SCR flue gas denitration intelligent control method based on multivariable collaborative optimization

Abstract

An SCR flue gas denitration intelligent control method based on multivariable collaborative optimization specifically comprises the following steps: S1, collecting multi-key variable data of SCR flue gas denitration, processing the multi-key variable data, and storing the processed multi-key variable data into a historical database; s2, on the basis of the processed data, constructing a multivariable correlation model, and predicting operation states of the denitration system under different working conditions; s3, according to the actual operation condition of the system and the equipment performance, determining constraint conditions and optimization targets of SCR flue gas denitration; s4, performing multivariable collaborative optimization calculation in combination with the multivariable correlation model, the optimization target and the constraint condition, and searching an optimal control variable combination; and S5, intelligent control is implemented according to the optimal variable combination, and re-optimization is performed by monitoring feedback deviation in real time. The system can keep stable control precision in different operation scenes, and denitration efficiency fluctuation or parameter adjustment lag caused by sudden change of working conditions is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of SCR flue gas denitrification, and in particular is an intelligent control method for SCR flue gas denitrification based on multivariable collaborative optimization. Background Art

[0002] Traditional SCR flue gas denitrification systems often utilize open-loop control with fixed parameters based on empirical settings. Operators pre-set control parameters, including ammonia injection rate and reaction temperature, based on past operating experience. This lacks dynamic response to real-time operating conditions. When key variables, such as flue gas flow rate and inlet NOx concentration, fluctuate, these fixed parameters make it difficult to maintain stable denitrification efficiency. In the event of sudden load changes, the ammonia injection rate cannot be adjusted in a timely manner, potentially leading to decreased denitrification efficiency or excessive ammonia escape rates, making it difficult to meet increasingly stringent environmental protection requirements.

[0003] In traditional control processes, the adjustment of system parameters relies heavily on the operator's experience and subjective judgment. When system operation anomalies occur, such as reduced denitrification efficiency or increased ammonia escape rate, manual analysis of the cause and adjustment of control parameters are required. This approach not only has a slow response time, but also results in varying control results due to differences in operator experience. Furthermore, manual operation makes it difficult to achieve coordinated optimization of multiple variables, making it impossible to balance denitrification efficiency with operating costs and equipment lifespan, limiting overall system performance.

[0004] Traditional SCR denitrification control typically adjusts individual variables independently, such as adjusting the ammonia injection rate based solely on the inlet NOx concentration, ignoring the interplay between these variables. However, in a denitrification system, flue gas flow, temperature, and ammonia-nitrogen ratio interact with each other, and single-variable control can easily trigger a chain reaction. Simply increasing the ammonia injection rate to improve denitrification efficiency can lead to increased ammonia slip, increasing equipment corrosion risk and operating costs, and failing to achieve optimal overall system operation.

[0005] While some traditional control methods use simple mathematical models to aid decision-making, these models are often based on ideal operating conditions or simplified assumptions and cannot accurately describe the complex nonlinear characteristics of denitrification systems. When faced with complex operating conditions such as fluctuating coal quality and catalyst activity degradation, the model predictions deviate significantly from the actual operating conditions, rendering the control strategy ineffective. As the catalyst ages, traditional models are unable to promptly correct parameters, resulting in a continuous decline in denitrification efficiency and a failure to ensure long-term stable system operation. Summary of the Invention

[0006] In view of this, the present invention aims to propose an intelligent control method for SCR flue gas denitrification based on multivariable collaborative optimization. By collecting multiple key variable data of SCR flue gas denitrification, storing them in a historical database after processing, and then constructing a multivariable correlation model based on the processed data, the operating status of the denitrification system under different working conditions is predicted. According to the actual operating conditions and equipment performance of the system, the constraints and optimization targets of SCR flue gas denitrification are determined. Combined with the multivariable correlation model, the optimization targets and constraints, multivariable collaborative optimization calculations are performed to search for the optimal combination of control variables, and then intelligent control is implemented based on the optimal combination of variables. Re-optimization is performed through real-time monitoring of feedback deviations, which effectively solves the problems mentioned in the background technology.

[0007] The object of the present invention can be achieved by the following technical solution: an intelligent control method for SCR flue gas denitrification based on multivariable collaborative optimization, characterized by comprising an acquisition and processing end, a model prediction end, a condition constraint end, a collaborative calculation end, and a monitoring feedback end, and specifically comprising the following steps:

[0008] S1. Collect multiple key variable data of SCR flue gas denitrification and store them in the historical database after processing;

[0009] S2. Based on the processed data, a multivariate correlation model is constructed to predict the operating status of the denitrification system under different working conditions;

[0010] S3. Determine the constraints and optimization targets for SCR flue gas denitrification based on the actual system operation and equipment performance;

[0011] S4. Combine the multivariable correlation model with the optimization objectives and constraints to perform multivariable collaborative optimization calculations and search for the optimal combination of control variables;

[0012] S5. Implement intelligent control based on the optimal variable combination and re-optimize through real-time monitoring of feedback deviations.

[0013] The multiple key variable data are specifically as follows: flue gas and pollutant related variables include flue gas flow rate, inlet NOx concentration, outlet NOx concentration, flue gas temperature, and flue gas composition; reducing agent and reaction control variables include ammonia injection rate, ammonia escape rate, reducing agent temperature and pressure; catalyst and equipment state variables include catalyst activity, catalyst temperature, and reactor pressure loss; auxiliary system operating parameters include fan parameters, temperature adjustment parameters, and reducing agent preparation system parameters; environmental protection and safety monitoring variables include denitrification efficiency and operating costs.

[0014] The method for constructing the multivariate association model is as follows: selecting an appropriate machine learning model based on the data characteristics and system complexity; performing feature screening and extraction on the preprocessed data, eliminating redundant variables, combining to generate new features and performing standardization processing; dividing the processed data into a training set, a validation set and a test set, iteratively training the model using the training set, and comprehensively evaluating the model using the test set.

[0015] The method for predicting the operating status of the denitrification system under different operating conditions is as follows: multivariate data collected and preprocessed in real time is input into a trained model, and the model outputs the predicted operating status of the denitrification system under the current operating conditions, including key indicators such as denitrification efficiency, ammonia escape rate, and outlet NOx concentration. When the system operating conditions change, the model quickly responds and dynamically adjusts the predicted results; the model is verified online through actual operating condition data, and the predicted results are compared with the actual operating status to further optimize the model parameters.

[0016] The constraints are determined as follows: the reaction temperature must be maintained within the catalyst's optimal activity temperature range; the upper limit of the ammonia injection amount is to avoid exceeding the ammonia escape rate due to excessive ammonia injection, and the lower limit is to reduce NOx to a standard concentration; the flue gas flow rate must be controlled within the rated processing range of the equipment; the internal pressure of the reactor must be maintained within the set safety threshold; the start-up, shutdown and switching of the equipment must follow specific operating sequences and time interval requirements; the concentration and purity of the reducing agent must meet the process requirements; and the pressure and flow of the supply system must be stable.

[0017] The specific method of the multivariable collaborative optimization calculation is as follows: the multivariable association model, the constraints and the optimization objectives are input into the optimization framework, and the control variables are initialized; a multi-objective intelligent optimization algorithm is used to search for the Pareto optimal solution set under the premise of satisfying dynamic constraints through non-dominated sorting and elite retention strategies; in combination with real-time working condition data, the constraint boundaries are updated through a rolling time domain optimization strategy, and nonlinear constraints are processed using the Lagrange multiplier method; the multi-objective solution set is comprehensively evaluated based on the fuzzy membership function, the entropy weight method is used to determine the weight coefficient, and finally the optimal control variable combination is output.

[0018] The method of re-optimization based on the real-time monitoring feedback deviation is as follows: real-time monitoring of the system's operating status, feeding back actual key indicators including denitrification efficiency and ammonia escape rate into the system; calculating the deviation between the measured value and the optimization target; triggering a re-optimization mechanism when the deviation exceeds a threshold or the operating condition suddenly changes; using an intelligent algorithm to combine historical data with the current operating condition, adjusting the control variables, generating a new optimization plan and executing it.

[0019] Combining all the above technical solutions, the present invention has the following positive effects: 1. By comprehensively collecting multi-dimensional operational data, including flue gas flow rate and ammonia escape rate, the present invention constructs a dynamic correlation model between variables, achieving coordinated optimization control of parameters including ammonia injection rate and reaction temperature. This mechanism transcends the technical limitations of traditional single-variable independent adjustment and can simultaneously optimize denitrification efficiency, ammonia escape rate, and operating costs based on variable coupling relationships. While ensuring that environmental indicators are met, it achieves a coordinated reduction in system energy consumption and reducing agent consumption, promoting the upgrade of denitrification control from single-indicator adjustment to global performance optimization.

[0020] 2. The multivariable correlation model constructed by this invention, leveraging a machine learning algorithm, accurately captures the nonlinear characteristics and variable coupling patterns of the denitrification system, providing enhanced dynamic characterization capabilities for complex operating conditions, including load fluctuations, catalyst activity decay, and flue gas composition changes. Compared to traditional empirical models, this model can learn from changing operating conditions in real time and adjust the prediction logic, enabling the system to maintain stable control accuracy across different operating scenarios, avoiding fluctuations in denitrification efficiency or lags in parameter adjustment caused by sudden changes in operating conditions.

[0021] 3. This invention uses high-frequency, real-time monitoring of system operating conditions to calculate deviations between key indicators, including denitrification efficiency and ammonia escape rate, and optimization targets. When operating conditions fluctuate or control performance deviates from expectations, a multivariable collaborative optimization mechanism is automatically triggered. This closed-loop control mode rapidly responds to sudden changes in flue gas parameters and equipment performance degradation, generating new control strategies through iterative optimization. This forms an adaptive cycle of monitoring, analysis, optimization, and execution, significantly improving the system's anti-interference capabilities and timely adjustment compared to traditional fixed-parameter control.

[0022] 4. Leveraging the massive operational data accumulated in a historical database and the iterative optimization capabilities of intelligent algorithms, this invention gradually reduces reliance on manual experience and enables automated updating and optimization of control strategies. Based on long-term operational data, the system mines implicit knowledge about equipment performance degradation patterns and operating condition trends, automatically adjusting control parameters and optimizing target weights. This shifts the denitrification control process from one driven by manual decision-making to one driven by intelligent data, simultaneously improving equipment safety, reliability, and operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0024] Figure 1 The present invention is a flowchart of the steps for implementing the method.

[0025] Figure 2 The present invention is a flowchart of the implementation steps that have been re-optimized. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0027] See also Figure 1 As shown, the present invention proposes an SCR flue gas denitrification intelligent control method based on multivariable collaborative optimization, including an acquisition and processing end, a model prediction end, a condition constraint end, a collaborative calculation end, and a monitoring feedback end.

[0028] In a more specific application of the present invention, during the operation of the SCR flue gas denitrification system, the data acquisition link achieves real-time capture of multi-dimensional variables through high-precision sensors deployed at each process node. For flue gas and pollutant-related variables, a thermal gas mass flowmeter is used to monitor the flue gas flow rate, an ultraviolet differential absorption spectrometer is used to detect the inlet and outlet NOx concentration, a thermocouple array is used to collect flue gas temperature, and an infrared gas analyzer is used to analyze the O2 and CO2 flue gas components. For reducing agent and reaction control variables, an electromagnetic flowmeter is used to monitor the ammonia injection rate, a tunable laser absorption spectrometer is used to measure the ammonia escape rate, and a pressure transmitter and temperature sensor are used to synchronously collect reducing agent medium parameters. Various sensors acquire data at a set sampling frequency and transmit it to the edge computing node through industrial communication protocols such as Modbus and OPCUA, forming a multivariate time series data stream.

[0029] The data preprocessing stage integrates signal processing and data cleaning techniques. Kalman filtering or wavelet transforms are first used to remove sensor measurement noise, eliminate pulse interference, and eliminate baseline drift. For variables of different dimensions, such as temperature, concentration, and flow rate, Min-Max normalization or Z-Score standardization is used to uniformly map the data to the [-1, 1] or [0, 1] intervals to ensure compatibility with subsequent modeling and analysis. Outliers, such as those outside the measurement range or those with physical and logical inconsistencies, are identified and marked using the Laida criterion or isolation forest algorithm. Correction is then performed using linear interpolation or historical mean substitution. The original data timestamps and outlier markers are retained, creating a traceable preprocessed dataset.

[0030] The historical database is constructed using a time-series database architecture, with InfluxDB or TimescaleDB, a storage system optimized for time series, being preferred. Data is stored in separate tables by data category, including flue gas parameters, reducing agent parameters, and equipment status, and is managed by time window partitions to improve query efficiency under large data volumes. The database design includes a metadata management module that structures the physical meaning, unit, acquisition frequency, and sensor model information of each variable to facilitate data understanding and application. To accommodate future system expansion needs, the database reserves extensible fields and distributed storage interfaces to support seamless integration of new monitoring indicators such as catalyst surface temperature and by-product concentration. Data backup and disaster recovery mechanisms are also used to ensure the security and integrity of historical data.

[0031] When building a multivariate correlation model for SCR flue gas denitrification, the appropriate machine learning architecture is first selected based on the data's time series characteristics and the system's nonlinear complexity. If the data exhibits strong time series characteristics, such as historical fluctuations in flue gas flow or temperature, long-short-term memory networks or gated recurrent units are preferred to capture dynamic dependencies. For example, to process flue gas flow time series data, a two-layer LSTM network with 128 neurons per layer can be constructed. The input is the flow sequence of the past hour, with 120 time steps. The network predicts the flow trend for the next 10 minutes and captures flow fluctuations caused by load changes, such as the flow rate increase when the unit increases its load.

[0032] If there is spatial coupling between variables, such as the correlation between temperature and activity at different points in the catalyst layer, convolutional neural networks or graph neural networks can be used to explore potential correlations. For example, the 27 temperature points in the catalyst layer can be modeled as a graph structure, with node features represented by temperature values ​​and edge weights represented by spatial distances. The edge weights for adjacent points are set to 0.8, and for non-adjacent points to 0.2. The correlation between temperature distribution and NOx conversion rate can be explored. When the temperature at a certain point falls below the catalyst activity threshold, the denitrification efficiency in that area is predicted to decrease.

[0033] During the feature engineering stage, core variables significantly related to denitrification efficiency, such as flue gas flow, temperature stratification, and initial NOx concentration, are screened through mutual information method or recursive feature elimination to eliminate redundant sensor signals. At the same time, combined features are generated based on the process mechanism, and then Z-score standardization is used to eliminate dimensional effects, so that the data distribution adapts to the model training requirements.

[0034] After completing the feature construction, the data set needs to be scientifically divided to ensure the generalization ability of the model. Usually, the data is divided into a training set covering typical steady-state and variable load conditions, a validation set for hyperparameter tuning, and a test set to simulate unknown conditions according to the time series. During the training process, a 50-fold time series cross-validation can be used, and the validation set loss function can be monitored by the early stopping method to avoid overfitting; at the same time, the Dropout layer or regularization term is introduced to suppress noise interference. When iteratively optimizing the model, focus on adjusting the number of network layers, the number of neurons, and the learning rate parameters until the prediction accuracy converges, such as the root mean square error of the NOx concentration prediction of the test set is lower than the set value. Finally, the test set is used to comprehensively evaluate the model performance. In addition to conventional indicators, it is also necessary to analyze the ammonia slip prediction deviation under different load ranges to ensure the reliability of the model under complex conditions.

[0035] The multivariate data collected and preprocessed in real time are input into the trained model, which then outputs the predicted operating status of the denitrification system under the current operating conditions, including key indicators such as denitrification efficiency, ammonia escape rate, and outlet NOx concentration. When the system operating conditions change, the model quickly responds and dynamically adjusts the predicted results. The model is verified online using actual operating data, and the predicted results are compared with the actual operating status to further optimize the model parameters.

[0036] When determining the constraints of an SCR flue gas denitrification system, they must be dynamically set based on the system's real-time operating parameters and equipment performance indicators. The reaction temperature must be maintained within the catalyst's optimal activity temperature range; the upper limit of ammonia injection is to avoid excessive ammonia escape rates due to excessive ammonia injection, and the lower limit is to reduce NOx to a standard concentration; the flue gas flow rate must be controlled within the equipment's rated processing range; the internal pressure of the reactor must be maintained within the set safety threshold; equipment startup, shutdown, and switching must follow specific operating sequences and time intervals; the concentration and purity of the reducing agent must meet process requirements; and the pressure and flow of the supply system must be stable.

[0037] Determining optimization targets for SCR flue gas denitrification systems requires dynamic quantification based on environmental regulations, operating costs, and equipment characteristics. The implementation process begins by setting core environmental targets based on national emission standards. Simultaneously, theoretical minimum ammonia consumption is calculated based on real-time inlet NOx concentration and flue gas flow rate, establishing the primary target of achieving minimum ammonia consumption while meeting emission standards. Second, by monitoring catalyst activity decay coefficients and historical ammonia slip data, extending catalyst life and controlling ammonia slip risk are converted into secondary targets. For example, ammonia injection fluctuations are limited to ≤5% to reduce thermal stress.

[0038] Combining the multivariable correlation model with the optimization objectives and constraints, multivariable collaborative optimization calculations are performed. The system integrates the constructed multivariable correlation model with the determined constraints such as the ammonia injection safety threshold, the catalyst temperature window, and the optimization objectives such as the denitrification efficiency and the ammonia escape rate into the optimization framework. The control variables to be initialized include the ammonia injection valve opening, the flue gas flow control valve position, and the catalyst layer temperature set value. At the same time, real-time operating data such as the inlet NOx concentration and the flue gas temperature are loaded. Based on the model predictive control architecture, an improved multi-objective optimization algorithm such as NSGA-II or the particle swarm algorithm is used to iteratively search for the Pareto optimal solution set in the dynamic constraint space through non-dominated sorting. In each round of iteration, the proxy model such as the Kriging model will accelerate the calculation of the complex mechanism equations, and the Lagrange multiplier method is used to handle the nonlinear constraints to ensure that the solution set meets the physical limits of the equipment.

[0039] The system incorporates a rolling horizon optimization strategy to dynamically adjust the feasible region boundaries based on real-time flue gas parameters. For example, when a decrease in catalyst activity is detected, the temperature constraint range is automatically narrowed to maintain reaction efficiency. The distributed parallel computing architecture employed in the optimization process significantly reduces the solution time for large-scale variable combinations. After the iterations are complete, the fuzzy comprehensive evaluation module performs an entropy-weighted analysis of the Pareto solution set, comprehensively weighing denitrification efficiency, ammonia escape rate, and energy costs. Ultimately, the system outputs the globally optimal combination of control variables, such as the precise ammonia injection rate and valve opening sequence.

[0040] The entire process achieves dynamic multivariable collaboration through a closed-loop mechanism of prediction, optimization, and decision-making. Its core lies in integrating the interpretability of mechanistic models with the adaptability of data-driven models, ensuring real-time performance while precisely balancing environmental performance indicators and economic requirements.

[0041] Reference Figure 2 As shown, intelligent control is implemented based on the optimal variable combination, and re-optimization is carried out through real-time monitoring of feedback deviations. The method of re-optimization through real-time monitoring of feedback deviations is to deploy high-precision sensors and monitoring equipment to monitor the operating status of the system in real time, and feed back the actual key indicators including denitrification efficiency and ammonia escape rate into the system;

[0042] Based on the preset target value of the process requirements, the deviation between the measured value and the optimization target is calculated. The single-point deviation is calculated by subtracting the target value from the measured value, and the relative fluctuation is quantified by the expression:

[0043]

[0044] Where K is the relative deviation, b is the measured value, and a is the target value.

[0045] Weight coefficients such as denitrification efficiency weight 0.7 and ammonia escape rate weight 0.3 are introduced into the comprehensive deviation to construct a multi-indicator comprehensive deviation model.

[0046] Static thresholds are preset and dynamic thresholds are adjusted. The thresholds can be adaptively adjusted through machine learning models by combining historical data distribution or operating condition fluctuation characteristics. Detection of sudden operating condition changes can use statistical process control methods to identify operating condition changes such as sudden increases and decreases in load and sudden changes in fuel composition by monitoring data slope and variance mutations. Pattern recognition of sensor data sequences based on deep learning models can be used to determine whether an unsteady operating condition has been entered. When the deviation exceeds the threshold or the operating condition suddenly changes, a re-optimization mechanism is triggered.

[0047] Intelligent algorithms can be selected based on the characteristics of the problem. Model predictive control can be combined with process mechanism models to predict future changes in indicators and optimize control variables. Reinforcement learning can dynamically adjust control strategies such as ammonia injection rate and reaction temperature through interactive learning with the system. Genetic algorithms can search for the global optimal solution in multivariable optimization scenarios. By combining historical data with current operating conditions, control variables can be adjusted to generate and execute new optimization plans.

[0048] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict.

[0049] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The SCR flue gas denitrification intelligent control method based on multivariable collaborative optimization is characterized by: It includes the acquisition and processing end, model prediction end, condition constraint end, collaborative calculation end, and monitoring feedback end, and specifically includes the following steps: S1. Collect multiple key variable data of SCR flue gas denitrification and store them in the historical database after processing; S2. Based on the processed data, a multivariate correlation model is constructed to predict the operating status of the denitrification system under different working conditions; S3. Determine the constraints and optimization targets for SCR flue gas denitrification based on the actual system operation and equipment performance; S4. Combine the multivariable correlation model with the optimization objectives and constraints to perform multivariable collaborative optimization calculations and search for the optimal combination of control variables; S5. Implement intelligent control based on the optimal variable combination and re-optimize through real-time monitoring of feedback deviations.

2. The SCR flue gas denitrification intelligent control method based on multivariable collaborative optimization according to claim 1 is characterized in that: The multiple key variable data are specifically as follows: flue gas and pollutant related variables include flue gas flow rate, inlet NOx concentration, outlet NOx concentration, flue gas temperature, and flue gas composition; reducing agent and reaction control variables include ammonia injection rate, ammonia escape rate, reducing agent temperature and pressure; catalyst and equipment state variables include catalyst activity, catalyst temperature, and reactor pressure loss; auxiliary system operating parameters include fan parameters, temperature adjustment parameters, and reducing agent preparation system parameters; environmental protection and safety monitoring variables include denitrification efficiency and operating costs.

3. The SCR flue gas denitrification intelligent control method based on multivariable collaborative optimization according to claim 1, characterized in that: The method for constructing the multivariate association model is as follows: selecting an appropriate machine learning model based on the data characteristics and system complexity; performing feature screening and extraction on the preprocessed data, eliminating redundant variables, combining to generate new features and performing standardization processing; dividing the processed data into a training set, a validation set and a test set, iteratively training the model using the training set, and comprehensively evaluating the model using the test set.

4. The SCR flue gas denitrification intelligent control method based on multivariable collaborative optimization according to claim 1, characterized in that: The method for predicting the operating status of the denitrification system under different operating conditions is as follows: multivariate data collected and preprocessed in real time is input into a trained model, and the model outputs the predicted operating status of the denitrification system under the current operating conditions, including key indicators such as denitrification efficiency, ammonia escape rate, and outlet NOx concentration. When the system operating conditions change, the model quickly responds and dynamically adjusts the predicted results; the model is verified online through actual operating condition data, and the predicted results are compared with the actual operating status to further optimize the model parameters.

5. The SCR flue gas denitrification intelligent control method based on multivariable collaborative optimization according to claim 1 is characterized in that: The constraints are determined as follows: the reaction temperature must be maintained within the catalyst's optimal activity temperature range; the upper limit of the ammonia injection amount is to avoid exceeding the ammonia escape rate due to excessive ammonia injection, and the lower limit is to reduce NOx to a standard concentration; the flue gas flow rate must be controlled within the rated processing range of the equipment; the internal pressure of the reactor must be maintained within the set safety threshold; the start-up, shutdown and switching of the equipment must follow specific operating sequences and time interval requirements; the concentration and purity of the reducing agent must meet the process requirements; and the pressure and flow of the supply system must be stable.

6. The SCR flue gas denitrification intelligent control method based on multivariable collaborative optimization according to claim 1, characterized in that: The specific method of the multivariable collaborative optimization calculation is as follows: the multivariable correlation model, the constraints and the optimization objectives are input into the optimization framework, and the control variables are initialized; a multi-objective intelligent optimization algorithm is used to search for the Pareto optimal solution set under the premise of satisfying dynamic constraints through non-dominated sorting and elite retention strategies; the constraint boundaries are updated through a rolling time domain optimization strategy based on real-time working condition data, and nonlinear constraints are processed using the Lagrange multiplier method; The multi-objective solution set is comprehensively evaluated based on the fuzzy membership function, the entropy weight method is used to determine the weight coefficient, and finally the optimal control variable combination is output.

7. The SCR flue gas denitrification intelligent control method based on multivariable collaborative optimization according to claim 1, characterized in that: The method of re-optimizing the real-time monitoring feedback deviation is: real-time monitoring of the system's operating status, and feeding back actual key indicators including denitrification efficiency and ammonia escape rate into the system; Calculate the deviation between the measured value and the optimization target; when the deviation exceeds the threshold or the operating condition changes suddenly, trigger the re-optimization mechanism; use intelligent algorithms to combine historical data with current operating conditions, adjust control variables, generate a new optimization plan and execute it.

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