AI intelligent efficient flue gas denitration system process

Abstract

The invention discloses an AI intelligent efficient flue gas denitration system process, and belongs to the technical field of flue gas denitration. Comprising the following steps: S1, temperature data acquisition and modeling; s2, simulating and predicting a hearth temperature field; s3, AI prediction control and optimization are carried out; s4, spray guns are arranged in a layered mode and accurately controlled; s5, carrying out closed-loop monitoring and adjustment on NOx; and S6, performing dynamic adaptive optimization operation. A hearth temperature field and flue gas characteristic data can be collected in real time, and a high-precision three-dimensional temperature model is established; the AI algorithm is used for temperature prediction and injection strategy optimization, and full-working-condition dynamic self-adaptive control is achieved; the SNCR denitration efficiency is improved to 70%-85%, NOx emission is effectively reduced, and ammonia escape is controlled within a safe range.

Description

Technical Field

[0001] This invention relates to the field of flue gas denitrification technology, and more specifically, to an AI-powered intelligent and efficient flue gas denitrification system process. Background Technology

[0002] With the acceleration of urbanization and the improvement of industrialization, the amount of municipal solid waste and industrial waste generated has increased significantly. Waste-to-energy incineration, as an important means of energy recovery and waste treatment, has been widely used. By the end of 2022, my country's municipal solid waste incineration capacity had reached approximately 804,700 tons / day, achieving the planned target ahead of schedule. However, the flue gas generated by waste incineration contains large amounts of nitrogen oxides (NOx), particulate matter, and other harmful gases, which seriously affect the environment and air quality. National environmental protection policies have increasingly stringent NOx emission standards for flue gas, and have imposed ultra-low emission requirements on waste incineration plants and small and medium-sized industrial boilers.

[0003] Currently, flue gas denitrification in waste incineration and some small and medium-sized industrial boilers mainly relies on selective non-catalytic reduction (SNCR) technology. SNCR technology achieves NOx reduction by injecting a reducing agent (such as ammonia or urea solution) into a suitable temperature range within the furnace. Existing SNCR processes have the following main problems: Traditional SNCR processes generally achieve denitrification efficiencies between 30% and 50%, which is insufficient to meet increasingly stringent ultra-low emission requirements. Especially when furnace temperature distribution is uneven or fuel properties change, the denitrification effect fluctuates significantly. Existing systems rely on limited temperature measurement points or single measurement methods for furnace temperature monitoring, failing to comprehensively reflect the three-dimensional temperature field distribution. This results in a large deviation between the reducing agent injection position and the optimal temperature zone, reducing denitrification efficiency and increasing the risk of ammonia escape. Traditional SNCR systems often use fixed parameter control or simple closed-loop control for injection control, lacking real-time dynamic prediction and adaptive adjustment of furnace operating conditions, making it difficult to achieve stable emissions across all operating conditions. Existing flue gas denitrification technologies have low levels of automation and lack real-time optimization control systems that are deeply integrated with big data and artificial intelligence (AI) technologies, making it difficult to achieve accurate and dynamic temperature prediction and injection optimization. Summary of the Invention

[0004] The purpose of this invention is to provide an AI-powered intelligent and efficient flue gas denitrification system process that can collect furnace temperature field and flue gas characteristic data in real time, establish a high-precision three-dimensional temperature model, use AI algorithms for temperature prediction and injection strategy optimization, achieve dynamic adaptive control under all operating conditions, improve SNCR denitrification efficiency to 70%–85%, effectively reduce NOx emissions, and control ammonia slip within a safe range.

[0005] A process for an AI-powered intelligent and efficient flue gas denitrification system includes the following technical steps: S1. Temperature data acquisition and modeling: No less than 20 temperature measurement points are arranged in the furnace of waste incineration boiler or small and medium-sized industrial boiler along the height, horizontal and vertical directions. Multi-mode temperature measurement is carried out using infrared thermal imager, acoustic temperature probe and fiber optic grating temperature probe. The sampling period is ≤1 second and the temperature measurement accuracy is ±2℃. The data acquisition module performs anomaly removal, filtering, and multi-source fusion on real-time temperature signals to form a high-precision, real-time updated three-dimensional temperature field model. The model takes into account changes in combustion load, fuel characteristics, and flue gas velocity fluctuations to ensure the accuracy and continuity of temperature data, providing high-quality input for denitrification control.

[0006] S2. Furnace Temperature Field Simulation and Prediction: Three-dimensional temperature reconstruction is performed by combining flame image recognition with infrared thermal imaging. A finite element calculation model of furnace combustion is established, and numerical simulations are performed on flow field velocity, turbulent energy, temperature distribution and reducing agent diffusion path. By weighted and fused real-time temperature measurement data with CFD simulation results, prediction errors are dynamically corrected to achieve future temperature trend predictions of 1 to 5 seconds. At the same time, high-temperature inefficiency zones, low-temperature stagnation zones, and temperature fluctuation zones are identified, providing a scientific basis for spray gun spraying strategies.

[0007] S3, AI Predictive Control and Optimization: Establish a combined model of deep neural network (DNN), reinforcement learning (RL) and adaptive time series analysis. The input parameters include the three-dimensional temperature field of the furnace, combustion load, flue gas velocity, NOx concentration, oxygen content and reducing agent concentration. The output parameters are injection flow rate, injection angle, spray gun start and stop status and injection duration. The AI ​​model employs an online learning mechanism, automatically updating weights based on historical data and real-time feedback. This enables adaptive optimization control of furnace load changes, fuel property changes, and temperature fluctuations, ensuring that the injection strategy is always in the optimal state and achieving an injection response time of ≤2 seconds.

[0008] S4. Layered Arrangement and Precise Control of Spray Guns: Spray gun groups are arranged in layers according to height and combustion zone in the furnace. Each layer of spray gun group includes 4 to 8 independently controlled spray guns. The spray angle is adjustable from 10° to 45° and the spray flow rate is adjustable from 0.5 to 2.5 L / min. The spray gun is made of high-temperature resistant and corrosion-resistant stainless steel or ceramic composite material to ensure that it will not deform during long-term operation; the spraying medium is ammonia or urea solution with a concentration of 10% to 25%; The spray gun's spray position, flow rate, and angle are dynamically optimized by an AI control module to ensure homogeneous mixing of the reducing agent within a temperature range of 850℃ to 1050℃, achieving a thorough denitrification reaction and minimizing ammonia slip to ≤8mg / Nm³. 3 .

[0009] S5. NOx Closed-Loop Monitoring and Regulation: Online NOx monitors are installed at the flue gas outlet and key intermediate points to collect NOx concentrations in real time. The data is transmitted to the AI ​​control module, which combines PID control algorithms and AI optimization strategies to correct injection parameters and achieve closed-loop control. The closed-loop system provides millisecond-level compensation for injection delay, temperature fluctuations, and changes in combustion load to ensure that NOx emissions remain stable below 100 mg / Nm³.

[0010] S6. Dynamic Adaptive Optimization Operation: When the furnace temperature, NOx concentration, or flue gas velocity deviates from the optimal range, the system triggers a dynamic optimization process. The AI ​​algorithm automatically corrects the injection strategy, including injection flow rate, injection angle, spray gun start / stop, and injection sequence, to ensure that the denitrification efficiency is maintained at 70% to 85% under all operating conditions. The system is highly adaptable to load fluctuations, changes in fuel type, and changes in ambient temperature, achieving fully automatic, high-precision, and stable control.

[0011] Preferably, the temperature data acquisition unit includes: an infrared thermal imager, an acoustic temperature probe, a fiber optic temperature probe, a data acquisition module, a data caching module, a filtering and anomaly removal module, and a cloud data interface, to achieve rapid acquisition, real-time processing, storage, and historical comparison of temperature data, ensuring that the temperature modeling accuracy is better than ±2℃.

[0012] Preferably, the CFD simulation module establishes a three-dimensional finite element combustion model, with inputs including furnace structure, burner layout, flue gas flow characteristics, and injection medium diffusion characteristics; and outputs local temperature gradient, turbulent energy, reducing agent mixing efficiency, and predicted NOx formation rate, providing accurate numerical support for the AI ​​control module.

[0013] Preferably, the AI ​​predictive control module adopts a deep neural network combined with reinforcement learning strategy to establish a nonlinear mapping model of temperature field-NOx generation rate-injection strategy. The inputs include furnace temperature, combustion load, flue gas velocity, NOx concentration and reducing agent concentration, and the outputs are injection flow rate, injection angle, spray gun start and stop status and injection duration. It also realizes online learning, weight adaptive update and self-learning control to ensure that the control accuracy error is ≤3%.

[0014] Preferably, the spray gun system adopts a modular layered spray structure, with each layer of spray gun group being independently controlled. The spray gun material is resistant to high temperature corrosion, the spray angle is adjustable from 10° to 45°, the spray flow rate is 0.5 to 2.5 L / min, and the spray sequence, spray level, and spray duration are dynamically optimized by the AI ​​control module to achieve homogeneous mixing and denitrification in the furnace.

[0015] Preferably, the spray medium is ammonia water or urea solution, with a spray concentration of 10% to 25%, and the spray temperature is controlled in the range of 850℃ to 1050℃. The AI ​​control module adjusts the spray level, spray flow rate and spray angle in real time to ensure that the reducing agent reacts fully, NOx emissions are ≤100mg / Nm³, and ammonia slip is ≤8mg / Nm³.

[0016] Preferably, the closed-loop feedback system includes an online NOx monitor, a data acquisition module, a PID controller, and an AI optimization algorithm module. The PID controller is used to compensate for injection delay and temperature fluctuations, and the AI ​​optimization module corrects the injection parameters to achieve millisecond-level response control, ensuring that the denitrification efficiency remains stable at 70% to 85% under all operating conditions.

[0017] Preferably, the system adopts a modular integrated design, including a metering and distribution module, a spraying module, a temperature measurement module, a CFD simulation module, an AI control module, and an industrial Ethernet high-speed data communication interface module. Each module works in real time to achieve high precision, multi-point control, and rapid data exchange.

[0018] Preferably, the AI ​​control system can automatically update the model parameters according to changes in furnace temperature field, fuel characteristics, combustion load and flue gas velocity, to achieve adaptive, closed-loop, and full-condition denitrification control, ensuring NOx emissions ≤100mg / Nm³ and ammonia slip ≤8mg / Nm³, while keeping the system energy consumption 30% to 40% lower than the traditional SNCR+SCR process.

[0019] Preferably, the system is characterized by being suitable for flue gas treatment in waste incineration power plants and small and medium-sized industrial boilers, achieving dynamic self-adaptation under all operating conditions, closed-loop feedback control, injection level optimization, temperature field prediction, denitrification efficiency improvement and ammonia escape control, while reducing operating energy consumption, investment costs and floor space.

[0020] Compared with the prior art, the advantages of this invention are: (1) By establishing a high-density three-dimensional temperature measurement point array in the furnace, combining flame image recognition and CFD simulation, a temperature field prediction method is used to achieve real-time and accurate monitoring and prediction of furnace temperature; by using the AI ​​intelligent control module to dynamically optimize the spray gun injection strategy, the SNCR denitrification efficiency is increased from the traditional 30% to 50% to 70% to 85%, significantly reducing the NOx concentration in flue gas and achieving stable emissions below 100 mg / Nm³.

[0021] (2) By controlling the spray gun in layers independently and dynamically adjusting the spray angle and flow rate, the reducing agent is homogeneously mixed in the optimal temperature range; the ammonia escape concentration is controlled within 8 mg / Nm³, effectively avoiding secondary pollution and meeting the requirements of environmental protection regulations.

[0022] (3) The system can respond in real time to furnace load fluctuations, fuel characteristic changes and ambient temperature changes, and realize closed-loop adaptive control under all working conditions; dynamically adjust the injection flow rate, injection angle, spray gun start and stop status and injection level, so that the denitrification effect remains stable when the load changes or working conditions fluctuate.

[0023] (4) Avoid the problems of catalyst use, heating system and high energy consumption in SCR process, reduce system footprint and investment cost; reduce ammonia water consumption and operating energy consumption through intelligent optimization control, and achieve energy saving and emission reduction effect.

[0024] (5) Through online learning and adaptive adjustment using AI algorithms, the system can achieve automated operation of spray gun control, temperature prediction and NOx closed-loop regulation; it can provide scientific identification of the high temperature and low efficiency zone, low temperature stagnation zone and temperature fluctuation zone in the furnace, provide decision-making basis for spray gun layout and operation, and improve system reliability and accuracy. Attached Figure Description

[0025] Figure 1 This is a process flow diagram of an AI-powered intelligent and efficient flue gas denitrification system according to the present invention. Detailed Implementation

[0026] Example 1: A process for an AI-powered intelligent and efficient flue gas denitrification system, comprising the following technical steps: S1. Temperature data acquisition and modeling: No less than 20 temperature measurement points are arranged in the furnace of waste incineration boiler or small and medium-sized industrial boiler along the height, horizontal and vertical directions. Multi-mode temperature measurement is carried out using infrared thermal imager, acoustic temperature probe and fiber optic grating temperature probe. The sampling period is ≤1 second and the temperature measurement accuracy is ±2℃. The data acquisition module performs anomaly removal, filtering, and multi-source fusion on real-time temperature signals to form a high-precision, real-time updated three-dimensional temperature field model. The model takes into account changes in combustion load, fuel characteristics, and flue gas velocity fluctuations to ensure the accuracy and continuity of temperature data, providing high-quality input for denitrification control.

[0027] S2. Furnace Temperature Field Simulation and Prediction: Three-dimensional temperature reconstruction is performed by combining flame image recognition with infrared thermal imaging. A finite element calculation model of furnace combustion is established, and numerical simulations are performed on flow field velocity, turbulent energy, temperature distribution and reducing agent diffusion path. By weighted and fused real-time temperature measurement data with CFD simulation results, prediction errors are dynamically corrected to achieve future temperature trend predictions of 1 to 5 seconds. At the same time, high-temperature inefficiency zones, low-temperature stagnation zones, and temperature fluctuation zones are identified, providing a scientific basis for spray gun spraying strategies.

[0028] 1. Data Collection Preparation Arrange a temperature measurement point array: 20 to 50 temperature measurement points are arranged along the height of the furnace, horizontally and vertically, covering the combustion core area, the upper flue gas area and the edge area of ​​the furnace.

[0029] The temperature measurement method adopts a multimodal approach: Infrared thermal imager: continuously images the furnace surface and flames.

[0030] Fiber Bragg grating temperature probe: embedded in the furnace wall to acquire local temperature in real time.

[0031] Acoustic temperature probe: for non-contact temperature measurement of high-temperature flue gas in the furnace.

[0032] Real-time data acquisition: Temperature sampling frequency is above 1Hz, and data is transmitted to the control computer in real time.

[0033] The collected data undergoes preliminary filtering, outlier removal, and time synchronization.

[0034] 2. Flame Image Recognition and 3D Temperature Reconstruction Flame image processing: Binarize flame images captured by infrared imaging or visible light cameras to extract flame boundaries and high-temperature core regions.

[0035] The contour of the flame region is obtained using image segmentation algorithms (such as Canny edge detection or deep learning segmentation models).

[0036] 3D reconstruction: The geometric model of the furnace is mapped to the flame image data, and the two-dimensional image is mapped onto the three-dimensional furnace mesh using the voxel method or beam projection method.

[0037] By combining the temperature measurement data, the temperature of each grid node in the furnace is initially interpolated to obtain the initial three-dimensional temperature field distribution.

[0038] 3. Establishment of CFD finite element calculation model Establish the furnace geometry model: Establish three-dimensional geometric models of equipment such as the furnace, burner, flue, and spray gun.

[0039] Divide the grid (structured or unstructured) to ensure that the grid fineness of the core combustion zone is ≤5cm.

[0040] Set boundary conditions and physical property parameters: fuel calorific value, combustion rate, oxygen content, flue gas velocity, fuel injection location, etc. as input conditions.

[0041] The wall boundary temperature, furnace pressure, and outlet conditions are set to actual operating data.

[0042] Numerical simulation: Turbulence models (such as k-ε or LES) are used to calculate the velocity field, turbulent energy, and temperature distribution inside the furnace.

[0043] The diffusion and reaction paths of the reducing agent (ammonia / urea) after injection were simulated to obtain the local temperature-concentration distribution.

[0044] 4. Data fusion and dynamic correction Real-time data fusion: The CFD simulation results are weighted and fused with real-time temperature measurement points and flame 3D reconstruction temperature data.

[0045] The fusion algorithm can use Kalman filtering or Bayesian filtering, taking the measurement data as the observation and the simulation results as the prior estimate to achieve dynamic correction.

[0046] Prediction error correction: Calculate the deviation between the simulated temperature and the actual measured temperature, and make online corrections to the boundary conditions, combustion rate, or turbulence parameters of the finite element model.

[0047] The updated model outputs the temperature field trend of the furnace for the next 1 to 5 seconds, ensuring that the predicted temperature deviation is ≤ ±5℃.

[0048] 5. Abnormal Area Identification and Spray Gun Optimization Basis High-temperature and low-efficiency zone: Temperature > 1050℃ but NOx reduction is insufficient, marked as high-temperature and low-efficiency zone.

[0049] Low-temperature retention zone: The temperature is <850℃ and the flue gas flow is slow. This zone is marked as a low-temperature retention zone.

[0050] Temperature fluctuation zone: Temperature fluctuations exceeding 30℃ within a short period of time are marked as fluctuation zones.

[0051] Provide a basis for spray gun layout: Based on the above-mentioned area location, determine the optimal spray level, flow rate and spray angle of the spray gun to achieve homogeneous mixing of the reducing agent in the optimal temperature range and ensure denitrification efficiency.

[0052] S3, AI Predictive Control and Optimization: Establish a combined model of deep neural network (DNN), reinforcement learning (RL) and adaptive time series analysis. The input parameters include the three-dimensional temperature field of the furnace, combustion load, flue gas velocity, NOx concentration, oxygen content and reducing agent concentration. The output parameters are injection flow rate, injection angle, spray gun start and stop status and injection duration. The AI ​​model employs an online learning mechanism, automatically updating weights based on historical data and real-time feedback. This enables adaptive optimization control of furnace load changes, fuel property changes, and temperature fluctuations, ensuring that the injection strategy is always in the optimal state and achieving an injection response time of ≤2 seconds.

[0053] S4. Layered Arrangement and Precise Control of Spray Guns: Spray gun groups are arranged in layers according to height and combustion zone in the furnace. Each layer of spray gun group includes 4 to 8 independently controlled spray guns. The spray angle is adjustable from 10° to 45° and the spray flow rate is adjustable from 0.5 to 2.5 L / min. The spray gun is made of high-temperature resistant and corrosion-resistant stainless steel or ceramic composite material to ensure that it will not deform during long-term operation; the spraying medium is ammonia or urea solution with a concentration of 10% to 25%; The spray gun's spray position, flow rate, and angle are dynamically optimized by an AI control module to ensure homogeneous mixing of the reducing agent within a temperature range of 850℃ to 1050℃, achieving a thorough denitrification reaction and minimizing ammonia slip to ≤8mg / Nm³. 3 .

[0054] S5. NOx Closed-Loop Monitoring and Regulation: Online NOx monitors are installed at the flue gas outlet and key intermediate points to collect NOx concentrations in real time. The data is transmitted to the AI ​​control module, which combines PID control algorithms and AI optimization strategies to correct injection parameters and achieve closed-loop control. The closed-loop system provides millisecond-level compensation for injection delay, temperature fluctuations, and changes in combustion load to ensure that NOx emissions remain stable below 100 mg / Nm³.

[0055] S6. Dynamic Adaptive Optimization Operation: When the furnace temperature, NOx concentration, or flue gas velocity deviates from the optimal range, the system triggers a dynamic optimization process. The AI ​​algorithm automatically corrects the injection strategy, including injection flow rate, injection angle, spray gun start / stop, and injection sequence, to ensure that the denitrification efficiency is maintained at 70% to 85% under all operating conditions. The system is highly adaptable to load fluctuations, changes in fuel type, and changes in ambient temperature, achieving fully automatic, high-precision, and stable control.

[0056] The temperature data acquisition unit includes: an infrared thermal imager, an acoustic temperature probe, a fiber optic temperature probe, a data acquisition module, a data caching module, a filtering and anomaly removal module, and a cloud data interface, enabling rapid acquisition, real-time processing, storage, and historical comparison of temperature data, ensuring that the temperature modeling accuracy is better than ±2℃.

[0057] The CFD simulation module establishes a three-dimensional finite element combustion model. The inputs include furnace structure, burner layout, flue gas flow characteristics, and injection medium diffusion characteristics. The outputs include local temperature gradient, turbulent energy, reducing agent mixing efficiency, and predicted NOx formation rate, providing accurate numerical support for the AI ​​control module.

[0058] The AI ​​predictive control module employs a deep neural network combined with reinforcement learning to establish a nonlinear mapping model of temperature field, NOx generation rate, and injection strategy. The inputs include furnace temperature, combustion load, flue gas velocity, NOx concentration, and reducing agent concentration. The outputs include injection flow rate, injection angle, spray gun start / stop status, and injection duration. It also enables online learning, adaptive weight updates, and self-learning control to ensure that the control accuracy error is ≤3%.

[0059] The spray gun system adopts a modular layered spray structure, with each layer of spray guns being independently controlled. The spray gun material is resistant to high temperature corrosion, the spray angle is adjustable from 10° to 45°, and the spray flow rate is 0.5 to 2.5 L / min. The spray sequence, spray level, and spray duration are dynamically optimized by the AI ​​control module to achieve homogeneous mixing and denitrification in the furnace.

[0060] The injection medium is ammonia water or urea solution, with an injection concentration of 10% to 25%. The injection temperature is controlled within the range of 850℃ to 1050℃. The AI ​​control module adjusts the injection level, injection flow rate and injection angle in real time to ensure that the reducing agent reacts fully. NOx emissions are ≤100mg / Nm³ and ammonia slip is ≤8mg / Nm³.

[0061] The closed-loop feedback system includes an online NOx monitor, a data acquisition module, a PID controller, and an AI optimization algorithm module. The PID controller is used to compensate for injection delay and temperature fluctuations, while the AI ​​optimization module corrects injection parameters to achieve millisecond-level response control, ensuring that the denitrification efficiency remains stable at 70% to 85% under all operating conditions.

[0062] The system adopts a modular integrated design, including a metering and distribution module, a spraying module, a temperature measurement module, a CFD simulation module, an AI control module, and an industrial Ethernet high-speed data communication interface module. All modules work together in real time to achieve high precision, multi-point control, and rapid data exchange.

[0063] The AI ​​control system can automatically update model parameters based on changes in furnace temperature field, fuel characteristics, combustion load and flue gas velocity, to achieve adaptive, closed-loop, and full-condition denitrification control, ensuring NOx emissions ≤100mg / Nm³ and ammonia slip ≤8mg / Nm³, while keeping system energy consumption 30% to 40% lower than the traditional SNCR+SCR process.

[0064] The system is suitable for flue gas treatment in waste incineration power plants and small and medium-sized industrial boilers. It achieves dynamic self-adaptation under all operating conditions, closed-loop feedback control, injection level optimization, temperature field prediction, denitrification efficiency improvement and ammonia escape control, while reducing operating energy consumption, investment costs and floor space.

[0065] 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 preferred examples of the present invention and are not intended to limit the present invention. Various changes and modifications can be made to the present invention without departing from the spirit and scope thereof, 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. An AI intelligent efficient flue gas denitration system process, characterized in that, The technical steps include the following: S1, temperature data acquisition and modeling: more than 20 temperature measurement point arrays are arranged in the furnace of a waste incineration boiler or a small and medium-sized industrial boiler along the height, transverse and longitudinal directions, multi-modal combined temperature measurement is adopted, the sampling period is less than or equal to 1 second, and the temperature measurement accuracy is ±2℃; The data acquisition module performs abnormal elimination, filtering processing and multi-source fusion on real-time temperature signals, and forms a high-precision and real-time updated three-dimensional temperature field model; the model considers the changes of combustion load, fuel characteristics and flue gas flow rate, ensures the accuracy and continuity of temperature data, and provides high-quality input for denitration control; S2, furnace temperature field simulation and prediction: three-dimensional temperature reconstruction is performed by combining flame image recognition with infrared thermal imaging, a finite element calculation model of the furnace combustion is established, and numerical simulation is performed on the flow field velocity, turbulent energy, temperature distribution and reducing agent diffusion path; The real-time temperature data and CFD simulation results are weighted and fused to dynamically correct the prediction error and realize the future temperature trend prediction of 1-5 seconds; meanwhile, the high-temperature low-efficiency area, low-temperature retention area and temperature fluctuation area are identified to provide a scientific basis for the injection strategy of the lance; S3, AI prediction control and optimization: a combined model of deep neural network (DNN), reinforcement learning (RL) and adaptive time series analysis is established, the input parameters include the three-dimensional temperature field of the furnace, the combustion load, the flue gas flow rate, the NOx concentration, the oxygen content and the reducing agent concentration, and the output parameters include the injection flow rate, the injection angle, the start-stop state of the lance and the injection duration; The AI model adopts an online learning mechanism, automatically updates the weights according to the historical data and real-time feedback, realizes adaptive optimization control of the changes of the furnace load, the changes of the fuel properties and the temperature fluctuations, ensures that the injection strategy is always in the optimal state, and realizes the injection response time of less than or equal to 2 seconds; S4, layered arrangement and precise control of the lance: the furnace is arranged with lance groups according to the height and the combustion area, each layer of the lance group includes 4-8 independently controlled lances, the injection angle can be adjusted by 10°-45°, and the injection flow rate can be adjusted by 0.5-2.5 L / min; The lance material is high-temperature-resistant and corrosion-resistant stainless steel or ceramic composite material to ensure that it does not deform during long-term operation; the injection medium is ammonia water or urea solution with a concentration of 10%-25%; The spraying position, flow and angle of the spray gun are dynamically optimized by the AI control module to ensure that the reductant is uniformly mixed in the temperature range of 850-1050°C, the denitration reaction is fully carried out, and the ammonia escape is ≤8 mg / Nm 3 ; S5, NOx closed-loop monitoring and adjustment: install online NOx monitor at the flue gas outlet and key intermediate points, collect NOx concentration in real time, transmit data to AI control module, combine PID control algorithm and AI optimization strategy, correct injection parameters, and realize closed-loop control; the closed-loop system compensates for millisecond-level injection delay, temperature fluctuations and combustion load changes, ensuring that NOx emissions are stably below 100 mg / Nm 3 ; S6, dynamic adaptive optimization operation: when the furnace temperature, NOx concentration or flue gas flow rate deviates from the optimal range, the system triggers the dynamic optimization process, and the AI algorithm automatically corrects the injection strategy, including the injection flow rate, the injection angle, the start-stop of the lance and the injection sequence, to ensure that the denitration efficiency is maintained at 70%-85% under all working conditions, the system has strong adaptability to load fluctuations, changes of fuel types and changes of environmental temperature, and realizes automatic, high-precision and stable control.

2. The AI intelligent efficient flue gas denitration system process according to claim 1, characterized in that, The temperature data acquisition unit includes an infrared thermal imager, an acoustic temperature probe, an optical fiber grating temperature probe, a data acquisition module, a data caching module, a filtering and abnormal elimination module and a cloud data interface, realizes rapid acquisition, real-time processing, storage and historical comparison of temperature data, and ensures that the temperature modeling accuracy is better than ±2℃.

3. The AI intelligent efficient flue gas denitration system process according to claim 1, characterized in that, The CFD simulation module establishes a three-dimensional finite element combustion model, and inputs include the furnace structure, burner layout, flue gas flow characteristics, and injection medium diffusion characteristics. The outputs are local temperature gradient, turbulent energy, reducing agent mixing efficiency, and predicted NOx generation rate, providing accurate numerical support for the AI control module.

4. The AI intelligent efficient flue gas denitration system process according to claim 1, characterized in that, The AI predictive control module uses a deep neural network combined with a reinforcement learning strategy to establish a nonlinear mapping model of the temperature field-NOx generation rate-injection strategy. The inputs include furnace temperature, combustion load, flue gas velocity, NOx concentration, and reducing agent concentration. The outputs are injection flow rate, injection angle, injection gun start-stop state, and injection duration. Online learning, weight adaptive update, and self-learning control are implemented to ensure a control accuracy error of ≤3%.

5. The AI intelligent efficient flue gas denitration system process according to claim 1, characterized in that, The injection gun system uses a modular layered injection structure, with each layer of gun group controlled independently. The gun material is resistant to high-temperature corrosion, the injection angle is adjustable from 10° to 45°, and the injection flow rate is 0.5-2.5 L / min. The injection sequence, injection level, and injection duration are dynamically optimized by the AI control module to achieve homogeneous mixing and denitrification in the furnace.

6. The AI intelligent efficient flue gas denitration system process according to claim 1, characterized in that, The injection medium is ammonia water or urea solution, the injection concentration is 10% to 25%, the injection temperature is controlled in the interval of 850°C to 1050°C, the AI control module adjusts the injection level, injection flow and injection angle in real time, ensures that the reducing agent fully reacts, and the NOx emission is ≤100 mg / Nm 3 , and the ammonia escape is ≤8 mg / Nm 3 .

7. The AI intelligent efficient flue gas denitration system process according to claim 1, characterized in that, The closed-loop feedback system includes an online NOx monitor, a data acquisition module, a PID controller, and an AI optimization algorithm module. The PID controller is used to compensate for injection delays and temperature fluctuations, and the AI optimization module corrects the injection parameters to achieve millisecond-level response control, ensuring that the denitrification efficiency is stable at 70%-85% under all operating conditions.

8. The AI intelligent efficient flue gas denitration system process according to claim 1, characterized in that, The system uses modular integrated design, including metering and distribution modules, injection modules, temperature measurement modules, CFD simulation modules, AI control modules, and industrial Ethernet high-speed data communication interface modules. Each module works in real time to achieve high-precision, multi-point control, and fast data exchange.

9. The AI intelligent efficient flue gas denitration system process according to claim 1, characterized in that, The AI control system can automatically update model parameters according to changes in the furnace temperature field, fuel characteristics, combustion load and flue gas flow rate, realize self-adaptive, closed-loop, full-working-condition denitration control, and ensure that the NOx emission is ≤100 mg / Nm 3 , the ammonia escape is ≤8 mg / Nm 3 , while keeping the system energy consumption 30%-40% lower than the traditional SNCR+SCR process.

10. The AI intelligent efficient flue gas denitration system process according to any one of claims 1-9, characterized in that, The system is suitable for waste incineration power plants and small and medium-sized industrial boiler flue gas treatment, achieving dynamic self-adaptation, closed-loop feedback control, injection level optimization, temperature field prediction, denitrification efficiency improvement, and ammonia escape control. It also reduces operating energy consumption, investment costs, and land area.

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