Combustion-supporting system and method for mixing waste nitrogen into air through flue gas heat exchange

Abstract

The invention discloses a combustion-supporting system and method for mixing waste nitrogen into air through flue gas heat exchange. The combustion-supporting system comprises a gas-nitrogen dynamic mixing balancing module, a self-adaptive grid combustion optimizing module, a flue gas flow field distribution predicting module, a combustion-supporting system big data studying and judging module, a flue gas heat exchange conduction module and an air waste nitrogen mixing module. According to the method, air and waste nitrogen are mixed according to an adjusting proportion through the air and waste nitrogen mixing module, and the air and the waste nitrogen are conveyed to a combustion area after being subjected to heat exchange heating through the flue gas heat exchange conduction module and high-temperature flue gas; the flue gas flow field distribution prediction module predicts flow field data based on flue gas parameters, and provides support for the adaptive grid combustion optimization module to optimize combustion parameters; the combustion-supporting system big data research and judgment module integrates parameters of all the modules to generate a regulation and control instruction, and the gas-nitrogen dynamic mixing and balancing module dynamically adjusts the mixing proportion according to the regulation and control instruction. Waste nitrogen recovery and flue gas waste heat utilization are achieved, the combustion efficiency and stability are improved, and the industrial combustion energy-saving requirement is met.

Description

Technical Field

[0001] This invention relates to the field of air and waste nitrogen mixing for combustion support technology, and more particularly to a system and method for air mixed with waste nitrogen and used for combustion support via flue gas heat exchange. Background Technology

[0002] In industrial combustion processes, achieving efficient recovery and utilization of waste nitrogen and waste heat from flue gas has become a crucial direction for reducing energy consumption and pollutant emissions. Current industrial production generates a large amount of waste nitrogen; direct emission not only wastes resources but may also increase the environmental burden. Simultaneously, the high-temperature flue gas generated during combustion carries a significant amount of heat, which, if not effectively recovered, leads to energy loss. To address these issues, the industry has gradually explored technical pathways for mixing air and waste nitrogen for combustion, hoping to improve the temperature of the mixed gas flow and optimize combustion efficiency through flue gas heat exchange. However, existing technologies have not yet formed a complete technical system in areas such as dynamic adjustment of the mixing ratio, precise optimization of combustion parameters, effective prediction of flue gas flow fields, and collaborative analysis of multi-system data. This makes it difficult to meet the dual demands of stability and efficiency in industrial combustion. Therefore, there is an urgent need to construct an integrated system and methodology to achieve synergistic optimization of waste nitrogen utilization and flue gas heat exchange.

[0003] The existing technology has two significant drawbacks: First, it lacks the ability to dynamically balance and adjust the mixing ratio during the air-nitrogen and waste gas mixing process. It cannot adjust the air-nitrogen and waste gas ratio in real time according to changes in combustion conditions, which easily leads to uneven heat and concentration distribution in the mixed gas flow, thereby affecting the stability of the subsequent combustion process and making it difficult to ensure continuous optimization of combustion efficiency. Second, the existing technology has not established an effective collaborative mechanism for flue gas flow field prediction, combustion parameter optimization, and system data analysis. The flue gas flow field distribution prediction results cannot provide accurate basis for combustion parameter optimization in a timely manner, and the operating parameters of each module of the system have not formed a unified big data analysis and judgment system. This results in lag in parameter control during combustion, making it impossible to respond to changes in operating conditions in a timely manner, which further limits the improvement of flue gas heat exchange efficiency and waste gas utilization. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides a system and method for incorporating polluted nitrogen into air and using flue gas heat exchange to aid combustion.

[0005] The technical solution adopted in this invention is an air-nitrogen mixed with flue gas heat exchange combustion system, comprising: a gas-nitrogen dynamic mixing and balancing module, an adaptive grid combustion optimization module, a flue gas flow field distribution prediction module, a combustion system big data analysis module, a flue gas heat exchange and conduction module, and an air-nitrogen mixing module; the gas-nitrogen dynamic mixing and balancing module receives real-time parameters of nitrogen and air flow output from the combustion system big data analysis module, dynamically adjusts the mixing ratio of nitrogen and air using a gas-nitrogen dynamic mixing and balancing model, and delivers the adjusted mixed airflow to the air-nitrogen mixing module; the adaptive grid combustion optimization module receives flow field velocity and temperature distribution data output from the flue gas flow field distribution prediction module, constructs a combustion zone grid model based on an adaptive grid combustion optimization algorithm, performs optimization calculations on combustion parameters, and transmits the optimization results to the combustion system big data analysis module; the flue gas flow field distribution prediction module receives output from the flue gas heat exchange and conduction module... Flue gas temperature and pressure parameters are predicted using a flue gas flow field distribution prediction model to determine the flue gas flow trajectory and distribution state. The predicted data is then sent to the adaptive grid combustion optimization module and the combustion system big data analysis module. The combustion system big data analysis module collects operating parameters from the gas-nitrogen dynamic mixing equilibrium module, the adaptive grid combustion optimization module, the flue gas flow field distribution prediction module, the flue gas heat exchange and conduction module, and the air-nitrogen mixing module. Through big data analysis, it generates parameter control commands, which are then sent to the respective modules. The flue gas heat exchange and conduction module receives the mixed airflow output from the air-nitrogen mixing module, exchanges heat with external flue gas, and delivers the heat-exchanged mixed airflow to the combustion device. Simultaneously, it transmits the flue gas heat exchange parameters to the flue gas flow field distribution prediction module. The air-nitrogen mixing module receives the adjustment signal output from the gas-nitrogen dynamic mixing equilibrium module, mixes air and nitrogen, and delivers the mixed airflow to the flue gas heat exchange and conduction module.

[0006] Furthermore, the expression for the gaseous-nitrogen dynamic mixing equilibrium model is as follows: in, This represents the theoretical heat value of the mixed airflow. The nitrogen-pollution mixing coefficient is given. This represents the real-time flow rate of nitrogen wastewater. The specific heat capacity of nitrogen at constant pressure is given by the polluted nitrogen. The initial temperature of the polluted nitrogen. The air mixing coefficient, Real-time airflow The specific heat capacity of air at constant pressure. The initial temperature of the air. This is the pressure influence coefficient. For the pressure difference between the inlet and outlet of the mixed system, This represents the total flow rate of the mixed airflow.

[0007] Furthermore, the expression for the adaptive grid combustion optimization algorithm is: ,in, To find the optimal objective function value for combustion, This represents the number of horizontal nodes in the grid. This represents the number of nodes in the vertical direction of the grid. For grid nodes The density of the mixed airflow at that location, For grid nodes At the airflow velocity, For grid nodes Lateral step size, For grid nodes Vertical step size, Temperature weighting coefficient, To optimize the time frame, For a moment Grid nodes Temperature, To set the combustion temperature.

[0008] Furthermore, the expression for the flue gas flow field distribution prediction model is as follows: ,in, Spatial coordinates flue gas velocity, The initial flow velocity at the flue gas inlet. This refers to the cross-sectional area of ​​the flue gas inlet. cross section Area of ​​flue gas circulation, Spatial coordinates flue gas temperature, The initial temperature at the flue gas inlet. The drag coefficient is along the height direction. For spatial height coordinates, This represents the total length of the flue gas passage.

[0009] Furthermore, the data processing model expression of the big data analysis module for the combustion-supporting system is as follows: ,in, Output control parameter values ​​to the module. For the number of data collection categories, For the first Class parameter weight coefficients For the first Real-time parameter acquisition, For the first Class parameter stability coefficient, The parameter change rate weighting coefficient, This represents the rate of change of the sum of all collected parameters.

[0010] Furthermore, the heat transfer calculation model expression for the flue gas heat transfer module is as follows: ,in, To exchange heat, The heat transfer coefficient, For heat exchange area, The flue gas inlet temperature, The flue gas outlet temperature, The inlet temperature of the mixed airflow. The outlet temperature of the mixed gas flow. The coefficient of efficiency for flue gas heat transfer. The heat absorption efficiency coefficient of the mixed airflow.

[0011] Furthermore, the flue gas flow field distribution prediction module includes a flue gas parameter acquisition unit, a flow field data preprocessing unit, a prediction model calculation unit, and a result output unit. The flue gas parameter acquisition unit continuously acquires temperature, pressure, and flow velocity data of flue gas at different cross-sections and heights using temperature sensors, pressure sensors, and flow velocity sensors located at different positions in the flue gas channel, and transmits the acquired raw data to the flow field data preprocessing unit. The flow field data preprocessing unit removes outliers from the received raw data, eliminates interference signals during the acquisition process through data smoothing, classifies and organizes the data according to spatial coordinates to form a structured flow field dataset, and transmits it to the prediction model calculation unit. The prediction model calculation unit calls the flue gas flow field distribution prediction model, substitutes the structured flow field dataset into the model for calculation, solves for the velocity, temperature, and pressure distribution values ​​of flue gas under different spatial coordinates, generates a flow field distribution prediction matrix, and transmits it to the result output unit. The result output unit converts the flow field distribution prediction matrix into a format that meets the data interface requirements of each associated module, and sends the prediction data to the adaptive grid combustion optimization module and the combustion-supporting system big data analysis module at a preset frequency.

[0012] Furthermore, the big data analysis module of the combustion-supporting system includes a parameter receiving unit, a data storage unit, an analysis and calculation unit, and an instruction generation unit. The parameter receiving unit receives operating parameters sent by the gas-nitrogen dynamic mixing and equalization module, the adaptive grid combustion optimization module, the flue gas flow field distribution prediction module, the flue gas heat transfer and conduction module, and the air-nitrogen mixing module through a data transmission interface. It verifies the format of the parameters to ensure data integrity before transmitting them to the data storage unit. The data storage unit categorizes and stores the received operating parameters according to timestamps, establishing a historical parameter database. It also backs up the data to prevent data loss and provides data query support for the analysis and calculation unit. The analysis and calculation unit retrieves real-time and historical parameters from the data storage unit, identifies parameter change trends using big data analysis algorithms, determines the operating status of different modules based on preset operating thresholds, calculates parameter control requirements, and generates a preliminary control plan. The instruction generation unit verifies the feasibility of the preliminary control plan, modifies the plan according to the parameter adjustment range of different modules, converts the modified control plan into specific parameter control instructions, and sends them to the relevant modules through the data transmission interface.

[0013] Furthermore, the flue gas heat exchange and conduction module includes a mixed airflow inlet unit, a flue gas inlet unit, a heat exchange core unit, and an airflow outlet unit. The mixed airflow inlet unit receives the mixed airflow delivered by the air-nitrogen mixing module and controls the inlet speed of the mixed airflow through a flow regulating valve, evenly distributing the mixed airflow to the inlet of the flow channel of the heat exchange core unit. The flue gas inlet unit receives high-temperature flue gas delivered from the outside and guides the flue gas into the heat exchange core unit along a preset path through a flow guiding structure, avoiding the formation of eddies at the inlet and ensuring that the flue gas flows evenly through the heat exchange core. The heat exchange core unit increases the heat exchange area through its internal fin structure, allowing the mixed airflow and high-temperature flue gas to exchange heat counter-currently within the core. The mixed airflow absorbs heat from the flue gas, increasing its temperature, while the flue gas releases heat, decreasing its temperature, thus completing the heat transfer process. The airflow outlet unit delivers the heated mixed airflow to the combustion device and simultaneously guides the cooled flue gas to subsequent processing stages. The pressure monitoring element monitors the airflow pressure at the outlet in real time and feeds the pressure data back to the combustion system big data analysis module.

[0014] An air-to-nitrogen mixture with flue gas heat exchange for combustion assistance method is described. This method, applied to an air-to-nitrogen mixture with flue gas heat exchange for combustion assistance system, includes the following steps: First, the air-to-nitrogen mixture module receives a mixing ratio adjustment signal from the dynamic mixing equilibrium module. Air and nitrogen are introduced into the mixing chamber according to the ratio set by the adjustment signal. A stirring structure within the chamber initially mixes the air and nitrogen to form an initial mixed airflow. Second, the initial mixed airflow enters the flue gas heat exchange module, where it exchanges heat with the high-temperature flue gas in the heat exchange core. The mixed airflow absorbs heat from the flue gas, causing its temperature to rise. The heat-exchanged mixed airflow is then transported to the combustion zone. Simultaneously, the flue gas heat exchange module transmits the temperature and pressure parameters from the heat exchange process to the flue gas flow field distribution prediction module. Third, the flue gas flow field distribution prediction module receives the parameters output by the flue gas heat exchange module and uses a flue gas flow field distribution prediction model to analyze the flow trajectory, velocity distribution, and temperature changes of the flue gas within the channel. The system performs calculations to generate flue gas flow field prediction data, which is then sent to the adaptive grid combustion optimization module. In the fourth step, the adaptive grid combustion optimization module receives the flue gas flow field prediction data, constructs a three-dimensional grid model of the combustion zone based on the adaptive grid combustion optimization algorithm, substitutes the flue gas flow field data into the model to optimize parameters related to combustion temperature and efficiency, obtains the optimal combination of combustion parameters, and transmits it to the combustion support system big data analysis module. In the fifth step, the combustion support system big data analysis module collects operating parameters transmitted from different modules, including mixed gas flow rate, temperature, flue gas pressure, and combustion parameters. Through big data analysis, it determines the current system operating status and generates a mixing ratio adjustment command for the gas-nitrogen dynamic mixing balance module. In the sixth step, the gas-nitrogen dynamic mixing balance module receives the adjustment command, calls the gas-nitrogen dynamic mixing balance model to dynamically adjust the mixing ratio of air and polluted nitrogen, and sends the adjusted ratio signal to the air-polluted nitrogen mixing module, forming a closed-loop system operation.

[0015] Beneficial Effects: This invention proposes a system and method for incorporating polluted nitrogen into air for combustion via flue gas heat exchange. Through the coordinated operation of a dynamic air-nitrogen mixing and balancing module, an adaptive grid combustion optimization module, a flue gas flow field distribution prediction module, a combustion system big data analysis module, a flue gas heat transfer module, and an air-nitrogen mixing module, it addresses the lack of dynamic balancing adjustment capabilities for mixing ratios in existing technologies. The dynamic air-nitrogen mixing and balancing module receives real-time parameters from the combustion system big data analysis module and dynamically adjusts the air-nitrogen mixing ratio, avoiding uneven heat and concentration distribution in the mixed airflow and ensuring stable combustion and continuous combustion efficiency. Optimization: Addressing the issue of the lack of a multi-stage collaborative mechanism in existing technologies, the flue gas flow field distribution prediction module transforms flue gas temperature and pressure parameters into flow field prediction data, providing accurate basis for the adaptive grid combustion optimization module. The combustion system big data analysis module integrates the operating parameters of each module to generate control commands, solving the problem of parameter control lag and improving flue gas heat exchange efficiency and waste nitrogen utilization. Overall, it achieves waste nitrogen resource recovery and efficient utilization of flue gas waste heat, reducing energy waste and environmental burden, meeting the dual requirements of industrial combustion for stability and efficiency, and forming an integrated technical system for the synergistic optimization of waste nitrogen utilization and flue gas heat exchange. Attached Figure Description

[0016] Figure 1 This is a diagram showing the system module composition of the present invention; Figure 2 This is a flowchart of the method steps of the present invention. Detailed Implementation

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] like Figure 1 As shown, the air mixed with polluted nitrogen is used in the flue gas heat exchange combustion system, which includes: a gas-nitrogen dynamic mixing and equalization module, an adaptive grid combustion optimization module, a flue gas flow field distribution prediction module, a combustion system big data analysis module, a flue gas heat exchange and conduction module, and an air-nitrogen mixing module.

[0019] The gas-nitrogen dynamic mixing and balancing module receives real-time parameters of waste nitrogen flow rate and air flow rate output by the big data analysis module of the combustion system, and dynamically adjusts the mixing ratio of waste nitrogen and air through the gas-nitrogen dynamic mixing and balancing model, and delivers the adjusted mixed airflow to the air-nitrogen mixing module.

[0020] Specifically, the gas-nitrogen dynamic mixing and balancing module enables real-time dynamic adjustment of the air-to-nitrogen mixing ratio. This process requires receiving real-time parameters of the nitrogen and air flow rates from the combustion system's big data analysis module. The nitrogen flow rate typically ranges from 50-200 m³ / h, while the air flow rate typically ranges from 100-300 m³ / h. Using a built-in gas-nitrogen dynamic mixing and balancing model, the module calculates the optimal mixing ratio based on the received real-time parameters, maintaining the mixing ratio adjustment accuracy within ±2% and achieving a response time of no more than 100 ms. In practical implementation, a flow regulating valve is installed within the module. The valve opening can be continuously adjusted based on the model calculation results. When the detected waste nitrogen flow rate is higher than 150 m³ / h, the valve will increase the air inflow accordingly. When the waste nitrogen flow rate is lower than 80 m³ / h, the valve will reduce the air inflow to ensure that the nitrogen content in the mixed airflow is stable between 25% and 35%. This provides a uniformly mixed airflow for subsequent heat exchange and combustion processes. The stable operation of this module can avoid the decrease in combustion efficiency caused by fluctuations in the mixing ratio and improve the overall energy utilization rate of the system.

[0021] The adaptive grid combustion optimization module receives the flow field velocity and temperature distribution data output by the flue gas flow field distribution prediction module, constructs a combustion area grid model based on the adaptive grid combustion optimization algorithm, performs optimization calculations on the combustion parameters, and transmits the optimization results to the combustion-supporting system big data analysis module.

[0022] Specifically, the adaptive grid combustion optimization module is used to accurately optimize the parameters of the combustion zone. During implementation, it receives flow field velocity and temperature distribution data output from the flue gas flow field distribution prediction module. The flow field velocity data includes velocity values ​​at different cross-sections within the combustion zone, with a velocity detection range of 0.5-5 m / s. The temperature distribution data includes temperature values ​​at different points within the combustion zone, with a temperature detection range of 800-1200℃. The module constructs a three-dimensional grid model of the combustion zone based on the adaptive grid combustion optimization algorithm. The grid density is dynamically adjusted according to the gradient of flow field velocity and temperature changes. In regions where the velocity change rate exceeds 0.8 m / s·m and the temperature change rate exceeds 100℃ / m, the grid cell size is reduced to 5-10 mm. In regions with gradual parameter changes, the grid cell size can be expanded to 20-30 mm to balance computational accuracy and efficiency. During the optimization process, the module iteratively calculates parameters such as combustion temperature, oxygen concentration, and fuel supply. Each iteration step is controlled between 5% and 10%, and the number of iterations is no less than 20, until the optimal parameter combination that achieves a combustion efficiency of over 90% and pollutant emissions of less than 50 mg / m³ is found. The optimization results are then transmitted to the big data analysis module of the combustion support system to provide combustion parameter basis for the overall system control.

[0023] The flue gas flow field distribution prediction module receives the flue gas temperature and pressure parameters output by the flue gas heat transfer and conduction module, uses the flue gas flow field distribution prediction model to predict the flue gas flow trajectory and distribution state, and sends the prediction data to the adaptive grid combustion optimization module and the combustion system big data analysis module respectively.

[0024] Specifically, the flue gas flow field distribution prediction module is responsible for predicting the flow trajectory and distribution of flue gas. During implementation, it needs to receive flue gas temperature and pressure parameters output from the flue gas heat exchange module. Temperature parameter detection points are set at the inlet, outlet, and intermediate section of the heat exchange module. The inlet temperature is typically 300-500℃, and the outlet temperature is typically 150-250℃. Pressure parameter detection points correspond to the temperature detection points; the inlet pressure is typically 0.12-0.15MPa, and the outlet pressure is typically 0.10-0.12MPa. The module uses the flue gas flow field distribution prediction model, taking the received temperature and pressure parameters as input, and combining them with the geometric dimensions of the flue gas channel (channel diameter typically 0.3-0.8m, length typically 5-10m), to calculate the velocity, temperature, and pressure distribution of the flue gas at different spatial coordinates within the channel. During the prediction process, the time step is set to 0.1-0.5s, and the spatial step is divided according to the channel size, with a horizontal step of 0.05-0.1m and a vertical step of 0.2-0.5m. The prediction results are presented in the form of a data matrix, including the flow field parameters of 100-200 monitoring points in the channel. The prediction data is sent to the adaptive grid combustion optimization module and the combustion system big data analysis module, respectively, to provide real-time flow field data support for combustion optimization and system control, and to avoid the problems of reduced heat exchange efficiency and combustion instability caused by flow field turbulence.

[0025] The combustion-supporting system big data analysis module collects the operating parameters of the gas-nitrogen dynamic mixing and balancing module, the adaptive grid combustion optimization module, the flue gas flow field distribution prediction module, the flue gas heat transfer and conduction module, and the air-nitrogen mixing module. It generates parameter control commands through big data analysis and sends them to different modules respectively.

[0026] Specifically, the big data analysis module of the combustion-supporting system is the central control unit of the system. During implementation, it needs to collect operating parameters from the gas-nitrogen dynamic blending and balancing module, the adaptive grid combustion optimization module, the flue gas flow field distribution prediction module, the flue gas heat transfer module, and the air-to-nitrogen blending module. The collected parameters include flow rate, temperature, pressure, blending ratio, and combustion efficiency. The parameter acquisition frequency is set to 1-5Hz to ensure real-time data. The module includes a data storage unit with a capacity of no less than 100GB, capable of storing nearly 30 days of historical operating data. It also incorporates a big data analysis algorithm to compare and analyze real-time parameters with historical data, identifying parameter change trends. When a parameter deviates from a preset threshold (e.g., temperature deviates from the set value by ±50℃, flow rate deviates from the set value by ±10%), the algorithm calculates the parameter adjustment requirements. In practice, the module generates parameter control instructions based on the analysis results. The instructions include the adjustment target values ​​of each module, such as the mixing ratio target value of the gas-nitrogen dynamic mixing and equalization module and the flow adjustment target value of the flue gas heat exchange and conduction module. The instruction transmission adopts industrial Ethernet with a transmission rate of not less than 100Mbps to ensure that the instructions are quickly delivered to each module, realize the real-time optimization of the system operation status, and improve the overall stability and reliability of the system.

[0027] The flue gas heat exchange and conduction module receives the mixed airflow output by the air-nitrogen mixing module, exchanges heat with the external flue gas, and delivers the heat-exchanged mixed airflow to the combustion device. At the same time, the flue gas heat exchange parameters are transmitted to the flue gas flow field distribution prediction module.

[0028] Specifically, the flue gas heat exchange module is responsible for the heat exchange between the mixed airflow and the flue gas. During implementation, it needs to receive the mixed airflow output from the air-to-nitrogen mixing module. The inlet temperature of the mixed airflow is typically 20-50℃, and the inlet flow rate is consistent with the output flow rate of the air-to-nitrogen mixing module, which is 150-500 m³ / h. A heat exchange core is installed within the module, made of 316L stainless steel. The heat exchange area is designed according to the flow rate and heat exchange requirements, typically 50-200 m². The core structure uses a finned design with a fin spacing of 5-10 mm to increase the heat exchange area. High-temperature flue gas enters from the other side of the module, with an inlet temperature of 300-500℃ and an inlet pressure of 0.12-0.15 MPa. The flue gas and the mixed airflow exchange heat in the core using a counter-current heat exchange method. After absorbing heat from the flue gas, the temperature of the mixed airflow rises to 100-200℃, and after releasing heat, the temperature of the flue gas drops to 150-250℃. During the heat exchange process, the module monitors the inlet and outlet temperatures in real time through temperature sensors and the inlet and outlet pressures through pressure sensors. It transmits the flue gas heat exchange parameters (including inlet and outlet temperatures, pressures, and heat exchange capacity) to the flue gas flow field distribution prediction module. At the same time, it delivers the mixed gas flow with increased temperature after heat exchange to the combustion device. The efficient operation of this module can realize the recovery and utilization of flue gas waste heat, reduce the energy consumption of the combustion process, and reduce the impact of direct emissions of high-temperature flue gas on the environment.

[0029] The air-nitrogen mixing module receives the adjustment signal output by the air-nitrogen dynamic mixing and equalization module, mixes the air and nitrogen, and delivers the mixed airflow to the flue gas heat exchange and conduction module.

[0030] Specifically, the air-nitrogen blending module is responsible for the initial blending of air and nitrogen. During implementation, it receives adjustment signals from the dynamic blending equilibrium module, including the target blending ratio of air to nitrogen, typically ranging from 1:1 to 3:1. The module contains a blending chamber with a volume determined by the design flow rate, usually 0.5-2 m³. Stirring blades are installed within the chamber, rotating at 100-300 r / min to promote thorough mixing. Air enters from one inlet on one side of the module, equipped with a flow control valve with an adjustment accuracy of ±1%. Nitrogen enters from the other inlet, also equipped with a flow control valve. Both valves adjust their openings synchronously according to the adjustment signals, controlling the inflow of air and nitrogen. In practice, when the mixing ratio is set to 2:1, the air flow control valve is adjusted to the corresponding 200 m³ / h position, and the waste nitrogen flow control valve is adjusted to the corresponding 100 m³ / h position. After the air and waste nitrogen are stirred by the stirring blades in the chamber for 10-30 seconds, a uniformly mixed airflow is formed. The uniformity error of the mixed airflow is controlled within ±3%. The mixed airflow is then delivered to the flue gas heat exchange and conduction module. The stable operation of this module can ensure the consistency of the mixed airflow composition during subsequent heat exchange and combustion processes, avoiding local combustion abnormalities caused by uneven mixing.

[0031] Preferably, the expression for the gaseous-nitrogen dynamic mixing equilibrium model is: in, This represents the theoretical heat value of the mixed airflow. The nitrogen-pollution mixing coefficient is given. This represents the real-time flow rate of nitrogen wastewater. The specific heat capacity of nitrogen at constant pressure is given by the polluted nitrogen. The initial temperature of the polluted nitrogen. The air mixing coefficient, Real-time airflow The specific heat capacity of air at constant pressure. The initial temperature of the air. This is the pressure influence coefficient. For the pressure difference between the inlet and outlet of the mixed system, This represents the total flow rate of the mixed airflow.

[0032] Specifically, the dynamic mixing equilibrium model of gas and nitrogen is used to accurately calculate the theoretical heat value of the mixed airflow. During implementation, multiple real-time parameters of waste nitrogen and air need to be obtained. The waste nitrogen mixing coefficient is typically adjusted between 0.3 and 0.7 based on the system load, while the air mixing coefficient is reverse-matched between 0.7 and 0.3 to ensure the sum of the two coefficients is 1. The real-time flow rate monitoring range for waste nitrogen is 50-200 m³ / h, with a constant-pressure specific heat capacity of 1.038 kJ / (kg・℃), and an initial temperature typically between 25-40℃. The real-time flow rate monitoring range for air is 100-300 m³ / h, with a constant-pressure specific heat capacity of 1.005 kJ / (kg・℃), and an initial temperature similar to that of waste nitrogen, between 20-35℃. The pressure influence coefficient is set to 0.02-0.05 through experimental calibration, the pressure difference between the inlet and outlet of the mixing system is controlled between 0.01-0.03 MPa, and the total flow rate of the mixed airflow is the sum of the waste nitrogen and air flow rates, i.e., 150-500 m³ / h. When implementing this model, the above parameters are first collected and input into the calculation. The theoretical heat value of the mixed airflow needs to be stable within 30-50 kJ / kg. If it deviates from this range, the module will automatically adjust the mixing coefficient to ensure that the heat of the mixed airflow meets the subsequent heat exchange and combustion requirements, avoid the decrease in combustion efficiency caused by the heat fluctuation of the mixed airflow, and provide a basic heat guarantee for the stable operation of the system. During the implementation process, the parameter acquisition frequency is 1 Hz, and the calculation response time does not exceed 50 ms to ensure real-time performance and accuracy.

[0033] Preferably, the adaptive grid combustion optimization algorithm is expressed as follows: ,in, To find the optimal objective function value for combustion, This represents the number of horizontal nodes in the grid. This represents the number of nodes in the vertical direction of the grid. For grid nodes The density of the mixed airflow at that location, For grid nodes At the airflow velocity, For grid nodes Lateral step size, For grid nodes Vertical step size, Temperature weighting coefficient, To optimize the time frame, For a moment Grid nodes Temperature, To set the combustion temperature.

[0034] Specifically, the adaptive grid combustion optimization algorithm is used to calculate the objective function value of combustion optimization to determine the optimal combustion parameters. During implementation, the combustion region needs to be divided into grids, with 20-40 horizontal nodes and 30-50 vertical nodes, forming 600-2000 grid nodes. The mixed airflow density at each grid node is calculated based on temperature and pressure, ranging from 0.8-1.2 kg / m³, and the airflow velocity monitoring range is 0.5-5 m / s. The horizontal and vertical step sizes are set to 0.05-0.1 m and 0.1-0.2 m respectively, depending on the node distribution. The temperature weighting coefficient is adjusted between 0.4 and 0.6 according to combustion stability requirements. The optimization time period is typically set to 5-10 minutes. The real-time temperature monitoring range at each grid node is 800-1200℃, and the set combustion temperature is fixed at 900-1100℃ based on the fuel type. During implementation, the parameters of each node are first collected by sensors and then substituted into the algorithm to calculate the objective function value. The objective function value needs to be controlled between 0.8 and 1.2. If it exceeds the range, the grid density and combustion parameters are adjusted, and the calculation is iterated until the target is met. Through precise optimization, the combustion efficiency is maintained above 90%, and the pollutant emission is below 50 mg / m³. The algorithm calculation cycle and parameter acquisition frequency are synchronized at 1 Hz to ensure that the optimization results can guide the combustion adjustment in real time.

[0035] Preferably, the expression for the flue gas flow field distribution prediction model is: ,in, Spatial coordinates flue gas velocity, The initial flow velocity at the flue gas inlet. This refers to the cross-sectional area of ​​the flue gas inlet. cross section Area of ​​flue gas circulation, Spatial coordinates flue gas temperature, The initial temperature at the flue gas inlet. The drag coefficient is along the height direction. For spatial height coordinates, This represents the total length of the flue gas passage.

[0036] Specifically, the flue gas flow field distribution prediction model is used to calculate the flue gas velocity under different spatial coordinates. During implementation, it is necessary to determine the initial flow velocity at the flue gas inlet. This velocity is calculated based on the flue gas flow rate and the inlet cross-sectional area, ranging from 3 to 8 m / s. The inlet cross-sectional area is calculated based on the channel diameter (0.3-0.8 m) and is 0.07-0.5 m². The flue gas flow area at the cross-section varies with the channel shape, ranging from 0.05 to 0.45 m² in a rectangular channel and consistent with the inlet cross-sectional area in a circular channel. The flue gas temperature monitoring range under different spatial coordinates is 150-500℃, and the initial inlet temperature is consistent with the high-temperature flue gas inlet temperature, which is 300-500℃. The drag coefficient along the height direction is experimentally determined to be 0.01-0.03. The spatial height coordinate is set to 0-10 m based on the channel length (5-10 m), and the total channel length is fixed at the design value. During implementation, temperature and pressure parameters at various spatial points are first collected and substituted into the model to calculate the flue gas velocity. The calculated velocity must be within ±5% of the actual monitored value. If the error exceeds the standard, the drag coefficient is corrected to accurately predict the flue gas field distribution, providing data support for adaptive grid combustion optimization and avoiding the reduction in heat transfer efficiency caused by flow field turbulence. The model calculation time step is 0.1-0.5s, which matches the parameter acquisition frequency.

[0037] Preferably, the data processing model expression of the big data analysis module of the combustion-supporting system is: ,in, Output control parameter values ​​to the module. For the number of data collection categories, For the first Class parameter weight coefficients For the first Real-time parameter acquisition, For the first Class parameter stability coefficient, The parameter change rate weighting coefficient, This represents the rate of change of the sum of all collected parameters.

[0038] Specifically, the big data analysis module of the combustion-supporting system uses a data processing model to calculate the control parameter values ​​output by the module. During implementation, the number of data collection types includes 8-12 categories such as flow rate, temperature, pressure, blending ratio, and combustion efficiency. The weighting coefficients for each parameter are set according to their importance, with combustion efficiency having the highest weighting coefficient of 0.2-0.3, followed by temperature and pressure at 0.15-0.25, and flow rate and blending ratio at 0.1-0.2. Each real-time collected parameter has a fixed monitoring range, such as temperature of 20-1200℃ and pressure of 0.1-0.15MPa. The stability coefficient is set according to parameter fluctuations; parameters with small fluctuations (such as isobaric specific heat capacity) have a stability coefficient of 0.9-0.95, while parameters with large fluctuations (such as flow rate) have a stability coefficient of 0.7-0.85. The parameter change rate weighting coefficient is 0.1-0.2, and the total change rate of all collected parameters is calculated based on system load changes, ranging from -5% to 5%. During implementation, the module collects various parameters every 1 second, substitutes them into the model to calculate the control parameter values, and the calculation results must conform to the adjustment range of each module. The data from multiple modules are integrated to generate precise control commands to ensure that all modules of the system operate in coordination. After the control commands are generated, they are transmitted to each module within 100ms via industrial Ethernet.

[0039] Preferably, the heat transfer calculation model expression for the flue gas heat transfer module is as follows: ,in, To exchange heat, The heat transfer coefficient, For heat exchange area, The flue gas inlet temperature, The flue gas outlet temperature, The inlet temperature of the mixed airflow. The outlet temperature of the mixed gas flow. The coefficient of efficiency for flue gas heat transfer. The heat absorption efficiency coefficient of the mixed airflow.

[0040] Specifically, the big data analysis module of the combustion-supporting system uses a data processing model to calculate the control parameter values ​​output by the module. During implementation, the number of data collection types includes 8-12 categories such as flow rate, temperature, pressure, blending ratio, and combustion efficiency. The weighting coefficients for each parameter are set according to their importance, with combustion efficiency having the highest weighting coefficient of 0.2-0.3, followed by temperature and pressure at 0.15-0.25, and flow rate and blending ratio at 0.1-0.2. Each real-time collected parameter has a fixed monitoring range, such as temperature of 20-1200℃ and pressure of 0.1-0.15MPa. The stability coefficient is set according to parameter fluctuations; parameters with small fluctuations (such as isobaric specific heat capacity) have a stability coefficient of 0.9-0.95, while parameters with large fluctuations (such as flow rate) have a stability coefficient of 0.7-0.85. The parameter change rate weighting coefficient is 0.1-0.2, and the total change rate of all collected parameters is calculated based on system load changes, ranging from -5% to 5%. During implementation, the module collects various parameters every 1 second, substitutes them into the model to calculate the control parameter values, and the calculation results must conform to the adjustment range of each module. The data from multiple modules are integrated to generate precise control commands to ensure that all modules of the system operate in coordination. After the control commands are generated, they are transmitted to each module within 100ms via industrial Ethernet.

[0041] Preferably, the flue gas flow field distribution prediction module includes a flue gas parameter acquisition unit, a flow field data preprocessing unit, a prediction model calculation unit, and a result output unit. The flue gas parameter acquisition unit continuously acquires temperature, pressure, and flow velocity data of flue gas at different cross-sections and heights using temperature sensors, pressure sensors, and flow velocity sensors located at different positions in the flue gas channel, and transmits the acquired raw data to the flow field data preprocessing unit. The flow field data preprocessing unit removes outliers from the received raw data, eliminates interference signals during the acquisition process through data smoothing, classifies and organizes the data according to spatial coordinates to form a structured flow field dataset, and transmits it to the prediction model calculation unit. The prediction model calculation unit calls the flue gas flow field distribution prediction model, substitutes the structured flow field dataset into the model for calculation, solves for the velocity, temperature, and pressure distribution values ​​of flue gas under different spatial coordinates, generates a flow field distribution prediction matrix, and transmits it to the result output unit. The result output unit converts the flow field distribution prediction matrix into a format that meets the data interface requirements of each associated module, and sends the prediction data to the adaptive grid combustion optimization module and the combustion-supporting system big data analysis module at a preset frequency.

[0042] Specifically, the flue gas flow field distribution prediction module includes four units: flue gas parameter acquisition, flow field data preprocessing, prediction model calculation, and result output. During implementation, these units operate collaboratively. The flue gas parameter acquisition unit uses temperature sensors (measurement range -40℃ to 800℃, accuracy ±0.5℃), pressure sensors (measurement range 0-0.5MPa, accuracy ±0.001MPa), and flow velocity sensors (measurement range 0-10m / s, accuracy ±0.01m / s) installed at the flue gas inlet and outlet, intermediate cross-section, and different heights to collect data every 0.5 seconds, ensuring coverage of 20-30 monitoring points within the channel. The flow field data preprocessing unit receives the raw data and uses a moving average method to remove outliers (data with a deviation exceeding 10% of the average is considered outlier), then processes the data... The data is categorized by spatial coordinates (x-axis 0-10m, y-axis 0-0.8m, z-axis 0-5m) to form a structured dataset, with processing time controlled within 0.3 seconds. The prediction model calculation unit calls the flue gas flow field distribution prediction model, substitutes the dataset into the calculation, and outputs the flow velocity, temperature, and pressure values ​​under different coordinates. The calculation error must be less than 5%, and the calculation time must not exceed 1 second. The result output unit converts the prediction matrix into RS485 protocol format and sends it to the associated module at a frequency of 2 seconds / time. This provides accurate flow field data for combustion optimization, avoids combustion parameter deviations caused by flow field data lag, and ensures combustion stability.

[0043] Preferably, the big data analysis module of the combustion-supporting system includes a parameter receiving unit, a data storage unit, an analysis and calculation unit, and an instruction generation unit. The parameter receiving unit receives operating parameters sent by the gas-nitrogen dynamic mixing and equalization module, the adaptive grid combustion optimization module, the flue gas flow field distribution prediction module, the flue gas heat transfer and conduction module, and the air-nitrogen mixing module through a data transmission interface. The parameters are format-verified to ensure data integrity before being transmitted to the data storage unit. The data storage unit categorizes and stores the received operating parameters according to timestamps, establishing a historical parameter database. Simultaneously, it backs up the data to prevent data loss and provides data query support for the analysis and calculation unit. The analysis and calculation unit retrieves real-time and historical parameters from the data storage unit, identifies parameter change trends using big data analysis algorithms, determines the operating status of different modules based on preset operating thresholds, calculates parameter control requirements, and generates a preliminary control plan. The instruction generation unit verifies the feasibility of the preliminary control plan, modifies the plan according to the parameter adjustment range of different modules, converts the modified control plan into specific parameter control instructions, and sends them to the relevant modules through the data transmission interface.

[0044] Specifically, the big data analysis module of the combustion-supporting system consists of four units: parameter reception, data storage, analysis and computation, and command generation. The implementation process emphasizes data integration and command accuracy. The parameter reception unit receives data from each module via an Ethernet interface (100Mbps transmission rate), verifies the data format (JSON format), and immediately reports any format errors, ensuring that 30-50 sets of parameters (including 8 categories of parameters such as flow rate, temperature, and pressure) are received every second. The data storage unit uses an SQL database (500GB capacity), storing data categorized by timestamp (accurate to milliseconds), retaining nearly 60 days of historical data, and simultaneously enabling a RAID1 backup mechanism to prevent data loss, with a data write speed of no less than 10MB / s. The analysis and computation unit retrieves real-time and historical data from the database. According to the data, the system identifies parameter changes through trend analysis (e.g., if the flow rate fluctuates by more than 8% every 5 minutes, it is marked), and calculates the control requirements based on preset thresholds (e.g., temperature threshold of 800-1200℃), generating 3-5 preliminary solutions with a calculation cycle of 2 seconds. The instruction generation unit verifies the feasibility of the solution (checking whether it exceeds the module adjustment range, such as the blending ratio adjustment range of 0.3-0.7), corrects it, and converts it into Modbus protocol instructions, which are sent to each module within 0.5 seconds via the industrial bus. This enables multi-module collaborative control, avoids the impact of single module parameter misalignment on the overall system efficiency, and improves the reliability of system operation.

[0045] Preferably, the flue gas heat exchange and conduction module includes a mixed airflow inlet unit, a flue gas inlet unit, a heat exchange core unit, and an airflow outlet unit. The mixed airflow inlet unit receives the mixed airflow delivered by the air-nitrogen mixing module and controls the inlet speed of the mixed airflow through a flow regulating valve to evenly distribute the mixed airflow to the inlet of the flow channel of the heat exchange core unit. The flue gas inlet unit receives high-temperature flue gas delivered from the outside and guides the flue gas into the heat exchange core unit along a preset path through a flow guiding structure to avoid the formation of eddies at the inlet and ensure that the flue gas flows evenly through the heat exchange core. The heat exchange core unit increases the heat exchange area through an internal fin structure, so that the mixed airflow and high-temperature flue gas exchange heat in the core in a countercurrent manner. The mixed airflow absorbs heat from the flue gas and its temperature rises, while the flue gas releases heat and its temperature drops, completing the heat transfer process. The airflow outlet unit delivers the heated mixed airflow to the combustion device and guides the cooled flue gas to the subsequent treatment stage. The pressure monitoring element monitors the airflow pressure at the outlet in real time and feeds the pressure data back to the big data analysis module of the combustion system.

[0046] Specifically, the flue gas heat exchange module comprises four units: a mixed airflow inlet, a flue gas inlet, a heat exchange core, and an airflow outlet. Implementation focuses on heat exchange efficiency and airflow stability. The mixed airflow inlet unit receives the mixed airflow (flow rate 150-500 m³ / h) output from the mixing module and controls the flow velocity (stabilizing at 2-5 m / s) via an electric regulating valve (adjustment accuracy ±1%), evenly distributing the airflow to 10-15 channels within the core. The flue gas inlet unit receives external high-temperature flue gas (temperature 300-500℃, pressure 0.12-0.15 MPa) and guides the airflow through a guide plate (tilt angle 30°-45°) to prevent turbulence and ensure uniform flow of the flue gas throughout the core. The heat exchange core unit employs a 316L stainless steel finned structure (fin spacing 5-8 mm, thickness 0 mm). The heat exchange area is 50-200m², with the mixed airflow and flue gas exchanging heat in a counter-current manner. This raises the temperature of the mixed airflow from 20-50℃ to 100-200℃, while the flue gas temperature drops to 150-250℃. The heat exchange efficiency must be maintained above 85%. The airflow outlet unit delivers the heated mixed airflow (pressure 0.1-0.12MPa) to the combustion device. At the same time, the outlet pressure is monitored by a pressure sensor (accuracy ±0.001MPa), and the data is fed back to the analysis module every second. This efficiently recovers the waste heat of the flue gas, reduces combustion energy consumption, and avoids core damage caused by airflow impact, thus extending the service life of the equipment.

[0047] The dynamic blending equilibrium model for air and nitrogen is a computational model used to dynamically adjust the blending ratio of air and waste nitrogen. Based on real-time collected airflow parameters, it calculates the optimal blending ratio through an algorithm to ensure stable composition and heat in the mixed airflow. In implementation, the model receives real-time parameters of waste nitrogen flow rate (50-200 m³ / h) and air flow rate (100-300 m³ / h) from the combustion system's big data analysis platform. Combining these parameters with fundamental properties such as the isobaric specific heat capacity and initial temperature of waste nitrogen and air, the model dynamically calculates the blending ratio using a built-in algorithm. During the calculation, the inlet and outlet pressure difference of the mixing system (0.01-0.03 MPa) is simultaneously referenced to ensure the blending ratio adjustment accuracy is controlled within ±2%, and the adjustment response time does not exceed 100 ms. Simultaneously, the flow control valve is driven to adjust its opening, stabilizing the nitrogen content in the mixed airflow at 25%-35%. This model provides a uniformly composed and thermally stable mixed airflow for subsequent heat exchange and combustion processes, preventing a decrease in combustion efficiency due to fluctuations in the blending ratio. This enables the precise utilization of waste nitrogen resources, reduces energy consumption during pure air combustion, lays the foundation for the overall stable operation of the system, and contributes to energy conservation and consumption reduction in the industrial combustion field.

[0048] The adaptive grid combustion optimization algorithm is a computational method for optimizing combustion zone parameters. It constructs a dynamic grid model and iteratively calculates optimal combustion parameters using flow field data to improve combustion efficiency and reduce pollutant emissions. In implementation, the algorithm first receives flow field velocity (0.5-5 m / s) and temperature distribution (800-1200℃) data from a flue gas flow field distribution prediction model. Based on the parameter variation gradient, it dynamically adjusts the three-dimensional grid density of the combustion zone—reducing the grid cell size to 5-10 mm in regions with velocity variation exceeding 0.8 m / s·m and temperature variation exceeding 100℃ / m, and expanding it to 20-30 mm in regions with relatively flat parameters. Subsequently, parameters such as mixed gas flow density (0.8-1.2 kg / m³) are substituted into the calculations, and combustion temperature, oxygen concentration, and fuel supply are iteratively calculated. Each iteration step is controlled at 5%-10%, and the number of iterations is no less than 20, until a parameter combination that achieves combustion efficiency of over 90% and pollutant emissions below 50 mg / m³ is obtained. This algorithm provides precise parameter guidance for the combustion process, ensuring that the combustion state is always at an optimal level. It breaks through the limitations of traditional fixed-parameter combustion, dynamically optimizing and balancing combustion efficiency with environmental requirements, adapting to the complex changes in industrial combustion conditions.

[0049] The flue gas flow field distribution prediction model is a computational model used to predict the flow trajectory and parameter distribution of flue gas in the channel. It extrapolates the flow field state through real-time parameters, providing data support for combustion optimization and system control. During implementation, the model needs to receive flue gas temperature (inlet 300-500℃, outlet 150-250℃) and pressure (inlet 0.12-0.15MPa, outlet 0.10-0.12MPa) parameters output from the flue gas heat exchange and conduction module. Combined with the flue gas channel geometry (diameter 0.3-0.8m, length 5-10m), and using a time step of 0.1-0.5s and a spatial step of 0.05-0.5m, it calculates the velocity, temperature, and pressure distribution of the flue gas at different spatial coordinates, covering 100-200 monitoring points within the channel. A flow field distribution prediction matrix is ​​generated, and the error between the calculated results and the actual monitored values ​​must be controlled within ±5%. Subsequently, the predicted data is converted into a format that meets the interface requirements and sent at a preset frequency to the adaptive grid combustion optimization algorithm and the combustion-supporting system big data analysis platform. This model provides real-time feedback on the flue gas flow field status, offering precise data for combustion parameter optimization and system control. To avoid reduced heat exchange efficiency and unstable combustion caused by turbulent flow field, this system achieves efficient recovery and utilization of waste heat from flue gas, and enhances overall system synergy.

[0050] The combustion system's big data analysis platform serves as the central control hub coordinating the operation of various modules. By integrating data from multiple modules and analyzing it, it generates control commands to achieve dynamic optimization of the entire system. In practice, the platform first receives operating parameters (covering 8-12 categories such as flow rate, temperature, and pressure) from modules including the gas-nitrogen dynamic mixing equilibrium model, the adaptive grid combustion optimization algorithm, and the flue gas flow field distribution prediction model via a data interface. The data is collected at a frequency of 1-5Hz and stored in a database with a capacity of at least 100GB, retaining historical data for the past 30 days. Then, big data analysis algorithms identify parameter change trends and, combined with preset thresholds (such as temperature deviation ±50℃ and flow rate deviation ±10%), determine the operating status, calculate control requirements, and generate a preliminary plan. Finally, the plan is validated for feasibility, revised, and converted into specific control commands, which are then sent to each module via industrial Ethernet (transmission rate at least 100Mbps), with a transmission time not exceeding 100ms. This platform enables data collaboration and precise control across modules, preventing system efficiency from being affected by the misalignment of a single module. By breaking down data barriers between modules and building an integrated control system, the system can be kept running stably under complex conditions, maximizing energy efficiency.

[0051] like Figure 2As shown, an air-nitrogen-mixed flue gas heat exchange combustion-assisted method is applied to an air-nitrogen-mixed flue gas heat exchange combustion-assisted system, comprising the following steps: First, the air-nitrogen mixing module receives a mixing ratio adjustment signal output from the air-nitrogen dynamic mixing equilibrium module, and introduces air and nitrogen into the mixing chamber according to the ratio set by the adjustment signal. The stirring structure within the chamber allows for initial mixing of the air and nitrogen, forming an initial mixed airflow. Second, the initial mixed airflow enters the flue gas heat exchange and conduction module, where it exchanges heat with the high-temperature flue gas in the heat exchange core. The mixed airflow absorbs heat from the flue gas, causing its temperature to rise. The heat-exchanged mixed airflow is then transported to the combustion zone. Simultaneously, the flue gas heat exchange and conduction module transmits the temperature and pressure parameters from the heat exchange process to the flue gas flow field distribution prediction module. Third, the flue gas flow field distribution prediction module receives the parameters output by the flue gas heat exchange and conducts a flue gas flow field distribution prediction model to predict the flow trajectory, velocity distribution, and temperature changes of the flue gas within the channel. The system performs calculations to generate flue gas flow field prediction data, which is then sent to the adaptive grid combustion optimization module. In the fourth step, the adaptive grid combustion optimization module receives the flue gas flow field prediction data, constructs a three-dimensional grid model of the combustion zone based on the adaptive grid combustion optimization algorithm, substitutes the flue gas flow field data into the model to optimize parameters related to combustion temperature and efficiency, obtains the optimal combination of combustion parameters, and transmits it to the combustion support system big data analysis module. In the fifth step, the combustion support system big data analysis module collects operating parameters transmitted from different modules, including mixed gas flow rate, temperature, flue gas pressure, and combustion parameters. Through big data analysis, it determines the current system operating status and generates a mixing ratio adjustment command for the gas-nitrogen dynamic mixing balance module. In the sixth step, the gas-nitrogen dynamic mixing balance module receives the adjustment command, calls the gas-nitrogen dynamic mixing balance model to dynamically adjust the mixing ratio of air and polluted nitrogen, and sends the adjusted ratio signal to the air-polluted nitrogen mixing module, forming a closed-loop system operation.

[0052] The air-nitrogen mixed-into-air combustion system and method utilizes a dynamic air-nitrogen mixing and equalization module in conjunction with a big data analysis module for the combustion system. The former receives real-time parameters such as nitrogen and air flow rates from the latter and dynamically adjusts the mixing ratio of air and nitrogen to avoid uneven heat and concentration distribution in the mixed airflow, thus ensuring the stability of the subsequent combustion process from the source. Addressing the issue of parameter control lag caused by the lack of a multi-stage collaborative mechanism in existing technologies, the flue gas flow field distribution prediction module converts the temperature and pressure parameters output by the flue gas heat transfer module into flow field prediction data, providing accurate input for the adaptive grid combustion optimization module. The big data analysis module for the combustion system integrates the operating parameters of each module to generate control commands, achieving seamless integration of flue gas flow field prediction, combustion parameter optimization, and system control, thus solving the parameter control lag problem.

[0053] Meanwhile, this system and method, through the cooperation of the flue gas heat exchange and conduction module and the air-nitrogen mixing module, mixes air and nitrogen, raises the temperature through flue gas heat exchange, and then participates in combustion, realizing the recovery of nitrogen resources and the efficient utilization of flue gas waste heat, reducing energy waste and environmental burden. On the other hand, with the help of the adaptive grid combustion optimization module to construct and optimize the grid model of the combustion area and the precise data support of the flue gas flow field distribution prediction module, the combustion parameters can be continuously optimized to improve combustion efficiency, meet the dual requirements of industrial combustion for stability and high efficiency, and ultimately form an integrated technical system for the coordinated operation of nitrogen utilization, flue gas heat exchange and combustion optimization, providing a reliable technical solution for energy saving and consumption reduction in the field of industrial combustion.

[0054] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0055] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. The air incorporation of waste nitrogen combustion system by flue gas heat exchange, its characterized in that, The air-nitrogen dynamic blending balance module receives the real-time parameters of the nitrogen flow and the air flow output by the combustion-supporting system big data research and judgment module, dynamically adjusts the blending ratio of the nitrogen and the air through the air-nitrogen dynamic blending balance model, and delivers the adjusted mixed gas flow to the air-nitrogen blending module; The adaptive grid combustion optimization module receives the flow field velocity and temperature distribution data output by the flue gas flow field distribution prediction module, constructs a grid model of the combustion area based on the adaptive grid combustion optimization algorithm, performs optimization calculation on the combustion parameters, and transmits the optimization results to the combustion-supporting system big data research and judgment module; The flue gas flow field distribution prediction module receives the flue gas temperature and pressure parameters output by the flue gas heat exchange and conduction module, predicts the flue gas flow trajectory and distribution state by using the flue gas flow field distribution prediction model, and sends the prediction data to the adaptive grid combustion optimization module and the combustion-supporting system big data research and judgment module respectively; the combustion-supporting system big data research and judgment module collects the operation parameters of the air-nitrogen dynamic blending balance module, the adaptive grid combustion optimization module, the flue gas flow field distribution prediction module, the flue gas heat exchange and conduction module, and the air-nitrogen blending module, generates parameter control instructions through big data analysis, and sends the instructions to different modules respectively; the flue gas heat exchange and conduction module receives the mixed gas flow output by the air-nitrogen blending module, exchanges heat with external flue gas, delivers the mixed gas flow after heat exchange to the combustion device, and transmits the flue gas heat exchange parameters to the flue gas flow field distribution prediction module; the air-nitrogen blending module receives the adjustment signal output by the air-nitrogen dynamic blending balance module, blends the air and the nitrogen, and delivers the mixed gas flow to the flue gas heat exchange and conduction module. The flue gas flow field distribution prediction module includes a flue gas parameter acquisition unit, a flow field data preprocessing unit, a prediction model operation unit, and a result output unit; the flue gas parameter acquisition unit continuously acquires the temperature, pressure, and flow rate data of the flue gas at different cross sections and different heights through temperature sensors, pressure sensors, and flow rate sensors arranged at different positions of the flue gas passage, and transmits the acquired raw data to the flow field data preprocessing unit; 2. The air incorporation system of claim 1, wherein the air incorporation system is a flue gas heat exchange combustion-supporting system. The expression of the air-nitrogen dynamic blending balance model is: Wherein, is the theoretical heat value of the mixed gas flow, is the nitrogen blending coefficient, is the real-time flow of the nitrogen, is the specific heat capacity of the nitrogen at constant pressure, is the initial temperature of the nitrogen, is the air blending coefficient, is the real-time flow of the air, is the specific heat capacity of the air at constant pressure, is the initial temperature of the air, is the pressure influence coefficient, is the pressure difference between the inlet and outlet of the mixing system, is the total flow of the mixed gas flow.

3. The system according to claim 1, wherein the system further comprises a flue gas heat exchange combustion-supporting system. The adaptive grid combustion optimization algorithm expression is: Wherein, is a combustion optimization target function value, is a grid transverse node number, is a grid longitudinal node number, is a grid node air flow density at the node, is a grid node air flow velocity at the node, is a grid node transverse step length, is a grid node longitudinal step length, is a temperature weight coefficient, is an optimization time period, is a time grid node temperature at the node, is a set combustion temperature.

4. The system according to claim 1, wherein the system further comprises a heat exchanger for exchanging heat between the flue gas and the air. The flue gas flow field distribution prediction model expression is: wherein, is a spatial coordinate is a flue gas flow rate at the spatial coordinate is an initial flue gas flow rate at the flue gas inlet is a flue gas inlet cross-sectional area is a cross-sectional is a flue gas flow passage area at the spatial coordinate is a spatial coordinate is a flue gas temperature at the spatial coordinate is an initial flue gas temperature at the flue gas inlet is a resistance coefficient in the height direction is a spatial height coordinate is a total length of the flue gas passage 5. The system as claimed in claim 1, wherein the system further comprises a heat exchanger (2) for exchanging heat between the flue gas and the air to be mixed with the exhaust gas. The combustion-supporting system big data judgment module data processing model expression is: Wherein, is a module output regulation parameter value, is a data collection category number, is a first category parameter weight coefficient, is a first category real-time collection parameter, is a first category parameter stability coefficient, is a parameter change rate weight coefficient, is a change rate of the sum of all collected parameters.

6. The air incorporation system of claim 1, wherein the air incorporation system is a flue gas heat exchange combustion-supporting system. The flue gas heat exchange conduction module heat exchange calculation model expression is: Q = (Tin - Tout) * A * U wherein, Q is a heat exchange amount, A is a heat exchange area, Tin is a flue gas import temperature, Tout is a flue gas export temperature, Tin is a mixed gas flow import temperature, Tout is a mixed gas flow export temperature, η is a flue gas heat exchange efficiency coefficient, η is a mixed gas flow heat absorption efficiency coefficient.

7. The system according to claim 1, wherein the system further comprises a flue gas heat exchange combustion-supporting system. The flow field data preprocessing unit performs outlier rejection on the received raw data, eliminates interference signals in the acquisition process through data smoothing processing, classifies and organizes the data according to spatial coordinates, forms a structured flow field data set, and transmits the data set to the prediction model operation unit; the prediction model operation unit calls the flue gas flow field distribution prediction model, substitutes the structured flow field data set into the model for calculation, solves the velocity, temperature, and pressure distribution values of the flue gas at different spatial coordinates, generates a flow field distribution prediction matrix, and transmits the matrix to the result output unit; the result output unit converts the flow field distribution prediction matrix into a format meeting the data interface requirements of each associated module, and sends the prediction data to the adaptive grid combustion optimization module and the combustion-supporting system big data research and judgment module according to a preset frequency. ​ 8. The air incorporation system of claim 1, wherein the air incorporation system is a flue gas heat exchange combustion-supporting system. The combustion-supporting system big data research and judgment module comprises a parameter receiving unit, a data storage unit, an analysis and operation unit, and an instruction generating unit. The parameter receiving unit receives operation parameters sent by the air-nitrogen dynamic blending and balancing module, the adaptive grid combustion optimization module, the flue gas flow field distribution prediction module, the flue gas heat exchange and conduction module, and the air-pollution-nitrogen blending module through a data transmission interface, performs format verification on the parameters to ensure data integrity, and then transmits the parameters to the data storage unit. The data storage unit stores the received operation parameters according to timestamps, establishes a parameter history database, simultaneously performs backup processing on the data to prevent data loss, and provides data query support for the analysis and operation unit. The analysis and operation unit calls real-time parameters and historical parameters from the data storage unit, identifies the parameter change trend through a big data analysis algorithm, judges the running states of different modules in combination with preset running thresholds, calculates parameter regulation requirements, and generates a preliminary regulation scheme. The instruction generating unit performs feasibility verification on the preliminary regulation scheme, corrects the scheme according to the parameter adjustment range of different modules, converts the corrected regulation scheme into specific parameter regulation instructions, and sends the instructions to each associated module through a data transmission interface.

9. The air incorporation system of claim 1, wherein the air incorporation system is a flue gas heat exchange combustion-supporting system. The flue gas heat exchange and conduction module comprises a mixed gas flow inlet unit, a flue gas inlet unit, a heat exchange core unit, and a gas flow outlet unit. The mixed gas flow inlet unit receives the mixed gas flow delivered by the air-pollution-nitrogen blending module, controls the mixed gas flow entering speed through a flow regulating valve, and uniformly distributes the mixed gas flow to the flow channel inlet of the heat exchange core unit. The flue gas inlet unit receives the high-temperature flue gas delivered externally, guides the flue gas to enter the heat exchange core unit according to a preset path through a flow guide structure, avoids vortex formation of the flue gas at the inlet, and ensures that the flue gas uniformly flows through the heat exchange core. The heat exchange core unit increases the heat exchange area through an internal fin structure, so that the mixed gas flow and the high-temperature flue gas perform counter-flow heat exchange in the core. The mixed gas flow absorbs the heat of the flue gas, the temperature of the mixed gas flow increases, the flue gas releases heat, the temperature of the flue gas decreases, and the heat transfer process is completed. The gas flow outlet unit delivers the mixed gas flow with increased temperature after heat exchange to the combustion device, simultaneously guides the flue gas with decreased temperature to a subsequent processing link, and monitors the gas flow pressure at the outlet in real time through a pressure monitoring element, and feeds back the pressure data to the combustion-supporting system big data research and judgment module.

10. The method for combustion-supporting heat exchange of flue gases with air- incorporated contaminated nitrogen, characterized in that, The method is applied to the air mixing dirty nitrogen combustion-supporting flue gas heat exchange system of claim 1, comprising the following steps: first, the air dirty nitrogen mixing module receives the mixing ratio adjustment signal output by the air dirty nitrogen dynamic mixing balance module, introduces air and dirty nitrogen into the mixing chamber according to the set ratio of the adjustment signal, and makes preliminary mixing of air and dirty nitrogen through the stirring structure in the chamber to form an initial mixed gas flow; second, the initial mixed gas flow enters the flue gas heat exchange and conduction module, and exchanges heat with the high-temperature flue gas in the module in the heat exchange core. After absorbing the heat of the flue gas, the temperature of the mixed gas flow is increased, and the mixed gas flow after heat exchange is transported to the combustion area. At the same time, the flue gas heat exchange and conduction module transmits the temperature and pressure parameters in the heat exchange process to the flue gas flow field distribution prediction module; third, the flue gas flow field distribution prediction module receives the parameters output by the flue gas heat exchange and conduction module, calls the flue gas flow field distribution prediction model to calculate the flow trajectory, velocity distribution and temperature change of the flue gas in the channel, generates flue gas flow field prediction data, and sends the prediction data to the adaptive grid combustion optimization module; fourth, the adaptive grid combustion optimization module receives the flue gas flow field prediction data, constructs a three-dimensional grid model of the combustion area based on the adaptive grid combustion optimization algorithm, substitutes the flue gas flow field data into the model to optimize the combustion temperature and combustion efficiency related parameters, obtains the optimal combustion parameter combination, and transmits it to the combustion-supporting system big data analysis module; fifth, the combustion-supporting system big data analysis module collects the operating parameters transmitted by different modules, including the flow, temperature of the mixed gas flow, flue gas pressure, combustion parameters and the like, judges the current system operating state through big data analysis, and generates mixing ratio adjustment instructions for the air dirty nitrogen dynamic mixing balance module; sixth, the air dirty nitrogen dynamic mixing balance module receives the adjustment instructions, calls the air dirty nitrogen dynamic mixing balance model to dynamically adjust the mixing ratio of air and dirty nitrogen, sends the adjusted ratio signal to the air dirty nitrogen mixing module, and forms a system operation closed loop.

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