Control method and device of denitration system and storage medium

Abstract

The invention relates to the technical field of denitration, and discloses a control method and device for a denitration system and a storage medium, and the method comprises the steps: obtaining flue gas components, flow data, load conditions, flue gas mixing effects and catalyst activity coefficients, and carrying out state calculation based on the flue gas components and the flow data to obtain flue gas state parameters. Afterwards, efficiency calculation is conducted by combining the flue gas state parameters and the load condition, a denitration efficiency value is obtained, and a denitration efficiency target is selected according to the denitration efficiency value. Thirdly, matching an injection strategy from the ammonia water injection strategy library, and calculating a preliminary ammonia water injection amount; and then correcting the initial injection amount by combining with the catalyst activity coefficient to obtain the ammonia water injection amount. On the basis, a non-dominated sorting genetic algorithm is adopted to evaluate the ammonia water injection amount and flue gas mixing effect, and injection parameters are optimized. And finally, inputting the ammonia water injection quantity and the injection parameters into a closed-loop control algorithm to obtain ammonia water control parameters. According to the method, the ammonia water injection strategy can be dynamically optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of denitration, and in particular to a control method, device and storage medium of a denitration system. Background Art

[0002] Denitrification technology is an important part of flue gas treatment in modern industry, especially in the control of high emission sources such as thermal power plants, where the denitrification system plays a vital role. Denitrification technology mainly reduces nitrogen oxides (NOx) in flue gas by injecting ammonia or other reducing agents, thereby reducing air pollutant emissions and protecting the environment. With the increasingly stringent global environmental protection requirements, the development of denitrification technology is also constantly advancing, and many new denitrification methods have emerged, especially in achieving precise control and efficient operation. The optimization of the control system, especially in the dynamic adjustment of ammonia injection amount, injection angle and flue gas composition, has become one of the key technologies to improve the denitrification effect and reduce energy consumption.

[0003] Existing denitrification technologies mainly include selective non-catalytic reduction (SNCR) and selective catalytic reduction (SCR). These technologies generally rely on fixed injection strategies and standard operating parameters to control the denitrification process. In these traditional technologies, the selection of injection volume and control parameters is often static, that is, the operation is based on empirical values ​​or preset strategies, and there is a lack of real-time response to flue gas composition, load and other dynamic factors. Although this method can achieve denitrification effects under certain conditions, it cannot effectively cope with the impact of real-time operating conditions such as load fluctuations and changes in flue gas composition.

[0004] The main problem with existing denitrification technology is its static control mechanism, which fails to dynamically adjust control parameters based on real-time flue gas data and operating conditions. Since flue gas composition and flow rate change at any time, traditional methods cannot optimize injection strategies in real time based on these changes. Summary of the invention

[0005] The present invention provides a control method, a device and a storage medium for a denitration system to realize dynamic optimization of an ammonia water injection strategy.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a control method for a denitration system, comprising: Obtain flue gas composition, flow data, load conditions, flue gas mixing effect and catalyst activity coefficient; Performing state calculation according to the smoke components and the flow data to obtain smoke state parameters; Performing efficiency calculation according to the flue gas state parameters and the load conditions to obtain a denitration efficiency value, and selecting a denitration efficiency target according to the denitration efficiency value; According to the denitration efficiency target, a corresponding injection strategy is selected from a preset ammonia injection strategy library to obtain a preliminary ammonia injection amount; Performing a correction calculation based on the preliminary ammonia water injection amount and the catalyst activity coefficient to obtain the ammonia water injection amount; Based on a non-dominated sorting genetic algorithm, the ammonia solution injection amount and the flue gas mixing effect are evaluated to obtain injection parameters; The ammonia water injection amount and the injection parameters are input into a closed-loop control algorithm to obtain ammonia water control parameters.

[0007] Preferably, the flue gas components include: nitrogen oxide concentration, sulfur dioxide concentration, carbon monoxide concentration, oxygen concentration and water vapor concentration; The load conditions include: unit load, combustion load and flue gas emission load; The smoke mixing effect includes: mixing uniformity, injection pressure and injection angle; The flow data includes: smoke flow and flow velocity.

[0008] Preferably, performing state calculation according to the smoke components and the flow data to obtain smoke state parameters includes: The denitrification effect parameters are calculated by the following formula: in, is the denitrification effect parameter, is the oxygen concentration, is the sulfur dioxide concentration, is the water vapor concentration, is the carbon monoxide concentration, is the concentration of nitrogen oxides, , , and is the reaction coefficient; The injection volume is calculated by the following formula: in, is the injection amount, is the reaction coefficient, is the flue gas flow rate, is the flue gas velocity; The flue gas state parameters include: denitration effect parameters and injection amount.

[0009] Preferably, the efficiency calculation is performed according to the flue gas state parameter and the load condition to obtain a denitration efficiency value, and a denitration efficiency target is selected according to the denitration efficiency value, including: The denitrification efficiency value is calculated by the following formula: in, is the denitrification efficiency value, is the unit load, is the combustion load, Flue gas emission load, is the denitrification effect parameter, is the injection volume; When the denitrification efficiency value is greater than or equal to the preset threshold, the maximum denitrification effect is selected as the denitrification efficiency target; When the denitrification efficiency value is less than the preset threshold, minimizing the ammonia escape amount is selected as the denitrification efficiency target.

[0010] Preferably, a correction calculation is performed based on the preliminary ammonia water injection amount and the catalyst activity coefficient to obtain the ammonia water injection amount, including: The ammonia injection amount is calculated by the following formula: in, is the amount of ammonia injection, is the initial ammonia injection amount, Catalyst activity coefficient, is the correction factor.

[0011] Preferably, based on a non-dominated sorting genetic algorithm, the ammonia solution injection amount and the flue gas mixing effect are evaluated to obtain injection parameters, including: Based on the flue gas mixing effect, the ammonia water injection amount is evaluated to obtain a preliminary evaluation result; The preliminary evaluation results are iteratively optimized based on a non-dominated sorting genetic algorithm to obtain injection parameters.

[0012] Preferably, the ammonia water injection amount and the injection parameters are input into a closed-loop control algorithm to obtain ammonia water control parameters, including: The ammonia water injection amount, the mixing uniformity, the injection pressure and the injection angle are input into the CFD model to obtain preliminary control parameters; Iteratively optimizing the preliminary control parameters based on a non-dominated sorting genetic algorithm to obtain optimized ammonia control parameters; The ammonia water control parameter is input into a closed-loop control algorithm, and feedback correction is performed to obtain the ammonia water control parameter.

[0013] In a second aspect, the present invention provides a control device for a denitration system, comprising: Data acquisition module, used to obtain flue gas composition, flow data, load conditions, flue gas mixing effect and catalyst activity coefficient; A state calculation module, used to perform state calculation according to the smoke components and the flow data to obtain smoke state parameters; A denitration efficiency module, for performing efficiency calculation according to the flue gas state parameters and the load conditions to obtain a denitration efficiency value, and selecting a denitration efficiency target according to the denitration efficiency value; A preliminary ammonia injection amount module is used to select a corresponding injection strategy from a preset ammonia injection strategy library according to the denitration efficiency target to obtain a preliminary ammonia injection amount; an ammonia water injection amount module, used for performing correction calculation according to the preliminary ammonia water injection amount and the catalyst activity coefficient to obtain the ammonia water injection amount; An injection parameter module, used for evaluating the ammonia water injection amount and the flue gas mixing effect to obtain injection parameters; The closed-loop control module is used to input the ammonia water injection amount and the injection parameters into a closed-loop control algorithm to obtain the ammonia water control parameters.

[0014] In a third aspect, the present invention further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements any one of the above-described control methods for the denitrification system when executing the computer program.

[0015] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for controlling the denitrification system.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention adopts flue gas state parameter calculation, denitrification efficiency value evaluation and closed-loop control algorithm to ensure that the ammonia injection amount is dynamically adjusted according to the flue gas composition, flow rate and load conditions, avoiding the lag and limitations of the traditional fixed injection strategy. Combined with the non-dominated sorting genetic algorithm, the ammonia injection amount and the flue gas mixing effect are optimized, the denitrification efficiency is significantly improved, the ammonia escape is reduced, and the overall stability and adaptability of the system are improved.

[0017] (2) The present invention calculates the denitration performance value (DPI) and dynamically selects "maximizing denitration efficiency" or "minimizing ammonia escape" as the optimization goal under different load conditions, and uses the ammonia injection strategy library to intelligently match the injection strategy that adapts to different working conditions. Compared with the traditional denitration control method, the present invention can dynamically optimize the ammonia injection strategy under different working conditions, improve the utilization rate of ammonia, reduce energy waste, and achieve precise denitration control.

[0018] (3) The present invention uses a CFD (computational fluid dynamics) model combined with a non-dominated sorting genetic algorithm to iteratively optimize the ammonia injection amount, injection pressure, and injection angle to ensure that the ammonia is evenly distributed in the flue and fully mixed with the flue gas to improve the denitrification reaction efficiency. This optimization strategy can effectively reduce the side reactions caused by excessive local ammonia concentration or uneven mixing, and improve the denitrification accuracy and stability of the system.

[0019] (4) The present invention adopts a closed-loop control algorithm to dynamically adjust the ammonia injection amount and injection strategy based on real-time feedback of flue gas composition and injection parameters to achieve intelligent adaptive control. Compared with the traditional open-loop control mode, this method can adapt to complex working conditions such as flue gas flow, pollutant concentration and load fluctuation, improve the response speed and control accuracy of the denitrification system, ensure the optimal ammonia dosage and maximize the denitrification efficiency.

[0020] In summary, the present invention optimizes the traditional denitration system in an all-round way by introducing technical means such as non-dominated sorting genetic algorithm, catalyst activity correction, CFD model optimization, closed-loop control mechanism, etc. Compared with the prior art, the present invention has higher denitration efficiency, lower ammonia escape, better energy utilization rate and stronger adaptive ability, and can be widely used in the field of industrial flue gas denitration such as thermal power plants, steel smelting, cement production, etc., providing a better solution for environmental protection and energy saving. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic flow chart of a control method for a denitration system provided by a first embodiment of the present invention; Figure 2 It is a schematic diagram of a control device of a denitration system provided in a second embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0023] Reference Figure 1 The first embodiment of the present invention provides a control method for a denitration system, comprising the following steps: S11, obtaining flue gas composition, flow data, load conditions, flue gas mixing effect and catalyst activity coefficient; S12, performing state calculation according to the smoke composition and the flow data to obtain smoke state parameters; S13, performing efficiency calculation according to the flue gas state parameter and the load condition to obtain a denitration efficiency value, and selecting a denitration efficiency target according to the denitration efficiency value; S14, according to the denitration efficiency target, selecting a corresponding injection strategy from a preset ammonia injection strategy library to obtain a preliminary ammonia injection amount; S15, performing a correction calculation according to the preliminary ammonia water injection amount and the catalyst activity coefficient to obtain the ammonia water injection amount; S16, evaluating the ammonia solution injection amount and the flue gas mixing effect based on a non-dominated sorting genetic algorithm to obtain injection parameters; S17, inputting the ammonia water injection amount and the injection parameters into a closed-loop control algorithm to obtain ammonia water control parameters.

[0024] In step S11, the flue gas components include: nitrogen oxide concentration, sulfur dioxide concentration, carbon monoxide concentration, oxygen concentration and water vapor concentration; The load conditions include: unit load, combustion load and flue gas emission load; The smoke mixing effect includes: mixing uniformity, injection pressure and injection angle; The flow data includes: smoke flow and flow velocity.

[0025] It is worth noting that in step S11, obtaining flue gas composition, flow data, load conditions, flue gas mixing effect and catalyst activity coefficient is the basis for ensuring the accurate operation of the denitration system. These parameters are crucial for subsequent system optimization and precise control. The following will describe in detail how to obtain these parameters, and explain the method and process of obtaining each parameter.

[0026] First of all, the acquisition of flue gas composition is one of the most critical parts, especially the concentration of oxides, carbon dioxide and water vapor. The concentration of oxides, especially nitrogen oxides (NOx), is measured by chemiluminescence. This method produces chemiluminescence by reacting nitrogen oxides with chlorine, and the intensity of the luminescence is proportional to the gas concentration, so the concentration of NOx can be accurately detected. For the measurement of carbon dioxide concentration, the infrared absorption method is used. This method is based on the characteristic that carbon dioxide molecules absorb infrared light of a specific wavelength, and the gas concentration is inferred by measuring the change in light intensity. The measurement of water vapor concentration uses a capacitive humidity sensor, which reflects the moisture content in the air by measuring the change in capacitance. These component data are monitored in real time by the flue gas analyzer, and the data is transmitted to the control system.

[0027] Obtaining flue gas flow and velocity is also an important part of the denitrification system. Flue gas flow measurement is completed using a flow meter. Common flow meters include differential pressure flow meters and thermal flow meters. The differential pressure flow meter calculates the flow rate by measuring the pressure difference generated when the flue gas flows in the pipeline. It is based on the Bernoulli principle and infers the flow rate by comparing the pressure of the fluid at different positions before and after the flow. The thermal flow meter measures the flow rate by the heat transfer characteristics of the gas flow. The heat taken away by the gas flow will affect the temperature sensor of the flow meter, and the flow rate is calculated by measuring the temperature change. The flow meter is installed in the flue, which can monitor the flow of flue gas in real time and transmit the data to the system for processing and control. In addition, the flue gas flow rate also needs to be detected by equipment. The commonly used methods are hot wire anemometers and differential pressure anemometers. The hot wire anemometer infers the flow rate by measuring the heat taken away by the airflow, while the differential pressure anemometer calculates the wind speed by measuring the pressure difference caused by the airflow. Through these flow and velocity data, accurate flue gas flow and velocity information can be obtained, which is very important for subsequent control and regulation.

[0028] The acquisition of load conditions is crucial for the optimization of the denitrification system. The load conditions mainly include three aspects: unit load, combustion load and flue gas emission load. Unit load refers to the current load state of the generator set, which is monitored in real time by the automatic control system of the generator set. The control system can directly read the load data of the unit, which reflects the output power of the generator set. The combustion load refers to the amount of fuel consumed during the combustion process, which is obtained through the fuel metering system. The fuel metering system determines the combustion load by measuring the flow rate and consumption of the fuel. These data can be obtained through devices such as flow meters and mass sensors installed on the fuel pipeline. The flue gas emission load refers to the amount of pollutants emitted in the flue gas, mainly nitrogen oxides, sulfur dioxide and other pollutants. The flue gas emission load is detected by flue gas analyzers and online monitoring equipment. The flue gas analyzer can measure the concentration of various pollutants in the flue gas in real time, and then calculate the emission load through the flow data. These load data are crucial for subsequent injection amount adjustment, denitrification efficiency evaluation and control strategy optimization.

[0029] The flue gas mixing effect is one of the important factors affecting the denitrification efficiency. The mixing effect mainly includes mixing uniformity, injection pressure and injection angle. The mixing uniformity reflects the degree of mixing of ammonia water and flue gas, which affects the denitrification efficiency. In order to evaluate the mixing effect of flue gas, joint measurements can be performed by using flow field monitoring equipment and flue gas analyzer. The flow field monitoring equipment can analyze the distribution of airflow and detect information such as the speed and direction of airflow, which is very helpful for analyzing the distribution and mixing uniformity of airflow. In addition, the mixing effect of flue gas can be further evaluated by simulating the mixing process of flue gas and ammonia water in combination with CFD (computational fluid dynamics) simulation technology. The injection pressure and injection angle are two important parameters for adjusting the mixing effect. The injection pressure determines the injection speed and diffusion range of ammonia water, and the injection pressure is controlled by adjusting the pressure control valve on the injection device. The injection angle affects the direction and coverage of the injection. A reasonable injection angle can ensure that ammonia water is fully in contact with flue gas, thereby improving the denitrification effect. The injection pressure and injection angle can be precisely controlled by adjusting the injection device to ensure that the mixing effect of flue gas and ammonia water is optimal.

[0030] Catalyst activity coefficient ( ) needs to be obtained in combination with real-time monitoring and data analysis methods to ensure that the denitrification system can accurately evaluate the current performance status of the catalyst and make reasonable adjustments to the injection volume. In actual engineering applications, the online monitoring method combined with the historical data regression analysis method is used to calculate the catalyst activity coefficient. For example, in the SCR denitrification system of a coal-fired power plant, an online catalyst activity monitoring sensor is installed. The sensor measures the NOx concentration at the inlet and outlet of the SCR, calculates the NOx removal rate, and dynamically calculates the catalyst activity coefficient in combination with the current flue gas temperature, pressure and other parameters. If the SCR inlet NOx concentration is 400 mg / Nm³ and the outlet NOx concentration is 40 mg / Nm³, the system detects that the catalyst's denitrification efficiency is about 90%, while under standard operating conditions the catalyst should achieve a denitrification efficiency of 95%, indicating that its activity has decreased. The system will further combine factors such as catalyst operating time, flue gas temperature (for example, 350°C), and the number of catalyst layers (such as a three-layer design) to calculate the current The system will calculate the catalyst aging trend based on the historical data regression analysis and the operation data of the catalyst in the past few months, and use the multivariate regression model or machine learning prediction model (such as BP neural network) to calculate the catalyst aging trend. The system can fit the historical data trend to predict that the activity will drop to 3.0 in the next 500 hours, and adjust the ammonia injection strategy in advance. In order to improve the accuracy of catalyst activity evaluation, some power plants also use chemical analysis methods to regularly sample catalyst active components (such as TiO2, V2O5, WO3 content), and combine physical detection methods (such as BET specific surface area analysis) to calculate The system calculates the catalyst activity coefficient accurately under different catalyst types (such as titanium-based or copper-based catalysts) and different operating conditions, ensuring that the denitration system always operates in the best state and effectively balancing the ammonia injection amount and NOx removal efficiency.

[0031] In step S12, a state calculation is performed according to the smoke composition and the flow data to obtain smoke state parameters, including: The denitrification effect parameters are calculated by the following formula: in, is the denitrification effect parameter, is the oxygen concentration, is the sulfur dioxide concentration, is the water vapor concentration, is the carbon monoxide concentration, is the concentration of nitrogen oxides, , , and is the reaction coefficient; The injection volume is calculated by the following formula: in, is the injection amount, is the reaction coefficient, is the flue gas flow rate, is the flue gas velocity; The flue gas state parameters include: denitration effect parameters and injection amount.

[0032] It is worth noting that in the denitrification system, in order to accurately calculate the denitrification effect and injection amount, we use multiple reaction coefficients (such as , , , and ) to describe the effect of flue gas composition on denitration reaction and injection amount. The setting of each reaction coefficient is based on experimental data, chemical reaction theory and engineering practice to ensure the best operation effect of the denitration system. The following is a detailed analysis of these reaction coefficients.

[0033] first, is the reaction coefficient of oxygen concentration to denitrification effect. The higher the oxygen concentration, the higher the efficiency of denitrification reaction. Take it as a positive value. Oxygen is an indispensable participant in the denitrification reaction and can accelerate the process of reducing nitrogen oxides to nitrogen. According to experimental data, The value of is 1, which can reflect the positive effect of oxygen concentration on denitrification efficiency and avoid the problem of overreaction due to excessive oxygen concentration in practical applications.

[0034] It is the reaction coefficient of water vapor concentration on denitrification effect. When the water vapor concentration is high, it will inhibit the denitrification reaction because water vapor and ammonia water will compete with each other, reducing the effective ammonia water participating in the denitrification reaction. Therefore, For negative values, set to -1. It can accurately reflect the inhibitory effect of water vapor concentration on the denitrification reaction, and when the water vapor concentration is low, the system can still maintain a high denitrification efficiency.

[0035] is the reaction coefficient of sulfur dioxide concentration to denitrification effect. Sulfur dioxide will react with ammonia to form by-products, which will inhibit the denitrification effect. Therefore, It is also taken as a negative value, and the value is -0.5. The value of this reaction coefficient can reasonably reflect the negative impact of sulfur dioxide concentration on denitrification efficiency and avoid excessive interference with the denitrification system.

[0036] is the reaction coefficient of CO concentration on denitrification effect. Carbon monoxide can promote the reduction reaction of nitrogen oxides, so it has a certain positive effect on denitrification efficiency. However, since the concentration of carbon monoxide is relatively low and the impact is small, The value of is set to a positive value, but a small value, set to 0.2, which can reflect the role of carbon monoxide in promoting the denitrification reaction without overestimating its impact.

[0037] at last, is the reaction coefficient directly related to the injection amount. In the calculation of the injection amount, It is used to reflect the proportional relationship between flue gas flow and injection amount. The larger the flue gas flow, the larger the required ammonia injection amount. It is a coefficient closely related to the flue gas flow rate. The value of 1.1 in this range can ensure that the matching relationship between the ammonia injection amount and the flue gas flow rate is reasonable, thereby achieving the best denitrification effect.

[0038] The value of determines how the injection volume matches the flue gas flow rate and velocity. The flue gas flow rate (Q) and velocity (V) directly affect the calculation of the injection volume. The flue gas flow rate and velocity are measured in real time by the flow meter and anemometer. The injection volume will be dynamically adjusted based on these data to ensure that the system can maintain optimal denitrification efficiency under different operating conditions. Reasonable setting ensures that the injection volume can adapt to the changes in flue gas flow, avoids excessive or insufficient ammonia injection, and ensures efficient operation of the system.

[0039] In step S13, efficiency calculation is performed according to the flue gas state parameters and the load conditions to obtain a denitrification efficiency value, and a denitrification efficiency target is selected according to the denitrification efficiency value, including: The denitrification efficiency value is calculated by the following formula: in, is the denitrification efficiency value, is the unit load, is the combustion load, Flue gas emission load, is the denitrification effect parameter, is the injection volume; When the denitrification efficiency value is greater than or equal to the preset threshold, the maximum denitrification effect is selected as the denitrification efficiency target; When the denitrification efficiency value is less than the preset threshold, minimizing the ammonia escape amount is selected as the denitrification efficiency target.

[0040] It is worth noting that this formula is used to calculate the denitrification effect value (DPI) and use it as a basis for optimization decisions. The calculation of DPI takes into account multiple factors, including unit load, combustion load, flue gas emission load and injection volume. Each parameter has a certain weight in the formula, and their product reflects the operating efficiency of the current denitrification system. This formula can comprehensively evaluate the effect of the current denitrification system through weighted calculation of these parameters, and determine the optimization direction based on the results.

[0041] First, the unit load, combustion load, flue gas emission load and injection volume in the formula directly affect the denitrification efficiency. ) represents the actual operating load of the generator set. As the load increases, the pollutant emissions from the flue gas will also increase accordingly, and the denitrification demand will increase accordingly. Therefore, the unit load is one of the key factors in evaluating the denitrification efficiency. Combustion load ( ) reflects the amount of fuel consumed during the combustion process. The greater the combustion load, the more flue gas is generated, and the corresponding denitrification demand will also increase. Flue gas emission load ( ) refers to the amount of pollutants discharged through the flue. The increase in flue gas emission load will lead to an increase in the concentration of pollutants in the flue gas, which directly affects the workload of the denitrification system. Injection volume ( ) is the amount of ammonia water injection required by the denitrification system to achieve the denitrification target. The increase in injection amount is positively correlated with the concentration of nitrogen oxides in the flue gas. The larger the injection amount, the more significant the denitrification effect. Therefore, the injection amount is also an important parameter that cannot be ignored in this formula.

[0042] The combined effect of these parameters is reflected in the DPI calculation by multiplication. The DPI value calculated by this formula can accurately reflect the overall efficiency of the denitrification system. When the DPI value is large, it means that the denitrification effect has reached a high level. At this time, the system should give priority to improving the denitrification efficiency. When the DPI value is small, it means that the current denitrification effect is low. The system needs to adjust the optimization target and give priority to reducing the ammonia escape.

[0043] The threshold setting in the formula is based on the actual needs and environmental standards of the denitrification system. The threshold is used to determine whether the DPI value reaches the expected target, and then determine the direction of system optimization. When the DPI value is greater than the threshold, the system chooses to maximize the denitrification efficiency as the goal to further improve the system performance; when the DPI value is less than the threshold, the system turns to reduce the amount of ammonia escape, reduce unnecessary waste of resources, and ensure the stability of the denitrification effect. This threshold is set according to the operating characteristics and target emission standards of the system, and the value is 0.8. The specific reason for selecting this value is based on the performance of the denitrification equipment, the operating requirements under different loads and flue gas components, and the threshold is set to 0.8 to cover most operating conditions, while ensuring the flexibility of the system under different operating conditions. After setting the threshold, the system can adaptively adjust the optimization target to ensure that while maintaining efficient denitrification, excessive ammonia injection is avoided, thereby reducing unnecessary emissions.

[0044] In short, the DPI calculation formula accurately reflects the operating effect of the denitrification system by reasonably weighting parameters such as unit load, combustion load, flue gas emission load and injection volume. The setting of the threshold ensures that the system can dynamically adjust the optimization target according to the actual effect, thereby ensuring the efficient and stable operation of the system and meeting environmental protection requirements.

[0045] In step S14, according to the denitration efficiency target, a corresponding injection strategy is selected from a preset ammonia injection strategy library to obtain a preliminary ammonia injection amount; It is worth noting that in step S14, the system selects the corresponding injection strategy from the preset injection strategy library according to the denitration efficiency target to obtain the preliminary ammonia injection amount. The key to this step is to determine the corresponding injection amount according to the two optimization goals, maximizing the denitration efficiency and reducing the ammonia slip, and select through the injection strategy library.

[0046] First, when maximizing the denitrification efficiency is the optimization goal, the system needs to provide enough ammonia to react with nitrogen oxides in the flue gas to ensure that the denitrification reaction is fully carried out. Under this goal, the injection amount will be higher, because more ammonia can better react with nitrogen oxides and reduce the concentration of nitrogen oxides in the emissions. According to actual conditions, the injection amount of ammonia will be adjusted according to the flue gas flow, nitrogen oxide concentration and the design capacity of the system. With the goal of maximizing the denitrification effect, the injection amount reaches a higher range, and the specific value depends on the flue gas load and pollutant concentration. For example, under high load, the injection amount of ammonia needs to be 600kg / h to ensure that nitrogen oxides in the flue gas are fully removed. Under low load, the injection amount can be relatively small, but the denitrification effect must still be maximized.

[0047] In contrast, the goal is to reduce the amount of ammonia escape. The core of reducing ammonia escape is to optimize the amount of ammonia injection to avoid excessive injection that causes ammonia escape and waste of resources. In this case, the injection amount will be adjusted to a more precise range, only meeting the minimum amount of ammonia required for denitrification, while avoiding the waste of ammonia caused by excessive injection. The injection amount will be adjusted according to the concentration and flow rate of nitrogen oxides in the flue gas. For example, when the concentration of nitrogen oxides in the flue gas is low, the injection amount can be taken as 200kg / h, which can not only meet the needs of denitrification effect, but also avoid the waste of ammonia. This process requires dynamic adjustment of the injection amount through feedback control to ensure that under different operating conditions, the injection amount can ensure that the denitrification target is achieved without causing unnecessary ammonia escape.

[0048] The injection strategy library is a collection of pre-set injection amounts and injection parameters. This library contains a variety of injection strategies based on different working conditions and optimization goals. In actual applications, the system will select the most appropriate injection strategy from this strategy library based on current flue gas composition, load, pollutant concentration and other parameters. The injection strategy library not only includes the settings of different injection amounts, but also involves factors such as injection angle and injection pressure to ensure that the injection effect of ammonia water can be fully mixed with the flue gas to optimize the denitrification reaction.

[0049] The role of the injection strategy library is to dynamically select the injection strategy that best suits the current operating conditions based on the real-time data of the system (such as flue gas flow, nitrogen oxide concentration, unit load, etc.). This strategy library is flexible and can select different injection amounts and injection parameters according to different denitrification efficiency goals (maximizing denitrification efficiency or reducing ammonia slip). For example, in the case of high pollution emissions, the system can select a higher injection amount to maximize the denitrification effect, while in the case of low pollution emissions, the injection strategy library can select a lower injection amount to reduce ammonia slip and resource waste.

[0050] In general, the injection strategy library in step S14 provides a mechanism for dynamically selecting the injection amount according to the target and real-time operating conditions. By selecting the appropriate injection strategy, the system can achieve the optimization target under different operating conditions. When maximizing the denitrification efficiency, the injection amount is large to ensure sufficient reaction; when reducing the ammonia slip, the injection amount is appropriately reduced to avoid waste. The flexibility and real-time nature of the injection strategy library enable the denitrification system to maintain the best operating state under changing operating conditions, thereby improving the denitrification efficiency and reducing emissions.

[0051] In step S15, a correction calculation is performed based on the preliminary ammonia water injection amount and the catalyst activity coefficient to obtain the ammonia water injection amount, including: The ammonia injection amount is calculated by the following formula: in, is the amount of ammonia injection, is the initial ammonia injection amount, Catalyst activity coefficient, is the correction factor.

[0052] It is worth noting that in the denitration system, the activity coefficient of the catalyst ( ) is an important parameter that affects the denitrification effect of the system. Different types of catalysts have different activity coefficients. It represents the catalytic efficiency of the catalyst under specific reaction conditions.

[0053] SCR catalyst is one of the most widely used catalysts in the field of denitrification. SCR catalyst catalyzes the reaction of ammonia and nitrogen oxides to generate nitrogen and water. The catalytic efficiency varies depending on the material and activity of the catalyst. Generally speaking, the activity coefficient of the SCR catalyst ( ) ranges from 2 to 4. For example, the SCR system using titanium-based catalysts has a higher catalytic activity. The value is set at 3; when copper-based catalysts are used, due to their higher reactivity, The value will be set to 4. These values ​​reflect the catalytic efficiency of different catalysts under the same conditions. The copper-based catalyst has a higher activity and can therefore achieve a higher denitrification effect at a lower ammonia injection rate.

[0054] Correction factor ( ) is used to further adjust the initial ammonia injection amount. The function of this factor is to appropriately increase or decrease the initial injection amount according to the different catalyst activity coefficients. Correction factor The value is between 0 and 2. The choice of value depends on the activity of the catalyst and the actual needs of the system. If the catalyst activity is low, the correction factor Need to be set to a higher value to increase the injection amount; if the catalyst activity is high, the correction factor The value is lower, thereby reducing the injection amount and avoiding the waste of ammonia.

[0055] The larger the value of , the more the system compensates for the low activity of the catalyst and increases the injection amount; When the value is small, it means that the catalyst is more active and the system injection volume can be relatively reduced. Reasonable setting of the value can effectively adjust the injection amount so that it always remains within the most suitable range, thereby achieving the best denitrification effect.

[0056] In step S16, the non-dominated sorting genetic algorithm is used to evaluate the ammonia solution injection amount and the flue gas mixing effect to obtain injection parameters, including: Based on the flue gas mixing effect, the ammonia water injection amount is evaluated to obtain a preliminary evaluation result; The preliminary evaluation results are iteratively optimized based on a non-dominated sorting genetic algorithm to obtain injection parameters.

[0057] It is worth noting that in step S16, the denitrification system optimizes the ammonia injection amount and the flue gas mixing effect through the non-dominated sorting genetic algorithm (NSGA-II) to obtain the optimal injection parameters. The core of this process is to dynamically adjust the ammonia injection amount according to the denitrification target through the optimization algorithm to maximize the denitrification efficiency or minimize the ammonia escape. The system will first evaluate the current ammonia injection amount and the flue gas mixing effect, calculate its impact on the denitrification effect, and provide data support for subsequent optimization. Through this step, the system can adaptively adjust the injection amount to ensure the best denitrification effect under different working conditions.

[0058] During the optimization process, the system uses a non-dominated sorting genetic algorithm, which is a multi-objective optimization algorithm that can handle multiple objectives at the same time, such as maximizing denitrification efficiency and reducing ammonia escape. The algorithm continuously iterates and selects the optimal solution by simulating the process of natural selection and evolution. In each generation, the system selects based on the fitness of the current solution, giving priority to solutions that perform well in both denitrification efficiency and ammonia escape. Unlike traditional single-objective optimization methods, NSGA-II can effectively handle the two conflicting objectives in the denitrification system and find the optimal balance point, which can not only ensure the optimal denitrification effect, but also minimize the waste of ammonia water.

[0059] Specifically, when executing the NSGA-II algorithm, the system will calculate the impact of different injection amounts and injection parameters on denitration efficiency and ammonia escape based on real-time data such as flue gas flow, nitrogen oxide concentration, and catalyst activity. Through the selection, crossover, mutation, and reselection process of the genetic algorithm, the system will continuously optimize the injection amount and injection parameters in multiple generations, and finally select the optimal solution that meets the requirements of denitration efficiency and ammonia escape. This optimal solution will be used as the final injection parameter to guide the actual operation of the denitration system.

[0060] As the non-dominated sorting genetic algorithm iterates, the system can continuously adjust the amount of ammonia injection to ensure that the denitrification effect reaches the expected standard. When the load is high or the pollutant concentration is high, the system will increase the amount of ammonia injection to ensure sufficient reaction; when the load is low or the pollutant concentration is low, the injection amount will be appropriately reduced to avoid resource waste and ammonia escape. In this way, the system can adapt to different working conditions in real time, optimize the amount of ammonia injection, and achieve the dual goal optimization of denitrification effect and ammonia escape.

[0061] Through this step, the denitrification system can not only improve the denitrification efficiency and reduce the amount of ammonia escape, but also automatically adjust the system operating parameters under different working conditions to ensure that the system always works in the best state. The application of non-dominated sorting genetic algorithm makes this process highly flexible and adaptable, and can continuously optimize the injection volume and injection parameters according to real-time feedback data. Ultimately, the system ensures efficient and environmentally friendly operation through this optimization strategy, maximizes resource utilization and meets emission standards.

[0062] In step S17, the ammonia water injection amount and the injection parameters are input into a closed-loop control algorithm to obtain ammonia water control parameters, including: Inputting the ammonia water injection amount, the mixing uniformity, the injection pressure and the injection angle into the CFD model to obtain preliminary control parameters; Iteratively optimizing the preliminary control parameters based on a non-dominated sorting genetic algorithm to obtain optimized ammonia control parameters; The ammonia water control parameter is input into a closed-loop control algorithm, and feedback correction is performed to obtain the ammonia water control parameter.

[0063] It is worth noting that in step S17, the denitrification system first inputs multiple injection parameters such as ammonia injection amount, flue gas mixing uniformity, injection pressure, injection angle, etc. into the CFD (computational fluid dynamics) model to calculate the preliminary control parameters. After these input parameters are processed by the CFD model, the preliminary injection control parameters are output to provide a basis for subsequent control. The role of the CFD model is to simulate and analyze the interaction between ammonia and flue gas, optimize the distribution and flow path of the injection, and thus improve the denitrification efficiency.

[0064] The CFD model is a computational tool used to simulate fluid flow, heat transfer, chemical reactions and other related processes. In the denitrification system, the CFD model is mainly used to simulate the injection effect of ammonia water and the mixing of flue gas. By establishing an accurate airflow and injection path model, CFD can describe in detail the distribution of ammonia water after injection, the change in pollutant concentration in the flue gas, and the influence of various control parameters on the denitrification efficiency. In this step, the CFD model generates the distribution map and concentration distribution of the flue gas after ammonia water injection through the input injection amount, flue gas flow rate, temperature, pressure and other data, so as to predict the effect of injection amount and flue gas mixing, and then obtain preliminary control parameters.

[0065] The application of CFD model is not limited to the evaluation of injection volume and flue gas mixing effect. It can also help design and optimize the injection system and find the best injection angle, injection position and injection pressure. These optimized control parameters can ensure that ammonia water is fully mixed with flue gas, improve the efficiency of denitrification reaction, and reduce ammonia escape. Through CFD model, the system can more intuitively understand the performance of ammonia water injection under different operating conditions, and then make reasonable control adjustments.

[0066] After obtaining the preliminary control parameters, the system is further optimized based on the non-dominated sorting genetic algorithm (NSGA-II). The core of this step is to adjust the preliminary control parameters through the algorithm to achieve better denitrification effect and ammonia escape. NSGA-II is a multi-objective optimization algorithm that can simultaneously consider the two goals of maximizing denitrification efficiency and minimizing ammonia escape. Through non-dominated sorting, NSGA-II can balance between multiple optimization objectives and find the best combination of control parameters. In this process, the system continuously optimizes through algorithm iteration to ensure that each adjustment is close to the optimal solution.

[0067] The execution process of the NSGA-II algorithm includes selection, crossover, and mutation steps. In the selection stage, the algorithm evaluates the fitness of the current solution and gives priority to solutions that can both improve denitrification efficiency and reduce ammonia escape. In the crossover and mutation stages, the algorithm generates and evaluates new solutions by combining and adjusting the parameters of the current solution. Through multiple iterations, the system can continuously fine-tune the control parameters and ultimately obtain a balanced optimal solution.

[0068] The optimized injection control parameters are input into the closed-loop control algorithm for feedback correction. The role of the closed-loop control algorithm is to make dynamic adjustments based on feedback data during real-time operation. Specifically, the closed-loop control system monitors the current denitration effect, analyzes the denitration efficiency and ammonia escape, and automatically adjusts the injection volume and other control parameters if deviations are found to ensure that the system always operates in the best operating state. This closed-loop control mechanism can quickly respond to changes in flue gas composition and other operating conditions, thereby ensuring the stability of the denitration effect.

[0069] During the feedback correction process, the control system will adjust the ammonia injection amount according to real-time data to ensure that the injection amount always matches the concentration of nitrogen oxides in the flue gas and avoid excessive or insufficient ammonia injection. When the denitrification effect does not meet expectations, the system will increase the injection amount; conversely, if the denitrification effect is too high or the ammonia escape increases, the system will automatically reduce the injection amount. Through this closed-loop regulation, the system can achieve automatic and precise ammonia control.

[0070] Once the optimized ammonia control parameters are obtained, these parameters will be directly input into the actuators of the denitration system for actual operation. These actuators include ammonia injection pumps, nozzle adjustment devices, etc. The actuators automatically adjust the injection volume, injection pressure, injection angle and other parameters according to the instructions of the control system to ensure that the ammonia injection plays the best effect in the entire denitration process. The actuators communicate with the control system in real time through electrical signals to feedback the current operating status. If an abnormality is found during the execution process, the system will quickly make adjustments to ensure the stable operation of the entire denitration system.

[0071] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0072] The working process of the present invention is described below using a relatively common scenario as an example.

[0073] During the operation of a typical coal-fired power plant denitrification system, the method of the present invention dynamically optimizes the ammonia injection strategy by real-time monitoring of flue gas composition, flow, load, flue gas mixing effect and catalyst activity coefficient to ensure the best denitrification efficiency and reduce ammonia slip. Figure 1 and Figure 2, understand the working principle of this method and its application in denitrification control systems.

[0074] When the power plant boiler is running, the flue gas enters the denitrification system. First, in step S11, multiple sensors obtain flue gas composition (such as NOx, O2, SO2, etc.), flow data (such as flow rate, flow), unit load conditions (such as combustion load, flue gas emission load), flue gas mixing effect (such as mixing uniformity) and catalyst activity coefficient ( ). These data are collected and input into the data acquisition module, and then transmitted to the processing unit for analysis.

[0075] Next, in S12, the system performs state calculation based on the obtained flue gas composition and flow data to obtain flue gas state parameters, such as current NOx concentration, flue gas temperature, flow rate, etc. The state calculation module uses the preset flue gas analysis model for calculation. For example, if the current detected NOx concentration is 450 mg / Nm³ and the flue gas flow is 3000 Nm³ / h, the system will determine whether the flue gas state is within the normal range.

[0076] In S13, the system calculates the denitrification efficiency value (DPI) based on the load conditions (such as the current operating load of the boiler is 80%) and the flue gas state parameters, and selects the denitrification efficiency target based on the DPI value. For example, under the current load, if the DPI value is high, the system sets the target to "minimize ammonia escape"; if the DPI value is low, the target is adjusted to "maximize denitrification efficiency". This calculation is completed by the denitrification efficiency module and provides a basis for the subsequent optimization of ammonia injection.

[0077] Subsequently, in S14, the system selects an appropriate injection strategy from the preset ammonia injection strategy library based on the selected denitration efficiency target to obtain a preliminary ammonia injection amount. Assuming that the current operating conditions require adjusting NOx emissions to below 100 mg / Nm³, the system selects a three-layer injection mode and calculates a preliminary injection amount of 120 kg / h. This process is completed by the preliminary ammonia injection amount module.

[0078] In S15, the system combines the catalyst activity coefficient ( ) to correct the initial injection amount to ensure that the injection amount is consistent with the current activity of the catalyst. For example, if the system detects =3.2 (indicating a decrease in catalyst activity), then the formula The corrected ammonia injection rate is calculated and adjusted from 120 kg / h to 135 kg / h to compensate for the decrease in catalyst activity. This calculation is done by the ammonia injection rate module.

[0079] In S16, the system evaluates the corrected ammonia injection amount and the flue gas mixing effect based on the non-dominated sorting genetic algorithm, and optimizes the injection parameters, such as injection angle, injection pressure, etc. For example, if the mixing uniformity is less than 90%, the system will optimize the injection pressure and adjust it from 2.0 MPa to 2.5 MPa to enhance the mixing effect. This process is performed by the injection parameter module.

[0080] Finally, in S17, the system inputs the calculated ammonia injection amount and optimized injection parameters into the closed-loop control algorithm, generates ammonia control parameters, and transmits them to the actuators (such as electric valves, injection pumps) to implement injection adjustments. The system will continuously monitor the injection effect and make dynamic adjustments based on feedback data. For example, if the NOx concentration is still higher than the target value after the injection adjustment, the system will optimize the injection amount or adjust the injection angle again to form an adaptive control mechanism. This process is completed by the closed-loop control module to ensure that the denitrification system always maintains efficient operation.

[0081] Reference Figure 2 The second embodiment of the present invention provides a control device for a denitration system, comprising: Data acquisition module, used to obtain smoke composition, flow data, load conditions and smoke mixing effects; A state calculation module, used to perform state calculation according to the smoke components and the flow data to obtain smoke state parameters; A denitration efficiency module, for performing efficiency calculation according to the flue gas state parameters and the load conditions to obtain a denitration efficiency value, and selecting a denitration efficiency target according to the denitration efficiency value; A preliminary ammonia injection amount module is used to select a corresponding injection strategy from a preset ammonia injection strategy library according to the denitration efficiency target to obtain a preliminary ammonia injection amount; an ammonia water injection amount module, used for performing correction calculation according to the preliminary ammonia water injection amount and the catalyst activity coefficient to obtain the ammonia water injection amount; An injection parameter module, used for evaluating the ammonia water injection amount and the flue gas mixing effect to obtain injection parameters; The closed-loop control module is used to input the ammonia water injection amount and the injection parameters into a closed-loop control algorithm to obtain the ammonia water control parameters.

[0082] It should be noted that the control device of a denitrification system provided in an embodiment of the present invention is used to execute all process steps of a control method of a denitrification system in the above embodiment, and the working principles and beneficial effects of the two correspond one to one, so they will not be repeated.

[0083] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a control program for a denitration system. When the processor executes the computer program, the steps in the above-mentioned control method for a denitration system are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are realized, such as a closed-loop control module.

[0084] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0085] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0086] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0087] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0088] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0089] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0090] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A control method for a denitration system, characterized in that: include: Obtain flue gas composition, flow data, load conditions, flue gas mixing effect and catalyst activity coefficient; Performing state calculation according to the smoke components and the flow data to obtain smoke state parameters; Performing efficiency calculation according to the flue gas state parameters and the load conditions to obtain a denitration efficiency value, and selecting a denitration efficiency target according to the denitration efficiency value; According to the denitration efficiency target, a corresponding injection strategy is selected from a preset ammonia injection strategy library to obtain a preliminary ammonia injection amount; Performing a correction calculation based on the preliminary ammonia water injection amount and the catalyst activity coefficient to obtain the ammonia water injection amount; Based on a non-dominated sorting genetic algorithm, the ammonia solution injection amount and the flue gas mixing effect are evaluated to obtain injection parameters; The ammonia water injection amount and the injection parameters are input into a closed-loop control algorithm to obtain ammonia water control parameters.

2. The control method of the denitration system according to claim 1, characterized in that: The flue gas components include: nitrogen oxide concentration, sulfur dioxide concentration, carbon monoxide concentration, oxygen concentration and water vapor concentration; The load conditions include: unit load, combustion load and flue gas emission load; The smoke mixing effect includes: mixing uniformity, injection pressure and injection angle; The flow data includes: smoke flow and flow velocity.

3. The control method of the denitration system according to claim 1, characterized in that: The state calculation is performed according to the smoke components and the flow data to obtain the smoke state parameters, including: The denitrification effect parameters are calculated by the following formula: in, is the denitrification effect parameter, is the oxygen concentration, is the sulfur dioxide concentration, is the water vapor concentration, is the carbon monoxide concentration, is the concentration of nitrogen oxides, , , and is the reaction coefficient; The injection volume is calculated by the following formula: in, is the injection amount, is the reaction coefficient, is the flue gas flow rate, is the flue gas velocity; The flue gas state parameters include: denitration effect parameters and injection amount.

4. The control method of the denitration system according to claim 1, characterized in that: The efficiency calculation is performed according to the flue gas state parameter and the load condition to obtain a denitration efficiency value, and a denitration efficiency target is selected according to the denitration efficiency value, including: The denitrification efficiency value is calculated by the following formula: in, is the denitrification efficiency value, is the unit load, is the combustion load, Flue gas emission load, is the denitrification effect parameter, is the injection volume; When the denitrification efficiency value is greater than or equal to the preset threshold, the maximum denitrification effect is selected as the denitrification efficiency target; When the denitrification efficiency value is less than the preset threshold, minimizing the ammonia escape amount is selected as the denitrification efficiency target.

5. The control method of the denitration system according to claim 1, characterized in that: The method of performing a correction calculation based on the preliminary ammonia water injection amount and the catalyst activity coefficient to obtain the ammonia water injection amount includes: The ammonia injection amount is calculated by the following formula: in, is the amount of ammonia injection, is the initial ammonia injection amount, Catalyst activity coefficient, is the correction factor.

6. The control method of the denitration system according to claim 1, characterized in that: The non-dominated sorting genetic algorithm is used to evaluate the ammonia water injection amount and the flue gas mixing effect to obtain injection parameters, including: Based on the flue gas mixing effect, the ammonia water injection amount is evaluated to obtain a preliminary evaluation result; The preliminary evaluation results are iteratively optimized based on a non-dominated sorting genetic algorithm to obtain injection parameters.

7. The control method of the denitration system according to claim 1, characterized in that: The step of inputting the ammonia water injection amount and the injection parameters into a closed-loop control algorithm to obtain the ammonia water control parameters includes: Inputting the ammonia water injection amount, the mixing uniformity, the injection pressure and the injection angle into the CFD model to obtain preliminary control parameters; Iteratively optimizing the preliminary control parameters based on a non-dominated sorting genetic algorithm to obtain optimized ammonia control parameters; The ammonia water control parameter is input into a closed-loop control algorithm, and feedback correction is performed to obtain the ammonia water control parameter.

8. A control device for a denitration system, characterized in that: include: Data acquisition module, used to obtain flue gas composition, flow data, load conditions, flue gas mixing effect and catalyst activity coefficient; A state calculation module, used to perform state calculation according to the smoke components and the flow data to obtain smoke state parameters; A denitration efficiency module, for performing efficiency calculation according to the flue gas state parameters and the load conditions to obtain a denitration efficiency value, and selecting a denitration efficiency target according to the denitration efficiency value; A preliminary ammonia injection amount module is used to select a corresponding injection strategy from a preset ammonia injection strategy library according to the denitration efficiency target to obtain a preliminary ammonia injection amount; an ammonia water injection amount module, used for performing correction calculation according to the preliminary ammonia water injection amount and the catalyst activity coefficient to obtain the ammonia water injection amount; An injection parameter module, used for evaluating the ammonia water injection amount and the flue gas mixing effect to obtain injection parameters; The closed-loop control module is used to input the ammonia water injection amount and the injection parameters into a closed-loop control algorithm to obtain the ammonia water control parameters.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the control method of the denitration system according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the control method of the denitration system according to any one of claims 1 to 7.

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