A method of reducing the carbon footprint during cremation

Abstract

The application provides a method for reducing carbon footprint in cremation process, comprising: obtaining turbulence intensity, turbulence scale turbulence parameters according to the internal structure and temperature distribution characteristics of the incinerator, simulating the internal flow field and temperature field of the incinerator, and obtaining the three-dimensional temperature distribution data inside the incinerator; for the three-dimensional temperature distribution data inside the incinerator, using temperature threshold method to divide the temperature field into regions, and obtaining the spatial range and volume proportion of high-temperature zone, medium-temperature zone and low-temperature zone; obtaining the activation energy and frequency factor of the chemical reaction inside the incinerator, judging the ammonia decomposition rate at different temperatures, fitting the relationship curve between temperature and ammonia decomposition rate, and determining the optimal ammonia concentration threshold of each temperature zone; according to the real-time operation monitoring data of the incinerator, using the correlation model to predict the denitration efficiency, if the predicted value is lower than the set threshold, triggering the dynamic optimization of ammonia injection scheme, and the dynamic optimization includes adjusting the injection position and injection amount.

Description

TECHNICAL FIELD

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

[0002] In large incinerator systems, there is a complex technical contradiction between ammonia injection and carbon emission control. The temperature distribution inside the incinerator is extremely uneven, forming multiple temperature gradient regions. In the high-temperature region, the ammonia decomposition rate is too fast, resulting in insufficient denitration reaction; while in the low-temperature region, the ammonia utilization rate is significantly reduced, causing 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 precisely control the amount and position of ammonia injection. Turbulence causes uneven distribution of ammonia in the furnace, which cannot fully contact and react with nitrogen oxides. Even increasing the amount of ammonia injection cannot make up for the uneven distribution problem, and may even cause ammonia escape. In addition, the ashes produced during the incineration process 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 cannot achieve such fine dynamic adjustment. These factors interact and constrain 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 for incinerator system optimization. SUMMARY

[0003] The present application provides a method for reducing carbon footprint in cremation, mainly comprising:

[0004] According to the internal structure and temperature distribution characteristics of the incinerator, the turbulence intensity, turbulence scale and turbulence parameters are obtained, the internal flow field and temperature field of 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, and the spatial range and volume proportion of the high-temperature zone, medium-temperature zone and low-temperature zone are obtained;

[0006] The activation energy and frequency factor of the chemical reaction inside the incinerator are obtained, the ammonia decomposition rate at different temperatures is determined, the relationship curve between temperature and ammonia decomposition rate is fitted, and the optimal ammonia concentration threshold of each temperature region is determined;

[0007] The physical properties and lift height parameters of the ashes are determined, and combined with the spatial range, volume proportion and optimal ammonia concentration threshold of each temperature region, a multi-point ammonia layered injection scheme is designed, and the spatial coordinates and corresponding ammonia injection amount of each injection point are determined;

[0008] Adopt the turbulent viscosity coefficient, turbulent diffusion coefficient parameter, build the ammonia diffusion model in the incinerator, 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, then iteratively adjust the injection parameters;

[0009] Obtain the cremated remains lifting speed and lifting amount parameters, analyze the influence of cremated remains lifting on ammonia distribution uniformity, predict the mixing degree and contact time of cremated remains and ammonia, judge the adsorption rate and catalytic decomposition rate of cremated remains on ammonia, and establish the correlation model of incinerator operation parameters and denitration efficiency;

[0010] According to the real-time operation monitoring data of the incinerator, the correlation model is used to predict the denitration efficiency, if the predicted value is lower than the set threshold, the dynamic optimization of ammonia injection scheme is triggered, and the dynamic optimization includes adjusting the injection position and injection amount.

[0011] The technical scheme provided by the embodiment of the present application can include the following beneficial effects:

[0012] The present application discloses a method for reducing carbon footprint in cremation process, which first obtains three-dimensional temperature distribution data by simulating the internal flow field and temperature field of the incinerator, and divides the temperature region, then designs a multi-point layered injection scheme according to the ammonia decomposition kinetics and the characteristics of cremated remains, simulates the concentration distribution after injection by building an ammonia diffusion model, and iteratively optimizes the injection parameters, while considering the influence of cremated remains lifting on ammonia distribution, establishes a correlation model of operation parameters and denitration efficiency, finally, predicts the denitration efficiency according to real-time monitoring data, and dynamically optimizes the ammonia injection scheme, realizes the accurate control of the denitration process of the incinerator and the reduction of carbon emissions. In summary, the present application makes the distribution of ammonia in the incinerator more uniform, improves the sufficiency and stability of the reaction, and through dynamic optimization of the ammonia injection scheme, it can adapt to the changes of the incinerator in real time, ensure the continuous good denitration effect, improve the denitration efficiency, reduce the emission of nitrogen oxides, meet the environmental protection requirements, optimize the use of ammonia, reduce the consumption of ammonia, and save costs. BRIEF DESCRIPTION OF DRAWINGS

[0013] Fig. 1 The flowchart of the method for reducing carbon footprint in cremation process of the present application.

[0014] Fig. 2 The schematic diagram of the method for reducing carbon footprint in cremation process of the present application.

[0015] Fig. 3 Another schematic diagram of the method for reducing carbon footprint in cremation process of the present application. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be described clearly and in detail below with reference to the drawings in the embodiments of the present application. The described embodiments are only some of the embodiments of the present application.

[0017] As Figs. 1-3 The method for reducing carbon footprint in the cremation process can specifically include the following steps.

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

[0019] A three-dimensional geometric model is established according to the internal structure size parameters and initial temperature conditions of the incinerator, grid division is performed and grid independence test is executed, and grid quality meeting the calculation requirements is obtained. The boundary conditions and initial conditions are set by using the grid quality, and the preliminary flow field and temperature field distribution data of the internal incinerator are obtained. The turbulent kinetic energy and dissipation rate are extracted from the preliminary flow field and temperature field distribution data, the turbulent intensity and turbulent characteristic length are calculated, and the coefficients in the standard k-ε model are adjusted according to the turbulent intensity and turbulent 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, the iterative calculation is continued. The energy equation is solved again by using the corrected flow field data, and a more accurate temperature field distribution is obtained. For the temperature field distribution, the discrete point temperature is interpolated to construct a continuous temperature distribution function. According to the continuous temperature distribution function, the local convective heat transfer coefficient is calculated, and the convective heat transfer amount is solved combined with the fluid physical property parameters; the solid wall surface radiation heat transfer amount is calculated, combined with the gray body radiation characteristics and the geometric view factor. The convective heat transfer amount and the radiation heat transfer amount are superimposed to obtain the total heat transfer amount distribution of the internal incinerator.

[0020] Specifically, according to the internal structure size parameters and initial temperature conditions of the incinerator, a three-dimensional geometric model was established using ANSYS Fluent software, mesh division was performed, and mesh independence test was carried out to ensure that the mesh quality met the calculation requirements. The boundary conditions and initial conditions were set, the standard k-ε turbulence model and the vortex 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 of the incinerator were obtained. The turbulent kinetic energy and dissipation rate were extracted from the solution results, the turbulent intensity and turbulent 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 calculation was carried out until the residual was less than 10^-4 or the change rate of key variables was less than 0.1%, and the corrected flow field data were obtained. The energy equation was solved again using the corrected flow field data to obtain a more accurate temperature field distribution. For the temperature field distribution data, a three-linear interpolation algorithm was used to interpolate the discrete point temperature, and a continuous temperature distribution function was constructed. The isothermal surface and temperature cloud chart of the incinerator were drawn using ParaView software, and the temperature gradient distribution at the key position was obtained. The numerical value 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 local convective heat transfer coefficient was calculated using the Dittus-Boelter correlation, and the convective heat transfer amount was solved combined with the fluid property parameters. The P1 radiation model was used to calculate the solid wall radiation heat transfer amount, considering the gray body radiation characteristics and geometric view factor. The total heat transfer amount distribution in the incinerator was obtained by superimposing the convective heat transfer amount and the radiation heat transfer amount. According to the total heat transfer amount distribution and temperature gradient data, the heat transfer law in 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 modeling process of the incinerator, a three-dimensional geometric model was constructed using ANSYS Fluent software, and the furnace size was 10 meters long, 3 meters wide and 5 meters high. Hexahedral structured mesh was used for mesh division, and the initial number of meshes was 1 million. After mesh independence test, the final number of meshes was determined to be 1.5 million. The boundary conditions included mass flow rate of 2000 kg / h at the inlet, air inlet velocity of 10 m / s, and outlet pressure of -50 Pa. The initial temperature field was set to 800℃. The standard k-ε turbulence model was selected, in which the initial value of turbulent kinetic energy k was set to 1 m 2 / s 2 , and the initial value of turbulent dissipation rate ε was set to 10 m 2 / s 3The vortex dissipation combustion model parameters were set to a vortex dissipation time scale of 0.1 s and a stoichiometric ratio of 2. During the solution process, the SIMPLE algorithm was used to couple the pressure and velocity fields, and the momentum and turbulence equations were discretized using a second-order upwind scheme. During the iterative calculation, the temperature change at the furnace center point was monitored; convergence was considered achieved when the temperature change rate was less than 0.1%. Turbulent kinetic energy and dissipation rate data were extracted from the convergence results, and the average turbulence intensity was calculated to be 15%, with a turbulence characteristic length of 0.5 m. Based on these parameters, the Cμ coefficient, C1ε coefficient, and C2ε coefficient in the standard k-ε model were adjusted to 0.09, 1.44, and 1.92, respectively. The calculation was iterated again to obtain the corrected flow and temperature field data. The discrete temperature data were processed using a trilinear interpolation algorithm with an interpolation grid spacing of 0.1 m. Isothermal surfaces were drawn using ParaView software, with a temperature range of 600℃ to 1200℃ and intervals of 100℃. The temperature contour map uses a red-blue color scheme, with red representing high-temperature regions and blue representing low-temperature regions. The temperature gradient distribution is obtained by calculating the temperature difference between adjacent grid points and dividing by the grid spacing. The convective heat transfer coefficient is calculated using the Dittus-Boelter correlation, with a Reynolds number of 5 × 10⁻⁶. 4 The Prandtl number is set to 0.7. In the P1 radiation model, the furnace wall is assumed to be ash, and the emissivity is set to 0.8. Considering the geometric perspective factor, the radiative heat transfer is calculated using the Monte Carlo method. The final total heat transfer distribution inside the incinerator is obtained, and the highest temperature zone is located in the middle of the furnace, with a temperature of approximately 1100℃, while the lowest temperature zone is located near the furnace wall, with a temperature of approximately 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 ratio 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 acquired, and the temperature value of each spatial point is stored in a three-dimensional array. A continuous temperature distribution function is generated based on the discrete temperature data in the three-dimensional array. Temperature thresholds for the high-temperature, medium-temperature, and low-temperature zones are determined for the continuous temperature distribution function. The temperature field is divided into regions using these temperature thresholds, and adjacent regions with the same temperature are merged to obtain the spatial ranges of the high-temperature, medium-temperature, and low-temperature zones. The volume of irregularly shaped regions is estimated for each of the spatial ranges of the high-temperature, medium-temperature, and low-temperature zones. Based on the volume estimation results, the volume ratio of each temperature zone is calculated, and the spatial range of each temperature zone is described using the coordinates of the smallest circumscribed cube.

[0023] Specifically, the three-dimensional temperature distribution data of the incinerator is obtained from the calculation results of the previous step, a three-dimensional array is used to store the temperature value of each space point, and a trilinear interpolation algorithm is used to process the discrete temperature data 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 weighted sum to get the interpolation result. According to the incinerator combustion temperature, flue gas outlet temperature and other process parameters, the K-means clustering algorithm is used to automatically determine the temperature threshold of the high temperature zone, medium temperature zone and low temperature zone, and the temperature data is divided into three categories, and the class center is the threshold. The temperature threshold is used to divide the temperature field into regions, and the DBSCAN algorithm is used to merge adjacent regions with the same temperature. The spatial range of the high temperature zone, medium temperature zone and low temperature zone is obtained. The DBSCAN algorithm uses a distance threshold and a minimum point number to classify adjacent points with the same temperature into a region. The volume of each temperature region is calculated, and the Monte Carlo integration method is used to estimate the volume of irregularly shaped regions. The Monte Carlo integration process includes generating random sampling points in the entire space range, determining whether the point is in the target temperature region, counting the number of points in the region, and estimating the volume according to the total number of sampling points and the number of points in the region. The volume of each temperature region is calculated, and the coordinates of the minimum circumscribed cube are used to describe the spatial range of each temperature zone, and a temperature region distribution report is generated. In the process of processing the temperature field of the incinerator, the three-dimensional temperature distribution data is obtained from the calculation results, the number of data points is 100x50x30, and there are 150000 space points. A three-dimensional array is used to store the temperature value, and the array size is 100x50x30. The trilinear interpolation algorithm is used to process the 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), select 8 points around the integer coordinate points (10, 25, 15), (11, 25, 15), etc. Calculate the interpolation weight to get the temperature value of the point. Interpolate all spaces to generate a continuous temperature distribution function. According to the incinerator process parameters, such as combustion temperature 1200℃, flue gas outlet temperature 200℃, the K-means clustering algorithm is used to determine the temperature threshold. Set the cluster number K=3, cluster the temperature data, get three class centers temperature 350℃, 800℃, 1100℃, as the division threshold of 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 point number MinPts=10. Traverse all temperature points, if the point has at least MinPts points in its ε neighborhood, then these points are divided into the same region. 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 region, 1000000 random sampling points are generated in the 10m x 5m x 3m space range, each point is judged to belong to which temperature region, and the number of points in each region is counted.Assuming that the point numbers of the high-temperature zone, the medium-temperature zone, and the low-temperature zone are 300,000, 500,000, and 200,000, respectively, the volume proportions of the respective zones are 30%, 50%, and 20%, respectively. The spatial ranges of the respective temperature zones are represented by the minimum circumscribed cubes, such as the range of the high-temperature zone being (2m, 1m, 1m) to (8m, 4m, 2m). Finally, a temperature zone distribution report is generated, including the volume proportions and spatial range coordinates of the respective temperature zones.

[0024] In S103, the activation energy and the frequency factor of the chemical reaction inside the incinerator are obtained, the ammonia decomposition rate at different temperatures is determined, a relationship curve between the temperature and the ammonia decomposition rate is fitted, and the optimal ammonia concentration threshold of each temperature zone is determined.

[0025] The activation energy and the frequency factor of the ammonia decomposition reaction are obtained, and the reaction rate constant at different temperatures is obtained according to the activation energy and the frequency factor. A differential equation describing the change of ammonia concentration with 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 through the solution of the differential equation to obtain the ammonia decomposition rate at different temperatures. The temperature and ammonia decomposition rate data are curve-fitted, a third-order polynomial function is selected as the fitting model, and the optimal fitting parameters are determined through iterative optimization. For the high-temperature zone, the medium-temperature zone, and the 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 the frequency factor of the ammonia decomposition reaction are determined through experiments or obtained from literature, the reaction rate constant at different temperatures is calculated using the Arrhenius equation, and the corresponding relationship between the temperature and the reaction rate constant is obtained. 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, and the change data of ammonia concentration with time at different temperatures are obtained. The change rate of ammonia concentration per unit time is calculated to obtain the ammonia decomposition rate at different temperatures. The temperature and ammonia decomposition rate data are curve-fitted using the least squares method, a third-order polynomial function is selected as the fitting model, the optimal fitting parameters are determined through iterative optimization, and the determination coefficient R 2The fitting quality is evaluated to obtain the relationship curve of temperature and ammonia decomposition rate. According to the high temperature zone, the medium temperature zone and the low temperature zone, combined with the ammonia decomposition rate curve and the incinerator process requirements, including the denitrification efficiency not less than 90% and the ammonia escape not more than 10ppm, the genetic algorithm is used to optimize the ammonia concentration threshold value of each temperature zone. The optimization objective function of genetic algorithm is set to maximize the denitrification efficiency while meeting the ammonia escape limit value, and the best ammonia concentration threshold setting value is obtained through multiple iteration optimization. In the process of analyzing the ammonia decomposition reaction in the incinerator, first, the activation energy E = 92.0 kJ / mol and the frequency factor A = 2.1 x 10^13 s^-1 of the ammonia decomposition reaction are obtained from the literature. The Arrhenius equation k = Aexp(-E / RT) is used to calculate the reaction rate constant at different temperatures, where k represents the reaction rate constant, A represents the pre-exponential factor, E represents the activation energy of the reaction, R represents the ideal gas constant, and T represents the thermodynamic temperature. For example, at 800℃, k = 0.0215 s^-1, and at 1000℃, k = 0.1876 s^-1. The differential equation dC / dt = -kC is established for the change of ammonia concentration, and the fourth-order Runge-Kutta method is used for solution, with a time step of 0.1 seconds and an initial ammonia concentration of 100ppm. The calculation results show that at 800℃, the ammonia concentration decreases to 27.3ppm after 60 seconds, and at 1000℃, the ammonia concentration decreases to 0.4ppm. By calculating the ammonia concentration change rate per unit time, the ammonia decomposition rate at 800℃ is 1.21ppm / s, and the ammonia decomposition rate at 1000℃ is 1.66ppm / s. The decomposition rate data of 13 temperature points with a temperature range of 600-1200℃ and an interval of 50℃ are fitted by a third-order polynomial, and the fitting function r = aT^3 + bT^2 + cT + d is obtained, where a = 1.2 x 10^-7, b = -3.5 x 10^-4, c = 0.42, and d = -156.8. 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 is not more than 10ppm, the genetic algorithm is used to optimize the ammonia concentration threshold value of each temperature zone. The population size is set to 100, the iteration number is set to 1000, the crossover probability is set to 0.8, and the mutation probability is set to 0.1. The optimization objective function is max(denitrification efficiency), and the constraint condition is ammonia escape ≤ 10ppm. After optimization, the ammonia concentration threshold value in the high temperature zone (i.e. > 1000℃) is 15ppm, the ammonia concentration threshold value in the medium temperature zone (i.e. 800-1000℃) is 30ppm, and the ammonia concentration threshold value in the low temperature zone (i.e. < 800℃) is 50ppm.

[0027] S104, the physical properties and the lifting height parameters of the cremated remains are determined, the multi-point ammonia layered injection scheme is designed according to the spatial range, the volume proportion and the best ammonia concentration threshold value of each temperature zone, and the spatial coordinates and the corresponding ammonia injection amount of each injection point are determined.

[0028] The temperature field data are acquired, specific boundary coordinates of high-temperature zone, medium-temperature zone and low-temperature zone are determined according to the temperature field data, and the temperature zones inside the incinerator are divided; according to the temperature zones inside the incinerator and the physical properties of the cremated remains, the motion trajectory of the cremated remains particles in the incinerator is simulated by using ANSYS Fluent software, and the lift height and residence time of the cremated remains particles with different particle sizes are obtained; according to the lift height and residence time of the cremated remains particles, the spatial coordinates of the ammonia injection points are determined; according to the minimum injection spacing and the volume proportion of the temperature zones, the number of injection points of the temperature zones is determined; for each injection point, the required ammonia injection amount is calculated, while the denitrification efficiency is optimized and the ammonia escape rate is minimized, and the accurate ammonia injection amount of each injection point is obtained.

[0029] Specifically, the specific boundary coordinates of high-temperature zone, medium-temperature zone and low-temperature zone are determined by using the previously obtained temperature field data, the temperature zones inside the incinerator are divided in combination with the spatial range and volume proportion of each temperature zone, the motion trajectory of the cremated remains particles in the incinerator is simulated by using ANSYS Fluent software according to the physical property data of the cremated remains, the lift height and residence time of the cremated remains particles with different particle sizes are calculated in combination with the temperature field distribution and flow field data. Based on the optimal ammonia concentration threshold of each temperature zone and the distribution of the cremated remains particles, the spatial coordinates of the ammonia injection points are optimized by using a genetic algorithm, and the optimization target is set to maximize the contact opportunity between ammonia and cremated remains particles, while considering the structure constraint of the incinerator and the flow field distribution. By setting the minimum injection spacing and the volume proportion of the temperature zones, the number of injection points of each temperature zone is determined. For each injection point, the required ammonia injection amount is calculated by using an improved Zeldovich mechanism as a reaction kinetics model, and the denitrification efficiency is optimized and the ammonia escape rate is minimized by using a non-dominated sorting genetic algorithm II, and the accurate ammonia injection amount of each injection point is obtained. In the design of the multi-point ammonia layered injection scheme of the incinerator, first, the temperature zone boundaries are determined by using the temperature field data, and the inside of the incinerator is divided into high-temperature zone (>1000℃, volume proportion 30%), medium-temperature zone (800-1000℃, proportion 50%) and low-temperature zone (<800℃, proportion 20%). Then, the motion of the cremated remains particles is simulated by using ANSYS Fluent software, the particle size range of the cremated remains is set to 50-200μm, the density is set to 2.5g / cm 3The results show that the average lift height of 50 pm particles is 1.5 m and the residence time is 60 s; the lift height of 200 pm particles is 0.8 m and the residence time is 30 s. Based on the optimal ammonia concentration threshold in each temperature zone (15 ppm in the high-temperature zone, 30 ppm in the medium-temperature zone, and 50 ppm in the low-temperature zone) and the distribution of bone ash particles, the coordinates of injection points are optimized using a genetic algorithm. The population size is set to 100, the number of iterations is 1000, the crossover probability is 0.8, and the mutation probability is 0.1. The optimization objective function is to maximize the contact probability of ammonia and bone ash particles, and the constraint conditions include the structure limitation of the incinerator and the minimum injection spacing of 0.5 m. Through calculation, 5 injection points are obtained in the high-temperature zone, 8 injection points in the medium-temperature zone, and 3 injection points in the low-temperature zone. For each injection point, the NOx generation rate is calculated using the improved Zeldovich mechanism, and the injection amount is optimized using the non-dominated sorting genetic algorithm II. The denitrification efficiency and ammonia escape rate are set as the two objective functions, the population size is set to 50, and the number of iterations is set to 500. Finally, the accurate coordinates of 16 injection points and the corresponding ammonia injection amounts are obtained, such as the center coordinates of the high-temperature zone (2.5 m, 1.5 m, 3.0 m) and the injection amount of 0.8 kg / h, which realizes the denitrification efficiency of 92% and the ammonia escape rate of 8 ppm.

[0030] When measuring the physical properties of bone ash, including particle size distribution, density, porosity, and specific surface area, the flowability and dispersibility of bone ash are evaluated, and the terminal settling velocity of bone ash with different particle sizes is calculated. The lifting trajectory and height distribution of bone ash in the incinerator are simulated, and the residence time of bone ash in each temperature zone is analyzed.

[0031] 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. According to the particle size distribution parameters and the density of bone ash measured by the specific gravity bottle method, the Carr index and Hausner ratio of bone ash are calculated. If the Carr index is greater than a predetermined threshold, it is determined that the bone ash has good flowability. For bone ash with good flowability, the terminal settling velocity of bone ash in the incinerator is determined. A temperature field model of the incinerator is established using ANSYS Fluent software, and the terminal settling velocity and temperature field model are used to simulate the lifting trajectory of bone ash in the incinerator. The residence time data of 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 bone ash and the residence time.

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

[0033] The adsorption capacity and adsorption kinetics of bone ash on ammonia gas are measured, the adsorption efficiency of bone ash on ammonia gas at different temperatures is evaluated, the influence of bone ash adsorption on local ammonia gas concentration is analyzed, the spatial coordinates of ammonia injection points are determined according to the distribution and motion characteristics of bone ash, and the ammonia injection amount of each injection point is adjusted in combination with the adsorption effect of bone ash.

[0034] The data of bone ash sample mass changing with time measured by a thermogravimetric analyzer provided with a temperature gradient and different ammonia gas concentrations are obtained, the isothermal adsorption line is plotted according to the data of bone ash sample mass changing with time, the Langmuir adsorption model is used to fit the isothermal adsorption line, the maximum adsorption capacity and equilibrium constant of bone ash on ammonia gas are obtained, the linear driving force model is used to fit the data of bone ash sample mass changing with time, the adsorption rate constant and effective diffusion coefficient at different temperatures are determined, the temperature-adsorption efficiency relationship model is established according to the maximum adsorption capacity, equilibrium constant, adsorption rate constant and effective diffusion coefficient in combination with the temperature distribution data in the incinerator, and whether the particle size of bone ash affects the adsorption efficiency is judged,

[0035] if yes, a multivariate regression model containing temperature and bone ash particle size is established, computational fluid dynamics software is used to simulate the ammonia gas concentration distribution in the incinerator, wherein the bone ash is regarded as a discrete phase, and the Lagrangian method is used to track the trajectory of bone ash particles, and the spatial coordinates of ammonia injection points are determined according to the multivariate regression model and the trajectory of bone ash particles.

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

[0037] S105, the turbulent viscosity coefficient and the turbulent diffusion coefficient 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, and if the uniformity of ammonia distribution does not reach the preset threshold, the injection parameters are iteratively adjusted.

[0038] The ANSYS Fluent software is used to calculate the turbulent viscosity coefficient and the turbulent diffusion coefficient, and a realizable k-ε turbulent flow model is set according to the turbulent viscosity coefficient and the turbulent diffusion coefficient; at the same time, ammonia is defined as an independent component, and the reaction kinetics parameters of ammonia and NOx are set to construct a complete ammonia diffusion model. Multiple mass inlet boundary conditions are set at different heights; the layered injection scheme is realized 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; the 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, the realizable k-ε turbulence model was set to solve the turbulent kinetic energy equation and turbulent dissipation rate equation, and ammonia was defined as an independent component, the reaction kinetics parameters of ammonia and NOx were set, and a complete ammonia diffusion model was constructed. The finite volume method was used to discretize the ammonia diffusion equation, multiple mass inlet boundary conditions were set at different heights to realize the layered injection scheme, and the SIMPLE algorithm was used to solve the discretized equation set. The implementation process of the SIMPLE algorithm includes pressure field guessing, momentum equation solving, pressure correction and velocity field correction, and iterative calculation until convergence, obtaining the three-dimensional concentration distribution of ammonia in the incinerator. The uniformity index of ammonia concentration distribution was calculated, defined as the coefficient of variation of ammonia concentration. If the uniformity index does not reach the preset threshold, the response surface method is used to optimize the injection parameters, including injection position, injection rate and injection angle, the response surface of parameters and uniformity index is established, and the optimal parameter combination is found. The Morris screening method is used for sensitivity analysis to evaluate the influence of different parameters on the uniformity of ammonia distribution, and to identify key parameters for subsequent optimization. According to the optimization results, the ammonia diffusion simulation is performed again until the uniformity requirement is met. In the process of constructing the ammonia diffusion model of the incinerator, ANSYS Fluent2021R1 software is used for simulation. The realizable k-ε turbulence model is set, the initial value of turbulent kinetic energy is 1m 2 / s 2 , the initial value of turbulent dissipation rate is 10m 2 / s 3 . Ammonia is defined as an independent component, and the reaction rate constant with NOx is set 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 for solving, the pressure-velocity coupling is set to PISO, and the discrete format of momentum and component equations is selected as the second-order upwind format. The convergence criterion is set to 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. The DesignExpert12 software is used for response surface optimization, the independent variables are the positions (x, y coordinates) and injection rates of the 15 injection ports, and there are 45 variables in total. Box-Behnken design is used, and 231 simulation experiments are performed. A quadratic polynomial response surface model is established, and the determination coefficient R 2The optimization objective is to minimize the coefficient of variation, and the constraint condition is the total injection amount. The optimization result shows that the coefficient of variation can be reduced to 0.18 under the optimal scheme. The sensitivity analysis is performed by using the Morris screening method, and the parameter perturbation range is ±20%, and 1000 samples are performed. The results show that the position and rate of the injection port at 10 m of the middle layer have the greatest influence on uniformity, and the sensitivity index μ* is 0.08 and 0.06, respectively. According to the optimization result, the injection parameters are adjusted, and the CFD simulation is performed again, and finally the uniformity requirement is met.

[0040] S106, obtain the cremated remains lifting speed and lifting amount parameters, analyze the influence of the cremated remains lifting on the ammonia gas distribution uniformity, predict the mixing degree and contact time of the cremated remains and ammonia gas, judge the adsorption rate and catalytic decomposition rate of the cremated remains on the ammonia gas, and establish a correlation model of the incinerator operation parameters and the denitration efficiency.

[0041] The trajectory of the cremated remains particles is simulated by using the computational fluid dynamics software, the lifting speed and lifting amount of the cremated remains with different particle sizes are calculated, and a cremated remains lifting characteristic database is obtained, the cremated remains lifting characteristic database includes particle size, density, lifting speed and lifting height; according to the cremated remains lifting characteristic database, the interaction between the cremated remains and the ammonia gas is simulated by using the Euler-Lagrange method, the mixing degree index and the average contact time are obtained, the mixing degree index is defined as the ratio of the standard deviation to the average value of the ammonia gas concentration; the adsorption rate of the cremated remains on the ammonia gas at different temperatures is measured by using the thermogravimetric analyzer, the catalytic decomposition rate is measured by using the fixed bed reactor, the Langmuir-Hinshelwood kinetic model is established by combining the mixing degree 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 operation parameters and the denitration efficiency are correlated to construct a prediction model, the incinerator operation parameters include temperature distribution, gas flow rate and cremated remains load; the accuracy and generalization ability of the prediction model are judged, if the accuracy and generalization ability of the prediction model meet the preset threshold, the prediction model is determined as an effective model.

[0042] Specifically, according to the flow field characteristics inside the incinerator and the physical properties of the cremated remains, the trajectory of the cremated remains particles was simulated using computational fluid dynamics software, the lift-off velocity and lift-off amount of cremated remains with different particle sizes were calculated, and a cremated remains lift-off characteristics database was established, including particle size, density, lift-off velocity, lift-off height, etc. Based on the ammonia diffusion model and the cremated remains lift-off characteristics data, the interaction between cremated remains and ammonia was simulated using the Euler-Lagrange method, the mixing index was defined as the ratio of the standard deviation to the average value of the ammonia concentration and the average contact time, and the influence of cremated remains lift-off on the uniformity of ammonia distribution was analyzed. The adsorption rate of cremated remains to ammonia at different temperatures was measured using a thermogravimetric analyzer, and the catalytic decomposition rate was measured using a fixed bed reactor. Combined with the mixing index and contact time data, a Langmuir-Hinshelwood kinetic model was established to describe the cremated remains-ammonia reaction process and predict the adsorption and decomposition effect under actual working conditions. Using the random forest regression algorithm, the incinerator operating parameters such as temperature distribution, gas flow rate, and cremated remains load were related to the denitrification efficiency, a prediction model was constructed, the interaction between parameters was considered, the influence of each parameter on the denitrification efficiency was analyzed using partial dependence plots, and the accuracy and generalization ability of the model were evaluated using cross-validation method. 3 The simulation results show that the average lift-off velocity of 100 μm cremated remains is 0.5 m / s, and the maximum lift-off height is 2.5 m. These data were stored in a MySQL database to create a cremated remains lift-off characteristics table. Subsequently, the interaction between cremated remains and ammonia was simulated using the Euler-Lagrange method, the grid was divided into 1 million hexahedral elements, the time step was 0.01 s, and the total simulation time was 60 s. The calculated mixing index was 0.15, and the average contact time was 8 s. The adsorption rate was measured using a TA Instruments Q5000 thermogravimetric analyzer, the temperature range was 200-800℃, the heating rate was 10℃ / min, and the ammonia concentration was 500 ppm. At 600℃, the adsorption rate reached a maximum of 0.85 mg / g. The catalytic decomposition rate was measured using a quartz tube fixed bed reactor with an inner diameter of 25 mm, the space velocity was 10000 h^-1, the ammonia concentration was 1000 ppm, and the catalytic decomposition rate reached 95% at 700℃. Based on the experimental data, a Langmuir-Hinshelwood kinetic model was established, the adsorption equilibrium constant was 0.05 L / mg, and the reaction rate constant was 0.03 s^-1. Finally, the random forest regression algorithm was implemented using the scikit-learn library in Python, the input variables included the average temperature 800-1200℃, the gas flow rate 5-15 m / s, and the cremated remains load 50-200 kg / h, and the output variable was the denitrification efficiency. The number of trees was set to 100, the maximum depth was 10, 5-fold cross-validation was used, and the model R2 reaches 0.92.

[0043] S107, according to the real-time operation monitoring data of the incinerator, the correlation model is used to predict the denitration efficiency, if the predicted value is lower than the set threshold, the dynamic optimization of ammonia gas injection scheme is triggered, and the dynamic optimization includes adjusting the injection position and the injection amount.

[0044] Obtain the operation parameters of the incinerator, the operation parameters include temperature distribution, gas flow rate and bone ash load, and store the operation parameters into InfluxDB time series database; according to the operation parameters stored in the time series database, calculate the denitration efficiency prediction value under the current working condition; judge whether the denitration efficiency prediction value is lower than the preset threshold value, if the denitration efficiency prediction value is lower than the preset threshold value, the dynamic optimization process of ammonia gas injection scheme is triggered; calculate the optimal ammonia gas injection scheme; execute ammonia gas injection according to the optimal ammonia gas injection scheme.

[0045] Specifically, a distributed data acquisition system is used to obtain real-time operation parameters of the incinerator, including temperature distribution, gas flow rate, and ash load, etc. The collected data is stored in an InfluxDB time series database and preprocessed. 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, a previously established random forest correlation model is used to calculate the denitration efficiency prediction value under the current working condition. The model features include temperature, flow rate, and ash load, etc. The number of trees is set to 100, and the maximum depth is 10. If the prediction value is lower than the set threshold, the dynamic optimization process of the ammonia injection scheme is triggered. The NSGA-II algorithm is called, and the maximum denitration efficiency and the minimum carbon emission are used as the multi-objective optimization function. The injection position and injection amount are used as the optimization variables. The population size is set to 50, and the iteration number is 100. Considering the structure and operation constraints of the incinerator, the optimal ammonia injection scheme is calculated. The optimized injection scheme is executed through the control system, and the carbon emission data before and after optimization is recorded. The sliding time window method is used to evaluate the optimization effect, and the window size is set to 24 hours with a sliding step of 1 hour. If the optimization effect is not good, the online update of the correlation model is triggered, and the incremental learning method is used to update the random forest model. The DeepQ-Network reinforcement learning method is introduced for long-term optimization of the injection strategy, considering the influence of historical decisions on future results. The experience replay pool size is set to 10,000, and the discount factor is 0.9. In the incinerator denitration efficiency optimization system, Siemens S7-1500PLC is used to collect real-time data such as temperature, flow rate, and ash load, etc. The sampling frequency is 1 Hz. The data is transmitted to the InfluxDB database through the OPCUA protocol. The median absolute deviation method is used to detect outliers, and the threshold is set to 3 times the standard deviation. 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 the temperature accounts for 50%, the flow rate accounts for 30%, and the ash load accounts for 20%. The denitration efficiency prediction threshold is set to 90%, and the optimization is triggered if the value is lower than this threshold. The NSGA-II algorithm has a population size of 50, 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 the height of 6.5 m in the furnace, and the injection amount is 20 kg / h. The denitration efficiency is improved to 95%, and the carbon emission is reduced by 10%. In the sliding time window evaluation, the window size is 24 hours, and the step is 1 hour. The average denitration efficiency and carbon emission are calculated. If the effect of the last three windows decreases, the model update is triggered, and 100 new samples are added each time for incremental learning. DeepQ-Network sets a double-layer neural network with 128 neurons in each layer. The experience replay pool size is 10,000. The initial ε value of the ε-greedy strategy is 1, and the decay rate is 0.995. The target network is updated every 1000 steps.

[0046] Although the present application has been described in detail with general description and specific embodiments above, it is obvious to those skilled in the art that some modifications or improvements can be made on the basis of the present application. Therefore, these modifications or improvements made on the basis of not deviating from the spirit of the present application, all belong to the scope of protection claimed by the present application.

Claims

1. A method of 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, turbulence scale turbulence parameters are obtained, the internal flow field and temperature field of 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 threshold method is used to divide the temperature field into regions, and the spatial range and volume ratio of the high-temperature zone, the medium-temperature zone and the low-temperature zone are obtained; The activation energy and frequency factor of the chemical reaction inside the incinerator are obtained, the ammonia decomposition rate at different temperatures is judged, the relationship curve between the generated temperature and the ammonia decomposition rate is fitted, and the optimal ammonia concentration threshold of each temperature zone is determined, which specifically comprises: The activation energy and frequency factor values of the ammonia decomposition reaction are obtained, and the reaction rate constant at different temperatures is obtained according to the activation energy and frequency factor values; According to the reaction rate constant and the ammonia decomposition reaction equation, a differential equation describing the change of ammonia concentration with time is established, the change rate of ammonia concentration per unit time is calculated through the solution of the differential equation, and the ammonia decomposition rate at different temperatures is obtained; The temperature and ammonia decomposition rate data are curve fitted, a third-order polynomial function is selected as the fitting model, and the optimal fitting parameters are determined through iterative optimization; For the high-temperature zone, the medium-temperature zone and the low-temperature zone, the ammonia concentration threshold of each temperature zone is optimized in combination with the ammonia decomposition rate curve and the process requirements of the incinerator; The physical properties and lift height parameters of the cremated remains are determined, the spatial range, volume ratio and optimal ammonia concentration threshold of each temperature zone are combined, a multi-point ammonia layered injection scheme is designed, and the spatial coordinates and corresponding ammonia injection amount of each injection point are determined; The turbulent viscosity coefficient and turbulent diffusion coefficient parameters are used to construct an ammonia diffusion model inside the incinerator, the three-dimensional concentration distribution of ammonia in the incinerator under the layered injection scheme is simulated, and if the ammonia distribution uniformity does not reach the preset threshold, the injection parameters are iteratively adjusted; The lift speed and lift amount parameters of the cremated remains are obtained, the influence of the lift of the cremated remains on the uniformity of ammonia distribution is analyzed, the mixing degree and contact time of the cremated remains and ammonia are predicted, the adsorption rate and catalytic decomposition rate of the cremated remains on ammonia are judged, and a correlation model of the operation parameters of the incinerator and the denitration efficiency is established; According to the real-time operation monitoring data of the incinerator, the correlation model is used to predict the denitration efficiency, and if the predicted value is lower than the set threshold, the dynamic optimization of the ammonia injection scheme is triggered, and the dynamic optimization includes adjusting the injection position and the injection amount.

2. The method of claim 1, wherein, According to the internal structure and temperature distribution characteristics of the incinerator, the turbulence intensity, turbulence scale turbulence parameters are obtained, the internal flow field and temperature field of the incinerator are simulated, and the three-dimensional temperature distribution data inside the incinerator is obtained; A three-dimensional geometric model is established according to the internal structure size parameters and initial temperature conditions of the incinerator, grid division is performed and grid independence test is executed, and grid quality meeting the calculation requirements is obtained; The grid quality is used to set boundary conditions and initial conditions, and the preliminary flow field and temperature field distribution data inside the incinerator are obtained; The 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 error is less than a preset value or the change rate of the key variable is less than a threshold percentage, the corrected flow field data is obtained; If the condition is not met, the iterative calculation is continued; The energy equation is solved again using the corrected flow field data to obtain a more accurate temperature field distribution; For the temperature field distribution, the discrete point temperature is interpolated to construct a continuous temperature distribution function; According to the continuous temperature distribution function, the local convective heat transfer coefficient is calculated, and the convective heat transfer amount is solved combined with the fluid physical property parameters; The solid wall surface radiation heat transfer amount is calculated, combined with the gray body radiation characteristics and the geometric view factor; The total heat transfer amount distribution inside the incinerator is obtained by superimposing the convective heat transfer amount and the radiation heat transfer amount.

3. The method of claim 1, wherein, The temperature threshold method is used to divide the temperature field into regions according to the three-dimensional temperature distribution data inside the incinerator, and the spatial range and volume proportion of the high temperature zone, medium temperature zone and low temperature zone are obtained, including: The three-dimensional temperature distribution data of the incinerator is obtained, and the temperature value of each space point is stored using a three-dimensional array; According to the discrete temperature data in the three-dimensional array, a continuous temperature distribution function is generated; For the continuous temperature distribution function, the temperature threshold of the high temperature zone, medium temperature zone and low temperature zone is determined; The temperature field is divided into regions using the temperature threshold, and adjacent regions with the same temperature are merged to obtain the spatial range of the high temperature zone, medium temperature zone and low temperature zone; For the spatial range of the high temperature zone, medium temperature zone and low temperature zone, the volume of irregularly shaped regions is estimated; According to the volume estimation result, the volume proportion of each temperature region is calculated, and the spatial range of each temperature region is described using the coordinates of the minimum circumscribed cube.

4. The method of claim 1, wherein, The physical properties of the bone ash and the lifting height parameters are determined, combined with the spatial range, volume proportion and optimal ammonia concentration threshold of each temperature region, a multi-point ammonia layered injection scheme is designed, the spatial coordinates of each injection point and the corresponding ammonia injection amount are determined, including: Obtain the temperature field data, determine the specific boundary coordinates of the high temperature zone, medium temperature zone and low temperature zone according to the temperature field data, and divide the temperature regions inside the incinerator; According to the temperature regions inside the incinerator and the determination of the physical properties of the bone ash, the motion trajectory of the bone ash particles in the incinerator is simulated using ANSYS Fluent software, and the lifting height and residence time of bone ash particles of different particle sizes are obtained; According to the lifting height and residence time of the bone ash particles, the spatial coordinates of the ammonia injection points are determined; According to the minimum injection spacing and the volume proportion of the temperature regions, the number of injection points for each temperature region is determined; 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 accurate ammonia injection amount of each injection point; It also includes: determining the physical properties of the bone ash, including particle size distribution, density, porosity and specific surface area, evaluating its flowability and dispersibility, then calculating the terminal settling velocity of bone ash of different particle sizes, simulating the lifting trajectory and height distribution of bone ash in the incinerator, and analyzing the residence time of bone ash in each temperature region; The adsorption capacity and kinetics of ammonia on bone ash are determined, the adsorption efficiency of ammonia on bone ash at different temperatures is evaluated, the influence of bone ash adsorption on local ammonia concentration is analyzed, the spatial coordinates of ammonia injection points are determined according to the distribution and motion characteristics of bone ash, and the ammonia injection amount of each injection point is adjusted in combination with the adsorption effect of bone ash.

5. The method of claim 4, wherein, The adsorption capacity and kinetics of ammonia on bone ash are determined, the adsorption efficiency of ammonia on bone ash at different temperatures is evaluated, the influence of bone ash adsorption on local ammonia concentration is analyzed, the spatial coordinates of ammonia injection points are determined according to the distribution and motion characteristics of bone ash, and the ammonia injection amount of each injection point is adjusted in combination with the adsorption effect of bone ash, including: Obtaining the data of bone ash sample mass change with time 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 data of bone ash sample mass change with time, fitting the isothermal adsorption line by using Langmuir adsorption model to obtain the maximum adsorption capacity and equilibrium constant of ammonia on bone ash; Fitting the data of bone ash sample mass change with time by using a linear driving force model to determine 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, in combination with the temperature distribution data in the incinerator; Judging whether the particle size of bone ash affects the adsorption efficiency, and if so, establishing a multivariate regression model containing temperature and bone ash particle size; Simulating the ammonia concentration distribution in the incinerator by using computational fluid dynamics software, wherein the bone ash is regarded as a discrete phase and the Lagrangian method is used to track the trajectory of bone ash particles; Determining the spatial coordinates of ammonia injection points according to the multivariate regression model and the trajectory of bone ash particles.

6. The method of claim 1, wherein, The turbulent viscosity coefficient and turbulent diffusion coefficient parameters are used to construct an ammonia diffusion model inside the incinerator, simulate the three-dimensional concentration distribution of ammonia in the incinerator under a layered injection scheme, and if the uniformity of ammonia distribution does not reach a preset threshold, iteratively adjust the injection parameters, including: Calculating the turbulent viscosity coefficient and turbulent diffusion coefficient by using ANSYS Fluent software, setting a realizable k-ε turbulent flow model according to the turbulent viscosity coefficient and the turbulent diffusion coefficient; At the same time, defining ammonia as an independent component, setting the reaction kinetics parameters of ammonia and NOx, and constructing a complete ammonia diffusion model; Setting multiple mass inlet boundary conditions at different heights; Realizing the layered injection scheme through the multiple mass inlet boundary conditions; Obtaining the three-dimensional concentration distribution of ammonia in the incinerator; Calculating the uniformity index of ammonia concentration distribution, which 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 parameters and the uniformity index; Finding the optimal parameter combination according to the response surface.

7. The method of claim 1, wherein, The ash lifting speed and lifting amount parameters are acquired, the influence of ash lifting on the uniformity of ammonia distribution is analyzed, the mixing degree and contact time of ash and ammonia are predicted, the adsorption rate and catalytic decomposition rate of ammonia by ash are judged, a correlation model of incinerator operation parameters and denitration efficiency is established, including: The computational fluid dynamics software is used to simulate the motion trajectory of ash particles, the lifting speed and lifting amount of ash of different particle sizes are calculated, and an ash lifting characteristic database is obtained, which includes particle size, density, lifting speed and lifting height; According to the ash lifting characteristic database, the interaction between ash and ammonia is simulated by using the Euler-Lagrange method to obtain the mixing degree index and average contact time, and the mixing degree index is defined as the ratio of the standard deviation to the average value of the ammonia concentration; The adsorption rate of ammonia by ash at different temperatures is measured by using a thermogravimetric analyzer, the catalytic decomposition rate is measured by using a fixed bed reactor, and a Langmuir-Hinshelwood kinetic model is established by combining the mixing degree index and average contact time; According to the Langmuir-Hinshelwood kinetic model, the adsorption and decomposition effects under actual working conditions are predicted; The operation parameters of the incinerator are correlated with the denitration efficiency to construct a prediction model, and the operation parameters of the incinerator include 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 the preset threshold, the prediction model is determined to be an effective model.

8. The method of claim 1, wherein, According to the real-time operation monitoring data of the incinerator, the denitration efficiency is predicted by using the correlation model, and if the predicted value is lower than the set threshold, the dynamic optimization of the ammonia injection scheme is triggered, including adjusting the injection position and injection amount, including: The operation parameters of the incinerator are acquired, including temperature distribution, gas flow rate and ash load, and the operation parameters are stored in an InfluxDB time series database; According to the operation parameters stored in the time series database, the denitration efficiency prediction value under the current working condition is calculated; It is judged whether the denitration efficiency prediction value is lower than the preset threshold, and if the denitration efficiency prediction value is lower than the preset threshold, the dynamic optimization process of the ammonia injection scheme is triggered; The optimal ammonia injection scheme is calculated; According to the optimal ammonia injection scheme, ammonia injection is performed.

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