Circulating fluidized bed gasifier gas-solid flow simulation modeling method, device and medium

Abstract

The application provides a circulating fluidized bed gasifier gas-solid flow simulation modeling method, equipment and medium, according to the actual structure of the circulating fluidized bed biomass gasification furnace, four small rooms are divided from the bottom to the top of the hearth, the continuous wide sieve particles are discretized into a limited number of particle size grades according to the standard sieve, the characteristic particle size and the initial mass fraction of each grade of particles are obtained, the geometric structure of the small room and the characteristic particle size and the initial mass fraction of each grade of particles are taken as inputs, the related parameters of each grade of particles are calculated, the dense phase zone height of each grade of particles is determined through a dynamic iteration algorithm, and an axial concentration profile reflecting the real particle size distribution is synthesized accordingly, the above-mentioned is integrated into a closed system with the number of variables and equations controlled, and the solid content of each small room and the dense phase zone height are synchronously updated through a numerical solver to realize real-time simulation; the application controls the complexity through axial partitioning and particle grading, and ensures the prediction accuracy and universality of key parameters through a dynamic mechanism algorithm.

Description

Technical Field

[0001] This invention relates to the fields of computer simulation and energy and chemical engineering technology, specifically to a method, equipment, and medium for simulating gas-solid flow in a circulating fluidized bed gasifier. Background Technology

[0002] Circulating fluidized bed biomass gasification technology, with its advantages of strong fuel adaptability, high gasification efficiency, and good environmental performance, has become an important pathway for the efficient conversion of biomass energy. The gas-solid two-phase flow within the furnace is closely coupled with heat transfer, mass transfer, and chemical reactions. The dynamic characteristics of this gas-solid flow directly determine the degree of mixing, residence time, and heat and mass transfer efficiency of the reactants, forming the core physical basis for overall operational performance and syngas quality. Therefore, establishing a simulation model that can accurately and efficiently simulate this flow behavior is crucial for the design optimization, operation control, and condition diagnosis of the gasifier.

[0003] Existing simulation modeling methods for circulating fluidized bed gasifiers face the following significant challenges when attempting to balance computational accuracy and simulation speed for real-time monitoring, operator training, or control strategy verification: The fundamental contradiction between model fidelity and computational real-time performance is that existing high-fidelity models, such as CPFD based on computational fluid dynamics, can accurately depict flow details, but the computation time is too long and cannot meet the requirements of real-time simulation for second-level or even millisecond-level response.

[0004] Conversely, while oversimplified empirical or semi-empirical models offer fast computation speeds, they neglect the non-uniformity and dynamics of flow, as well as the multi-scale characteristics of particulate systems. Relying on experimental data under specific operating conditions, they lack universality and struggle to accurately predict dynamic changes within circulating fluidized bed furnaces and behavior under undesigned conditions, leading to distorted simulation results and limited engineering application value.

[0005] In summary, existing technologies lack a dedicated simulation modeling method capable of accurately and reliably reproducing the core gas-solid flow dynamics characteristics within a circulating fluidized bed gasifier under strict real-time computational constraints. This deficiency has become a significant obstacle hindering the technology's progress towards digital monitoring, online optimization, and advanced control. Summary of the Invention This invention provides a simulation modeling method, equipment, and medium for gas-solid flow in a circulating fluidized bed gasifier. By controlling complexity through axial partitioning and particle grading, and by ensuring the prediction accuracy and universality of key parameters through dynamic mechanism algorithms, a simulation model with both high fidelity and real-time computing capabilities is achieved. This provides a reliable tool for the digital monitoring, operation optimization, and advanced control of circulating fluidized bed gasifiers, thereby solving the problems in the background technology.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: A simulation modeling method for gas-solid flow in a circulating fluidized bed gasifier involves the following steps performed using computer equipment: S1: Based on the actual structure of the target circulating fluidized bed biomass gasifier, establish a geometric model of the furnace; divide the furnace into four chambers from bottom to top: the first chamber dense phase zone, the second chamber transition zone, the third chamber lower dilute phase zone, and the fourth chamber upper dilute phase zone, and determine the geometric structure of each chamber; S2: Discretize the continuous wide-screen particles into a finite number of particle size ranges according to the standard sieve to obtain the characteristic particle size and initial mass share of each particle size range. S3: Using the geometry of the small chamber and the characteristic particle size and initial mass fraction of each particle grade as input, calculate the critical fluidization velocity, terminal velocity, dense phase porosity, saturated carry-over rate and axial attenuation coefficient of each particle grade. Determine the dense phase height of each particle grade through a dynamic iterative algorithm, and synthesize an axial concentration profile that reflects the true particle size distribution accordingly. S4: Integrate the above steps into a closed system with controlled variables and total number of equations, and use a numerical solver to synchronously update the solid content and dense phase height of each compartment to achieve real-time simulation.

[0007] Preferably, in S1: the first small chamber dense phase zone is: the furnace space from the upper surface of the air distribution plate to the surface of the dense phase zone. The height of the dense phase zone is a variable value that changes with the amount of bed material, particle size distribution, particle density, primary air volume, primary air temperature, etc. The second small chamber transition zone: the furnace space from the surface of the dense phase zone to the height of the lower secondary air inlet; The lower part of the dilute phase zone of the third small chamber: the furnace space from the lower secondary air inlet to the upper secondary air inlet; The upper part of the dilute phase zone of the fourth chamber: the furnace space from the upper secondary air inlet to the top of the furnace.

[0008] 3. The gas-solid flow simulation modeling method for a circulating fluidized bed gasifier as described in claim 1, characterized in that: the finite number of particle size ranges in S2 are five ranges: range 1: particle size range 0~0.063mm; range 2: particle size range 0.063~0.150mm; range 3: particle size range 0.15~1.0mm; range 4: particle size range 1.0~2.0mm; range 5: particle size range greater than 2mm.

[0009] Preferably, the specific process steps of S3 are as follows: S31: The total material content in the furnace is measured, and the mass percentage of each particle size is obtained through industrial sieving. Based on this, the actual material mass of each particle size is calculated. At the same time, the fluidization area corresponding to each particle size is allocated according to the mass ratio, and the static bed height of each particle size is calculated based on the packing void ratio. S32: Based on the determined particle size, density, gas properties and gas velocity in each chamber under the operating conditions, calculate the critical fluidization velocity, critical fluidization void fraction, average void fraction in the dense phase region, terminal velocity, saturated carry-over rate, far-field void fraction and axial attenuation coefficient in each chamber for each particle. S33: Using the obtained axial attenuation coefficients of each small chamber and the assumed value of the current dense phase zone height, the exponential attenuation distribution model is adopted to calculate the average particle volume concentration of each particle size in the four small chambers. Based on this, the concentration of each small chamber is spatially integrated to obtain the total calculated mass of particles in the furnace at the current assumed height. S34: Compare the calculated total mass in the furnace for each particle size range with the actual mass of the stored material. If the relative error exceeds the limit, adjust the assumed value of the dense phase region height for that range along the gradient direction and return to S33 to recalculate until all ranges meet the convergence conditions. After the iteration, the converged axial concentration distributions of each range are weighted and superimposed according to the mass share to obtain the total volume concentration and total particle mass of each chamber, forming the axial concentration distribution profile of the wide sieve in the furnace.

[0010] Preferably, the specific process steps of S31 are as follows: S311, Measure the total amount of material in the furnace (mm); S312, use an industrial sieve to measure the particle size distribution of the material in the furnace, divide it into 5 grades, and calculate the mass percentage of each grade bed(i), i=1,2,3,4,5, where i represents the i-th grade and bed represents the mass percentage; S313, calculate the mass of material in the furnace for each particle size, M(i), i=1,2,3,4,5

[0011] Where MM is the total mass of fluidized particles in the furnace, M(i) is the mass of particles in the i-th gear, and bed(i) is the mass percentage of particles in the i-th gear. S314, calculate the fluidization area areBED(i) of each particle based on its mass percentage;

[0012] Where are, areBED is the total fluidization area, and areBED(i) is the fluidization area of ​​each particle based on its mass percentage. S315, calculate the static bed height Hden(i) for each particle level.

[0013] Where 0.45 is the porosity of the material particles in the packing state, which can be adjusted according to the specific situation, and Hden(i) is the static bed height of the i-th particle.

[0014] Preferably, the specific process steps of S32 are as follows: S321: Calculate the bed height Hdenmax(i) in the dense phase region and the critical velocity of each particle:

[0015] And critical fluidization porosity:

[0016] in, Let be the critical velocity of the i-th particle. For gas phase density, Let be the critical fluidizing porosity for each particle size, Roup be the true density of the solid particle, Roug be the gas density, and g be the acceleration due to gravity. is the sphericity coefficient of the solid particles. The dynamic viscosity of the flue gas. The diameter of each particle;

[0017]

[0018] Average porosity in the dense phase region:

[0019]

[0020] in, This refers to the volume fraction of the bubble phase, that is, the volume fraction of bubbles in the dense phase region. eDen(i) is an intermediate variable used to calculate the empirical parameter of the bubble phase fraction, where eDen(i) is the porosity of the i-th particle in the dense phase region, and j is the index of the small chamber. Let Hdenmax(i) be the flue gas velocity in the j-th chamber, and Hdenmax(i) be the maximum bed height of each particle in the dense phase region. S322: Calculate the following parameters:

[0021] in, For density Froude number:

[0022]

[0023]

[0024] Where eeFar(i) is the porosity of the i-th particle at infinity. Let be the terminal velocity of the i-th particle, and Ar be the Archimedes number. This represents the saturated carrying rate of particles in the dense phase region for each particle level.

[0025]

[0026] in, The drag coefficient of a single particle. The terminal Reynolds number;

[0027]

[0028] in, The terminal velocity is the initial value for iteration, a simplified calculation value after ignoring the nonlinear term in the drag coefficient. It is only used as a starting guess for the iterative solution and is not the actual terminal velocity, but is used to estimate the iterative value:

[0029] in, Let be the attenuation coefficient of the i-th particle in the j-th chamber.

[0030] Preferably, the specific process steps of S33 are as follows: Calculate the axial material concentration distribution of particles in the i-th grade, and calculate the average particle volume concentration EEHHHEVEN(j,i) of particles in the i-th grade in the j-th chamber.

[0031]

[0032] Where hf(j) is the top height of the j-th chamber, and the furnace space mass Mehi(i) of the i-th batch of particles in the fluidized state is calculated, which is the sum of the mass integrals of the particles in the furnace:

[0033] Where VOLF(j,i) is the volume of the j-th chamber.

[0034] Preferably, the specific process steps of S34 are as follows: Check whether the mass of the i-th batch of particles is conserved, and calculate the mass difference dmehi(i) of the i-th batch of particles:

[0035] If the mass calculation error dmehipre(i) of the i-th particle is less than the set value, the calculation ends.

[0036] Where dmehipre(i) is the calculation error; Otherwise, if the integral mass of the furnace space Mehi(i) is greater than M(i); The iterative calculation is performed to gradually reduce the bed height Hdenmax(i) in the dense phase region while keeping the porosity of the dense phase region constant and the attenuation index of each cell constant. The calculation is continued until the calculation error dmehipre(i) is less than the set value, at which point the iterative calculation ends. If the integral mass of the furnace space Mehi(i) is less than M(i); The iterative calculation is performed, gradually increasing the bed height Hdenmax(i) in the dense phase region while keeping the porosity of the dense phase region constant and the attenuation index of each cell constant. The calculation is continued until the calculation error dmehipre(i) is less than the set value, at which point the iterative calculation ends. All particle size ranges are superimposed according to their respective mass fractions to calculate the overall axial material concentration distribution under wide sieve conditions, and the particle mass of the j-th chamber.

[0037] in, Let be the particle mass of the j-th chamber.

[0038] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0039] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0040] As can be seen from the above technical solution compared with the prior art, the present invention has the following beneficial effects: 1. This invention employs a multi-scale coupled modeling strategy of axial partitioning and particle size classification, which not only grasps the overall structure of gas-solid two-phase flow from a macroscopic perspective but also precisely describes the transport and distribution characteristics of particles of different sizes from a microscopic perspective. This effectively overcomes the inherent limitations of single particle size models or homogenization assumptions and significantly improves the simulation accuracy of key flow parameters. 2. This invention is successful. It abandons the reliance on empirical formulas for fixed operating conditions and adopts a dynamic iterative algorithm based on physical mechanisms, overcoming the problems of traditional empirical models being limited by experimental conditions and scale, and having poor universality. The iterative calculation of the dense phase region height is reasonable, ensuring that the model has reliable predictive capabilities under different operating conditions and on devices of different scales, significantly reducing computational costs and making dynamic simulation on an industrial scale possible. Attached Figure Description

[0041] Figure 1This is a schematic diagram of the solid content of particles inside the circulating fluidized bed boiler of the present invention; Figure 2 This is a schematic diagram of the method steps of the present invention; Figure 3 This is a schematic diagram of the specific steps in S3 of this embodiment of the invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0043] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention, but should not be used to limit the scope of the present invention.

[0044] Example: like Figure 1 As shown, this embodiment uses an operational industrial circulating fluidized bed biomass gasifier as the implementation object. The gasifier's furnace chamber contains a large amount of wide-screen bed material with a wide particle size range and a mixture of coarse and fine particles, exhibiting typical non-uniform distribution characteristics. To monitor the axial distribution of the material within the furnace and the changes in the height of the dense phase zone in real time, a simulation model that reflects the actual particle size distribution and gas-solid flow characteristics needs to be established.

[0045] Axial structural partitioning of the furnace based on fluid dynamics characteristics, such as Figure 1 As shown, the solid content in a circulating fluidized bed boiler typically exhibits an S-shaped distribution with a lower concentration and a higher concentration, and it is believed that multiple flow regimes coexist, such as bubbling fluidization in the bottom dense phase region and rapid fluidization in the upper dilute phase region.

[0046] like Figure 2 As shown, based on the differences in boiler structure, gas-solid flow state, gas mixing and mass transfer characteristics, the furnace can be further subdivided from bottom to top into a fully developed bubbling bed zone, a splash zone, and an upper dilute phase zone. From the bottom to the top of the furnace, it is divided into four small chambers: the first small chamber dense phase zone, the second small chamber transition zone, the lower part of the third small chamber dilute phase zone, and the upper part of the fourth small chamber dilute phase zone, and the geometric structure of each small chamber is determined.

[0047] The first small chamber dense phase zone is the furnace space from the upper surface of the air distribution plate to the surface of the dense phase zone. The height of the dense phase zone is a variable value that changes with the amount of bed material, particle size distribution, particle density, primary air volume, primary air temperature, etc. Second small chamber transition zone: the furnace space from the surface of the dense phase zone to the height of the lower secondary air inlet; The lower part of the dilute phase zone of the third chamber: the furnace space from the lower secondary air inlet to the upper secondary air inlet; Upper part of the dilute phase zone of the fourth chamber: the furnace space from the upper secondary air inlet to the top of the furnace.

[0048] The continuous wide-range sieve particle system is discretized into a finite number of representative particle size ranges, such as 5. The grading is based on industrial sieve standards, ensuring coverage of the entire particle size range. There are five particle size ranges: Range 1: 0~0.063mm; Range 2: 0.063~0.150mm; Range 3: 0.15~1.0mm; Range 4: 1.0~2.0mm; Range 5: Particle size greater than 2mm.

[0049] The smaller particle size range is more finely divided to capture its key impact on flow. This strategy effectively reconciles the contradiction between the oversimplification of single-particle-size models and the computational infeasibility of detailed multi-particle-size models in the background art.

[0050] It introduces the ability to describe particle diversity while keeping the number of model variables and equations within a reasonable range that allows for real-time solutions. This is the core design that balances model fidelity and computational complexity.

[0051] Calculation model for dense phase bed height and axial particle concentration distribution: like Figure 3 As shown, the initial conditions and inputs are as follows: based on the measured total amount of material in the furnace and the percentage of particle mass in each size range, an initial value for the height of the dense phase zone to be corrected is set for each particle size range. Measure the total amount of material in the furnace (MM); measure the particle size distribution of the material in the furnace using an industrial sieve, divide it into 5 grades, and calculate the mass percentage of each grade: bed(i), i=1,2,3,4,5, where i represents the i-th grade and bed represents the mass percentage. Calculate the mass of material in the furnace M(i) for each particle size, i=1,2,3,4,5

[0052] Where MM is the total mass of fluidized particles in the furnace, M(i) is the mass of particles in the i-th gear, and bed(i) is the mass percentage of particles in the i-th gear. Calculate the fluidization area areBED(i) of each particle based on its mass percentage;

[0053] Where are, areBED is the total fluidization area, and areBED(i) is the fluidization area of ​​each particle based on its mass percentage. Calculate the static bed height Hden(i) for each particle level.

[0054] Where 0.45 is the porosity of the material particles in the packing state, which can be adjusted according to the specific situation, and Hden(i) is the static bed height of the i-th particle.

[0055] Calculation of key physical properties and kinetic parameters: For each particle size, based on its particle size, density and other physical properties, as well as operating conditions (such as gas velocity and temperature), calculate its critical fluidization velocity, terminal velocity, critical fluidization void fraction, dense phase void fraction, saturated carryover rate and axial concentration decay coefficient. Calculate the bed height Hdenmax(i) in the dense phase region, and calculate the critical velocity of each particle:

[0056] And critical fluidization porosity:

[0057] in, Let be the critical velocity of the i-th particle. For gas phase density, Let be the critical fluidizing porosity for each particle size, Roup be the true density of the solid particle, Roug be the gas density, and g be the acceleration due to gravity. is the sphericity coefficient of the solid particles. The dynamic viscosity of the flue gas. The diameter of each particle;

[0058]

[0059] Average porosity in the dense phase region:

[0060]

[0061] in, This refers to the volume fraction of the bubble phase, that is, the volume fraction of bubbles in the dense phase region. eDen(i) is an intermediate variable used to calculate the empirical parameter of the bubble phase fraction, where eDen(i) is the porosity of the i-th particle in the dense phase region, and j is the index of the small chamber. Let Hdenmax(i) be the flue gas velocity in the j-th chamber, and Hdenmax(i) be the maximum bed height of each particle in the dense phase region. S321: Calculate the following parameters:

[0062] in, For density Froude number:

[0063]

[0064]

[0065] Where eeFar(i) is the porosity of the i-th particle at infinity. Let be the terminal velocity of the i-th particle, and Ar be the Archimedes number. This represents the saturated carrying rate of particles in the dense phase region for each particle level.

[0066]

[0067] in, The drag coefficient of a single particle. The terminal Reynolds number;

[0068]

[0069] in, The terminal velocity is the initial value for iteration, a simplified calculation value after ignoring the nonlinear term in the drag coefficient. It is only used as a starting guess for the iterative solution and is not the actual terminal velocity, but is used to estimate the iterative value:

[0070] in, Let be the attenuation coefficient of the i-th particle in the j-th chamber.

[0071] Axial Concentration Prediction and Mass Conservation Verification by Grade: Using an exponential decay distribution model, the average volumetric concentration distribution of each grade of particles in each axial zone (chamber) is calculated; then, the distribution is spatially integrated to obtain the total calculated mass of the particles in the furnace. Calculate the axial material concentration distribution of particles in the i-th grade, and calculate the average particle volume concentration EEHHHEVEN(j,i) of particles in the i-th grade in the j-th chamber.

[0072]

[0073] Where hf(j) is the top height of the j-th chamber, and the furnace space mass Mehi(i) of the i-th batch of particles in the fluidized state is calculated, which is the sum of the mass integrals of the particles in the furnace:

[0074] Where VOLF(j,i) is the volume of the j-th chamber.

[0075] Dynamic Iteration and High Convergence: The calculated total mass of each particle level is compared with its known input mass. The assumed value of the dense phase region height of that particle level is adjusted iteratively until the two satisfy the preset mass conservation error tolerance, thereby obtaining the dynamic dense phase region height of each particle level. The specific steps are as follows: Check whether the mass of the i-th batch of particles is conserved, and calculate the mass difference dmehi(i) of the i-th batch of particles:

[0076] If the mass calculation error dmehipre(i) of the i-th particle is less than the set value, the calculation ends.

[0077] Where dmehipre(i) is the calculation error; Otherwise, if the integral mass of the furnace space Mehi(i) is greater than M(i); The iterative calculation is performed to gradually reduce the bed height Hdenmax(i) in the dense phase region while keeping the porosity of the dense phase region constant and the attenuation index of each cell constant. The calculation is continued until the calculation error dmehipre(i) is less than the set value, at which point the iterative calculation ends. If the integral mass of the furnace space Mehi(i) is less than M(i); The iterative calculation is performed, gradually increasing the bed height Hdenmax(i) in the dense phase region while keeping the porosity of the dense phase region constant and the attenuation index of each cell constant. The calculation is continued until the calculation error dmehipre(i) is less than the set value, at which point the iterative calculation ends. All particle size ranges are superimposed according to their respective mass fractions to calculate the overall axial material concentration distribution under wide sieve conditions, and the particle mass of the j-th chamber.

[0078] in, Let be the particle mass of the j-th chamber.

[0079] This algorithm enables adaptive, online determination of the dense phase zone height based on total material quantity, air volume, and particle size distribution, fundamentally overcoming the limitations of static empirical formulas. Wide-sieve-range concentration distribution synthesis: After obtaining the dynamic height and corresponding axial concentration distribution for all particle size ranges, the concentration distributions of all particle size ranges are weighted and superimposed according to their respective mass fractions to finally generate an overall axial material concentration distribution that can characterize the multi-scale behavior of real particle systems. This synthesis method effectively simulates the differences between particles of different sizes, significantly improving the physical realism and accuracy of axial distribution prediction.

[0080] This step aims to systematically address the prediction distortion problem caused by existing technologies that rely on empirical models and particle homogenization assumptions. Its core is to determine the dense phase region height for each particle size group using a dynamic iterative algorithm, and then synthesize an axial concentration profile that reflects the true particle size distribution. The specific process is as follows: Finally, the above steps are integrated into a closed system with controlled variables and a total number of equations. A high-efficiency numerical solver enables synchronous and rapid updates of key state variables such as the total material quantity in the furnace, axial concentration distribution, and dense phase height, ensuring that the simulation speed meets stringent real-time requirements.

[0081] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0082] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0083] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the circulating fluidized bed gasifier gas-solid flow simulation modeling methods described in the above embodiments.

[0084] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0085] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0086] For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media.

[0087] The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid state disks (SSDs)).

[0088] It should be noted that in this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0089] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0090] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0091] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A simulation modeling method for gas-solid flow in a circulating fluidized bed gasifier, characterized in that, Perform the following steps using a computer device: S1: Based on the actual structure of the target circulating fluidized bed biomass gasifier, establish a geometric model of the furnace; divide the furnace into four chambers from bottom to top: the first chamber dense phase zone, the second chamber transition zone, the third chamber lower dilute phase zone, and the fourth chamber upper dilute phase zone, and determine the geometric structure of each chamber; S2: Discretize the continuous wide-screen particles into a finite number of particle size ranges according to the standard sieve to obtain the characteristic particle size and initial mass share of each particle size range. S3: Using the geometry of the small chamber and the characteristic particle size and initial mass fraction of each particle grade as input, calculate the critical fluidization velocity, terminal velocity, dense phase porosity, saturated carry-over rate and axial attenuation coefficient of each particle grade. Determine the dense phase height of each particle grade through a dynamic iterative algorithm, and synthesize an axial concentration profile that reflects the true particle size distribution accordingly. S4: Integrate the above steps into a closed system with controlled variables and total number of equations, and use a numerical solver to synchronously update the solid content and dense phase height of each compartment to achieve real-time simulation.

2. The simulation modeling method for gas-solid flow in a circulating fluidized bed gasifier as described in claim 1, characterized in that: In S1: the first small chamber dense phase zone is the furnace space from the upper surface of the air distribution plate to the surface of the dense phase zone. The height of the dense phase zone is a variable value that changes with the amount of bed material, particle size distribution, particle density, primary air volume, primary air temperature, etc. The second small chamber transition zone: the furnace space from the surface of the dense phase zone to the height of the lower secondary air inlet; The lower part of the dilute phase zone of the third small chamber: the furnace space from the lower secondary air inlet to the upper secondary air inlet; The upper part of the dilute phase zone of the fourth chamber: the furnace space from the upper secondary air inlet to the top of the furnace.

3. The simulation modeling method for gas-solid flow in a circulating fluidized bed gasifier as described in claim 1, characterized in that: The S2 has five particle size ranges: the first range is 0 to 0.063 mm; the second range is 0.063 to 0.150 mm; the third range is 0.15 to 1.0 mm; the fourth range is 1.0 to 2.0 mm; and the fifth range is greater than 2 mm.

4. The gas-solid flow simulation modeling method for a circulating fluidized bed gasifier as described in claim 1, characterized in that: The specific process steps of S3 are as follows: S31: Measure the total amount of material in the furnace and obtain the mass percentage of each particle size range through industrial sieving. Calculate the actual mass of each particle size range based on this. At the same time, allocate the fluidization area corresponding to each particle size range according to the mass ratio and calculate the static bed height of each particle size range based on the packing void ratio. S32: Based on the determined particle size, density, gas properties and gas velocity in each chamber under the operating conditions, calculate the critical fluidization velocity, critical fluidization void fraction, average void fraction in the dense phase region, terminal velocity, saturated carry-over rate, far-field void fraction and axial attenuation coefficient in each chamber for each particle. S33: Using the obtained axial attenuation coefficients of each small chamber and the assumed value of the current dense phase zone height, the exponential attenuation distribution model is adopted to calculate the average particle volume concentration of each particle size in the four small chambers. Based on this, the concentration of each small chamber is spatially integrated to obtain the total calculated mass of particles in the furnace at the current assumed height. S34: Compare the calculated total mass in the furnace for each particle size range with the actual mass of the stored material. If the relative error exceeds the limit, adjust the assumed value of the dense phase region height for that range along the gradient direction and return to S33 to recalculate until all ranges meet the convergence conditions. After the iteration, the converged axial concentration distributions of each range are weighted and superimposed according to the mass share to obtain the total volume concentration and total particle mass of each chamber, forming the axial concentration distribution profile of the wide sieve in the furnace.

5. The simulation modeling method for gas-solid flow in a circulating fluidized bed gasifier as described in claim 1, characterized in that: The specific process steps of S31 are as follows: S311, Measure the total amount of material in the furnace (mm); S312, use an industrial sieve to measure the particle size distribution of the material in the furnace, divide it into 5 grades, and calculate the mass percentage of each grade bed(i), i=1,2,3,4,5, where i represents the i-th grade and bed represents the mass percentage; S313, calculate the mass of material in the furnace for each particle size, M(i), i=1,2,3,4,5 Where MM is the total mass of fluidized particles in the furnace, M(i) is the mass of particles in the i-th gear, and bed(i) is the mass percentage of particles in the i-th gear. S314, calculate the fluidization area areBED(i) of each particle based on its mass percentage; Where are, areBED is the total fluidization area, and areBED(i) is the fluidization area of ​​each particle based on its mass percentage. S315, calculate the static bed height Hden(i) for each particle level. Where 0.45 is the porosity of the material particles in the packing state, which can be adjusted according to the specific situation, and Hden(i) is the static bed height of the i-th particle.

6. The gas-solid flow simulation modeling method for a circulating fluidized bed gasifier as described in claim 5, characterized in that: The specific process steps of S32 are as follows: S321: Calculate the bed height Hdenmax(i) in the dense phase region and the critical velocity of each particle: And critical fluidization porosity: in, Let be the critical velocity of the i-th particle. The density is the gas phase density. Let be the critical fluidizing porosity for each particle size, Roup be the true density of the solid particle, Roug be the gas density, and g be the acceleration due to gravity. is the sphericity coefficient of the solid particles. The dynamic viscosity of the flue gas. The diameter of each particle; Average porosity in the dense phase region: in, This refers to the volume fraction of the bubble phase, that is, the volume fraction of bubbles in the dense phase region. eDen(i) is an intermediate variable used to calculate the empirical parameter of the bubble phase fraction, where eDen(i) is the porosity of the i-th particle in the dense phase region, and j is the index of the small chamber. Let Hdenmax(i) be the flue gas velocity in the j-th chamber, and Hdenmax(i) be the maximum bed height of each particle in the dense phase region. S322: Calculate the following parameters: in, For density Froude number: Where eeFar(i) is the porosity of the i-th particle at infinity. Let be the terminal velocity of the i-th particle, and Ar be the Archimedes number. This represents the saturated carrying rate of particles in the dense phase region for each particle level. in, The drag coefficient of a single particle. The terminal Reynolds number; in, The terminal velocity is the initial value for iteration, a simplified calculation value after ignoring the nonlinear term in the drag coefficient. It is only used as a starting guess for the iterative solution and is not the actual terminal velocity, but is used to estimate the iterative value: in, Let be the attenuation coefficient of the i-th particle in the j-th chamber.

7. The simulation modeling method for gas-solid flow in a circulating fluidized bed gasifier as described in claim 6, characterized in that: The specific process steps of S33 are as follows: Calculate the axial material concentration distribution of particles in the i-th grade, and calculate the average particle volume concentration EEHHHEVEN(j,i) of particles in the i-th grade in the j-th chamber. Where hf(j) is the top height of the j-th chamber, and the furnace space mass Mehi(i) of the i-th batch of particles in the fluidized state is calculated, which is the sum of the mass integrals of the particles in the furnace: Where VOLF(j,i) is the volume of the j-th chamber.

8. The simulation modeling method for gas-solid flow in a circulating fluidized bed gasifier as described in claim 7, characterized in that: The specific process steps of S34 are as follows: Check whether the mass of the i-th batch of particles is conserved, and calculate the mass difference dmehi(i) of the i-th batch of particles: If the mass calculation error dmehipre(i) of the i-th particle is less than the set value, the calculation ends. Where dmehipre(i) is the calculation error; Otherwise, if the integral mass of the furnace space Mehi(i) is greater than M(i); The iterative calculation is performed to gradually reduce the bed height Hdenmax(i) in the dense phase region while keeping the porosity of the dense phase region constant and the attenuation index of each cell constant. The calculation is continued until the calculation error dmehipre(i) is less than the set value, at which point the iterative calculation ends. If the integral mass of the furnace space Mehi(i) is less than M(i); The iterative calculation is performed, gradually increasing the bed height Hdenmax(i) in the dense phase region while keeping the porosity of the dense phase region constant and the attenuation index of each cell constant. The calculation is continued until the calculation error dmehipre(i) is less than the set value, at which point the iterative calculation ends. All particle size ranges are superimposed according to their respective mass fractions to calculate the overall axial material concentration distribution under wide sieve conditions, and the particle mass of the j-th chamber. in, Let be the particle mass of the j-th chamber.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 8.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 8.

Images