A method for controlling nitrogen oxide emissions by efficiently optimizing a pnrc process

Abstract

The present application relates to a kind of high-efficiency optimization PNCR process nitrogen oxide emission control method, belong to atmospheric pollution control and industrial monitoring scheduling software field.The method is by real-time collection hearth flame image through industrial flame camera, combines with radiation heat transfer and spectral analysis algorithm to retrieve three-dimensional temperature field;Temperature field and combustion condition parameter are input embedded lightweight computational fluid dynamics model, predict high concentration area of nitrogen oxide;Based on the dynamic evolution trend in this area, real-time calculation and issue optimal reducing agent injection strategy, drive spray gun array millisecond level adjustment injection angle, pressure and timing, realize target denitration.The present application improves denitration efficiency by the above technical scheme, reduces ammonia escape and reducing agent consumption, and maintains stable control performance under non-steady state condition, while relying on hearth digital twin and self-optimization mechanism to enhance system long-term adaptability.

Description

Technical Field

[0001] This invention belongs to the field of air pollution control and industrial monitoring and scheduling software, specifically relating to a method for efficiently optimizing nitrogen oxide emission control in PNCR processes. Background Technology

[0002] With increasingly stringent environmental standards in industrial production, nitrogen oxide emission control technology is playing a crucial role in thermal energy engineering fields such as boilers and incinerators. Polymer reduction and denitrification (PNCR) technology, as a highly efficient non-catalytic reduction method, is widely used in flue gas treatment processes of various combustion equipment due to its advantages such as simple equipment, small footprint, and high denitrification efficiency. By injecting polymeric reducing agents into specific temperature zones of the furnace, the nitrogen oxide content in the flue gas can be reduced, meeting stringent environmental emission requirements.

[0003] The denitrification effect of the PNCR process highly depends on the thorough mixing of the reducing agent and flue gas within a suitable temperature window and the precise control of the reaction time. The core of this process lies in adjusting the operating parameters of the injection device to ensure the reducing agent precisely covers the high-concentration area of ​​nitrogen oxide generation. Given the extremely complex physicochemical environment inside the furnace, achieving a deep match between the reducing agent injection and the instantaneous dynamic flue gas flow field, and eliminating feedback delays in the control system's detection and execution stages, becomes crucial for improving the response speed and control accuracy of the denitrification system.

[0004] Existing control schemes typically rely solely on the nitrogen oxide concentration at the outlet as a feedback signal, exhibiting thermal lag and failing to cope with real-time, drastic fluctuations in combustion conditions. Traditional control systems lack the ability to perceive the local three-dimensional temperature field and complex flow field within the furnace in real time, resulting in an inability to precisely couple the reductant injection position with the high-concentration nitrogen oxide distribution area. Fixed or simplified adjustment mechanisms cannot capture the nonlinear changes in the flue gas flow trajectory, easily leading to uneven mixing of the reductant and flue gas, reducing denitrification efficiency, and increasing the risk of ammonia escape and material waste.

[0005] Therefore, a highly efficient method for controlling nitrogen oxide emissions from the PNCR process is desired. Summary of the Invention

[0006] The purpose of this invention is to provide a highly efficient and optimized method for controlling nitrogen oxide emissions in the PNCR process, which can solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: a method for efficiently optimizing nitrogen oxide emission control in PNCR process, comprising the following specific steps: Step 1: Real-time acquisition of flame image data inside the furnace using an industrial flame camera. The flame image data includes the flame's color distribution, brightness gradient, and texture structure features. Step 2: Based on the flame image data, the spatial distribution information of the three-dimensional temperature field inside the furnace is derived using radiation heat transfer and spectral analysis algorithms. Step 3: The three-dimensional temperature field information is input into an embedded lightweight computational fluid dynamics model. Combined with current combustion parameters, the flow trajectory of flue gas within the furnace and the high-concentration region of nitrogen oxide generation are predicted. Step 4: Based on the spatial location and dynamic evolution trend of the high-concentration region, the optimal reducing agent injection strategy is calculated in real-time. The injection strategy includes the injection angle, injection pressure, and start-stop sequence of each spray gun. Step 5: The optimal reducing agent injection strategy is sent to the actuator, driving the spray gun array to perform millisecond-level dynamic adjustments, ensuring the reducing agent precisely covers the nitrogen oxide generation region, thus completing the targeted denitrification reaction.

[0008] Preferably, in step 1, the industrial flame camera uses a high-temperature resistant optical lens and a high dynamic range imaging sensor, and is installed in multiple observation windows on the side wall of the furnace to ensure unobstructed continuous imaging of the entire area inside the furnace, and ensures the consistency of multi-view image frames through a time synchronization mechanism.

[0009] Preferably, in step 2, the radiation heat transfer and spectral analysis algorithm constructs the mapping relationship between flame radiation intensity and local temperature based on Planck's blackbody radiation law, combines the chromaticity coordinates and grayscale values ​​of image pixels, inverts the temperature values ​​pixel by pixel, and generates continuous temperature field data through three-dimensional interpolation and smoothing filtering.

[0010] Preferably, in step 3, the embedded lightweight computational fluid dynamics model adopts a simplified Navier-Stokes equations, ignores higher-order nonlinear terms, reduces computational complexity while ensuring the accuracy of flow field prediction, and has a model running cycle of less than 100 milliseconds, enabling real-time response to combustion load fluctuations and fuel composition changes.

[0011] Preferably, the combustion parameters in step 3 include fuel supply rate, primary air to secondary air ratio, furnace negative pressure and feeding rate. These parameters are collected in real time by a distributed sensor network and transmitted to the central processing unit as boundary condition inputs for flow field prediction.

[0012] Preferably, the calculation process of the optimal reducing agent injection strategy in step 4 introduces a multi-objective optimization function, with the comprehensive objective of minimizing the outlet nitrogen oxide concentration, ammonia slip, and reducing agent consumption. The constraint solver quickly searches for a set of control instructions that meet the process safety boundary in the feasible solution space.

[0013] Preferably, in step 5, the spray gun array consists of multiple independently controllable rotating nozzles. Each nozzle is equipped with a servo motor and a proportional adjustment valve, which can independently adjust the spray direction in two degrees of freedom, horizontal and vertical, and dynamically adjust the spray pressure according to the command, so as to achieve dual precise control of spatial directionality and flow rate.

[0014] Preferably, the method further includes establishing a digital twin of the furnace, fusing real-time temperature field and flow field prediction results with historical operating data, and using this data to calibrate computational fluid dynamics model parameters online, thereby improving prediction stability and adaptability under long-term operation.

[0015] Preferably, the digital twin continuously absorbs actual denitrification effect feedback data through an incremental learning mechanism, including outlet pollutant concentration and ammonia escape monitoring values, automatically corrects the temperature-nitrogen oxide generation correlation model, and forms a closed-loop self-optimizing control architecture.

[0016] Preferably, under non-steady-state conditions such as boiler startup, load change, and fuel switching, the method prioritizes the use of a predictive control mode based on historical similar conditions to activate the corresponding spray gun combination in advance, thereby avoiding control failure caused by model convergence delay.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention integrates computer vision and lightweight computational fluid dynamics technology to achieve, for the first time, real-time visual identification and dynamic tracking of the nitrogen oxide generation area in the furnace, breaking through the control lag bottleneck caused by the traditional PNCR system relying solely on outlet concentration feedback.

[0018] 2. This method can detect the spatial distribution of nitrogen oxides in the early stages of their formation and drive the spray gun to perform millisecond-level targeted spraying, improving the contact efficiency between the reducing agent and pollutants. This not only improves the overall denitrification efficiency but also controls ammonia slip to an extremely low level, reducing reducing agent consumption and operating costs.

[0019] 3. Because the system has the ability to actively sense and extrapolate complex flow and temperature fields, it can still maintain stable and efficient control performance under conditions of violent fluctuations in combustion conditions or non-steady-state operation, thus solving the core defects of existing technologies such as slow response and uneven mixing when dealing with dynamic disturbances.

[0020] 4. By constructing a digital twin of the furnace and a self-optimization mechanism, this invention further enhances the long-term adaptability and intelligence level of the system, providing a brand-new technical path for the refined and intelligent control of polymer denitrification process. Attached Figure Description

[0021] Figure 1 The flowchart is based on the present invention; Figure 2This is a schematic diagram of data flow according to the present invention; Figure 3 This is a flowchart illustrating the process of generating a three-dimensional temperature field based on radiation heat transfer and spectral analysis inversion of flame image data according to the present invention, and predicting high-concentration nitrogen oxide regions by combining a lightweight computational fluid dynamics model. Figure 4 This is a flowchart illustrating the calculation of the optimal reducing agent injection strategy based on the multi-objective optimization function of minimizing the outlet nitrogen oxide concentration, ammonia slip, and reducing agent consumption according to the present invention. Figure 5 This is a flowchart illustrating the online calibration and closed-loop self-optimization of model parameters based on the fusion of real-time monitoring data and historical operating data according to the present invention. Detailed Implementation

[0022] Example 1: Please refer to the appendix Figure 1 To be continued Figure 5 To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0023] In the efficient and optimized PNCR (polymer selective non-catalytic reduction) process nitrogen oxide emission control method provided by this invention, the core logic lies in constructing a fully closed-loop intelligent control architecture that encompasses visual perception, physical evolution, and precise execution. This method achieves microscopic dynamic control of the denitrification process by introducing multi-dimensional perception and computational fluid dynamics simulation in the extreme environment of a high-temperature furnace.

[0024] The method first performs step 1: real-time acquisition of flame image data inside the furnace using an industrial flame camera. This industrial flame camera is not a general-purpose monitoring device, but rather an imaging system specifically designed for the high-temperature environment of boilers exceeding 2000 degrees Celsius. The camera integrates a high-temperature resistant optical lens; its lens group is made of high-purity quartz material, coated with an infrared reflective film and a dustproof hard film, effectively filtering infrared interference from heat radiation and preventing fly ash adhesion. The camera is equipped with a high dynamic range imaging sensor, with a dynamic range exceeding 120 decibels, ensuring no overflow in the extremely bright area at the center of the flame and detail in the shadowed areas at the edge of the furnace.

[0025] In step 1, these cameras are strategically installed at multiple observation windows on the furnace sidewalls, forming a stereoscopic visual network covering the combustion zone, burnout zone, and lower superheater area. To ensure image quality, each camera is equipped with an air curtain cooling system, which continuously blows constant-pressure compressed air to the front of the lens, forming a protective air film. The acquired flame image data is not just a simple visual record; it includes the flame's color distribution, brightness gradient, and texture structure features. The color distribution is expressed by converting the acquired red, green, and blue three-channel color spaces into a chromaticity space that better reflects physical properties; the brightness gradient reflects the spatial rate of change of combustion intensity; and the texture structure features are extracted by calculating the image's gray-level co-occurrence matrix, extracting parameters such as energy, moment of inertia, correlation, and entropy to identify the degree of turbulence and stability of combustion. To ensure the accuracy of subsequent 3D reconstruction, the system establishes a strict time synchronization mechanism, sending synchronization pulse signals to all cameras through a central clock trigger to ensure that the deviation of the image frames acquired from multiple perspectives on the time axis is less than 10 milliseconds.

[0026] Step 2 is then executed: Based on the flame image data, the spatial distribution information of the three-dimensional temperature field inside the furnace is retrieved using radiation heat transfer and spectral analysis algorithms. This process is crucial for the transformation from "visualization" to "quantification." The radiation heat transfer algorithm is based on Planck's blackbody radiation law. During algorithm execution, the system first treats each pixel in the image as a tiny radiation detection unit. According to the physical law that the intensity of an object's radiation energy is inversely proportional to the fifth power of its wavelength and related to the result of an exponential function, the interference of emissivity on temperature measurement can be eliminated by performing a ratio calculation on the radiation intensity of specific wavelengths (such as the red light band and the green light band). This calculation logic converts the chromaticity coordinates and grayscale values ​​of image pixels into absolute temperature values.

[0027] Specifically, in step 2, the inversion process is divided into the following sub-steps: A. Establishing a furnace geometric model, dividing it into millions of tiny voxel units, each voxel representing a spatial volume element; B. Based on the projection relationship of multi-angle cameras, using tomographic reconstruction algorithms (such as algebraic reconstruction techniques), mapping the two-dimensional brightness information from each viewpoint back to the three-dimensional voxel space; C. Applying the radiative transfer equation in three-dimensional space, considering the absorption, emission, and scattering characteristics of the flue gas medium, and correcting the temperature values ​​voxel by voxel; D. Smoothing the discrete temperature points using a three-dimensional interpolation algorithm (such as Kriging interpolation) to ensure the spatial continuity of the temperature field. The final generated temperature field volume data not only includes the absolute temperature of each point but also the gradient field of temperature variation with spatial coordinates, which provides the basic physical boundary for subsequent prediction of nitrogen oxide generation.

[0028] Next, step 3 is executed: the three-dimensional temperature field information is input into the embedded lightweight computational fluid dynamics model, and combined with the current combustion parameters, the flow trajectory of flue gas in the furnace and the high-concentration region of nitrogen oxide generation are predicted. The embedded lightweight computational fluid dynamics model is the core derivation engine of this method. To achieve real-time response, the model deeply simplifies the traditional Navier-Stokes equations. Specifically, the model ignores high-order nonlinear fluctuations and extremely small-scale eddy fluctuations in the flow field, focusing on large-scale flow structures that affect the macroscopic flow direction. By employing a pre-integrated Reynolds-averaged method and introducing a modified ordinary differential operator, the computational complexity is reduced by two orders of magnitude.

[0029] In step 3, the model's runtime is strictly limited to within 100 milliseconds. To ensure prediction accuracy, the system collects combustion parameters from a distributed sensor network in real time as boundary conditions. These parameters include, but are not limited to: fuel supply rate (reflecting total heat input), primary and secondary air flow rates and ratios (determining the excess air coefficient and swirl intensity in the furnace), furnace negative pressure (affecting flue gas residence time), and feed rate (affecting the forces acting on solid particles in the flow field). Within the model, flue gas trajectory prediction is based on a coupling of the Lagrange particle tracking method and the Eulerian multiphase flow model. The system calculates the displacement vector of flue gas particles in the three-dimensional velocity field, where the displacement is equal to the product of velocity and time step. Simultaneously, a simplified chemical reaction kinetics module is embedded within the model to address nitrogen oxide formation. This module describes the exponential growth relationship between reaction rate and temperature according to the Arrhenius law. When the local temperature exceeds a specific threshold (e.g., 1400 degrees Celsius), the formation rate of thermal nitrogen oxides increases exponentially with increasing temperature. The model identifies regions in space where the concentration of nitrogen oxides exceeds a preset alarm value by integrating the generation rate within each voxel unit, defines them as high-concentration regions, and locks their three-dimensional spatial coordinates.

[0030] Step 4: Based on the spatial location and dynamic evolution trend of the high-concentration area, the optimal reducing agent injection strategy is calculated in real time. This step represents a leap from "prediction" to "decision-making." The so-called dynamic evolution trend refers to using the Kalman filter algorithm to predict the trajectory of the high-concentration area's location coordinates and determine its movement direction within the next 1 to 2 seconds. The calculation process of the optimal reducing agent injection strategy introduces a complex multi-objective optimization function. The objective of this function is to simultaneously optimize three indicators: first, to minimize the outlet nitrogen oxide concentration to below the national emission standard; second, to minimize the escape of the reducing agent (such as polymer particles or ammonia water) to prevent downstream equipment blockage or corrosion; and third, to minimize the unit consumption of the reducing agent to ensure operational economy. In Step 4, the constraint solver rapidly searches within the feasible solution space. The feasible solution space is defined by the physical characteristics of the spray guns, including the adjustable angle range of each spray gun, the upper limit of the injection pump pressure adjustment, and the minimum start-stop interval of the spray guns. The solver uses a heuristic search algorithm to evaluate thousands of possible control combinations within milliseconds. The final output strategy instruction set includes: the specific horizontal rotation angle value, vertical pitch angle value, opening degree of the proportional control valve (corresponding to the injection pressure) of each spray gun, and a timing table of which spray guns need to be in active state and which are in standby state.

[0031] Finally, step 5 is executed: the optimal reducing agent injection strategy is sent to the actuator, driving the spray gun array to perform millisecond-level dynamic adjustments. The actuator consists of multiple independently controllable rotating nozzles installed around the furnace. Each nozzle integrates a high-precision servo motor, receiving instructions from the central processing unit via a synchronous serial communication protocol. The servo motor drives the nozzle to rapidly deflect in both horizontal and vertical degrees of freedom, achieving a positioning accuracy of 0.1 degrees. Each spray gun pipe is equipped with an electronic proportional regulating valve, which adjusts the pressure and flow rate of the injection medium in real time according to instructions.

[0032] In step 5, as high-concentration nitrogen oxide agglomerates move upwards with the flue gas flow, the system's calculated commands drive the corresponding spray guns to adjust their direction in advance and increase the injection pressure. This allows the reducing agent particles to penetrate the mainstream flue gas under momentum, precisely covering and penetrating the high-concentration nitrogen oxide area. Through this "targeted injection," the reducing agent and nitrogen oxide molecules fully contact and undergo a reduction reaction within the optimal reaction temperature window, converting into nitrogen and water. Because the injection is based on a predicted trajectory, it offsets the lag time of mechanical action and chemical reaction, thus achieving precise suppression of pollutant emissions at the source.

[0033] To further enhance the long-term stability of the system, the method also includes establishing a digital twin of the furnace. This digital twin is a virtual furnace existing in computer memory. It not only mirrors the geometry of the real furnace but also synchronizes all sensor data, image data, and control commands in real time. By fusing the real-time observed temperature field and flow field prediction results with historical operating data stored in a high-speed database, the digital twin can calibrate key parameters of the computational fluid dynamics model online, such as drag coefficient, wall roughness, and heat loss coefficient.

[0034] The digital twin continuously evolves through an incremental learning mechanism. When the system detects abnormal fluctuations in outlet pollutant concentration or ammonia escape, the incremental learning module automatically backtracks to the control records and furnace field distribution data from the past 10 minutes. It employs a gradient-based optimization algorithm to fine-tune the weight parameters in the internal temperature-NOx generation correlation model. This closed-loop self-optimizing control architecture gives the system strong adaptability, enabling it to cope with furnace characteristic drift caused by changes in coal type, burner wear, or slagging on heating surfaces.

[0035] Under unsteady-state conditions such as boiler startup, load changes, and fuel switching, the physical field inside the furnace changes rapidly and exhibits high nonlinearity. In such cases, conventional model predictions may fail temporarily due to convergence speed issues. To address this, this method employs a predictive control mode based on matching historical similar operating conditions. The system searches the historical database for successful cases with the closest load change rate and fuel properties to the current situation, extracting the injection mode at that time as the initial control parameter to pre-activate the corresponding injection nozzle combination. This "memory triggering" mechanism ensures the system's robustness under extreme dynamic disturbances.

[0036] The following is a more detailed calculation and explanation of this embodiment using a specific application scenario of a 1000 MW supercritical coal-fired boiler. In this scenario, the boiler is operating in a load ramp-up phase, with the load increasing from 600 MW to 800 MW. In step 1, six high-temperature cameras installed 4 meters above the burners capture the flame center beginning to extend upwards. The image processing unit detects that the variance of the flame brightness gradient in the vertical direction has increased by 25%, and the texture structure features show that the turbulence intensity is enhanced in the right burner region. In step 2, the inversion algorithm receives the changes in image features. Planck radiation mapping shows that the temperature in the upper region of the furnace rises rapidly from 1100 degrees Celsius to 1350 degrees Celsius. The results of 3D interpolation show that a high-temperature core region with a volume of approximately 8 cubic meters appears on the right side wall of the boiler near the second corner. In step 3, the lightweight CFD model, combined with the current coal feed rate increase command, calculates that the flue gas velocity increases from 12 meters per second to 15 meters per second. The kinetics module predicts that due to the increase in local temperature, the nitrogen oxide generation rate in this region will increase from 0.02 kg / s to 0.05 kg / s. The predicted high-nitrogen oxide concentration area moves upward in a spiral, expected to reach the coverage height of the PNCR spray gun in 1.5 seconds. In step 4, the multi-objective optimization function detects this trend. Calculation results show that simply increasing the total amount of reducing agent will cause local ammonia escape to exceed the environmental limit of 3 mg / m³. Therefore, the optimization strategy decides to keep the pressure of the three spray guns on the left unchanged, adjust the pitch angle of the corresponding spray guns No. 4, 5, and 6 on the right by 12 degrees upward, and increase the injection pressure from 0.4 MPa to 0.55 MPa. In step 5, the actuator completes the angle adjustment and pressure switching within 150 milliseconds. The polymeric reducing agent is injected into the predicted nitrogen oxide-rich area with a higher initial velocity and a specific intersection angle. The final monitoring results showed that during the entire load variation process, the nitrogen oxide concentration at the boiler outlet was controlled within ±5 mg / m³, and ammonia slip remained at an extremely low level of less than 1.5 mg / m³, verifying the control performance of this method.

[0037] Example 2: Based on Example 1, this example further refines the specific implementation logic of the image feature-based radiation heat transfer and spectral analysis algorithm, and elaborates on the special processing scheme under different coal type switching conditions.

[0038] In the in-depth implementation of step 2, the radiation heat transfer and spectral analysis algorithm employs a textual logical expression of the dual-wavelength thermometry principle. The system defines the red channel in the flame image data as the first characteristic wavelength brightness and the green channel as the second characteristic wavelength brightness. The algorithm calculates the difference between the natural logarithms of the brightness values ​​of these two channels and maps it to the difference between the reciprocals of the two characteristic wavelengths. The scaling factor of this mapping relationship includes Planck's second constant and the emissivity ratio of the flame at different wavelengths. In this embodiment, through pre-calibration, the emissivity ratio is set as a dynamic variable related to the flame texture characteristics, rather than a fixed constant. Specifically, when the flame texture shows a high concentration of particulate matter, the emissivity ratio approaches 1; when the flame transparency is high, the emissivity ratio undergoes nonlinear compensation based on the spectral distribution law.

[0039] This pixel-by-pixel inversion logic generates a high-dimensional raw data matrix in computer memory. To transform it into a continuous three-dimensional temperature field, this embodiment introduces a voxel reconstruction compensation algorithm in step 2. This algorithm considers the shading effect of fly ash particles in the flue gas on light and uses the reverse evolution logic of optical path tracing to perform energy compensation calculations on the space behind the flame that is blocked by the flame in front. In this way, even when the flue gas concentration in the center of the furnace is extremely high, the temperature distribution in the central region can still be reconstructed.

[0040] For fuel switching scenarios, such as switching from high-volatile bituminous coal to low-volatile lean coal, the combustion center and nitrogen oxide generation characteristics within the furnace undergo drastic changes. In this case, the embedded lightweight computational fluid dynamics model in step 3 automatically activates the boundary condition correction module. This module monitors the feed torque and primary air pressure fluctuations of the fuel supply system in real time to determine the ignition delay characteristics of the current coal type. During the simulation in step 3, the model automatically adjusts the momentum transfer coefficient and mass diffusion coefficient based on the perceived coal characteristics. For example, when a decrease in fuel calorific value is detected, the flow field predicted by the model will show a downward trend in the flame core, and the fluid dynamics calculation will correspondingly increase the weight of the bottom swirl zone, thereby more accurately capturing the downward shift of the nitrogen oxide generation region at this time.

[0041] In the strategy generation stage of step 4, a robust weighting factor is introduced into the multi-objective optimization function to address the uncertainties brought about by coal type switching. This factor moderately sacrifices the consumption of reducing agent in exchange for a higher denitrification safety margin. The solver expands the activation coverage of the spray gun array, forming a "defensive spray curtain" to prevent nitrogen oxides from being missed due to flow field prediction errors.

[0042] In this embodiment, the digital twin also integrates a fault self-diagnosis function. During step 5, the system compares the feedback position of the actuator with the target command position in real time. If the servo motor response time of a certain spray gun exceeds the preset 200 milliseconds, or if the flow feedback and pressure of the proportional control valve do not match, the digital twin will immediately simulate the flow field impact after the spray gun fails in virtual space and automatically trigger step 4 to recalculate the compensation strategy, distributing the load of the failed spray gun to adjacent normal spray guns to ensure that the overall denitrification effect does not collapse due to a single point of hardware failure.

[0043] At the physical implementation level in step 5, this embodiment employs graded injection pressure control. For high-concentration nitrogen oxide areas at different distances, the system divides the injection pressure into three levels: far, medium, and near. The near-distance area uses a low-pressure, large-scattering-angle injection mode to enhance coverage uniformity; the far-distance area uses a high-pressure, high-collimation jet mode to ensure that the reducing agent particles can overcome the lateral wind resistance of the flue gas and reach the predetermined coordinate point. This dynamic switching of pressure levels is achieved by adjusting the frequency of the high-pressure pump's inverter and the scaling nozzle at the front of the spray gun.

[0044] Example 3: This example focuses on describing the application of the method of the present invention in scenarios where the fuel composition is extremely unstable, such as waste incineration power generation boilers, and elaborates in detail its special optimization mechanism for non-uniform flow fields.

[0045] In waste incinerators, the flow and temperature fields within the furnace exhibit high randomness and asymmetry due to the extreme fluctuations in fuel calorific value. To address this characteristic, this embodiment adds an infrared thermal imaging camera in step 1, which is then fused with a visible light flame camera using multispectral fusion. Infrared data can penetrate thick smoke and dust, capturing the initial combustion state above the grate, providing a deeper physical constraint for the temperature field inversion in step 2.

[0046] In step 2, the spectral analysis algorithm further incorporates correlation logic for the absorption lines of gas components. By monitoring the absorption intensity of the flame image in specific spectral bands, the algorithm can roughly estimate the concentration distribution of water vapor and carbon dioxide in the flue gas. These component concentrations, as intermediate variables, are used to correct the absorption coefficients in the radiative heat transfer model, thereby improving the accuracy of temperature field inversion under complex compositional environments.

[0047] Step 3 employs a multi-region decomposition calculation strategy when dealing with this type of non-uniform flow field. The system divides the furnace into a grate combustion zone, a first flue radiation zone, and a second flue convection zone. Different simplified governing equations are used for each zone. In the high-turbulence zone above the grate, the model focuses on calculating the average effects of large eddy simulation; in the convection zone, it simplifies to a quasi-one-dimensional flow model. This zoned calculation strategy ensures a high prediction resolution for key reaction regions while maintaining a constant total computational load.

[0048] In step 4, considering the explosive and localized nature of nitrogen oxides generated by waste incineration, the optimal reducing agent injection strategy introduces a "pulse injection" logic. When the system detects a sudden increase in nitrogen oxide concentration in a localized area, the command calculated in step 4 is no longer a constant injection pressure, but a series of pressure pulses with a frequency between 5 and 10 Hz. This pulse injection can generate strong disturbances, significantly enhancing the turbulent mixing effect between the reducing agent and the uneven flue gas.

[0049] In step 5, the spray gun array adopts a circumferential arrangement, and the nozzles have a wider deflection range. To cope with the extremely high corrosiveness of the waste incineration flue gas, all moving parts of the actuator are sealed in a protective cover filled with positive pressure air. The control logic of the servo motor has been enhanced with an automatic ash-cleaning reciprocating motion, meaning that during non-spraying periods, the nozzles will periodically rotate through their full stroke to prevent slag from coking at the nozzle.

[0050] In this embodiment, the digital twin is responsible for online fuel quality identification. By analyzing the temperature field fluctuation frequency output in step 2 and the flow field response velocity in step 3, the digital twin can infer the approximate classification of the current incinerated material (such as high-moisture waste, high-plastic-content waste, etc.) and send this identification result as a feedforward signal to step 4. For example, when identified as high-moisture fuel, the system will predict that the total amount of nitrogen oxides generated is low but the distribution is more dispersed, thus automatically switching to a wide-area low-density injection mode.

[0051] This embodiment also defines in detail the safety boundary conditions in the multi-objective optimization function. During waste incineration, the furnace temperature must be strictly maintained above 850 degrees Celsius for 2 seconds to ensure the complete decomposition of dioxins. In step 4, when calculating the injection strategy, this temperature control requirement will be taken as the first priority constraint. If the calculated reducing agent injection will cause the local temperature to drop below 850 degrees Celsius, the system will automatically reduce the injection volume in that area and link the combustion control system to increase the primary air temperature, ensuring an absolute balance between environmental compliance and process safety.

[0052] This method also includes real-time assessment of the system's operational energy efficiency. Within each control cycle, the system calculates the unit cost of denitrification, including the cost of reducing agent consumption, compressed air energy consumption, and the power cost of the servo mechanism. This energy efficiency indicator is fed back to the performance evaluation module of the digital twin for long-term operational mode optimization. By analyzing thousands of hours of operational data, the system can automatically discover the optimal control baseline curve for a specific season and specific waste source.

[0053] In actual operation, the system demonstrated exceptional adaptability. Even when the calorific value of the waste fluctuated by as much as 30% instantaneously, the mean square error of the outlet nitrogen oxide concentration was reduced compared to traditional control methods through millisecond-level linkage of steps 1 to 5. This fully demonstrates that the deep integration of computer vision, physical field simulation, and precise actuators can solve the challenge of precise pollutant control under extremely complex combustion environments.

[0054] The control architecture in this embodiment also supports remote cloud monitoring and multi-unit collaboration. Digital twin data from multiple furnaces can be transmitted to the cloud center via encrypted links. The cloud center utilizes a larger-scale deep learning model to perform horizontal comparisons of the operating data of all units, extract common patterns, and distribute them to each local unit, achieving collective intelligence-driven performance improvement.

[0055] 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 this invention is defined by the appended claims and their equivalents.

Claims

1. A highly efficient and optimized method for controlling nitrogen oxide emissions from a PNCR process, characterized in that, Includes the following steps: Step 1: Real-time acquisition of flame image data inside the furnace using an industrial flame camera. The flame image data includes the color distribution, brightness gradient, and texture structure features of the flame. Step 2: Based on the flame image data, the spatial distribution information of the three-dimensional temperature field inside the furnace is retrieved using radiation heat transfer and spectral analysis algorithms; Step 3: Input the three-dimensional temperature field information into the embedded lightweight computational fluid dynamics model, and combine it with the current combustion conditions to predict the flow trajectory of flue gas in the furnace and the high concentration area of ​​nitrogen oxides. Step 4: Based on the spatial location and dynamic evolution trend of the high-concentration area, calculate the optimal reducing agent injection strategy in real time. The optimal reducing agent injection strategy includes the injection angle, injection pressure and start-stop sequence of each spray gun. Step 5: The optimal reducing agent injection strategy is sent to the actuator to drive the spray gun array to make millisecond-level dynamic adjustments so that the reducing agent accurately covers the nitrogen oxide generation area and completes the targeted denitrification reaction.

2. The method for controlling nitrogen oxide emissions in a highly efficient and optimized PNCR process according to claim 1, characterized in that, The specific process of step 1 is as follows: An industrial flame camera with a high-temperature resistant optical lens is installed in multiple observation windows on the side wall of the furnace. The high dynamic range imaging sensor of the industrial flame camera is used to acquire flame images. The acquisition process is accompanied by air curtain cooling, which forms a protective air film at the front of the lens using constant pressure compressed air; Feature extraction is performed on the acquired flame images. The color distribution extraction process involves converting the red, green, and blue three-channel color space into a chromaticity space. The process of extracting the brightness gradient involves calculating the spatial rate of change of flame brightness; The process of extracting the texture structure features involves calculating the gray-level co-occurrence matrix of the image to obtain energy, moment of inertia, correlation, and entropy parameters. A central clock trigger sends synchronization pulse signals to all cameras, ensuring that the time deviation of image frames acquired from multiple perspectives is less than 10 milliseconds.

3. The method for controlling nitrogen oxide emissions of a highly efficient and optimized PNCR process according to claim 1, characterized in that, The specific process of step 2 is as follows: A mapping relationship between flame radiation intensity and local temperature was established based on Planck's blackbody radiation law. Each pixel in the flame image is used as a radiation detection unit. A first characteristic wavelength and a second characteristic wavelength are selected. The ratio of the first radiation intensity corresponding to the first characteristic wavelength to the second radiation intensity corresponding to the second characteristic wavelength is calculated. The interference of emissivity on temperature measurement is eliminated by the ratio. The chromaticity coordinates and grayscale values ​​of the image pixels are converted into absolute temperature values. During the conversion process, dynamic emissivity compensation is introduced. Based on the particulate matter concentration identified by the flame texture features, the emissivity ratio at different wavelengths is nonlinearly compensated and corrected.

4. The method for controlling nitrogen oxide emissions of a highly efficient and optimized PNCR process according to claim 1, characterized in that, Step 2 also includes a three-dimensional reconstruction process: Establish a geometric model of the furnace and divide it into voxel units; Based on the projection relationship of multi-angle cameras, the two-dimensional brightness information under each viewpoint is mapped back to the three-dimensional voxel space using an algebraic reconstruction tomography algorithm. In three-dimensional space, the radiative transfer equation is applied, and the absorption, emission and scattering characteristics of the flue gas medium are combined to correct the temperature value on a voxel-by-voxel basis. The reverse evolution logic of optical path tracing is used to compensate for the energy of the occluded space. Kriging interpolation is used to smooth discrete temperature points, generating continuous temperature field volume data and temperature gradient field.

5. The method for controlling nitrogen oxide emissions in a highly efficient and optimized PNCR process according to claim 1, characterized in that, The process of constructing and running the embedded lightweight computational fluid dynamics model in step 3 is as follows: The Navier-Stokes equations are simplified by ignoring high-order nonlinear wave terms and small-scale eddy fluctuations in the flow field. A pre-integrated Reynolds-averaged method is adopted and a modified ordinary differential operator is introduced to construct a lightweight flow control equation. Combustion operating parameters provided by a distributed sensor network in real time are used as boundary conditions. These combustion operating parameters include fuel supply rate, primary air and secondary air flow rate and ratio, furnace negative pressure, and feeding rate. The embedded lightweight computational fluid dynamics model has an operating cycle of less than 100 milliseconds and updates the boundary conditions in real time based on the collected combustion load fluctuations and fuel composition changes.

6. The method for controlling nitrogen oxide emissions of a highly efficient and optimized PNCR process according to claim 1, characterized in that, The specific logic for predicting high-concentration areas in step 3 is as follows: Based on the Lagrange particle tracking method and the Eulerian multiphase flow model, the displacement vector of the flue gas microparticle in the three-dimensional velocity field is calculated. The displacement vector is the product of velocity and time step. A chemical reaction kinetics module is embedded in the model to describe the exponential growth relationship between the formation rate of nitrogen oxides and temperature according to the Arrhenius law. The formation rate is proportional to the exponent of the natural logarithm base, and the exponent term of the exponent includes the negative activation energy, the molar gas constant, and the thermodynamic temperature. When the local temperature exceeds the set temperature threshold, the spatial range in which the nitrogen oxide concentration exceeds the preset alarm value is identified by the generation rate within the integral voxel unit, and its three-dimensional spatial coordinates are locked.

7. The method for controlling nitrogen oxide emissions of a highly efficient and optimized PNCR process according to claim 1, characterized in that, The specific logic for calculating the optimal reducing agent injection strategy in step 4 is as follows: The Kalman filter algorithm is used to predict the trajectory of the high-concentration area and determine the migration direction of the high-concentration area within a preset time. A multi-objective optimization function is constructed, wherein the optimization indices of the multi-objective optimization function include minimizing the outlet nitrogen oxide concentration, minimizing the reductant escape amount, and minimizing the reductant unit consumption. The constraint solver searches within the feasible solution space defined by the range of spray gun rotation angle, the upper limit of spray pressure adjustment, and the minimum interval between spray gun start and stop. The output is a set of control commands including the horizontal rotation angle value, vertical pitch angle value, proportional control valve opening degree, and spray gun activation sequence of each spray gun.

8. The method for controlling nitrogen oxide emissions of a highly efficient and optimized PNCR process according to claim 1, characterized in that, The specific process of driving the spray gun array in step 5 is as follows: The central processing unit sends position commands to the servo motors of the spray gun array via a synchronous serial communication protocol, driving the rotating nozzles to deflect in both horizontal and vertical degrees of freedom. The electronic proportional control valve adjusts the flow parameters of the injection medium in real time according to the pressure command; it adopts graded injection pressure control for high concentration areas of nitrogen oxides at different distances, using a low pressure and large scattering angle mode for close-range areas, and a high pressure and high collimation jet mode for long-range areas. When high-concentration nitrogen oxide agglomerates move, the corresponding spray gun is driven to adjust its direction and spray pressure in advance, so that the reducing agent can penetrate the mainstream of flue gas and cover the nitrogen oxide generation area.

9. The method for controlling nitrogen oxide emissions of a highly efficient and optimized PNCR process according to claim 1, characterized in that, The method also includes a self-optimization process based on digital twins: Construct a digital twin of a mirrored furnace geometry and synchronize sensor data, image data, and control commands; By fusing real-time temperature field and flow field prediction results with historical operating data, the drag coefficient, wall roughness, and heat loss coefficient parameters in the computational fluid dynamics model are calibrated online. The fluctuations in outlet pollutant concentration and ammonia escape were monitored by an incremental learning mechanism, and the weights of the correlation model between temperature and nitrogen oxide generation were fine-tuned by a gradient-based optimization algorithm. The system compares the feedback position of the actuator with the target command in real time. When an abnormal response of the spray gun is detected, the digital twin simulates the failure in virtual space and redistributes the control load of the remaining normal spray guns.

10. The method for controlling nitrogen oxide emissions of a highly efficient and optimized PNCR process according to claim 1, characterized in that, The method also includes processing procedures for unsteady operating conditions and special fuel operating conditions: During boiler startup, load change, or fuel switching, search the historical database for cases that match the current load change rate and fuel properties, extract the injection mode as the initial control parameter, and activate the injection gun combination in advance. In the context of waste incineration, infrared thermal imaging data and flame images are fused together using a multispectral method, and spectral analysis algorithms are used to monitor the absorption lines of gas components in order to correct the absorption coefficient in the radiation heat transfer model. For explosive nitrogen oxides generated by waste incineration, pulse injection logic is executed to output pressure pulse commands with a frequency between five and ten hertz. In the multi-objective optimization function, a temperature safety boundary constraint is set. When the injection of reducing agent causes the local temperature to fall below the temperature threshold required for dioxin decomposition, the injection amount is reduced and the combustion control system is linked to increase the air temperature.

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