband offset basis of the model. By determining the gas concentrations of background gasses in a mixture from the gas concentration measurement instrument 110, a mathematical algorithm and theoretical transformation can be implemented to properly fit the model.
As shown in
The method 200 includes obtaining 210 measured gas concentration data for each of the one or more background gases present in the fuel gas stream 101 from the concentration measurement instrument 110. In some embodiments, the measured gas concentration data is stored in the memory 130 of
The method 200 further includes determining 250 a calculated amount of the one or more target components within the sample gas mixture by applying the gas concentration fit coefficients and the target component fit coefficients to the measured light absorption data and to the measured gas concentration data.
In some embodiments, the processor 120 of
As shown in
The method 300 includes obtaining 310 measured gas concentration data for each of the one or more background gases present in the fuel gas stream 101 from the concentration measurement instrument 110. In some embodiments, the measured gas concentration data is stored in the memory 130 of
In some embodiments, the method 300 further includes applying 330 an iterative mathematical model to the measured light absorption data by iteratively adjusting gas concentration fit coefficients and target component fit coefficients until the measured light absorption data matches the chemometric model; wherein the chemometric model employs a broadband offset basis to a final basis set spectrum for the one or more gaseous target components.
The method 300 further includes applying 340 a correction function to the iterative mathematical model, wherein input parameters of the correction function are the measured gas concentration data and one or more of the iterated gas concentration fit coefficients and target component fit coefficients from the iterative mathematical model. The method 300 finally includes determining 350 a calculated amount of the one or more target components within the sample gas mixture by applying the fit coefficient to the measured light absorption data and to the measured gas concentration data.
In some embodiments, applying the correction function includes comparing the measured gas concentration data against a look up table stored in memory
Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed disclosure, from the study of the drawings, the disclosure, and the appended claims. In the claims the word “comprising” does not exclude other elements or steps and the indefinite article “a” or “an” does not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope of the claims.
While the disclosure has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive. It will be understood that changes and modifications may be made by those of ordinary skill within the scope of the following claims. In particular, the present disclosure covers further embodiments with any combination of features from different embodiments described above and below. Additionally, statements made herein characterizing the disclosure refer to an embodiment of the disclosure and not necessarily all embodiments.
The terms used in the claims should be construed to have the broadest reasonable interpretation consistent with the foregoing description. For example, the use of the article “a” or “the” in introducing an element should not be interpreted as being exclusive of a plurality of elements. Likewise, the recitation of “or” should be interpreted as being inclusive, such that the recitation of “A or B” is not exclusive of “A and B,” unless it is clear from the context or the foregoing description that only one of A and B is intended. Further, the recitation of “at least one of A, B and C” should be interpreted as one or more of a group of elements consisting of A, B and C, and should not be interpreted as requiring at least one of each of the listed elements A, B and C, regardless of whether A, B and C are related as categories or otherwise. Moreover, the recitation of “A, B and/or C” or “at least one of A, B or C” should be interpreted as including any singular entity from the listed elements, e.g., A, any subset from the listed elements, e.g., A and B, or the entire list of elements A, B and C.
This written description uses examples to disclose the disclosure, including the best mode, and also to enable any person skilled in the art to practice the disclosure, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the disclosure is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.
