An emergency sampling position optimization method for a chemical industrial park walk-around monitoring vehicle

Abstract

The application creatively applies source strength estimation error analysis to the emergency sampling position optimization problem of the walk-by monitoring vehicle, and proposes an emergency sampling position optimization method for the walk-by monitoring vehicle in a chemical industry park. The method can optimize the optimal emergency sampling position of the walk-by monitoring vehicle under the condition that the walk-by monitoring vehicle has measurement errors and the sampling position is limited, so as to complete the positioning of the over-standard emission source in the chemical industry park together with the fixed monitoring station. For the sampling point distributed in the upwind direction of the pollution source, the source strength estimation error of the sampling position set containing the sampling point and the sampling position set not containing the sampling point can be compared to exclude the sampling position. For the sampling position set distributed in the downwind direction of all pollution sources, the optimal emergency sampling position of the walk-by monitoring vehicle is optimized based on the Gaussian plume diffusion model. The source strength of each emission source is obtained by back calculation from the sampling data and the monitoring data of the fixed monitoring station. The over-standard emission source is positioned by comparing the emission limits of each emission source.

Description

Technical Field

[0001] This invention relates to a method for optimizing the emergency sampling location of a mobile monitoring vehicle in a chemical industrial park, belonging to the field of mobile monitoring vehicles and atmospheric environment monitoring. Background Technology

[0002] Excessive emissions of toxic and hazardous gases within chemical industrial parks can cause severe environmental damage. Therefore, accurately and quickly locating pollution sources is crucial for targeted air pollution control and the development of effective emergency response plans. Fixed monitoring stations are commonly used in chemical industrial parks. These stations are typically mounted on building rooftops, equipped with various gas and meteorological sensors, and capable of long-term stable operation. However, fixed monitoring stations are costly and cannot provide large-scale, high-density coverage across the entire park. Therefore, the number of fixed monitoring stations is often less than the number of gas emission sources within the park. With the development of many emerging technologies, mobile monitoring systems are increasingly being applied to gas monitoring in industrial parks, such as drones carrying miniature gas sensors and mobile monitoring vehicles carrying high-precision large-scale gas detectors. However, mobile monitoring systems also have certain limitations. For example, airflow disturbances caused by the movement of drones or mobile monitoring vehicles can lead to significant sampling and measurement errors, and the terrain limitations within chemical industrial parks restrict the available sampling locations.

[0003] Therefore, given the limitations of mobile monitoring vehicles in terms of measurement errors and sampling locations, when fixed monitoring stations detect excessive emissions from pollution sources within chemical industrial parks, it is crucial to quickly determine the emergency sampling location of the mobile monitoring vehicle and rapidly pinpoint the source of the excessive emissions using monitoring and sampling data. This is of great significance for developing effective pollution emergency response plans.

[0004] The paper "Research on Air Pollution Source Tracing in Chemical Industrial Parks Based on Mobile Monitoring Systems" (Zhou Wang, Zhejiang University, 2022) addresses the problem of large measurement errors by mobile sensors by quantitatively analyzing the impact of measurement errors on source strength estimation. Leveraging the flexibility of mobile monitoring systems in selecting sampling locations, a sampling path planning method for mobile monitoring systems is proposed based on error analysis theory. This method minimizes the impact of measurement errors on source strength estimation, and the method is validated through simulation and field experiments.

[0005] The technical problem to be solved by this invention is "the optimization of the emergency sampling location of mobile monitoring vehicles for sources of excessive pollutant gas emissions in chemical industrial parks". Summary of the Invention

[0006] The purpose of this invention is to accurately and quickly locate sources of excessive pollutant emissions within chemical industrial parks. It proposes an optimization method for emergency sampling locations using mobile monitoring vehicles in chemical industrial parks. Leveraging the flexibility of mobile monitoring vehicles in selecting sampling locations, and combining this with monitoring data from fixed monitoring stations, the method selects the optimal set of sampling locations for the mobile monitoring vehicle to reduce the source strength estimation error, thereby improving the accuracy of tracing excessive emissions. This method can optimize the sampling locations from multiple sets of locations limited by the terrain conditions of the chemical industrial park, even when measurement errors by the mobile monitoring vehicle are unavoidable. This invention addresses the emergency sampling problem of excessive pollutant emissions in chemical industrial parks by proposing an optimization method for emergency sampling locations using mobile monitoring vehicles. First, this invention assumes that there are k sources of pollutant emissions in a chemical industrial park, n fixed monitoring stations, and m locations where mobile monitoring vehicles can take samples. When a fixed monitoring station detects that a pollutant source is exceeding the emission standard, the optimal emergency sampling location of the mobile monitoring vehicle is optimized based on the Gaussian plume diffusion model. The source strength of each emission source is deduced from the sampling data and the monitoring data of the fixed monitoring stations. By comparing the emission limits of each emission source, the source of the exceeding emission standard is located.

[0007] A method for optimizing the emergency sampling location of a mobile monitoring vehicle in a chemical industrial park, the process is as follows: Figure 1 As shown, it includes the following steps:

[0008] Step 1: Within the chemical industrial park, fixed monitoring stations detected abnormally high concentration values.

[0009] Step 2: Initialize the emergency sampling location set for the mobile monitoring vehicle based on the park's terrain conditions;

[0010] Step 3: Determine whether each set of sampling locations includes sampling points upwind of the pollution source. If it does, proceed to step 4; otherwise, proceed to step 5.

[0011] Step 4: By analyzing the source strength estimation error formed by the source strength estimation of each sampling location set, sampling locations distributed upwind of the pollution source are initially excluded;

[0012] Step 5: Based on the source strength estimation error of each sampling location set, obtain the optimal sampling location set for the mobile monitoring vehicle;

[0013] Step 6: The mobile monitoring vehicle performs emergency sampling based on the optimal sampling location set to obtain sampling data, which is then analyzed in conjunction with data from fixed monitoring stations;

[0014] Step 7: Identify sources of excessive emissions.

[0015] The source strength estimation in step 4 uses a Gaussian plume diffusion model, as shown in the following formula:

[0016]

[0017] Where c is the emission gas concentration at (x,y,z), Q is the source strength of the emission source, u is the wind speed, and σ is the emission concentration at (x,y,z). y and σ z y and z are the atmospheric diffusion coefficients along the y-axis and z-axis, respectively, and H is the height of the emission source.

[0018] For multiple emission sources, the concentration value detected at each monitoring point is the sum of the concentration values ​​generated by the multiple emission sources individually. Let the number of emission sources be k and the number of monitoring points be j, the concentration can be expressed as:

[0019] c i =a1(p i )q1+…+a k (p i )q k i = 1, ..., j

[0020] Where c is the concentration value at the monitoring point, q is the source strength of each emission source, and a is the coefficient composed of other parameters besides the source strength in the Gaussian plume diffusion model. Assuming the concentration field is stable, a is a function of the sampling location p. For multiple emission sources and multiple monitoring points, it can be written in matrix form:

[0021] c = A(P)q

[0022] Where c is the concentration value matrix of the monitoring points, q is the source strength matrix of each emission source, and A is the matrix of coefficients a. For this linear equation system, when the number of sources is greater than the number of monitoring points (i.e., k > j), it is an underdetermined equation with no solution. The source strength of all emission sources cannot be uniquely estimated using only the data from these monitoring points. However, mobile monitoring vehicles can flexibly change sampling locations, making the equations solvable by significantly increasing the number of effective measurement points, thus allowing the calculation of source strength. In this case, the source strength of k emission sources can be estimated using j measurements according to the least squares method. The least squares solution for the source strength is:

[0023]

[0024] in, It is the pseudo-inverse of A.

[0025] The source strength estimation error in step 4 is caused by the concentration measurement error.

[0026]

[0027] Wherein, the concentration measurement error corresponding to the accurate concentration value c is δc, and the accurate source strength is... The corresponding source strength estimation error is By the properties of matrix norm:

[0028]

[0029] The relative error of the source strength estimation was measured. The relative error in the concentration measurement was measured. It is the relative error amplification factor. When the relative error of the measured concentration is an unavoidable objective fact, the upper limit of the relative error of the source strength estimation is limited by the amplification factor. When the amplification factor is reduced, the relative error of the source strength estimation will also be reduced. Since the coefficient matrix A is a function of the sampling point position, the amplification factor of this relative error can be reduced by optimizing the sampling position of the mobile monitoring vehicle, and thus the relative error of the source strength estimation is reduced in a statistical sense.

[0030] The beneficial effects of this invention are as follows:

[0031] This invention innovatively applies source strength estimation error analysis to the problem of optimizing the emergency sampling location of mobile monitoring vehicles, and proposes a method for optimizing the emergency sampling location of mobile monitoring vehicles in chemical industrial parks. This method can quickly determine the emergency sampling location of mobile monitoring vehicles when fixed monitoring stations detect excessive emissions from pollution sources in chemical industrial parks, even when concentration measurement errors are unavoidable and sampling locations are limited by the terrain conditions of chemical industrial parks. The method can also quickly locate the source of excessive emissions using monitoring data and sampling data.

[0032] After initializing the emergency sampling location set of the mobile monitoring vehicle, for sampling points distributed upwind of the pollution source, source strength estimation error analysis can be performed on multiple sets of sampling locations. The source strength estimation errors formed by the sampling location sets that include the sampling point and those that do not can be compared to exclude the sampling location.

[0033] For the set of multiple mobile monitoring vehicle emergency sampling locations formed by sampling points distributed downwind of all pollution sources, the optimal emergency sampling location of the mobile monitoring vehicle is optimized based on the Gaussian plume diffusion model. The source strength of each emission source is deduced from the sampling data and the monitoring data of the fixed monitoring station. By comparing the emission limits of each emission source, the source of the emission exceeding the standard is located. Attached Figure Description

[0034] Figure 1 This is a flowchart for locating excessive emission sources and optimizing the emergency sampling location of mobile monitoring vehicles.

[0035] Figure 2 This is a comparison chart excluding the relative error amplification factor of upwind sampling points.

[0036] Figure 3 This is a comparison chart of source strength estimation errors excluding upwind sampling points.

[0037] Figure 4 This is a schematic diagram of the emergency sampling location set excluding upwind sampling points.

[0038] Figure 5 This is a comparison chart of the relative error amplification factors of the downwind emergency sampling location set.

[0039] Figure 6 This is a comparison chart of source strength estimation errors for the downwind emergency sampling location set.

[0040] Figure 7 This is a schematic diagram of the optimal set of emergency sampling locations. Detailed Implementation

[0041] The specific implementation of the present invention is as follows:

[0042] A method for optimizing the emergency sampling location of a mobile monitoring vehicle in a chemical industrial park includes the following steps:

[0043] Step 1: Within the chemical industrial park, fixed monitoring stations detected abnormally high concentration values.

[0044] Step 2: Initialize the emergency sampling location set for the mobile monitoring vehicle based on the park's terrain conditions;

[0045] Step 3: Determine whether each set of sampling locations includes sampling points upwind of the pollution source. If it does, proceed to step 4; otherwise, proceed to step 5.

[0046] Step 4: By analyzing the source strength estimation error formed by the source strength estimation of each sampling location set, sampling locations distributed upwind of the pollution source are initially excluded;

[0047] Step 5: Based on the source strength estimation error of each sampling location set, obtain the optimal sampling location set for the mobile monitoring vehicle;

[0048] Step 6: The mobile monitoring vehicle performs emergency sampling based on the optimal sampling location set to obtain sampling data, which is then analyzed in conjunction with data from fixed monitoring stations;

[0049] Step 7: Identify sources of excessive emissions.

[0050] like Figure 2 As shown, sampling location sets 1 and 2, which contain sampling points upwind of the pollution source, have a greater relative error amplification factor than sampling location set 3, which does not contain sampling points upwind of the pollution source. Since the upper limit of the relative error in source strength estimation is limited by the amplification factor, the relative error of source strength estimation will decrease when the amplification factor decreases, as shown... Figure 3As shown, the source strength estimation error of sampling location set 1 and sampling location set 2 is greater than the source strength estimation error of sampling location set 3. Therefore, the sampling point upwind of the pollution source can be excluded.

[0051] like Figure 5 As shown, downwind of all pollution sources, there are multiple sets of emergency sampling locations for mobile monitoring vehicles. The relative error amplification factors of sampling location set 1, sampling location set 2, and sampling location set 3 are greater than the relative error amplification factor of sampling location set 4. For example... Figure 6 As shown, the source strength estimation error of sampling location set 1, sampling location set 2 and sampling location set 3 is greater than the source strength estimation error of sampling location set 4. Therefore, the optimal emergency sampling location set for the mobile monitoring vehicle is sampling location set 4.

[0052] The above are preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions made by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the emergency sampling location of a mobile monitoring vehicle in a chemical industrial park, characterized in that, Includes the following steps: Step 1: Within the chemical industrial park, fixed monitoring stations detected abnormally high concentration values. Step 2: Initialize the emergency sampling location set for the mobile monitoring vehicle based on the park's terrain conditions; Step 3: Determine whether each set of sampling locations includes sampling points upwind of the pollution source. If it does, proceed to step 4; otherwise, proceed to step 5. Step 4: By analyzing the source strength estimation error formed by the source strength estimation of each sampling location set, sampling locations distributed upwind of the pollution source are initially excluded; Step 5: Based on the source strength estimation error of each sampling location set, obtain the optimal sampling location set for the mobile monitoring vehicle; Step 6: The mobile monitoring vehicle performs emergency sampling based on the optimal sampling location set to obtain sampling data, which is then analyzed in conjunction with data from fixed monitoring stations; Step 7: Identify sources of excessive emissions; In step 4, source strength estimation involves summing the concentration values ​​detected at each monitoring point for multiple emission sources. Let the number of emission sources be k and the number of monitoring points be j. The concentration is expressed as: Where c is the concentration value at the monitoring point, q is the source strength of each emission source, and a is the coefficient composed of other parameters besides the source strength in the Gaussian plume diffusion model. Assuming the concentration field is stable, a is a function of the sampling location p. For multiple emission sources and multiple monitoring points, it can be written in matrix form as equation (1): （1） Where c is the concentration value matrix of the monitoring points, q is the source strength matrix of each emission source, and A is the matrix of coefficient a. For the linear equation system shown in equation (1), when the number of sources is greater than the number of monitoring points, i.e., k > j, it is an underdetermined equation with no solution. The source strength of all emission sources cannot be uniquely estimated using only the data from these monitoring points. However, the mobile monitoring vehicle flexibly changes the sampling position and makes the equation solvable by greatly increasing the number of effective measurement points, thereby calculating the source strength. At this time, the source strength of k emission sources is estimated using j measurement values ​​according to the least squares method. The least squares solution of the source strength is: in, , is the pseudo-inverse of A.

2. The method for optimizing the emergency sampling location of a mobile monitoring vehicle in a chemical industrial park according to claim 1, characterized in that... Source strength estimation error in step 4: Wherein, the concentration measurement error corresponding to the accurate concentration value c is c, accurate source strength The corresponding source strength estimation error is By the properties of matrix norm: in, The relative error of the source strength estimation was measured. The relative error in the concentration measurement was measured. It is the relative error amplification factor. When the relative error of the measured concentration is an unavoidable objective fact, the upper limit of the relative error of the source strength estimation is limited by the amplification factor. When the amplification factor is reduced, the relative error of the source strength estimation will also be reduced. Since the coefficient matrix A is a function of the sampling point position, the amplification factor of this relative error can be reduced by optimizing the sampling position of the mobile monitoring vehicle, and thus the relative error of the source strength estimation is reduced in a statistical sense.

3. The method for optimizing the emergency sampling location of a mobile monitoring vehicle in a chemical industrial park according to claim 1, characterized in that... In step 2, the set of emergency sampling locations is assumed to have multiple sets of mobile monitoring vehicle emergency sampling locations due to the terrain limitations of the chemical industrial park. For sampling points distributed upwind of the pollution source, source strength estimation error analysis can be performed on multiple sets of sampling locations. By comparing the source strength estimation errors formed by the sampling location sets that include the sampling point and those that do not, the sampling location can be excluded. For the set of sampling locations distributed downwind of all pollution sources, the optimal emergency sampling location of the mobile monitoring vehicle is optimized based on the Gaussian plume diffusion model. The source strength of each emission source is inferred from the sampling data and the monitoring data of the fixed monitoring station. By comparing the emission limits of each emission source, the source of the emission exceeding the standard can be located.

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