CN-116840187-B - TDLAS joint detection method

Abstract

The invention provides a TDLAS joint detection method, which is based on the existing method for reconstructing gas two-dimensional concentration distribution based on TDLAS telemetry, and uses a TDLAS in-situ detection system and a TDLAS telemetry system in a joint way to detect indoor gas concentration, so that the gas detection method is optimized, an iteration result is corrected by using in-situ detection data in each iteration of the reconstruction process, an image reconstruction algorithm is improved, and the method for reconstructing gas two-dimensional concentration distribution based on TDLAS joint detection is provided. The invention improves the reconstruction precision and reduces the reconstruction time.

Inventors

• CHEN JIUYING
• HU JIAN
• ZHOU MEI
• WANG PING
• ZHOU CHUNCHENG
• ZHANG HUIJING
• CHEN LINSHENG
• WU HAOHAO
• CHENG SAI
• YANG ZHOU

Assignees

• 中国科学院空天信息创新研究院

Dates

Publication Date

20260505

Application Date

20230627

Claims (6)

1. 1. The TDLAS joint detection method is characterized by comprising the following steps of: Step 1, acquiring TDLAS telemetry data of an indoor gas environment and TDLAS in-situ detection data of the indoor gas environment, and measuring indoor gas concentration data by using a TDLAS telemetry system and a TDLAS in-situ detection system along a detection route; Step 2, reconstructing a two-dimensional concentration distribution map of the indoor gas, and reconstructing the two-dimensional concentration distribution map of the indoor gas according to a joint correction algebraic iterative algorithm by using TDLAS telemetry data of the indoor gas environment and TDLAS in-situ detection data of the indoor gas environment; reconstructing a two-dimensional concentration profile of the gas in the chamber includes: reconstructing a two-dimensional concentration distribution map of indoor gas by using a joint correction algebraic iterative algorithm, and specifically comprising the following steps: Reconstructing a gas concentration two-dimensional distribution map by using an algebraic iterative algorithm; step (2) adopting a self-adaptive correction algebraic iterative algorithm to improve the reconstruction accuracy and the reconstruction speed; step (3) adopting non-negative constraint and smoothness criterion to constrain the reconstruction process; the step (4) of designing a joint correction algebraic iterative algorithm further improves the reconstruction accuracy and the reconstruction speed, and comprises the following steps: for the gas absorption coefficient at the grid where the in-situ measurement is positioned, adding an in-situ correction in each iteration, wherein the in-situ correction formula is as follows, and the correction factor in the formula is as follows : (9) In the above Is a grid In situ gas concentration measurements at.
2. 2. The TDLAS joint detection method according to claim 1, wherein in the step 1, obtaining TDLAS telemetry data of an indoor gas environment comprises: the detection route is designed, and the TDLAS telemetry system detects the concentration of the gas along the detection route at a certain frequency.
3. 3. The TDLAS joint detection method according to claim 2, wherein in the step 1, obtaining TDLAS in-situ detection data of the indoor gas environment comprises: while the TDLAS telemetry system detects gas concentration at a frequency, the TDLAS in-situ detection system detects gas concentration at the same frequency.
4. 4. The TDLAS joint detection method according to claim 1, wherein in the step (1), the image is first discretized, i.e. the whole gas concentration two-dimensional distribution image is formed Discretization into A grid whose internal gas parameters are considered uniform, whose gas concentration is obtained by inverting the gas absorption coefficient, and whose grid value f i represents the first Gas absorption coefficient within each grid , According to the beer-lambert law, when a certain beam of laser is used Through the target area, the center frequency is The integrated absorbance dispersion of the absorption line of (a) is: (1) Wherein, the Is the first The integrated absorbance of the bar of light; is the temperature of The line is strong; the above equation is the gas absorption equation, wherein, Is the total number of laser beams; is a grid Product of pressure, concentration and line intensity; Is a projection coefficient which is equal in value to the laser beam Quilt net The length of the cut-out; in the actual detection process, detection lasers are emitted from different directions and angles to scan a reconstruction area, each laser beam has a gas absorption equation as described in a formula (1), and the gas absorption equation of each laser beam is combined together and expressed by a linear equation set as shown in a formula (2): (2) obtaining a reconstruction value of the gas absorption coefficient by solving a linear equation set, and setting a gas absorption coefficient vector The initial value vector of (2) is zero vector, and the iterative calculation is carried out by substituting the zero vector into (2) to obtain the gas absorption coefficient vector Substituted into (2) th From the equations, we get The iterative formula of (2) is: (3) Wherein, the Is a factor of relaxation and is a factor of relaxation, Related to convergence speed and reconstruction quality, n represents Index of the dimensional image vector is performed, According to the above iteration, the method is represented by the formula (2) From equations And then, completing one complete iteration, judging whether the change of the gas absorption coefficient vector is larger than a threshold value and whether the iteration times are smaller than 10000 in each iteration, and then searching the line intensity at room temperature according to Hitran database to solve the gas concentration distribution.
5. 5. The TDLAS joint detection method of claim 4, wherein the step (2) comprises: the self-adaptive correction algebraic iterative algorithm is adopted, and the gas absorption coefficient in each grid is calculated The numerical value of (2) is solved by the following iterative formula: (4a) (4b) (4c) Wherein, the Representing the iteration times in MAART iteration processes; A value of 0.25 is taken in relation to the step size of the self-adaptive adjustment; for adaptive relaxation factor, represent the first Grid in multiple iterations Is used for the relaxation factor of (a), As a function of the remainder.
6. 6. The TDLAS joint detection method of claim 5, wherein the step (3) comprises: Adding non-negative constraint to gas absorption coefficient, non-negative constraint function As shown in formula (5): (5) While suppressing abrupt changes in the reconstruction result using smoothing criteria, in each iteration, a grid The gas absorption coefficient of (2) is smoothed by itself and 8 grids around it by 9 points, and the smoothing criterion is as shown in formula (6): (6) Wherein, the As a smoothing factor, in the above formula And Respectively row and column indexes of the absorption coefficient in the reconstruction region grid; to eliminate the pressure intensity Strong sum line The influence on the iteration result is defined as a new weight factor : (7) Wherein, the Is the first The lines of the grids are strong; The integral absorbance at this time, i.e. the projection value Expressed as: (8) Wherein, the Is a new weight factor; For each absorption line, if there is The projection light rays will be The absorption equation, a system of linear equations with respect to the gas absorption coefficient, is converted into a system of linear equations with respect to the concentration Is a linear system of equations of (2).

Description

TDLAS joint detection method Technical Field The invention belongs to the field of gas detection, and particularly relates to a TDLAS joint detection method. Background Most people are 90% of the time in a room where the gas environment is very important, and by gas profiling (Gas Distribution Mapping, GDM) techniques, the characteristics of the gas diffusion in the environment can be revealed. Currently, most methods for establishing GDM are obtained through a contact gas sensor or a sensor network, and the point measurement method cannot establish concentration distribution in the whole space due to sparse measurement points. To achieve higher spatial coverage, it is desirable to build an indoor GDM using a linear gas detection technique. The tunable semiconductor laser absorption spectrum (Tunable Diode Laser Absorption Spectroscopy, TDLAS) technology is an effective gas detection technology, which obtains the concentration of the detected substance by detecting the absorption intensity of the absorption spectrum line, has the advantages of non-contact, high sensitivity, high precision, high selectivity, rapid measurement, no pretreatment and the like, and has been widely used in various fields. The TDLAS technology can be classified into an in-situ detection technology and a telemetry technology according to a measurement mode, wherein the in-situ detection technology is to directly measure in a region to be measured, measure the concentration of gas to be measured in a gas pool, the measurement range is approximately regarded as a point, and the detection precision is higher than that of the telemetry technology. TDLAS telemetry can measure the average gas concentration on a laser path under a large-range open optical path, the telemetry distance can reach tens to hundreds of meters long, and the measuring range can be approximately regarded as a line. The TDLAS combined in-situ detection and telemetry technology can achieve higher spatial coverage and detection accuracy. Studies have not been presented to reconstruct GDM in a room using TDLAS joint detection techniques, and current scholars have only reconstructed gas two-dimensional concentration profiles using TDLAS telemetry. The GDM in the reconstruction chamber is an expansion application of reconstructing the two-dimensional concentration distribution of the gas, and the technical path for reconstructing the two-dimensional concentration distribution of the gas based on the TDLAS telemetry technology is to obtain spectral data on a laser line by using the TDLAS telemetry system, and then obtain the two-dimensional concentration distribution of the gas by algebraic reconstruction technology (Algebraic Reconstruction Technique, ART). The gas two-dimensional concentration distribution reconstruction technology based on TDLAS telemetry is widely applied to two-dimensional reconstruction of gas concentration in a flame flow field, and has more mature development, for example, the two-dimensional concentration distribution of CO, CO 2、O2、NOX and other gases in the combustion process is detected, and the combustion state information of a combustion furnace can be timely obtained so as to control combustion. However, the gas two-dimensional concentration distribution reconstruction technology based on TDLAS telemetry still has room for improvement in reconstruction accuracy, and the reconstruction time is long, so that the application of the technology in other fields is limited. In the conventional gas concentration distribution reconstruction process based on TDLAS telemetry, which reconstructs the gas concentration distribution of a flame flow field, a plurality of laser receiving and transmitting devices are required to be arranged to obtain different measurement data, so that the method has no exploratory property on an unknown space and is complex. The mobile TDLAS technology can obtain a large amount of measurement data by controlling the detection position of a TDLAS telemetry system through a mobile platform. In the process of reconstructing indoor GDM based on the mobile TDLAS technology, detection points are randomly distributed in grids or on grid lines of a reconstruction area, the existing TDLAS gas two-dimensional concentration distribution reconstruction technology cannot be directly applied to the mobile TDLAS technology for reconstructing indoor GDM, and research on a detection method suitable for a mobile TDLAS combined detection system is needed to improve reconstruction accuracy and reduce reconstruction time. In view of the above, it is important to detect the gas concentration distribution in an unknown indoor space and to establish an indoor GDM. Most researches are point type measuring methods, measuring points of the point type measuring methods are sparse, and the traditional TDLAS gas two-dimensional concentration distribution detection technology is only based on TDLAS telemetry, so that the reconstruction accuracy is low an