Reagent injection control method and system for impinging stream flue gas treatment

Abstract

The invention relates to the technical field of flue gas treatment, in particular to a reagent injection control method and system for impinging stream flue gas treatment. The method comprises the following steps: acquiring current flue gas working condition parameters of the impinging stream reactor; according to the current flue gas working condition parameters, the current airflow turbulence energy-containing scale is determined; determining the dynamic optimal instability wavelength range of the current reagent jet flow before impact based on the current airflow turbulence energy-containing scale and the physical properties of the reagent; determining a target reagent jet flow physical form according to the dynamic optimal instability wavelength range; a first control signal and a second control signal are generated such that the rupture instability wavelength of the current reagent jet is within the dynamic optimal instability wavelength range. The device can ensure that the reagent liquid drops are in full contact with pollutants in the flue gas.

Description

Technical Field

[0001] This application relates to the field of flue gas treatment technology, and in particular to a reagent injection control method and system for treating impinging flow flue gas. Background Technology

[0002] In industrial flue gas desulfurization, denitrification, and dust removal processes, impinging flow reactors have attracted widespread attention due to their highly efficient mass and heat transfer characteristics. The atomization quality of the reagents, especially the droplet size distribution, has a decisive impact on the pollutant removal efficiency.

[0003] In traditional techniques, reagent spraying typically uses nozzles with fixed parameters or is simply adjusted based on flue gas flow rate.

[0004] However, this extensive control method results in low reagent utilization and insufficient contact between droplets and contaminants, thus reducing treatment efficiency. Therefore, it urgently needs improvement. Summary of the Invention

[0005] Therefore, it is necessary to provide a reagent injection control method and system for impact flow flue gas treatment that solves the problem of insufficient contact between droplets and pollutants, in order to address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a reagent injection control method for treating impinging flow flue gas, executed by a controller, the method comprising: Obtain the current flue gas operating parameters of the impinging flow reactor; the current flue gas operating parameters include flue gas velocity, flue gas temperature, flue gas pollutant type and pollutant concentration; Determine the energy scale of the current airflow turbulence based on the current flue gas operating parameters; Based on the current energy scale of airflow turbulence and the physical properties of the reagent, the dynamic optimal instability wavelength range of the current reagent jet before impact is determined; wherein, the dynamic optimal instability wavelength range is dynamically adjusted according to the type and concentration of flue gas pollutants. The physical morphology of the target reagent jet is determined based on the dynamic optimal instability wavelength range; wherein, the physical morphology of the target reagent jet includes the target outlet diameter of the reagent jet, the target jet velocity, the target liquid film thickness, and the micro-perturbation frequency of the target jet surface; A first control signal and a second control signal are generated. The first control signal is transmitted to the mechanical adjustment mechanism of the reagent nozzle to drive the mechanical adjustment mechanism to change the geometry of the reagent nozzle. The second control signal is transmitted to the disturbance generator to drive the disturbance generator to apply micro-disturbance to the current reagent jet so that the breakup instability wavelength of the current reagent jet is within the dynamic optimal instability wavelength range.

[0007] Secondly, this application also provides a reagent injection control system for impinging flow flue gas treatment, the system including an impinging flow reactor and a controller, the controller being used to execute the above-described reagent injection control method for impinging flow flue gas treatment.

[0008] The aforementioned reagent injection control method and system for impinging flow flue gas treatment involves the controller first acquiring the current flue gas operating parameters of the impinging flow reactor, including flue gas velocity, flue gas temperature, flue gas pollutant types, and pollutant concentrations. Next, based on these parameters, the controller determines the current airflow turbulence energy scale, which reflects the disturbance characteristics of the flue gas turbulence on the reagent jet. Then, based on the current airflow turbulence energy scale and reagent physical properties, the controller determines the optimal dynamic instability wavelength range of the current reagent jet before impact, dynamically adjusted according to the flue gas pollutant types and concentrations. Following this optimal instability wavelength range, the controller determines the physical morphology of the target reagent jet, including the target outlet diameter, target injection velocity, target liquid film thickness, and the frequency of micro-disturbances on the target jet surface. Finally, a first control signal and a second control signal are generated. A mechanical adjustment mechanism changes the nozzle geometry, and a disturbance generator applies micro-disturbances to ensure that the fracture instability wavelength of the current reagent jet is within the optimal dynamic range.

[0009] On the one hand, since the dynamic optimal instability wavelength range is dynamically determined based on the current flue gas operating parameters, it can adapt to changes in flue gas operating conditions and therefore does not occupy additional real-time computing resources. On the other hand, by precisely controlling the jet breakup process through dual control methods (mechanical adjustment and micro-disturbance control), the size distribution of the formed droplets can be made most favorable for reaction with the current pollutants, thereby improving flue gas treatment efficiency. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a structural block diagram of an impinging flow flue gas treatment system in one embodiment; Figure 2 This is a schematic flowchart of a reagent injection control method for treating impinging flow flue gas, as shown in one embodiment. Figure 3 This is a flowchart illustrating the method for determining the energy content of airflow turbulence based on flue gas composition correction provided in this application embodiment; Figure 4This is a flowchart illustrating the method for determining the dynamic optimal instability wavelength range provided in the embodiments of this application; Figure 5 This is a schematic flowchart of the feedforward-feedback composite control method based on flue gas flow prediction provided in the embodiments of this application. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0013] See Figure 1 , Figure 1 This is a schematic diagram of the impact flow flue gas treatment system provided in an embodiment of this application. The system includes: Impinging flow reactor 10: includes mutually opposed flue gas inlets and reagent nozzles for forming a gas-liquid impingement zone; Flue gas parameter monitoring unit 20: used for real-time monitoring of flue gas velocity, temperature, pollutant type and concentration; Controller 30: Executes the reagent injection control method provided in the embodiments of this application; Reagent supply system 40: includes reagent storage tanks, transfer pumps, and pressure regulating devices; Reagent nozzle adjustment mechanism 50: includes a mechanical adjustment mechanism and a disturbance generator.

[0014] In this embodiment, the impinging flow reactor adopts an opposed design, with an impact angle of 90-120 degrees between the flue gas inlet and the reagent nozzle, which can be adjusted according to specific application scenarios. The flue gas parameter monitoring unit 50 includes multiple sensors that can collect flue gas operating parameters in real time; the controller 30 is implemented using an industrial-grade PLC or embedded system; the reagent nozzle adjustment mechanism 50 includes an adjustable mechanical structure and a disturbance generating device, which can dynamically change the jet characteristics.

[0015] Reference Figure 2 This embodiment provides a reagent injection control method for treating impinging flow flue gas, executed by controller 30. The method specifically includes the following steps: Step S101: Obtain the current flue gas operating parameters of the impinging flow reactor; In this embodiment, the controller 30 periodically acquires the current flue gas operating parameters of the impinging flow reactor. Specifically, the flue gas parameter monitoring unit collects parameters such as flue gas velocity, flue gas temperature, flue gas pollutant types, and pollutant concentrations in real time through a sensor array installed in the flue gas duct. In some embodiments, the flue gas velocity measurement range is 5-50 m / s, the flue gas temperature measurement range is 50-300℃, and the flue gas pollutant types include, but are not limited to, major pollutants such as SO2, NOx, and dust. The pollutant concentration measurement range varies depending on the type of pollutant.

[0016] In other embodiments, the data structure of the current flue gas operating parameters may include (flow rate value, temperature value, [(pollutant 1, concentration 1), (pollutant 2, concentration 2),]).

[0017] In this embodiment, the current flue gas operating parameters are acquired periodically. The acquisition period can be adjusted according to system response requirements and the rate of change of flue gas operating conditions, typically ranging from 0.5 to 5 seconds. In some embodiments, the acquisition period is set to 1 second to balance data real-time performance with system computational load. The current flue gas operating parameters are stored in the memory space of the controller 30 and can be acquired based on the storage location.

[0018] Step S102: Determine the energy scale of the current airflow turbulence based on the current flue gas operating parameters; Based on the current flue gas operating parameters obtained in step S101, the controller 30 determines the current energy scale of the airflow turbulence. In this embodiment, the energy scale of the airflow turbulence refers to the scale range in which energy is mainly concentrated in the flue gas turbulence, and is a key parameter affecting the breakup characteristics of the reagent jet. This parameter reflects the disturbance characteristics of the flue gas turbulence on the reagent jet, and is directly related to the breakup behavior of the reagent jet and the final droplet size distribution.

[0019] In some embodiments, the typical range of the current airflow turbulence energy scale is 0.1-10 mm, with the specific value dynamically varying with the flue gas conditions. When the flue gas velocity is high and the temperature is low, the airflow turbulence energy scale is usually small; when the flue gas contains a high concentration of fine particulate matter, the airflow turbulence energy scale may increase. In other embodiments, the current airflow turbulence energy scale can be expressed as L, where 0.1 mm ≤ L ≤ 10 mm.

[0020] In this embodiment, the process of determining the current airflow turbulence energy scale is based on the current flue gas operating parameters. In some embodiments, the basic airflow turbulence energy scale can be determined based on the flue gas velocity and temperature, and then corrected according to the type and concentration of flue gas pollutants to obtain the current airflow turbulence energy scale corrected by the flue gas composition. The specific implementation process can be referred to the following description.

[0021] Step S103: Based on the current energy scale of the airflow turbulence and the physical properties of the reagent, determine the optimal dynamic instability wavelength range of the current reagent jet before impact; Based on the current energy scale of the airflow turbulence determined in step S102 and the pre-stored physical properties of the reagent, the controller 30 determines the optimal dynamic instability wavelength range of the current reagent jet before impact. In this embodiment, the optimal dynamic instability wavelength range refers to the wavelength range in which the droplet size formed by the reagent jet breakup is most favorable for reaction with a specific contaminant. This range is determined based on fluid dynamics principles such as Rayleigh-Taylor instability and Kelvin-Helmholtz instability, and is directly related to the final droplet size distribution.

[0022] In some embodiments, the dynamic optimal instability wavelength range can be expressed as [λ]. min ,λ max ], where λ min and λ max These represent the minimum and maximum instability wavelengths, respectively. For example, the optimal dynamic instability wavelength range could be [1.2 mm, 2.8 mm]. In other embodiments, this range can be dynamically adjusted based on the type and concentration of the flue gas pollutants: when the main pollutant is SO2, the optimal dynamic instability wavelength range could be [1.5 mm, 3.0 mm]; when the main pollutant is NOx, the optimal dynamic instability wavelength range could be [0.8 mm, 1.8 mm].

[0023] In this embodiment, the optimal dynamic instability wavelength range is determined jointly based on the current energy scale of the airflow turbulence and the physical properties of the reagent. Reagent physical properties include, but are not limited to, reagent density, viscosity, and surface tension, which affect the breakup characteristics of the reagent jet. In some embodiments, the optimal dynamic instability wavelength range is proportional to the current energy scale of the airflow turbulence, typically with a proportionality coefficient of 1.0-3.0. The specific implementation process for determining the optimal dynamic instability wavelength range can be found in the subsequent description.

[0024] Step S104: Determine the physical morphology of the target reagent jet based on the dynamic optimal instability wavelength range; Based on the optimal dynamic instability wavelength range determined in step S103, the controller 30 determines the physical morphology of the target reagent jet. In this embodiment, the physical morphology of the target reagent jet includes the target outlet diameter, target jet velocity, target liquid film thickness, and the frequency of micro-disturbances on the target jet surface. These parameters collectively determine the initial conditions and instability development process of the reagent jet, directly affecting the final droplet size distribution.

[0025] In some embodiments, the physical morphology of the target reagent jet can be represented as (D, V, H, f), where D represents the target outlet diameter, V represents the target jet velocity, H represents the target liquid film thickness, and f represents the frequency of micro-disturbances on the target jet surface. For example, the physical morphology of the target reagent jet can be (1.5 mm, 20 m / s, 0.3 mm, 800 Hz).

[0026] In other embodiments, when the optimal dynamic instability wavelength range is [1.5 mm, 3.0 mm], the corresponding target outlet diameter can be [1.0 mm, 2.0 mm], the target jet velocity can be [15 m / s, 25 m / s], the target liquid film thickness can be [0.2 mm, 0.5 mm], and the target jet surface micro-perturbation frequency can be [500 Hz, 1000 Hz]. These parameter ranges are determined based on jet instability theory to ensure that the instability wavelength of the reagent jet falls within the optimal range before impact.

[0027] In this embodiment, the process of determining the physical morphology of the target reagent jet is based on the dynamic optimal instability wavelength range. In some embodiments, the Weber number and Oenizog number can be calculated based on the physical properties of the reagent and preset jet geometry parameters, and then combined with the current energy scale of the airflow turbulence to determine the physical morphology of the target reagent jet. The specific implementation process can be referred to the following description.

[0028] Step S105: Generate a first control signal and a second control signal. Transmit the first control signal to the mechanical adjustment mechanism of the reagent nozzle to drive the mechanical adjustment mechanism to change the geometry of the reagent nozzle. Transmit the second control signal to the disturbance generator to drive the disturbance generator to apply micro-disturbance to the current reagent jet so that the breakup instability wavelength of the current reagent jet is within the dynamic optimal instability wavelength range.

[0029] The controller 30 generates a first control signal and a second control signal based on the physical shape of the target reagent jet determined in step S104. In this embodiment, the first control signal is transmitted to the mechanical adjustment mechanism of the reagent nozzle, driving the mechanism to change the geometry of the reagent nozzle; the second control signal is transmitted to the disturbance generator, driving the disturbance generator to apply micro-disturbances to the current reagent jet.

[0030] In some embodiments, the mechanical adjustment mechanism includes an adjustable nozzle ring and a conical core, capable of continuously changing the cross-sectional shape and effective orifice diameter of the nozzle outlet. For example, the mechanical adjustment mechanism can adjust the nozzle outlet diameter from 1.0 mm to 2.0 mm and the liquid film thickness from 0.2 mm to 0.5 mm. In other embodiments, the disturbance generator, employing a piezoelectric ceramic element or an electromagnetic vibration device, is installed upstream of the nozzle and is capable of applying micro-disturbances to the reagent jet in the frequency range of 500-1000 Hz.

[0031] In this embodiment, the synergistic effect of a first control signal and a second control signal ensures that the breakup instability wavelength of the current reagent jet is within the dynamic optimal instability wavelength range. On one hand, a mechanical adjustment mechanism alters the nozzle geometry to adjust the jet's basic parameters; on the other hand, a precisely controlled micro-perturbation is applied via a perturbation generator to actively control the jet's instability development process. In some embodiments, when the dynamic optimal instability wavelength range is [1.5 mm, 3.0 mm], the control signal is adjusted to maintain the breakup instability wavelength of the current reagent jet within the range of [1.6 mm, 2.9 mm].

[0032] See Figure 3 This embodiment is a detailed implementation of the step of "determining the current airflow turbulence energy scale based on the current flue gas operating parameters," constituting a complete sub-technical solution. In this embodiment, by separating the effects of flue gas velocity, temperature, and pollutant composition, the basic airflow turbulence energy scale is first determined, and then corrected according to the flue gas composition, thereby obtaining a more accurate current airflow turbulence energy scale.

[0033] In some embodiments, the controller 30 is the main implementer of this sub-scheme, and the execution timing is after acquiring the current flue gas operating parameters and before determining the optimal dynamic instability wavelength range. This sub-scheme solves the problem in the prior art that only considers flue gas velocity and temperature while ignoring the influence of pollutant components, and can more accurately reflect the influence of actual flue gas operating conditions on reagent jet breakup characteristics, providing a more accurate basis for subsequent reagent jet control.

[0034] Step S201: Determine the basic energy scale of the airflow turbulence based on the flue gas velocity and temperature; In this embodiment, the controller 30 inputs the flue gas velocity and flue gas temperature obtained in step S101 into the turbulent kinetic energy level string model. Specifically, the flue gas velocity value (e.g., 15 m / s) and the flue gas temperature value (e.g., 120 °C) are provided to the model as input parameters. In some embodiments, the turbulent kinetic energy level string model is implemented using a lookup table method, and the model internally stores the mapping relationship between the flue gas velocity, flue gas temperature and the energy content scale of the basic airflow turbulence obtained through wind tunnel experiments.

[0035] In other embodiments, the turbulent kinetic level string model can be represented as the function L0 = f(v, T), where L0 represents the energy scale of the basic airflow turbulence, v represents the flue gas velocity, and T represents the flue gas temperature. In the embodiments of this application, the typical range of the energy scale L0 of the basic airflow turbulence is 0.5-8.0 mm, and the specific value varies with the flue gas velocity and temperature.

[0036] The controller 30 obtains the basic airflow turbulent energy scale from the turbulent kinetic energy level string model. In some embodiments, the model is pre-established through wind tunnel experiments. During the experiment, the flue gas composition is kept pure (e.g., containing only nitrogen and oxygen, with no pollutants), and the flue gas velocity (5-50 m / s) and temperature (50-300 °C) are systematically changed. The corresponding turbulent energy scale is measured, and a complete mapping relationship is established.

[0037] In other embodiments, the process of establishing the turbulent kinetic energy level series model includes: In a wind tunnel, a pure gas flow is generated, and the flow rate and temperature are controlled. Turbulent fields are measured using particle image velocimetry (PIV) technology; Determine the energy scale of turbulence through spectral analysis; Establish a mapping table between flow velocity, temperature, and turbulent energy scale.

[0038] Step S202: Determine the flue gas composition correction coefficient based on the types and concentrations of flue gas pollutants; (1) For each flue gas pollutant, determine the weight of the pollutant's influence on the physical properties of the flue gas; For each flue gas pollutant obtained in step S101, the controller 30 determines the influence weight of that pollutant on the physical properties of the flue gas. In this embodiment, the influence weight is determined based on the degree of influence of the pollutant on the density and viscosity of the flue gas.

[0039] In some embodiments, the influence weight can be represented as w i Where i represents the type of pollutant. For SO2, the influence weight w1 is determined based on the degree of influence of SO2 on flue gas density and viscosity; for NOx, the influence weight w2 is determined based on the degree of influence of NOx on flue gas density and viscosity. For example, the influence weight of SO2 can be 0.6, the influence weight of NOx can be 0.4, and the influence weight of dust can be 0.3.

[0040] In other embodiments, the process of determining the influencing weights includes: The impact of each pollutant on flue gas density at different concentrations was determined through theoretical calculations or experimental measurements. The effect of each pollutant on flue gas viscosity at different concentrations was determined through theoretical calculations or experimental measurements. The final influence weight is obtained by weighting the effects of density and viscosity.

[0041] In the embodiments of this application, the typical range of the influence weight is 0.1-1.0, and the specific value varies depending on the type of pollutant. The influence weight reflects the relative importance of the pollutant to the flue gas turbulence characteristics; the larger the weight, the more significant the influence of the pollutant on the energy scale of turbulence.

[0042] (2) Based on the types of flue gas pollutants, the concentration of each pollutant and its corresponding influence weight, calculate the weighted influence value of each pollutant, and sum all the weighted influence values ​​to obtain the comprehensive influence factor; The controller 30 calculates the weighted impact value of each pollutant based on the type of flue gas pollutant, the concentration of each pollutant, and its corresponding impact weight, and sums all the weighted impact values ​​to obtain the comprehensive impact factor. In this embodiment, the comprehensive impact factor IF can be expressed as: IF=Σ(c i ×w i ) Among them, c i w represents the concentration of the i-th pollutant. i This represents the influence weight of the i-th pollutant.

[0043] (3) Based on the comprehensive impact factor, the correction coefficient for flue gas composition is determined through a pre-calibrated mapping relationship: The controller 30 determines the flue gas composition correction coefficient based on the comprehensive influence factor and through a pre-calibrated mapping relationship. In this embodiment, the mapping relationship represents the functional relationship between the comprehensive influence factor and the flue gas composition correction coefficient.

[0044] In some embodiments, the flue gas composition correction coefficient K can be expressed as K=g(IF), where g represents the mapping relationship and IF represents the comprehensive influence factor. In other embodiments, the typical range of the flue gas composition correction coefficient K is 0.8-1.5, with the specific value varying with the comprehensive influence factor.

[0045] In this embodiment, the mapping relationship is obtained through experimental calibration. Specifically, in a wind tunnel experiment, different types and concentrations of pollutants are added to the pure gas, the ratio of the actual turbulent energy scale to the basic turbulent energy scale is measured, and the correspondence between the comprehensive influence factor and the correction coefficient is established.

[0046] Step S203: Determine the current airflow turbulence energy scale corrected by flue gas composition based on the basic airflow turbulence energy scale and the flue gas composition correction coefficient.

[0047] The controller 30 multiplies the basic airflow turbulence energy scale obtained in step S202 with the determined flue gas composition correction coefficient to obtain the current airflow turbulence energy scale corrected for flue gas composition. In this embodiment, the corrected current airflow turbulence energy scale L can be expressed as: L=L0×K Where L0 represents the basic airflow turbulence energy scale, and K represents the flue gas composition correction coefficient. In other embodiments, the typical range of the current airflow turbulence energy scale L after flue gas composition correction is 0.1-10 mm, and the specific value varies dynamically with the flue gas operating conditions.

[0048] In this embodiment, the controller 30 periodically executes step S203 to update the corrected current airflow turbulence energy scale. In some embodiments, the update cycle is the same as the flue gas condition parameter acquisition cycle, which is 1 second. In other embodiments, the update cycle can be extended when the flue gas condition changes little, and shortened when the flue gas condition changes rapidly.

[0049] By multiplying the basic airflow turbulence energy scale with the flue gas composition correction coefficient, this embodiment can comprehensively consider the influence of flue gas velocity, temperature and composition on turbulence characteristics, and obtain a more accurate current airflow turbulence energy scale.

[0050] See Figure 4 This embodiment provides a detailed implementation of the step of "determining the optimal dynamic instability wavelength range of the current reagent jet before impact," constituting a complete sub-technical solution. In this embodiment, by combining the energy scale of airflow turbulence, reagent physical properties, and flue gas pollutant characteristics, the breakup growth rate distribution of the reagent jet is accurately calculated, and the optimal dynamic instability wavelength range is determined accordingly.

[0051] In some embodiments, the controller 30 is the main implementer of this sub-scheme, and the execution timing is after determining the energy scale of the current airflow turbulence and before determining the physical morphology of the target reagent jet. This sub-scheme solves the problem in the prior art that it is impossible to dynamically adjust the optimal instability wavelength range according to the real-time flue gas conditions. It can accurately determine the optimal reagent jet breakup conditions for different pollutant types and concentrations, thereby forming the droplet size distribution most favorable to the reaction.

[0052] Step S301: Calculate the Weber number and Ounisoger number based on the physical properties of the reagent and the preset jet geometry parameters; In this embodiment, the controller 30 acquires the physical properties of the reagent and preset jet geometry parameters. Specifically, the physical properties of the reagent include the reagent density ρ. l , reagent viscosity μ I The surface tension σ of the reagent is a property that is pre-stored in the non-volatile memory of the controller 30. In some embodiments, when the reagent is a limestone slurry, the reagent density ρ is... l The reagent viscosity is 1100-1300 kg / m³. I The strength is 0.001-0.01 Pa·s, and the surface tension σ of the reagent is 0.05-0.07 N / m.

[0053] The preset jet geometry parameters include nozzle outlet diameter D, liquid film thickness H, etc., which are set based on historical data or experience. In other embodiments, the preset jet geometry parameters can be expressed as (D0, H0), where D0 represents the preset outlet diameter and H0 represents the preset liquid film thickness. For example, when processing flue gas from a coal-fired power plant, D0 can be 1.5 mm and H0 can be 0.3 mm. In the embodiments of this application, the data structure for reagent physical properties and preset jet geometry parameters may include (ρ l ,μ I ,σ,D0,H0).

[0054] Step S302: Calculate the Weber number and Ounisoger number based on the physical properties of the reagent and the preset jet geometry parameters; Based on the reagent physical properties and preset jet geometry parameters obtained in step S301, controller 30 calculates the Weber number We and the Ounizegg number Oh. In this embodiment, the Weber number We represents the ratio of inertial force to surface tension, and the Ounizegg number Oh represents the ratio of viscous force to the combined effect of surface tension and inertial force.

[0055] In some embodiments, the Weber number We can be expressed as: We=(ρ l ×V²×D) / σ Where, ρ l V represents the reagent density, V represents the reagent injection velocity, D represents the nozzle outlet diameter, and σ represents the reagent surface tension.

[0056] The Oh number can be represented as: Oh=μ I / √(ρ l ×σ×D) Where, μ I Indicates the viscosity of the reagent.

[0057] By calculating the Weber number and the Ounizogg number, this embodiment can accurately characterize the fundamental instability properties of the reagent jet.

[0058] Step S303: Calculate the breakup growth rate distribution of the current reagent jet using the aerodynamic instability spectrum model; The controller 30 inputs the current airflow turbulence energy scale L determined in step S102, the Weber number We, and the Oenisoger number Oh calculated in step S302 into the aerodynamic instability spectrum model. In this embodiment, the aerodynamic instability spectrum model is used to simulate the influence of the flue gas turbulence environment on the reagent jet breakup process.

[0059] In some embodiments, the aerodynamic instability spectrum model can be represented as a function Γ=M(L,We,Oh,λ), where Γ represents the breakup growth rate, λ represents the instability wavelength, and M represents the model function. This model, established through theoretical derivation and experimental verification, can accurately predict the breakup characteristics of reagent jets under different conditions. For example, when L=3.125mm, We=12000, and Oh=0.03, the model will calculate the breakup growth rate Γ corresponding to different wavelengths λ.

[0060] The controller 30 calculates the breakup growth rate distribution of the current reagent jet using an aerodynamic instability spectrum model. In this embodiment, the breakup growth rate distribution represents the breakup growth rate corresponding to different instability wavelengths, reflecting the instability development rate of the reagent jet under different wavelength perturbations.

[0061] In some embodiments, the fracture growth rate distribution can be represented as Γ(λ), where λ represents the instability wavelength. The controller 30 calculates the fracture growth rate at each wavelength point within the wavelength range [0.1 mm, 10 mm] in steps of 0.1 mm, forming a complete distribution curve.

[0062] In other embodiments, to improve computational efficiency, the controller 30 employs an adaptive step-size strategy: a smaller step size (0.05 mm) is used in regions where the fracture growth rate changes drastically, and a larger step size (0.2 mm) is used in regions where the change is gradual. This method reduces computation time by 30-40% while maintaining accuracy. In the embodiments of this application, the fracture growth rate distribution is stored in array form, with each element representing the fracture growth rate at a specific wavelength.

[0063] Furthermore, for each flue gas pollutant obtained in step S101, the controller 30 sets a corresponding weight adjustment coefficient for that pollutant. In this embodiment, the weight adjustment coefficient is pre-calibrated based on the degree of physical influence of the pollutant on the jet breakup characteristics.

[0064] In some embodiments, the weight adjustment coefficient can be expressed as k. i , where i represents the type of pollutant. For SO2, the weighting adjustment coefficient k1 is calibrated based on the degree of influence of SO2 on the reagent jet breakup characteristics; for NOx, the weighting adjustment coefficient k2 is calibrated based on the degree of influence of NOx on the reagent jet breakup characteristics.

[0065] In other embodiments, the calibration process for the weight adjustment coefficients includes: In the experimental setup, the flue gas was controlled to contain only a single pollutant, and the system was used to change the concentration of the pollutant. The reagent jet breakup process was observed using high-speed photography, and the main breakup wavelengths were measured. Analyze the relationship between pollutant concentration and the main breakup wavelength; Based on the analysis results, the weight adjustment coefficient for this pollutant was determined.

[0066] In the embodiments of this application, the typical range of the weighting adjustment coefficient is 0.3-1.0, and the specific value varies depending on the type of contaminant. The weighting adjustment coefficient reflects the relative influence of the contaminant on the breakup characteristics of the reagent jet; the larger the coefficient, the more significant the influence of the contaminant on the breakup wavelength.

[0067] Furthermore, the controller 30 calculates the actual weight adjustment factor for each pollutant type using a linear function based on the pollutant concentration of each pollutant type. In this embodiment, the actual weight adjustment factor reflects the degree of influence of the pollutant on the rupture characteristics at the current concentration.

[0068] In some embodiments, the actual weight adjustment factor a i It can be represented as: a i =k i ×(c i / c ref ) Where, k i c represents the weight adjustment factor. i c represents the concentration of the i-th pollutant. ref This indicates a reference concentration. In other embodiments, the reference concentration c... ref The settings are based on the type of pollutant.

[0069] Furthermore, the controller 30 performs a weighted sum of the actual weight adjustment factors for each pollutant type to obtain a comprehensive weight adjustment factor.

[0070] In this embodiment, the comprehensive weighting adjustment factor AF reflects the combined impact of all contaminants on the reagent jet breakup characteristics. In some embodiments, the comprehensive weighting adjustment factor AF can be expressed as: AF=Σ(w i ×a i ) Among them, w i a represents the weighting coefficient for the i-th pollutant. i This represents the actual weight adjustment factor for the i-th pollutant. In other embodiments, the weighting coefficient w i The w1 is set according to the importance of the pollutant to the target response; for example, the w1 of SO2 is 0.6 and the w2 of NOx is 0.4.

[0071] In the embodiments of this application, the typical range of the comprehensive weight adjustment factor AF is 0.3-1.7, and the specific value changes dynamically with the type and concentration of flue gas pollutants. AF=1.0 indicates that the influence of the current flue gas composition on the breakup characteristics is the same as the calibration conditions; AF>1.0 indicates that the pollutants enhance the instability of a specific wavelength; AF<1.0 indicates that the pollutants suppress the instability of a specific wavelength.

[0072] Furthermore, the controller 30 applies the comprehensive weight adjustment factor AF to the fracture growth rate distribution calculated in step S304 to obtain the adjusted fracture growth rate distribution. In this embodiment, the adjustment process takes into account the differentiated impact of pollutants on different wavelength regions.

[0073] In some embodiments, the adjusted fracture growth rate distribution Γ'(λ) can be expressed as: Γ'(λ)=Γ(λ)×[1+α(λ)×(AF-1)] Wherein, Γ(λ) represents the original fracture growth rate distribution, and α(λ) represents the wavelength-dependent influence coefficient. In other embodiments, the wavelength-dependent influence coefficient α(λ) is set according to the type of pollutant: for SO2, α(λ) is larger in the range of λ = 1.5-3.0 mm; for NOx, α(λ) is larger in the range of λ = 0.8-1.8 mm.

[0074] In this embodiment, the controller 30 employs a segmented adjustment strategy: based on the main pollutant type, the corresponding wavelength-affected region is selected for focused adjustment, while other regions are adjusted with smaller amplitudes. This strategy ensures the targeted nature and effectiveness of the adjustment process, avoiding distortion caused by globally uniform adjustment. By considering the influence of flue gas pollutants on the reagent jet breakup characteristics, this embodiment can more accurately determine the optimal dynamic instability wavelength range.

[0075] Step S304: Extract the wavelength range corresponding to the maximum value from the adjusted fracture growth rate distribution, and use it as the optimal dynamic instability wavelength range of the current reagent jet before impact.

[0076] The controller 30 extracts the wavelength range corresponding to the maximum value from the adjusted fracture growth rate distribution, and uses this as the dynamic optimal instability wavelength range of the current reagent jet before impact. In this embodiment, the dynamic optimal instability wavelength range refers to the wavelength interval where the fracture growth rate is near the peak value and not lower than 80% of the maximum value.

[0077] In some embodiments, the dynamic optimal instability wavelength range can be expressed as [λ]. min ,λ maxFor example, when the maximum fracture growth rate occurs at λ=2.0mm and the wavelength range of Γ(λ)≥0.8×Γmax is 1.6-2.4mm, the optimal wavelength range of dynamic instability is [1.6mm, 2.4mm].

[0078] In other embodiments, to prevent the wavelength range from being too narrow or too wide, the controller 30 sets a minimum width Δλ. min =0.3mm and maximum width Δλ max =1.5mm. If the calculated width is less than Δλ min Then, taking the peak position as the center, it extends to Δλ. min If it is greater than Δλ max Then retain Δλ near the peak. max The range of width.

[0079] In some embodiments, the controller 30 uses a peak detection algorithm to extract the dynamic optimal instability wavelength range. The algorithm steps include: Find the global maximum Γ in the fracture growth rate distribution. max and its corresponding wavelength λ peak ; From λ peak Searching to both sides, we find Γ(λ) = 0.8 × Γ max The wavelength point; Determine λ min and λ max This forms the wavelength range [λ] min ,λ max ]; Apply width limits and smoothing.

[0080] In other embodiments, to improve real-time performance, the controller 30 employs an approximate calculation method: pre-calculating typical wavelength ranges under different conditions and establishing a lookup table. By extracting the wavelength range corresponding to the maximum value as the dynamic optimal instability wavelength range, this embodiment can determine the jet breakup conditions most favorable for forming the target droplet size.

[0081] Furthermore, this embodiment provides a detailed implementation of the step of "driving the mechanical adjustment mechanism to change the geometry of the reagent nozzle," constituting a complete sub-technical solution. In this embodiment, through a dual control strategy—mechanical adjustment of the nozzle geometry and pressure control of the injection speed—the physical morphology of the target reagent jet is precisely achieved, ensuring that the wavelength of the reagent jet's breakup instability is within the dynamic optimal range.

[0082] In some embodiments, the controller 30 is the implementer of this sub-solution, and the execution timing is after the physical morphology of the target reagent jet is determined but before the actual change of the nozzle geometry. This sub-solution solves the problem in the prior art that it is impossible to simultaneously and accurately control the reagent jet outlet diameter, liquid film thickness, and jet velocity. It can adjust the reagent jet characteristics in real time according to the requirements of the dynamic optimal instability wavelength range to form the droplet size distribution most favorable to the reaction of pollutants.

[0083] In this embodiment, the controller 30 receives the target reagent jet physical morphology parameters determined in step S104. Specifically, the target reagent jet physical morphology includes the target outlet diameter D. t Target liquid film thickness H t Target jet speed V t and the frequency f of the micro-perturbation on the surface of the target jet t In some embodiments, the data structure of the physical morphological parameters of the target reagent jet can be represented as (D t H t V t ,f t In this embodiment of the application, the physical morphological parameters of the target reagent jet are stored in the memory space of the controller 30 and can be obtained based on the storage location.

[0084] The controller 30 drives a mechanical adjustment mechanism to adjust the instantaneous cross-sectional shape and effective aperture of the nozzle outlet, so that the current outlet diameter of the current reagent jet conforms to the target outlet diameter, and the current liquid film thickness of the current reagent jet conforms to the target liquid film thickness. In this embodiment, the mechanical adjustment mechanism includes an adjustable nozzle ring and a conical core, which can continuously change the geometric characteristics of the nozzle outlet.

[0085] In some embodiments, the mechanical adjustment mechanism comprises two main components: Nozzle ring: A radially movable annular component used to adjust the effective orifice diameter of the nozzle outlet; Conical core: An axially movable conical component used to adjust the liquid film thickness and outlet shape.

[0086] In other embodiments, the instantaneous cross-sectional shape of the nozzle outlet can be circular, elliptical, or a specific polygon, with the specific shape dynamically adjusted according to the current operating conditions. When the flue gas velocity is high, an elliptical outlet is used, with the major axis aligned with the flue gas flow direction; when the flue gas contains a high concentration of dust, a polygonal outlet is used to enhance jet instability.

[0087] By precisely adjusting the nozzle outlet shape and orifice diameter, this embodiment can simultaneously control the outlet diameter of the reagent jet and the liquid film thickness.

[0088] Controller 30 is based on target injection speed V tCalculate the required target reagent supply pressure P. t In this embodiment, there is a nonlinear relationship between the reagent supply pressure and the injection velocity, which is determined by the basic equations of fluid mechanics and the system characteristics.

[0089] In some embodiments, the target reagent supply pressure Pt can be approximately calculated using Bernoulli's equation: P t =P0+(1 / 2)×ρ I ×V t ² Where P0 represents the system back pressure, ρ I V represents the density of the reagent. t Indicates the target's jet speed.

[0090] In other embodiments, due to factors such as pipe resistance and valve characteristics in the actual system, the controller 30 uses a pre-calibrated pressure-velocity mapping table instead of theoretical calculations. The mapping table is established experimentally and stores the correspondence between injection velocity and required supply pressure under different nozzle geometries.

[0091] The controller 30 controls the reagent supply pressure to ensure that the current injection velocity of the reagent jet matches the target injection velocity. In this embodiment, the reagent supply system includes a reagent storage tank, a delivery pump, and a pressure regulating valve. The controller 30 controls the reagent supply pressure by adjusting the opening of the pressure regulating valve.

[0092] In some embodiments, the controller 30 generates a pressure control signal to adjust the opening of the pressure regulating valve so that the actual reagent supply pressure approaches the target value. When the actual injection speed is lower than the target value, the opening of the pressure regulating valve is increased; when the actual injection speed is higher than the target value, the opening of the pressure regulating valve is decreased.

[0093] By controlling the reagent supply pressure to achieve the target injection velocity, this embodiment can precisely control the kinetic energy characteristics of the reagent jet. In testing, compared to a fixed pressure supply system, this method improved the injection velocity control accuracy by 30% and reduced the response time by 40%.

[0094] See Figure 5 This embodiment is an enhanced implementation of the aforementioned embodiments, constituting a complete predictive control sub-technology solution. In this embodiment, the future trend of flue gas operating conditions is predicted by acquiring flue gas flow rate adjustment parameters, and the control parameters are adjusted in advance based on the prediction results. During the adjustment process, real-time monitoring and feedback correction are performed through a schlieren imaging sensor to achieve predictive optimization of reagent jet control.

[0095] This sub-solution addresses the response lag problem caused by control based solely on the current operating conditions in existing technologies. When flue gas conditions change abruptly (such as rapid changes in induced draft fan speed or sudden opening and closing of dampers), traditional control methods require 5-8 seconds for the reagent jet to adapt to the new conditions, resulting in a significant decrease in processing efficiency during this period. This embodiment, however, can detect and respond to these changes in advance, ensuring optimal droplet size distribution even during sudden changes in flue gas conditions, significantly improving the dynamic response performance and processing efficiency of the flue gas treatment system.

[0096] Step S501: Obtain the flue gas flow rate adjustment parameters of the impact flow reactor; In this embodiment, the controller 30 acquires the flue gas flow rate regulation parameters of the impinging flow reactor. Specifically, the flue gas flow rate regulation parameters include parameters that can reflect the flue gas flow rate regulation state, such as induced draft fan speed, damper opening, flue pressure, and combustion load change rate.

[0097] Step S502: Predict future trends in flue gas operating conditions based on flue gas flow rate regulation parameters; The controller 30 predicts future trends in flue gas operating conditions based on the acquired flue gas flow regulation parameters. In this embodiment, the prediction model combines the dynamic characteristics of the flue gas system with historical operating data, enabling it to accurately predict changes in flue gas operating conditions within the next 5-30 seconds.

[0098] In some embodiments, the prediction model employs a Long Short-Term Memory (LSTM) network, which is trained using historical operating data and is capable of capturing the nonlinear dynamic characteristics of the flue gas system.

[0099] In this embodiment, the prediction time window is dynamically adjusted based on the changing characteristics of the flue gas flow rate regulation parameters: when the parameters change drastically, the prediction time window is automatically shortened to 5-10 seconds; when the changes are gradual, the prediction time window is extended to 20-30 seconds. By acquiring the flue gas flow rate regulation parameters and predicting future flue gas operating conditions, this embodiment can detect impending changes in operating conditions in advance.

[0100] Step S503: Based on the predicted trend of flue gas operating conditions, adjust the parameters used to generate the first and second control signals in advance; The controller 30 calculates the advance adjustment amount of the parameters used to generate the first control signal and the second control signal based on the predicted trend of flue gas operating conditions. In this embodiment, the advance adjustment amount is calculated based on the difference between the predicted future flue gas operating conditions and the current flue gas operating conditions.

[0101] In some embodiments, the advance adjustment amount Δu can be expressed as: Δu=K×(y pred -y curr ) Where K represents the adjusted gain, ypred y represents the predicted future flue gas operating conditions. curr This indicates the current flue gas operating condition.

[0102] Based on the calculated advance adjustment amount, the controller 30 generates the advance adjusted control parameters, including the adjusted target outlet diameter, target liquid film thickness, target injection speed, and target micro-disturbance frequency.

[0103] Step S504: During the pre-adjustment process, the real-time breakup instability wavelength of the current reagent jet is continuously acquired through the schlieren imaging sensor; In this embodiment, the schlieren imaging sensor includes a light source system, an imaging system, and an image processing unit, and is installed at an appropriate location in the impinging flow reactor.

[0104] Step S505: Based on the deviation between the real-time fracture instability wavelength and the dynamic optimal instability wavelength range, the pre-adjusted parameters are corrected by feedback to optimize the generation of the first control signal and the second control signal.

[0105] The controller 30 calculates the real-time deviation based on the acquired real-time fracture instability wavelength and the dynamic optimal instability wavelength range. In this embodiment, the real-time deviation E can be expressed as: E=max(0,λ a -λ max )+max(0,λ min -λ a ) Where, λ a The wavelength representing the real-time fracture instability, [λ] min ,λ max [] indicates the dynamic optimal instability wavelength range.

[0106] The controller 30 generates a feedback correction signal based on the calculated real-time deviation. In this embodiment, the feedback correction signal Δu cb It can be represented as: Δu cb =K p ×E+K i ×ΣE+K a ×(EE prev ) Among them, K p K i K a These represent the proportional, integral, and derivative coefficients, respectively, and E represents the current deviation. prev This indicates the deviation from the previous cycle.

[0107] To adapt to the advance adjustment process, controller 30 uses adaptive PID parameters: when advance adjustment is in progress, K p =0.8, Ki =0.1, K a =0.05; When the adjustment is completed ahead of schedule, K p =1.2, K i =0.2, K a =0.1.

[0108] The controller 30 optimizes the first control signal and the second control signal based on the generated feedback correction signal. In this embodiment, the optimized first control signal U... opt It can be represented as: U opt =u pred +β×Δu cb Among them, u pred This represents the first control signal after pre-adjustment, and β represents the allocation coefficient.

[0109] In some embodiments, the controller 30 employs a dynamic allocation strategy: when the real-time fracture instability wavelength is too large, the first control signal (β=0.7) is mainly corrected; when the real-time fracture instability wavelength is too small, the second control signal (β=0.3) is mainly corrected.

[0110] Based on the same inventive concept, this application also provides a reagent injection control device for implementing the above-mentioned reagent injection control method for impinging flow flue gas treatment.

[0111] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the reagent injection control method for impact flow flue gas treatment described above.

[0112] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the reagent injection control method for treating impacted flow flue gas as described above.

[0113] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the reagent injection control method for treating impacted flow flue gas as described above.

[0114] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0115] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A reagent injection control method for treating impinging flow flue gas, characterized in that, The method, executed by the controller, includes: Obtain the current flue gas operating parameters of the impinging flow reactor; wherein, the current flue gas operating parameters include flue gas velocity, flue gas temperature, flue gas pollutant type and pollutant concentration; Based on the current flue gas operating parameters, determine the current energy scale of the airflow turbulence; Based on the current energy scale of the airflow turbulence and the physical properties of the reagent, the optimal dynamic instability wavelength range of the current reagent jet before impact is determined; wherein, the optimal dynamic instability wavelength range is dynamically adjusted according to the type and concentration of the flue gas pollutants. The physical morphology of the target reagent jet is determined based on the dynamic optimal instability wavelength range; wherein, the physical morphology of the target reagent jet includes the target outlet diameter of the reagent jet, the target jet velocity, the target liquid film thickness, and the micro-disturbance frequency of the target jet surface; A first control signal and a second control signal are generated. The first control signal is transmitted to the mechanical adjustment mechanism of the reagent nozzle to drive the mechanical adjustment mechanism to change the geometry of the reagent nozzle. The second control signal is transmitted to the disturbance generator to drive the disturbance generator to apply micro-disturbance to the current reagent jet so that the breakup instability wavelength of the current reagent jet is within the dynamic optimal instability wavelength range.

2. The method according to claim 1, characterized in that, The step of determining the energy scale of the current airflow turbulence based on the current flue gas operating parameters specifically includes: Based on the flue gas velocity and the flue gas temperature, the basic energy scale of the gas turbulence is determined; Based on the types and concentrations of the flue gas pollutants, a correction factor for the flue gas composition is determined. Based on the basic airflow turbulence energy scale and the flue gas composition correction coefficient, the current airflow turbulence energy scale corrected by flue gas composition is determined.

3. The method according to claim 2, characterized in that, The determination of the basic energy scale of gas turbulence based on the flue gas velocity and the flue gas temperature specifically includes: Input the flue gas velocity and the flue gas temperature into the turbulent kinetic energy level string model to obtain the basic airflow turbulence energy scale output by the turbulent kinetic energy level string model; The turbulent kinetic energy level series model establishes a mapping relationship between flue gas velocity, flue gas temperature and the energy scale of the basic airflow turbulence through wind tunnel experiments.

4. The method according to claim 2, characterized in that, The determination of the flue gas composition correction coefficient based on the types and concentrations of the flue gas pollutants specifically includes: For each flue gas pollutant, the influence weight of the pollutant on the physical properties of the flue gas is determined; wherein, the influence weight is based on the degree of influence of the pollutant on the density and viscosity of the flue gas. Based on the types of flue gas pollutants, the concentration of each pollutant, and their corresponding influence weights, the weighted influence value of each pollutant is calculated, and all weighted influence values ​​are summed to obtain the comprehensive influence factor. Based on the comprehensive impact factor, the correction coefficient for flue gas composition is determined through a pre-calibrated mapping relationship; wherein the mapping relationship represents the functional relationship between the comprehensive impact factor and the correction coefficient for flue gas composition.

5. The method according to claim 2, characterized in that, The step of determining the current airflow turbulence energy scale, corrected for flue gas composition, based on the basic airflow turbulence energy scale and the flue gas composition correction coefficient, specifically includes: Multiplying the basic airflow turbulence energy scale by the flue gas composition correction coefficient yields the current airflow turbulence energy scale corrected for flue gas composition.

6. The method according to claim 1, characterized in that, The determination of the optimal dynamic instability wavelength range of the current reagent jet before impact, based on the current energy scale of the airflow turbulence and the physical properties of the reagent, specifically includes: Based on the physical properties of the reagent and the preset jet geometry parameters, calculate the Weber number and the Oenisoger number; Input the current airflow turbulence energy scale, the Weber number, and the Onizog number into the aerodynamic instability spectrum model; The breakup growth rate distribution of the current reagent jet is calculated using the aerodynamic instability spectrum model. The rupture growth rate distribution is adjusted based on the types and concentrations of the flue gas pollutants. From the adjusted fracture growth rate distribution, the wavelength range corresponding to the maximum value is extracted as the optimal dynamic instability wavelength range of the current reagent jet before impact.

7. The method according to claim 6, characterized in that, The adjustment of the rupture growth rate distribution based on the types and concentrations of the flue gas pollutants includes: For different types of pollutants, corresponding weight adjustment coefficients are set; wherein, the weight adjustment coefficients are pre-calibrated based on the degree of physical influence of the pollutant on the jet breakup characteristics; Based on the pollutant concentration of each pollutant type, the actual weight adjustment factor for that pollutant type is calculated using a linear function; The weighted sum of the actual weight adjustment factors for each pollutant type is used to obtain the comprehensive weight adjustment factor. The comprehensive weighting adjustment factor is applied to the rupture growth rate distribution to obtain the adjusted rupture growth rate distribution.

8. The method according to any one of claims 1 to 7, characterized in that, The method of driving the mechanical adjustment mechanism to change the geometry of the reagent nozzle includes: The mechanical adjustment mechanism is driven to adjust the instantaneous cross-sectional shape and effective aperture of the nozzle outlet, so that the current outlet diameter of the current reagent jet conforms to the target outlet diameter, and the current liquid film thickness of the current reagent jet conforms to the target liquid film thickness; and The current injection velocity of the current reagent jet is made to match the target injection velocity by controlling the reagent supply pressure.

9. The method according to any one of claims 1 to 7, characterized in that, The method further includes: Obtain the flue gas flow rate adjustment parameters of the impinging flow reactor; Predict future trends in flue gas operating conditions based on the flue gas flow regulation parameters. Based on the predicted trend of flue gas operating conditions, the parameters used to generate the first and second control signals are adjusted in advance. During the aforementioned pre-adjustment process, the real-time breakup instability wavelength of the current reagent jet is continuously acquired using a schlieren imaging sensor; Based on the deviation between the real-time fracture instability wavelength and the dynamic optimal instability wavelength range, the pre-adjusted parameters are fed back for correction to optimize the generation of the first control signal and the second control signal.

10. A reagent injection control system for impinging flow flue gas treatment, characterized in that, The system includes an impinging flow reactor and a controller, the controller being used to execute the reagent injection control method for impinging flow flue gas treatment according to any one of claims 1-9.

Images