Method for reducing carbon footprints in cremation process

Abstract

The invention provides a method for reducing carbon footprints in a cremation process, which comprises the following steps: according to an internal structure and temperature distribution characteristics of an incinerator, obtaining turbulence intensity, turbulence scale and turbulence parameters, simulating an internal flow field and a temperature field of the incinerator, and obtaining internal three-dimensional temperature distribution data of the incinerator; according to the three-dimensional temperature distribution data in the incinerator, a temperature threshold method is adopted for carrying out regional division on a temperature field, and the space ranges and the volume ratios of a high-temperature region, a medium-temperature region and a low-temperature region are obtained; acquiring activation energy and frequency factors of chemical reaction in the incinerator, judging ammonia decomposition rates at different temperatures, fitting to generate a relation curve of the temperatures and the ammonia decomposition rates, and determining an optimal ammonia concentration threshold value of each temperature region; and according to real-time operation monitoring data of the incinerator, the denitration efficiency is predicted through the correlation model, if a predicted value is lower than a set threshold value, dynamic optimization of an ammonia gas injection scheme is triggered, and the dynamic optimization comprises adjustment of the injection position and the injection amount.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method for reducing carbon footprint in a cremation process. Background Art

[0002] In large-scale incinerator systems, there are complex technical contradictions between ammonia injection and carbon emission control. The temperature distribution inside the incinerator is extremely uneven, forming multiple temperature gradient areas. In the high-temperature area, the ammonia decomposition rate is too fast, resulting in insufficient denitration reaction; while in the low-temperature area, the ammonia utilization rate is significantly reduced, resulting in a large amount of ammonia waste. This uneven temperature distribution problem seriously affects the overall denitration efficiency. At the same time, there is a complex turbulent structure inside the incinerator, making it extremely difficult to accurately control the amount and position of ammonia injection. Turbulence causes ammonia to be unevenly distributed in the furnace and cannot fully contact and react with nitrogen oxides. Even if the amount of ammonia injection is increased, it is difficult to make up for the uneven distribution problem, but may cause ammonia escape. In addition, the ashes produced during the incineration process will interfere with the reaction of ammonia and nitrogen oxides. The ammonia injection system needs to be able to respond to these changes in real time, but current technology makes it difficult to achieve such fine dynamic adjustment. These factors influence and restrict each other, forming a complex technical problem. Therefore, how to achieve efficient utilization and precise control of ammonia under such harsh conditions while taking into account carbon emission control has become a key challenge in incinerator system optimization. Summary of the invention

[0003] The present invention provides a method for reducing the carbon footprint during cremation, which mainly includes:

[0004] According to the internal structure and temperature distribution characteristics of the incinerator, the turbulence intensity and turbulence scale turbulence parameters are obtained, the flow field and temperature field inside the incinerator are simulated, and the three-dimensional temperature distribution data inside the incinerator is obtained;

[0005] According to the three-dimensional temperature distribution data inside the incinerator, the temperature field is divided into regions using the temperature threshold method to obtain the spatial range and volume proportion of the high temperature zone, medium temperature zone and low temperature zone;

[0006] Obtain the activation energy and frequency factor of the chemical reaction inside the incinerator, determine the ammonia decomposition rate at different temperatures, fit the relationship curve between the generation temperature and the ammonia decomposition rate, and determine the optimal ammonia concentration threshold in each temperature zone;

[0007] Determine the physical properties of the ashes and the lifting height parameters, design a multi-point ammonia layered injection plan based on the spatial range, volume proportion and optimal ammonia concentration threshold of each temperature zone, and determine the spatial coordinates of each injection point and the corresponding ammonia injection amount;

[0008] The turbulent viscosity coefficient and turbulent diffusion coefficient parameters are used to construct an ammonia diffusion model inside the incinerator to simulate the three-dimensional concentration distribution of ammonia in the incinerator under the layered injection scheme. If the uniformity of ammonia distribution does not reach the preset threshold, the injection parameters are iteratively adjusted.

[0009] Obtain the parameters of the lifting speed and amount of ash, analyze the effect of ash lifting on the uniformity of ammonia distribution, predict the mixing degree and contact time of ash and ammonia, determine the adsorption rate and catalytic decomposition rate of ammonia by ash, and establish a correlation model between the incinerator operating parameters and denitrification efficiency;

[0010] Based on the real-time operation monitoring data of the incinerator, the correlation model is used to predict the denitrification efficiency. If the predicted value is lower than the set threshold, the dynamic optimization of the ammonia injection plan is triggered, which includes adjusting the injection position and injection amount.

[0011] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0012] The present invention discloses a method for reducing the carbon footprint during cremation. The method first obtains three-dimensional temperature distribution data and divides the temperature area by simulating the internal flow field and temperature field of the incinerator, and then designs a multi-point layered injection scheme according to the ammonia decomposition kinetics and the characteristics of the ash. By constructing an ammonia diffusion model, the concentration distribution after injection is simulated, and the injection parameters are iteratively optimized. At the same time, the influence of the ashes on the ammonia distribution is considered, and a correlation model between the operating parameters and the denitrification efficiency is established. Finally, the denitrification efficiency is predicted according to the real-time monitoring data, and the ammonia injection scheme is dynamically optimized, so as to achieve accurate control of the denitrification process of the incinerator and reduce carbon emissions. In summary, the present invention makes the distribution of ammonia in the incinerator more uniform, improves the sufficiency and stability of the reaction, and can adapt to the operation changes of the incinerator in real time by dynamically optimizing the ammonia injection scheme, ensure the continuous good denitrification effect, improve the denitrification efficiency, reduce the emission of nitrogen oxides, meet environmental protection requirements, optimize the use of ammonia, reduce ammonia consumption, and save costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The present invention is a flow chart of a method for reducing carbon footprint during cremation.

[0014] Figure 2 A schematic diagram of a method of reducing the carbon footprint during cremation according to the present invention.

[0015] Figure 3 It is another schematic diagram of a method for reducing the carbon footprint during cremation according to the present invention. DETAILED DESCRIPTION

[0016] The following will describe the technical solutions in the embodiments of the present invention in detail in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention.

[0017] like Figure 1-3 In this embodiment, a method for reducing carbon footprint during cremation may specifically include:

[0018] S101. According to the internal structure and temperature distribution characteristics of the incinerator, the turbulence intensity and turbulence scale turbulence parameters are obtained, the flow field and temperature field inside the incinerator are simulated, and the three-dimensional temperature distribution data inside the incinerator is obtained.

[0019] A three-dimensional geometric model is established according to the internal structural size parameters and initial temperature conditions of the incinerator, and mesh division is performed and mesh independence test is performed to obtain a mesh quality that meets the calculation requirements. The boundary conditions and initial conditions are set using the mesh quality to obtain the preliminary flow field and temperature field distribution data inside the incinerator. Turbulent kinetic energy and dissipation rate are extracted from the preliminary flow field and temperature field distribution data, the turbulence intensity and turbulence characteristic length are calculated, and the coefficients in the standard k-ε model are adjusted according to the turbulence intensity and turbulence characteristic length. If the residual is less than the preset value or the change rate of the key variable is less than the threshold percentage, the corrected flow field data is obtained; if the conditions are not met, the iterative calculation continues. The energy equation is re-solved using the corrected flow field data to obtain a more accurate temperature field distribution. For the temperature field distribution, the discrete point temperatures are interpolated to construct a continuous temperature distribution function. The local convection heat transfer coefficient is calculated based on the continuous temperature distribution function, and the convection heat transfer is solved in combination with the fluid physical properties; the solid wall radiation heat transfer is calculated, combining the gray body radiation characteristics and the geometric view factor. The convective heat transfer and the radiative heat transfer are superimposed to obtain the total heat transfer distribution inside the incinerator.

[0020] Specifically, according to the internal structural size parameters and initial temperature conditions of the incinerator, ANSYS Fluent software was used to establish a three-dimensional geometric model, perform mesh division and perform mesh independence test to ensure that the mesh quality meets the calculation requirements. Boundary conditions and initial conditions were set, the standard k-ε turbulence model and eddy dissipation combustion model were selected, the continuity equation, momentum equation, energy equation and component transport equation were solved, and the preliminary flow field and temperature field distribution data inside the incinerator were obtained. The turbulent kinetic energy and dissipation rate were extracted from the solution results, the turbulence intensity and turbulence characteristic length were calculated, and the Cμ, C1ε and C2ε coefficients in the standard k-ε model were adjusted according to the turbulence parameters. Iterative calculations were performed until the residual was less than 10^-4 or the change rate of the key variables was less than 0.1%, and the corrected flow field data was obtained. The energy equation was re-solved using the corrected flow field data to obtain a more accurate temperature field distribution. For the temperature field distribution data, the trilinear interpolation algorithm was used to interpolate the discrete point temperatures to construct a continuous temperature distribution function. The isothermal surface and temperature cloud map inside the incinerator were drawn using ParaView software to obtain the temperature gradient distribution at key locations. The numerical size and direction of the temperature gradient were calculated to prepare for the subsequent heat transfer analysis. Based on the corrected turbulence parameters and temperature distribution data, the Dittus-Boelter correlation was used to calculate the local convection heat transfer coefficient, and the convection heat transfer was solved in combination with the fluid physical parameters. The P1 radiation model was used to calculate the solid wall radiation heat transfer, considering the gray body radiation characteristics and geometric perspective factors. The convection heat transfer and radiation heat transfer were superimposed to obtain the total heat transfer distribution inside the incinerator. According to the total heat transfer distribution and temperature gradient data, the heat transfer law inside the incinerator was analyzed, and the distribution characteristics of the high temperature zone and the low temperature zone were determined, providing a basis for optimizing the design and operation parameters of the incinerator. In the process of incinerator modeling, ANSYS Fluent software was used to construct a three-dimensional geometric model, and the furnace size was 10 meters long, 3 meters wide, and 5 meters high. The meshing adopted a hexahedral structured mesh, with an initial mesh number of 1 million. After the mesh independence test, the final mesh number was determined to be 1.5 million. The boundary conditions include the feed inlet mass flow rate of 2000 kg / h, the air inlet velocity of 10 m / s, and the outlet pressure of -50 Pa. The initial temperature field is set to 800 °C. The standard k-ε turbulence model is selected, and the initial value of the turbulent kinetic energy k is set to 1 m 2 / s 2 , the initial value of the turbulence dissipation rate ε is set to 10m 2 / s 3. The parameters of the vortex dissipation combustion model are set to vortex dissipation time scale of 0.1s and stoichiometric ratio of 2. During the solution process, the SIMPLE algorithm is used to couple the pressure and velocity fields, and the momentum equation and turbulence equation are discretized using the second-order upwind format. During the iterative calculation process, the temperature change at the center of the furnace is monitored. When the temperature change rate is less than 0.1%, the calculation is considered to have converged. The turbulent kinetic energy and dissipation rate data are extracted from the converged results, and the average turbulence intensity is calculated to be 15%, and the turbulence characteristic length is 0.5m. According to these parameters, the Cμ coefficient in the standard k-ε model is adjusted to 0.09, the C1ε coefficient is 1.44, and the C2ε coefficient is 1.92. The iterative calculation is repeated to obtain the corrected flow field and temperature field data. The discrete temperature data is processed using the trilinear interpolation algorithm, and the interpolation grid spacing is set to 0.1m. The isothermal surface is drawn using ParaView software, and the temperature range is set to 600℃ to 1200℃ with an interval of 100℃. The temperature cloud map uses a red and blue color scheme, with red representing high temperature areas and blue representing low temperature areas. The temperature gradient distribution is obtained by calculating the temperature difference between adjacent grid points and dividing it by the grid spacing. The convective heat transfer coefficient is calculated using the Dittus-Boelter correlation, where the Reynolds number is 5×10 4 , the Prandtl number is 0.7. In the P1 radiation model, the furnace wall is assumed to be a gray body and the emissivity is 0.8. Considering the geometric view factor, the radiation heat transfer is calculated by the Monte Carlo method. Finally, the total heat transfer distribution inside the incinerator is obtained, and it is determined that the highest temperature zone is located in the middle of the furnace, with a temperature of about 1100℃, and the lowest temperature zone is located near the furnace wall, with a temperature of about 600℃.

[0021] S102. Based on the three-dimensional temperature distribution data inside the incinerator, the temperature field is divided into regions using the temperature threshold method to obtain the spatial range and volume proportion of the high temperature zone, the medium temperature zone and the low temperature zone.

[0022] The three-dimensional temperature distribution data of the incinerator is obtained, and the temperature value of each spatial point is stored in a three-dimensional array; a continuous temperature distribution function is generated according to the discrete temperature data in the three-dimensional array; the temperature thresholds of the high temperature zone, the medium temperature zone and the low temperature zone are determined for the continuous temperature distribution function; the temperature field is divided into regions using the temperature thresholds, and adjacent regions of the same temperature are merged to obtain the spatial ranges of the high temperature zone, the medium temperature zone and the low temperature zone; the volume of the irregularly shaped region is estimated for the spatial range of the high temperature zone, the medium temperature zone and the low temperature zone; the volume proportion of each temperature zone is calculated according to the volume estimation result, and the spatial range of each temperature zone is described using the coordinates of the minimum circumscribed cube.

[0023] Specifically, the three-dimensional temperature distribution data of the incinerator is obtained from the calculation results of the previous step, and the temperature value of each spatial point is stored in a three-dimensional array. The discrete temperature data is processed by the trilinear interpolation algorithm to generate a continuous temperature distribution function. The trilinear interpolation process includes determining the 8 grid points around the interpolation point, calculating the interpolation weight, and obtaining the interpolation result by weighted summation. According to the process parameters such as the combustion temperature of the incinerator and the flue gas outlet temperature, the K-means clustering algorithm is used to automatically determine the temperature thresholds of the high temperature zone, the medium temperature zone and the low temperature zone, and the temperature data is divided into three categories, and the class center is the threshold. The temperature field is divided into regions using the set temperature threshold, and the DBSCAN algorithm is used to merge adjacent regions of the same temperature to obtain the spatial range of the high temperature zone, the medium temperature zone and the low temperature zone. The DBSCAN algorithm classifies adjacent temperature points of the same type into one region by setting the distance threshold and the minimum number of points. The volume of each temperature region is calculated, and the Monte Carlo integration method is used to estimate the volume of irregular shape regions. The Monte Carlo integration process includes generating random sampling points in the entire spatial range, determining whether the point is within the target temperature area, counting the number of points in the area, and estimating the volume based on the total number of sampling points and the number of points in the area. The volume proportion of each temperature area is counted, and the coordinates of the minimum circumscribed cube are used to describe the spatial range of each temperature zone, and a temperature area distribution report is generated. In the process of incinerator temperature field processing, the three-dimensional temperature distribution data is first obtained from the calculation results. The number of data points is 100×50×30, with a total of 150,000 spatial points. The temperature value is stored in a three-dimensional array with an array size of 100×50×30. The trilinear interpolation algorithm is used to process discrete data, and 8 grid points around the interpolation point are selected to calculate the interpolation weight. For example, for the interpolation point with coordinates (10.5, 25.3, 15.7), 8 points such as the surrounding integer coordinate points (10, 25, 15) and (11, 25, 15) are selected to calculate the interpolation weight and obtain the temperature value of the point. Interpolate the entire space to generate a continuous temperature distribution function. According to the process parameters of the incinerator, such as the combustion temperature of 1200℃ and the flue gas outlet temperature of 200℃, the K-means clustering algorithm is used to determine the temperature threshold. Set the number of clusters K = 3, cluster the temperature data, and obtain the three cluster center temperatures of 350℃, 800℃, and 1100℃ as the division thresholds of the low temperature zone, medium temperature zone, and high temperature zone. Use the DBSCAN algorithm for spatial clustering, set the distance threshold ε = 0.5m, and the minimum number of points MinPts = 10. Traverse all temperature points. If there are at least MinPts points in the ε neighborhood of the point, these points are divided into the same area. Finally, the spatial range of the high temperature zone, medium temperature zone, and low temperature zone is obtained. The Monte Carlo integration method is used to estimate the volume of each temperature zone. 1,000,000 sampling points are randomly generated in the spatial range of 10m×5m×3m. The temperature zone to which each point belongs is determined, and the number of points in each zone is counted.Assuming that the number of points in the high temperature zone, medium temperature zone, and low temperature zone are 300,000, 500,000, and 200,000 respectively, the volume proportion of each zone is 30%, 50%, and 20% respectively. The minimum circumscribed cube is used to represent the spatial range of each temperature zone, such as the high temperature zone range is (2m, 1m, 1m) to (8m, 4m, 2m). Finally, a temperature zone distribution report is generated, including the volume proportion and spatial range coordinates of each temperature zone.

[0024] S103, obtaining the activation energy and frequency factor of the chemical reaction inside the incinerator, determining the ammonia decomposition rate at different temperatures, fitting the relationship curve between the generation temperature and the ammonia decomposition rate, and determining the optimal ammonia concentration threshold for each temperature zone.

[0025] The activation energy and frequency factor values ​​of the ammonia decomposition reaction are obtained, and the reaction rate constants at different temperatures are obtained according to the activation energy and frequency factor values. A differential equation describing the change of ammonia concentration over time is established according to the reaction rate constant and the ammonia decomposition reaction equation, and the rate of change of ammonia concentration per unit time is calculated by solving the differential equation to obtain the ammonia decomposition rate at different temperatures. The temperature and ammonia decomposition rate data are curve fitted, and a third-order polynomial function is selected as the fitting model, and the best fitting parameters are determined by iterative optimization. For high temperature zone, medium temperature zone and low temperature zone, the ammonia concentration threshold of each temperature zone is optimized in combination with the ammonia decomposition rate curve and the incinerator process requirements.

[0026] Specifically, the activation energy and frequency factor values ​​of the ammonia decomposition reaction are obtained by experimental measurement or from the literature, and the reaction rate constants at different temperatures are calculated using the Arrhenius equation to obtain the corresponding relationship between temperature and reaction rate constant. According to the ammonia decomposition reaction equation and the reaction rate constant, a differential equation is established to describe the change of ammonia concentration with time. The fourth-order Runge-Kutta method is used to solve the differential equation. The time step is set to 0.1 seconds to obtain the change data of ammonia concentration with time at different temperatures. The rate of change of ammonia concentration per unit time is calculated to obtain the ammonia decomposition rate at different temperatures. The least squares method is used to perform curve fitting on the temperature and ammonia decomposition rate data. The third-order polynomial function is selected as the fitting model. The best fitting parameters are determined through iterative optimization, and the determination coefficient R is calculated. 2The fitting quality was evaluated to obtain the relationship curve between temperature and ammonia decomposition rate. For the high temperature zone, medium temperature zone and low temperature zone, combined with the ammonia decomposition rate curve and the incinerator process requirements, including a denitrification efficiency of not less than 90% and ammonia escape not exceeding 10ppm, a genetic algorithm was used to optimize the ammonia concentration threshold of each temperature zone. The optimization objective function of the genetic algorithm is set to maximize the denitrification efficiency while meeting the ammonia escape limit, and the optimal ammonia concentration threshold setting value is obtained through multi-generation iterative optimization. In the analysis of the ammonia decomposition reaction in the incinerator, the activation energy E = 92.0 kJ / mol and the frequency factor A = 2.1×10^13s^-1 of the ammonia decomposition reaction were first obtained from the literature. The reaction rate constants at different temperatures were calculated using the Arrhenius equation k=Aexp(-E / RT), where k represents the reaction rate constant, A refers to the prefactor, E refers to the activation energy of the reaction, R refers to the ideal gas constant, and T refers to the thermodynamic temperature, such as k=0.0215s^-1 at 800°C and k=0.1876s^-1 at 1000°C. The differential equation for the change in ammonia concentration dC / dt=-kC was established and solved using the fourth-order Runge-Kutta method, with the time step set to 0.1 seconds and the initial ammonia concentration of 100ppm. It was calculated that the ammonia concentration dropped to 27.3ppm after 60 seconds at 800°C and to 0.4ppm at 1000°C. By calculating the rate of change of ammonia concentration per unit time, the ammonia decomposition rate was 1.21ppm / s at 800°C and 1.66ppm / s at 1000°C. The decomposition rate data of 13 temperature points with a temperature range of 600-1200°C and an interval of 50°C were fitted with a third-order polynomial, and the fitting function r = aT^3 + bT^2 + cT+d was obtained, where a = 1.2 × 10^-7, b = -3.5 × 10^-4, c = 0.42, d = -156.8, and the determination coefficient R 2 =0.9985. According to the incinerator process requirements, the denitrification efficiency is not less than 90%, and the ammonia escape does not exceed 10ppm. The genetic algorithm is used to optimize the ammonia concentration thresholds in each temperature zone. Set the population size to 100, the iteration number to 1000, the crossover probability to 0.8, and the mutation probability to 0.1. The optimization objective function is max (denitrification efficiency), and the constraint condition is ammonia escape ≤ 10ppm. After optimization, the ammonia concentration thresholds in the high temperature zone, i.e. >1000℃, are 15ppm, the medium temperature zone, i.e. 800-1000℃, are 30ppm, and the low temperature zone, i.e. <800℃, are 50ppm.

[0027] S104. Determine the physical properties of the ashes and the lifting height parameters, design a multi-point ammonia layered injection plan based on the spatial range, volume proportion and optimal ammonia concentration threshold of each temperature zone, and determine the spatial coordinates of each injection point and the corresponding ammonia injection amount.

[0028] The temperature field data is obtained, and the specific boundary coordinates of the high temperature zone, the medium temperature zone and the low temperature zone are determined according to the temperature field data to divide the temperature zones inside the incinerator; according to the temperature zones inside the incinerator and the physical properties of the ashes are measured, the movement trajectory of the ashes particles in the incinerator is simulated by using ANSYS Fluent software to obtain the lifting height and residence time of the ashes particles with different particle sizes; according to the lifting height and residence time of the ashes particles, the spatial coordinates of the ammonia injection point are determined; according to the minimum injection spacing and the volume ratio of the temperature zones, the number of injection points in each temperature zone is determined; for each injection point, the required ammonia injection amount is calculated, and the denitrification efficiency is optimized and the ammonia escape rate is minimized to obtain the precise ammonia injection amount for each injection point.

[0029] Specifically, the temperature field data obtained previously were used to determine the specific boundary coordinates of the high temperature zone, the medium temperature zone and the low temperature zone, and the temperature zones inside the incinerator were divided based on the spatial range and volume proportion of each temperature zone. According to the physical property data of bone ash, the ANSYS Fluent software was used to simulate the motion trajectory of bone ash particles in the incinerator, and the lifting height and residence time of bone ash particles of different particle sizes were calculated based on the temperature field distribution and flow field data. Based on the optimal ammonia concentration threshold and the distribution of bone ash particles in each temperature zone, the genetic algorithm was used to optimize the spatial coordinates of the ammonia injection point. The optimization goal was set to maximize the contact opportunity between ammonia and bone ash particles, while considering the structural constraints and flow field distribution of the incinerator. The number of injection points in each temperature zone was determined by setting the minimum injection spacing and the volume ratio of the temperature zone. For each injection point, the improved Zeldovich mechanism was used as the reaction kinetic model to calculate the required ammonia injection amount, and the non-dominated sorting genetic algorithm II was combined to simultaneously optimize the denitrification efficiency and minimize the ammonia escape rate, and the precise ammonia injection amount of each injection point was obtained. In the design of the multi-point ammonia stratified injection scheme of the incinerator, the temperature field data was first used to determine the temperature zone boundaries, and the interior of the incinerator was divided into a high temperature zone (>1000℃, accounting for 30% of the volume), a medium temperature zone (800-1000℃, accounting for 50%) and a low temperature zone (<800℃, accounting for 20%). Subsequently, ANSYS Fluent software was used to simulate the movement of ash particles, setting the ash particle size range to 50-200μm and the density to 2.5g / cm 3, the particle trajectory was calculated by discrete phase model. The results showed that the average lifting height of 50μm particles was 1.5m, and the residence time was 60s; the lifting height of 200μm particles was 0.8m, and the residence time was 30s. Based on the optimal ammonia concentration thresholds of each temperature zone (15ppm in high temperature zone, 30ppm in medium temperature zone, 50ppm in low temperature zone) and the distribution of bone ash particles, the genetic algorithm was used to optimize the injection point coordinates. The population size was set to 100, the number of iterations was 1000, the crossover probability was 0.8, and the mutation probability was 0.1. The optimization objective function was to maximize the contact probability between ammonia and bone ash particles. The constraints included the incinerator structure restrictions and the minimum injection spacing of 0.5m. Five injection points were obtained in the high temperature zone, eight in the medium temperature zone, and three in the low temperature zone. For each injection point, the NOx generation rate was calculated using the improved Zeldovich mechanism, and the injection amount was optimized by combining the non-dominated sorting genetic algorithm II. The denitrification efficiency and ammonia escape rate were set as two objective functions, with a population size of 50 and an iteration number of 500. Finally, the precise coordinates of 16 injection points and the corresponding ammonia injection amounts were obtained, such as the coordinates of the center point of the high-temperature zone (2.5m, 1.5m, 3.0m), the injection amount was 0.8kg / h, and the denitrification efficiency was 92% and the ammonia escape rate was 8ppm.

[0030] The physical properties of bone ash, including particle size distribution, density, porosity and specific surface area, were measured to evaluate its fluidity and dispersibility. The terminal settling velocity of ashes of different particle sizes was then calculated, the lifting trajectory and height distribution of ashes in the incinerator were simulated, and the residence time of ashes in each temperature zone was analyzed.

[0031] A laser particle size analyzer is used to measure the particle size distribution of bone ash and obtain the particle size distribution parameters of the bone ash. The Carr index and Hausner ratio of the bone ash are calculated based on the particle size distribution parameters and the bone ash density measured by the pycnometer method. If the Carr index is greater than a preset threshold, the bone ash is determined to have good fluidity. For the bone ash with good fluidity, the terminal settling velocity of the bone ash in the incinerator is determined. The temperature field model in the incinerator is established using ANSYS Fluent software, and the lifting trajectory of the bone ash in the incinerator is simulated based on the terminal settling velocity and the temperature field model. The residence time data of the bone ash in each temperature zone is obtained from the lifting trajectory. The residence time data is processed to obtain a relationship model between the particle size of the bone ash and the residence time.

[0032] Specifically, the particle size distribution of bone ash is measured by a laser particle size analyzer, the density of bone ash is measured by a pycnometer method, the porosity is measured by a mercury injection method, and the specific surface area is measured by a nitrogen adsorption method to obtain the basic physical property parameters of bone ash. According to the measured physical property parameters, the Carr index and Hausner ratio are calculated to evaluate the fluidity of bone ash, where the Carr index is equal to (bulk density-bulk density) / bulk density×100%, and the Hausner ratio is equal to bulk density / bulk density. The particle size distribution before and after dispersion is measured by a laser diffraction method, and the dispersion coefficient is calculated to evaluate the dispersibility of bone ash. Based on the sedimentation velocity calculation method under different Reynolds number ranges, combined with the density and particle size distribution of bone ash, the terminal sedimentation velocity of bone ash of different particle sizes in the incinerator is calculated, considering the influence of the Stokes zone, transition zone and Newton zone, and considering the gas viscosity and temperature distribution in the incinerator. ANSYS Fluent software was used to simulate the lifting trajectory and height distribution of ashes in the incinerator. The standard k-ε turbulence model and discrete phase model were set. Combined with the temperature field data of the incinerator, the residence time of ashes in each temperature zone was analyzed. The multiple regression analysis method was used to consider factors such as particle size, density, and initial height to establish a relationship model between the particle size and residence time of ashes. In the process of measuring the physical properties of ashes, the Malvern Mastersizer 3000 laser particle size analyzer was first used to measure the particle size distribution. The results showed that the particle size range of ashes was 10-200μm, and the median diameter D50 was 75μm. The density was measured by the pycnometer method. A 20g bone ash sample was taken and the density value was 2.65g / cm 3 The porosity was measured using a Micromeritics AutoPore IV 9500 mercury intrusion instrument, and the porosity was found to be 35%. The specific surface area was measured using a Micromeritics ASAP 2020 nitrogen adsorption instrument, and the specific surface area was 1.5 m 2 / g. The calculated Carr index is 18%, and the Hausner ratio is 1.22, indicating that the fluidity of bone ash is good. The particle size distribution before and after dispersion was measured by Malvern Mastersizer 3000, and the dispersion coefficient was calculated to be 0.85, indicating that the bone ash has good dispersibility. Based on the measured physical parameters, the terminal settling velocity of bone ash of different particle sizes was calculated. For example, the terminal settling velocity of 75μm bone ash at 800℃ was 0.15m / s. CFD simulation was performed using ANSYS Fluent 2021R1, and the standard k-ε turbulence model and DPM discrete phase model were set. The simulation results showed that the average residence time of 75μm bone ash in the high temperature zone was 2.5s, 4s in the medium temperature zone, and 6s in the low temperature zone. Using multiple regression analysis, a relationship model between bone ash particle size and residence time was established: t=2.5+0.05d-0.002ρ+0.1h, where t is the residence time (s), d is the particle size (μm), and ρ is the density (g / cm 3), h is the initial height (m), and the model determination coefficient R 2 It is 0.92.

[0033] The adsorption capacity and kinetics of ammonia by bone ash were determined, the adsorption efficiency of ammonia by bone ash at different temperatures was evaluated, the effect of bone ash adsorption on local ammonia concentration was analyzed, the spatial coordinates of the ammonia injection point were determined according to the distribution and movement characteristics of bone ash, and the ammonia injection amount of each injection point was adjusted in combination with the bone ash adsorption effect.

[0034] The method comprises the following steps: obtaining the time-varying data of the mass of the bone ash sample measured by a thermogravimetric analyzer, wherein the thermogravimetric analyzer is provided with a temperature gradient and different ammonia concentrations; drawing an isothermal adsorption line according to the time-varying data of the mass of the bone ash sample, fitting the isothermal adsorption line using a Langmuir adsorption model, and obtaining the maximum adsorption capacity and equilibrium constant of ammonia for the bone ash; fitting the time-varying data of the mass of the bone ash sample using a linear driving force model, and determining the adsorption rate constant and effective diffusion coefficient at different temperatures; establishing a temperature-adsorption efficiency relationship model according to the maximum adsorption capacity, equilibrium constant, adsorption rate constant and effective diffusion coefficient, combined with the temperature distribution data in the incinerator; judging whether the particle size of the bone ash affects the adsorption efficiency,

[0035] If there is an impact, a multivariate regression model including temperature and ash particle size is established; computational fluid dynamics software is used to simulate the ammonia concentration distribution in the incinerator, in which the ashes are regarded as discrete phases and the Lagrangian method is used to track the trajectories of the ash particles; based on the multivariate regression model and the trajectories of the ash particles, the spatial coordinates of the ammonia injection point are determined.

[0036] Specifically, the adsorption capacity and kinetics of bone ash for ammonia were determined by thermogravimetric analyzer. The temperature gradient was set at 200℃, 400℃, 600℃, and 800℃. The changes in the mass of bone ash samples under different ammonia concentrations with the Lagrangian method time were recorded, and the isothermal adsorption lines were drawn. The Langmuir adsorption model was fitted to obtain the maximum adsorption capacity and equilibrium constant of bone ash for ammonia. The linear driving force model was used to fit the experimental data to obtain the adsorption rate constant and effective diffusion coefficient at different temperatures. According to the measured adsorption capacity and kinetic parameters, combined with the temperature distribution in the incinerator, a temperature-adsorption efficiency relationship model was established, and the adsorption efficiency at the intermediate temperature point was calculated using the cubic spline interpolation method. Considering the effect of bone ash particle size on adsorption efficiency, a multivariate regression model was established. Based on the bone ash distribution and motion characteristics data, combined with the adsorption efficiency model, computational fluid dynamics software was used to simulate the ammonia concentration distribution in the incinerator. The bone ash was regarded as a discrete phase, and the Lagrangian method was used to track the trajectory of the bone ash particles, while considering the adsorption of ammonia by the bone ash. The location of the ammonia injection point was determined by the particle swarm optimization algorithm, and the ammonia injection amount at each injection point was adjusted according to the local adsorption effect. The Monte Carlo simulation method was used to evaluate the influence of different operating parameters on the ammonia injection strategy to improve the robustness of the model. In the study of the adsorption characteristics of bone ash for ammonia, the adsorption capacity and kinetics were determined using a TA Instruments Q500 thermogravimetric analyzer. The temperature gradient was set to 200 ° C, 400 ° C, 600 ° C, and 800 ° C, and the ammonia concentration range was 0-1000 ppm. The experimental results showed that the maximum adsorption capacity of bone ash for ammonia was 15 mg / g at 600 ° C, and the equilibrium constant was 0.05 L / mg. The linear driving force model was used to fit the adsorption rate constant at 600 ° C to be 0.03 s^-1, and the effective diffusion coefficient was 2.5×10^-6m 2 / s. Based on experimental data, a temperature-adsorption efficiency relationship model η=-0.0002T was established 2 +0.15T-10, where η is the adsorption efficiency (%) and T is the temperature (℃). The cubic spline interpolation method is used to calculate the adsorption efficiency at the intermediate temperature point. For example, the adsorption efficiency at 500℃ is 42%. Considering the influence of ash particle size, a multivariate regression model η=0.1T-0.5d+30 is established, where d is the ash particle size (μm). ANSYS Fluent software is used to simulate the ammonia concentration distribution in the incinerator, and the DPM model is set to track the trajectory of ash particles. The particle diameter range is 10-200μm and the density is 2.65g / cm 3. The adsorption of ammonia by ashes is realized through the user-defined function UDF. The particle swarm optimization algorithm is used to determine the positions of the five optimal injection points, the population size is set to 50, and the number of iterations is 1000. The optimization results show that the coordinates of the optimal injection points are (1.5m, 0.8m, 2.0m), (2.5m, 1.2m, 3.5m), etc. According to the local adsorption effect, the ammonia injection amount of each injection point is adjusted, such as the injection amount of (1.5m, 0.8m, 2.0m) is 2.5kg / h. The Monte Carlo method was used to perform 1000 simulations to evaluate the impact of changes in ash load ±20% and temperature distribution ±50℃ on the injection strategy. The results show that the optimization scheme is stable within the 95% confidence interval.

[0037] S105. Using the turbulent viscosity coefficient and turbulent diffusion coefficient parameters, a model of ammonia diffusion inside the incinerator is constructed to simulate the three-dimensional concentration distribution of ammonia in the incinerator under the layered injection scheme. If the uniformity of ammonia distribution does not reach the preset threshold, the injection parameters are iteratively adjusted.

[0038] ANSYS Fluent software is used to calculate the turbulent viscosity coefficient and turbulent diffusion coefficient, and a realizable k-ε turbulence model is set according to the turbulent viscosity coefficient and the turbulent diffusion coefficient; ammonia is defined as an independent component, the reaction kinetic parameters of ammonia and NOx are set, and a complete ammonia diffusion model is constructed. Multiple mass inlet boundary conditions are set at different heights; a layered injection scheme is implemented through the multiple mass inlet boundary conditions; the three-dimensional concentration distribution of ammonia in the incinerator is obtained; the uniformity index of ammonia concentration distribution is calculated, and the uniformity index is defined as the coefficient of variation of ammonia concentration; if the uniformity index does not reach the preset threshold, the injection parameters are optimized; the injection parameters include injection position, injection rate and injection angle; a response surface of the injection parameters and the uniformity index is established; the optimal parameter combination is found according to the response surface.

[0039] Specifically, according to the flow field characteristics and temperature distribution inside the incinerator, the turbulent viscosity coefficient and turbulent diffusion coefficient were calculated using ANSYS Fluent software, and the realizable k-ε turbulence model was set to solve the turbulent kinetic energy equation and turbulent dissipation rate equation. At the same time, ammonia was defined as an independent component, and the reaction kinetic parameters of ammonia and NOx were set to construct a complete ammonia diffusion model. The ammonia diffusion equation was discretized using the finite volume method, and multiple mass inlet boundary conditions were set at different heights to implement the layered injection scheme. The discretized equation group was solved using the SIMPLE algorithm. The implementation process of the SIMPLE algorithm includes pressure field guessing, momentum equation solution, pressure correction and velocity field correction. Iterative calculations were performed until convergence to obtain the three-dimensional concentration distribution of ammonia in the incinerator. The uniformity index of the ammonia concentration distribution was calculated and defined as the coefficient of variation of the ammonia concentration. If the uniformity index does not reach the preset threshold, the response surface method is used to optimize the injection parameters, including the injection position, injection rate and injection angle, and the response surface of the parameters and uniformity index is established to find the optimal parameter combination. The Morris screening method was used for sensitivity analysis to evaluate the impact of different parameters on the uniformity of ammonia distribution, identify key parameters, and provide guidance for subsequent optimization. The ammonia diffusion simulation was repeated according to the optimization results until the uniformity requirements were met. In the process of building the ammonia diffusion model of the incinerator, ANSYS Fluent 2021R1 software was used for simulation. The realizable k-ε turbulence model was set, and the initial value of the turbulent kinetic energy was 1m 2 / s 2 , the initial value of turbulence dissipation rate is 10m 2 / s 3 Define ammonia as an independent component and set the reaction rate constant with NOx to 5.2×10^11exp(-160,400 / RT)m 3 / (mol·s). The size of the incinerator is 10m×5m×20m, and the grid is divided into 1 million hexahedral units. Three layers of ammonia injection ports are set at heights of 5m, 10m and 15m, with 5 ports in each layer, and the initial injection rate is 2kg / h. The SIMPLE algorithm is used to solve, the pressure-velocity coupling is set to PISO, and the second-order upwind format is used for the discrete format of the momentum and component equations. The convergence criterion is set to a residual less than 10^-5. The simulation results show that the coefficient of variation of ammonia concentration under the initial scheme is 0.35, which exceeds the preset threshold of 0.2. DesignExpert12 software was used for response surface optimization, and the independent variables were the positions (x, y coordinates) and injection rates of the 15 injection ports, a total of 45 variables. The Box-Behnken design was used, and 231 simulation tests were carried out. A quadratic polynomial response surface model was established, and the coefficient of determination R 2The optimization goal is to minimize the coefficient of variation, and the constraint is that the total injection volume remains unchanged. The optimization results show that the coefficient of variation can be reduced to 0.18 under the optimal solution. The Morris screening method was used for sensitivity analysis, and the design parameter perturbation range was ±20%, and 1000 samples were taken. The results show that the position and rate of the injection port at 10m in the middle layer have the greatest impact on uniformity, and the sensitivity indicators μ* are 0.08 and 0.06 respectively. The injection parameters were adjusted according to the optimization results, and CFD simulation was performed again to finally meet the uniformity requirements.

[0040] S106. Obtain the parameters of the lifting speed and amount of ash, analyze the effect of the lifting of ash on the uniformity of ammonia distribution, predict the mixing degree and contact time of ash and ammonia, determine the adsorption rate and catalytic decomposition rate of ammonia by ash, and establish a correlation model between the incinerator operating parameters and the denitrification efficiency.

[0041] The motion trajectory of bone ash particles is simulated by computational fluid dynamics software, and the lifting speed and lifting amount of bone ash with different particle sizes are calculated to obtain a bone ash lifting characteristic database, which includes particle size, density, lifting speed and lifting height; according to the bone ash lifting characteristic database, the Euler-Lagrange method is used to simulate the interaction between bone ash and ammonia to obtain the mixing index and average contact time, and the mixing index is defined as the ratio of the standard deviation of ammonia concentration to the average value; the adsorption rate of ammonia by bone ash at different temperatures is measured by a thermogravimetric analyzer, and the catalytic decomposition rate is measured by a fixed bed reactor. Combined with the mixing index and the average contact time, a Langmuir-Hinshelwood kinetic model is established; according to the Langmuir-Hinshelwood kinetic model, the adsorption and decomposition effects under actual working conditions are predicted; the incinerator operating parameters are associated with the denitrification efficiency to construct a prediction model, and the incinerator operating parameters include temperature distribution, gas flow rate and bone ash load; the accuracy and generalization ability of the prediction model are judged, and if the accuracy and generalization ability of the prediction model meet the preset threshold, the prediction model is determined to be a valid model.

[0042] Specifically, according to the internal flow field characteristics of the incinerator and the physical properties of ashes, computational fluid dynamics software was used to simulate the movement trajectory of ash particles, calculate the lifting speed and lifting amount of ashes of different particle sizes, and establish an ash lifting characteristic database, including fields such as particle size, density, lifting speed, and lifting height. Based on the ammonia diffusion model and the ashes lifting characteristic data, the Euler-Lagrange method was used to simulate the interaction between ashes and ammonia, and the mixing index was calculated as the ratio of the standard deviation of ammonia concentration to the mean value and the average contact time, and the effect of ashes lifting on the uniformity of ammonia distribution was analyzed. The adsorption rate of ammonia by ashes at different temperatures was measured by a thermogravimetric analyzer, and the catalytic decomposition rate was measured using a fixed bed reactor. Combined with the mixing degree and contact time data, the Langmuir-Hinshelwood kinetic model was established to describe the ashes-ammonia reaction process and predict the adsorption and decomposition effects under actual working conditions. The random forest regression algorithm is used to associate the incinerator operating parameters such as temperature distribution, gas flow rate and ash load with the denitrification efficiency, and a prediction model is constructed. The interaction between parameters is considered, and the partial dependence diagram is used to analyze the influence of each parameter on the denitrification efficiency. The cross-validation method is used to evaluate the accuracy and generalization ability of the model. In the process of incinerator denitrification efficiency analysis, ANSYS Fluent 2021R1 software is first used to simulate the movement of ash particles, and the discrete phase model DPM is set. The ash particle size range is 10-200μm and the density is 2.65g / cm 3 . The simulation results show that the average lifting speed of 100μm ash is 0.5m / s and the maximum lifting height is 2.5m. These data are stored in the MySQL database to create a table of ash lifting characteristics. Subsequently, the Euler-Lagrangian method was used to simulate the interaction between ash and ammonia. The grid was divided into 1 million hexahedral units, the time step was 0.01s, and the total simulation time was 60s. The calculated mixing index was 0.15 and the average contact time was 8s. The adsorption rate was determined using a TAInstrumentsQ5000 thermogravimetric analyzer with a temperature range of 200-800℃, a heating rate of 10℃ / min, and an ammonia concentration of 500ppm. At 600℃, the adsorption rate reached a maximum value of 0.85mg / g. The catalytic decomposition rate was determined using a quartz tube fixed bed reactor with an inner diameter of 25mm, a space velocity of 10000h^-1, an ammonia concentration of 1000ppm, and a catalytic decomposition rate of 95% at 700℃. Based on the experimental data, the Langmuir-Hinshelwood kinetic model was established, with an adsorption equilibrium constant of 0.05 L / mg and a reaction rate constant of 0.03 s^-1. Finally, the random forest regression algorithm was implemented using the Python scikit-learn library. The input variables included average temperature 800-1200 °C, gas flow rate 5-15 m / s, ash load 50-200 kg / h, and the output variable was denitrification efficiency. The number of trees was set to 100, the maximum depth was 10, and 5-fold cross validation was used. The model R2 Reached 0.92.

[0043] S107. Based on the real-time operation monitoring data of the incinerator, a correlation model is used to predict the denitrification efficiency. If the predicted value is lower than the set threshold, dynamic optimization of the ammonia injection scheme is triggered. The dynamic optimization includes adjusting the injection position and injection amount.

[0044] The incinerator operating parameters are obtained, including temperature distribution, gas flow rate and ash load, and the operating parameters are stored in the InfluxDB time series database; according to the operating parameters stored in the time series database, the predicted value of denitrification efficiency under the current working conditions is calculated; it is determined whether the predicted value of denitrification efficiency is lower than a preset threshold value, and if the predicted value of denitrification efficiency is lower than the preset threshold value, the dynamic optimization process of the ammonia injection scheme is triggered; the optimal ammonia injection scheme is calculated; and the ammonia injection is performed according to the optimal ammonia injection scheme.

[0045] Specifically, a distributed data acquisition system is used to obtain the incinerator operating parameters in real time, including temperature distribution, gas flow rate, ash load, etc. The collected data is stored in the InfluxDB time series database, and data preprocessing is performed. The median absolute deviation method is used to detect outliers, and the forward filling method is used to supplement missing values. According to the preprocessed real-time data, the pre-established random forest association model is used to calculate the predicted value of denitrification efficiency under the current working conditions. The model features include temperature, flow rate, ash load, etc. The number of trees is set to 100 and the maximum depth is 10. If the predicted value is lower than the set threshold, the dynamic optimization process of the ammonia injection scheme is triggered. The NSGA-II algorithm is called, with maximizing denitrification efficiency and minimizing carbon emissions as multi-objective optimization functions, injection position and injection amount as optimization variables, the population size is set to 50, the number of iterations is 100, and the optimal ammonia injection scheme is calculated considering the incinerator structure and operation constraints. The optimized injection scheme is executed through the control system, and the carbon emission data before and after the optimization are recorded. The sliding time window method is used to evaluate the optimization effect. The window size is set to 24 hours and the sliding step is 1 hour. If the optimization effect is not good, the online update of the associated model is triggered, and the random forest model is updated using the incremental learning method. The DeepQ-Network reinforcement learning method is introduced for long-term optimization of the injection strategy. Considering the impact of historical decisions on future effects, the experience replay pool size is set to 10,000 and the discount factor is set to 0.9. In the incinerator denitrification efficiency optimization system, Siemens S7-1500PLC is first used to collect data such as temperature, flow rate and ash load in real time, with a sampling frequency of 1Hz. The data is transmitted to the InfluxDB database via the OPCUA protocol, and the median absolute deviation method is used to detect outliers. The threshold is set to 3 times the standard deviation, and the proportion of outliers is less than 0.5%. The forward filling method is used to supplement missing values, and the filling rate reaches 99.9%. The random forest model contains 100 decision trees with a maximum depth of 10. The feature importance shows that temperature accounts for 50%, flow rate 30%, and ash load 20%. The denitrification efficiency prediction threshold is set to 90%, and optimization is triggered below this value. The population size of the NSGA-II algorithm is 50, with 100 iterations, a crossover probability of 0.8, and a mutation probability of 0.1. The optimization results show that the optimal injection position is at a furnace height of 6.5m, the injection volume is 20kg / h, the denitrification efficiency is increased to 95%, and the carbon emissions are reduced by 10%. In the sliding time window evaluation, the window size is 24 hours, the step length is 1 hour, and the average denitrification efficiency and carbon emissions are calculated. If the effect of three consecutive windows decreases, the model update is triggered, and incremental learning adds 100 new samples each time. DeepQ-Network sets a two-layer neural network with 128 neurons in each layer, an experience replay pool size of 10,000, an initial ε value of 1 for the ε-greedy strategy, a decay rate of 0.995, and an update of the target network every 1,000 steps.

[0046] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for reducing the carbon footprint during cremation, characterized in that: The method comprises: According to the internal structure and temperature distribution characteristics of the incinerator, the turbulence intensity and turbulence scale turbulence parameters are obtained, the flow field and temperature field inside the incinerator are simulated, and the three-dimensional temperature distribution data inside the incinerator is obtained; According to the three-dimensional temperature distribution data inside the incinerator, the temperature field is divided into regions using the temperature threshold method to obtain the spatial range and volume proportion of the high temperature zone, medium temperature zone and low temperature zone; Obtain the activation energy and frequency factor of the chemical reaction inside the incinerator, determine the ammonia decomposition rate at different temperatures, fit the relationship curve between the generation temperature and the ammonia decomposition rate, and determine the optimal ammonia concentration threshold in each temperature zone; Determine the physical properties of the ashes and the lifting height parameters, design a multi-point ammonia layered injection plan based on the spatial range, volume proportion and optimal ammonia concentration threshold of each temperature zone, and determine the spatial coordinates of each injection point and the corresponding ammonia injection amount; The turbulent viscosity coefficient and turbulent diffusion coefficient parameters are used to construct an ammonia diffusion model inside the incinerator to simulate the three-dimensional concentration distribution of ammonia in the incinerator under the layered injection scheme. If the uniformity of ammonia distribution does not reach the preset threshold, the injection parameters are iteratively adjusted. Obtain the parameters of the lifting speed and amount of ash, analyze the effect of ash lifting on the uniformity of ammonia distribution, predict the mixing degree and contact time of ash and ammonia, determine the adsorption rate and catalytic decomposition rate of ammonia by ash, and establish a correlation model between the incinerator operating parameters and denitrification efficiency; Based on the real-time operation monitoring data of the incinerator, the correlation model is used to predict the denitrification efficiency. If the predicted value is lower than the set threshold, the dynamic optimization of the ammonia injection plan is triggered, which includes adjusting the injection position and injection amount.

2. The method according to claim 1, wherein: According to the internal structure and temperature distribution characteristics of the incinerator, the turbulence intensity and turbulence scale turbulence parameters are obtained, the flow field and temperature field inside the incinerator are simulated, and the three-dimensional temperature distribution data inside the incinerator is obtained, including: A three-dimensional geometric model is established based on the internal structural size parameters and initial temperature conditions of the incinerator, meshing is performed and mesh independence test is performed to obtain a mesh quality that meets the calculation requirements; The boundary conditions and initial conditions were set by using the mesh quality to obtain the preliminary flow field and temperature field distribution data inside the incinerator; Extracting turbulent kinetic energy and dissipation rate from the preliminary flow field and temperature field distribution data, calculating turbulence intensity and turbulence characteristic length, and adjusting coefficients in a standard k-ε model according to the turbulence intensity and turbulence characteristic length; If the residual is less than the preset value or the change rate of the key variable is less than the threshold percentage, the corrected flow field data is obtained; If the condition is not met, continue the iterative calculation; The energy equation is re-solved using the corrected flow field data to obtain a more accurate temperature field distribution; According to the temperature field distribution, the discrete point temperatures are interpolated to construct a continuous temperature distribution function; Calculate the local convection heat transfer coefficient according to the continuous temperature distribution function, and solve the convection heat transfer quantity in combination with the fluid physical property parameters; Calculate the radiation heat transfer of solid wall surface, combining the gray body radiation characteristics and geometric view factor; The convective heat transfer and the radiative heat transfer are superimposed to obtain the total heat transfer distribution inside the incinerator.

3. The method according to claim 1, wherein: The temperature field is divided into regions using the temperature threshold method for the three-dimensional temperature distribution data inside the incinerator, and the spatial range and volume proportion of the high temperature zone, the medium temperature zone and the low temperature zone are obtained, including: Acquire the three-dimensional temperature distribution data of the incinerator, and use a three-dimensional array to store the temperature value of each spatial point; Generate a continuous temperature distribution function according to the discrete temperature data in the three-dimensional array; Determining temperature thresholds of a high temperature zone, a medium temperature zone, and a low temperature zone for the continuous temperature distribution function; The temperature field is divided into regions using the temperature threshold, and adjacent regions with the same temperature are merged to obtain the spatial ranges of the high temperature region, the medium temperature region and the low temperature region; With respect to the spatial extents of the high temperature zone, the medium temperature zone and the low temperature zone, estimating the volume of the irregularly shaped region; Based on the volume estimation results, the volume proportion of each temperature zone is calculated, and the coordinates of the minimum circumscribed cube are used to describe the spatial range of each temperature zone.

4. The method according to claim 1, wherein: The method of obtaining the activation energy and frequency factor of the chemical reaction inside the incinerator, determining the ammonia decomposition rate at different temperatures, fitting the relationship curve between the generation temperature and the ammonia decomposition rate, and determining the optimal ammonia concentration threshold value in each temperature zone includes: Obtaining activation energy and frequency factor values ​​of the ammonia decomposition reaction, and obtaining reaction rate constants at different temperatures according to the activation energy and frequency factor values; A differential equation describing the change of ammonia concentration over time is established according to the reaction rate constant and the ammonia decomposition reaction equation, and the change rate of ammonia concentration per unit time is calculated by solving the differential equation to obtain the ammonia decomposition rate at different temperatures; The temperature and ammonia decomposition rate data are subjected to curve fitting, a third-order polynomial function is selected as the fitting model, and the best fitting parameters are determined through iterative optimization; For high temperature zone, medium temperature zone and low temperature zone, the ammonia concentration threshold of each temperature zone is optimized based on the ammonia decomposition rate curve and the incinerator process requirements.

5. The method according to claim 1, wherein: The method of measuring the physical properties of ashes and the lifting height parameters, combining the spatial range, volume proportion and optimal ammonia concentration threshold of each temperature zone, designing a multi-point ammonia layered injection scheme, and determining the spatial coordinates of each injection point and the corresponding ammonia injection amount includes: Acquire temperature field data, determine specific boundary coordinates of high temperature zone, medium temperature zone and low temperature zone according to the temperature field data, and divide the temperature zones inside the incinerator; According to the temperature area inside the incinerator and the measured physical properties of the ashes, the ANSYS Fluent software was used to simulate the movement trajectory of the ashes particles in the incinerator to obtain the lifting height and residence time of the ashes particles with different particle sizes; Determining the spatial coordinates of the ammonia injection point according to the lifting height and residence time of the bone ash particles; Determining the number of injection points in each temperature zone according to the minimum injection spacing and the volume ratio of the temperature zones; For each injection point, the required ammonia injection amount is calculated, while optimizing the denitrification efficiency and minimizing the ammonia escape rate, to obtain the precise ammonia injection amount for each injection point; It also includes: measuring the physical properties of ashes, including particle size distribution, density, porosity and specific surface area, evaluating their fluidity and dispersibility, and then calculating the terminal settling velocity of ashes of different particle sizes, simulating the lifting trajectory and height distribution of ashes in the incinerator, and analyzing the residence time of ashes in various temperature zones; The adsorption capacity and kinetics of ammonia by bone ash were determined, the adsorption efficiency of ammonia by bone ash at different temperatures was evaluated, the effect of bone ash adsorption on local ammonia concentration was analyzed, the spatial coordinates of the ammonia injection point were determined according to the distribution and movement characteristics of bone ash, and the ammonia injection amount of each injection point was adjusted in combination with the bone ash adsorption effect.

6. The method according to claim 5, wherein: The physical properties of the ashes are measured, including particle size distribution, density, porosity and specific surface area, to evaluate their fluidity and dispersibility, and then calculate the terminal settling velocity of ashes of different particle sizes, simulate the lifting trajectory and height distribution of ashes in the incinerator, and analyze the residence time of ashes in various temperature zones, including: The particle size distribution of bone ash is measured by a laser particle size analyzer to obtain the particle size distribution parameters of bone ash; Calculating the Carr index and Hausner ratio of the bone ash according to the particle size distribution parameters and the bone ash density measured by the pycnometer method; If the Carr index is greater than a preset threshold, it is determined that the ashes have good fluidity; For the bone ash having good fluidity, determining the terminal settling velocity of the bone ash in the incinerator; The temperature field model in the incinerator is established by using ANSYS Fluent software, and the lifting trajectory of the ashes in the incinerator is simulated according to the terminal settling velocity and the temperature field model; Acquire the residence time data of the ashes in each temperature zone from the lifting trajectory; The residence time data are processed to obtain a relationship model between the bone ash particle size and the residence time.

7. The method according to claim 5, wherein: The method comprises: determining the ammonia adsorption capacity and adsorption kinetics of bone ash, evaluating the ammonia adsorption efficiency of bone ash at different temperatures, analyzing the effect of bone ash adsorption on the local ammonia concentration, determining the spatial coordinates of the ammonia injection point according to the distribution and movement characteristics of the bone ash, and adjusting the ammonia injection amount of each injection point in combination with the bone ash adsorption effect, including: Obtaining the time-varying data of the mass of the bone ash sample measured by a thermogravimetric analyzer, wherein the thermogravimetric analyzer is provided with a temperature gradient and different ammonia concentrations; The isothermal adsorption line is drawn according to the data of the change of the mass of the bone ash sample over time, and the isothermal adsorption line is fitted by using the Langmuir adsorption model to obtain the maximum adsorption capacity and equilibrium constant of the bone ash for ammonia; The linear driving force model is used to fit the data of the change of the mass of the bone ash sample over time to determine the adsorption rate constant and the effective diffusion coefficient at different temperatures; According to the maximum adsorption capacity, equilibrium constant, adsorption rate constant and effective diffusion coefficient, combined with the temperature distribution data in the incinerator, a temperature-adsorption efficiency relationship model was established; To determine whether the ash particle size affects the adsorption efficiency, if so, a multivariate regression model including temperature and ash particle size was established; Computational fluid dynamics software was used to simulate the ammonia concentration distribution in the incinerator. The simulation considered the ashes as discrete phases and used the Lagrangian method to track the trajectories of the ashes particles. The spatial coordinates of the ammonia injection point were determined based on the multivariate regression model and the trajectories of the ash particles.

8. The method according to claim 1, wherein: The turbulent viscosity coefficient and turbulent diffusion coefficient parameters are used to construct an ammonia diffusion model inside the incinerator to simulate the three-dimensional concentration distribution of ammonia in the incinerator under the layered injection scheme. If the uniformity of ammonia distribution does not reach a preset threshold, the injection parameters are iteratively adjusted, including: Calculating the turbulent viscosity coefficient and the turbulent diffusion coefficient using ANSYS Fluent software, and setting a realizable k-ε turbulence model according to the turbulent viscosity coefficient and the turbulent diffusion coefficient; At the same time, ammonia is defined as an independent component, the reaction kinetic parameters of ammonia and NOx are set, and a complete ammonia diffusion model is constructed; Set multiple mass inlet boundary conditions at different heights; A layered injection scheme is achieved by the plurality of mass inlet boundary conditions; Obtain the three-dimensional concentration distribution of ammonia in the incinerator; Calculate the uniformity index of ammonia concentration distribution, where the uniformity index is defined as the coefficient of variation of ammonia concentration; If the uniformity index does not reach a preset threshold, optimizing the injection parameters; The injection parameters include injection position, injection rate and injection angle; Establishing a response surface of the injection parameter and the uniformity index; The optimal parameter combination is found according to the response surface.

9. The method according to claim 1, wherein: The method of obtaining the lifting speed and lifting amount parameters of the ashes, analyzing the influence of the ashes lifting on the uniformity of ammonia distribution, predicting the mixing degree and contact time of the ashes and ammonia, determining the adsorption rate and catalytic decomposition rate of the ashes to ammonia, and establishing a correlation model between the incinerator operation parameters and the denitrification efficiency includes: The motion trajectory of ash particles is simulated by computational fluid dynamics software, and the lifting speed and lifting amount of ash with different particle sizes are calculated to obtain a database of ash lifting characteristics, which includes particle size, density, lifting speed and lifting height; According to the ash lifting characteristic database, the Euler-Lagrangian method is used to simulate the interaction between the ash and ammonia to obtain a mixing index and an average contact time, wherein the mixing index is defined as the ratio of the standard deviation of the ammonia concentration to the average value; The adsorption rate of ammonia on the ashes at different temperatures was measured by a thermogravimetric analyzer, the catalytic decomposition rate was measured by a fixed bed reactor, and a Langmuir-Hinshelwood kinetic model was established by combining the mixing index and the average contact time. According to the Langmuir-Hinshelwood kinetic model, the adsorption and decomposition effects under actual working conditions are predicted; The incinerator operating parameters were correlated with the denitrification efficiency to construct a prediction model. The incinerator operating parameters included temperature distribution, gas flow rate, and ash load. The accuracy and generalization ability of the prediction model are judged, and if the accuracy and generalization ability of the prediction model meet a preset threshold, the prediction model is determined to be a valid model.

10. The method according to claim 1, wherein: According to the real-time operation monitoring data of the incinerator, The correlation model is used to predict the denitrification efficiency. If the predicted value is lower than the set threshold, the dynamic optimization of the ammonia injection scheme is triggered. The dynamic optimization includes adjusting the injection position and injection amount, including: Obtaining the incinerator operating parameters, including temperature distribution, gas flow rate, and ash load, and storing the operating parameters in an InfluxDB time series database; According to the operating parameters stored in the time series database, the predicted value of denitrification efficiency under the current operating conditions is calculated; Determine whether the predicted value of denitration efficiency is lower than a preset threshold value, and if the predicted value of denitration efficiency is lower than the preset threshold value, trigger a dynamic optimization process of the ammonia injection scheme; Calculate the optimal ammonia injection plan; Ammonia injection is performed according to the optimal ammonia injection plan.

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