Full-load adaptive ammonia injection dynamic coordination control method for SNCR-SCR coupling system

Abstract

The invention relates to a full-load self-adaptive ammonia spraying dynamic coordination control method for an SNCR-SCR coupling system, and belongs to the technical field of industrial flue gas denitration control. The method comprises the following steps: monitoring an SNCR reaction zone temperature field, SCR inlet multi-dimensional parameters and boiler operation characteristics in real time, constructing a coupling dynamic prediction model, and performing dynamic correction; a feedforward-feedback composite control framework and rolling time domain optimization are adopted, ammonia spraying modes are automatically switched, and system fluctuation is adapted; evaluating an activity state based on the catalyst activity influence factor, establishing a dynamic mapping relation between the activity state and an ammonia nitrogen molar ratio, and reversely correcting SNCR spray gun ammonia amount distribution by combining with SCR inlet reducing agent distribution uniformity; a risk assessment index is generated by fusing hearth temperature field uniformity and catalyst activity attenuation characteristics, and a dual-system boundary condition and operation state collaborative adaptive mechanism is established. The ammonia spraying dynamic coordination control in the full-load range is realized, the denitration efficiency and the operation stability are improved, and the ammonia consumption and the ammonia escape are reduced.

Description

Technical Field

[0001] This invention belongs to the field of industrial flue gas denitrification control technology, specifically relating to a full-load adaptive ammonia injection dynamic coordinated control method for SNCR-SCR coupled systems. Background Technology

[0002] In the field of industrial flue gas pollution control, nitrogen oxide emission control is one of the core requirements for protecting the ecological environment and achieving green development in industries. Among them, the selective non-catalytic reduction (SNR) and selective catalytic reduction (SCR) coupled systems are widely used in flue gas purification scenarios of combustion equipment such as thermal power plants and industrial boilers due to their balance between denitrification efficiency and operating costs, becoming the current mainstream nitrogen oxide control technology solution. However, existing coupled systems still have many technical bottlenecks in the dynamic coordination control of ammonia injection under full-load operating conditions, making it difficult to adapt to the complex and ever-changing operating states of industrial equipment.

[0003] First, in building system prediction models, traditional methods often rely on fixed process parameters and denitrification reaction mechanisms, failing to fully consider the impact of boiler load fluctuations, dynamic changes in flue gas residence time, and differences in combustion conditions on the reaction process. This makes the model prediction accuracy susceptible to operating condition disturbances, and the adjustment of ammonia injection volume is lagging, leading to fluctuations in denitrification efficiency or high ammonia consumption. Second, in designing ammonia injection distribution modes, existing schemes often adopt a single fixed mode or switching logic based on simple load thresholds, failing to autonomously optimize adjustment strategies based on dynamic characteristics such as load change rate and acceleration. When the boiler load rises or falls rapidly, situations such as untimely response of ammonia injection volume in the selective non-catalytic reduction system and deviations in ammonia compensation in the selective catalytic reduction system may occur, affecting not only the denitrification effect but also potentially leading to increased ammonia escape concentration and increased corrosion risk in subsequent equipment. Third, at the level of control architecture and parameter optimization, traditional feedforward control and feedback control often operate independently. The feedforward link lacks sufficient accuracy in predicting operating condition disturbances, and the feedback link responds slowly to deviation corrections. The two do not form effective synergy, making it difficult to cope with transient disturbances in nitrogen oxide generation within the furnace. Meanwhile, existing technologies lack adaptability to catalyst activity decay in selective catalytic reduction (SCR) systems. They fail to establish a dynamic mapping relationship between catalyst activity and the ammonia-nitrogen molar ratio, continuing to use initial ammonia injection parameters after activity decay can easily lead to localized catalyst overload or decreased denitrification efficiency. Furthermore, regarding the coordinated operation of the two systems, the ammonia injection temperature window boundary of the selective non-catalytic reduction (SNR) system and the ammonia slip safety limit of the SCR system are mostly fixed settings, not dynamically adjusted according to the system's operational risk status. Moreover, the ammonia distribution in each nozzle of the SNR system is not optimized in reverse, taking into account the uniformity of the reducing agent distribution at the SCR inlet, resulting in difficulty in matching the operating states of the two systems and limiting overall operational stability and economy. In summary, existing ammonia injection control methods for SNR-SCR coupled systems still have shortcomings in terms of operating condition adaptability, system coordination, and parameter optimization accuracy. There is an urgent need for a dynamic coordinated control scheme that can achieve full-load self-adaptation and multi-system coordination to improve denitrification efficiency stability, reduce ammonia consumption and ammonia slip risk, and meet the needs of efficient and environmentally friendly operation of industrial equipment. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this invention provides a full-load adaptive ammonia injection dynamic coordinated control method for SNCR-SCR coupled systems; The objective of this invention can be achieved through the following technical solutions: S1: By real-time multidimensional monitoring of distributed temperature field data in the SNCR reaction zone and multidimensional characteristic parameters of the SCR inlet, combined with precise analysis of flue gas residence time and dynamic trend of boiler load change and combustion condition characteristic parameters, based on historical big data mining and deep coupling analysis of denitrification reaction mechanism, an SNCR-SCR coupled dynamic prediction model is constructed, and the model prediction parameter matrix is ​​dynamically corrected based on real-time operating data, outputting a set of basic parameters for ammonia injection decision. S2: Based on the aforementioned basic parameter set, design a multi-mode adaptive ammonia injection coordination allocation decision, and autonomously switch the ammonia injection allocation mode through the dynamic threshold of load change rate; adopt a feedforward-feedback composite control architecture, calculate the ammonia injection ratio of SNCR and SCR in real time through rolling time domain optimization, introduce a system fluctuation adaptive adaptation mechanism to suppress the influence of system dynamic disturbance, and generate real-time ammonia injection control commands. S3: Obtain the SCR outlet ammonia slip concentration and catalyst bed pressure difference data, and construct a comprehensive evaluation system by combining catalyst activity-related influencing factors; establish a dynamic mapping relationship between the active state and the ammonia-nitrogen molar ratio, and adaptively match the ammonia demand threshold of the SCR system; at the same time, by monitoring the SCR inlet reducing agent distribution uniformity index, reverse the ammonia distribution coefficient of each spray gun in the SNCR, and feed the correction result back to the model prediction parameter matrix for secondary optimization. S4: By calculating the furnace temperature field uniformity index and catalyst activity decay acceleration in real time, a weighted nonlinear fusion algorithm is used to generate a comprehensive assessment index of system operation risk. The boundary of the SNCR ammonia injection temperature window and the safety limit of ammonia escape in the SCR system are dynamically adjusted to construct a closed-loop control system for the entire process of data interaction and risk assessment.

[0005] Specifically, the SNCR-SCR coupled dynamic prediction model includes: The spatial distribution and time-series characteristics of the temperature field in the SNCR reaction zone, the composition of the SCR inlet flue gas, boiler load, and historical operating data were acquired. An input matrix was formed through feature decoupling and standardization. A segmented coupling architecture was constructed based on the denitrification reaction kinetics. For the low-load section, a mechanistic analysis model embedding the relationship between temperature windows and flue gas velocity was used. For the high-load section, a data-driven model based on historical operating data mining was employed. This established the relationship between input parameters and SCR inlet NO. x The concentration mapping relationship is achieved by connecting two segments using dynamic weights generated based on load and historical deviation; the output SNCR outlet NO... x The concentration, SCR inlet concentration distribution and ammonia consumption prediction results are used to adjust the reaction parameters of the mechanism model in real time based on the measured deviations, and the data-driven model coefficients are corrected by combining the catalyst activity decay trend to form a dynamically adapted prediction output.

[0006] Specifically, the method for dynamically correcting the model prediction parameter matrix is ​​as follows: SNCR export NO is obtained based on a preset cycle. x Concentration, SCR outlet NO x The system collects real-time data sets of concentration and ammonia slip concentration, and calculates the absolute deviation value and deviation direction vector with the prediction data set output by the SNCR-SCR coupled dynamic prediction model at the same time. Based on the magnitude and direction of the deviation, the coupling coefficient describing the relationship between SNCR and SCR efficiency in the SNCR-SCR coupled dynamic prediction model is adjusted, and the correlation weight parameter of the influence of flue gas residence time on denitrification efficiency is corrected simultaneously. The updated parameter matrix is ​​substituted into the SNCR-SCR coupled dynamic prediction model to perform the prediction calculation for the next cycle, forming a dynamic iterative closed loop of acquisition-comparison-correction-verification.

[0007] Specifically, the autonomous switching of ammonia injection distribution mode is implemented using the following method: The system acquires real-time boiler load data, extracts load change characteristics, calculates load change rate and acceleration, and dynamically corrects load rise and fall thresholds based on historical fluctuation patterns. By determining the matching degree between real-time characteristic parameters and corrected thresholds, it autonomously selects and switches to the corresponding ammonia injection distribution mode. When the load rise characteristic matches, the SNCR ammonia injection quantity adjustment parameter within the mode is self-optimized based on historical response effects with the same trend. When the load fall characteristic matches, the SCR ammonia injection quantity adjustment coefficient is dynamically generated in association with the SCR inlet NOx gradient. The system continuously updates dynamic load characteristic parameters to drive the smooth switching process of the ammonia injection distribution mode. During the switching process, the ammonia injection quantity adjustment amplitude decays based on a preset decay curve gradient, suppressing instantaneous system fluctuations.

[0008] Specifically, the feedforward-feedback composite control architecture includes: The feedforward stage captures boiler load fluctuation trends and combustion condition adjustment signals, analyzes characteristic parameters to generate pre-adjustment commands for SNCR and SCR ammonia injection, and adapts to changes in NOx generation in the furnace. The feedback stage obtains the outlet NOx concentration, calculates the deviation characteristic quantity by the difference between the actual concentration and the target concentration, and generates ammonia injection compensation parameters based on the deviation characteristic quantity and a preset correction rule. The architecture integrates the feedforward pre-adjustment and feedback compensation parameters through dynamic weight allocation, outputs coordinated ammonia injection commands, and performs dynamic coordination of disturbance prediction and real-time correction.

[0009] Specifically, the method for calculating the ammonia injection ratio of SNCR and SCR in real time using a rolling time-domain optimization approach is as follows: The initial length of the rolling time-domain window is preset, and the predicted values ​​of denitrification performance parameters, ammonia consumption parameters, and related operating condition parameters output by the SNCR-SCR coupled dynamic prediction model are input into the rolling time-domain window. The relevant constraints of ammonia injection control are embedded in the objective function construction stage of the ammonia injection ratio variable. The constrained sequential quadratic programming algorithm is integrated into the window calculation process. The total ammonia consumption and denitrification deviation are used as the optimization objective, and the ammonia injection ratio variable is solved through multiple rounds of iteration. The model prediction value is calibrated online based on the real-time collected operating condition data, and the iteration step size and convergence accuracy threshold are dynamically adjusted. The calculated value of the objective function is monitored, and the iteration is terminated when the preset convergence state is reached. The SNCR and SCR ammonia injection ratios corresponding to the current window are output.

[0010] Specifically, the system fluctuation adaptation mechanism includes: The system acquires real-time dynamic characteristics of boiler load fluctuation amplitude, flue gas parameter transient rate, and denitrification efficiency fluctuation. Based on a preset three-dimensional classification criterion of amplitude, duration, and period, it identifies instantaneous disturbances, continuous fluctuations, and periodic oscillations according to fluctuation amplitude classification, duration segmentation, and periodic feature identification. It dynamically adjusts the feedforward-feedback control weights, ammonia injection ratio optimization step size, and mode switching transition coefficient for different fluctuation types. It also coordinates and matches the response thresholds of SNCR and SCR actuators to construct a multi-dimensional fluctuation buffer chain, enabling real-time adaptation of control parameters to system fluctuation characteristics.

[0011] Specifically, the method for constructing a comprehensive evaluation system by combining catalyst activity-related influencing factors is as follows: Real-time monitoring data and initial activity baseline parameters of catalyst activity-related influencing factors are collected to construct a two-level hierarchical structure of evaluation target-influencing factors. The relative importance of each influencing factor is determined pairwise, a pairwise comparison judgment matrix is ​​constructed and consistency verification is performed, and the weight coefficients of each influencing factor are calculated using the analytic hierarchy process (AHP). The real-time standardized data of each influencing factor are weighted and summed with their corresponding weight coefficients to generate a comprehensive catalyst activity evaluation index. This comprehensive catalyst activity evaluation index is quantitatively compared with a preset activity decay level threshold to define the current activity decay level of the catalyst, and a quantitative evaluation report including the index value, decay level, and contribution analysis of related influencing factors is output.

[0012] Specifically, the method for establishing the mapping relationship between the active state and the ammonia-nitrogen molar ratio is as follows: Historical operating data corresponding to each activity state level of the catalyst are retrieved, and a sample set of ammonia-nitrogen molar ratio and related operating parameters is split based on the activity state level. Outlier removal, normalization, and data smoothing are performed on the sample set. Using the activity state level as the input vector and the ammonia-nitrogen molar ratio as the output vector, a benchmark mapping curve for the ammonia-nitrogen molar ratio at each activity state is obtained through polynomial fitting. A correction term is established based on the correlation analysis between operating parameters and the ammonia-nitrogen molar ratio, and the deviation of real-time operating parameters is embedded into the benchmark mapping curve to construct a dynamic mapping relationship. Newly acquired operating data is used to verify the mapping accuracy, and the function parameters are continuously iterated and optimized to form a stable mapping relationship between the activity state and the ammonia-nitrogen molar ratio.

[0013] Specifically, the method for reverse correction of the ammonia distribution coefficient of each spray gun in SNCR is as follows: Acquire spatial distribution data of the reducing agent concentration field at the SCR inlet, identify abnormal regions deviating from the target uniform concentration and their concentration deviation amplitudes; establish a spatial coupling relationship between the spray coverage area of ​​each SNCR spray gun and the abnormal region at the SCR inlet, and quantify the concentration contribution weight coefficient of each spray gun to the abnormal region by combining the working condition correlation factors of spray gun spray angle and installation height; couple the contribution weight coefficient with the temperature field gradient and flue gas turbulence intensity of the corresponding region to generate a working condition dynamic adaptation factor; based on the quantitative correlation between the working condition dynamic adaptation factor and the concentration deviation amplitude, solve the correction coefficient of the ammonia distribution coefficient of each spray gun, and adjust the ammonia distribution ratio of each spray gun based on the correction coefficient to correct the spray guns in areas where the deviation exceeds the preset range.

[0014] Specifically, the method for generating the comprehensive risk assessment index for system operation is as follows: Using the uniformity of the furnace temperature field spatial distribution and the catalyst activity decay rate as core inputs, and based on the real-time operating load of the boiler, the weight values ​​of the uniformity coefficient of the furnace temperature field spatial distribution and the catalyst activity decay rate coefficient are dynamically calibrated. A dynamic coupling factor characterizing the interaction between the temperature field and catalyst activity is introduced to strengthen the dynamic correlation between the uniformity of the furnace temperature field spatial distribution and the catalyst activity decay rate. This dynamic coupling factor is then incorporated into a nonlinear weighted fusion calculation, and a comprehensive evaluation index that can characterize the system's operational risk level and evolution trend is generated through quantitative calculation.

[0015] Specifically, the closed-loop control system for the entire process of data interaction and risk assessment includes: A dynamic correlation adjustment rule base for the SNCR ammonia injection temperature window boundary and the SCR ammonia slip safety limit is constructed. By analyzing the correlation patterns between the SNCR ammonia injection temperature window boundary and the SCR ammonia slip safety limit as the operating conditions change in historical operating data, a collaborative adjustment logic based on operating load and flue gas composition is extracted. Under scenarios of changing system operating status, the generated comprehensive system operating risk assessment index is invoked to dynamically adjust the SNCR ammonia injection temperature window boundary, and the SCR ammonia slip safety limit is adjusted simultaneously based on the preset collaborative logic in the rule base. After adjustment, real-time temperature distribution in the SNCR reaction zone and SCR outlet ammonia slip concentration monitoring data are continuously collected. Deviation quantification analysis is used to verify the adaptability of the boundary conditions of the SCR and SNCR dual systems to the current operating status, forming a dynamic calibration closed loop for boundary parameters.

[0016] The beneficial effects of this invention are as follows: (1) This invention constructs a coupled prediction model that combines real-time monitoring data with dynamic correction, and combines it with a multi-mode ammonia injection allocation strategy that autonomously switches based on load dynamic characteristics. It can adapt to boiler load fluctuations, changes in combustion conditions and transient changes in flue gas parameters, effectively solving the problems of lagging ammonia injection adjustment and fluctuating denitrification efficiency in traditional control. At the same time, it optimizes the ammonia injection ratio to reduce overall ammonia consumption, taking into account both denitrification effect and operating economy.

[0017] (2) By comprehensively evaluating the catalyst's activity state, the present invention establishes a dynamic mapping relationship between activity and ammonia-nitrogen molar ratio, and can adaptively adjust the ammonia injection parameters according to the activity decay situation to avoid local overload or inefficient operation of the catalyst; at the same time, it combines the uniformity of SCR inlet reducing agent distribution to reverse correct the ammonia quantity distribution of SNCR spray gun, reduce the abnormal increase of ammonia escape concentration, reduce the risk of corrosion and blockage of subsequent equipment, and extend the service life of the catalyst and related equipment.

[0018] (3) This invention generates a comprehensive evaluation index of system operation risk and establishes a collaborative adaptive mechanism for the boundary conditions and operating status of the SNCR and SCR dual systems. It can dynamically match the operating parameters of the dual systems, avoid the interference of single system adjustment on the overall operating conditions, and improve the overall operating stability of the coupled system. At the same time, through the collaborative linkage of feedforward-feedback composite control, it can quickly respond to operating condition disturbances and ensure that the system meets environmental emission requirements. Attached Figure Description

[0019] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0020] Figure 1 This is a flowchart illustrating the full-load adaptive ammonia injection dynamic coordinated control method for SNCR-SCR coupled systems according to the present invention. Figure 2This is a structural block diagram of the full-load adaptive ammonia injection dynamic coordinated control method for SNCR-SCR coupled systems in this invention. Detailed Implementation

[0021] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0022] Please see Figure 1-2 A full-load adaptive ammonia injection dynamic coordinated control method for SNCR-SCR coupled systems includes: S1: By real-time multidimensional monitoring of distributed temperature field data in the SNCR reaction zone and multidimensional characteristic parameters of the SCR inlet, combined with precise analysis of flue gas residence time and dynamic trend of boiler load change and combustion condition characteristic parameters, based on historical big data mining and deep coupling analysis of denitrification reaction mechanism, an SNCR-SCR coupled dynamic prediction model is constructed, and the model prediction parameter matrix is ​​dynamically corrected based on real-time operating data, outputting a set of basic parameters for ammonia injection decision. S2: Based on the aforementioned basic parameter set, design a multi-mode adaptive ammonia injection coordination allocation decision, and autonomously switch the ammonia injection allocation mode through the dynamic threshold of load change rate; adopt a feedforward-feedback composite control architecture, calculate the ammonia injection ratio of SNCR and SCR in real time through rolling time domain optimization, introduce a system fluctuation adaptive adaptation mechanism to suppress the influence of system dynamic disturbance, and generate real-time ammonia injection control commands. S3: Obtain the SCR outlet ammonia slip concentration and catalyst bed pressure difference data, and construct a comprehensive evaluation system by combining catalyst activity-related influencing factors; establish a dynamic mapping relationship between the active state and the ammonia-nitrogen molar ratio, and adaptively match the ammonia demand threshold of the SCR system; at the same time, by monitoring the SCR inlet reducing agent distribution uniformity index, reverse the ammonia distribution coefficient of each spray gun in the SNCR, and feed the correction result back to the model prediction parameter matrix for secondary optimization. S4: By calculating the furnace temperature field uniformity index and catalyst activity decay acceleration in real time, a weighted nonlinear fusion algorithm is used to generate a comprehensive assessment index of system operation risk. The boundary of the SNCR ammonia injection temperature window and the safety limit of ammonia escape in the SCR system are dynamically adjusted to construct a closed-loop control system for the entire process of data interaction and risk assessment.

[0023] Specifically, the SNCR-SCR coupled dynamic prediction model includes: Obtain the spatial distribution and time series characteristics of the temperature field in the SNCR reaction zone, the flue gas components at the SCR inlet, the boiler load, and historical operation data, and form an input matrix through feature decoupling and standardization processing; construct a segmented coupling architecture based on the denitration reaction kinetic mechanism. In the low-load section, use a mechanism analysis model that embeds the correlation between the temperature window and the flue gas velocity, and in the high-load section, use a data-driven model constructed based on historical operation data mining to establish the mapping relationship between the input parameters and the NO concentration at the SCR inlet, and connect the two sections through dynamic weights generated based on the load and historical deviations; output the predicted results of the NO concentration at the SNCR outlet, the NO concentration distribution at the SCR inlet, and the ammonia consumption, and adjust the reaction parameters of the mechanism model in real time based on the measured deviation, and correct the coefficients of the data-driven model in combination with the catalyst activity decay trend to form a dynamically adaptable prediction output. x The mapping relationship between the input parameters and the NO concentration at the SCR inlet is established, and the two sections are connected through dynamic weights generated based on the load and historical deviations; output the predicted results of the NO concentration at the SNCR outlet, the NO concentration distribution at the SCR inlet, and the ammonia consumption, and adjust the reaction parameters of the mechanism model in real time based on the measured deviation, and correct the coefficients of the data-driven model in combination with the catalyst activity decay trend to form a dynamically adaptable prediction output. x concentration, and the predicted results of the NO concentration distribution at the SCR inlet and the ammonia consumption. Based on the measured deviation, adjust the reaction parameters of the mechanism model in real time, and combine the catalyst activity decay trend to correct the coefficients of the data-driven model to form a dynamically adaptable prediction output.

[0024] In this embodiment, a SNCR-SCR coupled denitration system supporting a 300MW subcritical pulverized coal boiler is used as the application object. The rated load of this boiler is 300MW, and the operating load covers 25%-100% (i.e., 75MW-300MW), mainly used in the thermal power generation scenario, and the coal is bituminous coal (volatile content 28%-32%, nitrogen content 1.2%-1.5%). The SNCR reaction zone is arranged in the upper furnace platen superheater area, and the SCR reactor uses a honeycomb vanadium-titanium-based catalyst (initial activity ≥90%, designed life 3 years). The core goal of this dynamic correction scheme is to control the prediction error of the SNCR-SCR coupled dynamic prediction model within 3% through real-time data feedback iteration, ensuring that the predicted values of the NO concentration at the SNCR outlet, the NO concentration at the SCR outlet, and the ammonia consumption output by the model are accurately matched with the actual working conditions, providing reliable parameter support for the ammonia injection control decision. x concentration, the NO concentration at the SCR outlet x concentration, and the predicted values of the ammonia consumption are accurately matched with the actual working conditions, providing reliable parameter support for the ammonia injection control decision.

[0025] Core preset parameters and setting basis (I) Preset cycle Set the preset cycle for data collection and parameter correction to 5 minutes. Setting basis: Considering the load fluctuation characteristics of this type of boiler (historical operation data shows that the load fluctuation cycle is mostly 5-15 minutes), a 5-minute cycle can not only capture the model prediction deviation caused by the working condition change in time, but also avoid parameter oscillation caused by too frequent correction, balancing the response speed and system stability.

[0026] (II) Deviation determination threshold Preset the deviation determination thresholds for three types of core parameters: NO xThe absolute deviation threshold for concentration is 5% (relative deviation), the absolute deviation threshold for ammonia slip concentration is 0.3 ppm, and the parameter adjustment step size threshold is 0.05. The settings are based on the allowable error requirements for online monitoring data in the "Technical Specification for Flue Gas Denitrification in Thermal Power Plants," combined with the statistical results from the system's historical operation that "the model stability is best when the prediction deviation is ≤5%." x Concentration deviation threshold: Ammonia escape concentration directly affects the corrosion risk of subsequent equipment. Based on the safe operation requirements of the air preheater (ammonia escape ≤3ppm), the deviation threshold is set to 0.3ppm to reserve a safety margin. The parameter adjustment step size threshold is determined through multiple simulation experiments. A step size of 0.05 can ensure smooth parameter adjustment and avoid sudden changes in model output caused by excessive adjustment in a single step.

[0027] (III) Initial parameter baseline values In the initial parameter matrix of the model, the initial value of the coupling coefficient for the relationship between SNCR and SCR efficiencies was set to 0.65, and the initial value of the correlation weight parameter for the influence of flue gas residence time on denitrification efficiency was set to 0.35. The basis for these settings is as follows: Based on the denitrification efficiency distribution ratio of SNCR and SCR in the boiler design document (SNCR denitrification efficiency 40% and SCR denitrification efficiency 85% under design conditions), the initial value of the coupling coefficient was derived through the denitrification reaction kinetic mechanism; the initial value of the correlation weight parameter was determined after numerical simulation calibration, referencing historical operating data statistics of similar boilers.

[0028] Implementation steps (I) Data Acquisition and Preprocessing Monitoring equipment deployment: Three laser-type NO monitoring units are evenly arranged along the width direction of the cross-section of the SNCR outlet flue. x Online concentration analyzer (measurement accuracy ≤ ±2 mg / m³) 3 One laser-type NOx generator is installed at the cross-section corresponding to the SCR outlet. x An online concentration analyzer (with the same accuracy) and one laser-type online ammonia slip monitor (measurement accuracy ≤ ±0.1ppm) were used. The sampling frequency of all devices was set to 1Hz (once per second) to ensure real-time data acquisition.

[0029] Data Extraction and Processing: Within each preset period (5 minutes), all sampling data from the three analyzers at the SNCR outlet are extracted. After removing outliers exceeding the 3σ criterion (σ is the standard deviation of the data within that period), the arithmetic mean is calculated as the SNCR outlet NO. x Real-time concentration data; similarly, extract NO from the SCR outlet. x The sampling data of concentration and ammonia slip concentration, after outlier removal, are used to calculate the arithmetic mean, forming the "SNCR outlet NO". x Concentration - SCR outlet NO xReal-time data set of "Concentration - Ammonia Escape Concentration".

[0030] Predictive data extraction: Simultaneously extract the corresponding predicted data output by the SNCR-SCR coupled dynamic prediction model within the 5-minute period, and calculate the arithmetic mean of the predicted values ​​within the period to form a prediction data group, ensuring that the time dimension of the real-time data and the predicted data are completely matched.

[0031] (II) Deviation Calculation Absolute deviation calculation: For the three types of core parameters, the absolute deviation between real-time data and predicted data is calculated separately. The formula is: Absolute deviation = |Average value of real-time data - Average value of predicted data|. For example, the SNCR export NO within a certain period... x The real-time average concentration was 180 mg / m³. 3 The predicted average is 172 mg / m³. 3 Then the absolute deviation value = 8 mg / m 3 The relative deviation is approximately 4.4% (8 / 180).

[0032] Deviation direction vector determination: The deviation direction vector is determined by the sign of the difference between "real-time data and predicted data". A positive difference indicates a positive deviation (predicted value is lower than measured value), and a negative difference indicates a negative deviation (predicted value is higher than measured value). For example, if the real-time average ammonia slip at the SCR outlet is 1.2 ppm and the predicted average is 1.4 ppm, the difference is -0.2 ppm, and the deviation direction vector is negative.

[0033] (III) Parameter Adjustment Rules and Execution Deviation level classification: Based on the preset deviation judgment threshold, the deviation level is divided into three levels: slight deviation (NO) x Relative concentration deviation ≤ 5% and absolute ammonia slip deviation ≤ 0.3 ppm), moderate deviation (5%) <NO x Relative concentration deviation ≤10% or 0.3ppm < absolute ammonia slip deviation ≤0.5ppm), and heavy deviation (NO) x (Relative concentration deviation > 10% or absolute ammonia slip deviation > 0.5 ppm).

[0034] Coupling coefficient adjustment: When a positive deviation occurs (predicted value is lower than measured value): If it is a slight deviation, adjust the coupling coefficient in the direction of increasing, with an adjustment amount of 0.02 × (absolute deviation value / deviation threshold); if it is a moderate deviation, adjust the adjustment amount of 0.03 × (absolute deviation value / deviation threshold); if it is a severe deviation, adjust the adjustment amount of 0.05 × (absolute deviation value / deviation threshold). However, after a single adjustment, the coupling coefficient must not exceed 0.8 (upper limit, to avoid over-reliance on SNCR efficiency) or be lower than 0.5 (lower limit, to ensure the basic denitrification contribution of SNCR).

[0035] When a negative deviation occurs (predicted value higher than measured value): the adjustment direction is opposite to that of a positive deviation, and the calculation logic for the adjustment magnitude is consistent, ensuring that the coupling coefficient fluctuates within a reasonable range of 0.5-0.8. For example, the SNCR export NO for a certain period... x If the relative concentration deviation is 6% (moderate positive deviation), then the adjustment range of the coupling coefficient is 0.03 × (6% / 5%) = 0.036. If the current coupling coefficient is 0.65, the adjusted value is 0.65 + 0.036 = 0.686.

[0036] Adjustment of associated weight parameters: Positive deviation scenario: If the deviation is mainly caused by the estimation deviation of flue gas residence time (determined by correlation analysis of historical data, when the flue gas flow fluctuation is >5%, the residence time has a significant impact), then increase the correlation weight parameter, and adjust the magnitude by 0.5 times the adjustment magnitude of the coupling coefficient; Negative bias scenario: Reduce the correlation weight parameter by 0.5 times the adjustment of the coupling coefficient, and maintain the correlation weight parameter within the range of 0.2-0.5 (the lower limit ensures that the impact of dwell time is not ignored, and the upper limit avoids excessive amplification). Continuing the previous example, if the coupling coefficient is adjusted by 0.036, the correlation weight parameter adjustment = 0.036 × 0.5 = 0.018. The current weight parameter is 0.35, and after adjustment, it becomes 0.35 + 0.018 = 0.368.

[0037] (iv) Iterative closed-loop verification Parameter update: Substitute the adjusted coupling coefficients and associated weight parameters into the model prediction parameter matrix, replace the original parameters, and complete the parameter matrix update.

[0038] Model prediction: The updated parameter matrix is ​​immediately used for the model prediction calculation in the next 5-minute cycle, and the model outputs a new set of prediction data based on the new parameters.

[0039] Deviation Verification: In the deviation calculation phase of the next cycle, the focus is on verifying the effect of the corrected parameters. If the deviation decreases to a slight deviation or below after correction, it indicates that the adjustment is effective, and the current adjustment logic is maintained; if the deviation is still moderate or severe, the same rules are continued in subsequent cycles until the deviation meets the requirements.

[0040] Stability control: If the deviation is ≤3% for 3 consecutive cycles, the current parameter is set as a temporary benchmark value, and the subsequent adjustment step size is halved (adjustment step size = 0.025) to avoid overcorrection that causes parameter fluctuations; if the deviation exceeds the threshold for 2 consecutive cycles, the adjustment is paused, the parameter value of the previous cycle is restored, the cause of the deviation is re-analyzed, and the adjustment is performed again.

[0041] Implementation effect verification (a) Accuracy Verification Three typical operating conditions (low load 100MW, medium load 200MW, and high load 280MW) were selected for continuous 72-hour verification. The results are as follows: Under low load conditions: The average prediction error of the model before correction was 8.2%, and the average error after correction was reduced to 2.3%, meeting the target requirement of ≤3%; Medium load condition: The average error was 7.5% before correction, and decreased to 1.8% after correction; High-load conditions: The average error before correction was 6.8%, which was reduced to 1.5% after correction. Under all three types of conditions, the prediction error of ammonia slip concentration decreased from 0.45 ppm before correction to below 0.12 ppm, which meets the preset deviation threshold requirements.

[0042] (II) Stability Verification After 30 days of continuous operation, the parameter adjustment process was smooth with no parameter oscillations, and the model's fault-free operation time was ≥99.8%. Under the scenario of sudden boiler load changes (load change rate >10% / min), the corrected model can still control the prediction error within 4% within 2 cycles, improving its ability to adapt to dynamic changes in operating conditions.

[0043] Specifically, the method for dynamically correcting the model prediction parameter matrix is ​​as follows: SNCR export NO is obtained based on a preset cycle. x Concentration, SCR outlet NO x The system collects real-time data sets of concentration and ammonia slip concentration, and calculates the absolute deviation value and deviation direction vector with the prediction data set output by the SNCR-SCR coupled dynamic prediction model at the same time. Based on the magnitude and direction of the deviation, the coupling coefficient describing the relationship between SNCR and SCR efficiency in the SNCR-SCR coupled dynamic prediction model is adjusted, and the correlation weight parameter of the influence of flue gas residence time on denitrification efficiency is corrected simultaneously. The updated parameter matrix is ​​substituted into the SNCR-SCR coupled dynamic prediction model to perform the prediction calculation for the next cycle, forming a dynamic iterative closed loop of acquisition-comparison-correction-verification.

[0044] Specifically, the autonomous switching of ammonia injection distribution mode is implemented using the following method: Obtain the real-time load data of the boiler, extract the load change characteristics to calculate the load change rate and acceleration, and dynamically correct the load rise and fall thresholds in combination with the historical fluctuation law; determine through the matching degree between the real-time characteristic parameters and the corrected thresholds, and independently select the corresponding ammonia injection distribution mode and execute the switch. When the load rise characteristics match, the SNCR ammonia injection volume adjustment parameters within the mode are self-optimized based on the historical response effect of the same trend; when the load fall characteristics match, the SCR ammonia injection volume adjustment coefficient is dynamically generated in association with the SCR inlet NOx gradient; continuously update the load dynamic characteristic parameters to drive the smooth switching process of the ammonia injection distribution mode, and the adjustment amplitude of the ammonia injection volume during the switching process decays based on the preset decay curve gradient to suppress the instantaneous fluctuation of the system.

[0045] In this embodiment, a SNCR-SCR coupled denitration system supporting a certain circulating fluidized bed boiler is used as the application object. When the boiler is in the conventional operating load range, due to the influence of coal particle size fluctuations, obvious load fluctuations often occur, and it is necessary to autonomously switch the ammonia injection distribution mode to ensure denitration stability. The system collects load data in real time through the boiler distributed control system (DCS), sets the sampling interval as a fixed value, and filters out instantaneous interference through a short-term sliding window, and extracts two core characteristic parameters: the load change rate r (reflecting the speed of load rise and fall) and the load acceleration a (reflecting the increase or decrease of the load change trend).

[0046] The system pre-calls the historical load fluctuation data of the same coal quality working conditions in the near future, analyzes and obtains the typical fluctuation rate range during load rise and the stable fluctuation range during load fall, and dynamically corrects the determination threshold based on this: clarify the load rise trigger threshold r and the load fall trigger threshold r (the negative sign indicates a decrease), and at the same time set the auxiliary determination condition of the acceleration a (the positive sign indicates an accelerating change, and the negative sign indicates a decelerating change). When it is real-time monitored that the load change rate r reaches the rise trigger threshold and the acceleration a > 0 (the load is accelerating upward), the system autonomously switches to the "load rise ammonia injection mode", and at this time, calls the ammonia injection response data of the historical same trend working conditions, and selects the SNCR ammonia injection volume adjustment coefficient with higher adaptability to execute the adjustment, and the SCR ammonia injection volume maintains an appropriate proportion of the basic value to reserve adjustment space. When it is monitored that the load change rate r reaches the fall trigger threshold and the acceleration a < 0 (the load is accelerating downward), the system switches to the "load fall ammonia injection mode", and combines the change trend of the SCR inlet NO x concentration to dynamically generate the SCR ammonia injection volume adaptability adjustment coefficient to avoid ammonia escape exceeding the standard after the NO x concentration decreases, and the SNCR ammonia injection volume remains stable to maintain the reaction zone temperature.

[0047] To avoid system fluctuations caused by sudden changes in ammonia injection rate during mode switching, a reasonable smooth transition phase is implemented. During the transition, the ammonia injection rate adjustment gradually decreases from its initial value, transitioning to the stable adjustment range of the target mode after multiple stages of attenuation. Actual operational data shows that after adopting this mode switching mechanism, the system outlet NO during load fluctuations... x Concentration fluctuations are controlled within a small range, and ammonia escape concentration remains at a low level. This improves the fluctuation control effect compared to the traditional fixed mode and is suitable for the load fluctuation characteristics of circulating fluidized bed boilers.

[0048] Specifically, the feedforward-feedback composite control architecture includes: The feedforward stage captures boiler load fluctuation trends and combustion condition adjustment signals, analyzes characteristic parameters to generate pre-adjustment commands for SNCR and SCR ammonia injection, and adapts to changes in NOx generation in the furnace. The feedback stage obtains the outlet NOx concentration, calculates the deviation characteristic quantity by the difference between the actual concentration and the target concentration, and generates ammonia injection compensation parameters based on the deviation characteristic quantity and a preset correction rule. The architecture integrates the feedforward pre-adjustment and feedback compensation parameters through dynamic weight allocation, outputs coordinated ammonia injection commands, and performs dynamic coordination of disturbance prediction and real-time correction.

[0049] Specifically, the method for calculating the ammonia injection ratio of SNCR and SCR in real time using a rolling time-domain optimization approach is as follows: The initial length of the rolling time-domain window is preset, and the predicted values ​​of denitrification performance parameters, ammonia consumption parameters, and related operating condition parameters output by the SNCR-SCR coupled dynamic prediction model are input into the rolling time-domain window. The relevant constraints of ammonia injection control are embedded in the objective function construction stage of the ammonia injection ratio variable. The constrained sequential quadratic programming algorithm is integrated into the window calculation process. The total ammonia consumption and denitrification deviation are used as the optimization objective, and the ammonia injection ratio variable is solved through multiple rounds of iteration. The model prediction value is calibrated online based on the real-time collected operating condition data, and the iteration step size and convergence accuracy threshold are dynamically adjusted. The calculated value of the objective function is monitored, and the iteration is terminated when the preset convergence state is reached. The SNCR and SCR ammonia injection ratios corresponding to the current window are output.

[0050] In this embodiment, a SNCR-SCR coupled denitrification system配套 with a 300MW subcritical pulverized coal boiler is taken as the application object. The daily operating load range of this boiler covers medium and low loads to the rated load. Affected by the fluctuation of coal calorific value and the unit peak shaving demand, the load fluctuation period is 5 - 15 minutes. It is necessary to calculate the optimal ammonia injection ratio in real time through rolling horizon optimization to adapt to the dynamic changes of the working conditions. The system first completes the preset of the rolling horizon window parameters: combining the load fluctuation period, the initial window length is set to 10 minutes (to ensure complete coverage of the fluctuation process), and the window rolling update period is 2 minutes (to ensure the real-time nature of the optimization result); the core optimization variable is defined as the SNCR ammonia injection ratio α (α ∈ (0, 1)), and the corresponding SCR ammonia injection ratio is 1 - α. The goal is to dynamically calculate the optimal value of α. <​​​​​​​​​​​​​​​​(α,t) represents the NO at the system outlet corresponding to the ammonia injection ratio α at time t. x Predicted concentration (unit: mg / m³) 3 ); C nox ^target is the system exit NO x Target concentration value (take 50 mg / m³) 3 (It meets the national emission standards for air pollutants from thermal power plants).

[0053] To ensure that the optimization results do not exceed the system safety and performance boundaries, the following ammonia injection control constraints are embedded: ① Ammonia injection ratio range constraint: 0.3≤α≤0.7 (to avoid excessively small α causing SCR overload and ammonia slip exceeding the standard, or excessively large α causing incomplete SNCR reaction and increased ammonia consumption); ② SNCR temperature window constraint: 850℃≤T≤1100℃ (this range is the optimal temperature range for SNCR reaction in pulverized coal boilers. If it exceeds this range, the α value will be forcibly reduced by 0.1 to ensure reaction efficiency); ③ SCR ammonia slip constraint: NH3^slip(t)≤3ppm (the predicted ammonia slip concentration at the SCR outlet at time t does not exceed 3ppm to prevent air preheater blockage and corrosion); ④ Total denitrification efficiency constraint: η1(t)×η2(t)≥85% (to ensure that the total denitrification efficiency of the system meets the standard and avoids exceeding environmental protection standards).

[0054] The constrained sequential quadratic programming algorithm is integrated into the window calculation process to perform iterative solution of the ammonia injection ratio: ① Initial parameter settings: Based on the optimal data of historical operating conditions, the initial ammonia injection ratio α0=0.5, the initial iteration step size h0=0.05, and the convergence accuracy threshold ε=10 are set. -4 (Conventional precision requirements in industrial control to ensure stable optimization results); ② Iterative calculation logic: In each iteration, the current αK is substituted into the objective function J, and the ammonia injection ratio α for the next round that satisfies all constraints is solved by a sequential quadratic programming algorithm. K+1 The core iterative relationship can be simplified as follows: , Among them, h k Let k be the iteration step size. ① is the partial derivative of the objective function J with respect to the ammonia injection ratio α (reflecting the combined influence trend of α change on total ammonia consumption and denitrification deviation; the negative sign indicates that α is updated along the direction of J decrease, ensuring iteration convergence); ③ Dynamic adjustment of iteration step size: if the boiler load fluctuation amplitude |ΔP / P(t)|>5% is detected within the window (a common scenario of large fluctuations in pulverized coal boilers), then hk is reduced to 0.02 to avoid sudden changes in α causing system fluctuations.

[0055] Online calibration of model predictions based on real-time operating data improves iterative accuracy: Boiler actual load P_real, furnace actual temperature T_real, and system outlet actual NO are collected every 2 minutes (window rolling update cycle). x After comparing the concentration C_real with the model prediction, calibration is performed: ① Ammonia consumption parameter calibration: If P_real > P(t) (actual load is higher than the predicted value), then q1(t) is corrected to q1'(t) = q1(t) × [1 + 0.1 × (P_real - P(t)) / P(t)] (when the load increases, the flue gas disturbance in the SNCR reaction zone increases, and a slight increase in unit ammonia consumption ensures reaction efficiency); ② Denitrification efficiency calibration: If C_real > C nox (α,t)(actual NO) x If the concentration is higher than the predicted value, then η2(t) is corrected to η2'(t) = η2(t) × 0.95 (after correction, the SCR efficiency decreases slightly, forcing the increase of 1-α value in the iteration process to compensate for the denitrification capacity); ③ Parameter update after calibration: Substitute the calibrated q1'(t) and η2'(t) into the objective function, and adjust the iteration step size h synchronously. k (When the load fluctuates greatly h) k =0.02, fluctuation hour h k =0.05).

[0056] Monitor the changes in the calculated objective function value and the ammonia injection ratio, determine the convergence of the iteration, and output the optimal ammonia injection ratio: The iteration terminates when any of the following conditions are met: ① Difference in objective function value: |J k+1 -J k |≤ε;②Change in ammonia injection ratio:|α k+1 -α k |≤ε. For example, when the boiler load is stable at 280MW and the furnace temperature is 980℃ (the optimal reaction temperature range), the convergence condition is reached after 5 iterations, and the optimal ammonia injection ratio α=0.53 is output, that is, SNCR ammonia injection ratio is 53% and SCR ammonia injection ratio is 47%.

[0057] After the window scrolls and updates, repeat the above process of "data acquisition - objective function calculation - iterative solution - real-time calibration - convergence output" to dynamically update the optimal ammonia injection ratio.

[0058] Specifically, the system fluctuation adaptation mechanism includes: The system acquires real-time dynamic characteristics of boiler load fluctuation amplitude, flue gas parameter transient rate, and denitrification efficiency fluctuation. Based on a preset three-dimensional classification criterion of amplitude, duration, and period, it identifies instantaneous disturbances, continuous fluctuations, and periodic oscillations according to fluctuation amplitude classification, duration segmentation, and periodic feature identification. It dynamically adjusts the feedforward-feedback control weights, ammonia injection ratio optimization step size, and mode switching transition coefficient for different fluctuation types. It also coordinates and matches the response thresholds of SNCR and SCR actuators to construct a multi-dimensional fluctuation buffer chain, enabling real-time adaptation of control parameters to system fluctuation characteristics.

[0059] Specifically, the method for constructing a comprehensive evaluation system by combining catalyst activity-related influencing factors is as follows: Real-time monitoring data and initial activity baseline parameters of catalyst activity-related influencing factors are collected to construct a two-level hierarchical structure of evaluation target-influencing factors. The relative importance of each influencing factor is determined pairwise, a pairwise comparison judgment matrix is ​​constructed and consistency verification is performed, and the weight coefficients of each influencing factor are calculated using the analytic hierarchy process (AHP). The real-time standardized data of each influencing factor are weighted and summed with their corresponding weight coefficients to generate a comprehensive catalyst activity evaluation index. This comprehensive catalyst activity evaluation index is quantitatively compared with a preset activity decay level threshold to define the current activity decay level of the catalyst, and a quantitative evaluation report including the index value, decay level, and contribution analysis of related influencing factors is output.

[0060] Specifically, the method for establishing the mapping relationship between the active state and the ammonia-nitrogen molar ratio is as follows: Historical operating data corresponding to each activity state level of the catalyst are retrieved, and a sample set of ammonia-nitrogen molar ratio and related operating parameters is split based on the activity state level. Outlier removal, normalization, and data smoothing are performed on the sample set. Using the activity state level as the input vector and the ammonia-nitrogen molar ratio as the output vector, a benchmark mapping curve for the ammonia-nitrogen molar ratio at each activity state is obtained through polynomial fitting. A correction term is established based on the correlation analysis between operating parameters and the ammonia-nitrogen molar ratio, and the deviation of real-time operating parameters is embedded into the benchmark mapping curve to construct a dynamic mapping relationship. Newly acquired operating data is used to verify the mapping accuracy, and the function parameters are continuously iterated and optimized to form a stable mapping relationship between the activity state and the ammonia-nitrogen molar ratio.

[0061] Specifically, the method for reverse correction of the ammonia distribution coefficient of each spray gun in SNCR is as follows: Acquire spatial distribution data of the reducing agent concentration field at the SCR inlet, identify abnormal regions deviating from the target uniform concentration and their concentration deviation amplitudes; establish a spatial coupling relationship between the spray coverage area of ​​each SNCR spray gun and the abnormal region at the SCR inlet, and quantify the concentration contribution weight coefficient of each spray gun to the abnormal region by combining the working condition correlation factors of spray gun spray angle and installation height; couple the contribution weight coefficient with the temperature field gradient and flue gas turbulence intensity of the corresponding region to generate a working condition dynamic adaptation factor; based on the quantitative correlation between the working condition dynamic adaptation factor and the concentration deviation amplitude, solve the correction coefficient of the ammonia distribution coefficient of each spray gun, and adjust the ammonia distribution ratio of each spray gun based on the correction coefficient to correct the spray guns in areas where the deviation exceeds the preset range.

[0062] Specifically, the method for generating the comprehensive risk assessment index for system operation is as follows: Using the uniformity of the furnace temperature field spatial distribution and the catalyst activity decay rate as core inputs, and based on the real-time operating load of the boiler, the weight values ​​of the uniformity coefficient of the furnace temperature field spatial distribution and the catalyst activity decay rate coefficient are dynamically calibrated. A dynamic coupling factor characterizing the interaction between the temperature field and catalyst activity is introduced to strengthen the dynamic correlation between the uniformity of the furnace temperature field spatial distribution and the catalyst activity decay rate. This dynamic coupling factor is then incorporated into a nonlinear weighted fusion calculation, and a comprehensive evaluation index that can characterize the system's operational risk level and evolution trend is generated through quantitative calculation.

[0063] Specifically, the closed-loop control system for the entire process of data interaction and risk assessment includes: A dynamic correlation adjustment rule base for the SNCR ammonia injection temperature window boundary and the SCR ammonia slip safety limit is constructed. By analyzing the correlation patterns between the SNCR ammonia injection temperature window boundary and the SCR ammonia slip safety limit as the operating conditions change in historical operating data, a collaborative adjustment logic based on operating load and flue gas composition is extracted. Under scenarios of changing system operating status, the generated comprehensive system operating risk assessment index is invoked to dynamically adjust the SNCR ammonia injection temperature window boundary, and the SCR ammonia slip safety limit is adjusted simultaneously based on the preset collaborative logic in the rule base. After adjustment, real-time temperature distribution in the SNCR reaction zone and SCR outlet ammonia slip concentration monitoring data are continuously collected. Deviation quantification analysis is used to verify the adaptability of the boundary conditions of the SCR and SNCR dual systems to the current operating status, forming a dynamic calibration closed loop for boundary parameters.

[0064] In this embodiment, taking the application of this method in an SNCR-SCR coupled denitrification system of a thermal power plant equipped with a W-type flame boiler as an example, the specific operation of dynamically correcting the predicted parameters of the model is as follows: Considering the characteristics of the W-type flame boiler with variable load and complex coal types, a preset cycle of five minutes is set for data acquisition and model correction to ensure adaptation to the rapid fluctuations of flue gas parameters in the furnace. In the data acquisition stage, three laser-type NOx concentration online analyzers are arranged at the cross-section of the SNCR outlet flue according to the flow characteristics of the W-type flame. The average measurement value of the first 30 seconds within the cycle is taken as the real-time data of NOx concentration at the SNCR outlet. At the corresponding monitoring section at the SCR outlet, one laser-type NOx concentration online analyzer and one laser-type ammonia slip online monitor are installed to simultaneously collect the average measurement value of the first 30 seconds within the cycle, which are respectively used as the real-time data of NOx concentration and ammonia slip concentration at the SCR outlet.

[0065] The three types of real-time average data are compared one by one with the corresponding predicted data output by the SNCR-SCR coupled dynamic prediction model in the same period. The absolute deviation value and direction of the "real-time data - predicted data" are calculated. At the same time, the predicted value of total ammonia consumption output by the model is correlated to form the "parameter deviation - total ammonia consumption" linkage analysis result. If the real-time value of the SNCR outlet NOx concentration is higher than the predicted value, and the predicted value of total ammonia consumption is higher than the best level under the same historical conditions, it indicates that the coefficient describing the relationship between SNCR efficiency and SCR efficiency in the model underestimates the contribution of SNCR denitrification. At this time, the coefficient is adjusted in the direction of deviation to enhance the proportion of SNCR efficiency, so as to reduce the total ammonia consumption by optimizing the synergy of the two systems. If the real-time value of the SCR outlet ammonia slip concentration is higher than the predicted value and close to the dynamic safety limit of SCR ammonia slip, it indicates that the correlation parameter setting of the effect of flue gas residence time on denitrification efficiency in the model is unreasonable. Over-reliance on extending the residence time to improve the denitrification effect leads to ammonia waste. At this time, the weight of the correlation parameter is appropriately reduced, while ensuring that the adjusted parameter does not exceed the dynamic boundary constraint of the SNCR ammonia injection temperature window.

[0066] Once the two types of core parameters have been adjusted, the updated coefficients and related parameters are immediately substituted into the SNCR-SCR coupled dynamic prediction model. The model automatically starts the prediction calculation for the next five-minute cycle based on the new parameters. During the prediction process, the comprehensive assessment index of system operation risk is called simultaneously to verify the matching degree of the boundary conditions of the two systems and the operational safety after parameter adjustment. This forms a closed-loop dynamic correction process of "data acquisition - coupling analysis - parameter optimization - model update - safety verification", ensuring that the model prediction accuracy matches the real-time operating conditions and meets the core objective of minimizing total ammonia consumption, thereby achieving synergistic optimization of denitrification efficiency and operational economy.

[0067] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A full load adaptive ammonia injection dynamic coordination control method for an SNCR-SCR coupled system, characterized in that, The method comprises the following steps: S1: Through real-time multi-dimensional monitoring of SNCR reaction zone temperature field distribution data and SCR inlet multi-dimensional characteristic parameters, combined with accurate analysis of flue gas residence time and dynamic change trend of boiler load, characteristic parameters of combustion condition, based on historical big data mining and deep coupling analysis of denitration reaction mechanism, a SNCR-SCR coupled dynamic prediction model is constructed, and the model prediction parameter matrix is dynamically corrected based on real-time operation data, and a basic parameter set for ammonia injection decision is output; S2: Based on the basic parameter set, a multi-mode adaptive ammonia injection coordination allocation decision is designed, the ammonia injection allocation mode is automatically switched through the load change rate dynamic threshold; a feedforward-feedback composite control architecture is adopted, the ammonia injection proportion of SNCR and SCR is calculated in real time through a rolling time domain optimization method, a system fluctuation adaptive adaptation mechanism is introduced to suppress the influence of system dynamic disturbance, and a real-time ammonia injection control instruction is generated; S3: The ammonia escape concentration and catalyst bed pressure difference data at the outlet of SCR are obtained, and a comprehensive evaluation system is constructed combined with the catalyst activity related influencing factors; a dynamic mapping relationship between the activity state and the ammonia nitrogen molar ratio is established, and the ammonia demand threshold of the SCR system is adaptively matched; at the same time, by monitoring the reducing agent distribution uniformity index at the inlet of SCR, the ammonia amount allocation coefficient of each SNCR lance is corrected in reverse, and the correction result is fed back to the model prediction parameter matrix for secondary optimization; S4: Through real-time calculation of the furnace temperature field uniformity index and the catalyst activity attenuation acceleration, a system operation risk comprehensive evaluation index is generated by using a weighted nonlinear fusion algorithm, the SNCR ammonia injection temperature window boundary and the SCR system ammonia escape safety limit value are dynamically adjusted, and a full-process closed-loop control system of data interaction and risk evaluation is constructed.

2. The method of claim 1, wherein, The SNCR-SCR coupled dynamic prediction model comprises: The spatial distribution and time sequence characteristics of the SNCR reaction zone temperature field, the SCR inlet flue gas components, the boiler load and historical operation data are obtained, and an input matrix is formed through characteristic decoupling and standardization processing; a segmented coupling architecture is constructed with the denitration reaction kinetics mechanism as a constraint, a mechanism analysis model associated with the temperature window and flue gas flow rate is used in the low load section, and a data-driven model constructed based on historical operation data mining is used in the high load section, so as to establish the mapping relationship between the input parameters and the SCR inlet NO x concentration, and the prediction results of the SNCR outlet NO x concentration, the SCR inlet concentration distribution and the ammonia consumption, real-time adjustment of the mechanism model reaction parameters based on the measured deviation, correction of the data-driven model coefficients combined with the catalyst activity attenuation trend, and formation of the dynamically adapted prediction output.

3. The method of claim 1, wherein, The dynamic correction model prediction parameter matrix comprises: SNCR export NO is obtained based on a preset cycle. x Concentration, SCR outlet NO x The system collects real-time data sets of concentration and ammonia slip concentration, and calculates the absolute deviation value and deviation direction vector with the prediction data set output simultaneously by the SNCR-SCR coupled dynamic prediction model. Based on the magnitude and direction vector of the deviation, it adjusts the coupling coefficient of the correlation between SNCR and SCR efficiency, and simultaneously corrects the correlation weight parameter of the influence of flue gas residence time on denitrification efficiency. The updated parameter matrix is ​​then substituted into the SNCR-SCR coupled dynamic prediction model to form a dynamic iterative closed loop of acquisition-comparison-correction.

4. The method of claim 1, wherein, The self-switching ammonia injection allocation mode comprises: Real-time boiler load data is obtained, load change characteristics are extracted to calculate load change rate and acceleration, and historical fluctuation rules are combined to dynamically correct load rising and falling thresholds; through matching degree judgment of real-time characteristic parameters and corrected thresholds, the corresponding ammonia injection allocation mode is selected and switched; load dynamic characteristic parameters are continuously updated to drive the smooth switching process of the ammonia injection allocation mode, and the ammonia injection amount adjustment amplitude is attenuated based on a preset attenuation curve gradient in the switching process to suppress system instantaneous fluctuation.

5. The method of claim 1, wherein, The feedforward-feedback composite control architecture comprises: The feedforward link captures boiler load fluctuation trend and combustion condition adjustment signal, analyzes characteristic parameters to generate SNCR and SCR ammonia injection amount pre-adjustment instruction, and adapts to NOx generation change in the furnace; the feedback link obtains outlet NOx concentration, generates deviation characteristic quantity through difference operation of actual concentration and target concentration, generates ammonia injection compensation parameter based on the deviation characteristic quantity by calling preset correction rules; the architecture fuses feedforward pre-adjustment and feedback compensation parameters through dynamic weight distribution, outputs collaborative ammonia injection instruction, and performs dynamic collaboration of disturbance prediction and real-time correction.

6. The method of claim 1, wherein, The ammonia injection proportion of SNCR and SCR is calculated in real time through a rolling time domain optimization method, and the specific method comprises: A preset rolling time domain window initial length is set, and the denitration performance parameters, ammonia consumption parameters and associated working condition parameter prediction values output by the SNCR-SCR coupling dynamic prediction model into the rolling time domain window are transmitted; the ammonia injection control related constraint conditions are embedded into the target function construction link of the ammonia injection proportion variable, the sequence quadratic programming algorithm with constraints is integrated into the window calculation process, and the ammonia injection proportion variable is implemented multiple rounds of iterative solution; Based on the model prediction value calibrated based on the real-time collected working condition data, the iteration step and the convergence precision threshold are dynamically adjusted, and the SNCR and SCR ammonia injection proportion corresponding to the current window is output.

7. The method of claim 1, wherein, The system fluctuation adaptation mechanism comprises: Real-time acquisition of boiler load fluctuation amplitude, flue gas parameter transient rate and denitration efficiency fluctuation dynamic characteristics, identification of instantaneous disturbance, sustained fluctuation and periodic oscillation type through preset amplitude-time period-three-dimensional classification criteria based on fluctuation amplitude grading, duration segmentation and period characteristic recognition; dynamically adjusting the feedforward-feedback control weight, ammonia injection proportion optimization step and mode switching transition coefficient for different fluctuation types; cooperatively matching the SNCR and SCR actuator response threshold, constructing a multi-dimensional fluctuation buffer chain, and performing real-time adaptation of control parameters and system fluctuation characteristics.

8. The method of claim 1, wherein, The specific method for constructing the comprehensive evaluation system combined with the catalyst activity associated influencing factors is: Collecting real-time monitoring data and initial activity benchmark parameters of catalyst activity associated influencing factors, constructing a two-level structure of evaluation target-influencing factor; comparing the importance of the influencing factors, constructing a judgment matrix and performing consistency check, and calculating the weight coefficients of each influencing factor by analytic hierarchy process; performing weighted summation operation on the real-time standardized data of each influencing factor and the corresponding weight coefficients to generate a catalyst comprehensive activity evaluation index; quantitatively comparing the catalyst comprehensive activity evaluation index with the preset activity attenuation level threshold to define the current activity attenuation level of the catalyst, and outputting a quantitative evaluation report containing index value, attenuation level and associated influencing factor contribution analysis.

9. The method of claim 1, wherein, The specific method for establishing the mapping relationship between the activity state and the ammonia nitrogen molar ratio is: Call the historical operation data corresponding to each activity state level of the catalyst, split the ammonia nitrogen molar ratio and the associated working condition parameter sample set based on the activity state level; perform outlier rejection, normalization and data smoothing processing on the sample set, and obtain the ammonia nitrogen molar ratio benchmark mapping curve under each activity state by polynomial fitting; establish a correction term based on the correlation analysis of working condition parameters and ammonia nitrogen molar ratio, embed the real-time working condition parameter deviation into the benchmark mapping curve, and construct a dynamic mapping relationship; verify the mapping accuracy based on newly collected operation data, continuously iterate and optimize the correction term, and form a stable activity state and ammonia nitrogen molar ratio mapping relationship.

10. The method of claim 1, wherein, The specific method for reverse correcting the ammonia amount distribution coefficient of each SNCR lance is: The SCR inlet reducing agent concentration field spatial distribution data is acquired, and an abnormal area deviating from a target uniform concentration and a concentration deviation amplitude are identified; a spatial coupling correlation between each SNCR spray gun spray coverage domain and the SCR inlet abnormal area is established, and a concentration contribution weight of each spray gun to the abnormal area is quantified in combination with a working condition influence factor; the concentration contribution weight is coupled with a temperature field gradient of the corresponding area and a flue gas turbulence intensity to calculate a working condition adaptation factor; based on a quantitative correlation between the working condition adaptation factor and the concentration deviation amplitude, a correction coefficient of an ammonia amount distribution coefficient of each spray gun is solved, and an ammonia amount distribution proportion of each spray gun is adjusted, and a spray gun in an area deviating beyond a preset range is corrected.

11. The method of claim 1, wherein, The generation system runs a comprehensive risk assessment index, and the specific method is: Taking the furnace temperature field spatial distribution uniformity characteristic quantity and the catalyst activity attenuation rate characteristic quantity as core inputs, the weight value of the furnace temperature field spatial distribution uniformity coefficient and the weight value of the catalyst activity attenuation rate coefficient are dynamically calibrated based on the real-time running load of the boiler; a dynamic coupling factor representing the interaction between the temperature field and the catalyst activity is introduced to strengthen the dynamic correlation coupling between the furnace temperature field spatial distribution uniformity characteristic quantity and the catalyst activity attenuation rate characteristic quantity, and the dynamic coupling factor is included in the nonlinear weighted fusion operation, and a comprehensive assessment index capable of representing the system running risk level and evolution trend is generated through quantitative operation.

12. The method of claim 1, wherein, The data interaction and risk assessment full-process closed-loop control system comprises: A dynamic correlation adjustment rule library of the SNCR ammonia injection temperature window boundary and the SCR ammonia escape safety limit value is constructed, the correlation between the SNCR ammonia injection temperature window boundary and the SCR ammonia escape safety limit value with the working condition is analyzed through historical operation data, and a collaborative adjustment logic based on the running load and the flue gas composition is refined; the system running risk comprehensive assessment index is called to dynamically adjust the SNCR ammonia injection temperature window boundary, and the SCR ammonia escape safety limit value is adjusted based on the preset collaborative logic in the rule library; after adjustment, the SNCR reaction zone real-time temperature distribution and the SCR outlet ammonia escape concentration monitoring data are continuously collected, the adaptability of the boundary conditions and the current running state of the SCR and SNCR double systems is verified through deviation quantization analysis, and a boundary parameter dynamic calibration closed loop is formed.

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