Flux inversion method using last cycle inversion information weighted radial plume mapping

Abstract

The application provides a flux inversion method using last cycle inversion information weighted radial plume mapping, relates to the field of oil and gas station equipment and safety and environmental protection technology, and comprises the following steps: correcting the measured path integral concentration data of the current cycle by using the reconstructed path integral concentration data of the last cycle to obtain the corrected path integral concentration data of the current cycle; based on the corrected path integral concentration data of the current cycle and the reconstructed path integral concentration data of the last cycle, using a weight function to update a weight coefficient to minimize the error sum of squares, combining a two-stage Gaussian smoothing base function minimization method to obtain the measurement plane concentration distribution of the current cycle; under the condition of simultaneously satisfying consistency, wind speed and wind direction, the flux is calculated by using the measurement plane concentration distribution and environmental wind data. The application is particularly suitable for the flux scene with obvious periodic change by weighting and correcting the measurement value of the current cycle by using the historical cycle data.

Description

Technical Field

[0001] This invention relates to the field of oil and gas station equipment and safety and environmental protection technology, specifically to a flux inversion method that uses weighted radial plume mapping based on the inversion information of the previous cycle. Background Technology

[0002] In oil and gas stations, the monitoring and management of escaping gases are crucial aspects of environmental protection and safe production. Currently, commonly used monitoring technologies mainly include direct detection methods and indirect detection methods.

[0003] Direct detection uses sensors and sampling equipment to detect emissions in real time near the emission source, but this method requires a large number of sensors to be deployed, is costly, and is difficult to cover large areas.

[0004] Indirect detection methods typically employ atmospheric diffusion models and inversion algorithms to estimate the source strength and emission location of fugitive gases by measuring their concentration distribution in the air. However, existing inversion algorithms face numerous challenges in practical applications, such as insufficient model accuracy, high computational complexity, and poor real-time performance. The radial plume mapping (VRPM) model is widely used for atmospheric pollutant inversion calculations; however, information based on single-period inversion often ignores the cumulative effect of historical data, making the inversion results susceptible to instability due to instantaneous measurement errors. To address this issue, more accurate and efficient flux inversion methods need to be developed, enabling real-time and reliable fugitive gas monitoring in complex environments to meet the application requirements of oil and gas stations.

[0005] To address the problems of existing technologies, this invention provides a flux inversion method that utilizes weighted radial plume mapping based on inversion information from the previous cycle. Summary of the Invention

[0006] To address the problems of existing technologies, this invention aims to provide a weighted modified flux inversion method based on radial plume mapping and previous cycle inversion information, enabling accurate monitoring of fugitive gas emissions from oil and gas stations. By introducing Gaussian smoothing basis functions and weighted error sum-of-squares optimization techniques, the accuracy and real-time performance of the inversion results are improved, reducing environmental pollution and energy waste, and contributing to the environmental protection and sustainable development of oil and gas stations.

[0007] This invention provides a flux inversion method using weighted radial plume mapping based on inversion information from the previous cycle, the method comprising:

[0008] The measured path integral concentration data for the current period is corrected using the reconstructed path integral concentration data from the previous period, resulting in the corrected path integral concentration data for the current period.

[0009] Based on the corrected path integral concentration data of the current period and the reconstructed path integral concentration data of the previous period, the weight coefficients are updated using a weight function to minimize the sum of squared errors. Combined with the two-stage Gaussian smoothing basis function minimization method, the measurement plane concentration distribution of the current period is obtained.

[0010] When the conditions of consistency, wind speed, and wind direction are met simultaneously, the flux is calculated using the measured plane concentration distribution and environmental wind data.

[0011] According to an embodiment of the present invention, the corrected path integral concentration data for the current period is obtained through the following steps:

[0012] The measurement plane is placed downwind of the area source, and five points in the measurement plane are periodically scanned using a path integral optical remote sensing system to obtain the measured path integral concentration data.

[0013] By correcting the measured path integral concentration data of the current period using the reconstructed path integral concentration data from the previous period and the weighted average coefficient, the corrected path integral concentration data for the current period is obtained.

[0014] According to an embodiment of the present invention, the corrected path integral concentration data for the current period is obtained by the following expression:

[0015]

[0016] in: The corrected path integral concentration data is for the i-th measurement point in the current period t; α is the weighted average coefficient. This refers to the measured path integral concentration data of the i-th measurement point in the current period t; This is the reconstructed path integral concentration data of the i-th measurement point in the previous period t-1.

[0017] According to an embodiment of the present invention, the reconstructed path integral concentration data is obtained through the following steps:

[0018] Integrating the measured plane concentration distribution for the current period yields the reconstructed path integrated concentration data for the current period.

[0019] According to an embodiment of the present invention, the measured plane concentration distribution is obtained through the following steps:

[0020] The corrected path integral concentration data of the near-ground measurement point in the current period is used for the first-stage Gaussian smoothing basis function minimization to obtain the peak position and standard deviation of the Gaussian function under the one-dimensional Gaussian distribution.

[0021] The corrected path integral concentration data, peak position, standard deviation, and updated weighting coefficients of all measurement points in the current period are used to minimize the Gaussian smoothing basis function in the second stage to obtain the concentration distribution of the measurement plane.

[0022] According to one embodiment of the present invention, the weighting coefficient is:

[0023]

[0024] The weighting function:

[0025] W(x) = e -x

[0026] in: The weighting coefficient for the i-th measurement point in the current period t; , which are the input independent variables of the weighting function W(x); The reconstructed path integral concentration data is for the i-th measurement point in the previous period t-1. This represents the measured path integral concentration data for the i-th measurement point in the current period t.

[0027] According to an embodiment of the present invention, the flux is calculated through the following steps:

[0028] The environmental wind data is obtained by measuring wind speed and direction information using a three-dimensional acoustic anemometer located in the vertical direction;

[0029] The wind speed and direction information are interpolated and extrapolated to obtain wind speed and direction data at various heights.

[0030] The concentration distribution of the measured plane is converted from polar coordinates to concentration in square basic units in Cartesian coordinates;

[0031] The flux is calculated by substituting the concentration of the square basic unit, the wind speed, and the wind direction data into the flux calculation expression.

[0032] According to an embodiment of the present invention, the flux calculation expression is as follows:

[0033]

[0034] Where: Flux is the flux; M is the molecular weight of the gas; G(y) i ,z i ) represents the concentration of a square basic unit; S area The area of ​​a square basic unit in a vertical plane; wind_speed i Wind speed; wind_direction i The wind direction.

[0035] According to another aspect of the invention, a storage medium is also provided, which includes instructions for performing the methods described in any of the preceding claims.

[0036] According to another aspect of the invention, a flux inversion system for weighted radial plume mapping using inversion information from the previous cycle is also provided, performing the method as described in any of the preceding claims, the system comprising:

[0037] The concentration correction module uses the reconstructed path integral concentration data from the previous period to correct the measured path integral concentration data for the current period, thus obtaining the corrected path integral concentration data for the current period.

[0038] The plane concentration module, based on the corrected path integral concentration data of the current period and the reconstructed path integral concentration data of the previous period, updates the weight coefficients using a weight function to minimize the sum of squared errors, and combines a two-stage Gaussian smoothing basis function minimization method to obtain the measured plane concentration distribution of the current period.

[0039] The flux calculation module calculates the flux by using the measured plane concentration distribution and environmental wind data, while simultaneously satisfying the conditions of consistency, wind speed, and wind direction.

[0040] This invention provides a flux inversion method using weighted radial plume mapping (VRPM) based on inversion information from the previous cycle. Compared with existing technologies, it has the following advantages: This invention significantly improves the accuracy and real-time performance of inverting fugitive methane emission fluxes at oil and gas stations. Traditional methods are limited by issues such as significant time variability and sensitivity to noise, while this invention effectively reduces errors by weighting and correcting current cycle measurements using historical cycle data, making it particularly suitable for flux scenarios with significant periodic variations. This technological innovation not only improves the accuracy of methane monitoring at oil and gas stations but also enables rapid response to real-time environmental changes, resulting in significant social and economic benefits.

[0041] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0042] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0043] Figure 1A flowchart illustrating the steps of a flux inversion method using weighted radial plume mapping with inversion information from the previous cycle, according to an embodiment of the present invention, is shown.

[0044] Figure 2 A schematic diagram of a VRPM measurement structure according to an embodiment of the present invention is shown;

[0045] Figure 3 A flowchart of a VRPM flux measurement method based on weighted average and weighted SSE at time t-1, according to an embodiment of the present invention, is shown.

[0046] Figures 4a-4c A schematic diagram of an exponential function, a linear function, and a negative exponential function according to an embodiment of the present invention is shown;

[0047] Figures 5a-5c A schematic diagram of flux inversion when the SSE function is weighted by three weighting functions according to an embodiment of the present invention is shown.

[0048] Figures 6a-6b The graph shows the trend of RMSE and average flux as a function of α according to an embodiment of the present invention;

[0049] Figure 7 A schematic diagram of the previous cycle reconstructed PIC weighted average flux measurement results when α = 0.5 is shown according to an embodiment of the present invention;

[0050] Figure 8 A schematic diagram of VRPM flux measurement results is shown, based on a combined method of weighted average PIC and WSSE using a PIC reconstructed from the previous cycle, according to an embodiment of the present invention.

[0051] In the accompanying drawings, the same parts use the same reference numerals. Also, the drawings are not drawn to scale. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0053] Determining the amount of volatile organic compounds (VOCs) from area sources is subject to significant uncertainty. In the last century, area source gas emission measurements primarily employed the soil balance method and flux chamber method, which suffered from considerable errors. Furthermore, these methods could only estimate gas exchange at a single point, failing to provide real-time monitoring or reflect spatiotemporal characteristics. Currently, micrometeorological techniques have proven accurate and practical for measuring area source gas emissions. Integral level flux (IHF) is a mass balance-based method that does not rely on similarity assumptions but requires accurate concentration distribution information. Backward Lagrange stochastic (bLS) is a technique that combines gas concentration and diffusion models to calculate VOC emission intensity. However, bLS requires consideration of data correction under non-ideal conditions (slope, obstacles, etc.).

[0054] The Vertical Radial Plume Mapping (VRPM) method developed by the EPA (U.S. Environmental Protection Agency) promises to eliminate the need for diffusion models and can be used to estimate directly measured emission factors. VRPM is designed to measure the mass flux of pollutants through the downwind vertical plane of the emission source. It applies a bivariate Gaussian smoothing basis function minimization method to reconstruct a crosswind smoothed mass equivalent concentration map on the vertical plane based on downwind PIC (path integral concentration data). The bivariate Gaussian function is assumed to represent the plume mass across the entire VRPM plane, and the parameters of the mass equivalent bivariate Gaussian function are reconstructed from the measured PIC. These reconstructed parameters are then used to reconstruct the concentration field across the entire VRPM plane. The reconstructed gas emission rates are calculated by numerical integration of the reconstructed bivariate Gaussian function with appropriate resolution.

[0055] However, the following problems exist when using VRPM to invert flux:

[0056] (1) High temporal variability. The VRPM measurement system is sequential, and the surface source gas flux is determined by the measurement value of only one cycle, making it highly sensitive to noise (e.g., CN117711515A). Previous studies have suggested using three to ten cycles of moving average PIC data as input to the VRPM algorithm, with a larger number of cycles resulting in better moving average performance and more accurate flux calculation. While this method can improve the accuracy of flux inversion, it introduces a time lag when detecting anomalous flux data, making it unsuitable for rapidly changing and aperiodic flux scenarios. The larger the number of cycles, the greater this adverse effect. Therefore, it is difficult to achieve a balance between the number of moving average cycles and flux accuracy.

[0057] (2) Since VRPM is a sequential sampling system, it will also be affected by time variability in the process of minimizing the sum of squared errors (SSE), which in turn affects the flux inversion results. The minimization process of SSE is actually a least squares process.

[0058] In summary, current techniques for measuring surface source gas emission fluxes primarily rely on gas diffusion models and require calculations based on meteorological data. However, these methods generally suffer from computational complexity, high temporal variability, and insufficient temporal resolution in practical applications, leading to unstable measurement results and limited accuracy.

[0059] This invention, based on the radial plume mapping (VRPM) method for quantifying area source gas emissions, accurately inverts gas fluxes by using previous period inversion information in the VRPM for weighting. Specifically, this invention uses previous period inversion information, including previous period measurements and reconstructed values, to weight the VRPM, thus addressing the problem of large time variability in VRPM. Given the lack of applications in the field of emission flux inversion based on previous period inversion information technology, this invention fills a gap in improving measurement accuracy and reducing time variability.

[0060] Figure 1 A flowchart illustrating the steps of a flux inversion method based on a weighted radial plume mapping using inversion information from the previous cycle, according to an embodiment of the present invention, is shown.

[0061] like Figure 1 As shown, in step S1, the reconstructed path integral concentration data of the previous period is used to correct the measured path integral concentration data of the current period, so as to obtain the corrected path integral concentration data of the current period.

[0062] like Figure 1 As shown, in step S2, based on the corrected path integral concentration data of the current period and the reconstructed path integral concentration data of the previous period, the weight coefficients are updated using a weight function to minimize the sum of squared errors. Combined with the two-stage Gaussian smoothing basis function minimization method, the measured plane concentration distribution of the current period is obtained.

[0063] like Figure 1 As shown, in step S3, when the conditions of consistency, wind speed and wind direction are met simultaneously, the flux is calculated by measuring the plane concentration distribution and the ambient wind data.

[0064] This invention is a flux inversion method using weighted radial plume mapping with inversion information from the previous period. By incorporating inversion information from the previous period into the radial plume mapping (VRPM), the measured values ​​of the current period are weighted and corrected to mitigate the impact of time variability on flux inversion. Through the weighted sum of squared errors (WSSE) method and the minimization of two-stage Gaussian smoothing basis functions, the accuracy and stability of gas flux inversion are improved, making it suitable for real-time monitoring of rapidly changing fugitive gas emissions.

[0065] Figure 2 A schematic diagram of a VRPM measurement structure according to an embodiment of the present invention is shown.

[0066] like Figure 2 As shown, the measurement structure includes: a path integral optical remote sensing system (PI-ORS), a three-dimensional anemometer, and measurement points (reflectors).

[0067] When a five-beam configuration is used, the measurement plane is placed downwind of the area source, and the path integral optical remote sensing system is used to periodically scan five points (a, b, c, d, e) in the two-dimensional measurement plane to obtain the measured path integral concentration data.

[0068] Specifically, when the position of the path integral optical remote sensing system is the origin (y O ,z O If (0,0) = 0, then the coordinates of the five measurement points are:

[0069] X a =(y a ,z a ),X b =(y b ,z b ),X c =(y c ,z c ),X d =(y d ,z d ),X e =(y e ,z e )

[0070] Furthermore, reflectors a, b, and c are positioned near the ground, with reflectors a and b located at the longest beam O. c The trisection points, the length of the beam from the surface source is O. c 1 / 3 to 1 / 2.

[0071] Two three-dimensional anemometers (e.g., W2 and W) are set up at different vertical heights. 10 (), to measure wind speed and wind direction information wind_speed j and wind_direction j , j = 2, 10.

[0072] Figure 3 A flowchart of a VRPM flux measurement method based on weighted average and weighted SSE at time t-1, according to an embodiment of the present invention, is shown.

[0073] like Figure 3 As shown, the measured PIC data for the current period t is obtained.

[0074] Specifically, such as Figure 2As shown, the measurement plane is placed downwind of the area source, and five points in the measurement plane are periodically scanned using a path integral optical remote sensing system to obtain measured path integral concentration data. Furthermore, when a five-beam configuration is used, five measured path integral concentration data (PIC) are obtained in the t-th period, denoted as...

[0075] like Figure 3 As shown, the current PIC is corrected using the PIC rebuilt in the previous cycle.

[0076] Specifically, the measured path integral concentration data for the current period is corrected using the reconstructed path integral concentration data from the previous period and a weighted average coefficient, resulting in the corrected path integral concentration data for the current period. Further, the corrected path integral concentration data for the current period is obtained using the following expression:

[0077]

[0078] in: The corrected path integral concentration data is for the i-th measurement point in the current period t; α is the weighted average coefficient. This refers to the measured path integral concentration data of the i-th measurement point in the current period t; This is the reconstructed path integral concentration data of the i-th measurement point in the previous period t-1.

[0079] like Figure 3 As shown, calculate the weights (coefficients) corresponding to the weighting function.

[0080] Specifically, based on the corrected path integral concentration data of the current period and the reconstructed path integral concentration data of the previous period, the weight coefficients are updated using a weight function to minimize the sum of squared errors.

[0081] Weighting coefficients:

[0082]

[0083] Weighting function:

[0084] W(x) = e -x

[0085] in: The weighting coefficient for the i-th measurement point in the current period t; , which are the input independent variables of the weighting function W(x); The reconstructed path integral concentration data is for the i-th measurement point in the previous period t-1. Let β(i) be the measured path integral concentration data of the i-th measurement point in the current period t. β(i) satisfies...

[0086] like Figure 3 The diagram illustrates the one-dimensional SBFM parameter optimization process. Specifically, the corrected path integral concentration data of near-ground measurement points in the current period are used for the first-stage minimization of the Gaussian smoothing basis function to obtain the peak position and standard deviation of the Gaussian function under the one-dimensional Gaussian distribution.

[0087] When using Figure 2 In the five-beam configuration shown, during the first-stage Gaussian Smooth Basis Function Minimization (SBFM), a one-dimensional SBFM reconstruction procedure is applied to the ground-segmented beam paths to find the crosswind concentration distribution. Specifically, the unknown parameters (m) of the Gaussian function are obtained by fitting corrected PIC data from three ground measurement points to a Gaussian function and minimizing the one-dimensional sum of errors (SSE) function. y ,σ y ).

[0088] Specifically, the PIC of the near-ground measurement point is used for the first-stage Gaussian smoothing basis function minimization, that is, the coordinates are X... a ,X b ,X c And their corresponding corrected PIC values Substituting the one-dimensional sum of errors (SSE) function and minimizing it using the simplex algorithm, we obtain the peak position m under the one-dimensional Gaussian distribution. y and the standard deviation σ of the Gaussian function y .

[0089] One-dimensional sum of errors (SSE) function:

[0090]

[0091] Where B is the area under a one-dimensional Gaussian distribution; m y It represents the peak position (mean) of a one-dimensional Gaussian distribution; σ y It is the standard deviation of the Gaussian function; PIC i For the path integral concentration data of the i-th measurement point, the corrected path integral concentration data is used; r i Let be the optical path length of the beam corresponding to the i-th measurement point.

[0092] like Figure 3 The diagram illustrates the two-dimensional SBFM parameter optimization process. Specifically, the corrected path integral concentration data, peak positions, standard deviations, and updated weighting coefficients of all measurement points in the current period are used to minimize the Gaussian smoothing basis function in the second stage to obtain the concentration distribution on the measurement plane.

[0093] When using Figure 2In the five-beam configuration, during the second-stage Gaussian Smooth Basis Function Minimization (SBFM), a two-dimensional SBFM reconstruction procedure is applied to all beam paths to find the concentration distribution in the measurement plane. This is achieved by fitting five PICs to a Gaussian function and obtaining the unknown parameters of the Gaussian function by minimizing the weighted sum of squared errors (WSSE).

[0094] Specifically, the corrected PIC and m for all measurement points y ,σ y For the second-stage Gaussian smoothing basis function minimization, i.e., X a ,X b ,X c ,X d ,X e and the corresponding corrected PIC value The updated weight coefficients β(i) are substituted into the weighted sum of squared errors (WSSE) function, and then the WSSE(A,σ) is minimized using the simplification method. z ), thus obtaining the remaining parameters A,σz.

[0095] Weighted sum of squared errors function:

[0096]

[0097] Among them: PIC i For the path integral concentration data of the i-th measurement point, the corrected path integral concentration data is used; G(r,θ) i A,m y ,σ y ,σ z ) represents the Gaussian function; θi is the angle of the beam corresponding to the i-th measurement point; A is the area under the two-dimensional Gaussian distribution; σ z denoted as the standard deviation of a two-dimensional Gaussian function.

[0098] like Figure 3 As shown, the planar concentration distribution G.

[0099] Specifically, the parameters (A, σ) z m y σ y Substituting the known value into the two-dimensional Gaussian function G(r,θ) to be fitted, we obtain the concentration distribution G in the measurement plane.

[0100] The two-dimensional Gaussian function to be fitted:

[0101]

[0102] like Figure 3 As shown, the reconstructed PIC value is obtained by integrating G.

[0103] Specifically, the reconstructed path integral concentration data is obtained through the following steps: Integrating the measured plane concentration distribution G of the current period, the reconstructed path integral concentration data of the current period is obtained.

[0104] like Figure 3 As shown, determine whether the condition (CCF≥0.8)∩(1≤s≤8)∩(-10≤d≤25) is satisfied.

[0105] Specifically, when consistency, wind speed, and wind direction conditions are simultaneously met, the flux is calculated by measuring the planar concentration distribution and ambient wind data. Further, the consistency condition is: a consistency correlation factor (CCF) greater than or equal to 0.8. The wind speed condition is: a wind speed *s* between 1 and 8 m / s. The wind direction condition is: a wind direction between -10° and 25°. If any condition is not met, the data for this period is not acceptable and should be discarded.

[0106] In one embodiment, the flux is calculated through the following steps: measuring wind speed and direction information using a three-dimensional acoustic anemometer located in the vertical direction to obtain environmental wind data; interpolating and extrapolating the wind speed and direction information to obtain wind speed and direction data at various heights; converting the measured planar concentration distribution G(r,θ) from polar coordinates to the concentration of square basic units in the Cartesian coordinate system (replaced by points); substituting the concentration of square basic units and the wind speed and direction data into the flux calculation expression to calculate the flux.

[0107] Flux calculation expression:

[0108]

[0109] Where: Flux is the flux; M is the molecular weight of the gas. The aim is to convert concentration values ​​from parts per million (ppmv) to grams per cubic meter (g / m³). 3 ); G(y i ,z i ) represents the concentration of a square basic unit; S area The area of ​​a square basic unit in a vertical plane; wind_speed i Wind speed; wind_direction i The wind direction.

[0110] This invention proposes a flux inversion method based on weighted radial plume mapping (VRPM) using inversion information from the previous period. By incorporating inversion information from the previous period, the measured values ​​for the current period are weighted and corrected, and the sum of squared errors is minimized through an updated weighting function, ultimately yielding an accurate flux calculation. This method addresses the problem of large time variability and significantly improves the accuracy and stability of flux inversion. This invention has broad application prospects in oil and gas stations. By monitoring methane flux in real time, greenhouse gas emissions can be effectively managed, station operations optimized, and environmental compliance improved.

[0111] The flux inversion method using weighted radial plume mapping based on previous cycle inversion information provided by this invention can also be used in conjunction with a computer-readable storage medium. The storage medium stores a computer program, which is executed to run the flux inversion method using weighted radial plume mapping based on previous cycle inversion information. The computer program can execute computer instructions, including computer program code, which can be in the form of source code, object code, executable file, or some intermediate form.

[0112] Computer-readable storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0113] It should be noted that the contents of computer-readable storage media may be appropriately added to or subtracted from the contents according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media may not include electrical carrier signals and telecommunication signals.

[0114] According to another aspect of the invention, a flux inversion system using weighted radial plume mapping with inversion information from the previous cycle is also provided, which performs a flux inversion method using weighted radial plume mapping with inversion information from the previous cycle.

[0115] In one embodiment, a flux inversion system using weighted radial plume mapping based on inversion information from the previous period includes: a concentration correction module, a plane concentration module, and a flux calculation module. The concentration correction module corrects the measured path integral concentration data for the current period using the reconstructed path integral concentration data from the previous period, obtaining the corrected path integral concentration data for the current period. The plane concentration module, based on the corrected path integral concentration data for the current period and the reconstructed path integral concentration data from the previous period, updates the weighting coefficients using a weighting function to minimize the sum of squared errors, and combines this with a two-stage Gaussian smoothing basis function minimization method to obtain the measured plane concentration distribution for the current period. The flux calculation module calculates the flux by measuring the plane concentration distribution and ambient wind data, while simultaneously satisfying consistency, wind speed, and wind direction conditions.

[0116] Previous research and recommended methods indicate that using a moving average of three or more periods for the PIC value in SSE minimization ensures the accuracy of the final flux calculation, and the more periods in the moving average, the smaller the calculation error. The expression for the weighted moving average method is shown below:

[0117]

[0118] The periodic moving average method is a special form of the weighted moving average method, which satisfies... Let represent the PIC value after correction in period t. To balance the resolution, real-time performance, and accuracy of flux inversion, only the measured PIC from the previous period is retained. For the case of n=2, this becomes the following formula:

[0119]

[0120] Although the plume reconstruction process used measurement information from the previous cycle However, relatively speaking, the reconstruction information from the previous cycle more accurately reflects the reconstruction quality and guides the reconstruction process in the current cycle. Additionally, the consistency correlation factor (CCF) is used to indicate the quality of the reconstruction fit. Generally, its value is high, close to 1, so this invention uses... replace The final version of the PIC correction expression provided by this invention is as follows:

[0121]

[0122] The initial PIC calibration value (t=1) is equal to the measured PIC value itself.

[0123] In one embodiment, three of the most typical weighting functions used to characterize data trends are analyzed, namely: Figure 4a exponential function, Figure 4b linear functions, Figure 4c It is a negative exponential function.

[0124] First, considering the weighting function W(x) = e x The exponential function form. For example... Figure 4a As shown in the exponential function, this function assigns higher weights to beams with inaccurate reconstructions when fitting the concentration distribution in the SBFM program, with particular emphasis on fitting the PIC data measured for these beams. When it is a linear function, it means that the weight β(i) is the same as the reconstructed difference.

[0125] Next, consider the weighting function W(x) = e -x The negative exponential function form is illustrated in the diagram below. Figure 4c As shown, the negative exponential weighting function assigns smaller weights to beams with large differences between historical reconstructed values ​​and current measurements. This weighting function assumes that beam measurements with large errors are unreliable, thus focusing on optical paths that significantly contribute to the fit. Using a negative exponential weighting function to weight the SSE function in SBFM helps eliminate the influence of outlier measurement PIC data on flux results, similar to the effect of signal filtering.

[0126] To find a suitable weighting function form for VRPM flux measurement, this invention ran three different weighting function WSSE minimization processes on ten sets of measurement data and calculated the corresponding fluxes, such as... Figure 5a , Figure 5b , Figure 5c As shown.

[0127] Figure 5a , Figure 5b , Figure 5c The black solid line in the figure represents the actual emission rate of the tracer gas, which is 0.11 g / s. The RMSE value calculated by the VRPM algorithm is 0.051, and the average flux MF is 0.128 g / s, which is close to the actual release value. However, using the weighting function W(x) = e x After weighted SSE, Figure 5a The calculated flux RMSE is 0.126, and the average flux MF is 0.185 g / s. This weighting function not only failed to improve the results but actually worsened the inversion results. Therefore, W(x) = e x This weighting function is not what this invention seeks.

[0128] When the weighting function is W(x) = x, that is, a linear function of the difference between the reconstructed value and the measured value, the results before and after weighting are compared as follows: Figure 5b As shown, the calculated RMSE value is 0.107, and the average flux is 0.17 g / s. Similarly, the weighted SSE of W(x) = x did not improve the flux calculation results, but it was similar to W(x) = e xIn comparison, the corresponding average flux MF and bias improve in a favorable direction.

[0129] Finally, this invention further defines W(x) = e -x As a weighting function in WSSE, the flux calculation result is as follows: Figure 5c As shown. It can be seen that after weighting the SSE function, the weight function W(x) = e -x This achieves a smoothing effect similar to filtering and periodic averaging, especially for overestimated flux calculations. The improved RMSE is 0.021, and the average flux MF is 0.99 g / s. Although the original VRPM algorithm calculated an average flux MF close to 0.11 g / s, the data fluctuated significantly. Compared to the original VRPM's RMSE of 0.051, minimizing the weight function W(x) = e -x The accuracy of the WSSE calculation is significantly improved, less than half that of the original VRPM method. Although there is a tendency to underestimate the data, it also helps to smooth out overestimated calculation values. Therefore, experiments show that the SBFM method can improve the accuracy of the SSE function W(x) = e -x Weighting, which assigns smaller weights to beams with significant differences between historically reconstructed PIC and measured PIC values, helps to address the impact of anomalous measured PIC data on flux results.

[0130] In one embodiment, to determine a suitable range of values ​​for α, this invention utilizes a grid search strategy to study the changing trends of the RMSE and average flux of the calculation results as α varies, such as... Figure 6a , Figure 6b As shown.

[0131] from Figure 6a It can be observed that when α is between [0.5, 0.6], the average flux is close to 0.11 g / s, and the RMSE value is also relatively small. Therefore, the effect of α variation was further investigated by setting the step size of α to 0.01. The results are plotted on... Figure 6b In the middle, by Figure 6b It can be seen that when 0.5≤α≤0.6, the calculated average flux and RMSE both fluctuate within a small range.

[0132] Figure 6a , Figure 6b This indicates that the method for weighting PIC in this invention is similar to the two-period moving average method, the difference being that the former uses reconstructed PIC values ​​as the weight term. In this embodiment, a weight α = 0.5, similar to that of the two-period moving average, is selected, and the corresponding flux calculation results are as follows: Figure 7 As shown.

[0133] from Figure 7It can be observed that the flux values ​​calculated after correction show a smoother data trend and reduced variance, similar to the smoothing effect of a moving average. The flux calculation values ​​calculated using the traditional moving average method also show... Figure 7 The average flux obtained was 0.123 g / s, and the RMSE was 0.041. Although the two-period moving average did have a smoothing effect and improved the calculation accuracy compared to the original algorithm, the effect was still not as good as the weighted correction using the reconstructed PIC value from the previous period proposed in this invention.

[0134] It can be seen that the present invention effectively improves the flux calculation results by using the weighted average PIC reconstructed from the previous cycle and the weighted SSE function. Therefore, this embodiment plots the flux results after the combination of the two improvements on... Figure 8 middle.

[0135] This invention combines the weighted average PIC (Picture Calculation) from the previous cycle and the weighted SSE (Strain Sequence Calculation) method into VRPM. The RMSE calculated using this method is 0.015, and the average flux is 0.98 g / s. Compared to the WSSE method, the results are similar, but the flux values ​​are more concentrated, around 0.11 g / s. The fitting results are significantly improved compared to the original VRPM method.

[0136] This invention addresses the problem of high time variability by incorporating the inversion information from the previous period into the weighted VRPM, thereby improving the accuracy of flux inversion. Experiments show that using a negative exponential function as the weighting function to weight the sum of squared errors (SSE) helps eliminate the influence of abnormal measurement data on flux results, similar to signal filtering, and significantly improves the accuracy and stability of flux inversion results.

[0137] In summary, this invention provides a flux inversion method using weighted radial plume mapping based on previous cycle inversion information. Compared with existing technologies, it has the following advantages: This invention significantly improves the accuracy and real-time performance of inverting fugitive methane emission fluxes at oil and gas stations using weighted radial plume mapping (VRPM) based on previous cycle inversion information. Traditional methods are limited by significant time variability and sensitivity to noise, while this invention effectively reduces errors by weighting and correcting current cycle measurements using historical cycle data, making it particularly suitable for flux scenarios with significant periodic variations. This technological innovation not only improves the accuracy of methane monitoring at oil and gas stations but also enables rapid response to real-time environmental changes, resulting in significant social and economic benefits.

[0138] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should be extended to equivalent substitutions of these features as understood by those skilled in the art. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0139] In the description of this invention, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," "tail," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0140] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0141] Certain terms are used throughout this application to refer to specific system components. As those skilled in the art will recognize, the same components may often be referred to by different names, and therefore this application is not intended to distinguish those components that differ only in name and not in function. In this application, the terms “comprise,” “include,” and “have” are used in an open-ended manner and should therefore be interpreted as meaning “including, but not limited to…”. Furthermore, the terms “substantially,” “materially,” or “approximately” as used herein refer to industry-accepted tolerances for the corresponding terms. The term “coupling,” as may be used herein, includes direct coupling and indirect coupling via additional components, elements, circuits, or modules, wherein, for indirect coupling, the intermediate component, element, circuit, or module does not alter the information of the signal but may adjust its current level, voltage level, and / or power level. Inferred coupling (e.g., one element is inferredly coupled to another element) includes direct and indirect coupling between two elements in the same manner as “coupling.”

[0142] The phrase "an embodiment" or "an embodiment" used in this specification means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "an embodiment" or "an embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0143] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

[0144] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A flux inversion method using weighted radial plume mapping based on inversion information from the previous cycle, characterized in that, The method includes: The measured path integral concentration data for the current period is corrected using the reconstructed path integral concentration data from the previous period, resulting in the corrected path integral concentration data for the current period. Based on the corrected path integral concentration data of the current period and the reconstructed path integral concentration data of the previous period, the weight coefficients are updated using a weight function to minimize the sum of squared errors. Combined with the two-stage Gaussian smoothing basis function minimization method, the measurement plane concentration distribution of the current period is obtained. When the conditions of consistency, wind speed, and wind direction are met simultaneously, the flux is calculated using the measured plane concentration distribution and environmental wind data.

2. The flux inversion method using weighted radial plume mapping based on previous cycle inversion information as described in claim 1, characterized in that, The corrected path integral concentration data for the current period is obtained through the following steps: The measurement plane is placed downwind of the area source, and five points in the measurement plane are periodically scanned using a path integral optical remote sensing system to obtain the measured path integral concentration data. By correcting the measured path integral concentration data of the current period using the reconstructed path integral concentration data from the previous period and the weighted average coefficient, the corrected path integral concentration data for the current period is obtained.

3. A flux inversion method using weighted radial plume mapping based on inversion information from the previous cycle, as described in claim 1 or 2, characterized in that... The corrected path integral concentration data for the current period is obtained using the following expression: in: The corrected path integral concentration data is for the i-th measurement point in the current period t; α is the weighted average coefficient. This refers to the measured path integral concentration data of the i-th measurement point in the current period t; This is the reconstructed path integral concentration data of the i-th measurement point in the previous period t-1.

4. A flux inversion method using weighted radial plume mapping based on inversion information from the previous cycle, as described in any one of claims 1-3, characterized in that... The reconstructed path integral concentration data is obtained through the following steps: Integrating the measured plane concentration distribution for the current period yields the reconstructed path integrated concentration data for the current period.

5. A flux inversion method using weighted radial plume mapping based on inversion information from the previous cycle, as described in any one of claims 1-4, characterized in that... The measured plane concentration distribution is obtained through the following steps: The corrected path integral concentration data of the near-ground measurement point in the current period is used for the first-stage Gaussian smoothing basis function minimization to obtain the peak position and standard deviation of the Gaussian function under the one-dimensional Gaussian distribution. The corrected path integral concentration data, peak position, standard deviation, and updated weighting coefficients of all measurement points in the current period are used to minimize the Gaussian smoothing basis function in the second stage to obtain the concentration distribution of the measurement plane.

6. A flux inversion method using weighted radial plume mapping based on inversion information from the previous cycle, as described in any one of claims 1-5, characterized in that... The weighting coefficients: The weighting function: W(x)=e -x in: The weighting coefficient for the i-th measurement point in the current period t; , which are the input independent variables of the weighting function W(x); The reconstructed path integral concentration data is for the i-th measurement point in the previous period t-1. This represents the measured path integral concentration data for the i-th measurement point in the current period t.

7. A flux inversion method using weighted radial plume mapping based on inversion information from the previous cycle, as described in any one of claims 1-6, characterized in that... The flux is calculated using the following steps: The environmental wind data is obtained by measuring wind speed and direction information using a three-dimensional acoustic anemometer located in the vertical direction; The wind speed and direction information are interpolated and extrapolated to obtain wind speed and direction data at various heights. The concentration distribution of the measured plane is converted from polar coordinates to concentration in square basic units in Cartesian coordinates; The flux is calculated by substituting the concentration of the square basic unit, the wind speed, and the wind direction data into the flux calculation expression.

8. The flux inversion method using weighted radial plume mapping based on previous cycle inversion information as described in claim 7, characterized in that, The flux calculation expression is as follows: Where: Flux is the flux; M is the molecular weight of the gas; G(y) i ,z i ) represents the concentration of a square basic unit; S area The area of ​​a square basic unit in a vertical plane; wind_speed i Wind speed; wind_direction i The wind direction.

9. A storage medium, characterized in that, It contains instructions for performing the method as described in any one of claims 1-8.

10. A flux inversion system using weighted radial plume mapping based on inversion information from the previous cycle, characterized in that, The system, which performs the method as described in any one of claims 1-8, comprises: The concentration correction module uses the reconstructed path integral concentration data from the previous period to correct the measured path integral concentration data for the current period, thus obtaining the corrected path integral concentration data for the current period. The plane concentration module, based on the corrected path integral concentration data of the current period and the reconstructed path integral concentration data of the previous period, updates the weight coefficients using a weight function to minimize the sum of squared errors, and combines a two-stage Gaussian smoothing basis function minimization method to obtain the measured plane concentration distribution of the current period. The flux calculation module calculates the flux by using the measured plane concentration distribution and environmental wind data, while simultaneously satisfying the conditions of consistency, wind speed, and wind direction.

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