A method for predicting particulate matter generation from solid fuel combustion

Abstract

This invention relates to the field of organic solid fuel combustion technology and discloses a method for predicting the amount of particulate matter generated during solid fuel combustion. The method involves: performing component analysis on the solid fuel; predicting the release concentration in the gas phase of the solid fuel combustion, the concentration of reduction reaction products, the particle size distribution curve of easily broken mineral particles after breakage, the particle size distribution curve of fuel particles after breakage, and the particle size distribution curve of intrinsic minerals after fuel particle combustion; generating source terms based on the predicted data; inputting the source terms into a group equilibrium model; outputting the particle size distribution curve of the particulate matter after the combustion reaction; and predicting the particulate matter emission amount based on the output particle size distribution curve. This method solves the technical problem that existing research on the generation of particulate matter from organic solid fuel combustion lacks a means to predict the generation of particulate matter from organic solid fuel combustion.

Description

Technical Field

[0001] This invention relates to the field of organic solid fuel combustion, and in particular to a method for predicting the amount of particulate matter generated during solid fuel combustion. Background Technology

[0002] Particulate matter pollution in my country is severe, posing a significant threat to the environment and human health. Fly ash particles, generated during the combustion of organic solid fuels and the resulting mineral migration and transformation, are a major source of atmospheric particulate matter, especially fine particles with a diameter less than 10 μm. Therefore, a thorough understanding of fly ash generation during organic solid fuel combustion is crucial for particulate matter emission control. However, existing research primarily measures particulate matter production experimentally, requiring substantial investment of manpower, resources, and experimental costs, and lacks methods for predicting particulate matter generation during organic solid fuel combustion. This invention analyzes the migration and transformation behavior of mineral elements during combustion and uses simulation calculations to predict particulate matter emissions from organic solid fuel combustion. This allows for the assessment of particulate matter emissions across different particle size ranges generated by fuel combustion with minimal computational resources. Summary of the Invention

[0003] To address the technical problem that existing methods for studying the generation of organic solid fuels require significant investment of human, material, and experimental resources, and lack means to predict the generation of particulate matter from the combustion of organic solid fuels, this invention provides a method for predicting the generation of particulate matter from the combustion of solid fuels.

[0004] To achieve the above objectives, the present invention adopts the following technical solution, including:

[0005] S1, perform component analysis on solid fuels to obtain their component characteristic data;

[0006] S2, Based on component characteristic data, determine the combustion kinetic parameters of the fuel, establish a fuel combustion model, and calculate the particulate surface temperature and reducing agent concentration during the combustion process;

[0007] S3, based on component characteristic data, combustion kinetic parameters, and particulate surface temperature during combustion, calculates the gas phase release concentration of fuel during combustion;

[0008] S4. Based on the component characteristic data and the concentration of reducing agent on the surface of particulate matter during combustion, calculate the concentration of reduction reaction products generated by the fuel during combustion.

[0009] S5, based on component characteristic data, simulate the crushing process of easily broken mineral particles in fuel and fuel particles, and obtain the particle size distribution curve of easily broken mineral particles after crushing and the particle size distribution curve of ash particles generated after crushing fuel particles.

[0010] S6, based on component characteristic data, simulates the combustion process of fuel particles to obtain the particle size distribution curve of the internal minerals after combustion of fuel particles;

[0011] S7 generates source terms based on the gas phase release concentration, reduction reaction product concentration, particle size distribution curve of easily broken mineral particles after crushing, particle size distribution curve of fuel particles after crushing, and particle size distribution curve of intrinsic minerals after fuel particle combustion. The source terms are then input into the group equilibrium model, and the particle size distribution curve of particulate matter after combustion reaction is output. The particulate matter emission is predicted based on the output particle size distribution curve.

[0012] Preferably, the specific operation steps of step S1 are as follows:

[0013] S11, Perform industrial analysis on the solid fuel to obtain the content of ash, volatile matter, fixed carbon, and moisture in the fuel;

[0014] S12, Perform elemental analysis on the solid fuel to obtain the content of carbon, hydrogen and oxygen elements in the fuel;

[0015] S13, perform ash composition analysis on the ash after combustion of solid fuel to obtain the content of Si, Al, Fe, Ca, Mg, Na and K elements in the ash;

[0016] S14, perform mineral speciation analysis on the solid fuel to obtain the content of water-soluble, organically bound, hydrochloric acid-soluble and insoluble minerals contained in the fuel;

[0017] S15, perform density sorting experiments and XRD analysis on the solid fuel to obtain the types and contents of intrinsic minerals, extrinsic minerals, and easily broken minerals contained in the fuel.

[0018] Preferably, the specific operation steps of step S2 are as follows:

[0019] S21. Based on the component characteristic data obtained in step S1, a thermogravimetric analysis experiment is performed on the solid fuel to obtain the combustion kinetic parameters of the fuel.

[0020] S22, Based on the combustion kinetic parameters of the fuel, establish a fuel combustion model and calculate the reaction rate of the fuel;

[0021] S23, based on the reaction rate of the fuel, obtains the particulate surface temperature and the concentration of the reducing agent during the combustion process.

[0022] Preferably, the specific operation steps of step S3 are as follows:

[0023] S31, Calculate the release rate of organically bound minerals contained in solid fuels during the pyrolysis stage. ,according to Calculate the gas phase release concentration of organically bound minerals during the pyrolysis stage. ;

[0024] S32, Calculate the release rate of water-soluble minerals contained in solid fuels during the pyrolysis stage. ,according to Calculate the gas phase release concentration of water-soluble minerals during the pyrolysis stage. ;

[0025] S33, where water-soluble minerals and organically bound minerals are released at the same rate during the coke combustion stage, is denoted as S33. ;according to Calculate the gas phase release concentration of water-soluble minerals during the coke combustion stage. Concentration of organically bound minerals released into the gas phase during coke combustion ;

[0026] S34, Calculate the total concentration of water-soluble and organically bound minerals released into the gas phase. That is, the concentration released in the gas phase:

[0027] ;

[0028] and, , , , All calculations are performed using the following formulas:

[0029] ;

[0030] in, For time, r Where is the fuel particle radius, ρ c For fuel particle density, θ ash This represents the mass fraction of ash in the fuel. θ am It is the sum of the mass fractions of water-soluble and organically bound minerals in the ash. θ rel,am The mass fraction of water-soluble and organically bound minerals released at time t. It represents the total molar mass of water-soluble and organically bound minerals.

[0031] Preferably, the specific operation steps of step S4 are as follows:

[0032] S41, Calculate the partial pressures of the hydrochloric acid soluble and insoluble minerals contained in the fuel when they react with the reducing agent CO at equilibrium;

[0033] S42, Calculate the partial pressures of the hydrochloric acid soluble and insoluble minerals contained in the fuel when they react with the reducing agent H2 to reach equilibrium;

[0034] S43, calculate the partial pressure of the hydrochloric acid soluble and insoluble minerals contained in the fuel when they react with the reducing agent H2O to reach equilibrium;

[0035] S44, Calculate the concentration of secondary oxides or hydroxides of hydrochloric acid-soluble and insoluble minerals reduced in the fuel. :

[0036] ;

[0037] in, η As an efficiency factor, N I =θ(r c / r m ) 3 This refers to the total number of mineral particles contained within a single coke particle. θ It is the volume fraction of total minerals. r c Let be the radius of the coke particle. r m Let be the radius of a single mineral particle inside the coke particle. D e It is the effective Kundsen diffusion coefficient. It is the sum of the partial pressures of soluble and insoluble minerals reacting with reducing agents CO and H2 at equilibrium. R is the partial pressure of the hydrochloric acid in its soluble and insoluble mineral states reacting with the reducing agent H2O at equilibrium. R is the molar gas constant, and T is the ambient temperature.

[0038] Preferably, in step S5, the specific steps for generating the particle size distribution curve of the easily breakable mineral particles after crushing are as follows:

[0039] S501, based on the analysis of solid fuel, determine the types and contents of external minerals in the fuel, as well as the particle size distribution curve of easily broken mineral particles before crushing and the content of easily broken mineral particles.

[0040] S502, input the particle size distribution curve of easily broken mineral particles before crushing and the content of easily broken mineral particles into the preset Poisson distribution model.

[0041] The Poisson distribution model is:

[0042] ;

[0043] in, Let be the probability that a fragile mineral particle breaks into j+1 fragments, where J is a characteristic parameter of the Poisson distribution, e is the Euler number, and j is the number of particles generated after the fragile mineral breaks, simulated by a Poisson distribution model. Represents the factorial symbol;

[0044] S503, Based on the Poisson distribution model, the crushing of easily broken mineral particles is simulated, and a minimum particle size threshold is preset as the termination condition for the crushing simulation.

[0045] S504, statistically simulates the distribution data of crushed particles in each particle size range, and outputs the particle size distribution curve of easily crushable mineral particles in the external mineral.

[0046] In step S5, the steps for generating the particle size distribution curve after fuel particle crushing are as follows:

[0047] S511, analyze the fuel to obtain the particle size distribution curve and the content of fuel particles before crushing;

[0048] S512, input the particle size distribution curve of fuel particles before crushing and the content of fuel particles into the preset DEM crushing model;

[0049] S513, Based on the DEM crushing model, the initial particle group of fuel is subjected to crushing simulation, and a minimum particle size threshold is preset as the termination condition for the crushing simulation.

[0050] S514, Obtain the particle size dataset of the fuel particles after crushing, and generate the particle size distribution curve of the fuel particles after crushing. This particle size distribution curve is represented as follows:

[0051] ;

[0052] or, ;

[0053] in, , It is a proportionality coefficient. It is the diameter of the coke fragments formed by the breakup. It is a breaking factor. It represents the number of minerals with a diameter of d on the surface of the coke. It is the reduction in coke radius after the gasification reaction. It is the crushing radius of the coke.

[0054] Preferably, the particle size distribution curves of easily breakable mineral particles before crushing and the particle size distribution curves of fuel particles before crushing are both measured using a laser particle size analyzer.

[0055] Preferably, in step S6, the operation steps for generating the particle size distribution curve of the intrinsic minerals after fuel particle combustion are as follows:

[0056] S61, Based on the analysis of solid fuels, determine the types and contents of the inherent minerals in the fuels;

[0057] S62, Establish a fuel particle model by inputting the types and contents of the inherent minerals in the fuel into the fuel particle model;

[0058] S63, based on the fuel particle model, simulate the process of the internal minerals of the fuel particles merging with or separating from the fuel particles during combustion until the fuel particles are completely burned;

[0059] S64, obtain the particle size dataset of the intrinsic mineral particles after fuel combustion, and generate the particle size distribution curve of the intrinsic mineral particles after fuel combustion.

[0060] Preferably, the specific operation steps of step S7 are as follows:

[0061] S71, Generate a direct gas phase release source term based on the gas phase release concentration. ;

[0062] S72, generating reduction reaction release source terms based on the concentration of reduction reaction products. ;

[0063] S73, Generate crushing source terms based on the particle size distribution curves of easily crushable mineral particles after crushing and the particle size distribution curves of fuel particles after crushing. ;

[0064] S74, generating intrinsic mineral source terms based on the particle size distribution curve of intrinsic minerals after fuel particle combustion.

[0065] S75, Generate source item :

[0066] = + + + ;

[0067] Source item The data is input into the group equilibrium model, and the output is the particle size distribution curve of particulate matter after the combustion reaction is completed, which is used to predict particulate matter emissions.

[0068] An electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for predicting the amount of particulate matter generated from solid fuel combustion.

[0069] The advantages of this invention are:

[0070] 1. This invention analyzes the composition of fuel to obtain the concentrations of water-soluble and organically bound minerals released into the gas phase, the concentrations of reduced secondary oxides or hydroxides, brittle mineral particles, the particle size distribution curves of fuel particles after breakage, and the particle size distribution curves of intrinsic minerals after combustion. These are then input into a group equilibrium model to predict particulate matter emissions. This invention solves the technical problem that existing methods for studying the generation of organic solid fuels require significant investment of manpower, resources, and experimental costs, and lack the means to predict the generation of particulate matter from the combustion of organic solid fuels.

[0071] 2. This invention uses simulation calculations to predict particulate matter from the combustion of organic solid fuels, and can complete the assessment of particulate pollutant emissions from different particle size ranges generated by fuel combustion with minimal computing resources.

[0072] 3. This invention analyzes the migration and transformation behavior of mineral elements during combustion and uses simulation calculations to accurately calculate the amount of particulate matter generated within different particle size ranges during the combustion of organic solid fuels. Strategies can be implemented in advance before fuel combustion to suppress particulate matter emissions and reduce environmental pollution. Attached Figure Description

[0073] Figure 1 This is a flowchart of the method for predicting the amount of particulate matter generated during the combustion of solid fuels provided in this embodiment.

[0074] Figure 2 XRD patterns of coal at different temperatures.

[0075] Figure 3 This is a particle size distribution curve of coal before combustion.

[0076] Figure 4 A curve showing the distribution of predicted and measured particulate matter emissions as a function of particle size when coal is burned at 1300℃ (error bar: measured value ± SD, n=3).

[0077] Figure 5 A bar chart comparing predicted and measured particulate matter emissions by particle size when coal is burned at 1300℃ (error bars: measured value ± SD, n=3). Detailed Implementation

[0078] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0079] It should be noted that when a component is said to be "installed on" another component, it can be directly on the other component or it may be in a component that is centered on it. When a component is said to be "set on" another component, it can be directly set on the other component or it may also be in a component that is centered on it. When a component is said to be "fixed to" another component, it can be directly fixed to the other component or it may also be in a component that is centered on it.

[0080] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0081] Please see Figure 1 , Figure 1 This embodiment provides a flowchart of a method for predicting the amount of particulate matter generated from solid fuel combustion. The method includes the following steps:

[0082] S1, perform component analysis on solid fuels to obtain their component characteristic data.

[0083] The specific steps for step S1 are as follows:

[0084] S11, Perform industrial analysis on the solid fuel to obtain the content of ash, volatile matter, fixed carbon, and moisture in the fuel.

[0085] S12, perform elemental analysis on the solid fuel to obtain the content of elements such as carbon, hydrogen, and oxygen in the fuel.

[0086] S13, perform compositional analysis on the ash after combustion of solid fuel to obtain the content of elements such as Si, Al, Fe, Ca, Mg, Na, and K in the ash.

[0087] S14, perform mineral speciation analysis on the solid fuel to obtain the content of water-soluble, organically bound, hydrochloric acid-soluble and insoluble minerals contained in the fuel.

[0088] S15, perform density sorting experiments and XRD analysis on the solid fuel to obtain the types and contents of external minerals, internal minerals, and easily broken minerals contained in the fuel.

[0089] In this embodiment, firstly, industrial and elemental analysis is performed on the solid fuel to obtain the fixed contents of ash, volatile matter, fixed carbon, and moisture in the fuel, as well as the contents of elements such as carbon, hydrogen, and oxygen in the fuel. This information is used to simulate the combustion process of the fuel and obtain key information such as the combustion process, combustion time, and particle surface temperature during combustion.

[0090] Then, ash analysis was performed on the solid fuel to obtain the content of elements such as Si, Al, Fe, Ca, Mg, Na, and K in the ash. Density sorting experiments and chemical fractionation methods were used to determine the types, contents, and occurrence forms of minerals in the fuel, in order to predict the amount of organic solid fuel converted into fly ash particles.

[0091] S2, Based on component characteristic data, determine the combustion kinetic parameters of the fuel, establish a fuel combustion model, and calculate the particulate surface temperature and reducing agent concentration during the combustion process;

[0092] The specific steps for step S2 are as follows:

[0093] S21. Based on the component characteristic data obtained in step S1, a thermogravimetric analysis experiment is performed on the solid fuel to obtain the combustion kinetic parameters of the fuel.

[0094] In this embodiment, based on the industrial analysis data, elemental analysis data, mineral morphology and content data, and brittle mineral type and content data obtained in step S1, a thermogravimetric analysis experiment is performed on the fuel to obtain the pre-exponential factor and activation energy of the fuel in the pyrolysis stage, and the pre-exponential factor and activation energy in the coke combustion stage.

[0095] S22, Based on the combustion kinetic parameters of the fuel, establish a fuel combustion model and calculate the reaction rate of the fuel;

[0096] In this embodiment, based on the combustion kinetic parameters of the fuel, the solid fuel pyrolysis process is simulated by CFD to obtain the concentration of oxidant or reductant on the surface of the fuel particles after the pyrolysis stage, and the reaction rate of the fuel is calculated based on the concentration of oxidant or reductant on the surface of the fuel particles.

[0097] S23, based on the reaction rate of the fuel, obtains the particulate surface temperature and the concentration of the reducing agent during the combustion process.

[0098] In this embodiment, the particle surface temperature and the concentration of reducing agents such as CO, H2, and H2O on the particle surface are obtained based on the reaction rate of the fuel during the combustion stage.

[0099] Specifically, in other embodiments, the industrial analysis data, elemental analysis data, mineral morphology and content data, and brittle mineral type and content data obtained in step S1 can be used to search for information on the fuel to obtain the activation energy of the fuel in the pyrolysis stage, the activation energy of the coke combustion stage, and the particle surface temperature.

[0100] S3 calculates the gas phase release concentration of fuel during combustion based on component characteristic data, combustion kinetic parameters, and particulate surface temperature during combustion.

[0101] The specific steps for step S3 are as follows:

[0102] S31, Calculate the release rate of organically bound minerals contained in solid fuels during the pyrolysis stage. ,according to Calculate the gas phase release concentration of organically bound minerals during the pyrolysis stage. ;

[0103] S32, Calculate the release rate of water-soluble minerals contained in solid fuels during the pyrolysis stage. ,according to Calculate the gas phase release concentration of water-soluble minerals during the pyrolysis stage. ;

[0104] S33, the release rates of water-soluble minerals and organically bound minerals are the same during the coke combustion stage; calculate the release rates of water-soluble minerals and organically bound minerals contained in solid fuels during the coke combustion stage. ;according to Calculate the gas phase release concentration of water-soluble minerals during the coke combustion stage. Concentration of organically bound minerals released into the gas phase during coke combustion ;

[0105] S34, Calculate the total concentration of water-soluble and organically bound minerals released into the gas phase. That is, the concentration released in the gas phase:

[0106] ;

[0107] , , , All concentrations were calculated using the formula for the gas phase release concentration, as shown below:

[0108] ;

[0109] in, For time,r Where is the fuel particle radius, ρ c For fuel particle density, θ ash This represents the mass fraction of ash in the fuel. θ am It is the sum of the mass fractions of water-soluble and organically bound minerals in the ash. θ rel,am The mass fraction of water-soluble and organically bound minerals released at time t. The total molar mass of water-soluble and organically bound minerals. k This corresponds to the release rate.

[0110] In this embodiment, the combustion process of solid fuel can be divided into two stages: pyrolysis and combustion. During the combustion process, the release of water-soluble and organically bound minerals continues, but at different rates.

[0111] During the pyrolysis stage, the release of organically bound minerals and the release of volatiles have the same kinetic parameters, and their release rate is... :

[0112] ;

[0113] Where C is the pre-exponential factor, E d It is the activation energy of the pyrolysis stage, R is the molar gas constant, and T is the activation energy. P It is the surface temperature of the particles.

[0114] according to Calculate the gas phase release concentration of organically bound minerals during the pyrolysis stage. ;

[0115] ;

[0116] During the pyrolysis stage, the release rate of water-soluble minerals is: :

[0117] ;

[0118] Where A and B are rate parameters, which include information such as fuel characteristics and ambient temperature during the fuel pyrolysis process, and t is the pyrolysis time.

[0119] according to Calculate the gas phase release concentration of water-soluble minerals during the pyrolysis stage. ;

[0120] ;

[0121] The release rates of water-soluble minerals and organically bound minerals are the same during the coke combustion stage; both have a release rate of [missing value]. :

[0122] ;

[0123] Where D is the pre-exponential factor, E g It is the activation energy of coke combustion, R is the molar gas constant, and T is the activation energy of coke combustion. P It is the surface temperature of the particles.

[0124] according to Calculate the gas phase release concentration of water-soluble minerals during the coke combustion stage. ;

[0125] ;

[0126] according to Calculate the gas phase release concentration of organically bound minerals during the coke combustion stage. ;

[0127] ;

[0128] Calculate the total concentration of water-soluble and organically bound minerals released into the gas phase. That is, the concentration released in the gas phase:

[0129] .

[0130] S4. Based on the component characteristic data and the concentration of reducing agent on the surface of particulate matter during combustion, calculate the concentration of reduction reaction products generated by the fuel during combustion.

[0131] The specific steps for step S4 are as follows:

[0132] S41, Calculate the partial pressures of the hydrochloric acid soluble and insoluble minerals contained in the fuel when they react with the reducing agent CO at equilibrium;

[0133] S42, Calculate the partial pressures of the hydrochloric acid soluble and insoluble minerals contained in the fuel when they react with the reducing agent H2 to reach equilibrium;

[0134] S43, calculate the partial pressure of the hydrochloric acid soluble and insoluble minerals contained in the fuel when they react with the reducing agent H2O to reach equilibrium;

[0135] S44, Calculate the concentration of secondary oxides or hydroxides of hydrochloric acid-soluble and insoluble minerals reduced in the fuel. :

[0136] ;

[0137] in, η As an efficiency factor, N I =θ(r c / r m ) 3 This refers to the total number of mineral particles contained within a single coke particle. θ It is the volume fraction of total minerals. r c Let be the radius of the coke particle. r m Let be the radius of a single mineral particle inside the coke particle. D e It is the effective Kundsen diffusion coefficient. It is the sum of the partial pressures of soluble and insoluble minerals reacting with reducing agents CO and H2 at equilibrium. R is the partial pressure of the hydrochloric acid in its soluble and insoluble mineral states reacting with the reducing agent H2O at equilibrium. R is the molar gas constant, and T is the ambient temperature.

[0138] In this embodiment, the hydrochloric acid soluble and insoluble minerals are mainly minerals containing Si, Al, Fe, Ca, and Mg. The hydrochloric acid soluble and insoluble minerals are rarely released below 1500℃. They are mainly released into the gaseous state through reduction reactions with CO, H2, H2O, C, etc. to generate secondary oxides or hydroxides. Among them, the gas-solid reaction is easier to carry out than the solid-solid reaction and is generally considered to be the main reaction for the gasification of refractory elements. The main reactions between refractory elements and gasifying agents are shown in Table 1.

[0139] Table 1: Major Reactions Between Refractory Elements and Gasifying Agents

[0140]

[0141] The partial pressures at equilibrium of the reaction between hydrochloric acid and soluble and insoluble minerals with the reducing agent CO are:

[0142] ;

[0143] in, metal oxides The partial pressure of the secondary oxide formed after reduction by the reducing agent CO. It is a reaction The equilibrium constant at a specific temperature. Reducing agent CO and oxidation products The ratio of partial pressures, It is the activity coefficient of CO.

[0144] The partial pressures of soluble and insoluble minerals reacting with reducing agent H2 at equilibrium are:

[0145] ;

[0146] in, It is a reaction The equilibrium constant at a specific temperature. It is the ratio of the partial pressure of the reducing agent H2 to the partial pressure of the oxidation product H2O. It is the activity coefficient of H2.

[0147] The partial pressures of soluble and insoluble minerals reacting with reducing agent H₂O at equilibrium are:

[0148] ;

[0149] in, It is a metal oxide The partial pressure of hydroxides formed after reduction by the reducing agent H2O It is a reaction The equilibrium constant at a specific temperature. It is the partial pressure of water vapor. It is the activity coefficient of H2O.

[0150] Calculate the concentrations of secondary oxides or hydroxides reduced by the soluble and insoluble minerals in hydrochloric acid at equilibrium with the reducing agents CO, H2, and H2O. :

[0151] ;

[0152] in, η Efficiency factor; N I =θ(r c / r m ) 3 This refers to the total number of mineral particles contained within a single coke particle. θ It is the volume fraction of total minerals. r c Let be the radius of the coke particle. r m The radius of a single mineral particle inside a coke particle. D e It is the effective Kundsen diffusion coefficient; It is the sum of the partial pressures of soluble and insoluble minerals reacting with reducing agents CO and H2 at equilibrium. ; R is the partial pressure at equilibrium when soluble and insoluble minerals react with the reducing agent H2O in hydrochloric acid; T is the molar gas constant; and T is the ambient temperature.

[0153] S5. Based on the component characteristic data, simulate the crushing process of the easily breakable mineral particles in the fuel and the fuel particles respectively, and obtain the particle size distribution curves of the easily breakable mineral particles after crushing and the particle size distribution curves of the fuel particles after crushing.

[0154] The specific steps for generating the particle size distribution curve of easily breakable mineral particles after crushing are as follows:

[0155] S501, the solid fuel was analyzed to obtain the particle size distribution curve of the easily broken mineral particles before crushing and the content of the easily broken mineral particles.

[0156] S502, input the particle size distribution curve of the easily breakable mineral particles before crushing and the content of the easily breakable mineral particles into the preset Poisson distribution model.

[0157] The Poisson distribution model is:

[0158] ;

[0159] in, Let be the probability that a fragile mineral particle breaks into j+1 fragments, where J is a characteristic parameter of the Poisson distribution, e is the Euler number, and j is the number of particles generated after the fragile mineral breaks, simulated by a Poisson distribution model. The factorial symbol is used to represent factorials.

[0160] S503, Based on the Poisson distribution model, the crushing of easily broken mineral particles is simulated, and a minimum particle size threshold is preset as the termination condition for the crushing simulation.

[0161] S504: Statistically simulate the distribution data of crushed particles in each particle size range, and output the particle size distribution curve of easily crushable mineral particles after crushing. .

[0162] The steps for generating the particle size distribution curve of the crushed fuel particles are as follows:

[0163] S511, analyze the fuel to obtain the particle size distribution curve and the content of fuel particles before crushing;

[0164] S512, input the particle size distribution curve of fuel particles before crushing and the content of fuel particles into the preset DEM crushing model (discrete element method).

[0165] S513, Based on the DEM crushing model, the initial particle group of fuel is subjected to crushing simulation, and a minimum particle size threshold is preset as the termination condition for the crushing simulation.

[0166] S514, Obtain the particle size dataset of the fuel particles after crushing, and generate the particle size distribution curve of the fuel particles after crushing. The distribution curve can be represented as:

[0167] ;

[0168] or, ;

[0169] in, , It is a proportionality coefficient. It is the diameter of the coke fragments formed by the breakup. It is a breaking factor. It represents the number of minerals with a diameter of d on the surface of the coke. It is the reduction in coke radius after the gasification reaction. It is the crushing radius of the coke.

[0170] Specifically, in this embodiment, the crushing process of brittle mineral particles in the fuel is simulated using a Poisson distribution model, and the particle size distribution curve of the crushed brittle mineral particles is obtained accordingly. Different brittle minerals have different characteristic parameters; for example, J=3 when pyrite is crushed.

[0171] The particle size distribution curves of easily breakable mineral particles before crushing and the particle size distribution curves of fuel particles before crushing can both be measured using a laser particle size analyzer.

[0172] S6, based on component characteristic data, simulates the combustion process of fuel particles to obtain the particle size distribution curve of the internal minerals after combustion of fuel particles.

[0173] The steps for generating the particle size distribution curve of the intrinsic minerals after fuel particle combustion are as follows:

[0174] S61, Based on the analysis of solid fuels, determine the types and contents of the inherent minerals in the fuels;

[0175] S62, Establish a fuel particle model by inputting the types and contents of the inherent minerals in the fuel into the fuel particle model;

[0176] S63, based on the fuel particle model, simulate the process of the internal minerals of the fuel particles merging with or separating from the fuel particles during combustion until the fuel particles are completely burned;

[0177] S64, obtain the particle size dataset of the intrinsic mineral particles after fuel combustion, and generate the particle size distribution curve of the intrinsic mineral particles after fuel combustion.

[0178] In this embodiment, the fuel particle model simulates the process by which the minerals within the fuel particles merge with or detach from the fuel particles during combustion, specifically:

[0179] S631, the fuel particle model randomly generates N intrinsic minerals within the fuel particles (the number of N is determined based on the type and density of the mineral particles, as well as the ash content in the fuel, etc.). Furthermore, the positions of the mineral particles are randomly generated and assigned a specific three-dimensional coordinate. By calculating the distance between the mineral particles, it is ensured that the mineral particles do not overlap.

[0180] S632, based on the reaction rate of fuel particles, calculates the particle size reduction value of fuel particles after undergoing combustion reaction;

[0181] S633, based on the reduction value of the fuel particle size, calculate the number of mineral particles exposed on the surface of the fuel particles and the spatial distance between the mineral particles; if the center distance between the mineral particles is less than the sum of their radii, it is considered that the particles have collided and merged into one particle, and the particle size information of the collided and merged particles is recorded.

[0182] S634, calculate the centrifugal force on the mineral particles and the adhesion force of the fuel particles to them, and determine whether the mineral particles detach or adhere to the fuel surface. If the centrifugal force is greater than the adhesion force, it is considered that the particles detach from the fuel particle surface to form fly ash particles and the particle size information is recorded; otherwise, they remain on the fuel particle surface, and the collision process described in S633 is continued.

[0183] S635 continuously calculates the combustion process of fuel and the collision, aggregation, adsorption and desorption processes of mineral particles until the fuel particles are completely burned, and outputs the particle size distribution curve of the internal minerals.

[0184] S7 generates source terms based on the gas phase release concentration, reduction reaction product concentration, particle size distribution curve of easily broken mineral particles after crushing, particle size distribution curve of fuel particles after crushing, and particle size distribution curve of intrinsic minerals after fuel particle combustion. The source terms are then input into the group equilibrium model, and the particle size distribution curve of particulate matter after combustion reaction is output. The particulate matter emission is predicted based on the output particle size distribution curve.

[0185] The specific steps are as follows:

[0186] S71, Generate a direct gas phase release source term based on the gas phase release concentration. ;

[0187] S72, generating reduction reaction release source terms based on the concentration of reduction reaction products. ;

[0188] S73, Generate crushing source terms based on the particle size distribution curves of easily crushable mineral particles after crushing and the particle size distribution curves of fuel particles after crushing. ;

[0189] S74, generating intrinsic mineral source terms based on the particle size distribution curve of intrinsic minerals after fuel particle combustion.

[0190] S75, Generate source item :

[0191] = + + + ;

[0192] S76, source item The data is input into the group equilibrium model, and the output is the particle size distribution curve of particulate matter after the combustion reaction is completed, which is used to predict particulate matter emissions.

[0193] The group equilibrium model is as follows:

[0194] ;

[0195] Where t is time; v, , All figures represent particle volume. It is the rate of change of the number of particles over time, that is, the number of particles with volume v at time t; Let v be the rate of increase in the number of particles of volume v due to collision-aggregation. The integral from 0 to v; For volume is and The rate at which two particles collide and merge into a new particle; For volume The number density of particles; For volume The number density of particles; The integral variable is ;- Let v be the rate of decrease in the number of particles of volume v due to collision-aggregation. This is the condensation kernel function.

[0196] To verify the accuracy of the prediction method described in this invention, a type of coal was selected, and its basic properties were analyzed:

[0197] The coal was subjected to industrial analysis, and the industrial analysis data of the coal were obtained, as shown in Table 2.

[0198] Table 2: Industrial Analysis Data of Coal

[0199]

[0200] Elemental analysis was performed on the coal, and the elemental analysis data of the coal are shown in Table 3.

[0201] Table 3: Elemental Analysis Data of Coal

[0202]

[0203] Ash content analysis was performed on the ash after the coal was burned, and the ash content analysis data of the coal is shown in Table 4.

[0204] Table 4: Ash content analysis data of coal

[0205]

[0206] XRD analysis was performed on coal to obtain XRD patterns of coal at different temperatures, such as... Figure 2 As shown, where:

[0207] 1: Quartz (SiO2); 2: Siderite (FeCO3); 3: Anhydrite (CaSO4); 4: Hematite (Fe2O3); 5: Nepheline (NaAlSiO4).

[0208] The particle size of the coal was measured using a laser particle size analyzer to obtain the particle size distribution curve before combustion, such as... Figure 3 As shown.

[0209] Based on the compositional characteristics data of the coal, a method for predicting particulate matter generation from solid fuel combustion, provided by this invention, is used to predict particulate matter emissions during combustion at 1300°C. Please refer to [link / reference]. Figure 4 and Figure 5 , Figure 4 This is a curve comparing the predicted and measured values ​​of particulate matter emissions with particle size (Dp) when coal is burned at 1300℃. The horizontal axis represents particulate matter particle size, the vertical axis represents particulate matter emissions, and the error bars represent the standard deviation (±SD, n=3) of three repetitions of the measured values. Figure 5 This is a bar chart comparing predicted and measured particulate matter emissions, categorized by particle size, when coal is burned at 1300℃. The horizontal axis represents ultrafine particulate matter (PM2.5). 0.1 Particulate matter (PM1, ≤0.1μm), fine particulate matter (PM2.5, ≤1μm), and inhalable particulate matter (PM4.5) 10The graphs show the particle size ≤ 10 μm, with the vertical axis representing particulate matter emissions and the error bars representing the standard deviation (±SD, n=3) of three replicates of the measured values. Combining the two graphs, it is clear that the predicted and measured values ​​for coal emissions using this invention exhibit a high degree of consistency. The predicted values ​​for all data points fall within the range of the measured value error bars (±SD, n=3), fully demonstrating the high accuracy and reliability of the method used in this invention for predicting particulate matter emissions from coal.

[0210] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting the amount of particulate matter generated during the combustion of solid fuels, characterized in that, Includes the following steps: S1, perform component analysis on solid fuels to obtain their component characteristic data; S2, Based on component characteristic data, determine the combustion kinetic parameters of the fuel, establish a fuel combustion model, and calculate the particulate surface temperature and reducing agent concentration during the combustion process; S3, based on component characteristic data, combustion kinetic parameters, and particulate surface temperature during combustion, calculates the gas phase release concentration of fuel during combustion; S4. Based on the component characteristic data and the concentration of reducing agent on the surface of particulate matter during combustion, calculate the concentration of reduction reaction products generated by the fuel during combustion. S5, based on component characteristic data, simulate the crushing process of easily broken mineral particles in fuel and fuel particles, and obtain the particle size distribution curve of easily broken mineral particles after crushing and the particle size distribution curve of ash particles generated after crushing fuel particles. S6, based on component characteristic data, simulates the combustion process of fuel particles to obtain the particle size distribution curve of the internal minerals after combustion of fuel particles; S7 generates source terms based on the gas phase release concentration, reduction reaction product concentration, particle size distribution curve of easily broken mineral particles after crushing, particle size distribution curve of fuel particles after crushing, and particle size distribution curve of intrinsic minerals after fuel particle combustion. The source terms are then input into the group equilibrium model, and the particle size distribution curve of particulate matter after combustion reaction is output. The particulate matter emission is predicted based on the output particle size distribution curve.

2. The method for predicting particulate matter generation from solid fuel combustion according to claim 1, characterized in that, The specific steps for step S1 are as follows: S11, Perform industrial analysis on the solid fuel to obtain the content of ash, volatile matter, fixed carbon, and moisture in the fuel; S12, Perform elemental analysis on the solid fuel to obtain the content of carbon, hydrogen and oxygen elements in the fuel; S13, perform ash composition analysis on the ash after combustion of solid fuel to obtain the content of Si, Al, Fe, Ca, Mg, Na and K elements in the ash; S14, perform mineral speciation analysis on the solid fuel to obtain the content of water-soluble, organically bound, hydrochloric acid-soluble and insoluble minerals contained in the fuel; S15, perform density sorting experiments and XRD analysis on the solid fuel to obtain the types and contents of intrinsic minerals, extrinsic minerals, and easily broken minerals contained in the fuel.

3. The method for predicting particulate matter generation from solid fuel combustion according to claim 1, characterized in that, The specific steps of step S2 are as follows: S21. Based on the component characteristic data obtained in step S1, a thermogravimetric analysis experiment is performed on the solid fuel to obtain the combustion kinetic parameters of the fuel. S22, Based on the combustion kinetic parameters of the fuel, establish a fuel combustion model and calculate the reaction rate of the fuel; S23, based on the reaction rate of the fuel, obtains the particulate surface temperature and the concentration of the reducing agent during the combustion process.

4. The method for predicting particulate matter generation from solid fuel combustion according to claim 1, characterized in that, The specific steps for step S3 are as follows: S31, Calculate the release rate of organically bound minerals contained in solid fuels during the pyrolysis stage. ,according to Calculate the gas phase release concentration of organically bound minerals during the pyrolysis stage. ; S32, Calculate the release rate of water-soluble minerals contained in solid fuels during the pyrolysis stage. ,according to Calculate the gas phase release concentration of water-soluble minerals during the pyrolysis stage. ; S33, where water-soluble minerals and organically bound minerals are released at the same rate during the coke combustion stage, is denoted as S33. ;according to Calculate the gas phase release concentration of water-soluble minerals during the coke combustion stage. Concentration of organically bound minerals released into the gas phase during coke combustion ; S34, Calculate the total concentration of water-soluble and organically bound minerals released into the gas phase. That is, the concentration released in the gas phase: ； and, , , , All calculations are performed using the following formulas: ； in, For time, r Where is the fuel particle radius, ρ c For fuel particle density, θ ash This represents the mass fraction of ash in the fuel. θ am It is the sum of the mass fractions of water-soluble and organically bound minerals in the ash. θ rel,am for t The mass fraction of water-soluble and organically bound minerals released at that time. The total molar mass of water-soluble and organically bound minerals. k This corresponds to the release rate.

5. The method for predicting particulate matter generation from solid fuel combustion according to claim 1, characterized in that, The specific steps for step S4 are as follows: S41, Calculate the partial pressures of the hydrochloric acid soluble and insoluble minerals contained in the fuel when they react with the reducing agent CO at equilibrium; S42, Calculate the partial pressures of the hydrochloric acid soluble and insoluble minerals contained in the fuel when they react with the reducing agent H2 to reach equilibrium; S43, calculate the partial pressure of the hydrochloric acid soluble and insoluble minerals contained in the fuel when they react with the reducing agent H2O to reach equilibrium; S44, Calculate the concentration of secondary oxides or hydroxides of hydrochloric acid-soluble and insoluble minerals reduced in the fuel. : ； in, η As an efficiency factor, N I =θ(r c / r m ) 3 This refers to the total number of mineral particles contained within a single coke particle. θ It is the volume fraction of total minerals. r c Let be the radius of the coke particle. r m Let be the radius of a single mineral particle inside the coke particle. D e It is the effective Kundsen diffusion coefficient. It is the sum of the partial pressures of soluble and insoluble minerals reacting with reducing agents CO and H2 at equilibrium. R is the partial pressure of the hydrochloric acid in its soluble and insoluble mineral states reacting with the reducing agent H2O at equilibrium. R is the molar gas constant, and T is the ambient temperature.

6. The method for predicting particulate matter generation from solid fuel combustion according to claim 1, characterized in that, In step S5, the specific steps for generating the particle size distribution curve of easily breakable mineral particles after crushing are as follows: S501, based on the analysis of solid fuel, determine the types and contents of external minerals in the fuel, as well as the particle size distribution curve of easily broken mineral particles before crushing and the content of easily broken mineral particles. S502, input the particle size distribution curve of easily broken mineral particles before crushing and the content of easily broken mineral particles into the preset Poisson distribution model. The Poisson distribution model is: ； in, Let be the probability that a fragile mineral particle breaks into j+1 fragments, where J is a characteristic parameter of the Poisson distribution, e is the Euler number, and j is the number of particles generated after the fragile mineral breaks, simulated by a Poisson distribution model. Represents the factorial symbol; S503, Based on the Poisson distribution model, the crushing of easily broken mineral particles is simulated, and a minimum particle size threshold is preset as the termination condition for the crushing simulation. S504, statistically simulates the distribution data of crushed particles in each particle size range, and outputs the particle size distribution curve of easily crushable mineral particles in the external mineral. In step S5, the steps for generating the particle size distribution curve after fuel particle crushing are as follows: S511, analyze the fuel to obtain the particle size distribution curve and the content of fuel particles before crushing; S512, input the particle size distribution curve of fuel particles before crushing and the content of fuel particles into the preset DEM crushing model; S513, Based on the DEM crushing model, the initial particle group of fuel is subjected to crushing simulation, and a minimum particle size threshold is preset as the termination condition for the crushing simulation. S514, Obtain the particle size dataset of the fuel particles after crushing, and generate the particle size distribution curve of the fuel particles after crushing. This particle size distribution curve is represented as follows: ； or, ; in, , It is a proportionality coefficient. It is the diameter of the particles formed by the breakup. It is a breaking factor. It represents the number of minerals with a diameter of d on the surface of the coke. It is the reduction in coke radius after the gasification reaction. It is the crushing radius of the coke.

7. The method for predicting particulate matter generation from solid fuel combustion according to claim 6, characterized in that, The particle size distribution curves of easily breakable mineral particles and fuel particles before crushing were both measured using a laser particle size analyzer.

8. The method for predicting particulate matter generation from solid fuel combustion according to claim 1, characterized in that, In step S6, the steps for generating the particle size distribution curve of the intrinsic minerals after fuel particle combustion are as follows: S61, Based on the analysis of solid fuels, determine the types and contents of the inherent minerals in the fuels; S62, Establish a fuel particle model by inputting the types and contents of the inherent minerals in the fuel into the fuel particle model; S63, based on the fuel particle model, simulate the process of the internal minerals of the fuel particles merging with or separating from the fuel particles during combustion until the fuel particles are completely burned; S64, obtain the particle size dataset of the intrinsic mineral particles after fuel combustion, and generate the particle size distribution curve of the intrinsic mineral particles after fuel combustion.

9. The method for predicting particulate matter generation from solid fuel combustion according to claim 1, characterized in that, The specific steps for step S7 are as follows: S71, Generate a direct gas phase release source term based on the gas phase release concentration. ; S72, generating reduction reaction release source terms based on the concentration of reduction reaction products. ; S73, Generate crushing source terms based on the particle size distribution curves of easily crushable mineral particles after crushing and the particle size distribution curves of fuel particles after crushing. ; S74, generating intrinsic mineral source terms based on the particle size distribution curve of intrinsic minerals after fuel particle combustion. ; S75, Generate source item : = + + + ； S76, source item The data is input into the group equilibrium model, and the output is the particle size distribution curve of particulate matter after the combustion reaction is completed, which is used to predict particulate matter emissions.

10. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting particulate matter generation from solid fuel combustion as described in any one of claims 1-9.

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