Waste incineration SNCR (Selective Non-Catalytic Reduction) precise control method based on mechanism model and dynamic prediction

Abstract

The embodiment of the invention discloses a waste incineration SNCR precise control method based on a mechanism model and dynamic prediction, and relates to the technical field of waste incineration power plant flue gas denitration. The method comprises the steps that operation parameters of the garbage incinerator are obtained; constructing a mechanism model, namely a thermodynamic-combustion coupling model, and realizing three-dimensional flow field reconstruction and axial temperature field estimation by utilizing a virtual sensing technology and based on an energy conservation equation, a radiation transfer equation and CFD reverse solution and inversion of a flue gas temperature and a flow field velocity vector; combining the mechanism model and actually measured data, optimizing spray gun parameters by using a particle swarm optimization algorithm, and realizing multi-target balance of denitration efficiency and ammonia escape; and the flow, angle and layout of the spray gun are dynamically adjusted according to the optimized parameters. The method is high in denitration efficiency and low in reducing agent consumption.

Description

Technical Field

[0001] The present invention relates to the technical field of flue gas denitrification in waste incineration power plants, and particularly to a precise control method for waste incineration SNCR based on a mechanism model and dynamic prediction. Background Art

[0002] In the prior art, the SNCR (Selective Non-Catalytic Reduction) denitrification system of waste incinerators generally relies on direct sensing technologies in high-temperature areas (such as optical fiber temperature measurement, acoustic velocity measurement, TDLAS gas analysis) to obtain the furnace temperature field and flow field information. However, there are the following technical bottlenecks in the waste incineration process:

[0003] 1. High-temperature interference problem: The temperature in the incinerator is as high as over 1200 °C, and traditional sensors are easily affected by heat radiation and corrosion, resulting in a decrease in measurement accuracy or even failure.

[0004] 2. Dynamic response lag: The waste components are complex (the moisture content and calorific value fluctuate by ±30%), and the combustion conditions change frequently. Existing fixed-parameter adjustment strategies are difficult to match the dynamic balance of optimal NOx generation and reduction in real time.

[0005] 3. Lack of spatial regulation accuracy: Single-point temperature measurement and fixed spray gun layout result in a reductant injection coverage rate of only 85%, and ammonia escape is likely to exceed the standard (>8 mg / Nm³).

[0006] 4. Limitations of model simplification: Existing mechanism models mostly assume ideal combustion conditions and do not incorporate the coupling of multiple physical fields, resulting in large prediction deviations.

[0007] Patent application CN116808804A discloses an SNCR control method based on the dynamic generation characteristics of NOx in the furnace. This method divides the NOx generation cycle into 4 stages (rapid decline, low level, rapid rise, high level), adjusts the reductant dosage according to the stages, and establishes a dynamic control curve (such as the proportional relationship between the maximum dosage Hh and the minimum dosage Hl). The applicable scenario of this method is the periodic NOx fluctuation of vibrating grate boilers.

[0008] This patent application mainly has the following disadvantages:

[0009] Strong furnace type specificity: It depends on the periodic law of the vibrating grate and has insufficient adaptability to non-periodic fluctuations (such as random fluctuations in waste calorific value).

[0010] Simplification of control parameters: Only dividing the stages based on NOx concentration fluctuations, without integrating key reaction parameters such as fly ash catalytic effect and three-dimensional temperature field distribution, the optimization range of reductant dosage is limited.

[0011] Patent application CN106362561A discloses a cluster SNCR control method based on the in - furnace flow field. This method divides the boiler into groups, adjusts the injection amount of the reducing agent based on the fuzzy inference algorithm, and dynamically distributes the flow rate of the spray guns in combination with the in - furnace temperature field (five temperature intervals), flue gas volume, and unit load. The control logic of this method is fuzzy PID + fixed temperature partition.

[0012] This patent application mainly has the following disadvantages:

[0013] Coarse division of the temperature field: It is only divided into 5 groups according to the average temperature (such as 900 - 1000 °C), ignoring the influence of temperature gradient and local hot spots on the reaction efficiency;

[0014] Simplification of the flow field simulation: It relies on the operating status of the burners and the flue gas volume to simulate the flow field, and the accuracy of the flow field parameters (such as velocity vector) is insufficient;

[0015] Lag in the control strategy: It is a feedback control based on the deviation of the outlet NOx concentration, lacking feed - forward prediction and responding slowly to sudden changes in the garbage composition (such as sudden changes in calorific value).

[0016] In summary, due to single - parameter dependence, simplified mechanism modeling, and insufficient dynamic adaptability in the existing technology, the denitration efficiency of the SNCR system is low (40 - 60%) and the consumption of the reducing agent is high. Summary of the Invention

[0017] In view of this, an embodiment of the present invention provides a precise control method for garbage incineration SNCR based on a mechanism model and dynamic prediction, which has high denitration efficiency and low consumption of the reducing agent.

[0018] A precise control method for garbage incineration SNCR based on a mechanism model and dynamic prediction includes:

[0019] Step S101: Obtain the operating parameters of the garbage incinerator, where an array of wall temperatures and an array of pressure sensors are provided in the garbage incinerator;

[0020] Step S102: Construct a mechanism model, namely a thermodynamics - combustion coupling model. Using virtual sensing technology, based on the energy conservation equation, radiation transfer equation, and CFD inverse solution, inversely calculate the flue gas temperature and the flow field velocity vector, and realize three - dimensional flow field reconstruction and axial temperature field estimation. The thermodynamics - combustion coupling model includes a combustion and heat transfer sub - model and a NOx generation sub - model;

[0021] Step S103: Combine the mechanism model and the measured data, and use the particle swarm optimization algorithm to optimize the spray gun parameters to achieve a multi - objective balance between denitration efficiency and ammonia slip;

[0022] Step S104: Dynamically adjust the flow rate, angle, and layout of the spray guns according to the optimized parameters.

[0023] Preferably, in the step S101, the operating parameters include fuel calorific value, flue gas mass flow rate, combustion air mass flow rate, flue gas oxygen content, furnace inlet air temperature, and fuel nitrogen content.

[0024] Preferably, in the step S102, the combustion and heat transfer sub-model is as follows:

[0025] ,

[0026] ,

[0027] where m fuel is the fuel mass flow rate, LHV is the fuel calorific value, Q comb is the effective heat released by combustion, Q loss is the furnace heat loss, m flue is the flue gas mass flow rate, C p is the flue gas specific heat at constant pressure, T flue is the flue gas temperature, T air is the furnace inlet air temperature.

[0028] Preferably, in the step S102, the NOx generation sub-model is as follows:

[0029] ,

[0030] where, is the NOx generation rate, A is the pre-exponential factor, E is the activation energy, R is the gas constant, T is the combustion zone temperature, is the oxygen volume fraction, [Fuel-N] is the fuel nitrogen content.

[0031] Preferably, the step S102 includes:

[0032] Based on the pressure sensor array, by inversely solving the continuity equation through CFD, minimizing the residual of the mass conservation equation, and inversely calculating the flow field velocity vector ,

[0033] where u is the flow field velocity vector, ρ is the flue gas density, calculated according to the flue gas temperature and pressure P , R S is the flue gas gas constant, S m is the mass source term, calibrated by the flue gas mass flow rate m flue and the combustion air mass flow rate m air and the furnace volume V, .

[0034] Preferably, the step S102 includes:

[0035] Using the radiation transfer equation, the axial temperature distribution of the furnace is fitted based on the wall temperature array,

[0036] According to the single-point wall heat flux density ,

[0037] invert the flue gas temperature ,

[0038] wherein, is the wall heat flux density, is the wall temperature, is the Stefan-Boltzmann constant, is the wall emissivity.

[0039] Preferably, the step S103 includes:

[0040] Step S1031: Automatically adjust the CFD calculation grid density according to the fuel calorific value wave;

[0041] Step S1032: Optimize the lance parameters with the goal of maximizing the denitration efficiency and minimizing the ammonia slip;

[0042] Step S1033: Calculate the lance adjustment amount using an improved PID algorithm, and a mechanism model prediction correction amount and a dynamic compensation coefficient α are introduced in the improved PID algorithm.

[0043] Preferably, in the step S1031, the grid resolution in the key area is increased to at least 1 m.

[0044] Preferably, in the step S1032, the lance parameter optimization formula is: ,

[0045] wherein, is the negative weight, is the ammonia slip, is the weight coefficient;

[0046] Constraint conditions: injection coverage rate ≥ 96%, ammonia-nitrogen molar ratio ≤ 1.2, single lance flow rate fluctuation ≤ 20%.

[0047] Preferably, in the step S1033, the improved PID algorithm formula is:

[0048] ,

[0049] wherein, is the flow rate adjustment amount of the (i,j)-th lance, Kp, Ki, and Kd are PID control parameters, e(t) is the NOx concentration deviation, which is equal to the measured value - the set value, α is the dynamic compensation coefficient, is the flow rate correction amount predicted by the mechanism model.

[0050] The present invention has the following beneficial effects:

[0051] Mechanism-data dual-driven modeling: Combining the thermodynamic equation of waste incineration to achieve dynamic calibration of theoretical prediction and measured data, and solve the problem of direct sensing failure in high-temperature areas;

[0052] Virtual sensing technology: Using the furnace pressure and wall multi-point temperature array (8 channels) newly added to collect furnace pressure and wall temperature data, inversely solve the flow field continuity equation through CFD (Computational Fluid Dynamics software), and combine the radiation transfer equation (RTE) to invert the axial temperature distribution to achieve non-contact three-dimensional field reconstruction, improving the reliability and accuracy of parameter measurement under complex working conditions;

[0053] Adaptive injection optimization: Using the particle swarm optimization algorithm (PSO) to dynamically adjust the spray gun layout and injection parameters, improving the matching accuracy of ammonia-nitrogen molar ratio, suppressing ammonia escape, increasing the injection coverage rate, ensuring full mixing of the reducing agent and flue gas, with high denitrification efficiency and low reducing agent consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0055] Figure 1 It is a schematic flow chart of the method for precise control of waste incineration SNCR based on mechanism model and dynamic prediction of the present invention;

[0056] Figure 2 It is a system architecture diagram of the method for precise control of waste incineration SNCR based on mechanism model and dynamic prediction of the present invention;

[0057] Figure 3 It is a schematic flow chart for realizing virtual sensing technology in the method of the present invention;

[0058] Figure 4 It is a closed-loop control flow chart of the injection strategy optimization algorithm in the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] The following will describe the embodiments of the present invention in detail with reference to the drawings.

[0060] It should be clear that the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts belong to the scope of protection of the present invention.

[0061] An embodiment of the present invention provides a method for precise control of efficient SNCR in waste incineration based on a (multi-dimensional) mechanism model and dynamic prediction, as Figure 1-2 shown, including:

[0062] Step S101: Obtain the operating parameters of the waste incinerator, wherein a wall temperature array and a pressure sensor array are provided in the waste incinerator;

[0063] As an optional embodiment, in step S101, the operating parameters include fuel calorific value, flue gas mass flow rate, combustion air mass flow rate, flue gas oxygen content, inlet air temperature, and fuel nitrogen content.

[0064] In specific implementation, in terms of hardware configuration, newly added: pressure and multi-point furnace wall temperature array (8 channels, temperature measurement range 0°C to 1200°C, accuracy 1°C; pressure measurement range -500Pa to 500Pa, accuracy 0.1Pa); continued to use: original fuel calorific value (LHV), flue gas mass flow rate (m flue ), combustion air mass flow rate (m air ), flue gas oxygen content (ε), inlet air temperature (T air ), fuel nitrogen content ([Fuel-N]) and other parameters through the DCS system.

[0065] Step S102: Construct a mechanism model, namely a thermodynamics-combustion coupling model, and use virtual sensing technology to inversely calculate the flue gas temperature and flow field velocity vector based on the energy conservation equation, radiation transfer equation and CFD inverse solution, so as to realize three-dimensional flow field reconstruction and axial temperature field estimation, wherein the thermodynamics-combustion coupling model includes a combustion and heat transfer sub-model and a NOx generation sub-model;

[0066] In this step, the thermodynamics-combustion coupling model includes two core sub-models, namely a combustion and heat transfer sub-model and a NOx generation sub-model.

[0067] As an optional embodiment, the combustion and heat transfer sub-model is as follows:

[0068] Based on the energy conservation equation:

[0069] Wherein,

[0070] Parameter meaning:

[0071] mfuel : The fuel mass flow rate (kg / s), measured by the feeding system;

[0072] LHV: The calorific value of the fuel (kJ / kg);

[0073] Q comb : The effective heat released by combustion (kJ / s);

[0074] Q loss : The heat loss due to furnace radiation (kJ / s), which is taken as a fixed proportion of the fuel calorific value according to the waste incinerator design manual, usually 3% - 5% of the fuel calorific value under rated load;

[0075] m flue : The flue gas mass flow rate (kg / s);

[0076] C p : The specific heat at constant pressure of the flue gas ( ), calculated according to the flue gas composition ( etc.) and related to the temperature (can be found in the thermodynamic property table);

[0077] T flue : The flue gas temperature (K), retrieved through the wall temperature array (not directly detected, calculated by the radiation heat transfer equation);

[0078] T air : The temperature of the incoming air (K).

[0079] As another alternative embodiment, the NOx generation sub - model is as follows (combining the fuel nitrogen conversion mechanism and reaction kinetics):

[0080] , but considering that the main NOx generation path in the domestic waste incinerator is (> 93.7% of the total NOx content), so the NOx generation (rate) sub - model for the waste incinerator is:

[0081]

[0082] Parameter meanings:

[0083] r NOx : The NOx generation rate ;

[0084] A: The pre - exponential factor, reflecting the frequency and direction suitability of reactant molecule collisions, with a value of 10 12 ;

[0085] E: The activation energy (J / mol), the minimum energy required for the reaction to occur, with a value of 120000 J / mol.

[0086] R: The gas constant, with a value of 。

[0087] T is the temperature of the combustion zone (K), which is retrieved through the wall temperature array and the radiation heat transfer equation (indirect calculation instead of direct detection).

[0088] ε is the volume fraction of oxygen (%), corresponding to the oxygen content in the flue gas, obtained from the on-site measurement of DCS.

[0089] [Fuel-N] is the fuel nitrogen content (%), obtained through fuel composition analysis.

[0090] In this way, the construction of the multi-dimensional mechanism model can be completed through the above process, that is, the thermodynamic-combustion coupling model is constructed.

[0091] As another alternative embodiment, the step S102 includes:

[0092] Based on the pressure sensor array (accuracy ), by inversely solving the continuity equation through CFD and minimizing the residual of the mass conservation equation, the flow field velocity vector is retrieved ,

[0093] Parameter meaning:

[0094] u: flow field velocity vector (m / s);

[0095] ρ: flue gas density (kg / m³), calculated according to the flue gas temperature and pressure P ,R S is the flue gas gas constant, approximately ;

[0096] S m : mass source term , representing the mass input rate per unit volume, used to calibrate the source term of mass conservation in the furnace, reflecting the mass inflow of flue gas and air during the fuel combustion process, calibrated through the flue gas mass flow rate m flue and the combustion air mass flow rate m air and the furnace volume V, 。

[0097] In this way, the three-dimensional flow field reconstruction can be completed through this step.

[0098] As yet another alternative embodiment, the step S102 includes:

[0099] Using the radiation transfer equation (RTE), based on the wall temperature array (8 channels), the axial temperature distribution of the furnace is fitted, and according to the single-point wall heat flux density ,

[0100] the flue gas temperature is retrieved ,

[0101] Meanings of key parameters:

[0102] : Wall heat flux density, indicating the radiative heat received or dissipated per unit area of the wall, typical value for a waste incinerator .

[0103] : Wall temperature, obtained through a multi-point temperature array.

[0104] : Stefan-Boltzmann constant .

[0105] : Wall emissivity (0 - 1, depending on the wall material, approximately 0.8 for refractory bricks in a waste incinerator).

[0106] In this way, the estimation of the axial temperature field can be completed through this step.

[0107] The three-dimensional flow field reconstruction and the axial temperature field estimation can be specifically as Figure 3 shown in the figure, which shows the non-contact three-dimensional field reconstruction technology, solves the problem of direct sensing failure in high-temperature areas through wall sensors and inversion algorithms, reconstructs the furnace temperature field and flow field through non-contact methods, and improves the reliability and accuracy of parameter measurement under complex working conditions.

[0108] Step S103: Combine the mechanism model and the measured data, and use the particle swarm optimization algorithm to optimize the spray gun parameters to achieve the multi-objective balance of denitration efficiency and ammonia slip;

[0109] In this step, dynamically fuse the DCS measured parameters (flue gas mass flow rate, oxygen content, etc.) with the predicted values of the mechanism model, and correct the non-directly detected parameters in the high-temperature area such as Tflue and the flow field velocity vector. Specifically, during implementation, as Figure 4 shown in the figure, which shows the closed-loop control process of dynamic optimization of spray gun parameters, fuses the mechanism model prediction and real-time feedback to achieve precise injection control, and the process can be as follows:

[0110] 1. Input data drive: Use the measured NOx concentration deviation e(t), fuel calorific value fluctuation (LHV ± 10%) and the predicted correction amount of the mechanism model as the input to drive the control algorithm.

[0111] 2. Dual-algorithm collaborative optimization:

[0112] PSO algorithm: Aim to maximize the denitration efficiency and minimize the ammonia slip, optimize the spray gun flow rate, angle and layout, and meet the constraint conditions such as spray coverage rate ≥ 96% and ammonia-nitrogen molar ratio ≤ 1.2.

[0113] Improved PID algorithm: Introduce the prediction correction amount of the mechanism model and the dynamic compensation coefficient α (0.3 - 0.8) to solve the problem of response lag of traditional PID to complex working conditions.

[0114] 3. Optimized parameter-driven spray gun adjustment: Combine dynamic grid division (grid resolution of the key area is 1m) to improve the spatial coverage accuracy, and use the measured emission data to feed back the model to form an iterative optimization closed loop.

[0115] That is to say, as an alternative embodiment, the step S103 adopts an adaptive spraying strategy, including:

[0116] Step S1031 (dynamic grid division): Automatically adjust the CFD calculation grid density according to the fuel calorific value wave;

[0117] In this step, the CFD calculation grid density can be automatically adjusted according to the fuel calorific value fluctuation (LHV ± 10%), and the grid resolution of the key area (such as the burner and the spray gun layer) is increased to 1m.

[0118] Step S1032 (Particle Swarm Optimization Algorithm (PSO)): Optimize the spray gun parameters with the goal of maximizing the denitration efficiency and minimizing the ammonia slip;

[0119] In this step, optimize the spray gun parameters with the goal of maximizing the denitration efficiency and minimizing the ammonia slip: ,

[0120] That is, in actual optimization, it is necessary to convert into a negative weight to ensure that the algorithm can improve the denitration efficiency and reduce the ammonia slip while reducing the objective function value. Ammonia slip : Directly aim at minimizing the ammonia slip, and the weight coefficients ω1 and ω2 are dynamically configured according to engineering requirements (such as ω1 > ω2 when giving priority to ensuring the denitration efficiency, for example: ω1 = 0.7, ω2 = 0.3).

[0121] Constraint conditions: Spray coverage rate ≥ 96%; Ammonia-nitrogen molar ratio ≤ 1.2; Single spray gun flow rate fluctuation ≤ 20%.

[0122] Step S1033: Calculate the spray gun adjustment amount using the improved PID algorithm, and introduce the prediction correction amount of the mechanism model and the dynamic compensation coefficient α in the improved PID algorithm.

[0123] In this step, the spray gun adjustment amount is calculated using the improved PID algorithm. On the basis of traditional PID control, the prediction correction amount of the mechanism model and the dynamic compensation coefficient α are introduced to solve the problem of response lag of pure data-driven PID to complex working conditions:

[0124]

[0125] In the formula:

[0126] : The flow rate adjustment of the (i,j)-th spray gun (L / h);

[0127] Kp, Ki, Kd: PID control parameters, where Kp is the proportional coefficient, Ki is the integral coefficient, and Kd is the differential coefficient;

[0128] e(t) is the NOx concentration deviation (mg / Nm³), which is equal to the measured value - the set value;

[0129] α is the dynamic compensation coefficient (adaptively adjusted according to the fuel calorific value volatility, with a range of 0.3 - 0.8);

[0130] is the flow rate correction calculated by the mechanism model, based on the combustion and heat transfer sub-model and the NOx generation sub-model. The spray gun adjustment amount and the NOx generation rate There is a tight feedforward-feedback coupling relationship in the calculation. The NOx generation rate sub-model is one of the core bases for realizing "mechanism prediction-driven dynamic adjustment of the spray gun".

[0131] For example: When rises, the mechanism model will calculate that the injection amount of the reducing agent needs to be increased > 0, otherwise it will be reduced.

[0132] Step S104: Dynamically adjust the flow rate, angle, and layout of the spray gun according to the optimized parameters.

[0133] The system architecture diagram (four-layer architecture) corresponding to the present invention can be as Figure 2 shown. This diagram shows the overall architecture of the SNCR precise control system for the waste incinerator, and adopts a hierarchical design to realize the closed-loop process from data acquisition, model calculation, intelligent decision-making to execution control, specifically including:

[0134] 1. Data acquisition layer: The operating parameters obtained through the DCS interface, combined with the newly added wall temperature array and pressure sensor array, are used to collect key data in real time.

[0135] 2. Model calculation layer: Construct a thermodynamics-combustion coupling model (combustion and heat transfer sub-model, NOx generation sub-model) and a virtual sensing module, and based on the energy conservation equation, radiation transfer equation (RTE) and CFD inverse solution, invert non-directly detected parameters such as flue gas temperature and flow field velocity vector.

[0136] 3. Intelligent decision-making layer: Calibrate the mechanism model with the measured data, and optimize the spray gun parameters in combination with the particle swarm optimization algorithm (PSO) to achieve the multi-objective balance of denitrification efficiency and ammonia slip.

[0137] 4. Execution layer: Dynamically adjust the spray gun flow rate, angle, and layout according to the optimized parameters, and improve the injection coverage rate of the reducing agent through the atomization system to form a closed-loop control of "data collection - model calculation - intelligent decision-making - execution feedback".

[0138] In summary, the present invention has the following beneficial effects:

[0139] Mechanism-data dual-driven modeling: Combining the thermodynamic equation of waste incineration to achieve dynamic calibration of theoretical prediction and measured data, and solve the problem of direct sensing failure in high-temperature areas;

[0140] Virtual sensing technology: Using the furnace pressure and wall surface temperature data collected by the newly added furnace pressure and multi-point wall surface temperature array (8 channels), reverse-solving the flow field continuity equation through CFD (Computational Fluid Dynamics software), and combining the radiation transfer equation (RTE) to invert the axial temperature distribution to achieve non-contact three-dimensional field reconstruction, improving the reliability and accuracy of parameter measurement under complex working conditions;

[0141] Adaptive injection optimization: Using the particle swarm optimization algorithm (PSO) to dynamically adjust the spray gun layout and injection parameters, improving the matching accuracy of the ammonia-nitrogen molar ratio, suppressing ammonia escape, improving the injection coverage rate, ensuring full mixing of the reducing agent and flue gas, with high denitrification efficiency and low reducing agent consumption.

[0142] The present invention specifically relates to an SNCR dynamic regulation method integrating a thermodynamic-combustion coupling model, virtual sensing technology, and intelligent optimization algorithm, which is applicable to removing nitrogen oxides (NOx) in flue gas under complex working conditions of waste incinerators. Through multi-dimensional modeling and intelligent optimization, the present invention fills the gap in control accuracy and robustness under complex working conditions, and is especially applicable to scenarios of high-temperature sensor failure and waste component fluctuations.

[0143] Implementation case (a waste incineration power plant in Zhejiang):

[0144] Treatment scale: 750 t / d of domestic waste;

[0145] Inlet NOx concentration: 300 mg / Nm³ (standard state, )

[0146] Daily average value of outlet index , and the process detection value is ;

[0147] Reducing agent: 20% ammonia water;

[0148] (1) Application calculation of the mechanism model

[0149] 1) Inversion of combustion heat and flue gas temperature

[0150] Lower Heating Value (LHV) of fuel: Assume the calorific value of the waste is 6000 kJ / kg.

[0151] Fuel mass flow rate ,

[0152] Combustion heat: ,

[0153] Wall heat flux density:

[0154] Flue gas temperature inversion: The corresponding wall temperature in the combustion zone is measured to be 950 °C (1223 K) through the wall temperature array, and calculated in combination with the radiation heat transfer equation (considering the correction of heat dissipation loss).

[0155] 2) Calculation of NOx generation rate

[0156] Volume fraction of oxygen (ε): Measured to be 5% (standard condition), ε = 0.05

[0157] The fuel nitrogen content is 1.5%, then [Fuel-N] = 0.015;

[0158]

[0159] 3) Calculation of spray gun adjustment amount

[0160] NOx concentration deviation: ;

[0161] Dynamic compensation coefficient: Assume the fuel calorific value fluctuates by +10%, α = 0.6;

[0162] Prediction correction amount of mechanism model: (indicating that the predicted reductant is in excess and the injection needs to be reduced)

[0163]

[0164] (K p = 0.8, K i = 0.05, K d = 0.2, assume the differential term is 0 at steady state)

[0165] , adjusted gradually to balance by time integration.

[0166] (2) Virtual sensing and injection optimization

[0167] 3D Flow Field Reconstruction: Detect the furnace negative pressure of -100 Pa through a pressure sensor array (accuracy 0.1 Pa), combine with the flue gas temperature of 1400 K, use CFD inverse solution to obtain the flow velocity vector, adjust the spray gun layout to the high flow velocity area, and increase the spray coverage rate from 85% to 96% to ensure full mixing of the reducing agent and the flue gas.

[0168] Dynamic Mesh Division: When the fuel calorific value fluctuates by +10%, the mesh resolution in the key area is increased to 1 m, optimize the atomization path of the reducing agent, and reduce ammonia escape.

[0169] (3)Effect Verification

[0170] Spatial Coverage: The spray coverage rate of the spray gun is increased to 96% (85% in the traditional scheme);

[0171] Emission Performance: The hourly average value of NOx emissions ≤ 150 mg / Nm³, the daily average value ≤ 100 mg / Nm³ (a 40% reduction), and ammonia escape ≤ 8 mg / Nm³ (a 60% reduction);

[0172] Economy: The denitrification efficiency exceeds 70%, the consumption of 20% ammonia water as the reducing agent is saved by 18.3%, and the energy consumption of compressed air is reduced by 15.1%.

[0173] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A precise control method for SNCR of waste incineration based on mechanism model and dynamic prediction, characterized in that: include: Step S101: obtaining operating parameters of a waste incinerator, wherein a wall temperature array and a pressure sensor array are provided in the waste incinerator; Step S102: construct a mechanism model, i.e., a thermodynamic-combustion coupling model, and utilize virtual sensing technology to invert the flue gas temperature and flow field velocity vector based on the energy conservation equation, the radiation transfer equation, and CFD inverse solution to achieve three-dimensional flow field reconstruction and axial temperature field estimation, wherein the thermodynamic-combustion coupling model includes a combustion and heat transfer sub-model and a NOx generation sub-model; Step S103: combining the mechanism model and measured data, optimizing the spray gun parameters using a particle swarm optimization algorithm to achieve a multi-objective balance between denitrification efficiency and ammonia slip; Step S104: dynamically adjusting the flow rate, angle and layout of the spray gun according to the optimized parameters.

2. The method according to claim 1, characterized in that: In the step S101, the operating parameters include fuel calorific value, flue gas mass flow rate, combustion air mass flow rate, flue gas oxygen content, furnace air temperature, and fuel nitrogen content.

3. The method according to claim 1, characterized in that: In step S102, the combustion and heat transfer sub-models are as follows: ， ， Among them, m fuel is the fuel mass flow rate, LHV is the fuel calorific value, Q comb is the effective heat released by combustion, Q loss is the heat loss of the furnace, m flue is the flue gas mass flow rate, C p is the constant pressure specific heat of flue gas, T flue is the flue gas temperature, T air is the temperature of the air entering the furnace.

4. The method according to claim 1, characterized in that In step S102, the NOx generation sub-model is as follows: ， in, is the NOx generation rate, A is the pre-exponential factor, E is the activation energy, R is the gas constant, T is the combustion zone temperature, ε is the oxygen volume fraction, and [Fuel-N] is the fuel nitrogen content.

5. The method according to claim 1, characterized in that The step S102 includes: Based on the pressure sensor array, the continuity equation is solved by CFD inversely, the residual of the mass conservation equation is minimized, and the flow field velocity vector is inverted. , Among them, u is the flow field velocity vector, ρ is the smoke density, which is calculated based on the smoke temperature and pressure P , R S is the flue gas constant, S m is the mass source term, through the flue gas mass flow m flue and combustion air mass flow m air And the furnace volume V calibration, .

6. The method according to claim 1, characterized in that The step S102 includes: Using the radiation transfer equation, the axial temperature distribution of the furnace is fitted based on the wall temperature array. According to the single point wall heat flux , Inverse Flue Gas Temperature , in, is the wall heat flux density, is the wall temperature, is the Stefan-Boltzmann constant, is the wall emissivity.

7. The method according to claim 1, characterized in that The step S103 includes: Step S1031: Automatically adjust the CFD calculation grid density according to the fuel calorific value wave; Step S1032: Optimizing spray gun parameters with the goal of maximizing denitration efficiency and minimizing ammonia escape; Step S1033: Calculate the spray gun adjustment amount using an improved PID algorithm, in which a mechanism model is introduced to predict the correction amount. and the dynamic compensation coefficient α.

8. The method according to claim 7, characterized in that In step S1031, the grid resolution of the key area is increased to at least 1 m.

9. The method according to claim 7, characterized in that: In step S1032, the spray gun parameter optimization formula is: , in, is a negative weight, Ammonia escape, is the weight coefficient; Constraints: spray coverage ≥ 96%, ammonia nitrogen molar ratio ≤ 1.2, single spray gun flow fluctuation ≤ 20%.

10. The method according to claim 7, characterized in that In step S1033, the improved PID algorithm formula is: , in, is the flow adjustment value of the (i, j)th spray gun, Kp, Ki, Kd are PID control parameters, e(t) is the NOx concentration deviation, which is equal to the measured value - the set value, α is the dynamic compensation coefficient, is the flow correction value predicted by the mechanism model.

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