Method, device and medium for optimizing flow field of selective catalytic reduction denitration system

Abstract

The application relates to the field of flue gas purification technology of coal-fired power plants, in particular to a flow field optimization method and device of a selective catalytic reduction denitration system and a medium, wherein the method comprises the following steps: identifying average flow velocities of a first region and a second region; calculating a region flow ratio according to the average flow velocities of the first region and the second region, and determining an adjustment strategy according to the region flow ratio and a current decision threshold value, wherein the current decision threshold value comprises a first decision threshold value and a second decision threshold value, and the first decision threshold value is greater than the second decision threshold value; obtaining a current unit load, determining reference guide plate angle values corresponding to the first region and the second region according to the current unit load, and adjusting a current actual guide plate angle to the reference guide plate angle in the adjustment strategy. Therefore, the problems that control parameters of flow field optimization of the selective catalytic reduction denitration system are fixed in the related art, and the flow field optimization effect is attenuated after long-term operation are solved.

Description

Technical Field

[0001] This application relates to the field of flue gas purification technology for coal-fired power plants, and in particular to a flow field optimization method, apparatus and medium for a selective catalytic reduction denitrification system. Background Technology

[0002] SCR (Selective Catalytic Reduction) technology is currently the most widely used flue gas denitrification technology in coal-fired power plants. In an SCR denitrification system, the uniformity of the flue gas flow field upstream of the catalyst bed directly affects the denitrification efficiency, ammonia slip rate, and catalyst lifespan. Engineering practice typically requires that the relative standard deviation of the flue gas velocity upstream of the catalyst be controlled within 15% to ensure efficient and stable system operation.

[0003] In related technologies, to achieve flow field homogenization, existing techniques mainly employ the method of arranging fixed guide vanes within the flue, based on static arrangements using simulation results under design conditions. However, in actual operation of coal-fired units, loads fluctuate frequently, and long-term operation leads to ash accumulation and wear within the flue, causing flow field characteristics to drift. Fixed guide vanes cannot adapt to these changes in operating conditions and long-term drift, resulting in a gradual deterioration of flow field optimization. While manual or simple automatic adjustments can be used to adapt to these changes, control parameters (such as decision thresholds for feedback regulation) are typically fixed and cannot automatically evolve based on actual operating data. As operating time increases, the fixed thresholds gradually deviate from the actual optimal range, leading to a decrease in the adjustment effect and making it difficult to achieve long-term stable flow field optimization. Summary of the Invention

[0004] This application provides a flow field optimization method, apparatus and medium for a selective catalytic reduction denitrification system, in order to solve the problem that the control parameters for flow field optimization in selective catalytic reduction denitrification systems are fixed, leading to the decay of the flow field optimization effect after long-term operation.

[0005] The first aspect of this application provides a flow field optimization method for a selective catalytic reduction (SCR) denitrification system. The method is applied to the flue section of the catalyst layer in the SCR system. An adjustable guide vane is installed within the flue section, and the partition boundaries of the guide vane divide the cross-section of the flue section of the catalyst layer into a first region and a second region. The method includes the following steps: identifying the average flow velocity of the first region and the second region; calculating the region flow ratio based on the average flow velocities of the first region and the second region; determining an adjustment strategy based on the region flow ratio and a current decision threshold, wherein the current decision threshold includes a first decision threshold and a second decision threshold, and the first decision threshold is greater than the second decision threshold; obtaining the current unit load; determining reference guide vane angle values ​​corresponding to the first region and the second region based on the current unit load; and gradually adjusting the current actual guide vane angle to the reference guide vane angle using the adjustment strategy.

[0006] Optionally, determining the adjustment strategy based on the regional flow ratio and the current decision threshold includes: identifying the sensitivity of the first region and the second region to the regional flow ratio; determining the adjustment step size corresponding to the first region and the second region based on the sensitivity; and determining the adjustment strategy based on the sensitivity, the regional flow ratio, the adjustment step size, and the decision threshold.

[0007] Optionally, determining the adjustment strategy based on the sensitivity, the regional flow ratio, the adjustment step size, and the decision threshold includes: if the regional flow ratio is greater than a first decision threshold, then gradually decreasing the guide vane angle with the adjustment step size corresponding to the region with high sensitivity, and gradually increasing the guide vane angle with the adjustment step size corresponding to the region with low sensitivity; if the regional flow ratio is less than the first decision threshold, then gradually increasing the guide vane angle with the adjustment step size corresponding to the region with high sensitivity, and gradually decreasing the guide vane angle with the adjustment step size corresponding to the region with low sensitivity.

[0008] Optionally, determining the reference guide vane angle values ​​corresponding to the first region and the second region based on the current unit load includes: obtaining a first mapping table, wherein the first mapping table is a mapping table between unit load and reference guide vane angle values; querying the first mapping table using the current unit load as an index to determine the reference guide vane angle values ​​corresponding to the first region and the second region.

[0009] Optionally, before determining the adjustment strategy based on the regional flow ratio and the current decision threshold, the method includes: extracting historical optimization data corresponding to the cumulative adjustment operations of the current operating cycle, wherein the historical optimization data are the regional flow ratio and flow field uniformity index after the adjustment has stabilized; filtering target historical optimization data where the flow field uniformity index reaches a preset index threshold; if the total number of target historical optimization data reaches a preset number threshold, calculating the quantile of the regional flow ratio in all target historical optimization data, and updating the current decision threshold based on the quantile to generate a target decision threshold.

[0010] Optionally, after generating the target decision threshold by updating the current decision threshold based on the quantile, the process includes: if the target decision threshold is greater than or equal to a preset safety boundary, then applying the current decision threshold to the next operating cycle; calculating the change in the target decision threshold relative to the current decision threshold, and if the change exceeds a preset range of the current decision threshold, then applying the current decision threshold to the next operating cycle; after applying the target decision threshold to the next operating cycle, if the flow field compliance rate in the next operating cycle decreases by more than a preset ratio compared to the current cycle, then automatically reverting to the current decision threshold and applying the current decision threshold to the next operating cycle.

[0011] A second aspect of this application provides a flow field optimization device for a selective catalytic reduction (SCR) denitrification system, comprising: an identification module for identifying the average flow velocity of a first region and a second region; a calculation module for calculating a region flow ratio based on the average flow velocities of the first region and the second region, and determining an adjustment strategy based on the region flow ratio and a decision threshold, wherein the decision threshold includes a first decision threshold and a second decision threshold, and the first decision threshold is greater than the second decision threshold; and an adjustment module for acquiring the current unit load, determining a reference guide vane angle value corresponding to the first region and the second region based on the current unit load, and gradually adjusting the current actual guide vane angle to the reference guide vane angle using the adjustment strategy.

[0012] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform a flow field optimization method for a selective catalytic reduction denitrification system as described in the above embodiments.

[0013] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to perform the flow field optimization method for a selective catalytic reduction denitrification system as described in the above embodiments.

[0014] A fifth aspect of this application provides a computer program product, including a computer program or instructions, which, when executed, implement the flow field optimization method for a selective catalytic reduction denitrification system as described in the above embodiments.

[0015] Therefore, this application has at least the following beneficial effects: This embodiment of the application divides the upstream flue section of the catalyst layer into a first region and a second region, identifies the average flow velocity of the two regions in real time, calculates the region flow ratio, and simultaneously obtains the reference guide vane angle value corresponding to the current unit load. The region flow ratio is compared with the current decision threshold (including the first decision threshold and the second decision threshold) to determine the adjustment strategy. The current actual guide vane angle is gradually adjusted to the reference guide vane angle using this adjustment strategy. Since the region flow ratio reflects the uniformity of the flow velocity distribution on the inner and outer sides of the flue section, the decision threshold dynamically defines the allowable deviation range. When the region flow ratio exceeds the threshold, the adjustment strategy triggers the guide vane angle adjustment to bring the region flow ratio back within the threshold. At the same time, the reference guide vane angle is preset based on the load to ensure that the adjustment direction matches the changes in operating conditions. The two work together to enable the flow field optimization to respond quickly to load changes and accurately correct local flow velocity deviations, thereby maintaining the uniformity of the upstream flow field of the catalyst layer in the long term, improving denitrification efficiency and reducing ammonia slip.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a flow field optimization method for a selective catalytic reduction denitrification system according to an embodiment of this application; Figure 2 This is a schematic diagram of the module architecture and data flow according to the embodiments of this application; Figure 3 This is a flowchart of a three-layer adaptive control method provided according to an embodiment of this application; Figure 4 This is a sub-flowchart illustrating the adaptive updating of the decision threshold according to the embodiments of this application; Figure 5 This is a comparison chart of the long-term effects of a fixed threshold and an adaptive threshold provided according to embodiments of this application; Figure 6 This is a block diagram of the flow field optimization device for a selective catalytic reduction denitrification system provided in the embodiments of this application; Figure 7This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0018] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0019] Existing technologies mainly employ fixed guide vanes, statically arranged based on CFD simulations of design operating conditions, which cannot adapt to load fluctuations and long-term operational drift. Some existing technologies propose adjustable guide vane solutions, which adapt to changes in operating conditions through manual or simple automatic adjustments; however, the control parameters are fixed and cannot automatically evolve with operating data, leading to performance degradation after long-term operation.

[0020] Therefore, this application provides a flow field adaptive optimization method and system for a selective catalytic reduction (SCR) denitrification system. The system continuously records historical data from each optimization adjustment, extracts the actual R-value distribution that achieves the flow field target, and dynamically updates the threshold range using statistical quantiles, ensuring that the control strategy always approaches the optimal range under the current operating conditions. Through a two-layer control system of feedforward preset and primary-secondary collaborative feedback, it achieves rapid response to load changes and precise correction of flow field deviations. Through a threshold self-learning mechanism based on the quantiles of the target data, the decision threshold automatically evolves with operational drift, offsetting the long-term effects of ash accumulation and wear. This method is particularly suitable for SCR denitrification systems of coal-fired units where flow field characteristics drift after long-term operation. Due to factors such as catalyst surface ash accumulation, flue wall wear, and mechanical aging of the guide vanes, the optimal guide vane angle combination under the same load will shift after 6 months to 1 year of unit operation, and the fixed regional flow ratio threshold range will gradually deviate from the actual optimal range. Therefore, this application continuously accumulates operational data and dynamically updates the decision threshold using statistical methods, so that the control strategy always approaches the optimal range under the current operating conditions, achieving an adaptive effect that becomes more accurate with use and effectively offsetting the effects of long-term drift.

[0021] The flow field optimization method, apparatus, and medium of the selective catalytic reduction denitrification system according to embodiments of this application are described below with reference to the accompanying drawings.

[0022] Specifically, Figure 1 This is a schematic flowchart of a flow field optimization method for a selective catalytic reduction denitrification system provided in an embodiment of this application.

[0023] like Figure 1As shown, the flow field optimization method for the selective catalytic reduction (SCR) denitrification system is applied to the flue section of the catalyst layer in the system. An adjustable guide vane is installed within the flue section, and the guide vane's partition boundary divides the cross-section of the flue section into a first region and a second region. The method includes the following steps: In step S101, the average flow velocity of the first region and the second region is identified. It is understood that the embodiments of this application can obtain the velocity distribution characteristics of different spatial locations of the upstream flue section of the catalyst layer by identifying the average flow velocity of the first region and the second region, so as to facilitate the subsequent calculation of the regional flow ratio.

[0024] It should be noted that the first region and the second region are divided according to the partition boundary of the guide plate, and correspond to the typical airflow channels on the inner and outer sides of the flue respectively. That is, the first region corresponds to the flue cross-sectional area covered by the inner partition of the guide plate, and the second region corresponds to the flue cross-sectional area covered by the outer partition of the guide plate. The embodiments of this application are described in terms of the inner and outer sides.

[0025] In step S102, the regional flow ratio is calculated based on the average flow velocity of the first region and the second region, and an adjustment strategy is determined based on the regional flow ratio and the current decision threshold. The current decision threshold includes a first decision threshold and a second decision threshold, and the first decision threshold is greater than the second decision threshold. It is understood that the regional flow ratio can be obtained by calculating the ratio of the average flow velocity of the first region to that of the second region. This ratio quantitatively characterizes the flue gas flow distribution state between the inside and outside of the flue. The current decision threshold includes the first decision threshold as the upper limit and the second decision threshold as the lower limit. The two define the qualified range of the regional flow ratio, transforming the complex flow field uniformity problem into a simple threshold comparison decision. This gives the adjustment strategy a clear physical basis, avoids blind adjustment, and ensures that the direction of the guide vane movement is accurately matched with the flow field correction requirements.

[0026] It should be noted that, as Figure 2 As shown, this application arranges a flow velocity measurement array on the upstream section of the catalyst layer through an online flow field sensing module, dividing the flue section into an inner region and an outer region along the width direction, and calculating the regional flow ratio R in real time; where R is defined as the ratio of the average flow velocity in the inner region to the average flow velocity in the outer region; the division of the inner and outer regions is determined based on the partition boundary of the guide vane actuator to ensure that the measuring points and the adjustment partitions correspond in space.

[0027] The regional flow ratio can quantitatively reflect the degree of balance in the flow distribution between the inner and outer sides. When the average flow velocity on the inner side is relatively low, it indicates that the airflow is biased to both sides, which can easily lead to insufficient denitrification efficiency in the central area. When the average flow velocity on the inner side is relatively high, it indicates that the airflow is concentrated in the middle, which can easily lead to increased ammonia escape in the side wall area.

[0028] The correspondence between the measuring point area and the guide vane partition: The arrangement of the velocity measurement array (measuring points) must correspond one-to-one with the adjustment partition of the guide vane in space; that is, the measuring points in the inner area correspond to the inner guide vane partition, and the measuring points in the outer area correspond to the outer guide vane partition.

[0029] Adjustable baffle actuator module: Includes an adjustable baffle installed in the flue, divided into an inner section and an outer section along the width direction, with each section being independently adjustable. The baffle's sectioning structure is that the adjustable baffle in the flue is divided into two independent adjustment sections along the width direction: an inner section and an outer section; each section has an independent actuator, allowing for independent angle adjustment.

[0030] Method for determining zone boundaries: Assuming the flue width is W, the area from 0 to W / 2 of the guide vane in the width direction is the inner zone, and the area from W / 2 to W is the outer zone; therefore, the boundary between the inner and outer regions of the velocity measuring point should also be set at W / 2. In this way, the average velocity calculated from the measuring points in the inner region directly reflects the adjustment effect of the inner guide vane. The zone boundary refers to the physical boundary line of the adjustable guide vane actuator in the flue width direction, usually located at the midpoint of the flue width. The measuring points in the inner region correspond spatially to the inner guide vane zones, and the measuring points in the outer region correspond spatially to the outer guide vane zones, ensuring that the measured zone flow ratio R accurately reflects the actual effect of the guide vane adjustment.

[0031] For example, taking a 660MW unit: The inner zone is equipped with 6 guide vanes, primarily regulating the flue gas flow distribution in the central area of ​​the flue; the outer zone is also equipped with 6 guide vanes, primarily regulating the flue gas flow distribution near the wall. The guide vanes in both zones are driven by independent actuators, allowing for independent angle adjustment. The primary and secondary adjustment relationship between the inner and outer zones is determined through CFD simulation or on-site calibration tests: the zone with higher sensitivity to the regional flow ratio R is the primary adjustment target, and the other zone is the secondary adjustment target.

[0032] In this embodiment of the application, before determining the adjustment strategy based on the regional flow ratio and the current decision threshold, the process includes: extracting historical optimization data corresponding to the cumulative adjustment operations of the current operating cycle, wherein the historical optimization data are the regional flow ratio and flow field uniformity index after the adjustment has stabilized; screening target historical optimization data whose flow field uniformity index reaches a preset index threshold; if the total number of target historical optimization data reaches a preset number threshold, calculating the quantile of the regional flow ratio in all target historical optimization data, and updating the current decision threshold based on the quantile to generate the target decision threshold.

[0033] Among them, the preset index threshold is a critical value used to determine whether the uniformity of the flow field meets the standard. It can be set according to actual needs. For example, it is usually set to 15% without specific limitation.

[0034] It is understood that the embodiments of this application can obtain effective samples that enable the flow field to meet the standards in actual operation by extracting the regional flow ratio and flow field uniformity index after adjustment and stabilization within the current operating cycle, and screening out the target historical optimization data that the flow field uniformity index reaches the preset threshold. When the number of target data reaches the preset threshold, the quantiles of the regional flow ratio in these data are calculated. The obtained quantiles reflect the typical distribution range of the regional flow ratio under the standard operating conditions. The current decision threshold is updated with the quantiles to generate the target decision threshold, which is equivalent to adjusting the threshold to the statistical range of regional flow ratios that have been verified in history to enable the flow field to meet the standards. As the operating cycle accumulates, the newly included standard-compliant data continuously optimizes the quantile estimation, so that the decision threshold gradually approaches the true optimal range corresponding to the uniform flow field under the current flue structure. This effectively offsets the impact of long-term drift caused by ash accumulation, wear, etc., and ensures that the subsequent adjustment strategy is always based on the threshold that matches the actual operating conditions, thereby improving the adaptive capability and long-term stability of the flow field optimization.

[0035] It should be noted that the preset threshold is usually set at 15%, which is the generally accepted standard for flow field uniformity in SCR denitrification engineering practice. When the relative standard deviation of the monitoring section is less than or equal to 15%, it indicates that the flue gas velocity distribution is relatively uniform in space, and the flow field meets the requirements for efficient and stable operation of the denitrification system; when the relative standard deviation is greater than 15%, it indicates that the velocity distribution deviation is too large, and the guide vanes need to be adjusted.

[0036] In this embodiment of the application, after generating a target decision threshold by updating the current decision threshold based on quantiles, the process includes: if the target decision threshold is greater than or equal to a preset safety boundary, then applying the current decision threshold to the next operating cycle; calculating the change in the target decision threshold relative to the current decision threshold, and if the change exceeds a preset range of the current decision threshold, then applying the current decision threshold to the next operating cycle; after applying the target decision threshold to the next operating cycle, if the flow field compliance rate in the next operating cycle decreases by more than a preset ratio compared to the current cycle, then automatically reverting to the current decision threshold and applying the current decision threshold to the next operating cycle.

[0037] Among them, the preset safety boundary refers to the absolute limit range within which the decision threshold is allowed to change during the update process. It can be set according to actual needs without specific limitations.

[0038] It is understood that the embodiments of this application can perform multiple checks on the target decision threshold generated by quantile updates by setting preset safety boundaries, variation range limits, and a compliance rate rollback mechanism. When the target decision threshold exceeds the preset safety boundary, it indicates that the threshold has deviated from the physical range allowed by engineering. If it is forcibly applied, it may lead to flow field runaway. Therefore, the current threshold is maintained to ensure system safety. When the variation range of the target decision threshold relative to the current decision threshold exceeds the preset range, it indicates that the single update is too drastic and may cause oscillation or overshoot in the adjustment system. Therefore, the update is temporarily suspended to maintain the stability of the adjustment process. In the next operating cycle after the actual application of the target decision threshold, if the flow field compliance rate decreases by more than a preset ratio compared to the current cycle, it indicates that although the threshold is based on historical data statistics, it has failed to adapt to recent changes in operating conditions or has statistical bias. At this time, it automatically rolls back to the current decision threshold to avoid long-term performance degradation caused by an improper update. The above mechanisms together constitute a safety constraint closed loop for decision threshold updates, ensuring that while the self-learning process improves the flow field optimization effect, it always maintains the gradualness, safety, and reversibility of threshold changes, prevents threshold drift out of control, and ensures long-term stable operation of the system.

[0039] It should be noted that the preset safety boundary is set based on the initial decision threshold, for example, ±30% of the initial threshold. Assuming the initial decision threshold is [0.85, 1.15], the safety boundary is a lower limit of no less than 0.70 (0.85 × 70%) and an upper limit of no more than 1.30 (1.15 × 130%). Any new decision threshold calculated using quantiles that is lower than 0.70 or higher than 1.30 is considered to exceed the safety boundary, and the system will reject the update, maintaining the current threshold for continued use.

[0040] The physical meaning of this safety boundary is as follows: when the regional flow ratio R exceeds the range of 0.70 to 1.30, it indicates that the air flow distribution in the flue has been seriously unbalanced. At this time, even if it is recommended to update the threshold based on the statistical results of historical compliance data, the representativeness of the sample data or the abnormal change of the flue structure should be suspected. Forcing the threshold to be extended to such extreme ranges may cause the system to still be judged as qualified when the flow field is seriously uneven, thus losing the adjustment ability. Therefore, a preset safety boundary is used as a hard constraint for threshold self-learning to ensure that the decision threshold is always within the physically acceptable range of the project and guarantee the basic safety of system operation.

[0041] Specifically, as Figure 4 shown, after running for the preset period, the self-learning correction unit executes: (1) Extract historical records according to load intervals; (2) Screen compliance records: the screening condition is that the relative standard deviation after adjustment < 15% (independent of the current threshold to avoid circular verification); (3) If the number of compliance records ≥ the preset value, set the 10% quantile of the R value as the new second decision threshold and the 90% quantile as the new first decision threshold.

[0042] (4) Safety constraint: The new threshold must be within the preset safety boundary (such as ±30% of the initial threshold), and the single change amplitude does not exceed ±15% of the initial threshold; if the new threshold causes the compliance rate in the next cycle to decrease by more than 5%, automatically roll back to the previous version threshold.

[0043] The updated threshold replaces the initial value and is used for subsequent feedback decisions. Through this mechanism, the decision threshold always covers the actual R value distribution that makes the flow field reach the standard under the current working conditions, offsetting the influence of long-term drift.

[0044] In the embodiment of this application, the adjustment strategy is determined according to the regional flow ratio and the current decision threshold, including: identifying the sensitivities of the first region and the second region to the regional flow ratio; determining the adjustment step sizes corresponding to the first region and the second region according to the sensitivities; and determining the adjustment strategy according to the sensitivities, the regional flow ratio, the adjustment step sizes and the decision threshold.

[0045] It is understood that the embodiments of this application can quantify the impact of changes in the angle of the guide vanes in each region on the flow distribution by identifying the sensitivity of the first and second regions to the regional flow ratio. Based on the sensitivity difference, a larger main adjustment step size is assigned to the high-sensitivity region, allowing it to undertake the main adjustment task, while a smaller auxiliary adjustment step size is assigned to the low-sensitivity region, allowing it to only play a supporting role. After determining the step size, sensitivity, real-time regional flow ratio, adjustment step size, and decision threshold are all incorporated into the strategy formulation: when the regional flow ratio exceeds the upper limit of the threshold, if the contribution of the high-sensitivity region to the flow ratio is positive, its angle is reduced and the angle of the low-sensitivity region is increased to quickly reduce the ratio; if the contribution is negative, the opposite operation is performed; the same applies when the regional flow ratio is below the lower limit of the threshold. This mechanism ensures that each adjustment is led by the high-sensitivity region with a large step size to guide the correction direction, while the low-sensitivity region compensates for side effects with a small step size, thereby pulling the regional flow ratio back to the qualified range within the shortest number of adjustment steps, while avoiding mutual cancellation or overshoot oscillation that may be caused by equal step size adjustments in both regions, significantly improving the convergence speed and stability of flow field optimization.

[0046] Specifically, such as Figure 3 As shown, based on the comparison between the real-time monitored R value and the current decision threshold, the guide vane angle is adjusted: when the area flow ratio is lower than the lower limit threshold, it is determined that the inner flow is insufficient; at this time, the zone with higher sensitivity to the area flow ratio is taken as the primary adjustment target, mainly increasing the guide vane angle of that zone; at the same time, the zone with lower sensitivity is taken as the secondary adjustment target, assisting in reducing its guide vane angle. This operation aims to increase the inner flow and reduce the outer flow, so that the area flow ratio returns to the threshold range.

[0047] When the zone flow ratio exceeds the upper threshold, it is determined that there is excess flow on the inner side. In this case, the zone with higher sensitivity is the primary adjustment target, mainly by reducing its deflector angle; simultaneously, the zone with lower sensitivity is the secondary adjustment target, assisting by increasing its deflector angle. This operation aims to reduce the inner flow and increase the outer flow, causing the zone flow ratio to fall back within the threshold range.

[0048] When the regional flow ratio is between the lower and upper thresholds, the flow field is deemed to meet the standard, and the current guide vane angle remains unchanged.

[0049] The main adjustment step size is fixed at 2°, and the auxiliary adjustment step size is fixed at 1°, with the adjustment range of the outer zone not exceeding 50% of the adjustment range of the inner zone. That is, when the inner flow is insufficient (R is too small), it indicates that the inner resistance is too high or the flow area is too small. Increasing the angle of the inner guide vane can guide more flue gas into the inner side; simultaneously, it helps to reduce the outer angle to prevent the outer flow from becoming excessive. The two work together to increase the R value. When the inner flow is excessive (R is too large), the opposite operation is performed: reducing the inner angle and helping to increase the outer angle, together reducing the R value.

[0050] The primary and secondary relationship is not fixed, with the inner or outer side always taking the lead. Instead, the sensitivity coefficients of the inner and outer zones to the regional flow ratio are pre-determined through computational fluid dynamics simulations or field calibration tests. The zone with higher sensitivity is designated as the primary control target, and the other zone as the secondary control target. For example, if the inner zone is more sensitive than the outer zone, then the inner zone is the primary control target and the outer zone is the secondary control target; conversely, if the inner zone is less sensitive than the outer zone, then the outer zone is the primary control target and the inner zone is the secondary control target. This differentiated step size and primary-secondary coordination mechanism ensure that each adjustment brings the regional flow ratio back to the acceptable range with maximum efficiency, avoiding ineffective adjustment or flow field oscillations.

[0051] In this embodiment, the adjustment strategy is determined based on sensitivity, regional flow ratio, adjustment step size, and decision threshold, including: if the regional flow ratio is greater than a first decision threshold, the guide vane angle is gradually decreased with the adjustment step size corresponding to the region with high sensitivity, and the guide vane angle is gradually increased with the adjustment step size corresponding to the region with low sensitivity; if the regional flow ratio is less than a first decision threshold, the guide vane angle is gradually increased with the adjustment step size corresponding to the region with high sensitivity, and the guide vane angle is gradually decreased with the adjustment step size corresponding to the region with low sensitivity.

[0052] It is understood that the embodiments of this application can form a directional adjustment rule by jointly deciding on sensitivity, regional flow ratio, adjustment step size, and decision threshold; when the regional flow ratio exceeds the first decision threshold, i.e., the upper limit, it indicates that the inner flow is relatively excessive and the regional flow ratio needs to be reduced; at this time, the region with high sensitivity is used as the main adjustment target, and its guide vane angle is gradually reduced to reduce the equivalent effect of the inner flow or increase the outer flow, while the region with low sensitivity is used as the secondary adjustment target, and its guide vane angle is gradually increased to assist in completing the flow redistribution; when the regional flow ratio is lower than the second decision threshold... When the threshold, or lower limit, is reached, it indicates that the flow rate on the inner side is relatively insufficient, and the flow ratio of the region needs to be increased. At this time, the guide vane angle of the high-sensitivity region is gradually increased, while the guide vane angle of the low-sensitivity region is gradually decreased. This rule uses the large step size of the high-sensitivity region to quickly change the dominant direction of the flow ratio of the region, and uses the small step size of the low-sensitivity region for fine-tuning. This ensures that no matter whether the flow ratio of the region is too high or too low, the adjustment action always points in the direction that brings the ratio back to the threshold range. The primary and secondary actions are clearly distinguished and the step sizes are matched, so as to achieve flow field correction with the fewest number of adjustments and avoid directional errors and ineffective adjustments.

[0053] In step S103, the current unit load is obtained, and the reference guide vane angle values ​​corresponding to the first and second regions are determined based on the current unit load. The actual guide vane angle is gradually adjusted to the reference guide vane angle using an adjustment strategy. It is understood that the embodiments of this application can achieve proactive prediction of changes in operating conditions by obtaining the current unit load and determining the reference guide vane angle values ​​corresponding to the first and second regions. The current actual angle is gradually adjusted to the reference angle instead of jumping directly, avoiding flow field oscillations and local turbulence caused by sudden angle changes. During the gradual adjustment process, it is ensured that the system can respond to changes in operating conditions in seconds in coal-fired units with frequent load fluctuations, and can smoothly transition to the target angle, thereby maintaining the continuous uniformity of the upstream flow field of the catalyst layer and improving the dynamic response capability and operational stability of the denitrification system.

[0054] It should be noted that when a change in unit load triggers the issuance of a feedforward command, the system compares the target feedforward angle with the current actual angle, gradually approaching the target angle in steps not exceeding the main control step size, thus avoiding flow field oscillations caused by angle jumps. After the feedforward command is executed, the feedback control mechanism is activated normally, and subsequent adjustments are made based on the feedforward command for fine-tuning.

[0055] In this embodiment of the application, determining the reference guide vane angle values ​​corresponding to the first region and the second region based on the current unit load includes: obtaining a first mapping relationship table, wherein the first mapping relationship table is a mapping relationship table between the unit load and the reference guide vane angle values; using the current unit load as an index, querying the first mapping relationship table to determine the reference guide vane angle values ​​corresponding to the first region and the second region.

[0056] It is understood that, in this embodiment of the application, a first mapping table between unit load and reference guide vane angle value can be pre-established to store the ideal guide vane angle for uniform flow field under different load conditions in data form. When the current unit load is obtained, the mapping table can be directly queried using the load as an index to quickly determine the reference guide vane angle value corresponding to the first and second regions. This mapping table is usually constructed based on the computational fluid dynamics simulation results of typical load conditions. For atypical loads, it can be supplemented by interpolation to ensure the continuity and accuracy of the reference angle across the entire load range. The query method achieves a second-level response of feedforward control, enabling the guide vane angle to be adjusted in real time with load changes, avoiding the lag of feedback adjustment, while reducing the computing power consumption of real-time calculation on the control system, and improving the tracking speed of flow field optimization for load fluctuations and engineering practicality.

[0057] Specifically, such as Figure 3As shown in the figure, a computational fluid dynamics simulation preset database is established for typical load conditions as the first mapping table, as shown in Table 1 below. Among them, the inner angle is the current deflection angle of the second-region deflector, and the inner partition corresponds to the deflector blades near the flue center. The outer angle is the current deflection angle of the first-region partition deflector. When the load changes, the corresponding preset angle is read according to the real-time load (linear interpolation for intermediate loads) and issued as an initial command to achieve a second-level response. Among them, linear interpolation: when the real-time load P is between two known load points P1 and P2 (P1 < P < P2), the interpolation calculation is as follows: inner angle = α1 + (α2 - α1) × (P - P1) / (P2 - P1); outer angle = β1 + (β2 - β1) × (P - P1) / (P2 - P1); where: P1, P2 are adjacent preset load points; α1, α2 are the corresponding inner angles; β1, β2 are the corresponding outer angles.

[0058]

[0059] In summary, as Figure 5 shown, compared with the fixed threshold scheme, after the same unit has been running for 6 months, the passing rate of the flow field has increased from 85% to 96%, the average monthly adjustment times have decreased from 28 to 18, the deviation of the velocity distribution is stably controlled below 15% and continuously optimized with the running time. At the same time, through the safety constraint and fallback mechanism, the self-learning process is ensured to be stable and reliable, effectively avoiding the out-of-control threshold drift.

[0060] According to the flow field optimization method of the selective catalytic reduction denitration system proposed in the embodiments of the present application, by dividing the flue duct section upstream of the catalyst layer into a first region and a second region, the average flow velocities of the two regions are identified in real time and the regional flow ratio is calculated. At the same time, the reference deflector angle value corresponding to the current unit load is obtained, and the regional flow ratio is compared with the current decision thresholds (including the first decision threshold and the second decision threshold) to determine the adjustment strategy, and the current actual deflector angle is gradually adjusted to the reference deflector angle with this adjustment strategy. Since the regional flow ratio reflects the uniform degree of the flow velocity distribution inside and outside the flue duct cross-section, and the decision thresholds dynamically define the allowed deviation range, when the regional flow ratio exceeds the threshold, the adjustment strategy triggers the adjustment of the deflector angle to make the regional flow ratio return within the threshold; at the same time, the reference deflector angle is preset based on the load to ensure that the adjustment direction matches the change of the working condition. The two work together to enable the flow field optimization to quickly respond to the load change and accurately correct the local flow velocity deviation, thereby maintaining the uniformity of the flow field upstream of the catalyst layer for a long time, improving the denitration efficiency and reducing ammonia escape.

[0061] Next, the flow field optimization method of the selective catalytic reduction denitration system of the present application will be elaborated in detail in combination with Figure 3 specific embodiments as follows: (1) Taking a 660MW coal-fired unit as an example, this application arranges 20 velocity measurement points at a cross section 0.8m upstream of the catalyst. The flow channels are divided into an inner region (corresponding to the inner guide plate section) and an outer region (corresponding to the outer guide plate section) according to the width direction of the flue. Each region has 10 measurement points. The guide plates are divided into an inner section (6 pieces) and an outer section (6 pieces).

[0062] It should be noted that the first mapping table (pre-configured database) is as follows:

[0063] Determination of primary and secondary control relationship: Through computational fluid dynamics simulation analysis, the sensitivity coefficient of the inner partition to the R value is 0.82, and that of the outer partition is 0.31. Therefore, the inner partition is set as the primary control object, and the outer partition as the secondary control object.

[0064] (2) Threshold self-learning process: Initial threshold: 0.85~1.15 (safety boundary preset to 0.70~1.30) 1) First iteration (after 30 days of operation): Twenty records met the standard in the 50%-75% load range (screening criteria: adjusted RSD < 15%, independent of the current threshold), with the following R value distribution: 0.78, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.98.

[0065] The 10th percentile of the R-value distribution is 0.81, and the 90th percentile is 0.96. Therefore, the new decision threshold is 0.81 to 0.96 (within the safety margin range of 0.70 to 1.30, the variation is within the limit and is updated). 2) Second iteration (after 90 days of operation): Thirty records met the standards, and the R-value distribution further converged to 0.82–0.94, with the 10th percentile at 0.83 and the 90th percentile at 0.93.

[0066] After six months of operation, the compliance rate of this solution stabilized at over 96%, and the number of adjustments decreased from an average of 28 times per month for the fixed threshold solution to 18 times, significantly improving system stability.

[0067] In summary, this application's system adopts a three-layer architecture of "feedforward pre-setting + primary and secondary collaborative feedback + threshold self-learning": When the load changes, it reads the angle combination from the CFD pre-set database; based on the deviation of the regional flow ratio R, it uses the more sensitive area as the primary adjustment target and the other area as the secondary adjustment target for feedback adjustment; it accumulates historical data and dynamically updates the decision threshold using the statistical quantile of the compliant records, while applying safety constraints. Through the adaptive evolution of the decision threshold, it solves the problem that fixed parameters cannot offset long-term operational drift, achieving continuous improvement in flow field optimization over operating time, and is suitable for SCR denitrification systems in coal-fired units.

[0068] Next, with reference to the accompanying drawings, a flow field optimization device for a selective catalytic reduction denitrification system according to an embodiment of this application is described.

[0069] Figure 6 This is a block diagram of the flow field optimization device of the selective catalytic reduction denitrification system according to an embodiment of this application.

[0070] like Figure 6 As shown, the flow field optimization device 10 of the selective catalytic reduction denitrification system is applied to the flue section of the catalyst layer of the selective catalytic reduction denitrification system. An adjustable guide plate is provided in the flue section. The partition boundary of the guide plate divides the cross section of the flue section of the catalyst layer into a first region and a second region. The device 10 includes: an identification module 100, a calculation module 200 and an adjustment module 300.

[0071] The identification module 100 is used to identify the average flow velocity of the first region and the second region; the calculation module 200 is used to calculate the region flow ratio based on the average flow velocity of the first region and the second region, and to determine the adjustment strategy based on the region flow ratio and the decision threshold, wherein the decision threshold includes a first decision threshold and a second decision threshold, and the first decision threshold is greater than the second decision threshold; the adjustment module 300 is used to obtain the current unit load, determine the reference guide vane angle value corresponding to the first region and the second region based on the current unit load, and gradually adjust the current actual guide vane angle to the reference guide vane angle using the adjustment strategy.

[0072] like Figure 2 As shown, the system of this application also includes an online flow field sensing module, an adjustable guide vane actuator module, an intelligent control and decision-making module, and a system integration and communication module.

[0073] The online flow field sensing module is located at the upstream section of the catalyst layer and contains a flow velocity measurement array to collect the flow velocity at each measuring point in real time and calculate the flow ratio R between the inner and outer regions based on the boundary of the guide vane partition.

[0074] The adjustable deflector actuator module includes an inner section (e.g., 6 sections) and an outer section (e.g., 6 sections) divided along the width of the flue. Each section is equipped with an independent drive device, which can receive angle commands and independently adjust the deflection angle of the deflector.

[0075] The intelligent control and decision-making module has a built-in computational fluid dynamics simulation pre-set database, flow field evaluation model, and optimization decision-making unit. This module receives the regional flow ratio R from the online flow field sensing module and the unit load signal from the system integration and communication module. It obtains the feedforward angle by querying the pre-set database, judges the flow field uniformity through the flow field evaluation model, and generates primary and secondary coordinated feedback adjustment commands through the optimization decision-making unit.

[0076] The system integration and communication module is responsible for data communication with the power plant's distributed control system, acquiring unit load signals in real time, and sending angle commands generated by the control module to the guide vane actuator module. At the same time, it transmits the operating status and historical data back to the distributed control system.

[0077] The four modules mentioned above form a closed loop through data flow and control commands: the sensing module collects the flow velocity and calculates the R value; the decision module combines the load signal and the R value to generate feedforward and feedback commands; the communication module transmits the commands; the execution module adjusts the angle of the guide vane; the adjusted flow field changes are captured again by the sensing module, thereby achieving continuous adaptive optimization.

[0078] According to the flow field optimization device of the selective catalytic reduction denitrification system proposed in this application, the upstream flue section of the catalyst layer is divided into a first region and a second region. The average flow velocity of the two regions is identified in real time and the region flow ratio is calculated. At the same time, the reference guide vane angle value corresponding to the current unit load is obtained. The region flow ratio is compared with the current decision threshold (including the first decision threshold and the second decision threshold) to determine the adjustment strategy. The current actual guide vane angle is gradually adjusted to the reference guide vane angle according to the adjustment strategy. Since the region flow ratio reflects the uniformity of the flow velocity distribution on the inner and outer sides of the flue section, the decision threshold dynamically defines the allowable deviation range. When the region flow ratio exceeds the threshold, the adjustment strategy triggers the guide vane angle adjustment to bring the region flow ratio back to within the threshold. At the same time, the reference guide vane angle is preset based on the load to ensure that the adjustment direction matches the change of operating conditions. The two work together to enable the flow field optimization to respond quickly to load changes and accurately correct local flow velocity deviations, thereby maintaining the uniformity of the upstream flow field of the catalyst layer in the long term, improving denitrification efficiency and reducing ammonia slip.

[0079] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 701, the processor 702, and the computer program stored on the memory 701 and executable on the processor 702.

[0080] When the processor 702 executes the program, it implements the flow field optimization method for the selective catalytic reduction denitrification system provided in the above embodiments.

[0081] Furthermore, electronic devices also include: Communication interface 703 is used for communication between memory 701 and processor 702.

[0082] The memory 701 is used to store computer programs that can run on the processor 702.

[0083] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0084] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0085] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0086] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0087] This application also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the flow field optimization method for the selective catalytic reduction denitrification system described above.

[0088] This application also provides a computer program product, including a computer program or instructions, which, when executed, implement the flow field optimization method for the selective catalytic reduction denitrification system described above.

[0089] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0090] 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 number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0091] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0092] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0093] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A flow field optimization method for a selective catalytic reduction denitrification system, characterized in that, The method is applied to the flue section of the catalyst layer in a selective catalytic reduction denitrification system, wherein an adjustable guide vane is provided within the flue section, and the partition boundary of the guide vane divides the cross-section of the flue section of the catalyst layer into a first region and a second region, wherein the method includes the following steps: Identify the average flow velocity in the first and second regions; The regional flow ratio is calculated based on the average flow velocity of the first region and the second region. An adjustment strategy is determined based on the regional flow ratio and the current decision threshold. The current decision threshold includes a first decision threshold and a second decision threshold, and the first decision threshold is greater than the second decision threshold. Obtain the current unit load, determine the reference guide vane angle values ​​corresponding to the first and second regions based on the current unit load, and gradually adjust the current actual guide vane angle to the reference guide vane angle using the adjustment strategy.

2. The flow field optimization method for the selective catalytic reduction denitrification system according to claim 1, characterized in that, The step of determining the adjustment strategy based on the regional flow ratio and the current decision threshold includes: Sensitivity of identifying the first and second regions to the regional flow ratio; The adjustment step size corresponding to the first region and the second region is determined based on the sensitivity. The adjustment strategy is determined based on the sensitivity, the regional flow ratio, the adjustment step size, and the decision threshold.

3. The flow field optimization method for the selective catalytic reduction denitrification system according to claim 2, characterized in that, The step of determining the adjustment strategy based on the sensitivity, the regional flow ratio, the adjustment step size, and the decision threshold includes: If the flow ratio of the region is greater than the first decision threshold of the decision threshold, then the guide vane angle is gradually reduced with the adjustment step size corresponding to the region with high sensitivity, and the guide vane angle is gradually increased with the adjustment step size corresponding to the region with low sensitivity. If the flow ratio of the region is less than the first decision threshold, then the guide vane angle is gradually increased with the adjustment step size corresponding to the region with high sensitivity, and the guide vane angle is gradually decreased with the adjustment step size corresponding to the region with low sensitivity.

4. The flow field optimization method for the selective catalytic reduction denitrification system according to claim 3, characterized in that, The step of determining the reference guide vane angle values ​​corresponding to the first and second regions based on the current unit load includes: Obtain the first mapping table, which is a mapping table between unit load and reference guide vane angle value; Using the current unit load as an index, query the first mapping table to determine the reference guide vane angle values ​​corresponding to the first and second regions.

5. The flow field optimization method for the selective catalytic reduction denitrification system according to claim 1, characterized in that, Before determining the adjustment strategy based on the regional flow ratio and the current decision threshold, the following steps are included: Extract the historical optimization data corresponding to the cumulative adjustment operations of the current operating cycle. The historical optimization data includes the regional flow ratio and flow field uniformity index after the adjustment has stabilized. Filter historical optimization data that achieve the preset threshold for the flow field uniformity index; If the total number of target historical optimization data reaches a preset threshold, then the quantile of the regional flow ratio in all target historical optimization data is calculated, and the current decision threshold is updated based on the quantile to generate the target decision threshold.

6. The flow field optimization method for the selective catalytic reduction denitrification system according to claim 5, characterized in that, After generating the target decision threshold by updating the current decision threshold based on the quantile, the process includes: If the target decision threshold is greater than or equal to the preset safety boundary, the current decision threshold will be applied to the next running cycle. Calculate the change in the target decision threshold relative to the current decision threshold. If the change exceeds a preset range of the current decision threshold, then apply the current decision threshold to the next running cycle. After applying the target decision threshold to the next operating cycle, if the flow field compliance rate in the next operating cycle decreases by more than a preset ratio compared to the current cycle, the current decision threshold will be automatically reverted and applied to the next operating cycle.

7. A flow field optimization device for a selective catalytic reduction denitrification system, characterized in that, The device is applied to the flue section of the catalyst layer in a selective catalytic reduction denitrification system. An adjustable guide vane is installed within the flue section, and the guide vane's partition boundary divides the cross-section of the flue section of the catalyst layer into a first region and a second region. The device includes: An identification module is used to identify the average flow velocity in the first and second regions. The calculation module is used to calculate the regional flow ratio based on the average flow velocity of the first region and the second region, and to determine the adjustment strategy based on the regional flow ratio and the decision threshold, wherein the decision threshold includes a first decision threshold and a second decision threshold, and the first decision threshold is greater than the second decision threshold; The adjustment module is used to obtain the current unit load, determine the reference guide vane angle values ​​corresponding to the first and second regions based on the current unit load, and gradually adjust the current actual guide vane angle to the reference guide vane angle using the adjustment strategy.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the flow field optimization method for a selective catalytic reduction denitrification system as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by the processor, they are used to implement the flow field optimization method for the selective catalytic reduction denitrification system as described in any one of claims 1-6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed, they implement the flow field optimization method for the selective catalytic reduction denitrification system as described in any one of claims 1-6.

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