High-temperature high-dust flue gas self-adaptive soot blower

Abstract

The present application relates to the technical field of flue gas denitration, and discloses a high-temperature high-dust flue gas self-adaptive soot blower, aiming at the defects of traditional soot blowing strategies, such as response lag, inability to perceive local phase change risk, and fixed soot blowing medium energy, the present application comprises: a multi-physical field data acquisition module; a space heat field feature extraction module, which is used for calculating the standard deviation of the effective temperature gradient considering the local acid dew point approximation effect and quantifying the risk of cohesive ash generation; a time flow field feature extraction module, which extracts a transient high-frequency differential pressure signal to identify the precursor of fly ash dynamic bridging. A multi-parameter coupling solution module combines the critical shear stress of the ash deposition layer inversed by powder mechanics, and considers the device aging and flue gas cross-flow interference, to solve the self-adaptive soot blowing fluid dynamic pressure instruction value; finally, a closed-loop self-adaptive execution module adjusts the fluid valve in real time based on the pipeline pneumatic transfer function. The present application realizes early warning of the ash deposition state and intelligent and accurate matching of the soot blowing intensity.

Description

Technical Field

[0001] This invention belongs to the field of flue gas denitrification technology, and particularly relates to an adaptive soot blower for high-temperature, high-dust flue gas. Background Technology

[0002] Selective catalytic reduction (SCR) technology, as the core process for industrial flue gas denitrification, has been widely used in high-emission industries such as coal-fired power plants, metallurgy, and chemicals. Under high-temperature and high-dust operating conditions, a large number of fly ash particles carried in the flue gas are prone to non-uniform deposition inside the catalyst channels when passing through the SCR reactor, forming a dense ash layer. This ash accumulation not only leads to a continuous increase in system pressure drop and increased induced draft fan energy consumption, but also blocks the active channels of the catalyst, causing a decrease in denitrification efficiency or even permanent catalyst deactivation, seriously threatening the long-term stable operation of the denitrification system. To maintain normal operating conditions, a soot blowing device must be configured for online cleaning.

[0003] Current soot blowing control strategies commonly used in industrial practice have significant limitations. Most systems rely on preset time intervals or fixed differential pressure thresholds as trigger conditions, i.e., soot blowing is initiated when the macroscopic differential pressure between the catalyst bed inlet and outlet exceeds a certain empirical value. This control mechanism is essentially a hysteresis response mode, unable to effectively identify the early dynamic characteristics of soot accumulation. The macroscopic differential pressure signal can only reflect the static accumulation result after the soot has been severely compacted, making it difficult to capture the aerodynamic instability precursors caused by dynamic bridging of fly ash particles within the channels. Furthermore, existing control logic completely ignores the local distortion effect of the temperature distribution in the reactor inlet cross-section, especially when the local temperature approaches or falls below the acid dew point of ammonium bisulfate (ABS), changes in flue gas composition can induce liquid-phase condensation, leading to a sharp increase in the adhesion between fly ash particles. Due to the lack of real-time perception of thermodynamic risks, the system often intervenes only after the soot has developed into hard blockage, significantly reducing the soot removal effect.

[0004] Furthermore, the method for setting the energy of the soot blowing medium has a fundamental flaw. Traditional methods use a fixed soot blowing pressure calibrated at the factory, a parameter that does not consider the dynamic evolution of the physical properties of the ash layer during actual operation. The critical shear stress of the ash layer is constantly changing due to factors such as the risk of adhesive ash formation, porosity fluctuations, and catalyst aging, making it impossible for a fixed pressure to accurately match dynamic requirements. Simultaneously, the attenuation effect of flue gas crossflow on the soot jet trajectory is not taken into account, resulting in a significant deviation between the actual dynamic pressure applied to the ash layer and the theoretical requirement. When the soot blowing energy is insufficient, it is difficult to remove highly viscous ash; while excessive energy accelerates catalyst surface wear and wastes media. This crude control strategy leaves the system in a long-term predicament of both low cleaning efficiency and the risk of equipment damage. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is as follows: A high-temperature, high-dust adaptive sootblower for flue gas, comprising: a multi-physics data acquisition module, used to acquire in real-time spatial temperature array data of the SCR reactor inlet section, real-time pressure difference signals at the catalyst layer inlet and outlet, and real-time flue gas composition data, and to obtain the median particle size of flue gas fly ash, the cumulative service time of the catalyst, and a preset catalyst surface roughness reference decay constant; a spatial thermal field feature extraction module, used to calculate the effective temperature gradient standard deviation, taking into account the local acid dew point approximation effect, based on the spatial temperature array data and real-time flue gas composition data, to quantify the risk of adhesive ash formation; and time. The flow field feature extraction module is used to extract transient high-frequency disturbances from the real-time differential pressure signal and calculate the dynamic blockage high-frequency fluctuation energy that characterizes the dynamic bridging characteristics of fly ash in the duct. The multi-parameter coupled solution module is used to use the effective temperature gradient standard deviation and the dynamic blockage high-frequency fluctuation energy as interactive control variables, and combine the powder mechanics model to invert the critical shear stress of the ash layer. It also combines the fluid free jet attenuation theory, cumulative service time and surface roughness reference attenuation constant to solve and output the adaptive soot blowing fluid dynamic pressure command value. The closed-loop adaptive execution module is used to drive the fluid regulating valve to automatically adjust the medium injection intensity according to the adaptive soot blowing fluid dynamic pressure command value.

[0007] In a further technical solution, the calculation logic of the effective temperature gradient standard deviation in the spatial thermal field feature extraction module is as follows: using the degree to which the local actual temperature approaches the absolute temperature of the acid dew point as the driving potential, a nonlinear exponential amplification weight is constructed; the local spatial temperature gradient of each measuring point in the spatial temperature array data is multiplied by the nonlinear exponential amplification weight, and the spatial dispersion between it and the weighted average temperature gradient is calculated, thereby obtaining the quantized effective temperature gradient standard deviation.

[0008] In a further technical solution, the calculation logic for the dynamic blockage high-frequency fluctuation energy in the time flow field feature extraction module is as follows: the real-time differential pressure signal is filtered and separated to extract the transient high-frequency differential pressure signal; the square integral of the deviation between the transient high-frequency differential pressure signal and the average macroscopic differential pressure within a set time window is calculated, and the dimension is normalized using the reference standard atmospheric pressure difference to obtain the dynamic blockage high-frequency fluctuation energy.

[0009] A further technical solution involves configuring the multi-parameter coupled solution module to perform interactive inversion of microscopic ash accumulation characteristics. Specifically, this includes: based on the environmental aerosol phase change theory, using the temperature difference between the local minimum absolute temperature and the acid dew point absolute temperature as the subcooling independent variable, constructing a positively correlated exponential function to obtain the equivalent value of the phase change liquid bridge; introducing dynamic compression characteristic values ​​generated by fitting long-period macroscopic differential pressure data, and using the high-frequency fluctuation energy of dynamic blockage as the attenuation independent variable, establishing an exponential decay model to invert and calculate the real-time dynamic porosity of the ash accumulation layer.

[0010] In a further technical solution, the multi-parameter coupled solution module is also configured to calculate the critical shear stress of the ash layer. Specifically, this includes: linearly amplifying and compensating the intrinsic surface energy benchmark value of the dry fly ash particles by using the product of the phase change liquid bridge equivalent value and the standard deviation of the effective temperature gradient, and calculating the microscopic surface bonding energy after being modified by the liquid film adhesion; based on the powder structure fracture mechanics model, setting the critical shear stress to be directly proportional to the microscopic surface bonding energy, and inversely proportional to the apparent volume parameters of the median particle size and real-time dynamic porosity of the flue gas fly ash.

[0011] A further technical solution involves the multi-parameter coupled solution module introducing a momentum transfer attenuation rate characterizing equipment aging and a crossflow deflection attenuation factor characterizing flow field disturbance when calculating the adaptive sootblowing fluid dynamic pressure command value. The momentum transfer attenuation rate is set as a function that decays exponentially with the cumulative service time of the catalyst, and its time decay rate is uniquely determined by a preset catalyst surface roughness reference attenuation constant. The crossflow deflection attenuation factor is calculated based on the vector summation ratio of the flue gas velocity upstream of the catalyst layer and the rated design jet velocity of the sootblowing medium at the nozzle outlet.

[0012] A further technical solution is that the final calculation logic of the adaptive soot blowing fluid dynamic pressure command value is as follows: based on the attenuation law of free turbulent jet dynamics, the adaptive soot blowing fluid dynamic pressure command value is set to be proportional to the critical shear stress, and at the same time, it is set to be inversely proportional to the momentum transfer attenuation rate, the square term of the geometric diffusion factor of the soot blowing device, and the crossflow deflection attenuation factor.

[0013] A further technical solution is provided, wherein the closed-loop adaptive execution module includes: a programmable logic controller and a fluid regulating valve driven by the controller; the fluid regulating valve is one of a soot blowing steam fluid regulating valve or a compressed air fluid regulating valve; the programmable logic controller receives the adaptive soot blowing fluid dynamic pressure command value as a set value, and adjusts the valve opening in real time according to the pneumatic transfer function of the soot blowing pipeline system, so that the dynamic pressure head at the nozzle outlet approaches the adaptive soot blowing fluid dynamic pressure command value.

[0014] The beneficial effects of this invention are as follows:

[0015] 1. This invention abandons the traditional mode of passively triggering by relying solely on macroscopic pressure difference exceeding limits. It innovatively extracts the energy of high-frequency pressure difference fluctuations and the standard deviation of effective temperature gradient, which can accurately capture the aerodynamic precursors of dynamic bridging of fly ash in the duct and predict in advance the risk of forming sticky ash due to ammonium bisulfate condensation induced by local temperature distortion. This achieves a fundamental shift from "post-blockage treatment" to "initial intervention".

[0016] 2. This invention constructs a deep fluid-structure interaction model, which inverts the real-time critical shear stress based on the dynamic porosity of ash accumulation and the adhesion energy of microscopic surfaces. Furthermore, it incorporates catalyst service aging degradation and flue gas crossflow deflection compensation when calculating the soot blowing dynamic pressure. This mechanism ensures that the soot blowing intensity dynamically adapts to actual operating conditions, effectively removing highly viscous ash while avoiding severe catalyst wear and media waste caused by excessive pressure.

[0017] 3. During execution, the system uses a programmable logic controller (PLC) to take the calculated adaptive dynamic pressure command as the setpoint and deeply integrates the pneumatic transfer function of the sootblowing pipeline system itself for real-time compensation and adjustment. This effectively eliminates execution errors caused by pipeline resistance and fluid inertia, ensuring that the actual dynamic pressure head at the nozzle outlet can approach the theoretical optimal value in milliseconds, significantly improving the system's control accuracy and sootblowing reliability. Attached Figure Description

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] Figure 1 A system functional module block diagram of a high-temperature, high-dust adaptive sootblower provided by the present invention;

[0020] Figure 2 The multi-parameter coupling and physical-mechanical solution logic diagram provided for this invention. Detailed Implementation

[0021] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0022] like Figure 1 and Figure 2 As shown, an adaptive soot blower for high-temperature, high-dust flue gas is provided according to an embodiment of the present invention, comprising:

[0023] The multiphysics data acquisition module is used to acquire real-time spatial temperature array data at the SCR reactor inlet section, real-time differential pressure signals at the catalyst inlet and outlet, and real-time flue gas composition data. It also obtains the median fly ash particle size, the cumulative service time of the catalyst, and a preset reference decay constant for catalyst surface roughness. For example, a distributed temperature sensor network can be used to acquire spatial temperature array data, a differential pressure transmitter can acquire real-time differential pressure signals, and a flue gas analyzer can acquire flue gas composition data. The median fly ash particle size can be obtained using an online particle size analyzer, while the cumulative service time of the catalyst can be accumulated from system operation records. The reference decay constant for catalyst surface roughness can be provided by the catalyst supplier or calibrated experimentally. This data provides comprehensive input for subsequent ash accumulation risk assessment and soot blowing parameter calculation.

[0024] A spatial thermal field feature extraction module is used to calculate the effective temperature gradient standard deviation, taking into account the local acid dew point approximation effect, based on spatial temperature array data and real-time flue gas composition data, to quantify the risk of fly ash formation. For example, this module can receive temperature readings from multiple temperature sensors and calculate the acid dew point by combining SO3 and water vapor concentrations in the flue gas. Subsequently, the effective temperature gradient standard deviation is obtained by weighting the local temperature gradient, where the weighting factors are related to the degree to which the local temperature approaches the acid dew point. This processing method can more accurately reflect the risk of enhanced fly ash adhesion caused by local temperature unevenness and the acid dew point effect, rather than relying solely on average temperature or a fixed threshold.

[0025] The time-flow field feature extraction module is used to extract transient high-frequency disturbances from the real-time differential pressure signal and calculate the dynamic blockage high-frequency fluctuation energy characterizing the dynamic bridging characteristics of fly ash within the pores. For example, the real-time differential pressure signal can be high-pass filtered to separate transient high-frequency components, and then energy integration calculations can be performed on these high-frequency components within a certain time window. This method can capture the small, rapid differential pressure fluctuations generated in the early stage of fly ash formation within the catalyst pores due to interparticle interactions and local airflow disturbances, thus identifying early precursors of dynamic bridging of ash accumulation before the macroscopic differential pressure increases significantly.

[0026] The multi-parameter coupled solution module uses the effective temperature gradient standard deviation and the high-frequency fluctuation energy of dynamic blockage as interactive control variables, combined with a powder mechanics model to invert the critical shear stress of the ash layer; and combines the fluid free jet attenuation theory, cumulative service time, and surface roughness reference attenuation constant to calculate and output an adaptive sootblowing fluid dynamic pressure command value. For example, this module can use powder mechanics theory, taking the risk of adhesive ash formation (quantified by the effective temperature gradient standard deviation) and the pore blockage state (quantified by the high-frequency fluctuation energy of dynamic blockage) as inputs, and calculate the minimum shear stress required for the current ash layer through a series of physical models. It can comprehensively calculate the sootblowing medium injection dynamic pressure required to reach the critical shear stress based on the injection characteristics of the sootblowing medium, the distance between the sootblowing gun and the catalyst surface, the flue gas crossflow velocity, and the wear of the catalyst surface over time. This multi-parameter coupled calculation method allows the sootblowing dynamic pressure command value to accurately adapt to the complex and variable ash accumulation state inside the SCR reactor.

[0027] The closed-loop adaptive execution module is used to automatically adjust the injection intensity of the fluid regulating valve based on the adaptive soot blowing fluid dynamic pressure command value. For example, this module can receive the dynamic pressure command value output by the multi-parameter coupled solution module and use it as the target setpoint. The flow rate or pressure of the soot blowing medium (such as steam or compressed air) is adjusted through a proportional-integral-derivative (PID) controller or other advanced control algorithm, thereby controlling the opening of the fluid regulating valve. This closed-loop control mechanism ensures that the actual injection intensity of the soot blowing medium accurately tracks the command value, achieving effective removal of accumulated soot while avoiding catalyst wear and media waste caused by excessive soot blowing.

[0028] The following specific example will provide a more in-depth explanation of the above technical solution:

[0029] Suppose that the catalyst bed is operating in an SCR denitrification unit of a coal-fired power plant. Due to the large amount of fly ash in the flue gas and the complex and variable operating conditions, there is a risk of ash accumulation and the formation of sticky ash.

[0030] First, the multiphysics data acquisition module operates in real time, continuously acquiring spatial temperature array data at the SCR reactor inlet section, real-time differential pressure signals at the catalyst inlet and outlet, and real-time flue gas composition data. Simultaneously, this module obtains the current median fly ash particle size, the catalyst's cumulative service time (e.g., 10,000 hours of operation), and a preset reference decay constant for catalyst surface roughness. This data is then transmitted to the subsequent processing module.

[0031] Next, the spatial thermal field feature extraction module receives spatial temperature array data and real-time flue gas composition data. Based on this data, the module calculates the effective temperature gradient standard deviation under the current operating conditions, taking into account the local acid dew point approach effect. For example, when a local area of ​​the SCR reactor experiences a low temperature and approaches the acid dew point, the effective temperature gradient standard deviation calculated by this module will increase significantly, thereby quantifying the risk of adhesive ash formation in that area.

[0032] Meanwhile, the time-flow field feature extraction module analyzes the real-time differential pressure signal. This module extracts transient high-frequency disturbances from the differential pressure signal and calculates the dynamic blockage high-frequency fluctuation energy, which characterizes the dynamic bridging characteristics of fly ash within the channels. For example, when fly ash particles begin to form loose bridging structures within the catalyst channels, the airflow passing through these structures generates small, rapid differential pressure fluctuations. These fluctuations are captured by the module and converted into higher dynamic blockage high-frequency fluctuation energy, thus identifying early signs of ash accumulation before the macroscopic differential pressure has significantly increased.

[0033] Subsequently, the multi-parameter coupled solution module receives the effective temperature gradient standard deviation and the dynamic blockage high-frequency fluctuation energy. This module uses these two parameters as interactive control variables, combined with a preset powder mechanics model, to inversely calculate the critical shear stress of the current ash layer. For example, if the effective temperature gradient standard deviation is high, indicating a greater risk of adhesive ash, the calculated critical shear stress will increase accordingly; if the dynamic blockage high-frequency fluctuation energy is high, indicating a more severe blockage of the channels, the critical shear stress will also be adjusted accordingly. Based on this, the module further combines the fluid free-jet attenuation theory, the cumulative service time of the catalyst, and the catalyst surface roughness reference attenuation constant to comprehensively calculate the currently required adaptive sootblowing fluid dynamic pressure command value. For example, considering that the catalyst has been in service for a long time, its surface roughness may have increased, leading to a decrease in momentum transfer efficiency; therefore, the calculated command value will be appropriately increased to compensate for this attenuation.

[0034] Finally, the closed-loop adaptive execution module receives the adaptive sootblowing fluid dynamic pressure command value output by the multi-parameter coupled solution module. Based on this command value, the module drives the fluid regulating valve to automatically adjust the media injection intensity. For example, if the command value requires a higher sootblowing dynamic pressure, the valve opening will be increased, thereby increasing the injection intensity of the sootblowing medium. In this way, the sootblower can provide just the right amount of sootblowing kinetic energy based on the actual adhesion and blockage degree of the ash inside the SCR reactor, as well as the service condition of the catalyst itself, achieving precise and efficient sootblowing.

[0035] In summary, this application overcomes the technical bottlenecks of existing SCR denitrification soot blowing systems, such as lagging control triggering mechanisms, lack of thermodynamic sensing, and lack of adaptive matching of soot blowing kinetic energy, by constructing a complete closed-loop system from multi-physics field data sensing to adaptive soot blowing execution. It achieves precise, efficient, and economical ash accumulation control for the SCR denitrification process of high-temperature and high-dust flue gas.

[0036] This application further proposes the following calculation logic for the effective temperature gradient standard deviation in the spatial thermal field feature extraction module: A nonlinear exponential amplification weight is constructed using the degree to which the local actual temperature approaches the absolute temperature of the acid dew point as the driving potential; the local spatial temperature gradient of each measuring point in the spatial temperature array data is multiplied by the nonlinear exponential amplification weight, and the spatial dispersion between this multiplication and the weighted average temperature gradient is calculated to obtain the quantized effective temperature gradient standard deviation; the specific formula for calculating the effective temperature gradient standard deviation is as follows:

[0037]

[0038] In the formula, This represents the standard deviation of the effective temperature gradient taking into account the acid dew point approximation effect. This indicates the total number of temperature sensor points at the inlet cross-section; i represents the sensor point number. This represents the local spatial temperature gradient at the i-th measurement point; This represents the absolute temperature of the acid dew point calculated based on real-time flue gas composition. This represents the real-time absolute temperature at the i-th measuring point; This represents the weighted average temperature gradient of all measuring points after acid dew point weighting.

[0039] Effective temperature gradient standard deviation It is a comprehensive indicator used to quantify the non-uniformity of the temperature field at the inlet section of an SCR reactor, with particular emphasis on the risk of adhesive ash formation when local temperatures approach the acid dew point. Its calculation results directly reflect the likelihood and severity of adhesive ash accumulation. (Total number of inlet section temperature sensor measuring points) This represents the total number of sensor points used to collect spatial temperature array data at the inlet section of the SCR reactor. These points can be evenly distributed across the inlet section, for example, using a grid arrangement, or optimized according to the flue gas flow characteristics to ensure a comprehensive and accurate reflection of the temperature distribution across the entire section. The point number *i* identifies each individual point in the inlet section temperature sensor array. By numbering each point, the data collected from each point can be processed and analyzed independently, thereby achieving a refined assessment of the local temperature field. The local spatial temperature gradient of the *i*-th point. This represents the rate of temperature change at the i-th measuring point at the inlet section. This gradient can be calculated as the ratio of the temperature difference between adjacent measuring points to their distance, for example, by using the finite difference method or interpolation from spatial temperature array data. It reflects the non-uniformity of temperature distribution in a local area. The absolute temperature of the acid dew point is calculated based on real-time flue gas composition. This refers to the acid dew point temperature of flue gas, calculated based on real-time collected flue gas composition data (e.g., SO2, H2O content). This calculation can be based on known thermodynamic models or empirical formulas, such as by consulting acid dew point charts or using specialized calculation software. Specifically, the absolute temperature of the acid dew point... The empirical formula can be solved using the classic Verhoff-Banchero formula, whose mathematical expression is: In the formula, This refers to the partial pressure of water vapor in the flue gas. This represents the partial pressure of sulfur trioxide in the flue gas; the partial pressure data is acquired in real time by the multiphysics data acquisition module. and The volume percentage concentration is obtained by multiplying the current total absolute pressure of the flue gas. This formula ensures millisecond-level dynamic response for acid dew point sensing, reaching the level of gas phase partial pressure. Acid dew point temperature is a key parameter for determining whether ammonium bisulfate (ABS) will condense. The real-time absolute temperature at the i-th measuring point... This represents the absolute temperature of the flue gas measured in real time at the i-th measuring point at the inlet section. This data is acquired in real time by a multiphysics data acquisition module, typically obtained directly by temperature sensors such as thermocouples or infrared thermometers. The weighted average temperature gradient of all measuring points is adjusted for acid dew point weighting. This represents the weighted average value calculated after correcting for the acid dew point approximation effect on the local spatial temperature gradients at all measuring points. This average value serves as a benchmark to measure the overall trend of the corrected temperature gradient distribution across the entire inlet cross-section, thus more accurately reflecting the dispersion of the corrected temperature gradient when calculating the standard deviation.

[0040] The solution proposed in this application introduces an exponential correction term to adjust the real-time absolute temperature at each measuring point. Compared with the absolute temperature of acid dew point calculated based on real-time flue gas composition The degree of approximation between them is taken into consideration. Specifically, when the temperature at the measuring point... The closer to the acid dew point At that time, the exponent term The larger the value, the more it affects the original local spatial temperature gradient. A greater weighting correction is applied. This correction mechanism amplifies the effective temperature gradient in high-risk areas where local temperatures approach the acid dew point, thus more accurately reflecting the risk of increased fly ash adhesion due to ammonium bisulfate condensation in these areas. This scheme compares the corrected effective temperature gradient at all measuring points with the weighted average of the acid dew point-weighted temperature gradients at all measuring points. The deviations between the values ​​are squared, summed, averaged, and squared to obtain the effective temperature gradient standard deviation that takes into account the acid dew point approximation effect. Standard deviation, as an indicator of data dispersion, accurately reflects the non-uniformity of temperature gradient distribution at the inlet section of the SCR reactor. Given that non-uniform temperature gradients are a significant contributing factor to the localized formation of adhesive ash, and that the closer the local temperature is to the acid dew point, the higher the risk of adhesive ash formation, this corrected standard deviation allows for a more accurate and comprehensive quantification of the risk of adhesive ash formation. This scheme works in conjunction with a multi-physics data acquisition module to acquire the required spatial temperature array data and real-time flue gas composition data in real time, ensuring the real-time performance and accuracy of the calculations. In this way, the spatial thermal field feature extraction module can output a more representative quantitative index of adhesive ash formation risk, corrected for the acid dew point effect. This provides a more accurate and reliable input for the subsequent multi-parameter coupled solution module to invert the critical shear stress of the ash layer, thereby improving the control accuracy and effectiveness of the entire adaptive soot blowing system.

[0041] As a specific implementation method, a temperature sensor array consisting of multiple thermocouples can be deployed at the inlet section of the SCR denitrification unit. For example, 20 K-type thermocouples can be evenly distributed across the section to collect flue gas temperature data at each measuring point in real time. Simultaneously, the SO2 and H2O content in the flue gas is monitored in real time using a flue gas analyzer. After conversion to millimeters of mercury (mmHg), the partial pressure of water vapor (PH2O) is obtained as 76 mmHg (corresponding to approximately 10% by volume), and the partial pressure of sulfur trioxide (PSO3) is 0.0152 mmHg (corresponding to approximately 20 ppm). The collected and converted flue gas partial pressures are then substituted into a corrected empirical formula for calculation. First, calculate the natural logarithm of each partial pressure. , Substitute into the right side of the equation and expand step by step: Taking the reciprocal of both sides of the equation and multiplying it by 1000, we can obtain the current absolute temperature of the acid dew point. Once the multiphysics data acquisition module obtains this data, the space thermal field feature extraction module can begin calculations. First, for each thermocouple measuring point i in the array, based on its own temperature... The local spatial temperature gradient at a given measuring point is calculated using the finite difference method, based on temperature data from adjacent measuring points. For example, the local lowest absolute temperature After array data extraction, the total number of measuring points was... The weighted average temperature gradient after acid dew point weighting Next, the local temperature gradient at each measuring point is adjusted using the corrected acid dew point index weights. The weighted average is calculated as the sum of squares of the deviations from the mean. Substituting into the standard deviation formula: This calculation process can be performed by a microprocessor or digital signal processor (DSP) embedded in the spatial thermal field feature extraction module, ensuring the real-time performance and accuracy of the calculation.

[0042] Through the above technical solution, this application clearly provides a specific calculation method for the effective temperature gradient standard deviation considering the local acid dew point approximation effect. This method, by introducing acid dew point-related weight corrections, makes the quantification of the risk of sticky ash formation more accurate and precise. The closer the local temperature is to the acid dew point, the larger the correction term, thus assigning a higher weight to the risk of sticky ash formation in that area. This highly matches the phenomenon of a sharp increase in fly ash stickiness caused by ammonium bisulfate condensation in actual operating conditions. This corrected effective temperature gradient standard deviation not only reflects the non-uniformity of the temperature field distribution, but more importantly, it solves the problem of inaccurate risk quantification caused by the inability of traditional methods to accurately reflect the influence of acid dew point. Therefore, this solution can provide a more realistic quantification result for the risk of sticky ash formation, providing an accurate and reliable basic input for the subsequent multi-parameter coupled solution module to invert the critical shear stress of the ash layer and calculate the adaptive soot blowing fluid dynamic pressure command value. This significantly improves the adaptive control accuracy and cleaning efficiency of the entire SCR denitrification soot blowing system, effectively avoiding catalyst hard clogging or wear and media waste caused by excessive soot blowing due to risk judgment errors.

[0043] This application further proposes the following calculation logic for the dynamic blockage high-frequency fluctuation energy in the time-flow field feature extraction module: The real-time differential pressure signal is filtered and separated to extract the transient high-frequency differential pressure signal; the square integral of the deviation between the transient high-frequency differential pressure signal and the average macroscopic differential pressure within a set time window is calculated, and dimension normalization is performed using the reference standard atmospheric pressure difference to obtain the dynamic blockage high-frequency fluctuation energy; the specific formula for calculating the dynamic blockage high-frequency fluctuation energy is as follows:

[0044]

[0045] In the formula, This indicates the high-frequency fluctuation energy of dynamic blockage; Indicates the length of the time window for high-frequency energy integration; This represents the atmospheric pressure difference used as a reference standard for dimensional standardization. This represents the real-time high-frequency differential pressure transient value at the inlet and outlet of the catalyst layer at time t; Indicates time window The average macroscopic differential pressure within; This represents the time differential element.

[0046] Dynamic blockage high-frequency fluctuation energy This is a quantitative indicator used to characterize the dynamic bridging of fly ash within catalyst channels. Its concept lies in capturing transient, high-frequency fluctuations in the differential pressure signal caused by early fly ash bridging, rather than simply focusing on the static accumulation of macroscopic differential pressure. A higher energy value indicates a greater risk of dynamic fly ash bridging and a more pronounced clogging trend, providing crucial information for subsequent soot blowing decisions. The time window length for high-frequency energy integration is also relevant. This defines the time frame considered when calculating the high-frequency fluctuation energy of dynamic blockage. Its purpose is to ensure that the calculation covers a sufficient duration of dynamic changes while avoiding the introduction of outdated data, thus ensuring that the calculation results reflect the current state of fly ash accumulation. For example, the length of this time window can be empirically set based on the operating characteristics of the SCR reactor and the dynamic process of fly ash accumulation, such as 5 to 30 minutes, or the optimal time scale can be determined through spectral analysis and autocorrelation analysis of historical operating data. Atmospheric pressure difference is used as the reference standard for dimensional uniformity. This is used to perform dimensionless processing on the calculation results, making the dynamic blockage high-frequency fluctuation energy a unified, quantitative indicator unaffected by specific operating conditions. By introducing a reference standard atmospheric pressure difference, the dimensional differences under different operating pressure conditions can be eliminated, thus facilitating subsequent coupling calculations with other parameters and improving the compatibility and accuracy of the calculation. For example, this reference standard atmospheric pressure difference can be set to 101325 Pa (standard atmosphere), or a representative system rated operating pressure difference value can be selected as a reference based on the system design and operating characteristics. The real-time high-frequency differential pressure transient value at the catalyst layer inlet and outlet at time t. This represents the high-frequency pressure difference change between the inlet and outlet of the catalyst layer at a given instant t. It is acquired in real time by a high-precision differential pressure sensor and undergoes appropriate signal processing to filter out macroscopic and low-frequency pressure difference changes, thus highlighting transient fluctuations caused by dynamic bridging of fly ash. For example, the original pressure difference signal can be processed using a digital high-pass filter, or high-frequency components can be extracted using wavelet transform. This transient value is key data for capturing the early dynamic characteristics of fly ash accumulation. Time Window Average macro pressure within Indicates the length of the specified time window. The average pressure difference between the inlet and outlet of the catalyst layer reflects the overall macroscopic resistance of the catalyst layer and is the basis for traditional ash accumulation assessment. This average value can be obtained by performing a moving average calculation on the real-time collected pressure difference data, or by performing an arithmetic average at the end of each time window. By subtracting this average macroscopic pressure difference from the real-time high-frequency transient pressure value, the pure transient fluctuation component can be effectively separated, thereby more accurately capturing the early signals of fly ash dynamic bridging.

[0047] This application solves the problem of traditional methods failing to accurately quantify the dynamic bridging characteristics of fly ash by providing a clear mathematical formula for calculating the high-frequency fluctuation energy of dynamic blockage. The core of this formula lies in capturing transient high-frequency disturbances in the differential pressure signals at the catalyst bed inlet and outlet. Specifically, it first obtains the real-time transient high-frequency differential pressure values ​​at the catalyst bed inlet and outlet at time t. This transient value reflects the subtle and rapid pressure difference changes caused by the dynamic bridging of fly ash within the ducts. Simultaneously, it calculates the pressure difference over a certain time window. Average macro pressure within This average value represents the overall static resistance of the catalyst layer. This is calculated by subtracting the real-time high-frequency differential pressure transient value from the average macroscopic differential pressure. This effectively isolates the pure pressure difference fluctuation component, which is an early aerodynamic precursor to fly ash dynamic bridging. Subsequently, the squares of these fluctuation components are integrated within a time window. The energy of these high-frequency fluctuations was quantified by averaging within the range. Finally, a standard atmospheric pressure difference was introduced for dimensional unification. Dimensionless processing is performed to make the calculated dynamic blockage high-frequency fluctuation energy... This method serves as a unified and universally applicable indicator, adaptable to various working conditions, and provides accurate and reliable input parameters for retrieving the critical shear stress of the fly ash layer in subsequent multi-parameter coupled solution modules. This calculation method can accurately extract the dynamic characteristics of early-stage fly ash accumulation, effectively solving the problem that conventional sootblowing cannot capture early signs of dynamic bridging of fly ash within the ducts, and providing crucial foundational data for achieving adaptive sootblowing.

[0048] As a specific implementation method, during the operation of an SCR denitrification system, a high-precision differential pressure sensor can be used to monitor the pressure difference between the inlet and outlet of the catalyst bed in real time. This sensor can be configured with a high sampling frequency to capture transient high-frequency disturbances in the pressure difference signal. The acquired raw pressure difference data is preprocessed by a signal processing unit, for example, by filtering out low-frequency macroscopic pressure difference changes using a digital high-pass filter, thereby obtaining the real-time high-frequency transient differential pressure values ​​at the inlet and outlet of the catalyst bed at time t. Meanwhile, the signal processing unit or another data processing module will continuously calculate within a preset time window. (For example, Average macroscopic differential pressure within) The processed data is then fed into a calculation module that contains the formula for calculating the high-frequency fluctuation energy of dynamic blockage. During the calculation, a standard atmospheric pressure difference can be preset for dimensional standardization. Within this window, the integral of the square of the deviation between the transient high-frequency differential pressure signal and the average macroscopic differential pressure is measured. , time window Substituting the result of the square integral into the normalization formula This outputs the current dynamic congestion high-frequency fluctuation energy. .this The value can then be used as input to a multi-parameter coupled solution module to further invert the real-time dynamic porosity of the ash layer, and ultimately affect the calculation of the adaptive soot blowing fluid dynamic pressure command value.

[0049] Through the above technical solution, this application provides a unified and accurate method for calculating the high-frequency fluctuation energy of dynamic blockage. This method can reliably and accurately obtain a quantitative index reflecting the degree of dynamic bridging blockage of fly ash, solving the problem of the lack of standardized calculation methods in the past. By capturing the fluctuation of the difference between the real-time high-frequency differential pressure transient value and the average macroscopic differential pressure, this scheme can accurately extract the dynamic characteristics of early ash accumulation, thus effectively solving the problem that conventional sootblowing cannot capture the early signs of dynamic bridging of fly ash in the duct. In addition, the introduction of a dimensionlessly standardized reference standard atmospheric pressure difference makes this energy value a universal index, improving its compatibility and accuracy under different operating conditions. This provides accurate and reliable basic parameters for the subsequent multi-parameter coupled solution module to calculate the adaptive sootblowing fluid dynamic pressure command value, thereby supporting the precise operation of the entire adaptive sootblowing system.

[0050] This application further proposes a multi-parameter coupled solution module configured to perform interactive inversion of microscopic ash accumulation characteristics. Specifically, this includes: based on the environmental aerosol phase change theory, using the temperature difference between the local minimum absolute temperature and the acid dew point absolute temperature as the independent variable of supercooling, constructing a positively correlated exponential function to obtain the equivalent value of the phase change liquid bridge; introducing dynamic compression characteristic values ​​generated by fitting long-period macroscopic differential pressure data, and using the high-frequency fluctuation energy of dynamic blockage as the attenuation independent variable, establishing an exponential decay model to invert and calculate the real-time dynamic porosity of the ash accumulation layer; the multi-parameter coupled solution module is also configured to solve the critical shear stress of the ash accumulation layer. Specifically, this includes: using the inherent surface energy benchmark value of dry fly ash particles, linearly amplifying and compensating for the product of the equivalent value of the phase change liquid bridge and the standard deviation of the effective temperature gradient, to calculate the microscopic surface bonding energy after being modified by liquid film adhesion; based on the powder structure fracture mechanics model, setting the critical shear stress to be positively proportional to the microscopic surface bonding energy, and inversely proportional to the apparent volume parameters of the median particle size and real-time dynamic porosity of flue gas fly ash. The formula for constructing a positively correlated exponential function to obtain the equivalent value of the phase change fluid bridge is as follows:

[0051]

[0052] In the formula, This indicates the equivalent value of the phase change fluid bridge. The reference capillary constant for ammonium bisulfate solution; This indicates the absolute temperature of the acid dew point; This represents the local lowest absolute temperature in the spatial temperature array data; the environmental aerosol phase transition theory describes the process by which gaseous substances condense into liquid or solid particles under specific temperature and pressure conditions. Here, it specifically refers to ammonium bisulfate transforming from a gaseous state to a liquid state below its acid dew point temperature, forming liquid bridges between fly ash particles, thereby significantly enhancing the interparticle adhesion. This calculation can be performed by real-time monitoring of the temperature distribution within the SCR reactor, particularly the local lowest absolute temperature. The absolute temperature of acid dew point was calculated by combining flue gas composition data. Using the preset capillary constant of ammonium bisulfate solution as a reference Through the given mathematical model Calculations can be performed. Alternatively, a numerical model based on thermodynamic equilibrium and mass transport can be established to simulate the phase transition behavior of ammonium bisulfate under different temperature gradients, thereby deriving the equivalent value of the phase change liquid bridge. .

[0053] The formula for calculating the real-time dynamic porosity of the ash accumulation layer using an exponential decay model is as follows:

[0054]

[0055] In the formula, This indicates the real-time dynamic porosity of the ash accumulation layer in the catalyst channels; This represents the initial inherent porosity of the catalyst matrix; This represents the dynamic compression feature value generated by fitting long-period macroscopic differential pressure data; This represents the high-frequency fluctuation energy of dynamic blockage. Utilizing this energy to invert the real-time dynamic porosity of the ash layer aims to assess the density or looseness of the ash layer within the catalyst channels. Porosity is a parameter measuring the proportion of pore volume to total volume in porous media. For ash layers, changes in porosity directly reflect the deposition and bridging of fly ash. (Dynamic blockage high-frequency fluctuation energy) This characterizes the early aerodynamic precursors of dynamic bridging in fly ash within the ducts; a higher value generally indicates more severe duct blockage and lower porosity. This inversion can be established by pre-testing in the laboratory or through long-term operational data, based on high-frequency fluctuations in dynamic blockage energy. Porosity of ash layer Empirical relationship models between them, such as those obtained by fitting an exponential decay function. The high-frequency fluctuation energy collected in real time is input into the model for calculation. The long-period macroscopic differential pressure data refers to the historical data of macroscopic resistance at the inlet and outlet of the catalyst layer continuously collected over 72 to 168 hours under stable operating conditions of the SCR denitrification system. The specific implementation method of the fitting generation is as follows: in offline mode, through experiments or historical ash removal operation records, several sets of real-time dynamic porosity of the ash layer under different degrees of looseness are obtained. and the corresponding dynamic blockage high-frequency fluctuation energy This serves as the training sample set; subsequently, the least squares method is used to evaluate the exponentially decaying benchmark function. The unknown coefficients of the convergence equation obtained by performing nonlinear curve regression fitting are the dynamic compression eigenvalues. Once this value is fitted and determined, it is fixed as a constant in the multi-parameter coupled solution module. Alternatively, it can be combined with computational fluid dynamics (CFD) simulations to simulate the pressure difference fluctuation characteristics of fluid passing through the catalyst layer under different degrees of blockage, and compared with the actual measured dynamic blockage high-frequency fluctuation energy. By making comparisons, the corresponding real-time dynamic porosity can be derived. .

[0056] The formula for calculating the adhesion energy of the microscopic surface after liquid film adhesion modification is as follows:

[0057]

[0058] In the formula, Indicates the bonding energy of microscopic surfaces; The reference value representing the intrinsic surface energy of dried fly ash particles; This indicates the equivalent value of the phase change fluid bridge. This represents the standard deviation of the effective temperature gradient taking into account the acid dew point approximation effect. This represents the reference temperature gradient standard deviation baseline value; The method of obtaining the data is as follows: Under the initial operating conditions where the new catalyst in the SCR reactor has just been put into operation and the catalyst matrix surface is completely clean and free of any ash accumulation, the boiler is controlled to operate continuously and stably under rated design load for 24 hours, and the standard deviation of the effective temperature gradient is calculated by the space thermal field feature extraction module during this period. The arithmetic mean of the values ​​is used as the dimensionless denominator for the spatial dispersion of the system's thermal field and is pre-stored in the controller's memory. The micro-surface bonding energy of the fly ash particle group after adhesive modification is calculated using the equivalent value of the phase change liquid bridge and the standard deviation of the effective temperature gradient. This aims to accurately quantify the true bonding strength of fly ash particles under actual operating conditions, affected by ammonium bisulfate liquid bridges and local temperature field distortions. Micro-surface bonding energy This is a physical quantity describing the interparticle interaction forces, which directly determines the ability of the ash layer to resist shear forces. By considering the liquid bridging effect and the risk of adhesive ash formation caused by temperature gradients, the adhesion characteristics of fly ash can be assessed more accurately. This solution can convert the previously calculated equivalent value of phase change liquid bridges... and effective temperature gradient standard deviation As a correction factor, it is based on the intrinsic surface energy reference value of dry fly ash particles. Through the given mathematical formula Weighted calculations are performed to obtain the corrected microscopic surface adhesion energy. Furthermore, by establishing a multi-scale model that combines macroscopic temperature field information with microscopic liquid bridge formation mechanisms, and using molecular dynamics simulations or mesoscopic simulations, the interaction potential energy of particles under liquid bridge action can be calculated, thereby deriving the microscopic surface adhesion energy. .

[0059] The formula for calculating the critical shear stress of the ash accumulation layer is:

[0060]

[0061] In the formula, Indicates the critical shear stress; This indicates the real-time dynamic porosity of the ash accumulation layer in the catalyst channels; Indicates the bonding energy of microscopic surfaces; This represents the median particle size of the flue gas fly ash. Calculating the critical shear stress of the ash layer is one of the ultimate goals of the entire calculation process; it determines the minimum shear force required to blow the ash layer away from the catalyst surface. Critical shear stress It is a key parameter in powder mechanics, directly guiding the setting of the sootblowing media injection intensity. Accurate calculation of the critical shear stress is crucial. This is key to achieving adaptive soot blowing. This solution can convert the previously calculated real-time dynamic porosity... Microscopic surface adhesion energy and the median particle size of flue gas fly ash obtained in advance Substitute into the critical shear stress calculation formula commonly used in powder mechanics models. The solution is then performed. Alternatively, a more complex nonlinear regression model can be established by combining experimental data, such as conducting shear tests on dust layers with different porosities and bonding energies under laboratory conditions, and using the aforementioned parameters as inputs to predict the critical shear stress. .

[0062] The multi-parameter coupled solution module of this application aims to accurately obtain the critical shear stress of the ash accumulation layer through a series of interactive calculations, thereby providing accurate dynamic pressure command values ​​for adaptive soot blowing. The operating logic of this module is as follows: First, based on the environmental aerosol phase transition theory, the absolute temperature of the acid dew point is calculated using real-time flue gas composition data collected by the multi-physics data acquisition module. and the local lowest absolute temperature in the space temperature array data The equivalent value of the phase change liquid bridge was calculated. This step quantifies the enhancing effect of ammonium bisulfate condensation forming liquid bridges on fly ash adhesion in local low-temperature regions, solving the problem of inaccurate adhesion assessment caused by neglecting the influence of phase change in traditional methods. Secondly, this module utilizes the dynamic blockage high-frequency fluctuation energy provided by the time-flow field feature extraction module. Combined with the initial inherent porosity of the catalyst matrix and pre-fitted dynamic compression eigenvalues The real-time dynamic porosity of the ash layer was inverted. This method can reflect the porosity changes caused by dynamic bridging of fly ash within the channels in real time, overcoming the errors introduced by fixed porosity parameters and making the description of the physical state of the ash layer more accurate. Next, the calculated equivalent value of the phase change liquid bridge is... The effective temperature gradient standard deviation provided by the space thermal field feature extraction module, taking into account the acid dew point approximation effect. Combined, and based on the intrinsic surface energy of dry fly ash particles. Based on this, the microscopic surface adhesion energy of fly ash particle groups after adhesive modification was calculated. This step comprehensively considers the dual effects of ammonium bisulfate liquid bridging and local temperature field distortion on fly ash adhesion, ensuring an accurate assessment of actual adhesion and avoiding the risk of underestimating the adhesion of fly ash. Finally, the aforementioned real-time dynamic porosity... Microscopic surface adhesion energy And the median particle size of flue gas fly ash obtained by the multiphysics data acquisition module Substituting the values ​​into the powder mechanics model, the critical shear stress of the ash accumulation layer was finally calculated. The entire calculation process forms a logically rigorous chain, comprehensively considering various factors affecting ash adhesion from multiple dimensions and physical field perspectives, including thermal field distortion, phase change liquid bridge formation, and porosity changes caused by dynamic blockage, thus ensuring the obtained critical shear stress... This method accurately reflects the minimum shear force required to remove the current ash layer. This refined calculation significantly improves the accuracy of ash accumulation perception, providing a solid foundation for generating adaptive soot blowing fluid dynamic pressure command values. This effectively avoids the problems of low soot blowing efficiency or excessive catalyst wear caused by inaccurate assessment of critical shear stress in traditional soot blowing schemes.

[0063] As a specific implementation method, the multi-parameter coupled solution module can be configured as an industrial control computer or embedded processor, running a pre-programmed algorithm. The multi-physics data acquisition module acquires real-time spatial temperature array data of the SCR reactor inlet section, real-time differential pressure signals at the catalyst layer inlet and outlet, and real-time flue gas composition data, and obtains the median particle size of the fly ash in the flue gas. ( ), cumulative service life of catalyst and the preset catalyst surface roughness reference attenuation constant Then, this data is transmitted to the multi-parameter coupled solution module via the data bus. For example, the module first receives the absolute temperature of the acid dew point calculated from the real-time flue gas composition data. (For example, calculating the local lowest absolute temperature from SO2 and H2O concentrations obtained using an infrared gas analyzer) and space temperature array data. (For example, acquired via a thermocouple array or infrared thermal imager). Subsequently, the module calls upon a preset reference capillary constant for the ammonium bisulfate solution. And according to the formula Calculate the equivalent value of the phase change fluid bridge. Simultaneously, this module receives the dynamic blockage high-frequency fluctuation energy calculated by the time-flow field feature extraction module. Combined with dynamic compression feature values ​​obtained in advance through long-term running data. and the initial intrinsic porosity of the catalyst matrix The module will use the formula Calculate the real-time dynamic porosity of the ash accumulation layer Next, the module receives the standard deviation of the effective temperature gradient, taking into account the acid dew point approximation effect, calculated by the spatial thermal field feature extraction module. Combined with the calculated equivalent value of the phase change fluid bridge Inherent surface energy reference value of dry fly ash particles and the reference temperature gradient standard deviation benchmark value The module will use the formula The microscopic surface bonding energy of fly ash particle groups after adhesive modification was calculated. Finally, the module will use the real-time dynamic porosity calculated above. Microscopic surface adhesion energy The median particle size of flue gas fly ash provided by the multiphysics data acquisition module. Substitute into the powder mechanics model formula to calculate Thus, the critical shear stress of the ash accumulation layer can be finally calculated. All these calculation results will serve as input for subsequent steps, such as calculating the adaptive sootblowing fluid dynamic pressure command value.

[0064] Through the above technical solution, the multi-parameter coupled solution module of this application can combine various operating parameters collected on-site to quantify, step by step, and accurately, various factors affecting the adhesion of fly ash, thereby obtaining the critical shear stress actually required by the current fly ash layer. Specifically, by calculating the equivalent value of phase change liquid bridge based on the environmental aerosol phase change theory, this solution can quantify the degree of local low temperature inducing ammonium bisulfate condensation to form a liquid bridge into a specific parameter, effectively avoiding the calculation deviation caused by ignoring the influence of phase change, making the assessment of fly ash adhesion more consistent with the actual characteristics of phase change fly ash in high-temperature, high-dust flue gas. At the same time, by using the high-frequency fluctuation energy of dynamic blockage to invert the real-time dynamic porosity of the fly ash layer, it can reflect the porosity change of the fly ash layer caused by the gradual deposition and bridging of fly ash in real time, correcting the calculation error caused by the fixed porosity parameter, making the porosity assessment more consistent with the actual state of the current fly ash layer. Furthermore, by combining the equivalent value of the phase change fluid bridge with the standard deviation of the effective temperature gradient to calculate the microscopic surface adhesion energy, this scheme can accurately quantify the increase in adhesion energy of the adhesive ash due to temperature field distortion and the phase change of ammonium bisulfate. This ensures an accurate grasp of the actual ash adhesion characteristics and avoids underestimating the actual adhesion force of the adhesive ash. Finally, the critical shear stress calculated by integrating these real-time parameters accurately reflects the minimum shear force required to blow the current ash layer off the catalyst surface. This provides a reliable basis for subsequent calculation of accurate adaptive soot blowing fluid dynamic pressure command values, fundamentally solving the problem of inaccurate calculation of critical shear stress in traditional schemes leading to inappropriate soot blowing pressure. It effectively avoids the dilemma of insufficient soot blowing pressure causing the adhesive ash to be unable to be blown away, or excessive soot blowing pressure causing catalyst wear and media waste.

[0065] This application further proposes a multi-parameter coupled solution module that, when calculating the adaptive sootblowing fluid dynamic pressure command value, introduces a momentum transfer attenuation rate characterizing equipment aging and a crossflow deflection attenuation factor characterizing flow field disturbance. The momentum transfer attenuation rate is set as a function that decays exponentially with the cumulative service time of the catalyst, and its time decay rate is uniquely determined by a preset catalyst surface roughness reference attenuation constant. The crossflow deflection attenuation factor is calculated based on the vector summation ratio of the flue gas velocity upstream of the catalyst layer and the rated design jet velocity of the sootblowing medium at the nozzle outlet. The final calculation logic for the adaptive sootblowing fluid dynamic pressure command value is as follows: based on the attenuation law of free turbulent jet dynamics, the adaptive sootblowing fluid dynamic pressure command value is set to be proportional to the critical shear stress, and simultaneously set to be inversely proportional to the momentum transfer attenuation rate, the square term of the geometric diffusion factor of the sootblowing device, and the crossflow deflection attenuation factor. The formula for calculating the momentum transfer attenuation rate is:

[0066]

[0067] In the formula, Indicates the rate of momentum transfer decay; Indicates the reference shear coefficient under the initial operating conditions; This represents the reference attenuation constant for catalyst surface roughness, pre-calibrated by the factory bench wear test. Indicates the cumulative service life of the catalyst; This represents the geometric angle between the centerline of the sootblowing nozzle jet and the axis of the catalyst channel; momentum transfer attenuation rate. This parameter characterizes the degree to which the efficiency of the sootblowing jet in transferring momentum to the ash accumulation layer varies with time and geometric angle. Its function is to correct the sootblowing dynamic pressure, enabling it to effectively overcome the adhesion force of the ash accumulation layer. This attenuation rate can be calculated using a pre-established mathematical model or obtained by fitting experimental data. The reference shear coefficient under initial commissioning conditions... This is a baseline value for the momentum transfer efficiency between the soot jet and the catalyst surface in a brand-new state, serving as the starting reference point for calculating the momentum transfer attenuation rate. This coefficient can be calibrated through bench tests at the time of catalyst delivery or obtained through theoretical calculations combined with material properties. The catalyst surface roughness reference attenuation constant is pre-calibrated by the factory bench wear test. The rate at which catalyst surface roughness decreases with service time was quantified. Its function is to reflect the impact of surface wear on momentum transfer efficiency during long-term catalyst service. This constant can be obtained by performing simulated wear tests before catalyst production or shipment, recording the relationship between surface roughness change and time, and fitting the data; or by establishing a decay model through surface morphology analysis of catalyst samples with different service times. The specific testing method for the factory bench wear test is a conventional cold-state accelerated sand milling test: in a simulated wind tunnel bench, a brand-new catalyst sample is exposed to an accelerated wear airflow containing a set concentration of standard quartz sand, and the micro-roughness morphology of the catalyst surface is measured periodically using a laser confocal microscope; the surface roughness is then expressed as a function of sandblasting time (which is equivalent to actual service time). Substituting the decay curve of the function into the negative exponential function for regression calculation, the decay constant is determined. The reference shear coefficient under the initial commissioning state. This is determined by spraying a standard rated sootblowing jet in a clean, cold test bench, measuring the actual shear force on the catalyst wall using a grid of micro-pressure sensors, and calculating its ratio to the rated design dynamic pressure at the nozzle outlet. The cumulative service time of the catalyst is also calibrated. This is the total operating time of the catalyst from its initial use to the present moment. It serves as the time variable for calculating the momentum transfer decay rate, reflecting the degree of catalyst aging. This time can be accumulated through system operation records or obtained in real time through a catalyst life monitoring system. The geometric angle between the centerline of the sootblowing nozzle jet and the axis of the catalyst channel. This is the angle between the direction of the sootblowing medium injection and the direction of the catalyst channels, reflecting the influence of the relative geometric relationship between the sootblowing jet and the catalyst channels on momentum transfer efficiency. This angle can be determined by the design parameters of the sootblowing system or obtained through on-site installation and measurement.

[0068] The formula for calculating the adaptive sootblowing fluid dynamic pressure command value is:

[0069]

[0070] In the formula, This indicates the adaptive sootblowing fluid dynamic pressure command value; This represents the calculated critical shear stress; This represents the calculated momentum transfer attenuation rate; This indicates the initial design diameter of the soot blowing nozzle; This indicates the designed axial distance from the sootblowing gun to the catalyst surface; This indicates the flue gas velocity upstream of the catalyst layer; This indicates the rated design jet velocity of the sootblowing medium at the nozzle exit. Adaptive sootblowing fluid dynamic pressure command value. This is the optimal target value for the dynamic pressure of the blowing medium, calculated by the system based on the current ash accumulation state and catalyst characteristics. Its function is to guide the fluid regulating valve in adjusting the blowing intensity to achieve efficient and low-loss cleaning. This command value can be calculated by a multi-parameter coupled solution module according to the above formula, or obtained by interpolation using a preset lookup table combined with real-time operating parameters. The calculated critical shear stress... This is the minimum shear stress required to remove the accumulated ash layer, serving as the target for calculating the soot blowing dynamic pressure command value, ensuring that the soot blowing intensity is sufficient to remove the ash. This stress is calculated by a multi-parameter coupled solution module based on a powder mechanics model, or by experimentally measuring the critical shear stress under different ash accumulation conditions and establishing a model. The initial diameter of the soot blowing nozzle is also considered. This is the standard outlet diameter of the sootblowing nozzle during design, serving as a geometric parameter in free jet attenuation theory, affecting jet diffusion and momentum decay. This diameter is provided by the sootblowing nozzle manufacturer or obtained through actual measurement. The design axial distance from the sootblowing gun to the catalyst surface. This is the straight-line distance from the outlet of the sootblowing nozzle to the surface of the catalyst layer. As a propagation distance parameter in the free jet attenuation theory, it affects the momentum attenuation of the jet. This distance is determined by the sootblowing system installation design or obtained through on-site measurement. The flue gas velocity upstream of the catalyst layer... This is the average velocity of the flue gas flowing upstream of the catalyst layer, used as an input to the crossflow deflection attenuation factor, reflecting the influence of flue gas crossflow on the sootblowing jet. This velocity can be measured in real time by a flow velocity sensor inside the flue, or calculated using a flue gas flow meter combined with the flue cross-sectional area. The rated design jet velocity of the sootblowing medium at the nozzle outlet. This is the theoretical maximum velocity of the sootblowing medium at the nozzle exit. It serves as the input to the crossflow deflection attenuation factor and reflects the kinetic energy of the sootblowing jet itself. This velocity is determined by the design parameters of the sootblowing system or obtained through nozzle performance testing.

[0071] The proposed solution utilizes a multi-parameter coupled solution module to fully consider the attenuation of catalyst surface roughness during long-term service, the influence of the geometric angle between the jet centerline of the sootblowing nozzle and the catalyst channel axis on momentum transfer efficiency, and the attenuation effect of free turbulent jet dynamics and flue gas crossflow on the deflection of the sootblowing jet when calculating the adaptive sootblowing fluid dynamic pressure command value. Specifically, firstly, a reference attenuation constant for catalyst surface roughness is introduced. and the cumulative service life of catalysts Combined with the reference shear coefficient under the initial commissioning state The momentum transfer decay rate over time was calculated. This decay rate was also determined by the geometric angle between the jet centerline of the sootblowing nozzle and the catalyst channel axis. Corrections are made to accurately reflect the actual efficiency of the catalyst in transferring soot jet flow under different service stages and soot blowing angles. Secondly, based on the attenuation law of free turbulent jet dynamics and combined with the crossflow deflection attenuation factor derived from velocity vector synthesis, the adaptive soot blowing fluid dynamic pressure command value is calculated in reverse. This calculation process considers not only the initial design diameter of the sootblowing nozzle. and the designed axial distance from the blow gun to the catalyst surface The effect on jet attenuation is also influenced by the flue gas velocity upstream of the catalyst layer. and the rated design jet velocity of the blowing medium at the nozzle outlet The effects of flue gas crossflow on the deflection and dynamic pressure attenuation of the sootblowing jet were quantified. Finally, the modified momentum transfer attenuation rate and crossflow deflection attenuation factor were combined with the critical shear stress of the ash layer obtained by the multi-parameter coupled solution module based on the powder mechanics model. By combining these methods, an adaptive sootblowing fluid dynamic pressure command value that can accurately match the current ash accumulation state and catalyst characteristics is derived in reverse. This series of calculation steps are organically combined, enabling the sootblowing dynamic pressure command value to dynamically adapt to complex operating conditions, ensuring that the sootblowing intensity can effectively remove ash accumulation while minimizing catalyst wear and media waste.

[0072] As a specific implementation, the multi-parameter coupled solution module can be implemented by a high-performance industrial controller (e.g., a Siemens S7-1500 series PLC or a Rockwell ControlLogix series PAC). This controller has a built-in floating-point unit, enabling it to efficiently perform complex mathematical operations. When calculating the momentum transfer attenuation rate, the reference shear coefficient under the initial commissioning state is used. The value can be preset to 0.85, obtained through standard bench testing before the catalyst leaves the factory. The catalyst surface roughness is referenced to the attenuation constant. The cumulative service time of the catalyst can be obtained through data fitting based on the wear curve provided by the catalyst supplier, for example, by setting it to 0.0001 / hour. The cumulative service time is accumulated by an internal timer from the initial operation of the catalyst, for example, 10,000 hours. The geometric angle between the centerline of the sootblowing nozzle jet and the axis of the catalyst channel is also recorded. The installation design drawings for the sootblowing gun can be used to determine the method; for example, for vertical spraying, It is 0 degrees; for tilt spray, The value is 15 degrees. The critical shear stress calculated during the reverse calculation of the adaptive sootblowing fluid dynamic pressure command value is... The initial diameter of the sootblowing nozzle is calculated by a multi-parameter coupled solution module based on real-time collected flue gas data and catalyst parameters. The design axial distance from the sootblower to the catalyst surface can be set to 10 mm. It can be set to 500 mm. The flue gas velocity upstream of the catalyst layer. Real-time measurements are taken using a Pitot tube or ultrasonic flow meter installed inside the flue and transmitted to the controller, for example... The rated design jet velocity of the blowing medium at the nozzle outlet. The pressure of the blowing medium (such as steam or compressed air) and the nozzle characteristic curve can be determined, for example, by setting it to 200 m / s. The controller substitutes all these parameters into the formula above. , , , It can calculate the precise adaptive sootblowing fluid dynamic pressure command value in real time.

[0073] Through the above technical solution, this application can accurately calculate the dynamic pressure command value of the soot blowing fluid, which is adapted to the current ash accumulation state and catalyst characteristics. This solution fully considers the impact of the decrease in surface roughness on momentum transfer efficiency during long-term catalyst service, allowing the soot blowing intensity to be reasonably adjusted as the catalyst ages, avoiding insufficient soot blowing due to decreased momentum transfer efficiency caused by catalyst wear. Simultaneously, by introducing the geometric angle between the soot blowing nozzle jet centerline and the catalyst channel axis, the calculation of the momentum transfer attenuation rate is made closer to the actual installation conditions, improving the accuracy of the soot blowing effect. Furthermore, based on the free turbulent jet dynamics attenuation law and combined with the crossflow deflection attenuation factor, the soot blowing dynamic pressure command value is calculated inversely, effectively compensating for the natural attenuation of the soot blowing jet during propagation, as well as the deflection and dynamic pressure loss caused by the crossflow of flue gas, ensuring that the actual dynamic pressure reaching the ash accumulation layer can accurately match the critical shear stress required for ash removal. This fundamentally solves the engineering dilemma of traditional soot blowing systems, which suffer from "insufficient pressure to blow away adhering soot" and "excessive pressure to severely wear down the catalyst and waste media" due to the mismatch between the soot blowing kinetic energy and the actual working conditions. It achieves precise adaptive matching of soot blowing intensity, significantly improves soot removal efficiency, extends catalyst lifespan, and reduces operating costs.

[0074] This application further proposes a closed-loop adaptive execution module including: a programmable logic controller and a fluid regulating valve driven by the controller; the fluid regulating valve is one of a soot blowing steam fluid regulating valve or a compressed air fluid regulating valve; the programmable logic controller receives the adaptive soot blowing fluid dynamic pressure command value as a set value, and adjusts the valve opening in real time according to the pneumatic transfer function of the soot blowing pipeline system, so that the dynamic pressure head at the nozzle outlet approaches the adaptive soot blowing fluid dynamic pressure command value.

[0075] A programmable logic controller (PLC) is a digital electronic system designed specifically for industrial environments. Internally, it uses a programmable memory to store instructions for performing logical operations, sequential control, timing, counting, and arithmetic operations. It controls various types of machinery or production processes through digital or analog input / output. In soot blowing systems, the PLC can serve as the core control unit, receiving adaptive soot blowing fluid dynamic pressure commands from a multi-parameter coupled solver module. Based on a preset control algorithm and the pneumatic transfer function of the piping system, it outputs control signals to adjust the opening of fluid regulating valves. Implementation methods can include, but are not limited to, using industrial-grade PLCs such as Siemens S7 series, Rockwell ControlLogix series, or Mitsubishi Q series. These controllers feature high reliability, strong anti-interference capabilities, and flexible programming, and can adapt to high-temperature and high-dust industrial environments.

[0076] Fluid regulating valves are actuators used to control parameters such as flow rate and pressure of fluids (e.g., steam or compressed air). In sootblowing systems, they directly determine the injection intensity of the sootblowing medium. These valves typically consist of a valve body, valve core, valve seat, and actuator. They adjust the flow area between the valve core and valve seat by receiving signals from a programmable logic controller (PLC), thereby regulating the flow rate and outlet pressure of the medium. Implementation methods include, but are not limited to: using an electric regulating valve, where a motor drives the valve stem to change the opening; or using a pneumatic regulating valve, where air pressure drives the actuator to change the opening. These valves typically possess high-precision regulation capabilities and rapid response characteristics to ensure accurate control of the sootblowing dynamic pressure. Fluid regulating valves are specifically configured according to the type of sootblowing medium used. Sootblowing steam fluid regulating valves are specifically designed to control the flow rate and pressure of high-temperature, high-pressure steam, and typically require characteristics such as high temperature resistance, corrosion resistance, and good sealing performance. Compressed air fluid regulating valves are used to control the flow rate and pressure of compressed air, and typically require fast response and high control precision. These two types of valves may differ in structure and material selection, but their core function is to achieve precise adjustment of the intensity of soot blowing media injection.

[0077] The programmable logic controller (PLC) receives an adaptive sootblowing fluid dynamic pressure command value as a setpoint; this technical feature describes the input stage of the control system. As the control core, the PLC's control objective is to ensure that the actual sootblowing dynamic pressure reaches the adaptive sootblowing fluid dynamic pressure command value calculated by a multi-parameter coupled solution module. Therefore, this command value is set as the controller's target value or setpoint. The controller continuously monitors the actual output (e.g., the dynamic head at the nozzle outlet) and compares it with this setpoint, generating a deviation signal to drive the adjustment action. The controller's core adjustment logic is described by adjusting the valve opening in real time according to the pneumatic transfer function of the sootblowing pipeline system. The sootblowing pipeline system exhibits complex hydrodynamic characteristics from the fluid regulating valve to the nozzle outlet, including friction resistance, local resistance, and fluid inertia. These characteristics can be described by a pneumatic transfer function. The PLC utilizes this transfer function to predict the dynamic head that may be generated at the nozzle outlet at a given valve opening, or to calculate the valve opening required to achieve the target dynamic head. Through real-time monitoring and calculation, the controller can dynamically adjust the valve opening to compensate for the influence of pipeline characteristics, ensuring that the actual dynamic head at the nozzle outlet accurately approximates the command value. This technical feature, ensuring the dynamic head at the nozzle outlet closely approximates the adaptive sootblowing fluid dynamic pressure command value, is the ultimate control objective of the entire closed-loop adaptive execution module. Dynamic head refers to the pressure generated by the fluid during its movement; it directly reflects the impact force of the sootblowing medium on the ash layer. By precisely controlling the opening of the fluid regulating valve, the actual dynamic head at the nozzle outlet is kept consistent with or very close to the adaptive sootblowing fluid dynamic pressure command value, thereby ensuring that the sootblowing intensity accurately matches the current ash adhesion characteristics and blockage degree, achieving efficient and non-destructive cleaning.

[0078] In this application, to achieve precise adaptive adjustment of the sootblowing medium injection intensity, a closed-loop adaptive execution module is designed as a feedback control system with a programmable logic controller (PLC) at its core. This module receives the adaptive sootblowing fluid dynamic pressure command value output from a multi-parameter coupled solver module and uses it as the set target for the control system. Based on this set value and an understanding of the inherent aerodynamic transfer function of the sootblowing pipeline system, the PLC calculates and adjusts the opening of the fluid regulating valve in real time. The fluid regulating valve, acting as the actuator, precisely controls the flow rate and pressure of the sootblowing medium (steam or compressed air) according to the controller's instructions, thereby directly affecting the dynamic head at the nozzle outlet. The entire process forms a closed loop; the controller continuously monitors or calculates the actual dynamic head at the nozzle outlet and compares it with the command value. By continuously correcting the valve opening, deviations are eliminated, ultimately ensuring that the dynamic head at the nozzle outlet accurately approximates the adaptive sootblowing fluid dynamic pressure command value. This design cleverly solves the problem of lack of refined control at the execution level in traditional sootblowing systems. By introducing a PLC, stable and reliable operation in industrial settings is achieved, and the system can flexibly adapt to the needs of different sootblowing media (steam or compressed air). More importantly, this solution fully considers the fluid dynamics characteristics of the sootblowing pipeline system itself, namely the aerodynamic transfer function. In actual operation, factors such as pipeline length, bends, and valves all affect fluid pressure and flow rate, resulting in a non-linear relationship between valve opening and nozzle outlet dynamic pressure. By incorporating the aerodynamic transfer function into the control algorithm, the programmable logic controller (PLC) can predict and compensate for the effects of these pipeline characteristics, thereby more accurately predicting and achieving the target dynamic head at the nozzle outlet when adjusting the valve opening. This enables the entire sootblowing system to truly transform the refined adaptive sootblowing fluid dynamic pressure command value obtained from upstream multiphysics data acquisition, spatial thermal field feature extraction, temporal flow field feature extraction, and multi-parameter coupled solution into a precise physical impact force at the nozzle outlet, ensuring a high degree of matching between sootblowing intensity and ash accumulation characteristics, and avoiding problems such as insufficient soot removal or excessive catalyst wear.

[0079] The following is a concrete example. In a typical SCR denitrification unit, the closed-loop adaptive execution module can be implemented as follows: A Siemens S7-1500 series PLC can be selected as the programmable logic controller (PLC). This series of PLCs has powerful processing capabilities and rich communication interfaces, enabling stable operation in industrial environments. The fluid regulating valve can be a DN50 electric regulating ball valve, whose actuator is controlled by the PLC via a 4-20mA analog signal to achieve 0-100% opening adjustment. If the soot blowing medium is steam, a high-temperature and high-pressure resistant stainless steel valve body and valve core are selected; if the soot blowing medium is compressed air, carbon steel or cast iron can be used. In actual operation, the multi-parameter coupled solution module calculates an adaptive soot blowing fluid dynamic pressure command value, for example, requiring the nozzle outlet dynamic head to reach 1.5 bar. This command value is sent to the Siemens S7-1500 PLC via an industrial Ethernet (such as Profinet) as the setpoint for its PID control loop. The PLC pre-stores the pneumatic transfer function model of the sootblowing pipeline system, which can be obtained through field experimental calibration or fluid simulation. For example, this transfer function might be a nonlinear function describing the relationship between valve opening and nozzle outlet dynamic head, taking into account factors such as pipeline length, number of bends, and medium viscosity. Based on the current command value of 1.5 bar, the PLC uses the pneumatic transfer function to calculate the required valve opening, for example, determining that the valve opening needs to be adjusted to 75%. Subsequently, the PLC outputs a corresponding 4-20mA signal to the actuator of the electrically controlled ball valve, driving the valve opening to 75%. Simultaneously, the PLC can receive feedback signals from pressure sensors near the nozzle (if configured) or predict the actual dynamic head at the nozzle outlet using the model and compare it with the 1.5 bar setpoint. If a deviation exists, the PLC will fine-tune the valve opening in real time using a PID algorithm until the dynamic head at the nozzle outlet stabilizes around 1.5 bar, thus achieving precise approximation of the adaptive sootblowing fluid dynamic pressure command value.

[0080] Through the above technical solution, this application clarifies the specific composition and control logic of the closed-loop adaptive execution module, enabling the previously calculated adaptive sootblowing fluid dynamic pressure command value to be truly implemented. This solution, using a programmable logic controller (PLC) as its core, ensures the stability and reliability of the control system, allowing it to adapt to harsh industrial environments. Simultaneously, the fluid regulating valve can be selected according to the type of sootblowing medium (steam or compressed air), improving the system's versatility and applicability. More importantly, this solution uses the adaptive sootblowing fluid dynamic pressure command value as the control setpoint and, combined with the pneumatic transfer function of the sootblowing pipeline system, adjusts the valve opening in real time, effectively compensating for the influence of pipeline characteristics on the sootblowing dynamic pressure. This achieves precise approximation of the command value to the dynamic pressure head at the nozzle outlet. This ensures that the actual sootblowing intensity accurately matches the current ash adhesion characteristics and blockage degree, avoiding problems such as insufficient cleaning, excessive catalyst wear, and waste of sootblowing medium caused by pressure mismatch in traditional sootblowing systems, significantly improving the operating efficiency and economy of the SCR denitrification system.

[0081] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-temperature, high-dust flue gas adaptive soot blower, characterized in that, include: The multi-physics data acquisition module is used to acquire in real time the spatial temperature array data of the inlet section of the SCR reactor, the real-time differential pressure signal of the catalyst layer inlet and outlet, and the real-time flue gas composition data, and to obtain the median particle size of the flue gas fly ash, the cumulative service time of the catalyst, and the preset catalyst surface roughness reference decay constant. The spatial thermal field feature extraction module is used to calculate the standard deviation of the effective temperature gradient, taking into account the local acid dew point approximation effect, based on spatial temperature array data and real-time flue gas composition data, in order to quantify the risk of adhesive ash formation. The time-flow field feature extraction module is used to extract transient high-frequency disturbances in real-time differential pressure signals and calculate the dynamic blockage high-frequency fluctuation energy that characterizes the dynamic bridging characteristics of fly ash in the duct. The multi-parameter coupled solution module is used to use the effective temperature gradient standard deviation and dynamic blockage high-frequency fluctuation energy as interactive control variables, and combine the powder mechanics model to invert the critical shear stress of the ash layer; and combine the fluid free jet attenuation theory, cumulative service time and surface roughness reference attenuation constant to solve and output the adaptive soot blowing fluid dynamic pressure command value. The closed-loop adaptive execution module is used to drive the fluid regulating valve to automatically adjust the intensity of media injection based on the adaptive soot blowing fluid dynamic pressure command value.

2. The high-temperature, high-dust flue gas adaptive soot blower according to claim 1, characterized in that, In the spatial thermal field feature extraction module, the calculation logic for the effective temperature gradient standard deviation is as follows: Using the degree to which the local actual temperature approaches the absolute temperature of the acid dew point as the driving potential, a nonlinear exponential amplification weight is constructed. The local spatial temperature gradient of each measuring point in the spatial temperature array data is multiplied by the nonlinear exponential amplification weight, and the spatial dispersion between it and the weighted average temperature gradient is calculated, thereby obtaining the quantized effective temperature gradient standard deviation.

3. The high-temperature, high-dust adaptive soot blower according to claim 1, characterized in that, In the time-flow field feature extraction module, the calculation logic for the high-frequency fluctuation energy of dynamic blockage is as follows: The real-time differential pressure signal is filtered and separated to extract the transient high-frequency differential pressure signal; The square integral of the deviation between the transient high-frequency differential pressure signal and the average macroscopic differential pressure within a set time window is calculated, and the dimension is normalized using the reference standard atmospheric pressure difference to obtain the dynamic blockage high-frequency fluctuation energy.

4. The high-temperature, high-dust adaptive soot blower according to claim 1, characterized in that, The multi-parameter coupled solution module is configured to perform interactive inversion of microscopic ash accumulation characteristics, specifically including: Based on the environmental aerosol phase change theory, the temperature difference between the local minimum absolute temperature and the acid dew point absolute temperature is used as the independent variable of supercooling, and a positively correlated exponential function is constructed to obtain the equivalent value of the phase change liquid bridge. A dynamic compression characteristic value generated by fitting long-cycle macro differential pressure data is introduced, and an exponential decay model is established with the high-frequency fluctuation energy of dynamic blockage as the decay independent variable to invert and calculate the real-time dynamic porosity of the ash accumulation layer.

5. The high-temperature, high-dust flue gas adaptive soot blower according to claim 4, characterized in that, The multi-parameter coupled solution module is also configured to calculate the critical shear stress of the ash accumulation layer, specifically including: The intrinsic surface energy benchmark value of dry fly ash particles is linearly amplified and compensated by the product of the phase change liquid bridge equivalent value and the standard deviation of the effective temperature gradient, and the microscopic surface adhesion energy after being modified by liquid film adhesion is calculated. Based on the fracture mechanics model of powder structure, the critical shear stress is set to be directly proportional to the microscopic surface bonding energy, and inversely proportional to the apparent volume parameters of the median particle size and real-time dynamic porosity of flue gas fly ash.

6. The high-temperature, high-dust adaptive soot blower according to claim 5, characterized in that, When solving the adaptive soot blowing fluid dynamic pressure command value, the multi-parameter coupled solution module introduces the momentum transfer attenuation rate, which characterizes equipment aging, and the crossflow deflection attenuation factor, which characterizes flow field disturbance. The momentum transfer attenuation rate is set as a function that decays exponentially with the cumulative service time of the catalyst, and its time attenuation rate is uniquely determined by a preset catalyst surface roughness reference attenuation constant; the crossflow deflection attenuation factor is calculated based on the vector summation ratio of the flue gas velocity upstream of the catalyst layer and the rated design jet velocity of the soot blowing medium at the nozzle outlet.

7. The high-temperature, high-dust flue gas adaptive soot blower according to claim 6, characterized in that, The final calculation logic for the adaptive sootblowing fluid dynamic pressure command value is as follows: Based on the attenuation law of free turbulent jet dynamics, the adaptive soot blowing fluid dynamic pressure command value is set to be proportional to the critical shear stress, while it is set to be inversely proportional to the momentum transfer attenuation rate, the square term of the geometric diffusion factor of the soot blowing device, and the crossflow deflection attenuation factor.

8. The high-temperature, high-dust flue gas adaptive soot blower according to claim 1, characterized in that, The closed-loop adaptive execution module includes: A programmable logic controller and a fluid regulating valve driven by the controller; the fluid regulating valve is one of a soot blowing steam fluid regulating valve or a compressed air fluid regulating valve; The programmable logic controller receives the adaptive soot blowing fluid dynamic pressure command value as a set value, and adjusts the valve opening in real time according to the pneumatic transfer function of the soot blowing pipeline system, so that the dynamic pressure head at the nozzle outlet approaches the adaptive soot blowing fluid dynamic pressure command value.

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