SCR denitrification process precise ammonia injection and sampling system

Abstract

This invention discloses a precise ammonia injection and sampling system for SCR denitrification processes, relating to the field of industrial flue gas treatment technology. It includes a flow field optimization module, a distributed sampling module, an intelligent analysis and control module, a cyclic measurement module, an execution module, and a remote monitoring module. These modules form a closed loop of simulation-evaluation-sampling-control-feedback. Through dynamic adaptive multi-dimensional simulation by the flow field optimization module, combined with a multi-scale grid pool and a mechanical delay compensation mechanism for the ammonia injection valve, the uniformity of the flue gas cross-sectional velocity distribution is ≥95%. The intelligent analysis and control module, relying on a PID neural network algorithm and triple control logic, can flexibly switch adjustment modes according to NOx concentration deviations, stabilizing the deviations within ≤10mg / m³. It can also activate a compensation mode based on the catalyst activity state, significantly improving denitrification efficiency and avoiding resource waste caused by excessive ammonia injection and denitrification failure due to insufficient ammonia in traditional processes.

Description

Technical Field

[0001] This invention relates to the field of industrial flue gas treatment technology, and in particular to a precision ammonia injection and sampling system for SCR denitrification processes. Background Technology

[0002] In the field of industrial flue gas treatment, selective catalytic reduction (SCR) denitrification technology is the core technology for controlling nitrogen oxide (NOx) emissions and is widely used in industries such as power, chemical, and metallurgy. However, with the increasing complexity of operating conditions, traditional SCR denitrification processes suffer from uneven velocity distribution due to load fluctuations and flue structure affecting the flue gas flow field within the reactor, resulting in vortex zones in some areas. Furthermore, traditional sampling systems employ a "uniform distribution" approach, failing to consider concentration gradient differences, leading to insufficient sampling in high-gradient areas and redundancy in low-gradient areas. This fails to eliminate ±30% of flue gas turbulence pulsation interference. The sampling hardware lacks heating, anti-ash accumulation, and automatic purging designs, resulting in data deviations exceeding 10% from the actual concentration. This makes it impossible to provide a reliable basis for ammonia injection adjustment, creating a vicious cycle of "blind ammonia injection - inaccurate data - control failure." Additionally, traditional systems rely on fixed PID algorithms without incorporating parameters such as AMI and catalyst activity, failing to compensate in a timely manner when catalyst activity is below 80%. The flow field simulation uses a fixed grid, which reduces accuracy under large operating conditions and lacks a self-learning mechanism, leading to a decline in adjustment accuracy over long-term operation. Remote control also lacks hierarchical access control and dual-person confirmation, easily causing operational errors and safety accidents. Summary of the Invention

[0003] The purpose of this invention is to provide a precise ammonia injection and sampling system for SCR denitrification process, which solves the technical problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a precision ammonia injection and sampling system for SCR denitrification process, including a flow field optimization module, a distributed sampling module, an intelligent analysis and control module, a cyclic measurement module, an execution module, and a remote monitoring module. Each module forms a simulation-evaluation-sampling-control-feedback closed loop, with the feedback priority being to first correct the ammonia injection command and then adjust the flow field simulation parameters.

[0005] The flow field optimization module optimizes the flue gas flow field, ammonia flow field and gas-gas mixture flow field in the reactor through dynamic adaptive multi-dimensional simulation, including operating condition perception, dynamic switching of multi-scale grids and mechanical delay compensation mechanism of ammonia injection valve.

[0006] Distributed sampling modules are deployed at the reactor inlet, mixing, and outlet sections, with sampling points arranged according to the principle of "intensified sampling in high gradient regions and sparse sampling in low gradient regions," where the high gradient regions represent the concentration gradient. In the region where the gradient is low, the region is a concentration gradient. In the region, a "multi-point synchronization + polling" sampling mode is adopted and wavelet transform is used to eliminate turbulent pulsation interference;

[0007] The intelligent analysis and control module receives sampled data and generates ammonia injection adjustment commands based on the quantitative index of ammonia-fume mixing efficiency, the PID neural network algorithm model, and the catalyst activity state.

[0008] The circulating measurement module is located at the catalyst outlet and monitors gas parameters according to the concentration gradient priority.

[0009] The execution module regulates the ammonia injection rate by controlling the valves;

[0010] The remote monitoring module displays system parameters in real time, and the distributed sampling data is transmitted to the intelligent analysis and control module in real time. The cyclic measurement data is used for verification, and the intelligent analysis and control module sends instructions based on the verification data.

[0011] Preferably, the flow field optimization module includes:

[0012] The flue gas flow guiding submodule employs dynamic adaptive computational fluid dynamics technology, defining an "operating condition fluctuation intensity factor" to quantify real-time fluctuations in flue gas velocity, temperature, NOx, and NH3 concentrations. The calculation formula for the operating condition fluctuation intensity factor is as follows: ,in For flow velocity fluctuation value, As the reference flow rate, For temperature fluctuation values, As the reference temperature, NOx concentration fluctuation value, Using the baseline concentration, a "multi-scale grid pool" containing coarse, medium, and fine grids was established. The coarse grid corresponds to the operating condition of F≤0.1, the medium grid corresponds to the operating condition of 0.1<F≤0.3, and the fine grid corresponds to the operating condition of F>0.3. Automatic grid switching was triggered by online sensors such as Pitot tube velocity measurement and infrared temperature measurement. A mechanical delay compensation of 0.5~2s for the ammonia injection valve was introduced. Based on the simulation results, the three-dimensional layout of the guide vane and the tilt angle of 30°-60° were designed to make the uniformity of flue gas cross-section velocity distribution ≥95%.

[0013] The ammonia injection optimization submodule plans the orifice diameter gradient distribution and spatial density based on the ammonia flow field simulation results and AMI index.

[0014] The hybrid enhancement submodule is equipped with a detachable static mixer. The blade angle is adjusted in real time based on the sampling data of the mixing section and the feedback of the AMI index to maintain AMI≥0.85.

[0015] Preferably, the distributed sampling module includes:

[0016] The inlet sampling unit collects the original flue gas NOx concentration and oxygen content, and the sampling points avoid the eddy region with a flow velocity of <0.5m / s;

[0017] The mixed section sampling unit is located 1-2m downstream of the ammonia injection grid to collect NOx concentration and ammonia slip concentration. One sampling point is set every 0.3m in the high gradient zone and one sampling point is set every 1.5m in the low gradient zone.

[0018] The sampling unit at the outlet section is located 0.5-1m downstream of the catalyst layer. It collects flue gas parameters after the reaction by using "multi-point synchronous sampling + polling sampling". After collecting at least 100 sets of data, wavelet transform is used to eliminate ±30% turbulent fluctuation interference. The wavelet transform uses db4 wavelet basis and 3-level decomposition. The threshold is 1.5 times the standard deviation to obtain the time-averaged concentration.

[0019] Each sampling unit is equipped with a heated sampling tube, an automatic purging device that purges every 5 minutes at a pressure of 0.6-0.8 MPa, and a tapered anti-dust probe made of Inconel 625 with a temperature resistance of ≤600℃. The probe has a built-in water-cooled jacket, and the cooling medium is deionized water with a flow rate of 0.5-1 L / min, ensuring that the sampling data deviates from the actual ammonia escape by less than 3%.

[0020] Preferably, the sampling points of the mixed segment sampling unit are set to satisfy the following:

[0021] Based on both reactor cross-sectional area and concentration gradient, the high gradient region represents the concentration gradient. In the region where the gradient is low, the region is a concentration gradient. In the high gradient region, one sampling point is set every 0.3m, and in the low gradient region, one sampling point is set every 1.5m, avoiding the eddy current region with a flow velocity <0.5m / s;

[0022] The sampling tube is inserted to a depth of 1 / 3 to 2 / 3 of the pipe diameter, and the sampling direction is arranged at a 45° angle to the flue gas flow direction, with the same cross section being uniformly distributed in a matrix pattern.

[0023] The sampled data were processed by wavelet transform to eliminate turbulence and fluctuation interference. The wavelet transform used a db4 wavelet basis and a 3-level decomposition, with a threshold of 1.5 times the standard deviation to ensure data authenticity.

[0024] Preferably, the intelligent analysis and control module includes:

[0025] The data preprocessing unit performs temperature and humidity compensation, 3σ criterion outlier removal, and wavelet transform to eliminate ±30% turbulent fluctuation interference on the sampled data. The wavelet transform uses the db4 wavelet basis and 3-level decomposition, with a threshold of 1.5 times the standard deviation.

[0026] The concentration field reconstruction unit, combined with the dynamic adaptive CFD simulation results, uses the Kriging interpolation algorithm to construct a NOx concentration distribution cloud map of the reactor cross section with a spatial resolution of ≤0.5m×0.5m;

[0027] The adjustment decision unit adopts a PID neural network algorithm. Based on the NOx concentration deviation, deviation change rate, AMI index and catalyst activity status, it outputs instructions. When the catalyst activity retention rate is <80%, the compensation mode is activated, and a mechanical delay compensation of 0.5~2s for the ammonia injection valve is introduced simultaneously.

[0028] The self-learning unit trains the model based on historical data of NOx concentration, ammonia slip, AMI index, and catalyst activity over the past 30 days. The parameters are updated every 24 hours to optimize the adjustment accuracy and the matching degree of catalyst-ammonia injection strategy. The updated parameters are then synchronously fed back to the grid switching logic of the flow field optimization module.

[0029] Preferably, the adjustment decision unit is equipped with triple control logic:

[0030] When the NOx concentration deviation is >50mg / m³, the rapid adjustment mode is activated, and the valve is adjusted by 5%-10% / s until the deviation is ≤30mg / m³.

[0031] When the deviation is ≤50mg / m³, the fine adjustment mode is activated, with an adjustment range of 0.5%-2% / s, to stabilize the deviation at ≤10mg / m³.

[0032] When AMI < 0.85, the mixing-ammonia injection coordinated adjustment is triggered. First, the angle of the static mixer blades is adjusted, with an adjustment range of 5°-10° / time. If AMI does not rise to ≥ 0.85 within 30 seconds, the ammonia injection amount is corrected according to the fine adjustment mode.

[0033] Preferably, the cyclic measurement module includes:

[0034] The movable sampling probe can switch points along a preset track with a switching response time of ≤3s. The track covers the high and low gradient areas of the exit section. The probe structure and materials are consistent with the distributed sampling unit.

[0035] The measurement sequence generation unit, based on the NOx concentration gradient of each point in the past hour, sets the sampling frequency of the high gradient area to be 2-3 times that of the low gradient area, and completes a full cross-section polling once every 1 minute;

[0036] The backflush cleaning unit automatically performs a 3-second high-pressure purge after each point switch, with a purge pressure of 0.6-0.8 MPa. It also performs an additional purge every 5 minutes to remove particles from the probe.

[0037] The data storage unit stores time-averaged concentration data processed by wavelet transform in real time for AMI calculation and trend analysis.

[0038] Preferably, the polling measurement module and the distributed sampling module form a triple data verification mechanism:

[0039] When the NOx concentration deviation is greater than 10%, the ammonia slip concentration deviation is greater than 8%, or the AMI index deviation is greater than 5% in the same area, the self-test program will be automatically started.

[0040] The self-test sequentially checks the patency of the sampling pipeline, the calibration status of the analyzer, the accuracy of the probe position, and the dynamic adaptive CFD grid switching logic. The patency of the sampling pipeline is judged by the purge pressure decay rate; decay ≤10% / min indicates patency.

[0041] Preferably, the execution module includes:

[0042] The control valve is electro-hydraulic servo driven, with a working pressure of 10-15MPa. It has a built-in high-frequency response valve core, and the full stroke adjustment time is ≤0.5s. The flow characteristics are matched with the orifice gradient of the ammonia injection grid.

[0043] The intelligent locator uses digital signal processing technology, with a positioning accuracy of ≤0.1%FS. It supports high-speed PWM control signals with a frequency of 10-20kHz and receives AMI index feedback in real time.

[0044] The dual-channel feedback unit uses a dual-parameter feedback mechanism consisting of an absolute encoder and a pressure sensor to input the NOx degradation rate at the catalyst outlet, thus forming a "opening degree-flow rate-reaction effect" verification.

[0045] The pre-action compensation unit, based on the predicted adjustment trend and a mechanical delay of 0.5~2s, pre-adjusts the valve 0.1-0.2s in advance to counteract mechanical inertia;

[0046] When the actual opening deviation from the command is greater than 1%, the deviation compensation is completed within 50ms; when the AMI does not meet the standard after valve adjustment, it is automatically fed back to the intelligent analysis and control module to adjust the grid simulation parameters.

[0047] Preferably, the remote monitoring module includes:

[0048] The data display unit shows in real time dynamic adaptive CFD flow field simulation, sampling point concentration, AMI index, valve status, and catalyst activity;

[0049] The data query unit supports querying and trend analysis of historical data for the past year, with dimensions including NOx concentration, ammonia slip, AMI index, catalyst activity, and valve adjustment records.

[0050] The early warning unit will push an early warning to the designated terminal within 10 seconds when the NOx outlet concentration exceeds the set value by 10%, ammonia slip is >8ppm, AMI is <0.8, or valve opening deviation is >2%, probe purging pressure is abnormal, analyzer calibration fails, or catalyst NOx degradation rate decreases by >5% / 24h.

[0051] The remote control unit supports authorized users in issuing commands to adjust the grid template and modify purging parameters. The access control is set to allow administrators to modify all parameters, while maintenance personnel can query data and modify purging parameters. Commands must be confirmed by two people before execution, and the results are fed back in real time after the command is executed.

[0052] Compared with related technologies, the SCR denitrification process precision ammonia injection and sampling system provided by this invention has the following advantages:

[0053] 1. This invention provides a precise ammonia injection and sampling system for SCR denitrification process. Through dynamic adaptive multi-dimensional simulation of the flow field optimization module, combined with a multi-scale grid pool and a mechanical delay compensation mechanism of the ammonia injection valve, the uniformity of flue gas cross-sectional velocity distribution is ≥95%. At the same time, the distributed sampling module is precisely arranged according to the principle of high gradient region densification and low gradient region sparseness, and wavelet transform is used to eliminate turbulent pulsation interference, ensuring that the deviation between the sampled data and the actual ammonia escape is <3%. On this basis, the intelligent analysis and control module relies on PID neural network algorithm and triple control logic to flexibly switch the adjustment mode according to the NOx concentration deviation, stabilizing the deviation at ≤10mg / m³, and can start the compensation mode in combination with the catalyst activity state. This not only significantly improves the denitrification efficiency, but also avoids the resource waste caused by excessive ammonia injection and the denitrification failure caused by insufficient ammonia in traditional processes, realizing the precise and efficient utilization of ammonia resources.

[0054] 2. This invention provides a precise ammonia injection and sampling system for SCR denitrification processes. Through a flow field optimization module, a "condition fluctuation intensity factor F" is defined, which can quantify the fluctuations in flue gas velocity, temperature, NOx, and NH3 concentrations in real time. Online sensors trigger automatic switching between coarse, medium, and fine grids to adapt to different fluctuation intensities. The detachable static mixer in the mixing enhancement submodule can adjust the blade angle in real time based on the sampling data from the mixing section and the AMI index to maintain an AMI ≥ 0.85. Furthermore, the cyclic measurement module and the distributed sampling module form a triple data verification mechanism. When data deviation exceeds limits, a self-check is automatically initiated to investigate key aspects such as pipeline patency and analyzer calibration status, ensuring data reliability. Simultaneously, the self-learning unit of the intelligent analysis and control module updates model parameters daily based on nearly 30 days of historical data, continuously optimizing the adjustment accuracy and catalyst-ammonia injection strategy matching degree. This enables the system to cope with both short-term condition fluctuations and long-term catalyst aging, significantly enhancing long-term operational stability.

[0055] 3. This invention provides a precise ammonia injection and sampling system for SCR denitrification processes. A remote monitoring module enables real-time visualization of core system parameters, supporting multi-dimensional historical data queries and trend analysis over the past year. This facilitates maintenance personnel in quickly understanding operating conditions and tracing the root causes of problems. The early warning unit can push warnings to designated terminals within 10 seconds for various risk scenarios such as excessive NOx outlet concentration, abnormal ammonia escape, and valve opening deviation, reducing the delay in handling anomalies. The remote control unit employs a hierarchical permission design, with administrators and maintenance personnel each having their own responsibilities. Commands require double confirmation before execution, avoiding the risk of misoperation. Furthermore, the full-stroke adjustment time of the control valve in the execution module is ≤0.5 seconds, and the pre-action compensation unit can offset mechanical inertia in advance, completing opening deviation compensation within 50ms, reducing the frequency of manual intervention. Each sampling unit is equipped with an automatic purging device and an anti-dust accumulation probe, reducing the probability of probe blockage and equipment damage, decreasing the workload of on-site maintenance personnel and equipment repair costs, improving overall maintenance efficiency, and making management safer and more standardized. Attached Figure Description

[0056] Figure 1 This is a flowchart of the present invention;

[0057] Figure 2 This is an extended flowchart of the flow field optimization module of the present invention;

[0058] Figure 3 This is an extended flowchart of the distributed sampling module of the present invention;

[0059] Figure 4 This is an extended flowchart of the intelligent analysis and control module of the present invention;

[0060] Figure 5 This is an extended flowchart of the cyclic measurement module of the present invention;

[0061] Figure 6 This is an extended flowchart of the execution module of the present invention;

[0062] Figure 7 This is an extended flowchart of the remote monitoring module of the present invention. Detailed Implementation

[0063] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0064] Example 1:

[0065] Please see Figures 1-7This invention provides a technical solution: a precise ammonia injection and sampling system for SCR denitrification process, including a flow field optimization module, a distributed sampling module, an intelligent analysis and control module, a cyclic measurement module, an execution module, and a remote monitoring module. Each module forms a simulation-evaluation-sampling-control-feedback closed loop, with the feedback priority being to first correct the ammonia injection command and then adjust the flow field simulation parameters.

[0066] The flow field optimization module optimizes the flue gas flow field, ammonia flow field and gas-gas mixture flow field in the reactor through dynamic adaptive multi-dimensional simulation, including operating condition perception, dynamic switching of multi-scale grids and mechanical delay compensation mechanism of ammonia injection valve.

[0067] The flow field optimization module includes:

[0068] The flue gas flow guiding submodule employs dynamic adaptive computational fluid dynamics technology, defining an "operating condition fluctuation intensity factor" to quantify real-time fluctuations in flue gas velocity, temperature, NOx, and NH3 concentrations. The calculation formula for the operating condition fluctuation intensity factor is as follows: ,in For flow velocity fluctuation value, As the reference flow rate, For temperature fluctuation values, As the reference temperature, NOx concentration fluctuation value, Using the baseline concentration, a "multi-scale grid pool" containing coarse, medium, and fine grids was established. The coarse grid corresponds to the operating condition of F≤0.1, the medium grid corresponds to the operating condition of 0.1<F≤0.3, and the fine grid corresponds to the operating condition of F>0.3. Automatic grid switching was triggered by online sensors such as Pitot tube velocity measurement and infrared temperature measurement. A mechanical delay compensation of 0.5~2s for the ammonia injection valve was introduced. Based on the simulation results, the three-dimensional layout of the guide vane and the tilt angle of 30°-60° were designed to make the uniformity of flue gas cross-section velocity distribution ≥95%.

[0069] The ammonia injection optimization submodule plans the orifice diameter gradient distribution and spatial density based on the ammonia flow field simulation results and AMI index.

[0070] The hybrid enhancement submodule is equipped with a detachable static mixer. The blade angle is adjusted in real time based on the sampling data of the mixing section and the feedback of the AMI index to maintain AMI≥0.85.

[0071] In this implementation scheme, the synergistic effect of the flue gas guiding submodule, ammonia injection optimization submodule, and mixing enhancement submodule specifically addresses the technical pain points of traditional SCR denitrification processes, such as uneven flue gas flow field distribution, low mixing efficiency of ammonia injection and flue gas, and poor adaptability to operating condition fluctuations. Specifically, the "operating condition fluctuation intensity factor F" defined by the flue gas guiding submodule quantifies real-time fluctuations in flue gas velocity, temperature, NOx, and NH3 concentrations. Combined with a "multi-scale grid pool," automatic grid switching is achieved. When operating conditions are stable, a coarse grid is used to improve simulation efficiency; when operating conditions fluctuate significantly, a fine grid is switched to ensure simulation accuracy. This, along with online sensors such as Pitot tube velocimeters and infrared thermometers, ensures a high degree of matching between the flow field simulation and actual operating conditions. A mechanical delay compensation of 0.5~2s for the ammonia injection valve is introduced to avoid excessive or insufficient ammonia caused by valve lag. Finally, through the three-dimensional layout of the 30°-60° inclined guide plate, the uniformity of the flue gas cross-section velocity distribution is ≥95%, laying the foundation for subsequent ammonia-fume mixing. The ammonia injection optimization submodule plans the gradient distribution and spatial density of the injection hole diameter based on the ammonia flow field simulation results and ammonia-fume mixing efficiency index to avoid local ammonia concentrations that are too high or too low. The detachable static mixer of the mixing enhancement submodule can adjust the blade angle in real time according to the sampling data of the mixing section and the AMI index, forcibly maintaining AMI≥0.85, effectively solving the problem of fixed blade angle and decreased mixing efficiency with changes in operating conditions in traditional mixers.

[0072] Distributed sampling modules are deployed at the reactor inlet, mixing, and outlet sections, with sampling points arranged according to the principle of "intensified sampling in high gradient regions and sparse sampling in low gradient regions," where the high gradient regions represent the concentration gradient. In the region where the gradient is low, the region is a concentration gradient. In the region, a "multi-point synchronization + polling" sampling mode is adopted and wavelet transform is used to eliminate turbulent pulsation interference;

[0073] The distributed sampling module includes:

[0074] The inlet sampling unit collects the original flue gas NOx concentration and oxygen content, and the sampling points avoid the eddy region with a flow velocity of <0.5m / s;

[0075] The mixed section sampling unit is located 1-2m downstream of the ammonia injection grid to collect NOx concentration and ammonia slip concentration. One sampling point is set every 0.3m in the high gradient zone and one sampling point is set every 1.5m in the low gradient zone.

[0076] The sampling unit at the outlet section is located 0.5-1m downstream of the catalyst layer. It collects flue gas parameters after the reaction by using "multi-point synchronous sampling + polling sampling". After collecting at least 100 sets of data, wavelet transform is used to eliminate ±30% turbulent fluctuation interference. The wavelet transform uses db4 wavelet basis and 3-level decomposition. The threshold is 1.5 times the standard deviation to obtain the time-averaged concentration.

[0077] Each sampling unit is equipped with a heated sampling tube, an automatic purging device that purges every 5 minutes at a pressure of 0.6-0.8 MPa, and a tapered anti-dust probe made of Inconel 625 with a temperature resistance of ≤600℃. The probe has a built-in water-cooled jacket, and the cooling medium is deionized water with a flow rate of 0.5-1 L / min, ensuring that the deviation between the sampling data and the actual ammonia slip is <3%.

[0078] The sampling point settings of the mixed-segment sampling unit meet the following requirements:

[0079] Based on both reactor cross-sectional area and concentration gradient, the high gradient region represents the concentration gradient. In the region where the gradient is low, the region is a concentration gradient. In the high gradient region, one sampling point is set every 0.3m, and in the low gradient region, one sampling point is set every 1.5m, avoiding the eddy current region with a flow velocity <0.5m / s;

[0080] The sampling tube is inserted to a depth of 1 / 3 to 2 / 3 of the pipe diameter, and the sampling direction is arranged at a 45° angle to the flue gas flow direction, with the same cross section being uniformly distributed in a matrix pattern.

[0081] The sampled data were processed by wavelet transform to eliminate turbulence fluctuation interference. The wavelet transform used a db4 wavelet basis and a 3-level decomposition, with a threshold of 1.5 times the standard deviation to ensure data authenticity.

[0082] This implementation scheme addresses the problems of unreasonable sampling point distribution, significant data interference from turbulence and ash accumulation, and poor representativeness in traditional sampling systems through a design that combines segmented sampling, gradient sampling, precise data processing, and hardware anti-interference. The inlet sampling unit is located 3-5D before the reactor inlet, avoiding eddy currents with velocities <0.5m / s, ensuring that the collected raw flue gas NOx concentration and oxygen content data accurately reflect the inlet operating conditions. The mixing section sampling unit is strategically located 1-2m downstream of the ammonia injection grid, with a density-sparse distribution principle of "one point every 0.3m in high gradient zones and one point every 1.5m in low gradient zones" to accurately capture ammonia fumes. The concentration gradient change after mixing; the sampling unit at the outlet section collects at least 100 sets of data through the "multi-point synchronization + polling" mode, and then performs wavelet transform processing with db4 wavelet basis, 3-level decomposition, and 1.5 times standard deviation threshold to eliminate ±30% turbulent pulsation interference and obtain reliable time-averaged concentration. In terms of hardware, the heated sampling tubes configured in each sampling unit can prevent flue gas condensation. The automatic purging device with a pressure of 0.6-0.8MPa every 5 minutes, combined with the tapered anti-ash probe, effectively avoids probe ash accumulation and high temperature damage, and finally ensures that the deviation between the sampling data and the actual ammonia escape is <3%, providing high-quality data input for subsequent intelligent analysis.

[0083] The intelligent analysis and control module receives sampled data and generates ammonia injection adjustment commands based on the quantitative index of ammonia-fume mixing efficiency, the PID neural network algorithm model, and the catalyst activity state.

[0084] The intelligent analysis and control module includes:

[0085] The data preprocessing unit performs temperature and humidity compensation, 3σ criterion outlier removal, and wavelet transform to eliminate ±30% turbulent fluctuation interference on the sampled data. The wavelet transform uses the db4 wavelet basis and 3-level decomposition, with a threshold of 1.5 times the standard deviation.

[0086] The concentration field reconstruction unit, combined with the dynamic adaptive CFD simulation results, uses the Kriging interpolation algorithm to construct a NOx concentration distribution cloud map of the reactor cross section with a spatial resolution of ≤0.5m×0.5m;

[0087] The adjustment decision unit adopts a PID neural network algorithm. Based on the NOx concentration deviation, deviation change rate, AMI index and catalyst activity status, it outputs instructions. When the catalyst activity retention rate is <80%, the compensation mode is activated, and a mechanical delay compensation of 0.5~2s for the ammonia injection valve is introduced simultaneously.

[0088] The self-learning unit trains the model based on historical data of NOx concentration, ammonia slip, AMI index, and catalyst activity over the past 30 days. The parameters are updated every 24 hours to optimize the adjustment accuracy and the matching degree of catalyst-ammonia injection strategy. After the update, the parameters are synchronously fed back to the grid switching logic of the flow field optimization module.

[0089] The adjustment decision unit is equipped with a triple control logic:

[0090] When the NOx concentration deviation is >50mg / m³, the rapid adjustment mode is activated, and the valve is adjusted by 5%-10% / s until the deviation is ≤30mg / m³.

[0091] When the deviation is ≤50mg / m³, the fine adjustment mode is activated, with an adjustment range of 0.5%-2% / s, to stabilize the deviation at ≤10mg / m³.

[0092] When AMI < 0.85, the mixing-ammonia injection coordinated adjustment is triggered. First, the angle of the static mixer blades is adjusted, with an adjustment range of 5°-10° / time. If AMI does not rise to ≥ 0.85 within 30 seconds, the ammonia injection amount is corrected according to the fine adjustment mode.

[0093] This implementation scheme addresses the problems of traditional SCR control systems, such as reliance on human experience, low adjustment accuracy, slow response, and lack of adaptive operation, through a complete process design of "data preprocessing - concentration field reconstruction - dynamic decision-making - self-learning optimization". The data preprocessing unit uses temperature and humidity compensation to eliminate environmental influences and the 3σ criterion to remove outliers. Combined with wavelet transform technology consistent with the distributed sampling module, the reliability of the sampled data is further guaranteed. The concentration field reconstruction unit combines the results of dynamic adaptive CFD simulation and uses the Kriging interpolation algorithm to construct a NOx concentration distribution cloud map with a spatial resolution of ≤0.5m×0.5m. This visually presents the uneven concentration areas within the reactor, providing a visual basis for precise adjustment and decision-making. The unit is based on a PID neural network algorithm. According to the NOx concentration deviation, the rate of change of deviation, combined with the AMI index and the catalyst activity status, the output command is: when the catalyst activity retention rate is <80%, the compensation mode is activated to avoid the decrease in denitrification efficiency caused by catalyst aging; a mechanical delay compensation of 0.5~2s for the ammonia injection valve is introduced simultaneously to offset the effect of valve action lag; the triple control logic can be flexibly switched according to the actual working conditions to ensure that the NOx concentration deviation is stable at ≤10mg / m³. The self-learning unit updates the model parameters daily based on the historical data of the past 30 days, optimizes the adjustment accuracy and the matching degree of the catalyst-ammonia injection strategy, and feeds the parameters back to the flow field optimization module to adjust the grid switching logic, so as to achieve continuous optimization of the overall system performance.

[0094] The circulating measurement module is located at the catalyst outlet and monitors gas parameters according to the concentration gradient priority.

[0095] The cyclic measurement module includes:

[0096] The movable sampling probe can switch points along a preset track with a switching response time of ≤3s. The track covers the high and low gradient areas of the exit section. The probe structure and materials are consistent with the distributed sampling unit.

[0097] The measurement sequence generation unit, based on the NOx concentration gradient of each point in the past hour, sets the sampling frequency of the high gradient area to be 2-3 times that of the low gradient area, and completes a full cross-section polling once every 1 minute;

[0098] The backflush cleaning unit automatically performs a 3-second high-pressure purge after each point switch, with a purge pressure of 0.6-0.8 MPa. It also performs an additional purge every 5 minutes to remove particles from the probe.

[0099] The data storage unit stores time-averaged concentration data processed by wavelet transform in real time for AMI calculation and trend analysis.

[0100] The polling measurement module and the distributed sampling module form a triple data verification mechanism:

[0101] When the NOx concentration deviation is greater than 10%, the ammonia slip concentration deviation is greater than 8%, or the AMI index deviation is greater than 5% in the same area, the self-test program will be automatically started.

[0102] The self-test sequentially checks the patency of the sampling pipeline, the calibration status of the analyzer, the accuracy of the probe position, and the dynamic adaptive CFD grid switching logic. The patency of the sampling pipeline is judged by the purge pressure decay rate, and decay ≤10% / min is considered patency.

[0103] This implementation scheme addresses the limitations of traditional fixed-point measurements, such as limited coverage, poor data timeliness, and lack of self-verification mechanisms, through a design that combines "mobile monitoring + gradient frequency conversion + active cleaning + triple verification." The mobile sampling probe switches locations along a preset track with a switching response time of ≤3 seconds. The track covers both high and low gradient zones at the catalyst outlet cross-section, and the probe structure and materials are consistent with the distributed sampling unit, ensuring uniform measurement standards. The measurement sequence generation unit sets the sampling frequency in the high gradient zone to 2-3 times that in the low gradient zone based on the NOx concentration gradient at each location over the past hour, completing a full-section poll every minute. This ensures monitoring density in key high-gradient areas while avoiding redundant measurements in low-gradient areas, improving monitoring efficiency. The backflushing cleaning unit performs cleaning at each location... After switching, a high-pressure purging of 0.6-0.8 MPa is automatically performed for 3 seconds, with an additional purging every 5 minutes to effectively remove particulate matter from the probe surface and prevent blockage from affecting measurement accuracy. The data storage unit stores time-averaged concentration data processed by wavelet transform in real time, providing continuous data support for AMI calculation and denitrification trend analysis. In addition, the triple data verification mechanism formed by the cyclic measurement module and the distributed sampling module automatically starts self-checking when the NOx concentration deviation is >10%, the ammonia slip concentration deviation is >8%, or the AMI index deviation is >5% in the same area. It sequentially checks the unobstructedness of the sampling pipeline, the calibration status of the analyzer, the probe position accuracy, and the CFD grid switching logic to ensure the reliability of the measurement data and provide a reliable verification basis for the intelligent analysis and control module.

[0104] The execution module regulates the ammonia injection rate by controlling the valves;

[0105] The execution module includes:

[0106] The control valve is electro-hydraulic servo driven, with a working pressure of 10-15MPa. It has a built-in high-frequency response valve core, and the full stroke adjustment time is ≤0.5s. The flow characteristics are matched with the orifice gradient of the ammonia injection grid.

[0107] The intelligent locator uses digital signal processing technology, with a positioning accuracy of ≤0.1%FS. It supports high-speed PWM control signals with a frequency of 10-20kHz and receives AMI index feedback in real time.

[0108] The dual-channel feedback unit uses a dual-parameter feedback mechanism consisting of an absolute encoder and a pressure sensor to input the NOx degradation rate at the catalyst outlet, thus forming a "opening degree-flow rate-reaction effect" verification.

[0109] The pre-action compensation unit, based on the predicted adjustment trend and a mechanical delay of 0.5~2s, pre-adjusts the valve 0.1-0.2s in advance to counteract mechanical inertia;

[0110] When the actual opening deviation from the command is greater than 1%, the deviation compensation is completed within 50ms; when the AMI does not meet the standard after valve adjustment, it is automatically fed back to the intelligent analysis and control module to adjust the grid simulation parameters.

[0111] This implementation scheme solves the problems of slow response, low positioning accuracy, large adjustment deviation, and lack of effect verification of traditional ammonia injection actuators by adopting a design of "high-precision control + real-time feedback + pre-compensation". The control valve adopts electro-hydraulic servo drive, with a working pressure of 10-15MPa, and has a built-in high-frequency response valve core. The full stroke adjustment time is ≤0.5s, and the flow characteristics are matched with the aperture gradient of the ammonia injection grid, ensuring that the ammonia injection quantity can quickly and accurately follow the adjustment command changes. The intelligent positioner adopts digital signal processing technology, with a positioning accuracy of ≤0.1%FS, supports high-speed PWM control signals of 10-20kHz, and provides real-time feedback. The system receives AMI (Adjustment Minimum Mixing Parameter) feedback and dynamically corrects valve positioning based on mixing efficiency to avoid ineffective adjustments. A dual-channel feedback unit uses both absolute encoder and pressure sensor feedback, along with the catalyst outlet NOx degradation rate, to form a closed-loop verification of "opening degree-flow rate-reaction effect," ensuring that adjustments effectively improve denitrification. A pre-action compensation unit pre-adjusts the valve 0.1-0.2 seconds in advance based on adjustment trend prediction and a 0.5-2s valve mechanical delay, offsetting the effects of mechanical inertia. When the actual opening degree deviates from the command by more than 1%, deviation compensation is completed within 50ms, further improving adjustment accuracy. If the AMI still fails to meet the standard after valve adjustment, the execution module automatically feeds back to the intelligent analysis and control module, triggering flow field simulation parameter adjustments, forming a secondary closed loop of "execution-verification-optimization."

[0112] The remote monitoring module displays system parameters in real time, distributed sampling data is transmitted to the intelligent analysis and control module in real time, the cyclic measurement data is used for verification, and the intelligent analysis and control module sends instructions based on the verification data.

[0113] The remote monitoring module includes:

[0114] The data display unit shows in real time dynamic adaptive CFD flow field simulation, sampling point concentration, AMI index, valve status, and catalyst activity;

[0115] The data query unit supports querying and trend analysis of historical data for the past year, with dimensions including NOx concentration, ammonia slip, AMI index, catalyst activity, and valve adjustment records.

[0116] The early warning unit will push an early warning to the designated terminal within 10 seconds when the NOx outlet concentration exceeds the set value by 10%, ammonia slip is >8ppm, AMI is <0.8, or valve opening deviation is >2%, probe purging pressure is abnormal, analyzer calibration fails, or catalyst NOx degradation rate decreases by >5% / 24h.

[0117] The remote control unit supports authorized users to issue commands for adjusting grid templates and modifying purging parameters. The permission hierarchy is set so that administrators can modify all parameters, while maintenance personnel can query data and modify purging parameters. Commands must be confirmed by two people before execution, and the results are fed back in real time after the command is executed.

[0118] This implementation plan addresses the problems of information lag, difficult data querying, slow anomaly response, and chaotic operation permissions in traditional monitoring systems through a design that combines real-time visualization, data traceability, proactive early warning, and hierarchical control. The data display unit shows real-time dynamic adaptive CFD flow field simulation results, concentration data at each sampling point, AMI index, valve operating status, and catalyst activity, presenting the overall system operation status in a visual interface, facilitating real-time monitoring of core parameters by maintenance personnel. The data query unit supports querying and trend analysis of historical data for the past year, covering multi-dimensional data such as NOx concentration, ammonia slip, AMI index, catalyst activity, and valve adjustment records, assisting maintenance personnel in tracing the root cause of problems and optimizing operational strategies. The early warning unit pushes early warning information to designated terminals within 10 seconds for various key anomaly scenarios, ensuring timely detection and handling of abnormal situations. The remote control unit adopts a hierarchical permission design: administrators can modify all parameters, while maintenance personnel can only query data and modify purging parameters, and all commands require double confirmation before execution to avoid misoperation. Real-time feedback of results after command execution ensures the safety and effectiveness of remote operation, achieving efficient remote operation and maintenance of the SCR denitrification system.

[0119] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A precision ammonia injection and sampling system for SCR denitrification process, characterized in that, It includes a flow field optimization module, a distributed sampling module, an intelligent analysis and control module, a cyclic measurement module, an execution module, and a remote monitoring module. Each module forms a simulation-evaluation-sampling-control-feedback closed loop. The feedback priority is to first correct the ammonia injection command, and then adjust the flow field simulation parameters. The flow field optimization module optimizes the flue gas flow field, ammonia flow field and gas-gas mixture flow field in the reactor through dynamic adaptive multi-dimensional simulation, including operating condition perception, dynamic switching of multi-scale grids and mechanical delay compensation mechanism of ammonia injection valve. Distributed sampling modules are deployed at the reactor inlet, mixing, and outlet sections, with sampling points arranged according to the principle of "intensified sampling in high gradient regions and sparse sampling in low gradient regions," where the high gradient regions are the concentration gradients. In the region where the gradient is low, the gradient region is the concentration gradient. In the region, a "multi-point synchronization + polling" sampling mode is adopted and wavelet transform is used to eliminate turbulent pulsation interference; The intelligent analysis and control module receives sampled data and generates ammonia injection adjustment commands based on the quantitative index of ammonia-fume mixing efficiency, the PID neural network algorithm model, and the catalyst activity state. The circulating measurement module is located at the catalyst outlet and monitors gas parameters according to the concentration gradient priority. The execution module regulates the ammonia injection rate by controlling the valves; The remote monitoring module displays system parameters in real time, distributed sampling data is transmitted to the intelligent analysis and control module in real time, the cyclic measurement data is used for verification, and the intelligent analysis and control module sends instructions based on the verification data. The flow field optimization module includes: The flue gas flow guiding submodule employs dynamic adaptive computational fluid dynamics technology, defining an "operating condition fluctuation intensity factor" to quantify real-time fluctuations in flue gas velocity, temperature, NOx, and NH3 concentrations. The calculation formula for the operating condition fluctuation intensity factor is as follows: ,in For flow velocity fluctuation value, As the reference flow rate, For temperature fluctuation values, As the reference temperature, NOx concentration fluctuation value, Using the baseline concentration, a "multi-scale grid pool" containing coarse, medium, and fine grids was established. The coarse grid corresponds to the operating condition of F≤0.1, the medium grid corresponds to the operating condition of 0.1<F≤0.3, and the fine grid corresponds to the operating condition of F>0.

3. Automatic grid switching was triggered by online sensors such as Pitot tube velocities and infrared thermometers. A mechanical delay compensation of 0.5~2s for the ammonia injection valve was introduced. Based on the simulation results, a three-dimensional layout of the guide vane and an inclination angle of 30°-60° were designed to ensure that the uniformity of flue gas cross-section velocity distribution is ≥95%. The ammonia injection optimization submodule plans the orifice diameter gradient distribution and spatial density based on the ammonia flow field simulation results and AMI index. The hybrid enhancement submodule is equipped with a detachable static mixer. The blade angle is adjusted in real time based on the sampling data of the mixing section and the feedback of the AMI index to maintain AMI≥0.

85.

2. The precision ammonia injection and sampling system for SCR denitrification process according to claim 1, characterized in that: The distributed sampling module includes: The inlet sampling unit collects the original flue gas NOx concentration and oxygen content, and the sampling points avoid the eddy region with a flow velocity of <0.5m / s; The mixed section sampling unit is located 1-2m downstream of the ammonia injection grid to collect NOx concentration and ammonia slip concentration. One sampling point is set every 0.3m in the high gradient zone and one sampling point is set every 1.5m in the low gradient zone. The sampling unit at the outlet section is located 0.5-1m downstream of the catalyst layer. It collects flue gas parameters after the reaction by using "multi-point synchronous sampling + polling sampling". After collecting at least 100 sets of data, wavelet transform is used to eliminate ±30% turbulent fluctuation interference. The wavelet transform uses db4 wavelet basis and 3-level decomposition. The threshold is 1.5 times the standard deviation to obtain the time-averaged concentration. Each sampling unit is equipped with a heated sampling tube, an automatic purging device that purges every 5 minutes at a pressure of 0.6-0.8 MPa, and a tapered anti-dust probe made of Inconel 625 with a temperature resistance of ≤600℃. The probe has a built-in water-cooled jacket, and the cooling medium is deionized water with a flow rate of 0.5-1 L / min, ensuring that the sampling data deviates from the actual ammonia escape by less than 3%.

3. The precision ammonia injection and sampling system for SCR denitrification process according to claim 2, characterized in that: The sampling point settings of the hybrid segment sampling unit satisfy the following: Based on both reactor cross-sectional area and concentration gradient, the high gradient region represents the concentration gradient. In the region where the gradient is low, the gradient region is the concentration gradient. In the high gradient region, one sampling point is set every 0.3m, and in the low gradient region, one sampling point is set every 1.5m, avoiding the eddy current region with a flow velocity <0.5m / s; The sampling tube is inserted to a depth of 1 / 3 to 2 / 3 of the pipe diameter, and the sampling direction is arranged at a 45° angle to the flue gas flow direction, with the same cross section being uniformly distributed in a matrix pattern. The sampled data were processed by wavelet transform to eliminate turbulence and fluctuation interference. The wavelet transform used a db4 wavelet basis and a 3-level decomposition, with a threshold of 1.5 times the standard deviation to ensure data authenticity.

4. The precision ammonia injection and sampling system for SCR denitrification process according to claim 1, characterized in that: The intelligent analysis and control module includes: The data preprocessing unit performs temperature and humidity compensation, 3σ criterion outlier removal, and wavelet transform to eliminate ±30% turbulent fluctuation interference on the sampled data. The wavelet transform uses the db4 wavelet basis and 3-level decomposition, with a threshold of 1.5 times the standard deviation. The concentration field reconstruction unit, combined with the dynamic adaptive CFD simulation results, uses the Kriging interpolation algorithm to construct a NOx concentration distribution cloud map of the reactor cross section with a spatial resolution of ≤0.5m×0.5m; The adjustment decision unit adopts a PID neural network algorithm. Based on the NOx concentration deviation, deviation change rate, AMI index and catalyst activity status, it outputs instructions. When the catalyst activity retention rate is <80%, the compensation mode is activated, and a mechanical delay compensation of 0.5~2s for the ammonia injection valve is introduced simultaneously. The self-learning unit trains the model based on historical data of NOx concentration, ammonia slip, AMI index, and catalyst activity over the past 30 days. The parameters are updated every 24 hours to optimize the adjustment accuracy and the matching degree of catalyst-ammonia injection strategy. The updated parameters are then synchronously fed back to the grid switching logic of the flow field optimization module.

5. The SCR denitrification process precision ammonia injection and sampling system according to claim 4, characterized in that: The adjustment decision unit is equipped with triple control logic: When the NOx concentration deviation is >50mg / m³, the rapid adjustment mode is activated, and the valve is adjusted by 5%-10% / s until the deviation is ≤30mg / m³. When the deviation is ≤50mg / m³, the fine adjustment mode is activated, with an adjustment range of 0.5%-2% / s, to stabilize the deviation at ≤10mg / m³. When AMI < 0.85, the mixing-ammonia injection coordinated adjustment is triggered. First, the angle of the static mixer blades is adjusted, with an adjustment range of 5°-10° / time. If AMI does not rise to ≥ 0.85 within 30 seconds, the ammonia injection amount is corrected according to the fine adjustment mode.

6. The precision ammonia injection and sampling system for SCR denitrification process according to claim 1, characterized in that: The polling measurement module includes: The movable sampling probe can switch points along a preset track with a switching response time of ≤3s. The track covers the high and low gradient areas of the exit section. The probe structure and materials are consistent with the distributed sampling unit. The measurement sequence generation unit, based on the NOx concentration gradient of each point in the past hour, sets the sampling frequency of the high gradient area to be 2-3 times that of the low gradient area, and completes a full cross-section polling once every 1 minute; The backflush cleaning unit automatically performs a 3-second high-pressure purge after each point switch, with a purge pressure of 0.6-0.8 MPa. It also performs an additional purge every 5 minutes to remove particles from the probe. The data storage unit stores time-averaged concentration data processed by wavelet transform in real time for AMI calculation and trend analysis.

7. The precision ammonia injection and sampling system for SCR denitrification process according to claim 6, characterized in that: The polling measurement module and the distributed sampling module form a triple data verification mechanism: When the NOx concentration deviation is greater than 10%, the ammonia slip concentration deviation is greater than 8%, or the AMI index deviation is greater than 5% in the same area, the self-test program will be automatically started. The self-test sequentially checks the patency of the sampling pipeline, the calibration status of the analyzer, the accuracy of the probe position, and the dynamic adaptive CFD grid switching logic. The patency of the sampling pipeline is judged by the purge pressure decay rate; decay ≤10% / min indicates patency.

8. The precision ammonia injection and sampling system for SCR denitrification process according to claim 1, characterized in that: The execution module includes: The control valve is electro-hydraulic servo driven, with a working pressure of 10-15MPa. It has a built-in high-frequency response valve core, and the full stroke adjustment time is ≤0.5s. The flow characteristics are matched with the orifice gradient of the ammonia injection grid. The intelligent locator uses digital signal processing technology, with a positioning accuracy of ≤0.1%FS. It supports high-speed PWM control signals with a frequency of 10-20kHz and receives AMI index feedback in real time. The dual-channel feedback unit uses a dual-parameter feedback mechanism of an absolute encoder and a pressure sensor to input the NOx degradation rate at the catalyst outlet, forming a "opening degree-flow rate-reaction effect" verification. The pre-action compensation unit, based on the predicted adjustment trend and a mechanical delay of 0.5~2s, pre-adjusts the valve 0.1-0.2s in advance to counteract mechanical inertia; When the actual opening deviation from the command is greater than 1%, the deviation compensation is completed within 50ms; when the AMI does not meet the standard after valve adjustment, it is automatically fed back to the intelligent analysis and control module to adjust the grid simulation parameters.

9. The precision ammonia injection and sampling system for SCR denitrification process according to claim 1, characterized in that: The remote monitoring module includes: The data display unit shows in real time dynamic adaptive CFD flow field simulation, sampling point concentration, AMI index, valve status, and catalyst activity; The data query unit supports querying and trend analysis of historical data for the past year, with dimensions including NOx concentration, ammonia slip, AMI index, catalyst activity, and valve adjustment records. The early warning unit will push an early warning to the designated terminal within 10 seconds when the NOx outlet concentration exceeds the set value by 10%, ammonia slip is >8ppm, AMI is <0.8, or valve opening deviation is >2%, probe purging pressure is abnormal, analyzer calibration fails, or catalyst NOx degradation rate decreases by >5% / 24h. The remote control unit supports authorized users in issuing commands to adjust the grid template and modify purging parameters. The access control is set to allow administrators to modify all parameters, while maintenance personnel can query data and modify purging parameters. Commands must be confirmed by two people before execution, and the results are fed back in real time after the command is executed.

Images