Method, device, program product and storage medium for monitoring flow field of flue gas of scr denitration

Abstract

The application discloses an SCR denitration flue gas flow field monitoring method, device, program product and storage medium, and relates to the technical field of flue gas treatment. The method comprises the following steps: uniformly dividing an SCR inlet section according to the number of ammonia injection lattices to obtain a plurality of monitoring areas; acquiring flue gas flow velocity data of the monitoring areas, and constructing a flow velocity matrix of each monitoring area according to the flue gas flow velocity data; calculating a comprehensive flow field coordination index of the whole SCR inlet section according to the flow velocity matrix of each monitoring area; determining target monitoring areas that need to be controlled by ammonia injection according to the comprehensive flow field coordination index; calculating a flow compensation coefficient of each target monitoring area according to the flow velocity matrix of the adjacent monitoring area corresponding to each target monitoring area; and controlling each target monitoring area by ammonia injection according to the flow compensation coefficient. The technical scheme provided by the application can improve the accuracy of ammonia injection amount control.

Description

Technical Field

[0001] This application relates to the technical field of flue gas treatment, specifically to a method, equipment, program product, and storage medium for monitoring the flue gas flow field of SCR denitrification. Background Technology

[0002] Selective catalytic reduction (SCR) technology is the mainstream technology for denitrification in thermal power plants. In an SCR denitrification system, ammonia gas, a reducing agent, is injected into the flue gas through an ammonia injection grid. It undergoes a selective catalytic reduction reaction with nitrogen oxides in the flue gas, producing nitrogen and water. To ensure denitrification efficiency and catalyst lifespan, the ammonia injection rate through the ammonia injection grid needs to be precisely controlled.

[0003] Currently, the commonly used method for controlling ammonia injection rate is based on the distribution characteristics of the flue gas flow field at the SCR inlet. By arranging velocity measuring devices at the SCR inlet cross-section to acquire flue gas velocity data at each measuring point, the ammonia injection rate of the corresponding area's ammonia injection grid is adjusted according to the velocity distribution. This control method can adapt to changes in the flue gas flow field and improve the operating efficiency of the denitrification system.

[0004] However, due to the uneven spatial distribution and mutual influence between adjacent areas in the flue gas flow field at the SCR inlet, relying solely on the velocity data at each measurement point can only assess the distribution of the flue gas flow field at each measurement point, which can easily lead to insufficient accuracy in the overall ammonia injection rate control. Summary of the Invention

[0005] This application provides a method, device, program product, and storage medium for monitoring the airflow field of SCR denitrification flue gas, which can improve the accuracy of ammonia injection quantity control.

[0006] The first aspect of this application provides a method for monitoring the airflow field of SCR denitrification, specifically including: The SCR inlet section is evenly divided according to the number of ammonia injection grids to obtain multiple monitoring areas; Obtain flue gas velocity data for each of the monitoring areas, and construct a velocity matrix for each monitoring area based on the flue gas velocity data; The overall comprehensive flow field compatibility index of the SCR inlet section is calculated based on the flow velocity matrix of each monitoring area. The target monitoring area requiring ammonia injection control is determined based on the comprehensive flow field coordination index. Calculate the flow compensation coefficient for each target monitoring area based on the flow velocity matrix of the adjacent monitoring areas corresponding to each target monitoring area; Ammonia injection is regulated for each target monitoring area based on the flow compensation coefficients described above.

[0007] By adopting the above technical solution, multiple monitoring areas are obtained by uniformly dividing the SCR inlet section according to the number of ammonia injection grids. Based on the velocity matrix of each monitoring area, the overall comprehensive flow field coordination index of the SCR inlet section is calculated, which can comprehensively evaluate the overall distribution of the flue gas flow field. Furthermore, the target monitoring area that needs to be controlled by ammonia injection is determined according to the comprehensive flow field coordination index, and the flow compensation coefficient is calculated by combining the velocity matrix of adjacent monitoring areas. When controlling ammonia injection, the mutual influence between areas is fully considered, thereby improving the accuracy of ammonia injection quantity control.

[0008] Optionally, constructing the velocity matrix for each monitoring area based on the flue gas velocity data includes: Calculate the mean flow velocity and variance of the flue gas velocity data in each monitoring area. When the deviation of the flue gas velocity data in any monitoring area from the mean flow velocity exceeds a preset multiple of the variance, the corresponding flue gas velocity data is determined to be an outlier. Obtain flue gas velocity data of adjacent monitoring areas corresponding to each of the abnormal values, and calculate the mean value of the velocity data of each of the adjacent monitoring areas; When an outlier deviates from the mean of the flow velocity data in the adjacent monitoring area by more than a preset deviation threshold, the mean of the flow velocity data in the adjacent monitoring area is used as the target flue gas velocity data for the corresponding monitoring area, and a flow velocity matrix for each monitoring area is constructed based on the flue gas velocity data and the target flue gas velocity data.

[0009] By adopting the above technical solution, the mean and variance of flue gas velocity data in each monitoring area are calculated, and anomalies are identified by using a preset multiple of the velocity variance as a judgment criterion. The flow velocity data of adjacent monitoring areas are then used for correction, which can effectively eliminate abnormal data caused by measurement errors and ensure the reliability of the flow velocity matrix data. This provides a more accurate data foundation for subsequent ammonia injection control based on the comprehensive flow field coordination index.

[0010] Optionally, the step of calculating the overall comprehensive flow field compatibility index of the SCR inlet section based on the flow velocity matrix of each of the monitored areas includes: The non-uniformity of each monitoring area is calculated based on the standard deviation and average value of the flue gas velocity data in each monitoring area. Calculate the average value of the non-uniformity of each monitoring area to obtain the overall coordination evaluation value of the SCR inlet section. Identify all pairs of adjacent monitoring areas, and use the ratio of the average velocity difference between each monitoring area pair to the distance between the centers of the corresponding monitoring area pairs as the velocity gradient of each monitoring area pair; Based on the flow velocity gradient of each monitoring area pair and the area ratio of the corresponding monitoring area, the inter-regional coordination evaluation value is calculated. The comprehensive flow field coordination index is calculated based on the coordination evaluation values ​​within the region and the coordination evaluation values ​​between regions.

[0011] By adopting the above technical solution, the coordination within each monitoring area is assessed by calculating the non-uniformity and its average value. At the same time, the coordination between areas is assessed based on the velocity gradient and area ratio of adjacent monitoring areas. The distribution characteristics of the flue gas flow field are quantitatively assessed from two dimensions: within the area and between areas. The comprehensive flow field coordination index can more comprehensively reflect the overall flow field state of the SCR inlet section, providing a reliable basis for accurately identifying the target monitoring area that needs to be regulated.

[0012] Optionally, determining the target monitoring area requiring ammonia injection control based on the comprehensive flow field coordination index includes: When the comprehensive flow field coordination index exceeds a preset acceptable threshold, the internal contribution of each monitoring area to the comprehensive flow field coordination index is calculated based on the degree of deviation between the non-uniformity of each monitoring area and the coordination evaluation value within the area. Based on the degree of deviation between each velocity gradient and the inter-regional coordination evaluation value, the external contribution of each monitoring region to the comprehensive flow field coordination index is calculated. Based on the internal and external contribution of each monitoring area, the total contribution of each monitoring area is calculated, and the monitoring areas with a total contribution greater than a preset contribution threshold are identified as target monitoring areas that require ammonia injection regulation.

[0013] By adopting the above technical solution, the internal contribution is calculated based on the degree of deviation between the non-uniformity of each monitoring area and the coordination evaluation value within the area, and the external contribution is calculated based on the degree of deviation between the velocity gradient and the coordination evaluation value between areas. This allows for a quantitative assessment of the impact of each monitoring area on the overall flow field coordination. Furthermore, by calculating the total contribution, the monitoring areas with a significant impact on flow field non-coordination can be accurately identified as control targets, providing a more targeted basis for subsequent ammonia injection control.

[0014] Optionally, calculating the flow compensation coefficient for each target monitoring area based on the flow velocity matrix of the adjacent monitoring areas corresponding to each target monitoring area includes: Calculate the flow loss coefficient of the target monitoring area; Based on the velocity gradient between each target monitoring area and its corresponding adjacent monitoring area, and the distance between the center of each target monitoring area and its corresponding adjacent monitoring area, the flow field coupling influence coefficient of each adjacent monitoring area on the target monitoring area is calculated. The flow field coupling influence coefficients of all adjacent monitoring areas are normalized to obtain the influence weights of each adjacent monitoring area. A flow compensation coefficient calculation model is established based on the flow missing coefficient and the influence weight. The flow field characteristic parameters of the target monitoring area and the flow field coupling influence coefficient of each adjacent monitoring area are input into the flow compensation coefficient calculation model to obtain the flow compensation coefficient of each target monitoring area.

[0015] By adopting the above technical solution, a flow compensation coefficient calculation model considering spatial location relationships was established by calculating the flow loss coefficient of the target monitoring area and combining it with the flow field coupling influence coefficient calculated based on the velocity gradient and the distance between the center of the area. This model can scientifically quantify the degree of influence of adjacent monitoring areas on the target monitoring area, thereby obtaining a more accurate flow compensation coefficient and providing a reliable compensation basis for achieving precise ammonia injection control.

[0016] Optionally, the step of adjusting the ammonia injection for each target monitoring area according to each of the flow compensation coefficients includes: Calculate the ammonia injection rate adjustment value of the ammonia injection grid corresponding to each of the target monitoring areas based on the flow compensation coefficients, and calculate the first expected flow velocity after regulation for each of the target monitoring areas based on the ammonia injection rate adjustment value. Calculate the difference between the first expected flow velocity and the current flow velocity to obtain the flow velocity change in each of the target monitoring areas. Based on the flow velocity change in each of the target monitoring areas and the flow field coupling influence coefficient, calculate the influence of the flow velocity change in each of the target monitoring areas on the flow velocity of the adjacent monitoring areas corresponding to the target monitoring area. The second expected flow velocity of each non-target monitoring area is obtained by superimposing all the influence quantities of each non-target monitoring area, and the predicted comprehensive flow field coordination index after regulation is calculated based on each second expected flow velocity. When the predicted comprehensive flow field coordination index is within the preset ideal value range, the ammonia injection grid corresponding to each of the target monitoring areas is adjusted according to the ammonia injection quantity adjustment value. When the predicted comprehensive flow field coordination index is not within the preset ideal value range, the flow compensation coefficient and ammonia injection adjustment value are recalculated.

[0017] By adopting the above technical solution, the expected flow velocity change in the target monitoring area after ammonia injection regulation is calculated, and its impact on adjacent non-target monitoring areas is evaluated based on the flow field coupling influence coefficient. At the same time, the regulation effect is pre-verified by calculating and predicting the comprehensive flow field coordination index. This forms a closed-loop regulation mechanism of prediction-verification-execution, which can effectively avoid the adverse effects of chain reactions in the regulation process on the overall flow field, ensure the systematicness and reliability of ammonia injection regulation, and further improve the operating efficiency of the SCR denitrification system.

[0018] Optionally, after adjusting the ammonia injection of the ammonia grid corresponding to each of the target monitoring areas according to the ammonia injection quantity adjustment value, the method further includes: During the preset stable time period after the ammonia injection regulation is executed, the actual flow velocity data of each monitoring area after regulation is collected, and the actual flow velocity matrix after regulation is constructed based on the actual flow velocity data. The prediction accuracy is calculated based on the first expected flow velocity and the adjusted actual flow velocity matrix. When the predicted accuracy is lower than a preset accuracy threshold, the flow compensation coefficient is adjusted according to the prediction deviation. The flow velocity matrix before regulation, the flow compensation coefficient, the ammonia injection adjustment value, the actual flow velocity matrix after regulation, and the predicted consistency are stored in the flow field monitoring database.

[0019] By adopting the above technical solution, the actual flow velocity data after regulation is collected and compared with the expected flow velocity. The prediction accuracy is calculated and the flow compensation coefficient is adjusted in a timely manner according to the prediction deviation. This forms a verification and optimization mechanism based on actual results. At the same time, the relevant data is stored in the flow field monitoring database. This not only enables adaptive optimization of the regulation parameters, but also accumulates historical data for continuous improvement of subsequent regulation strategies, further improving the accuracy and reliability of ammonia injection regulation.

[0020] In a second aspect, this application provides an SCR denitrification flue gas flow field monitoring device, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the SCR denitrification flue gas flow field monitoring device to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer program product containing instructions that, when the computer program product is run on an SCR denitrification flue gas flow field monitoring device, cause the SCR denitrification flue gas flow field monitoring device to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer-readable storage medium including instructions that, when executed on an SCR denitrification flue gas flow field monitoring device, cause the SCR denitrification flue gas flow field monitoring device to perform the method described in the first aspect and any possible implementation thereof. Attached Figure Description

[0023] Figure 1 This is a schematic flowchart of a method for monitoring the airflow field of SCR denitrification provided in an embodiment of this application; Figure 2 This is a schematic diagram of the SCR inlet section monitoring area segmentation provided in an embodiment of this application; Figure 3 This is an exemplary hardware structure diagram of an SCR denitrification flue gas flow field monitoring device provided in an embodiment of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0025] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0026] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0027] This application provides a method for monitoring the flue gas flow field in SCR denitrification, referencing... Figure 1 , Figure 1 This is a flowchart illustrating a method for monitoring the flue gas flow field in SCR denitrification according to an embodiment of this application, including steps S101 to S106, as follows: S101: Divide the SCR inlet section evenly according to the number of ammonia injection grids to obtain multiple monitoring areas.

[0028] In this embodiment, the monitoring area refers to an independent flow field monitoring unit formed by uniformly dividing the SCR inlet section according to the number of ammonia injection grids. Each monitoring area and its corresponding ammonia injection grid maintain a one-to-one spatial correspondence, used to achieve independent acquisition and analysis of flue gas velocity within that area. For example, when the SCR inlet section is configured with 4×6 ammonia injection grids (24 in total), the section is correspondingly divided into 24 monitoring areas.

[0029] Specifically, the SCR inlet section is uniformly divided into grids based on the number of ammonia injection grilles arranged horizontally and vertically. First, the number of ammonia injection grilles in the horizontal and vertical directions is determined, and the total number of grilles is calculated. The horizontal length of the SCR inlet section is divided equally according to the number of horizontal ammonia injection grilles, yielding the horizontal dimension of each monitoring area; similarly, the vertical length is divided equally according to the number of vertical ammonia injection grilles, yielding the vertical dimension of each monitoring area. A two-dimensional coordinate system is established, with the lower left corner of the SCR inlet section as the origin. Each monitoring area is numbered and its location is defined according to rows and columns, ensuring that each monitoring area has a clear spatial coordinate range. After division, all monitoring areas have completely equal areas and continuously cover the entire SCR inlet section in space, forming monitoring areas with the same number of ammonia injection grilles.

[0030] like Figure 2 As shown, Figure 2 This is a schematic diagram of the SCR inlet section monitoring area segmentation provided in an embodiment of this application.

[0031] The SCR inlet cross-section has a rectangular planar structure. The "30" indicates the thickness of the SCR inlet cross-section, while "600" and "800" correspond to its horizontal and vertical lengths, respectively, defining the overall size of the cross-section. The surface of the plate is divided into multiple uniform regions by a regular grid. These regions are arranged in rows and columns, each corresponding to a numerical identifier, such as 5.0, 6.0, 7.0, 8.0, 9.0 in the diagram, and 11.0, 11.5, 12.0, 12.5 below. These numbers are the location markers for each segment, clearly indicating its spatial position. These segmented regions have equal areas and continuously cover the entire cross-section of the plate, forming the corresponding monitoring area.

[0032] S102: Obtain flue gas velocity data for each monitoring area, and construct a velocity matrix for each monitoring area based on the flue gas velocity data.

[0033] In this embodiment, the velocity matrix refers to a two-dimensional data structure formed by standardizing and organizing the flue gas velocity data collected from each monitoring area within a specific time period according to their spatial correspondence. It is used to represent the velocity distribution and variation characteristics of the entire SCR inlet section. For example, for 24 monitoring areas divided into 4×6 sections, the velocity matrix is ​​a data matrix containing 24 velocity values ​​arranged in a 4x6 spatial layout.

[0034] Specifically, flue gas velocity data is collected in real time by velocity sensors deployed in each monitoring area. Outlier detection and data cleaning are performed on the raw velocity data. The mean and variance of the flue gas velocity data for all monitoring areas are calculated. The flue gas velocity data for each monitoring area are compared with the mean. If the deviation of the flue gas velocity data for any monitoring area from the mean exceeds a preset multiple of the variance, the flue gas velocity data is marked as an outlier. For detected outliers, the flue gas velocity data of adjacent monitoring areas are obtained, and the mean of the velocity data for these adjacent monitoring areas is calculated. If the deviation of the outlier from the mean of the velocity data of adjacent monitoring areas exceeds a preset deviation threshold, the mean of the velocity data of adjacent monitoring areas is used to replace the outlier as the target flue gas velocity data. The flue gas velocity data of each monitoring area after outlier processing are arranged in a matrix according to their actual spatial position on the SCR inlet section, constructing a velocity matrix that completely corresponds to the spatial distribution of the monitoring areas.

[0035] S103: Calculate the overall comprehensive flow field compatibility index of the SCR inlet section based on the flow velocity matrix of each monitoring area.

[0036] In this embodiment, the comprehensive flow field coordination index is a numerical indicator that comprehensively reflects the overall stability and coordination level of the flow field by quantitatively evaluating the uniformity of the velocity distribution in each monitoring area within the SCR inlet section and the smoothness of the velocity change between adjacent areas. It is used to represent the quality of the flow field distribution. For example, when the comprehensive flow field coordination index is close to 1, it indicates that the flow field distribution is relatively ideal, while a low value indicates that there is obvious velocity non-uniformity or local turbulence.

[0037] Specifically, the intra-regional and inter-regional coordination are calculated separately based on the constructed velocity matrix. For intra-regional coordination evaluation, the non-uniformity of each monitoring region is calculated based on the ratio of the standard deviation to the average value of the flue gas velocity data within each region. This reflects the dispersion of the velocity distribution within each region. Then, the average value of the non-uniformity of all monitoring regions is calculated to obtain the overall intra-regional coordination evaluation value of the SCR inlet section. For inter-regional coordination evaluation, all pairs of adjacent monitoring regions in the velocity matrix are identified, including horizontally adjacent, vertically adjacent, and diagonally adjacent pairs. The ratio of the average velocity difference between each pair of monitoring regions to the distance between the centers of the corresponding pairs is calculated as the velocity gradient of each pair. Then, the inter-regional coordination evaluation value is obtained by weighting the velocity gradient of each pair of monitoring regions with the area proportion of the corresponding monitoring region. The intra-regional and inter-regional coordination evaluation values ​​are then comprehensively calculated according to preset weights to obtain a comprehensive flow field coordination index that fully reflects the flow field distribution state of the SCR inlet section.

[0038] S104: Determine the target monitoring area that requires ammonia injection control based on the comprehensive flow field coordination index.

[0039] In this embodiment, the target monitoring area refers to the monitoring area that has a major impact on the overall flow field incoordination of the SCR inlet section. The contribution of each monitoring area to the deterioration of the comprehensive flow field coordination index is identified through quantitative analysis, thus determining the key areas requiring focused ammonia injection control. For example, if the flow velocity in a certain monitoring area is significantly higher or there is a large velocity difference compared to adjacent areas, that area may be identified as the target monitoring area.

[0040] Specifically, when the comprehensive flow field coordination index exceeds a preset acceptable threshold, indicating a problem with the flow field distribution coordination, the internal and external contributions of each monitoring area are calculated separately. For the internal contribution calculation, the degree of deviation between the non-uniformity of each monitoring area and the coordination evaluation value within the area is used to quantify the impact of the non-uniform velocity distribution within each monitoring area on the overall coordination index. The larger the deviation, the more unstable the velocity distribution within that area, and the higher the internal contribution. For the external contribution calculation, the degree of deviation between the velocity gradient formed by each monitoring area and its adjacent areas and the coordination evaluation value between areas is used to quantify the impact of the velocity difference between each monitoring area and its surrounding areas on the overall coordination. The larger the velocity gradient deviation, the less smooth the velocity connection between that area and its surrounding environment, and the higher the external contribution. The internal and external contributions of each monitoring area are then comprehensively calculated to obtain the total contribution of each monitoring area to the deterioration of the comprehensive flow field coordination index. Monitoring areas with a total contribution exceeding a preset contribution threshold are identified as target monitoring areas requiring ammonia injection control.

[0041] S105: Calculate the flow compensation coefficient for each target monitoring area based on the flow velocity matrix of the adjacent monitoring areas corresponding to each target monitoring area.

[0042] In this embodiment, the flow compensation coefficient refers to a correction parameter used to quantify and determine the required flow adjustment range for the target monitoring area based on the flow field interaction between the target monitoring area and its surrounding adjacent areas. It represents the degree of flow compensation required for the target area to achieve optimized flow field coordination. For example, a positive flow compensation coefficient indicates that the flow in the area needs to be increased, while a negative value indicates that the flow in the area needs to be decreased.

[0043] Specifically, firstly, the flow loss coefficient for each target monitoring area is calculated. By comparing the difference between the actual flow velocity and the ideal uniform flow velocity in the target monitoring area, the degree of deviation in flow distribution in that area is quantified. Then, based on the magnitude of the flow velocity gradient between each target monitoring area and its corresponding adjacent monitoring areas, as well as the distance between the center of the target monitoring area and each adjacent monitoring area, the flow field coupling influence coefficient of each adjacent monitoring area on the target monitoring area is calculated, reflecting the degree of influence of the flow field state of adjacent areas on the flow compensation requirement of the target area. The flow field coupling influence coefficients of all adjacent monitoring areas are normalized to ensure that the sum of each influence coefficient is 1, thus obtaining the influence weight of each adjacent monitoring area. Based on the flow loss coefficient and influence weight, a flow compensation coefficient calculation model is established. This model comprehensively considers the flow deviation of the target area itself and the coupling influence of the surrounding areas. The flow field characteristic parameters of the target monitoring area and the flow field coupling influence coefficients of each adjacent monitoring area are used as input variables into the calculation model, and the flow compensation coefficient of each target monitoring area is obtained through weighted calculation.

[0044] S106: Ammonia injection is regulated for each target monitoring area based on the flow compensation coefficient.

[0045] In this embodiment, ammonia injection control refers to a control process that, based on the flow compensation coefficient of each target monitoring area, precisely controls the ammonia injection amount of the corresponding ammonia injection grid to achieve flow field redistribution and coordination optimization, thereby improving the flow velocity distribution at the SCR inlet section. For example, when a target monitoring area needs to increase its flow rate, the ammonia injection amount of the ammonia injection grid in that area is reduced accordingly to lower flow resistance; conversely, the ammonia injection amount is increased.

[0046] Specifically, the ammonia injection rate adjustment value for the corresponding ammonia injection grid in each target monitoring area is calculated based on the flow compensation coefficient. By utilizing the conversion relationship between the flow compensation coefficient and the ammonia injection rate adjustment range, the specific ammonia injection rate that needs to be increased or decreased for each ammonia injection grid is determined. Based on the ammonia injection rate adjustment value, the first expected flow velocity after regulation in each target monitoring area is calculated. The difference between the first expected flow velocity and the current flow velocity is calculated to obtain the flow velocity change in each target monitoring area. Based on the flow velocity change in each target monitoring area and the previously calculated flow field coupling influence coefficient, the impact of the flow velocity change in each target monitoring area on the flow velocity of its corresponding adjacent monitoring areas is calculated, quantifying the chain effect of flow field regulation. The impact of the flow velocity changes in all target monitoring areas on each non-target monitoring area is superimposed to obtain the second expected flow velocity for each non-target monitoring area. Combining the first expected flow velocity of each target monitoring area and the second expected flow velocity of each non-target monitoring area, the predicted comprehensive flow field coordination index of the entire SCR inlet section after regulation is calculated. When the predicted comprehensive flow field coordination index is within the preset ideal value range, it indicates that the control scheme is feasible. Actual ammonia injection control is carried out on the ammonia injection grid corresponding to each target monitoring area according to the calculated ammonia injection adjustment value. When the predicted comprehensive flow field coordination index is not within the preset ideal value range, it indicates that the current control scheme may be excessive or insufficient. It is necessary to re-optimize the flow compensation coefficient and the ammonia injection adjustment value until the control effect that meets the requirements is obtained.

[0047] Based on the above embodiments, as an optional embodiment, S102: the step of constructing the velocity matrix of each monitoring area based on the flue gas velocity data may specifically include the following steps: S201: Calculate the mean and variance of flue gas velocity data for each monitoring area. When the deviation between the flue gas velocity data of any monitoring area and the mean velocity exceeds a preset multiple of the variance, the corresponding flue gas velocity data is determined to be an outlier.

[0048] In this embodiment, outliers refer to values ​​in the flue gas velocity data collected from various monitoring areas that significantly deviate from the normal distribution range. These values ​​are identified and marked using statistical analysis methods for subsequent data cleaning and correction. For example, when the velocity data in a certain monitoring area is significantly higher or lower than the overall average level and exceeds a reasonable fluctuation range, the data is determined to be an outlier.

[0049] Specifically, flue gas velocity data from all monitoring areas at the current sampling time are collected, and the velocity values ​​from each monitoring area are aggregated to form a complete dataset. Based on this dataset, the overall velocity mean is calculated, which is the arithmetic mean of the velocity data from all monitoring areas. Simultaneously, the velocity variance is calculated to reflect the dispersion of the velocity data from each monitoring area relative to the mean. An outlier detection method based on standard deviation is used to compare the flue gas velocity data from each monitoring area with the velocity mean, calculating the absolute deviation of each data point from the mean. When the deviation of the flue gas velocity data from any monitoring area exceeds a preset multiple of the velocity variance, for example, exceeding 2 or 3 times the standard deviation, the flue gas velocity data is marked as an outlier.

[0050] S202: Obtain flue gas velocity data of adjacent monitoring areas corresponding to each outlier, and calculate the mean value of the velocity data of each adjacent monitoring area.

[0051] In this embodiment, the adjacent monitoring area refers to other monitoring areas that are physically adjacent to the monitoring area containing the outlier, including all horizontally adjacent, vertically adjacent, and diagonally adjacent areas, used to provide a reference benchmark for outlier correction. For example, when there is an outlier in the monitoring area of ​​row 2, column 3, its adjacent monitoring areas include the eight surrounding areas of row 1, column 2, row 1, column 3, row 1, column 4, row 2, column 2, row 2, column 4, row 3, column 2, row 3, column 3, and row 4.

[0052] Specifically, for each monitoring area containing outliers, its spatial adjacency is determined based on the meshing results of the SCR inlet section. By traversing the row and column coordinates of the outlier monitoring area in the velocity matrix, all valid monitoring areas within ±1 of the row coordinate and ±1 of the column coordinate are identified as adjacent monitoring areas. Flue gas velocity data for each adjacent monitoring area at the current sampling time are extracted from the data storage system, and other outliers or invalid data are removed. The arithmetic mean of the valid flue gas velocity data for each adjacent monitoring area is calculated by adding the velocity values ​​of all adjacent areas and dividing by the total number of adjacent areas.

[0053] S203: When there is an outlier and the deviation of the mean flow velocity data of the corresponding adjacent monitoring area exceeds the preset deviation threshold, the mean flow velocity data of the adjacent monitoring area is used as the target flue gas velocity data of the corresponding monitoring area, and a flow velocity matrix of each monitoring area is constructed based on each flue gas velocity data and each target flue gas velocity data.

[0054] In this embodiment, the target flue gas velocity data refers to the corrected velocity value used to replace outliers. It is determined based on the average velocity of adjacent monitoring areas to ensure the rationality and consistency of all data in the velocity matrix. For example, if the outlier velocity value of a certain monitoring area is 15 m / s and the average velocity of its adjacent areas is 8 m / s, and the deviation between the two exceeds a preset threshold, then 8 m / s is taken as the target flue gas velocity data for that area.

[0055] Specifically, each outlier is compared numerically with the mean velocity data of its corresponding adjacent monitoring area, and the absolute deviation between the two is calculated. When the absolute deviation of the outlier from the mean velocity data of the adjacent monitoring area exceeds a preset deviation threshold, the outlier is determined to need correction and replacement, and the mean velocity data of the corresponding adjacent monitoring area is determined as the target flue gas velocity data for that monitoring area. When the deviation does not exceed the preset deviation threshold, the original flue gas velocity data is retained without correction. All monitoring areas are traversed, and the final velocity data after outlier detection and correction are collected, including the original flue gas velocity data without outliers and the corrected target flue gas velocity data. According to the spatial distribution of each monitoring area at the SCR inlet section, all the final velocity data are arranged and combined in row and column order to construct a complete velocity matrix.

[0056] Based on the above embodiments, as an optional embodiment, S103: the step of calculating the overall comprehensive flow field compatibility index of the SCR inlet section based on the flow velocity matrix of each monitoring area may specifically include the following steps: S301: Calculate the non-uniformity of each monitoring area based on the standard deviation and average value of the flue gas velocity data in each monitoring area; calculate the average value of the non-uniformity of each monitoring area to obtain the overall coordination evaluation value of the SCR inlet section.

[0057] In this embodiment, non-uniformity refers to the degree of dispersion of flue gas velocity data within a single monitoring area relative to the average velocity of that area. It is calculated by the ratio of the standard deviation to the average value and is used to quantify the uniformity level of velocity distribution within the monitoring area. For example, when the standard deviation of velocity in a certain monitoring area is 1.5 m / s and the average value is 10 m / s, the non-uniformity of that area is 0.15. The smaller the value, the more uniform the velocity distribution within the area.

[0058] Specifically, for each monitoring area, flue gas velocity data is collected, and the arithmetic mean and standard deviation of these velocity data are calculated. The non-uniformity of each monitoring area is obtained by dividing the standard deviation by the mean: Non-uniformity = Standard Deviation / Mean. All monitoring areas at the SCR inlet section are traversed, and the calculated non-uniformity values ​​for each area are obtained. The arithmetic mean of the non-uniformity values ​​for all monitoring areas is calculated by summing the non-uniformity values ​​for each area and dividing by the total number of monitoring areas, thus obtaining the overall regional compatibility evaluation value for the SCR inlet section.

[0059] S302: Identify all pairs of adjacent monitoring areas, and use the ratio of the average velocity difference between each monitoring area pair to the distance between the centers of the corresponding monitoring area pairs as the velocity gradient of each monitoring area pair; calculate the inter-regional coordination evaluation value based on the velocity gradient of each monitoring area pair and the area proportion of the corresponding monitoring area.

[0060] In this embodiment, the velocity gradient refers to a quantitative indicator of the rate of change of velocity between two adjacent monitoring areas. It is calculated as the ratio of the difference in average velocity between the two areas to the distance between their centers, and is used to characterize the drastic degree of velocity change between adjacent areas. For example, when the average velocities of two adjacent monitoring areas are 8 m / s and 10 m / s respectively, and the distance between their centers is 0.5 m, the velocity gradient of this monitoring area pair is (10-8) / 0.5 = 4 s⁻¹.

[0061] Specifically, all monitoring areas of the SCR inlet section are traversed, and all pairs of adjacent monitoring areas are identified based on spatial relationships, including horizontally adjacent and vertically adjacent combinations. For each monitoring area pair, the average flue gas velocity data of the two areas are calculated, and the absolute value of the difference between the two average velocities is obtained. The straight-line distance between the geometric center points of the two areas in each monitoring area pair is measured or calculated. The velocity gradient of each monitoring area pair is obtained by dividing the average velocity difference by the distance between the centers, i.e., velocity gradient = |average velocity difference| / distance between centers. The ratio of the sum of the areas of the two areas in each monitoring area pair to the total area of ​​the SCR inlet section is calculated as a weighting coefficient. The velocity gradient of each monitoring area pair is weighted and averaged with the corresponding area ratio to obtain the inter-regional coordination evaluation value.

[0062] S303: Calculate the comprehensive flow field coordination index based on the coordination evaluation values ​​within the region and between regions.

[0063] Specifically, the intra-regional and inter-regional coordination evaluation values ​​calculated in the previous steps are obtained as basic data. According to a preset weighting scheme, corresponding weight coefficients are assigned to the intra-regional and inter-regional coordination evaluation values, with the sum of these weight coefficients equal to 1. Through a weighted average mathematical operation, the product of the intra-regional coordination evaluation value and its corresponding weight, and the product of the inter-regional coordination evaluation value and its corresponding weight, are summed. That is, the comprehensive flow field coordination index = intra-regional coordination evaluation value × weight 1 + inter-regional coordination evaluation value × weight 2, yielding the comprehensive flow field coordination index.

[0064] Based on the above embodiments, as an optional embodiment, S104: the step of determining the target monitoring area requiring ammonia injection control based on the comprehensive flow field coordination index may specifically include the following steps: S401: When the comprehensive flow field coordination index exceeds the preset acceptable threshold, the internal contribution of each monitoring area to the comprehensive flow field coordination index is calculated based on the degree of non-uniformity of each monitoring area and the degree of deviation of the coordination evaluation value within the area.

[0065] In this embodiment, the internal contribution rate refers to the quantitative contribution index of the non-uniformity of flow velocity within each monitoring area to the degree of deterioration of the overall flow field coordination at the SCR inlet section. It is calculated by the deviation between the non-uniformity of each monitoring area and the coordination evaluation value within the area, and is used to identify the problem areas that have the most significant impact on flow field coordination. For example, when the non-uniformity of a certain monitoring area is 0.25 and the coordination evaluation value within the area is 0.15, the deviation rate of that area is 0.10, indicating that the flow velocity distribution within that area is significantly worse than the overall average level.

[0066] Specifically, when the comprehensive flow field compatibility index exceeds a preset acceptable threshold, it is determined that the current SCR inlet section flow field compatibility does not meet operational requirements, necessitating problem area identification and optimization. The non-uniformity values ​​of each monitoring area and the regional compatibility evaluation values ​​calculated in previous steps are obtained as basic data. For each monitoring area, the difference between the non-uniformity of that area and the regional compatibility evaluation value is calculated, i.e., deviation degree = non-uniformity of monitoring area - regional compatibility evaluation value. A positive value indicates that the velocity distribution within that area is worse than the overall average level. The deviation degree of each monitoring area is normalized by dividing the deviation degree of each area by the sum of the deviation degrees of all areas, yielding the internal contribution of each monitoring area to the comprehensive flow field compatibility index.

[0067] S402: Based on the degree of deviation between each velocity gradient and the inter-regional coordination evaluation value, the external contribution of each monitoring region to the comprehensive flow field coordination index is calculated.

[0068] In this embodiment, the external contribution degree refers to the quantitative contribution index of the influence of each monitoring area on the velocity difference of the adjacent area on the degree of deterioration of the overall flow field coordination of the SCR inlet section. It is calculated using a method based on information entropy theory and statistical analysis, and is used to identify the problem area with the most significant impact on the velocity incoordination of the adjacent area.

[0069] Specifically, the velocity gradient values ​​for each monitoring area and the inter-regional coordination evaluation values ​​calculated in previous steps are obtained as basic data. For each monitoring area, a velocity gradient distribution matrix is ​​constructed, containing the velocity gradient vectors of that area and all its neighboring areas. Based on the coefficient of variation analysis method in statistics, the dispersion index of the velocity gradient distribution in each monitoring area is calculated. By logarithmic transformation of the ratio of standard deviation to mean, the velocity gradient fluctuation intensity of each area is obtained. The information entropy value of the velocity gradient in each monitoring area is calculated using the entropy weight method, and then expressed as a formula. Calculation, where Let be the probability density of the j-th gradient value in the i-th region. When using Mahalanobis distance to measure the deviation of the velocity gradient distribution in each monitoring region from the overall consistency benchmark, a multi-dimensional vector space containing the velocity gradient characteristics of each monitoring region is first constructed. Let the velocity gradient feature vector of the i-th monitoring region be . ,in This represents the velocity gradient value between this region and the j-th adjacent region, and then the overall compatibility reference vector is calculated. Assuming an ideal velocity gradient distribution, the covariance matrix of the sample set, composed of the eigenvectors of the velocity gradients in all monitored areas, is then calculated using the Mahalanobis distance formula. The deviation of the i-th monitoring area from the overall coordination benchmark is calculated. This distance value comprehensively considers the correlation and variance structure differences among the various velocity gradient variables. In the TOPSIS comprehensive evaluation process, the calculated velocity gradient fluctuation intensity, information entropy value, and Mahalanobis distance are used to construct an evaluation index matrix. The normalized matrix Z is obtained by standardizing the matrix, where Then determine the ideal solution. and negative ideal solution Calculate the Euclidean distance from each monitoring area to the positive ideal solution. Distance to the negative ideal solution Ultimately, it is determined through the relative closeness formula. The external contribution of each monitoring area to the comprehensive flow field coordination index was obtained.

[0070] S403: Based on the internal and external contributions of each monitoring area, the total contribution of each monitoring area is calculated, and the monitoring areas with a total contribution greater than the preset contribution threshold are identified as the target monitoring areas that need to be controlled by ammonia injection.

[0071] In this embodiment, the total contribution refers to a composite evaluation index obtained by comprehensively quantifying the internal and external influences of each monitoring area on the deterioration of the overall flow field coordination index. This index comprehensively reflects the overall contribution level of the region to the degree of incoordination in the overall flow field. For example, when the internal contribution of a monitoring area is 0.25 and the external contribution is 0.18, a specific fusion algorithm can yield a total contribution of 0.31 for that area, indicating that the region has a significant comprehensive impact on the deterioration of the overall flow field coordination.

[0072] Specifically, the internal and external contribution values ​​of each monitoring area calculated in the previous steps are used as the basic input data. A nonlinear fusion method is used to calculate the total contribution of each monitoring area. This is achieved by multiplying the internal contribution value by a first weighting coefficient and then squaring it, and by multiplying the external contribution value by a second weighting coefficient and then squaring it. Simultaneously, the product of the internal and external contributions is multiplied by the interaction coefficient. The sum of these three values ​​is then taken as the square root to obtain the total contribution value. The sum of the squares of the first and second weighting coefficients is equal to 1 to ensure weight normalization. The interaction coefficient reflects the coupling effect between internal and external contributions. The calculated total contribution value of each monitoring area is compared with a preset contribution threshold. All monitoring areas with a total contribution value greater than the preset threshold are selected, and their numbers and location information are recorded in the target monitoring area list, identifying them as the target monitoring areas requiring ammonia injection control.

[0073] Based on the above embodiments, as an optional embodiment, S105: the step of calculating the flow compensation coefficient of each target monitoring area according to the flow velocity matrix of the adjacent monitoring areas corresponding to each target monitoring area may specifically include the following steps: S501: Calculate the flow missing coefficient for the target monitoring area.

[0074] In this embodiment, the flow missing coefficient is a quantitative indicator of the deviation between the current flow distribution state and the ideal uniform flow distribution state in the target monitoring area. It is used to represent the amount of flow adjustment required to achieve optimal flow field coordination in the area. For example, when the actual flow rate of a target monitoring area is 8 cubic meters per second, while the ideal flow rate is 12 cubic meters per second, the flow missing coefficient of the area is 0.33, indicating that a 33% increase in flow compensation is needed to achieve optimized flow field coordination.

[0075] Specifically, the actual flow rates of all target monitoring areas and the total flow rate data of the SCR inlet section are obtained as the basis for calculation. The standard flow rate value that each target monitoring area should possess under ideal conditions is calculated by dividing the total flow rate of the SCR inlet section by the total number of monitoring areas to obtain the ideal average flow rate benchmark value for a single area. For each target monitoring area, the difference between the actual flow rate and the ideal average flow rate benchmark value is calculated. A positive difference indicates insufficient flow when the actual flow rate is less than the ideal flow rate, and a negative difference indicates excessive flow when the actual flow rate is greater than the ideal flow rate. The flow rate difference of each target monitoring area is normalized by dividing it by the corresponding ideal average flow rate benchmark value to obtain the flow rate missing coefficient for each target monitoring area.

[0076] S502: Calculate the flow field coupling influence coefficient of each adjacent monitoring area on the target monitoring area based on the velocity gradient between each target monitoring area and its corresponding adjacent monitoring area, as well as the distance between the center of each target monitoring area and its corresponding adjacent monitoring area.

[0077] In this embodiment, the flow field coupling influence coefficient is a quantitative indicator of the comprehensive influence of adjacent monitoring areas on the flow field state changes of the target monitoring area. It is used to represent the strength of the effect of the flow field characteristics of adjacent areas on the coordinated control effect of the flow field in the target area through spatial transmission. For example, when the velocity gradient between an adjacent monitoring area and the target monitoring area is 2.5 meters per second per meter, and the distance between the centers of the two areas is 0.8 meters, the flow field coupling influence coefficient of the adjacent area on the target area is 0.42, indicating that the adjacent area has a significant coupling influence on the ammonia injection control effect of the target area.

[0078] Specifically, the velocity gradient values ​​between each target monitoring area and all its adjacent monitoring areas, as well as the spatial coordinates of the center points of each area, are obtained as the basic data for calculation. The Euclidean distance between the centers of each target monitoring area and its adjacent monitoring areas is calculated, and the actual spatial distance is obtained by taking the square root of the sum of the squares of the coordinate differences between the two points. For each pair of target monitoring areas and adjacent monitoring areas, the absolute value of the velocity gradient between the two areas is used as the flow field intensity influence factor, and the reciprocal of the distance between the center points is used as the spatial attenuation influence factor. The initial coupling strength value is obtained by multiplying the absolute value of the velocity gradient by the reciprocal of the distance. An exponential attenuation correction is applied to the initial coupling strength value using an exponential function with a base of the natural constant and a negative ratio of distance to characteristic length as the exponent, resulting in the flow field coupling influence coefficient of each adjacent monitoring area on the target monitoring area.

[0079] S503: Normalize the flow field coupling influence coefficients of all adjacent monitoring areas to obtain the influence weights of each adjacent monitoring area; establish a flow compensation coefficient calculation model based on the flow missing coefficient and influence weights; input the flow field characteristic parameters of the target monitoring area and the flow field coupling influence coefficients of each adjacent monitoring area into the flow compensation coefficient calculation model to obtain the flow compensation coefficients of each target monitoring area.

[0080] In this embodiment, the flow compensation coefficient is a quantitative indicator of the amount of flow regulation required to achieve optimal flow field coordination in the target monitoring area. It represents the actual ammonia injection control intensity needed in the area, considering the coupling effects of adjacent areas. For example, if the flow missing coefficient of a target monitoring area is 0.25, after correction by the influence weight of adjacent areas, the flow compensation coefficient of that area is 0.32, indicating that a 32% flow compensation regulation needs to be applied in that area to achieve the optimal flow field coordination state.

[0081] Specifically, the flow field coupling influence coefficients of each adjacent monitoring area on the target monitoring area, calculated in the previous steps, are used as input data for normalization. For each target monitoring area, the flow field coupling influence coefficients of all its adjacent monitoring areas are summed to obtain the total influence coefficient. Then, the flow field coupling influence coefficient of each adjacent area is divided by the total influence coefficient to obtain the standardized influence weight of that adjacent area, ensuring that the sum of the influence weights of all adjacent areas equals 1. A flow compensation coefficient calculation model is constructed, with the formula as follows: ,in Let be the flow compensation coefficient for target region i. Let be the flow missing coefficient for target region i, α be the coupling effect adjustment coefficient, and β, γ, and δ be the weighting coefficients of the flow field characteristic parameters, respectively. Let $\frac{i}{i}$ represent the mean flow velocity, turbulence intensity, and variance of flow direction angle, respectively. The flow field characteristic parameters of each target monitoring area and the flow field coupling influence coefficient of adjacent areas are substituted into the above formula for calculation to obtain the flow compensation coefficient for each target monitoring area.

[0082] Based on the above embodiments, as an optional embodiment, S106: the step of adjusting ammonia injection for each target monitoring area according to each flow compensation coefficient may specifically include the following steps: S601: Calculate the ammonia injection rate adjustment value of the ammonia injection grid corresponding to each target monitoring area based on each flow compensation coefficient, and calculate the first expected flow velocity after regulation for each target monitoring area based on the ammonia injection rate adjustment value.

[0083] In this embodiment, the ammonia injection rate adjustment value refers to the specific numerical value of the amount of ammonia injection flow rate adjustment required for the corresponding ammonia injection grid to achieve flow field coordination optimization in the target monitoring area. It represents the adjustment range of the difference between the current ammonia injection rate and the target ammonia injection rate. For example, when the flow compensation coefficient of a target monitoring area is 0.28 and the current ammonia injection rate of the corresponding ammonia injection grid is 150 kg / h, the calculated ammonia injection rate adjustment value is 42 kg / h, indicating that an additional 42 kg / h of ammonia injection is needed at the grid to achieve the flow field optimization target.

[0084] Specifically, the flow compensation coefficient of each target monitoring area and the current ammonia injection rate benchmark value of the corresponding ammonia injection grid are obtained as calculation input data. A linear mapping relationship between the flow compensation coefficient and the ammonia injection rate adjustment value is established. The ammonia injection rate adjustment value for each target monitoring area is calculated by multiplying the flow compensation coefficient and the ammonia injection rate adjustment sensitivity coefficient, and then multiplying the result by the rated ammonia injection rate of the corresponding grid. When the flow compensation coefficient is positive, the adjustment value is positive, indicating that the ammonia injection rate needs to be increased; when the flow compensation coefficient is negative, the adjustment value is negative, indicating that the ammonia injection rate needs to be decreased. First, the basic relationship of mass transfer in the ammonia injection process is established, and the basic physical property parameters such as flue gas density, temperature, and pressure of the target monitoring area, as well as the geometric dimensions and spatial distribution information of the ammonia injection grid, are obtained. The impact of the ammonia injection rate change on the total mass flow rate in the area is analyzed using the principle of mass conservation. The ammonia injection rate adjustment value is multiplied by the ammonia density to obtain the change in the injected mass flow rate, and then this change is superimposed with the original flue gas mass flow rate to obtain the adjusted total mass flow rate value. A momentum conservation equation is established to describe the influence mechanism of ammonia injection on the flow field momentum, analyzing the momentum input effect of ammonia injection and the momentum transfer effect of the mixing process. The velocity difference between the ammonia injection velocity and the original flue gas velocity is calculated. Using the momentum theorem, the adjusted ammonia injection rate, the ammonia injection velocity, and the original flue gas momentum are vector-synthesized to obtain the composite momentum value after ammonia injection. Considering the turbulent mixing effect during ammonia injection, a turbulent kinetic energy correction coefficient is used to correct the composite momentum for turbulence enhancement. This correction coefficient is related to the ratio of the ammonia injection velocity to the flue gas velocity and the Reynolds number. A model is constructed to analyze the effects of fluid property changes on flow velocity, analyzing the density and viscosity changes after ammonia and flue gas mixing. Using the mixture density calculation formula, the equivalent density after mixing is obtained by weighted averaging based on the mass fractions of ammonia and flue gas, while considering the correction effect of temperature changes on density. The dynamic viscosity change of the mixed fluid is calculated, and the equivalent viscosity value of the mixed fluid is obtained by logarithmic weighted averaging of the component viscosities. A correlation and conversion relationship between velocity, momentum, and density is established. The theoretical velocity value is obtained by dividing the regulated composite momentum by the equivalent density after mixing and the effective cross-sectional area of ​​the corresponding monitoring area. Spatial distribution correction is applied to the theoretical velocity, considering the non-uniform influence of the ammonia injection grid position, injection angle, and diffusion range on the velocity distribution at different spatial locations. A spatial weighting function is used to locally correct the theoretical velocity. Finally, a comprehensive velocity prediction model is established. The calculated ammonia injection adjustment value is added to the current ammonia injection amount to obtain the regulated target ammonia injection amount. The velocity prediction model converts the target ammonia injection amount into the corresponding change in flow field momentum. Then, combined with the current velocity benchmark value and regional geometric characteristic parameters of the target monitoring area, the velocity increment is calculated to obtain the first expected velocity after regulation for each target monitoring area.

[0085] S602: Calculate the difference between the first expected flow velocity and the current flow velocity to obtain the flow velocity change in each target monitoring area. Based on the flow velocity change in each target monitoring area and the flow field coupling influence coefficient, calculate the influence of the flow velocity change in each target monitoring area on the flow velocity of the adjacent monitoring areas corresponding to the target monitoring area.

[0086] In this embodiment, the impact of velocity change refers to the quantified value of the degree to which the velocity regulation change of the target monitoring area itself transmits to the velocity state of its adjacent monitoring areas through the flow field coupling mechanism. It is used to represent the intensity of the velocity transmission effect of inter-regional interaction during the flow field regulation process. For example, when the velocity change of a target monitoring area is 1.8 meters per second, and the flow field coupling influence coefficient between this area and its adjacent areas is 0.35, the calculated velocity change impact is 0.63 meters per second, indicating that the velocity regulation of the target area will cause the velocity of the adjacent areas to change by a trend of 0.63 meters per second.

[0087] Specifically, the expected flow velocity and the current actual flow velocity after regulation are obtained for each target monitoring area as the basis for calculation. The flow velocity difference is calculated for each target monitoring area by subtracting the current flow velocity from the expected flow velocity. A positive difference indicates an increase in flow velocity, while a negative difference indicates a decrease. This value reflects the direct impact of ammonia injection regulation on the flow velocity in the target area. A calculation model for the transmission of flow velocity changes is established, based on the momentum transfer principle and spatial coupling effect mechanism in fluid dynamics. For each target monitoring area, the flow field coupling influence coefficient between this area and all its adjacent monitoring areas is obtained. The impact of the flow velocity change in the target area on the flow velocity of its adjacent areas is obtained by multiplying the flow velocity change in the target monitoring area with the flow field coupling influence coefficient of the corresponding adjacent monitoring area.

[0088] S603: Superimpose all the influence quantities on each non-target monitoring area to obtain the second expected flow velocity of each non-target monitoring area, and calculate the predicted comprehensive flow field coordination index after regulation based on each second expected flow velocity.

[0089] In this embodiment, the second expected velocity refers to the expected velocity state of a non-target monitoring area after being affected by the coupled transmission of velocity regulation changes from all surrounding target monitoring areas. It is used to represent the velocity response result of a non-directly regulated area during the flow field coordination regulation process. For example, when the current velocity of a certain non-target monitoring area is 12.5 meters per second, and the velocity changes from three adjacent target monitoring areas are 0.8 meters per second, -0.3 meters per second, and 0.6 meters per second, respectively, the superimposed second expected velocity is 13.6 meters per second, indicating the expected velocity state that the non-target area will reach after flow field regulation.

[0090] Specifically, a mechanism for collecting and superimposing the influence of non-target monitoring areas is established, traversing all non-target monitoring areas and identifying target monitoring areas that influence the velocity of each non-target area. For each non-target monitoring area, the influence values ​​of velocity changes caused by all adjacent or flow-field-coupled target monitoring areas are collected. The principle of vector superposition is used to synthesize multiple influence quantities, considering the directionality and magnitude of each influence quantity. The total influence quantity of the same non-target monitoring area is obtained by algebraically summing all velocity influence quantities received by that area. The current velocity of each non-target monitoring area is superimposed with the corresponding total influence quantity to obtain the second expected velocity value for each non-target monitoring area. A calculation model for the predicted integrated flow field coordination index after regulation is constructed, which comprehensively considers the first expected velocity of all target monitoring areas and the second expected velocity of all non-target areas. The mean and variance of the expected flow velocity in all monitoring areas are calculated. The reciprocal of the ratio of variance to mean is used to characterize the uniformity of the flow velocity distribution. At the same time, the average value of the expected flow velocity gradient between adjacent monitoring areas is calculated to characterize the smoothness of the flow field. The uniformity index and the smoothness index are weighted and averaged to obtain the predicted comprehensive flow field coordination index after regulation.

[0091] S604: When the predicted comprehensive flow field coordination index is within the preset ideal value range, the ammonia injection grid corresponding to each target monitoring area is adjusted according to the ammonia injection quantity adjustment value; when the predicted comprehensive flow field coordination index is not within the preset ideal value range, the flow compensation coefficient and the ammonia injection quantity adjustment value are recalculated.

[0092] Specifically, an evaluation and judgment mechanism for the predicted comprehensive flow field coordination index is established, using the upper and lower limits of the system's preset ideal value range as judgment benchmarks. The calculated predicted comprehensive flow field coordination index is compared with the preset ideal value range to determine whether the index falls between the upper and lower limits of the ideal value range. When the predicted comprehensive flow field coordination index is greater than or equal to the lower limit and less than or equal to the upper limit, it is confirmed that the currently calculated ammonia injection adjustment value can achieve the expected flow field coordination optimization target. The system performs actual ammonia injection flow control operations on the corresponding ammonia injection grid according to the ammonia injection adjustment value corresponding to each target monitoring area, achieving precise ammonia injection control by adjusting parameters such as grid opening, injection pressure, or injection angle. When the predicted comprehensive flow field coordination index is less than the lower limit or greater than the upper limit, it indicates that the current control scheme cannot achieve the ideal flow field coordination effect or may generate the risk of over-control. The system initiates an iterative optimization mechanism to recalculate the parameters. During the recalculation process, the calculation weight and correction factor of the flow compensation coefficient are adjusted according to the degree of deviation between the predicted index and the ideal value range. The flow compensation coefficient value is corrected by increasing or decreasing the compensation intensity. Then, the ammonia injection adjustment value of each target monitoring area is recalculated based on the corrected flow compensation coefficient. The subsequent expected flow velocity calculation and coordination index evaluation process are repeated until the predicted comprehensive flow field coordination index meets the preset ideal value range requirements.

[0093] Based on the above embodiments, as an optional embodiment, S604: after the step of adjusting the ammonia injection grid corresponding to each target monitoring area according to the ammonia injection quantity adjustment value, a step of adjustment verification is also included, which may specifically include the following steps: S701: During the preset stable time period after the ammonia injection regulation is executed, collect the actual flow velocity data of each monitoring area after regulation, and construct the actual flow velocity matrix after regulation based on the actual flow velocity data.

[0094] Specifically, a mechanism for monitoring and acquiring data on the stability of the flow field after regulation is established, and an appropriate preset stabilization time period is set based on the flow field response characteristics and system dynamic features. After the ammonia injection regulation operation is completed, a stabilization time timer is started, and data acquisition is paused to wait for the flow field to reach a new steady-state equilibrium. The setting of the preset stabilization time period needs to consider factors such as flue geometry, fluid velocity, turbulence intensity, and ammonia diffusion rate, and the waiting time is ensured to be sufficient through hydrodynamic time constant calculation. After the preset stabilization time period ends, the velocity sensors in all monitoring areas are simultaneously activated to acquire actual velocity data. The acquisition frequency and duration are set according to the data statistical accuracy requirements to ensure that representative velocity measurement values ​​are obtained. The acquired actual velocity data of each monitoring area are statistically processed, and the influence of random fluctuations is eliminated by time averaging to obtain the stable actual velocity values ​​of each monitoring area after regulation. Following the same spatial arrangement and numbering rules as the initial velocity matrix, the actual velocity values ​​of each monitoring area after regulation are arranged in a matrix according to their corresponding row and column positions to construct the actual velocity matrix after regulation.

[0095] S702: Calculate the prediction accuracy based on the first expected flow rate and the actual flow rate matrix after adjustment; when the prediction accuracy is lower than the preset accuracy threshold, adjust the flow compensation coefficient according to the prediction deviation.

[0096] Specifically, a prediction consistency calculation and evaluation mechanism is established. The actual flow velocity values ​​at corresponding locations of each target monitoring area are extracted from the adjusted actual flow velocity matrix and compared one by one with the first expected flow velocity value for that target monitoring area. A combination of relative error and root mean square error is used to calculate the prediction accuracy. The relative error is obtained by dividing the absolute difference between the expected and actual flow velocities in each target monitoring area by the actual flow velocity. The average of the relative errors for all target areas is then converted to a percentage to obtain the overall prediction consistency value. The calculated prediction consistency is compared with a preset consistency threshold. When the prediction consistency is greater than or equal to the preset threshold, the prediction model accuracy meets the requirements. When the prediction consistency is less than the preset threshold, the flow compensation coefficient optimization and adjustment mechanism is activated. During the adjustment process, the direction and magnitude of the deviation between the expected and actual flow velocities in each target monitoring area are analyzed, and the statistical characteristics of the prediction deviation are calculated, including the mean deviation, standard deviation, and distribution pattern of the deviation. The flow compensation coefficient is adjusted according to the systematic characteristics of the prediction deviation. When the predicted value is generally higher than the actual value, the flow compensation coefficient is reduced to reduce the prediction sensitivity. When the predicted value is generally lower than the actual value, the flow compensation coefficient is increased to improve the prediction responsiveness. The adjustment range is related to the magnitude and distribution characteristics of the prediction deviation.

[0097] S703: Store the flow velocity matrix before regulation, flow compensation coefficient, ammonia injection adjustment value, actual flow velocity matrix after regulation, and prediction consistency in the flow field monitoring database.

[0098] Specifically, a data storage and management mechanism for the flow field monitoring database is established. A primary key structure including timestamps, control serial numbers, and monitoring area codes is designed to ensure the uniqueness and traceability of data records. The pre-control velocity matrix is ​​used as the baseline data storage, recording the initial velocity distribution and corresponding flow field coordination index of each monitoring area, providing a reference baseline for comparing control effects. The flow compensation coefficient values ​​and their calculation parameters, determined after iterative optimization, are stored, including the initial value, adjustment process, and final value of the compensation coefficient. The correlation between the compensation coefficient and flow field characteristic parameters is recorded to provide a reference for rapid parameter setting under similar operating conditions. The ammonia injection adjustment value and control strategy information corresponding to each target monitoring area are saved, including detailed parameters such as adjustment amplitude, adjustment direction, and execution time, ensuring the complete reproducibility of control operations. The actual velocity matrix data after control is stored, recording the actual velocity distribution and flow field coordination improvement effect of each monitoring area after control, establishing a complete comparative dataset of flow field states before and after control. The prediction fit and its calculation details are stored in the database, including prediction error analysis of each target area and overall prediction accuracy evaluation results, providing a quantitative basis for continuous optimization and parameter adjustment of the prediction model.

[0099] The following describes an exemplary SCR denitrification flue gas flow field monitoring device provided in an embodiment of this application. Figure 3 This is an exemplary hardware structure diagram of an SCR denitrification flue gas flow field monitoring device provided in an embodiment of this application.

[0100] In some embodiments, the SCR denitrification flue gas flow field monitoring device is a computer device or includes a computer device in the SCR denitrification flue gas flow field monitoring device. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0101] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0102] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0103] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0104] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0105] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for monitoring the flue gas flow field in SCR denitrification, characterized in that, The method includes: The SCR inlet section is evenly divided according to the number of ammonia injection grids to obtain multiple monitoring areas; Obtain flue gas velocity data for each of the monitoring areas, and construct a velocity matrix for each monitoring area based on the flue gas velocity data; The overall comprehensive flow field compatibility index of the SCR inlet section is calculated based on the flow velocity matrix of each monitoring area. The target monitoring area requiring ammonia injection control is determined based on the comprehensive flow field coordination index. Calculate the flow compensation coefficient for each target monitoring area based on the flow velocity matrix of the adjacent monitoring areas corresponding to each target monitoring area; Ammonia injection is regulated for each target monitoring area based on the flow compensation coefficients described above.

2. The SCR denitrification flue gas flow field monitoring method according to claim 1, characterized in that, The step of constructing a velocity matrix for each monitoring area based on the flue gas velocity data includes: Calculate the mean flow velocity and variance of the flue gas velocity data in each monitoring area. When the deviation of the flue gas velocity data in any monitoring area from the mean flow velocity exceeds a preset multiple of the variance, the corresponding flue gas velocity data is determined to be an outlier. Obtain flue gas velocity data of adjacent monitoring areas corresponding to each of the abnormal values, and calculate the mean value of the velocity data of each of the adjacent monitoring areas; When an outlier deviates from the mean of the flow velocity data in the adjacent monitoring area by more than a preset deviation threshold, the mean of the flow velocity data in the adjacent monitoring area is used as the target flue gas velocity data for the corresponding monitoring area, and a flow velocity matrix for each monitoring area is constructed based on the flue gas velocity data and the target flue gas velocity data.

3. The SCR denitrification flue gas flow field monitoring method according to claim 1, characterized in that, The calculation of the overall flow field compatibility index of the SCR inlet section based on the flow velocity matrix of each monitoring area includes: The non-uniformity of each monitoring area is calculated based on the standard deviation and average value of the flue gas velocity data in each monitoring area. Calculate the average value of the non-uniformity of each monitoring area to obtain the overall coordination evaluation value of the SCR inlet section. Identify all pairs of adjacent monitoring areas, and use the ratio of the average velocity difference between each monitoring area pair to the distance between the centers of the corresponding monitoring area pairs as the velocity gradient of each monitoring area pair; Based on the flow velocity gradient of each monitoring area pair and the area ratio of the corresponding monitoring area, the inter-regional coordination evaluation value is calculated. The comprehensive flow field coordination index is calculated based on the coordination evaluation values ​​within the region and the coordination evaluation values ​​between regions.

4. The SCR denitrification flue gas flow field monitoring method according to claim 3, characterized in that, The determination of the target monitoring area requiring ammonia injection regulation based on the comprehensive flow field coordination index includes: When the comprehensive flow field coordination index exceeds a preset acceptable threshold, the internal contribution of each monitoring area to the comprehensive flow field coordination index is calculated based on the degree of deviation between the non-uniformity of each monitoring area and the coordination evaluation value within the area. Based on the degree of deviation between each velocity gradient and the inter-regional coordination evaluation value, the external contribution of each monitoring region to the comprehensive flow field coordination index is calculated. Based on the internal and external contribution of each monitoring area, the total contribution of each monitoring area is calculated, and the monitoring areas with a total contribution greater than a preset contribution threshold are identified as target monitoring areas that require ammonia injection regulation.

5. The SCR denitrification flue gas flow field monitoring method according to claim 1, characterized in that, The step of calculating the flow compensation coefficient for each target monitoring area based on the flow velocity matrix of the adjacent monitoring areas corresponding to each target monitoring area includes: Calculate the flow loss coefficient of the target monitoring area; Based on the velocity gradient between each target monitoring area and its corresponding adjacent monitoring area, and the distance between the center of each target monitoring area and its corresponding adjacent monitoring area, the flow field coupling influence coefficient of each adjacent monitoring area on the target monitoring area is calculated. The flow field coupling influence coefficients of all adjacent monitoring areas are normalized to obtain the influence weights of each adjacent monitoring area. A flow compensation coefficient calculation model is established based on the flow missing coefficient and the influence weight. The flow field characteristic parameters of the target monitoring area and the flow field coupling influence coefficient of each adjacent monitoring area are input into the flow compensation coefficient calculation model to obtain the flow compensation coefficient of each target monitoring area.

6. The SCR denitrification flue gas flow field monitoring method according to claim 5, characterized in that, The step of regulating ammonia injection in each target monitoring area according to each of the flow compensation coefficients includes: Calculate the ammonia injection rate adjustment value of the ammonia injection grid corresponding to each of the target monitoring areas based on the flow compensation coefficients, and calculate the first expected flow velocity after regulation for each of the target monitoring areas based on the ammonia injection rate adjustment value. Calculate the difference between the first expected flow velocity and the current flow velocity to obtain the flow velocity change in each of the target monitoring areas. Based on the flow velocity change in each of the target monitoring areas and the flow field coupling influence coefficient, calculate the influence of the flow velocity change in each of the target monitoring areas on the flow velocity of the adjacent monitoring areas corresponding to the target monitoring area. The second expected flow velocity of each non-target monitoring area is obtained by superimposing all the influence quantities of each non-target monitoring area, and the predicted comprehensive flow field coordination index after regulation is calculated based on each second expected flow velocity. When the predicted comprehensive flow field coordination index is within the preset ideal value range, the ammonia injection grid corresponding to each of the target monitoring areas is adjusted according to the ammonia injection quantity adjustment value. When the predicted comprehensive flow field coordination index is not within the preset ideal value range, the flow compensation coefficient and ammonia injection adjustment value are recalculated.

7. The SCR denitrification flue gas flow field monitoring method according to claim 6, characterized in that, After adjusting the ammonia injection rate of the ammonia injection grid corresponding to each target monitoring area according to the ammonia injection rate adjustment value, the method further includes: During the preset stable time period after the ammonia injection regulation is executed, the actual flow velocity data of each monitoring area after regulation is collected, and the actual flow velocity matrix after regulation is constructed based on the actual flow velocity data. The prediction accuracy is calculated based on the first expected flow velocity and the adjusted actual flow velocity matrix. When the predicted accuracy is lower than a preset accuracy threshold, the flow compensation coefficient is adjusted according to the prediction deviation. The flow velocity matrix before regulation, the flow compensation coefficient, the ammonia injection adjustment value, the actual flow velocity matrix after regulation, and the predicted consistency are stored in the flow field monitoring database.

8. A flue gas flow field monitoring device for SCR denitrification, characterized in that, The SCR denitrification flue gas flow field monitoring device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the SCR denitrification flue gas flow field monitoring device to perform the method as described in any one of claims 1-7.

9. A computer program product containing instructions, characterized in that, When the computer program product is run on the SCR denitrification flue gas flow field monitoring device, the SCR denitrification flue gas flow field monitoring device performs the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the SCR denitrification flue gas flow field monitoring device, the SCR denitrification flue gas flow field monitoring device performs the method as described in any one of claims 1-7.

Images