A denitration ammonia injection flue gas mixing system and an ammonia injection control method thereof

Abstract

The present application relates to the technical field of flue gas pollutant control of thermal power plants, and discloses a denitration ammonia injection flue gas mixing system and an ammonia injection control method thereof, comprising: a sensing module configured to measure three-dimensional spatial distribution data of a flue gas flow velocity field, a temperature field and a component concentration field in an SCR reactor inlet flue in real time; a prediction module connected with the output end of the sensing module; a collaborative decision module connected with the output end of the prediction module; and a jet flow module connected with the output end of the collaborative decision module. In the present application, the function of the control system is expanded from the traditional chemical process regulation to the active shaping and management of the reactor physical environment. Because the system can accurately predict the deposition position of fly ash through its solid particle transport model, the double-channel jet flow module can be instructed to perform targeted air flow injection on high-risk areas before the formation of fly ash deposition, thereby changing the local flow field and making it difficult for fly ash particles to adhere and accumulate.

Description

Technical Field

[0001] This invention relates to the field of flue gas pollutant control technology in thermal power plants, and in particular to a denitrification ammonia injection flue gas mixing system and its ammonia injection control method. Background Technology

[0002] In the flue gas pollutant control system of thermal power plants, selective catalytic reduction (SCR) technology is currently the most widely used core technology for controlling nitrogen oxide emissions. Its basic working principle is as follows: in the reactor, a reducing agent with ammonia as the main component is injected into the flue gas using the catalytic effect of a catalyst, so that it reacts chemically with the nitrogen oxides in the flue gas to generate nitrogen and water that are harmless to the atmosphere. In typical existing technology practices, this process is achieved by setting a fixed ammonia injection grid upstream of the catalyst bed to inject ammonia, and by using a feedback control loop based on the NOx concentration monitoring value at the SCR reactor outlet to adjust the total amount of ammonia injected, in order to control the final NOx emission concentration within the environmental protection standard limit.

[0003] However, the aforementioned existing technologies have revealed several inherent defects in practical applications, especially when dealing with the frequent load changes of current generator sets. First, their core hardware, the ammonia injection grid and control logic, are based on an idealized assumption that the flue gas in the flue is uniform. However, in actual operation, the spatial distribution of flue gas velocity, temperature, and NOx concentration is extremely uneven and dynamically variable. A fixed ammonia injection pattern cannot adapt to such changes, leading to a mismatch in the molar ratio of ammonia to NOx on the catalyst surface in local areas. This not only reduces denitrification efficiency and ammonia utilization but also causes ammonia escape problems. Second, this uneven reaction environment causes irreversible damage to the catalyst itself. Some areas are accelerated to sinter and deactivate due to long-term exposure to high temperatures or high ammonia concentrations, while other areas may be poisoned and blocked due to undertemperature or byproduct generation. Ultimately, this leads to uneven aging of the catalyst as a whole, significantly shortening its effective lifespan. Finally, the uneven flue gas flow field can also induce the deposition of fly ash particles in specific low-velocity areas, gradually forming physical ash accumulation and blockage. This increases system resistance, increases operating energy consumption, and in severe cases, even threatens the safe and stable operation of the unit. Summary of the Invention

[0004] The technical problem to be solved by this invention is that the fixed ammonia injection and control mode in the prior art cannot adapt to the dynamic and uneven distribution of flue gas parameters in space during actual operation, resulting in low denitrification efficiency, ammonia escape, uneven aging of catalyst and ash accumulation and blockage. To this end, we propose a denitrification ammonia injection flue gas mixing system and its ammonia injection control method.

[0005] To achieve the above objectives, this application adopts the following technical solution: a denitrification ammonia injection flue gas mixing system, characterized in that it comprises:

[0006] The sensing module is configured to measure the three-dimensional spatial distribution data of the flue gas velocity field, temperature field, and component concentration field in the inlet flue of the SCR reactor in real time.

[0007] The prediction module is connected to the output of the sensing module. The prediction module has embedded a flue gas flow model, a catalytic reaction kinetic model and a solid particulate matter transport model. It is configured to predict the three-dimensional flue gas field distribution at future times, generate an activity state distribution map of each region of the catalyst bed and generate an expected deposition risk distribution map of fly ash particles in the flue gas at the catalyst inlet section based on the three-dimensional spatial distribution data and unit operating parameters.

[0008] The collaborative decision-making module is connected to the output of the prediction module and is configured to generate zoned control commands containing ammonia injection parameters and gas jet parameters based on the predicted three-dimensional flue gas field, catalyst activity state distribution map and fly ash deposition risk distribution map, with the optimization objectives of system denitrification efficiency, catalyst activity uniformity and catalyst bed physical unobstructedness.

[0009] The jet module is connected to the output of the collaborative decision module. The jet module has multiple independently controllable jet units on the flue section. Each jet unit has a liquid channel for injecting ammonia liquid and a gas channel for injecting compressed gas, so as to execute the ammonia liquid injection parameters and gas jet parameters respectively.

[0010] Preferably, the sensing module includes: an acoustic tomography sensor array for measuring the temperature field and the flow velocity field, and a laser spectral absorption sensor grid for measuring the component concentration field.

[0011] Preferably, the prediction module uses the Lagrange particle tracking algorithm, combined with the predicted three-dimensional flue gas velocity field, to calculate and generate the fly ash deposition risk distribution map.

[0012] Preferably, each jet unit of the jet module has a liquid control valve with adjustable jet flow rate at the outlet of its liquid channel and a gas control valve with an independent liquid control valve for switching and flow rate adjustment at the outlet of its gas channel.

[0013] Preferably, the collaborative decision-making module is configured to execute a preventive dust removal operation mode. In this mode, the collaborative decision-making module identifies high-risk areas based on the fly ash deposition risk distribution map and issues instructions to the jet units in the corresponding areas to spray pulsed or continuous gas jets through their gas channels to change the local flue gas flow lines and prevent fly ash particles from depositing in the area.

[0014] Preferably, the collaborative decision-making module calculates each jet unit using the following formula. ammonia injection mass flow rate :

[0015] ,in and For predicted values, The comprehensive decision weight factor is calculated by the collaborative decision-making module.

[0016] Preferably, the comprehensive decision weighting factor Weighted by reaction efficiency Catalyst activity maintenance weight And physical fluency weight A joint decision.

[0017] Preferably, the collaborative decision-making module determines the catalyst activity maintenance weight. At that time, regions with high activity in the catalyst activity state distribution diagram are assigned a weight coefficient less than 1, and regions with low activity are assigned a weight coefficient greater than 1, in order to balance the long-term chemical load of the catalyst bed.

[0018] Furthermore, the present invention also relates to an embodiment, specifically a method for controlling ammonia injection in a denitrification ammonia injection flue gas mixing system, comprising the following steps:

[0019] S1: Real-time measurement of the three-dimensional spatial distribution parameters of flue gas in the inlet flue of the SCR reactor;

[0020] S2: Based on the three-dimensional spatial distribution parameters, predict the flue gas field distribution at future times, and evaluate the distribution map of the active state of the generated catalyst bed and the distribution map of fly ash deposition risk.

[0021] S3: Taking denitrification efficiency, catalyst activity uniformity and bed physical unobstructedness as comprehensive optimization objectives, control commands including ammonia injection parameters and gas jet parameters are independently calculated for multiple regions divided on the flue section.

[0022] S4: Control the jet unit corresponding to each region to independently perform ammonia injection operation and / or gas jet operation according to the control command.

[0023] Preferably, step S4 includes: when the predicted risk value of fly ash deposition in any area exceeds a preset threshold, prioritizing the execution of a gas jet operation for that area to proactively intervene in the local aerodynamic environment and prevent ash accumulation and blockage.

[0024] The technical effects and advantages of this invention are as follows:

[0025] In this invention, the function of the control system is expanded from traditional chemical process regulation to the active shaping and management of the reactor's physical environment. Because the system can accurately predict the deposition location of fly ash through its solid particulate transport model, it can instruct the jet module to perform targeted airflow injection on high-risk areas before ash accumulation. This active aerodynamic intervention changes the local flow field, making it difficult for fly ash particles to adhere and accumulate. Its direct effect is to avoid physical blockage of the catalyst, thereby eliminating the resulting increase in flue gas pressure differential, increased power consumption of the induced draft fan, and unplanned shutdown purging, significantly improving the unit's availability and operational economy.

[0026] In this invention, catalyst life can be extended through two approaches. First, by balancing the catalyst activity maintenance weight, premature failure of some catalyst regions due to long-term overwork is avoided, thus achieving balanced decay of chemical activity. Second, by using a preventive cleaning function, catalyst failure due to physical blockage is avoided. Since the integrity of both chemical activity and physical structure is maintained simultaneously, the overall effective life of the catalyst is maximized.

[0027] In this invention, three previously independent or even conflicting objectives—denitrification efficiency, catalyst lifespan, and equipment uptime—are incorporated into a unified collaborative decision-making framework for global optimization. The decisions are based on comprehensive future predictions, enabling the system to make globally optimal choices while ensuring the safety of the outlet. Under the premise of meeting the standards, operation is achieved with minimal catalyst loss and blockage risk, thereby minimizing the total cost of the system's entire life cycle and maximizing overall operating efficiency. Attached Figure Description

[0028] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts:

[0029] Figure 1 This is a module relationship diagram of the system of the present invention;

[0030] Figure 2 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0031] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0032] like Figure 1As shown, a denitrification ammonia injection flue gas mixing system consists of four core modules: a sensing module, a prediction module, a collaborative decision-making module, and a jet module. These four modules are progressive in function and tightly coupled in data, forming a complete forward-looking intelligent control system.

[0033] For the perception module:

[0034] This sensing module serves as the system's real-time data source. Its function is to comprehensively and accurately measure the changing flue gas state within the SCR reactor inlet flue. To achieve this function, the module preferably consists of two sensor arrays:

[0035] Acoustic tomography sensor array: Multiple pairs of ultrasonic transmitters and receivers are arranged in a matrix along the circumference and axial direction of the flue wall. By measuring the propagation time of sound waves on different paths, algebraic reconstruction technology and tomographic imaging algorithm are used to perform high-speed inversion calculations on a large amount of path data. This array can reconstruct the temperature field and velocity field distribution of any cross section inside the flue in real time.

[0036] Laser spectral absorption sensor grid: Multiple pairs of tunable diode lasers and photodetectors are arranged on the same or adjacent flue gas sections to form a measurement grid covered by multiple intersecting optical paths. By analyzing the absorption attenuation of laser beams of specific wavelengths after passing through the flue gas and combining the absorption data from different optical paths, this grid can measure the concentration of laser light in the flue gas, either in sections or as a whole. Concentration and Real-time distribution of concentration.

[0037] The data from these two sensor arrays are aligned and integrated in time and space by a data fusion processor, and finally output to the downstream prediction module in the form of a three-dimensional data matrix.

[0038] For the prediction module:

[0039] This prediction module is responsible for sensing the current state and predicting the future state. It receives real-time 3D data streams from the sensing module and global operating parameters from the power plant's distributed control system. Internally, this module runs three interconnected prediction and evaluation models for different physicochemical processes in parallel:

[0040] Flue gas flow model: This model is a data-enhanced model based on computational fluid dynamics principles and continuously corrected using historical operating data. It can quickly solve the simplified Navier-Stokes equations based on the current boundary conditions, thereby accurately predicting the evolution trend of the three-dimensional flue gas velocity field and temperature field of the entire flue in a short period of time in the future.

[0041] Catalytic reaction kinetics model: This model generates a real-time distribution map of the catalyst's active state, logically dividing the catalyst bed into segments corresponding to jet units. Each infinitesimal element, for each infinitesimal element The model can continuously track its cumulative operating time, temperature history, and reactant concentration history, and calculate a quantified relative activity coefficient online based on a pre-defined catalyst performance degradation sub-model based on the Arrhenius equation and empirical deactivation formula. .

[0042] Solid-phase particulate transport model: This model is used to predict physical blockage risk and generate a fly ash deposition risk distribution map. Using the Lagrange particle tracking method, a large number of virtual fly ash particles are released into the predicted future flue gas flow field. By calculating the forces acting on each particle in the complex flow field, including drag, gravity, and lift, as well as its trajectory, the model can statistically determine the value of each micro-element at the catalyst inlet cross-section. The above figure represents the number of particles that collide or may deposit due to low velocity per unit time. After normalization, this statistical result yields the fly ash deposition risk index for that unit. .

[0043] For the collaborative decision-making module:

[0044] This module is the system's decision-making center. It receives flue gas field, activity map, and risk map from the prediction module and executes a multi-objective optimization algorithm. This algorithm is used to optimize the performance of each jet unit. Calculate an optimal set of control commands, namely the mass flow rate of ammonia injection. and gas jet flow rate .

[0045] The specific formula for calculating the ammonia injection flow rate in this module is as follows:

[0046] ,in: To assign to the The mass flow rate of ammonia in jet unit No. 1, in units of .

[0047] The future time obtained by the prediction module area Concentration, in units of .

[0048] The future time obtained by the prediction module The normal velocity of flue gas in the region, in units of .

[0049] For the first The cross-sectional area of ​​the region is a design constant, and its unit is 1000 m². .

[0050] The target global ammonia-nitrogen molar ratio set for the system is a dimensionless operating parameter.

[0051] , Here, are the molar masses of ammonia and nitrogen oxides, respectively; are physical constants, with units of . .

[0052] It is a comprehensive decision weighting factor, dimensionless, which couples multiple optimization objectives of different dimensions through a multiplicative model. Its expression is: ,in: This is the reaction efficiency weight, which is mainly determined by the predicted temperature. The decision was made that the functional relationship is set to be close to 1 within the optimal activity temperature range of the catalyst, and less than 1 when deviating from the optimal range, so as to ensure that the chemical reaction takes place under the most efficient conditions. This is the catalyst activity maintenance weight, which is used to achieve long-term equilibrium of the chemical load on the catalyst bed. Its preferred calculation formula is: ,in, It is calculated by the prediction module The relative activity coefficient of the catalyst in a unit; It is the average activity coefficient of the entire bed, representing the current overall health level of the catalyst; It is an adjustment coefficient with a value between 0 and 1, used to adjust the strength of the balancing strategy. The mechanism of this formula is that for areas with activity higher than the average value, its weight will be less than 1, thereby appropriately reducing its ammonia injection load. Conversely, for areas with activity lower than the average value, its weight will be greater than 1, so as to appropriately increase its load, thereby delaying the overall aging.

[0053] This is the physical accessibility weight, which is directly related to the risk of fly ash deposition. The preferred calculation formula is as follows:

[0054] ,in It is calculated by the prediction module The fly ash deposition risk index of the unit; It is an adjustable influence coefficient. This formula allows for a proactive reduction in the ammonia injection weight in areas with higher ash accumulation risk, because liquid injection increases particle adhesion, and reducing the amount of ammonia injected helps to slow down ash accumulation.

[0055] In addition, the gas jet flow rate of this module The decision-making logic is set as a threshold-based triggering mechanism: when Exceeding a preset security threshold At that time, the gas channel of the unit is activated. It is set to a flow rate value sufficient to generate effective purging or disturbance, thereby performing preventative dust removal operations.

[0056] For the jet module:

[0057] This module is the physical execution mechanism for implementing the above decisions. It abandons the traditional ammonia injection grid and uses a [mechanism / mechanism] to achieve the desired results. A matrix composed of independently addressable jet units, each jet unit featuring a dual-channel hardware structure:

[0058] Liquid channel: Its inlet is connected to the main ammonia supply pipe, and its outlet is equipped with a liquid control valve for rapid and precise flow regulation, preferably a pulse width modulation solenoid valve, used to execute ammonia injection commands. .

[0059] Gas passage: Its inlet connects to the main compressed air supply line, and its outlet is equipped with a gas control valve that can be opened, closed, and its flow rate regulated independently of the liquid control valve. This passage is used to execute gas jet commands. .

[0060] The nozzles of the two channels are designed as a single unit to ensure an effective jet pattern is formed when spraying alone or in combination. This separation of functions in the hardware gives the system flexibility, allowing it to function as both a chemical reactant dispenser and an aerodynamic intervention tool, thus providing a physical basis for advanced maintenance functions such as preventative cleaning.

[0061] This invention, through the precise collaboration of the above four modules, integrates the originally fragmented denitrification efficiency control, catalyst life management, and equipment physical maintenance into an organic and intelligent whole, achieving a fundamental shift from passive response to proactive management.

[0062] like Figure 2 As shown, the present invention also relates to an ammonia injection control method for a denitrification ammonia injection flue gas mixing system, specifically including the following steps:

[0063] Step 1: Perform the sensing step. By deploying a sensing module at the inlet of the SCR reactor, continuously measure the three-dimensional spatial distribution parameters of flue gas velocity, temperature, and component concentration in real time, providing the system with accurate, full-section, real-time operating data.

[0064] Step 2: Perform the prediction and evaluation steps. Input the sensed real-time data and unit operating parameters into the prediction module. This module not only predicts the three-dimensional flue gas field distribution in the short future period, but more importantly, it also uses the built-in catalyst performance degradation model and particulate transport model to evaluate and generate two key diagnostic maps online: one is a catalyst activity state distribution map reflecting the chemical health status of each region of the catalyst, and the other is a fly ash deposition risk distribution map that warns of physical blockage risk.

[0065] Step 3: Execute the decision-making step. The collaborative decision-making module receives the above prediction results and diagnostic map. This module performs comprehensive optimization based on three objectives: system denitrification efficiency, long-term equilibrium of catalyst activity, and physical unobstructedness of the catalyst bed. Through a core algorithm containing multiple weighting factors, it independently calculates the optimal control command for multiple regions divided on the flue section. The command specifically includes two parts: ammonia injection parameters for chemical reaction and gas jet parameters for physical intervention.

[0066] Step 4, execution step: The jet module receives and precisely executes the decision instructions. Each jet unit independently performs ammonia injection or gas jet operation through its liquid channel and / or gas channel according to its received specific instructions. This step includes a priority processing logic: when the predicted value of fly ash deposition risk in a region exceeds a preset threshold, the system will prioritize the gas jet operation for that region, actively intervene in the local aerodynamic environment to prevent ash accumulation and blockage, thereby placing the protection of the equipment's physical structure in an important position.

[0067] The detailed working principle is as follows: This system operates as a continuous, closed-loop process of sensing, prediction, decision-making, and execution. First, the sensing module continuously collects acoustic and spectral data from the flue gas cross-section, generating real-time data reflecting the current flue gas temperature, flow rate, and... The three-dimensional distribution map of the concentration is then generated. The prediction module receives this real-time data and, on the one hand, uses a fluid dynamics model to deduce the dynamics of the flue gas over the next few minutes. On the other hand, it updates the active state distribution map of the catalyst bed based on real-time operating conditions and historical performance. Simultaneously, it calculates the fly ash deposition risk index for each region, which is then transmitted to the collaborative decision-making module. This module's optimization algorithm comprehensively considers: how much ammonia is needed in each region to achieve the denitrification target; whether certain regions need to bear a lower chemical load to protect the catalyst; and whether airflow intervention is needed in certain regions to prevent blockage. Based on these considerations, the collaborative decision-making module calculates the optimal ammonia injection rate for the unit using the aforementioned weighting factor formula. and gas injection volume Finally, the command is sent to the jet module, which... Each unit executes the received instructions precisely: the liquid channels of most units spray precisely metered ammonia liquid as needed, while the gas channels of a few units located in the dust accumulation risk area may be activated to spray pulsed airflow, actively changing the local aerodynamic environment, thereby achieving a high degree of unity between efficient denitrification and equipment self-maintenance.

[0068] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A denitrification ammonia injection flue gas mixing system, characterized in that, include: The sensing module is configured to measure the three-dimensional spatial distribution data of the flue gas velocity field, temperature field, and component concentration field in the inlet flue of the SCR reactor in real time. The prediction module is connected to the output of the sensing module. The prediction module has embedded a flue gas flow model, a catalytic reaction kinetic model and a solid particulate matter transport model. It is configured to predict the three-dimensional flue gas field distribution at future times, generate an activity state distribution map of each region of the catalyst bed and generate an expected deposition risk distribution map of fly ash particles in the flue gas at the catalyst inlet section based on the three-dimensional spatial distribution data and unit operating parameters. The collaborative decision-making module is connected to the output of the prediction module and is configured to generate zoned control commands containing ammonia injection parameters and gas jet parameters based on the predicted three-dimensional flue gas field, catalyst activity state distribution map and fly ash deposition risk distribution map, with the optimization objectives of system denitrification efficiency, catalyst activity uniformity and catalyst bed physical unobstructedness. The jet module is connected to the output of the collaborative decision module. The jet module has multiple independently controllable jet units on the flue section. Each jet unit has a liquid channel for injecting ammonia liquid and a gas channel for injecting compressed gas, so as to execute the ammonia liquid injection parameters and gas jet parameters respectively. The collaborative decision-making module is configured to execute a preventive dust removal operation mode. In this mode, the collaborative decision-making module identifies high-risk areas based on the fly ash deposition risk distribution map and issues instructions to the jet units in the corresponding areas to spray pulsed or continuous gas jets through their gas channels to change the local flue gas flow lines and prevent fly ash particles from depositing in the area. The collaborative decision-making module calculates each jet unit using the following formula. ammonia injection mass flow rate : ,in and For predicted values, The future time obtained by the prediction module area Concentration, in units of , The future time obtained by the prediction module The normal velocity of flue gas in the region, in units of , The comprehensive decision weight factor is calculated by the collaborative decision-making module. For the first The cross-sectional area of ​​the region The target global ammonia nitrogen molar ratio set for the system, , These are the molar masses of ammonia and nitrogen oxides, respectively. The comprehensive decision weighting factor Weighted by reaction efficiency Catalyst activity maintenance weight And physical fluency weight A joint decision.

2. The denitrification ammonia injection flue gas mixing system according to claim 1, characterized in that: The sensing module includes: an acoustic tomography sensor array for measuring the temperature field and the flow velocity field, and a laser spectral absorption sensor grid for measuring the component concentration field.

3. The denitrification ammonia injection flue gas mixing system according to claim 1, characterized in that: The prediction module uses the Lagrange particle tracking algorithm, combined with the predicted three-dimensional flue gas velocity field, to calculate and generate the fly ash deposition risk distribution map.

4. The denitrification ammonia injection flue gas mixing system according to claim 1, characterized in that: Each jet unit of the jet module has a liquid control valve at the outlet of its liquid channel that can adjust the jet flow rate, and a gas control valve at the outlet of its gas channel that can be switched on and off and have its flow rate adjusted independently of the liquid control valve.

5. The denitrification ammonia injection flue gas mixing system according to claim 1, characterized in that: The catalyst activity maintenance weight The formula for calculating the long-term equilibrium of the chemical load on the catalyst bed is as follows: ,in, It is calculated by the prediction module The relative activity coefficient of the catalyst in a unit; It is the average activity coefficient of the entire bed, representing the current overall health level of the catalyst; It is an adjustment coefficient with a value between 0 and 1, used to adjust the strength of the equilibrium strategy; The collaborative decision-making module determines the catalyst activity maintenance weight. At that time, regions with high activity in the catalyst activity state distribution diagram are assigned a weight coefficient less than 1, and regions with low activity are assigned a weight coefficient greater than 1, in order to balance the long-term chemical load of the catalyst bed.

6. A method for controlling ammonia injection in a denitrification ammonia injection flue gas mixing system, applied to the denitrification ammonia injection flue gas mixing system as described in claim 1, characterized in that, Includes the following steps: S1: Real-time measurement of the three-dimensional spatial distribution parameters of flue gas in the inlet flue of the SCR reactor; S2: Based on the three-dimensional spatial distribution parameters, predict the flue gas field distribution at future times, and evaluate the distribution map of the active state of the generated catalyst bed and the distribution map of fly ash deposition risk. S3: Taking denitrification efficiency, catalyst activity uniformity and bed physical unobstructedness as comprehensive optimization objectives, control commands including ammonia injection parameters and gas jet parameters are independently calculated for multiple regions divided on the flue section. S4: Control the jet unit corresponding to each region to independently perform ammonia injection operation and / or gas jet operation according to the control command.

7. The ammonia injection control method for the denitrification ammonia injection flue gas mixing system according to claim 6, characterized in that, Step S4 includes: when the predicted risk value of fly ash deposition in any area exceeds a preset threshold, performing a gas jet operation for that area to actively intervene in the local aerodynamic environment and prevent the occurrence of ash accumulation and blockage.

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