Method for detecting concentration of carbon dioxide in pressure environment
The gas concentration detection data under high-pressure environment was processed by segmented linear interpolation, which solved the difficulty in calculating concentration caused by the widening of line width of the gas absorption spectrum under high pressure, and achieved accurate carbon dioxide concentration detection within a wide pressure range.
Patent Information
- Application Number
- CN202510181538.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-19
- Publication Date
- 2025-06-17
AI Technical Summary
In a high-pressure environment, the line width of the gas absorption spectrum increases, resulting in difficulty in calculating the gas concentration obtained by direct scanning, and the prior art is difficult to effectively solve this problem.
The gas concentration value value is calculated by collecting the measured gas concentration value data at each pressure point, pre-processing and data segmentation, linear interpolation and correction.
Accurate detection of carbon dioxide concentration under negative pressure to high pressure environments is achieved, and the problem of pressure affecting gas absorption coefficient and linearity is avoided.
Smart Images
Figure CN120161014A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of carbon dioxide concentration detection, and particularly to a method for detecting carbon dioxide concentration under a pressure environment. Background Art
[0002] TDLAS technology can be divided into two methods in the way of characterizing the concentration of the gas to be measured. One is the direct absorption method, and the other is the wavelength modulation method. Each method has its own advantages and disadvantages. The direct absorption method measures the relative intensity of the characteristic spectral lines of the gas after direct absorption. Its implementation is simple, omitting the need for hardware for lock-in amplification and phase-sensitive detection. The waveform can intuitively reflect the absorption situation, and it has a good effect on the situation where the selected absorption spectral line is wide and the absorption peaks are messy. The wavelength modulation method uses a high-frequency wave in the kHz range to modulate the low-frequency laser sweeping within the characteristic spectral line, effectively suppressing background noise and greatly improving the detection sensitivity; this method is more suitable for the situation where the absorption spectral line is single and the absorption intensity is small. In actual use, the two may be mixed to focus on different needs.
[0003] Since the line width of the gas absorption spectral line is relatively narrow under normal pressure, the gas concentration can be calculated by directly scanning a single gas spectral line and according to the Beer-Lambert law. However, under a high-pressure environment, due to the influence of collision broadening, the linearity of the gas absorption spectral line is significantly broadened. A larger scanning range of the laser is required to directly scan the complete spectral line shape, and all spectral lines are superimposed on each other, which brings great difficulties to the on-line monitoring and concentration calculation of high-pressure gases. Therefore, it is necessary to design a method for detecting carbon dioxide concentration under a pressure environment. Summary of the Invention
[0004] This application provides a detection method with a wide applicable range, and the working conditions can cover negative pressure to high pressure. By setting reasonable segmented interpolation intervals, accurate calculation of spectral line parameters can be achieved, avoiding the detection method of carbon dioxide concentration in a pressure environment where pressure has an impact on the absorption coefficient and linearity of the gas, so as to solve the problems pointed out in the background art.
[0005] This application provides a method for detecting carbon dioxide concentration under a pressure environment. The detection method includes the following steps:
[0006] A. Collect data on the concentration values of the gas to be measured at each pressure point;
[0007] B. Preprocess the collected concentration value data;
[0008] C. Then divide the pressure range into several data segments;
[0009] D. Perform linear interpolation on each pressure interval data segment;
[0010] E. The collected pressure data and the calibration point data of the gas concentration to be measured are embedded into the algorithm in the form of a table, and the gas concentration value predicted by the piecewise linear interpolation method is obtained.
[0011] Preferably, the gas concentration data to be measured in step A is collected by a TDLAS laser gas detector.
[0012] Preferably, the data preprocessing method in step B is as follows:
[0013] a. Extracting a plurality of noise data from the gas concentration value data to be processed as sample data;
[0014] b. performing transformation processing on each of the sample data to obtain transformation data of each of the sample data;
[0015] c. Using a pre-trained data classification model, perform label prediction on each of the sample data and each of the transformed data to determine the target label and target label probability of each sample data;
[0016] d. According to the target label and target label probability of each sample data, each sample data is screened to obtain the target data.
[0017] Preferably, the data segmentation process in step C is as follows:
[0018] a. Partially segment the pressure data segment to be processed and extract it, and store the extracted data in a separate database to avoid data damage and loss caused by mixing and garbled characters during the data segment segmentation process;
[0019] b. Perform security checks on the extracted data, remove garbled and mixed data, and conduct comprehensive security checks on the data;
[0020] c. Construct a secondary feature matrix for the data after risk assessment to facilitate data segmentation, data organization and improve data processing efficiency;
[0021] d. Segment the data after the secondary feature matrix is constructed into multiple sub-data files, and automatically generate the segmentation status of each sub-data file.
[0022] Preferably, the linear interpolation method in step D is as follows:
[0023] 1) According to the collected data, the nearest neighbor interpolation method is used to interpolate and fill in the data on the twelve edges of the rectangular body of the space to be measured;
[0024] 2) Create a three-dimensional array with a size of L * W * H, where L is the sum of the number of data acquisition points and the number of data points to be interpolated in the length of the space to be measured, W is the sum of the number of data acquisition points and the number of data points to be interpolated in the width of the space to be measured, and H is the sum of the number of data acquisition points and the number of data points to be interpolated in the height of the space to be measured;
[0025] 3) For the point P to be interpolated, traverse the array to find the point closest to point P, and the concentration of this point is the carbon dioxide concentration of point P.
[0026] Preferably, in the step E, data correction is further included, and the data correction method is as follows:
[0027] Obtain the relevant concentration data of each surrounding node, where the surrounding nodes are the nodes adjacent to the data of the node to be processed;
[0028] Determine the correction layer number, the weight values and offset values of each surrounding node in each correction layer according to the relevant data of the node to be processed and each surrounding node;
[0029] Perform correction processing on the carbon dioxide concentration data according to the correction layer number, the weight values and offset values of each surrounding node in each correction layer to obtain the final correction result.
[0030] Beneficial effects: The detection method adopted by the present invention has a wide application range, and the working conditions can cover negative pressure to high pressure. By setting a reasonable segmented interpolation interval, accurate calculation of spectral line parameters can be achieved, and the influence of pressure on the absorption coefficient and linearity of the gas can be avoided.
[0031] The above description is only an overview of the technical solutions of the embodiments of the present application. In order to be able to understand the technical means of the embodiments of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the embodiments of the present application more obvious and understandable, the following specifically gives the specific implementation manners of the present application. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.
[0033] Figure 1 It is a flow chart of the detection method of the present invention;
[0034] Figure 2 It is the CO2 absorption spectral line of 2.004 - 2.015 μm;
[0035] Figure 3Absorption spectra of CO2 at a wavelength of 2.008μm under different pressures. Detailed implementation manners
[0036] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without making creative efforts based on the embodiments in the present application belong to the scope of protection of the present application.
[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the drawings are intended to cover non-exclusive inclusion.
[0038] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of this application. The phrase "embodiment" appearing in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.
[0039] To enable those in the technical field of this application to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.
[0040] Please refer to Figure 1 , this application provides a method for detecting the concentration of carbon dioxide in a pressure environment, and the detection method includes the following steps:
[0041] A. Collect data on the concentration values of the gas to be measured at each pressure point;
[0042] B. Preprocess the collected concentration value data;
[0043] C. Then divide the pressure range into several data segments;
[0044] D. Perform linear interpolation on each pressure range data segment;
[0045] E. Embed the collected pressure data and the calibration point data of the gas concentration to be measured into the algorithm in the form of a table, and the gas concentration value predicted by the piecewise linear interpolation method can be obtained.
[0046] In the present invention, the concentration value data of the gas to be measured in step A is collected by a TDLAS laser gas detector.
[0047] In the present invention, the data preprocessing method in step B is as follows:
[0048] a. Extracting a plurality of noise data from the gas concentration value data to be processed as sample data;
[0049] b. performing transformation processing on each of the sample data to obtain transformation data of each of the sample data;
[0050] c. Using a pre-trained data classification model, perform label prediction on each of the sample data and each of the transformed data to determine the target label and target label probability of each sample data;
[0051] d. According to the target label and target label probability of each sample data, each sample data is screened to obtain the target data.
[0052] The data preprocessing method adopted in the present invention can screen the data of Egypt to obtain the target data, thereby improving the detection efficiency.
[0053] In the present invention, the data segmentation process in step C is as follows:
[0054] a. Partially segment the pressure data segment to be processed and extract it, and store the extracted data in a separate database to avoid data damage and loss caused by mixing and garbled characters during the data segment segmentation process;
[0055] b. Perform security checks on the extracted data, remove garbled and mixed data, and conduct comprehensive security checks on the data;
[0056] c. Construct a secondary feature matrix for the data after risk assessment to facilitate data segmentation, data organization and improve data processing efficiency;
[0057] d. Segment the data after the secondary feature matrix is constructed into multiple sub-data files, and automatically generate the segmentation status of each sub-data file.
[0058] In the present invention, the linear interpolation method in step D is as follows:
[0059] 1) According to the collected data, the nearest neighbor interpolation method is used to interpolate and fill in the data on the twelve edges of the rectangular body of the space to be measured;
[0060] 2) Create a three-dimensional array of size L*W*H, where L is the sum of the number of data points collected and the number of data points to be interpolated in the length of the space to be measured, W is the sum of the number of data points collected and the number of data points to be interpolated in the width of the space to be measured, and H is the sum of the number of data points collected and the number of data points to be interpolated in the height of the space to be measured;
[0061] 3) For the point P to be interpolated, traverse the array to find the point closest to point P, and the concentration of this point is the carbon dioxide concentration of point P.
[0062] The linear interpolation method adopted by the present invention can consider the influence of the spatial heterogeneity of carbon dioxide concentration data distribution, thereby improving the accuracy of carbon dioxide concentration detection.
[0063] In addition, in the present invention, in step E, data correction is also included, and the data correction method is as follows:
[0064] Obtain the relevant concentration data of each surrounding node, where the surrounding nodes are the nodes adjacent to the data of the node to be processed;
[0065] Determine the correction layer number, the weight value and the offset value of each surrounding node in each correction layer according to the relevant data of the node to be processed and each surrounding node;
[0066] Perform correction processing on the carbon dioxide concentration data according to the correction layer number, the weight value and the offset value of each surrounding node in each correction layer to obtain the final correction result.
[0067] The data correction method adopted by the present invention can make the final correction result closer to the data to be measured, reducing the error in the subsequent application process.
[0068] Common carbon dioxide has absorption at wavelengths of 1.437μm, 1.578μm, 2.004μm, 2.015μm, etc. (center wavelengths), but the absorption peaks at each wavelength have relatively narrow spacings, and there are multiple small and chaotic absorption peaks at the baseline. Considering that pressure broadening brings serious interference in a high-pressure environment, the absorption peak at 2.008μm with a relatively wide absorption peak spacing is finally selected as the measurement object in this scheme. See Figure 2 .
[0069] Usually, the carbon dioxide detection module preferentially selects the harmonic absorption method for measurement, and there are relatively chaotic small absorption peaks near the carbon dioxide absorption peak. After comprehensive consideration, the direct absorption method is selected to realize the detection of carbon dioxide concentration.
[0070] Figure 3The absorption spectrum lines of carbon dioxide at (0 - 6) atmospheric pressures are given. It can be seen from the figure that at 6 atmospheric pressures, the shift of the baseline on the absorption spectrum line of carbon dioxide has exceeded the absorption peak under normal pressure, and there is no identifiable absorption peak under high pressure, making it difficult to implement dynamic baseline calculation. After comprehensive consideration, the carbon dioxide detection module uses a static baseline as the base value, and calculates the integral of the distance between the absorption spectrum line and the baseline as the characterization parameter of concentration. In order to suppress sensing drift, its baseline is not completely fixed. When the pressure is less than 110 kPa and the concentration is less than 0.3%, it will be iteratively updated every 20 s to automatically eliminate the relatively serious zero drift caused by various reasons. The final test proves that the direct absorption method selected for the carbon dioxide module can meet the requirements of this invention.
[0071] In summary, the detection method adopted by this invention has a wide range of applications, and the working conditions can cover negative pressure to high pressure. By setting reasonable segmented interpolation intervals, accurate calculation of spectral line parameters can be achieved, avoiding the influence of pressure on the absorption coefficient and linearity of the gas.
[0072] As mentioned above, the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of this application.
Claims
1. A method for detecting carbon dioxide concentration under pressure environment, characterized in that: The detection method includes the following steps: A. Collect the concentration data of the gas to be tested at each pressure point; B. Preprocess the collected concentration value data; C. Then divide the pressure interval into several data segments; D. Perform linear interpolation on each pressure interval data segment; E. The collected pressure data and the calibration point data of the gas concentration to be measured are embedded into the algorithm in the form of a table, and the gas concentration value predicted by the piecewise linear interpolation method is obtained.
2. The method for detecting carbon dioxide concentration under pressure environment according to claim 1, characterized in that: The gas concentration data to be measured in step A is collected by a TDLAS laser gas detector.
3. The method for detecting carbon dioxide concentration under pressure environment according to claim 1, characterized in that: The data preprocessing method in step B is as follows: a. Extracting a plurality of noise data from the gas concentration value data to be processed as sample data; b. performing transformation processing on each of the sample data to obtain transformation data of each of the sample data; c. Using a pre-trained data classification model, perform label prediction on each of the sample data and each of the transformed data to determine the target label and target label probability of each sample data; d. According to the target label and target label probability of each sample data, each sample data is screened to obtain the target data.
4. The method for detecting carbon dioxide concentration under pressure environment according to claim 1, characterized in that: The data segmentation process in step C is as follows: a. Partially segment the pressure data segment to be processed and extract it, and store the extracted data in a separate database to avoid data damage and loss caused by mixing and garbled characters during the data segment segmentation process; b. Perform security checks on the extracted data, remove garbled and mixed data, and conduct comprehensive security checks on the data; c. Construct a secondary feature matrix for the data after risk assessment to facilitate data segmentation, data organization and improve data processing efficiency; d. Segment the data after the secondary feature matrix is constructed into multiple sub-data files, and automatically generate the segmentation status of each sub-data file.
5. The method for detecting carbon dioxide concentration under pressure environment according to claim 1, characterized in that: The linear interpolation method in step D is as follows: 1) According to the collected data, the nearest neighbor interpolation method is used to interpolate and fill in the data on the twelve edges of the rectangular body of the space to be measured; 2) Create a three-dimensional array of size L*W*H, where L is the sum of the number of data points collected and the data points to be interpolated in the length of the space to be tested, W is the sum of the number of data points collected and the data points to be interpolated in the width of the space to be tested, and H is the sum of the number of data points collected and the data points to be interpolated in the height of the space to be tested; 3) For the point P to be interpolated, traverse the array and find the point closest to point P. The concentration of this point is the carbon dioxide concentration at point P.
6. The method for detecting carbon dioxide concentration under pressure environment according to claim 1, characterized in that: The step E also includes data correction, and the data correction method is as follows: Acquire concentration data related to each peripheral node, wherein the peripheral nodes are nodes adjacent to the node data to be processed; Determine the number of correction layers, the weights and offset values of each surrounding node in each correction layer according to the relevant data of the node to be processed and each surrounding node; The carbon dioxide concentration data is corrected according to the number of correction layers, the weights of each surrounding node in each correction layer, and the offset value to obtain the final correction result.