A method for simulating fluid flow and adsorption in biochar

Abstract

The present invention relates to the fields of energy utilization and computational fluid dynamics, and more particularly to a method for simulating fluid flow and adsorption within biochar. The specific simulation method comprises: obtaining a computed tomography grayscale image of the biochar, performing image binarization processing to generate a binarized image comprising a matrix region and a pore region, and establishing a biochar pore geometry model; establishing a lattice Boltzmann adsorption model for coupled convection-diffusion surface adsorption of the biochar; and simulating the flow process of the fluid within the biochar pore geometry model and the surface adsorption process to obtain an adsorption kinetics model characterizing the flow and diffusion laws of the fluid within the biochar pores and the adsorption laws within the biochar matrix. While improving accuracy and efficiency, the present invention reduces the time and cost required for actual experiments, addressing the problems of insufficient accuracy and low efficiency of traditional image processing and fluid simulation techniques when processing large-particle biochar.

Description

Technical Field

[0001] The present invention relates to the technical field of energy utilization and computational fluid dynamics, and in particular to a method for simulating fluid flow and adsorption in biochar. Background Art

[0002] Biochar is a carbon-based material with excellent physical and chemical properties, widely used in water treatment, soil improvement, and pollutant adsorption. Traditionally used biochar is in powder form. However, using large-particle biomass as raw material, it can be produced through pyrolysis and gasification without the need for fine grinding. Large-particle biomass, especially fibrous biomass, is often obtained directly from agricultural and forestry waste. The technology for producing biochar from large-particle biomass fully utilizes the natural properties of biomass raw materials, offering advantages such as reduced costs, increased efficiency, and improved process stability.

[0003] Recent studies have shown that pore structure significantly influences the adsorption properties of biochar, particularly when treating volatile organic fluids such as toluene. The fluid flow and adsorption characteristics within biochar directly determine its effectiveness in environmental remediation. As research deepens, in-depth analysis of the pore structure of biochar and the behavior of fluids within it has become a crucial approach to improving its application efficiency. Therefore, effectively evaluating the pore structure and fluid behavior of large-particle biochar has become a key research topic.

[0004] Currently, traditional image processing and fluid simulation techniques suffer from inaccuracies and low efficiency when dealing with large-particle biochar. Specifically, these techniques struggle when attempting to characterize media with complex porous structures. Because large-particle biochar is inherently porous, volatile organic fluids like toluene not only flow within its micro- and nanoscale pores but also undergo adsorption and desorption processes on its surface. These dynamic behaviors complicate simulation.

[0005] Although a large number of studies have summarized and evaluated the adsorption of volatile organic fluids such as toluene on large-particle biochar through experimental means, they are still insufficient in terms of in-depth exploration of the fluid dynamics and adsorption inside the pores. Traditional image processing and fluid simulation methods have encountered insurmountable obstacles when trying to capture these physical phenomena at the microscale. Among them, the high complexity of large-particle biochar makes it difficult to guarantee the reliability of any simulation analysis or semi-analytical model based on simplified assumptions. In addition, at the microscale, the gas flow mechanism in the pores is extremely complex, involving diffusion, adsorption and other physical processes that are difficult to accurately model. Therefore, traditional image processing and numerical simulation methods seem to be unable to accurately describe the flow and adsorption behavior of volatile organic fluids at the micro / nanoscale in porous media such as large-particle biochar, and it is difficult to provide satisfactory accuracy and efficiency. Summary of the Invention

[0006] In order to solve the problems of insufficient accuracy and low efficiency of traditional image processing and fluid simulation technologies when processing large-particle biochar, the present invention provides a simulation method for fluid flow and adsorption in biochar.

[0007] The Lattice Boltzmann method (LBM) is a mesoscopic method, situated between the macroscopic and microscopic scales. It can capture flow behavior at two scales: free flow through regular pore channels and gas transport accompanied by adsorption within a dense matrix with very small pores. Therefore, this paper attempts to use the LBM to simulate the adsorption of volatile organic compounds (VOCs) by large-particle biochar, aiming to uncover the microscale mechanism of VOC adsorption by large-particle biochar.

[0008] However, the current challenge in accurately describing the flow and adsorption of organic matter in porous media using the lattice Boltzmann method is how to maximize computational efficiency while ensuring accuracy. To this end, this paper combines image processing with the lattice Boltzmann method to achieve a detailed simulation of the flow and surface adsorption of volatile organic compounds within biochar pores. This approach improves accuracy and efficiency while reducing the time and cost of actual experiments. This allows for in-depth study of the behavioral characteristics of biochar during fluid flow and adsorption, providing theoretical support for optimizing its application.

[0009] The present invention combines CT imaging, image processing and the lattice Boltzmann method to form an innovative biochar research method. The method of the present invention helps to promote the progress of biochar research and provide new ideas for subsequent technological development.

[0010] To achieve the above objectives, the technical solutions of the present invention are as follows.

[0011] The present invention provides a method for simulating the flow and adsorption of fluid in biochar, wherein the fluid is volatile organic compound, and the method comprises the following steps:

[0012] A computed tomography grayscale image of biochar was obtained, and then the image was binarized to identify the pore and matrix distribution characteristics of the biochar, generate a binary image containing the matrix area and the pore area, and establish a biochar pore geometry model; the biochar pore geometry model was matched with the computational domain of the lattice Boltzmann method, and the fluid characteristic parameters and boundary conditions were set to establish a lattice Boltzmann adsorption model of biochar convection-diffusion coupled surface adsorption; based on the lattice Boltzmann adsorption model, the flow process of the fluid in the biochar pore geometry model and the surface adsorption process were simulated. By analyzing the changes in the concentration of the fluid in the pores and the adsorption amount in the biochar matrix at different time steps, an adsorption kinetic model was obtained to characterize the flow and diffusion laws of the fluid in the biochar pores and the adsorption laws in the biochar matrix.

[0013] Preferably, the method for performing image binarization processing is:

[0014] The computed tomography grayscale images of biochar were binarized using Gaussian blur algorithm, fixed threshold segmentation, morphological operation and connected component analysis to identify the pore and matrix distribution characteristics of biochar. The grayscale value of the matrix area was defined as 0, and the grayscale value of the pore area was defined as 255 to generate a binary image containing the matrix area and the pore area.

[0015] Currently, grayscale images are binarized directly using a fixed threshold. However, the resulting binarized images are not ideal, often containing significant noise and inaccurately processed regions. To suppress this noise and accurately extract the biochar pore structure, this paper uses a Gaussian blur algorithm, fixed threshold segmentation, morphological operations, and connected component analysis to binarize biochar computed tomography grayscale images. This method identifies the distribution characteristics of the biochar's pores and matrix, yielding a binary image containing both matrix and pore regions.

[0016] Preferably, the method for performing image binarization processing on the computed tomography grayscale image of biochar using Gaussian blur algorithm, fixed threshold segmentation, morphological operation and connected component analysis is:

[0017] From the computed tomography grayscale image of biochar, characteristic meta-scale regions with permeability equivalent to that of the biochar slices were screened and segmented into multiple sub-regions. A Gaussian blur algorithm was used to smooth each sub-region to suppress fine-grained noise and retain the pore structure characteristics. The smoothed sub-regions were segmented with a fixed threshold, with pixels below the threshold set to RGB values ​​of 0 and pixels above or equal to the threshold set to RGB values ​​of 255. Morphological operations were used to open and close the sub-regions after fixed threshold segmentation to remove isolated noise points. Connected component analysis was performed on the sub-regions after opening and closing operations, and they were filtered according to area size. Sub-regions with an area larger than the set threshold were regarded as matrix regions, generating a binary image containing matrix regions and pore regions.

[0018] In the present invention, the Gaussian blur algorithm is mainly used to reduce fine-grained noise in the image, helping subsequent steps to better identify image features; fixed threshold segmentation is mainly used to achieve image binarization, where an RGB value of 0 represents black and an RGB value of 255 represents white; morphological operations are used to remove noise, and small noise points in the image are further reduced through opening and closing operations; connected component analysis is used to traverse all connected areas and filter out areas that are too small based on area, and areas with an area greater than the threshold are regarded as solid areas and retained in the mask image to remove weakly connected objects caused by noise. The present invention achieves accurate extraction of the pore structure of biochar through multi-level noise suppression, while retaining the spatial representation capability of permeability characteristics, providing a reliable foundation for the subsequent establishment of a lattice Boltzmann adsorption model.

[0019] Preferably, the method for obtaining a computed tomography grayscale image of the biochar is to perform high-resolution imaging of the biochar using computed tomography technology to obtain a computed tomography grayscale image of the biochar. In the present invention, the computed tomography grayscale image of the biochar can characterize the microstructure of the biochar.

[0020] Preferably, the fluid is a volatile organic compound, for example, toluene.

[0021] Preferably, the method for establishing a lattice Boltzmann adsorption model for biochar convection-diffusion coupled surface adsorption comprises the following steps:

[0022] According to the distribution characteristics of pores and matrix in the biochar pore geometry model, the biochar pore geometry model is divided into several lattice units, each of which represents a lattice node.

[0023] Set the simulated fluid characteristic parameters and physical parameters and perform initialization processing; the fluid characteristic parameters include fluid density, viscosity, pore diffusion coefficient, inlet velocity and concentration, and matrix diffusion coefficient; the physical parameters include simulation domain, grid step and time step.

[0024] A single relaxation time BGK collision operator model is constructed, and macroscopic quantities are calculated; the single relaxation time BGK collision operator model includes a lattice velocity model, an equilibrium distribution function, and an evolution equation of collision migration; the macroscopic quantities include density, velocity, concentration, and adsorption amount.

[0025] Boundary conditions were set to establish a lattice Boltzmann adsorption model for convection-diffusion coupled surface adsorption of biochar.

[0026] Preferably, the expression of the lattice velocity model is as follows:

[0027]

[0028] Where i represents the direction of velocity; c i represents the discrete lattice velocity in the i-th direction; c′ is a constant, c′=1.

[0029] The evolution equation of collision migration is as follows:

[0030]

[0031] Among them, f i represents the density distribution function; f i eq represents the density equilibrium distribution function; f i (r+Δtc i , t+Δt) represents the density distribution function after migration; f i (r, t) represents the density distribution function before migration; represents the density equilibrium distribution function at time t; g i represents the concentration distribution function; represents the concentration equilibrium distribution function; g i +(r+Δtc i , t+Δt) represents the concentration distribution function after migration; g i (r, t) represents the concentration distribution function before migration; represents the concentration equilibrium distribution function at time t; r represents the position of the grid node; t represents time; Δt represents the time step; τ f represents the dimensionless relaxation time of the flow field; τ g represents the dimensionless relaxation time of the concentration field; τ f and τ g The calculation formula is:

[0032] υ=C s 2 (τ f -1 / 2); D s =C s2 (τ g -1 / 2);

[0033] Where, υ represents the kinematic viscosity of the fluid; s represents the volatile component of the fluid; C s represents the concentration of adsorbate s; D s The diffusion coefficient represents the concentration of the s component.

[0034] The equilibrium distribution function is as follows:

[0035]

[0036] in, represents the density equilibrium distribution function at time t; represents the concentration equilibrium distribution function at time t; ω i is the weight coefficient used to calculate the particle interaction of different velocity components during the collision process; ρ represents the density of the fluid; c i represents the discrete grid velocity in the i-th direction; u represents the velocity of the fluid; C s Represents the concentration of the s component adsorbate.

[0037] Preferably, the calculation formula of the macroscopic amount is as follows:

[0038] The relationship between the density ρ of the fluid is: The relationship between the velocity u of the fluid is: The concentration C of the adsorbate of component s in the fluid s The calculation formula is: The calculation formula for the adsorption capacity of biochar surface is: N (t+1) -N=[k1C s (N m -N)-k -1 N]Δt;

[0039] Among them, f i represents the density distribution function, c i represents the discrete grid velocity in the i-th direction; g i represents the concentration distribution function, k1 represents the adsorption rate constant; C s represents the concentration of adsorbate of component s; k -1 represents the desorption rate constant; N m Indicates the saturated adsorption capacity, N indicates the current adsorption capacity; N (t+1) It represents the adsorption amount after the layer update at time t+1 that needs to be solved in the solid matrix.

[0040] Preferably, the method for simulating the flow process of the fluid in the biochar pore geometry model and the surface adsorption process is as follows:

[0041] The lattice Boltzmann adsorption model was used to simulate the flow and mass transfer processes of the fluid in the biochar pore geometry model, and the flow field and concentration field in the biochar pores were calculated.

[0042] The Langmuir adsorption kinetic model was used to analyze the adsorption amount and adsorption kinetics in the matrix region, and the concentration of the fluid in the pores and the changes in the adsorption amount in the biochar matrix were obtained.

[0043] Preferably, the expression of the Langmuir adsorption kinetic model is as follows:

[0044]

[0045] Where R represents the reaction source / sink at the gas-solid interface; t represents time; k1 represents the adsorption rate constant; k -1 represents the desorption rate constant; N m Indicates saturated adsorption capacity, N indicates current adsorption capacity; C s Represents the concentration of the s component adsorbate.

[0046] When considering the source / sink terms, the particle concentration evolution function is as follows:

[0047]

[0048] Among them, Q i is the mass source / sink term, t represents time; g i represents the concentration distribution function; represents the concentration equilibrium distribution function; g i (r+Δtc i , t+Δt) represents the concentration distribution function after migration; g i (r, t) represents the concentration distribution function before migration; represents the concentration equilibrium distribution function at time t; r represents the position of the grid node, which can be expressed as (x, y) in the Cartesian coordinate system; Δt represents the time step; c i represents the discrete grid velocity in the i-th direction; τ g represents the dimensionless relaxation time of the concentration field.

[0049] Among them, the mass source / sink term Q i The concentration conservation equation is as follows:

[0050]

[0051] Among them, τ g represents the dimensionless relaxation time of the concentration field; ω i is the weight coefficient; R represents the reaction source / sink at the gas-solid interface.

[0052] The concentration conservation equation of the s component adsorbate is as follows:

[0053]

[0054] Among them, g i represents the concentration distribution function; R represents the reaction source / sink term at the gas-solid interface.

[0055] Preferably, the adsorption kinetics model that characterizes the flow and diffusion laws of the fluid in the biochar pores and the adsorption laws in the biochar matrix is ​​specifically simulated and calculated and converged through the following process:

[0056] Initialization parameters: Set the initialization speed and concentration, adsorption parameters, and CT scan slice images of biochar, and perform binarization on the images to identify the pore structure of biochar.

[0057] Select model: Use D2Q9 model and D2Q5 model to define the motion and interaction of fluid particles in different directions in two-dimensional space.

[0058] Solve the flow and mass transfer equations: Use the equilibrium distribution function to solve the flow and mass transfer evolution equations in the pore space. Use the obtained fluid particle velocity to drive the volume concentration of the adsorbate to simulate the fluid particle collision and migration process.

[0059] Calculation of Langmuir adsorption kinetics: At the interface between the fluid and biochar, after determining the matrix interface adsorbate concentration, the interfacial adsorption amount is updated, and then the adsorption process of the adsorbate is calculated using the Langmuir adsorption kinetics equation.

[0060] Calculate the diffusion of adsorption within the matrix: Based on the homogeneous solid diffusion model, calculate the diffusion law of adsorption within the matrix.

[0061] Calculate macroscopic quantities: Calculate the macroscopic quantities of the fluid to observe the change pattern of the interface adsorption amount over time; the macroscopic quantities are the density, velocity, concentration and adsorption amount of the fluid.

[0062] Reaching adsorption saturation: Continue calculating the macroscopic quantity until the interface adsorption reaches saturation, that is, the adsorption amount no longer changes significantly with time. At this time, the calculation converges to a certain value and the program ends.

[0063] Through the above steps, the present invention can simulate the flow and adsorption process of the fluid in the biochar in detail, providing theoretical support for studying the adsorption performance of the biochar.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] 1. The present invention combines image processing with the lattice Boltzmann method to achieve detailed simulation of the flow and surface adsorption process of volatile organic compounds in the pores of biochar. While improving accuracy and efficiency, it reduces the time and cost required for actual experiments, improves the adsorption efficiency of biochar, and solves the problems of insufficient accuracy and low efficiency of traditional image processing and fluid simulation technologies when processing large-particle biochar.

[0066] 2. This invention uses image binarization processing technology for computed tomography grayscale images of biochar to accurately identify and analyze the pore structure of large-particle biochar, providing important basic data for optimizing biochar preparation and application. This technology then combines image binarization processing technology with the lattice Boltzmann method to simulate the flow and adsorption of volatile organic compounds. This allows for rapid computer-generated data on the adsorption properties of biochar, reducing the time and cost of actual experiments. This allows for in-depth research into the behavioral characteristics of biochar during fluid flow and adsorption processes, providing theoretical support for optimizing biochar applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flowchart of a lattice Boltzmann simulation method for large-particle biochar image binarization and toluene flow and adsorption.

[0068] Figure 2 This figure shows the image binarization process for large-particle biochar. Figure 1 shows the original biochar image, 2 shows the meta-scale region, 3 shows the Gaussian blur algorithm, 4 shows the fixed threshold segmentation, 5 shows the morphological operation, and 6 shows the connected component analysis.

[0069] Figure 3 Schematic diagram and simulation boundary conditions for the adsorption of gaseous toluene by large-particle biochar.

[0070] Figure 4 Flowchart for calculating the flow, diffusion, and adsorption of gaseous toluene within large-particle biochar for LBM.

[0071] Figure 5The velocity and streamline distribution of gaseous toluene in the biochar pores at different times. (a1) is the velocity distribution of gaseous toluene in the biochar pores at 1.7 μs; (a2) is the velocity distribution of gaseous toluene in the biochar pores at 17 μs; (a3) ​​is the velocity distribution of gaseous toluene in the biochar pores at 170 μs; (a4) is the velocity distribution of gaseous toluene in the biochar pores at 1700 μs; (b1) is the streamline distribution of gaseous toluene in the biochar pores at 1.7 μs; (b2) is the streamline distribution of gaseous toluene in the biochar pores at 17 μs; (b3) is the streamline distribution of gaseous toluene in the biochar pores at 170 μs; (b4) is the streamline distribution of gaseous toluene in the biochar pores at 1700 μs.

[0072] Figure 6 The concentration and adsorption amount distribution of gaseous toluene adsorbed by biochar at different times are shown in Figure 1. (a1) is the concentration distribution of gaseous toluene adsorbed by biochar at 10 μs; (a2) is the concentration distribution of gaseous toluene adsorbed by biochar at 100 μs; (a3) ​​is the concentration distribution of gaseous toluene adsorbed by biochar at 1000 μs; (a4) is the concentration distribution of gaseous toluene adsorbed by biochar at 10,000 μs; (b1) is the adsorption amount distribution of gaseous toluene adsorbed by biochar at 10 μs; (b2) is the adsorption amount distribution of gaseous toluene adsorbed by biochar at 100 μs; (b3) is the adsorption amount distribution of gaseous toluene adsorbed by biochar at 1000 μs; and (b4) is the adsorption amount distribution of gaseous toluene adsorbed by biochar at 10,000 μs.

[0073] Figure 7 This is the average adsorption kinetic results of gaseous toluene by biochar. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0075] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0076] At present, biochar is mainly in powder form. Large-particle biochar can be directly obtained by pyrolysis and gasification of large-particle biomass, which has the advantages of reducing processing costs and energy consumption. Among them, the pore structure of biochar has an important influence on its adsorption performance. The flow and adsorption characteristics of biochar directly determine its effectiveness in environmental governance. Traditional image processing and fluid simulation technologies are often unable to fully and accurately describe the microstructure and fluid behavior of biochar, and have problems of insufficient accuracy and low efficiency. In view of this, the present invention combines biochar image binarization processing with the lattice Boltzmann method to achieve a detailed simulation of the flow and surface adsorption process of volatile organic compounds in the pores of biochar, while improving accuracy and efficiency. It reduces the time and cost required for actual experiments, and provides theoretical support for optimizing the application of biochar.

[0077] The technical solution of the present invention is clearly and completely described below through specific embodiments.

[0078] A method for simulating fluid flow and adsorption in biochar comprises the following steps:

[0079] Step 1: Obtain a computed tomography grayscale image of biochar, then perform image binarization processing to identify the pore and matrix distribution characteristics of the biochar, generate a binary image containing the matrix area and the pore area, and establish a biochar pore geometry model.

[0080] Step 1.1: Obtain a computed tomography grayscale image of the biochar.

[0081] The method for obtaining a computed tomography grayscale image of biochar involves performing high-resolution imaging of the biochar using computed tomography (CT) technology to obtain a CT grayscale image of the biochar. In the embodiments of the present invention, CT stands for Computerized Tomography (CT). The CT grayscale image of the biochar can characterize the microstructure of the biochar.

[0082] Step 1.2: perform image binarization on the biochar computed tomography grayscale image.

[0083] The method for image binarization is as follows: Gaussian blur algorithm, fixed threshold segmentation, morphological operation and connected component analysis are used to perform image binarization on the computed tomography grayscale image of biochar to identify the pore and matrix distribution characteristics of biochar. The grayscale value of the matrix area is defined as 0, and the grayscale value of the pore area is defined as 255, to generate a binary image containing the matrix area and the pore area.

[0084] Reasonable image segmentation and processing are the basis for accurate LBM calculation. During the research process, it was found that the current method of binarizing grayscale images is to directly use a fixed threshold for binarization. However, the resulting binarized image is not ideal. The resulting binarized image contains a lot of noise and incorrectly processed areas.

[0085] In order to suppress noise and achieve accurate extraction of the pore structure of biochar, the embodiment of the present invention uses Gaussian blur algorithm, fixed threshold segmentation, morphological operation and connected component analysis to perform image binarization processing on the computed tomography grayscale image of biochar to obtain a binary image containing a matrix area and a pore area. In the binarized image, the pore area is marked with a grayscale value corresponding to white, and the matrix area is marked with a grayscale value corresponding to black. In order to be able to accurately identify the pore area and matrix area of ​​biochar based on the size distribution of the voxel grayscale values ​​in the binarization processing results, a binary data set of biochar is obtained. This process involves the reconstruction of the geometric shape of the porous medium to ensure the accuracy and rationality of the model. This step is implemented based on the C++Open CV library. After completing the image binarization processing, the pore geometry model of biochar is constructed based on the extracted binary data set of large-particle biochar, and appropriate boundary conditions are set for the calculation domain of fluid flow.

[0086] Specifically, the method for image binarization of biochar computed tomography grayscale images using Gaussian blur algorithm, fixed threshold segmentation, morphological operation and connected component analysis is as follows:

[0087] From the computed tomography grayscale image of biochar, characteristic meta-scale regions with permeability equivalent to that of the biochar slices were screened and segmented into multiple sub-regions. A Gaussian blur algorithm was used to smooth each sub-region to suppress fine-grained noise and retain the pore structure characteristics. The smoothed sub-regions were segmented with a fixed threshold, with pixels below the threshold set to RGB values ​​of 0 and pixels above or equal to the threshold set to RGB values ​​of 255. Morphological operations were used to open and close the sub-regions after fixed threshold segmentation to remove isolated noise points. Connected component analysis was performed on the sub-regions after opening and closing operations, and they were filtered according to area size. Sub-regions with an area larger than the set threshold were regarded as matrix regions, generating a binary image containing matrix regions and pore regions.

[0088] In the embodiment of the present invention, the size of each divided sub-region may be determined according to actual conditions. For example, the size of each sub-region may be 400 pixels×260 pixels.

[0089] The embodiment of the present invention mainly uses the Gaussian blur algorithm to reduce fine-grained noise in the image, helping subsequent steps to better identify image features; the fixed threshold segmentation is mainly used to achieve image binarization, where an RGB value of 0 represents black and an RGB value of 255 represents white; morphological operations are used to remove noise, and small noise points in the image are further reduced through opening and closing operations; through connected component analysis, by traversing all connected areas and filtering out areas that are too small based on area, areas with an area greater than the threshold are regarded as solid areas and retained in the mask image to remove weakly connected objects caused by noise. Thus, through multi-level noise suppression, the precise extraction of the pore structure of biochar is achieved, while retaining the spatial representation capability of the permeability characteristics, providing a reliable foundation for the subsequent establishment of the lattice Boltzmann adsorption model.

[0090] Step 2: Match the biochar pore geometry model to the computational domain of the lattice Boltzmann method, set fluid characteristic parameters and boundary conditions, and establish a lattice Boltzmann adsorption model for biochar convection-diffusion coupled surface adsorption. The fluid is a volatile organic compound, such as toluene.

[0091] A method for establishing a lattice Boltzmann adsorption model for biochar convection-diffusion coupled surface adsorption includes the following steps:

[0092] Step 2.1: Divide the biochar pore geometry model into several grid units according to the pore and matrix distribution characteristics in the biochar pore geometry model, where each grid unit represents a grid node.

[0093] In the embodiments of the present invention, the lattice Boltzmann adsorption model is primarily based on numerical simulation methods of fluid dynamics and can effectively handle complex flows and fluid behavior in porous media. LBM uses discrete lattice structures and distribution functions to describe fluid flow characteristics, offering advantages such as high computational efficiency and ease of implementing boundary conditions. Therefore, before establishing the lattice Boltzmann adsorption model, the lattice units must first be divided. The density of the required lattice nodes must be matched to the pore and matrix distribution characteristics of the biochar pore geometry model to ensure the accuracy of the calculation results.

[0094] Step 2.2 sets the simulation's fluid and physical parameters and performs initialization. The fluid parameters include density, viscosity, pore diffusion coefficient, inlet velocity and concentration, and matrix diffusion coefficient. Physical parameters include the simulation domain, mesh step, and time step. For example, the fluid density is the density of gaseous toluene within the biochar pores.

[0095] Step 2.3: Construct a single-relaxation-time BGK collision operator model and calculate macroscopic quantities. The single-relaxation-time BGK collision operator model includes a lattice velocity model, an equilibrium distribution function, and an evolution equation for collisional transport. The macroscopic quantities include density, velocity, concentration, and adsorption amount. The single-relaxation-time BGK collision operator model is known as the Single Relaxation Time Bhatnagar-Gross-Krook Collision Operator Model (SRT-BGK model).

[0096] Step 2.2: Set boundary conditions to establish a lattice Boltzmann adsorption model for the convection-diffusion coupled surface adsorption of biochar.

[0097] In the lattice Boltzmann adsorption model, physical space is discretized into a set of lattice nodes. In embodiments of the present invention, the lattice velocity model uses a two-dimensional nine-speed lattice model and a two-dimensional five-speed lattice model to establish the governing equations for fluid flow and mass transfer. The two-dimensional nine-speed lattice model is referred to as the D2Q9 model, while the two-dimensional five-speed lattice model is referred to as the D2Q5 model.

[0098] The expression of the lattice velocity model is as follows:

[0099]

[0100] Where i represents the direction of velocity; c i represents the discrete lattice velocity in the i-th direction; c′ is a constant, c′=1 degree.

[0101] For the D2Q9 model, there are 9 velocity directions; while in the D2Q5 model, only the vectors of the first 5 velocity directions of the D2Q9 model are taken.

[0102] The evolution equation of collision migration describes the time evolution of the particle velocity distribution function and the fluid concentration distribution function in the absence of external forces. The evolution equation of collision migration is as follows:

[0103]

[0104] Among them, f i represents the density distribution function; f i eq represents the density equilibrium distribution function; f i (r+Δtc i , t+Δt) represents the density distribution function after migration; f i (r, t) represents the density distribution function before migration; represents the density equilibrium distribution function at time t; g i represents the concentration distribution function; represents the concentration equilibrium distribution function; g i (r+Δtc i , t+Δt) represents the concentration distribution function after migration; g i (r, t) represents the concentration distribution function before migration; represents the concentration equilibrium distribution function at time t; r represents the position of the grid node, which can be expressed as (x, y) in the Cartesian coordinate system; t represents time; Δt represents the time step, Δt = 1; τ f represents the dimensionless relaxation time of the flow field, which is determined by the kinematic viscosity υ of the fluid; τ g The dimensionless relaxation time of the concentration field is expressed by the diffusion coefficient D of the s component concentration. s Decide.

[0105] τ f and τ g The calculation formula is as follows: υ=C s 2 (τ f -1 / 2); Ds=Cs 2 (τg-1 / 2); where υ represents the kinematic viscosity of the fluid; s represents the volatile component of the fluid; C s represents the concentration of adsorbate s; D s represents the diffusion coefficient of the s component concentration; τ f represents the dimensionless relaxation time of the flow field; τ g represents the dimensionless relaxation time of the concentration field.

[0106] The distribution function of the particle first executes the collision term on the right side of the equation, and then executes the migration term on the left side to complete the evolution.

[0107] Establish the equilibrium distribution function:

[0108] The local equilibrium distribution function f of the particle velocity and the s component i eq and The expression is as follows:

[0109]

[0110] Among them, f i eq (r, t) represents the density equilibrium distribution function at time t; represents the concentration equilibrium distribution function at time t; ω i is the weight coefficient used to calculate the particle interaction of different velocity components during the collision process; ρ represents the density of the fluid; c i represents the discrete grid velocity in the i-th direction; u represents the velocity of the fluid; C s Represents the concentration of the s component adsorbate.

[0111] In the D2Q9 model, ω0=4 / 9, ω 1-4 =1 / 9,ω 5-8 =1 / 36. In the D2Q5 model, ω0=1 / 3, ω 1-4 = 1 / 6. cs is the lattice sound velocity. In the D2Q9 model and the D2Q5 model, c s Take both

[0112] Calculate macro quantities:

[0113] The relationship between the density ρ of the fluid is:

[0114] The relationship between the velocity u of the fluid is:

[0115] The concentration C of the adsorbate of component s in the fluid s The calculation formula is:

[0116] According to the mass transfer and adsorption process, the adsorption amount is calculated:

[0117] In this example, to simulate the adsorption process, an absorption source per unit volume was used to incorporate the adsorption effect into the governing equation. For the concentration boundary, the adsorption of gaseous toluene by biochar occurs at the gas-solid interface, where the mass transfer governing equation is:

[0118]

[0119] Among them, C s represents the concentration of the adsorbate of component s, u represents the fluid velocity, and D s represents the diffusion coefficient of the s component concentration, R is the reaction source / sink term at the gas-solid interface, which is used to simulate surface adsorption / desorption and is set to zero in the pore space;

[0120] The adsorption process at the biochar interface conforms to the typical Langmuir adsorption kinetic model, and the reaction term R can be expressed as the adsorption-desorption equilibrium, as shown below:

[0121]

[0122] Where R represents the reaction source / sink at the gas-solid interface; t is time, k1 represents the adsorption rate constant; k -1 represents the desorption rate constant; N m Indicates saturated adsorption capacity, N indicates current adsorption capacity; C s It represents the concentration of the adsorbate of the s component and can be calculated by the convection-diffusion model of LBM.

[0123] Introducing source / sink terms in the lattice Boltzmann mass transfer equation is a common method to account for the effects of chemical reactions. When considering source / sink terms, the evolution equation for collisional transport can be modified to:

[0124]

[0125] Among them, Q i represents the mass source / sink term; t represents time; g i represents the concentration distribution function; represents the concentration equilibrium distribution function; g i (r+Δtc i , t+Δt) represents the concentration distribution function after migration; g i (r, t) represents the concentration distribution function before migration; represents the concentration equilibrium distribution function at time t; r represents the position of the grid node, which can be expressed as (x, y) in the Cartesian coordinate system; Δt represents the time step; c i represents the discrete grid velocity in the i-th direction; τ g represents the dimensionless relaxation time of the concentration field.

[0126] Mass source term Q i The conservation equations for the concentrations of the and s component adsorbates are as follows:

[0127]

[0128] Among them, τ g represents the dimensionless relaxation time of the concentration field; ω i is the weight coefficient, g i represents the concentration distribution function; R represents the reaction source / sink term at the gas-solid interface.

[0129] After Langmuir adsorption occurs at the boundary, the adsorbed gaseous toluene is transported by complete diffusion, i.e., the convective velocity is zero. Under the influence of the concentration gradient of the gaseous toluene, it diffuses from the gas-solid boundary into the interior of the solid matrix. The mass balance equation for the intramatrix transport of the adsorbed gas is described by the homogeneous solid diffusion model (HDSM):

[0130]

[0131] Among them, D sp is the solid phase diffusion coefficient, which is usually smaller than the diffusion coefficient in the pore space; N is the current adsorption amount; t is the time; x and y are Cartesian coordinates.

[0132] Solid phase diffusion is given by the equation To describe, we only need to adjust the diffusion coefficient, that is, change D s =C s2 (τ g -1 / 2) dimensionless relaxation time τ of the concentration field g . Among them g i represents the concentration distribution function; represents the concentration equilibrium distribution function; g i (r+Δtc i , t+Δt) represents the concentration distribution function after migration; g i (r, t) represents the concentration distribution function before migration; represents the concentration equilibrium distribution function at time t; r represents the position of the grid node; t represents time; Δt represents the time step; C s represents the concentration of adsorbate s; D s The diffusion coefficient represents the concentration of the s component.

[0133] The adsorption process in biochar follows Fick's second diffusion law, which is expressed by the formula The adsorption amount on the biochar surface is calculated. Where R represents the reaction source / sink at the gas-solid interface; t is time, k1 represents the adsorption rate constant; k -1 represents the desorption rate constant; N m Indicates saturated adsorption capacity, N indicates current adsorption capacity; C s It represents the concentration of the adsorbate of the s component and can be calculated by the convection-diffusion model of LBM.

[0134] The calculation formula for the adsorption amount on the biochar surface is as follows:

[0135] N (t+1) -N=[k1C s (N m -N)-k -1 N]Δt;

[0136] Among them, N (t+1) represents the adsorption amount after the layer update at time t+1 that needs to be solved in the solid matrix; k1 represents the adsorption rate constant; C s represents the concentration of adsorbate of component s; k -1 represents the desorption rate constant; N m Indicates the saturated adsorption capacity, and N indicates the current adsorption capacity.

[0137] Set boundary conditions: the upper and lower walls are set as no-slip boundary conditions; the biochar surface velocity is set as a no-slip boundary, implemented in a half-step rebound format; the inlet is driven by a parabolic velocity and the concentration is a constant; the outlet is a fully developed velocity and concentration boundary to simulate the actual fluid flow situation.

[0138] In the embodiment of the present invention, the adsorption of gaseous toluene on the surface of biochar conforms to the classic Langmuir model. The Langmuir model sets different maximum adsorption capacities for different biochars, and the same adsorption rate constant and desorption rate constant.

[0139] In each time step, the LBM simulation process is carried out in the order of the adsorption process, that is, from the convection diffusion of gas adsorbate between the matrices, to the Langmuir adsorption reaction in the outermost layer of the biochar matrix, and then to the pure diffusion process driven by the concentration gradient in the matrix after adsorption. The schematic diagram and boundary conditions of the simulation are shown in Figure 2. Figure 3 and as shown in Table 1.

[0140] Table 1 Boundary conditions of LBM simulation adsorption process

[0141]

[0142] Step 3: Based on the lattice Boltzmann adsorption model, the flow process of the fluid in the biochar pore geometric model and the surface adsorption process are simulated. By changing the concentration of the fluid in the pores and the adsorption amount in the biochar matrix at different time steps, an adsorption kinetic model is obtained to characterize the flow and diffusion laws of the fluid in the biochar pores and the adsorption laws in the biochar matrix.

[0143] Specifically, the method for simulating the flow process of the fluid in the biochar pore geometry model and the surface adsorption process is as follows:

[0144] The lattice Boltzmann adsorption model was used to simulate the flow and mass transfer processes of the fluid in the biochar pore geometry model, and the flow field and concentration field in the biochar pores were calculated.

[0145] The Langmuir adsorption kinetic model was used to analyze the adsorption amount and adsorption kinetics in the matrix region, and the concentration of the fluid in the pores and the changes in the adsorption amount in the biochar matrix were obtained.

[0146] Preferably, the adsorption kinetics model that characterizes the flow and diffusion laws of the fluid in the biochar pores and the adsorption laws in the biochar matrix is ​​specifically simulated and calculated and converged through the following process:

[0147] Initialization parameters: Set the initialization speed and concentration, adsorption parameters, and CT scan slice images of biochar, and perform binarization on the images to identify the pore structure of biochar.

[0148] Select model: Use D2Q9 model and D2Q5 model to define the motion and interaction of fluid particles in different directions in two-dimensional space.

[0149] Solve the flow and mass transfer equations: Use the equilibrium distribution function to solve the flow and mass transfer evolution equations in the pore space. Use the obtained fluid particle velocity to drive the volume concentration of the adsorbate to simulate the fluid particle collision and migration process.

[0150] Calculation of Langmuir adsorption kinetics: At the interface between the fluid and biochar, after determining the matrix interface adsorbate concentration, the interfacial adsorption amount is updated, and then the adsorption process of the adsorbate is calculated using the Langmuir adsorption kinetics equation.

[0151] Calculate the diffusion of adsorption within the matrix: Based on the homogeneous solid diffusion model, calculate the diffusion law of adsorption within the matrix.

[0152] Calculate macroscopic quantities: Calculate the macroscopic quantities of the fluid to observe the change pattern of the interface adsorption amount over time; the macroscopic quantities are the density, velocity, concentration and adsorption amount of the fluid.

[0153] Reaching adsorption saturation: Continue calculating the macroscopic quantity until the interface adsorption reaches saturation, that is, the adsorption amount no longer changes significantly with time. At this time, the calculation converges to a certain value and the program ends.

[0154] Through the above steps, the embodiment of the present invention can simulate the flow and adsorption process of the fluid in the biochar in detail, providing theoretical support for studying the adsorption performance of the biochar.

[0155] Taking toluene as an example, the established lattice Boltzmann adsorption model is used to simulate the flow and surface adsorption of gaseous toluene in the pores of biochar. An adsorption kinetics model is obtained to characterize the flow and diffusion laws of the fluid in the pores of biochar and the adsorption laws within the biochar matrix, which is used to analyze the flow characteristics and adsorption performance of biochar.

[0156] Flow simulation: LBM is used to simulate the flow of toluene within the biochar pores, primarily calculating the velocity field. Prior to the simulation, a flow model was established based on the biochar pore structure, matching the LBM computational domain, and setting appropriate toluene physical properties such as density, viscosity, pore diffusion coefficient, and matrix diffusion coefficient. By solving the flow equations, the flow path and velocity distribution within the biochar were determined, providing a foundation for subsequent adsorption simulations.

[0157] Adsorption simulation: Building on the flow simulation, we further simulate the mass transfer and surface adsorption of gaseous toluene within biochar. The biochar adsorption process is divided into three stages: convection diffusion between biochars, adsorption on the biochar surface, and diffusion within the biochar. Gaseous toluene reaches the biochar surface through external diffusion and convection at a constant flow rate and concentration. First, the diffusion behavior of gaseous toluene within the pores is simulated, specifically the concentration distribution of gaseous toluene within the pores. Second, when gaseous toluene encounters the biochar surface, the Langmuir model, an adsorption kinetic model, is used to describe its adsorption behavior on the biochar surface. Through Langmuir surface adsorption, gaseous toluene adsorbs on the biochar surface. The adsorbed gaseous toluene then diffuses further within the biochar. At the mesoscale, biochar has macropores on the exterior and numerous micropores, typically at the nanoscale, within the interior. Mass transfer within biochar also involves two steps: diffusion within the biochar and adsorption within the biochar. Because the micropores are nanoscale, convection effects within the pores are negligible, allowing gaseous toluene to diffuse further into the biochar's micropores using the homogeneous solid diffusion model (HDSM). Finally, if the gaseous toluene that has diffused into the biochar further adsorbs on the micropore surfaces, adsorption within the biochar can occur. By calculating the concentration changes of gaseous toluene at different time steps, adsorption isotherms of gaseous toluene within the biochar pores were obtained, and adsorption kinetics, such as adsorption rate and saturation adsorption capacity, were analyzed.

[0158] In this embodiment, the schematic diagram of the LBM calculation flow process and surface adsorption process is shown in Figure 4 First, the input parameters are read, including the CT scan slice image of the biochar, image binarization processing, flow and adsorption parameters, and weights of each direction.

[0159] Then, the flow evolution equation in the pore space is solved and according to the evolution equation The obtained velocity is used to drive the volume concentration of the adsorbate. Calculate the interfacial Langmuir adsorption kinetics. After determining the matrix interface adsorbate concentration, according to the formula N (t+1) -N=[k1C s (N m -N)-k -1 N]Δt updates the interface adsorption amount.

[0160] Finally, according to the HDSM theory, the concentration gradient within the matrix drives the diffusion process of the adsorbed amount, as shown in the formula By observing the variation of the interfacial adsorption amount with time at different time steps, it was found that the entire mass transfer system was unstable until it reached adsorption saturation.

[0161] In LBM adsorption simulations, all variables are dimensionless lattice quantities, based on the same dimensionless principle. This allows for conversion between lattice units and real physical units. Table 2 lists the correspondence between dimensionless lattice units and physical units for adsorption parameters. The symbol "^" represents a physical parameter.

[0162] Table 2 Conversion between physical units and grid units of simulated adsorption process parameters

[0163]

[0164]

[0165] Note: Symbols marked with ^ are physical parameters; adsorption temperature is 150℃; Indicates the molar concentration of the gas; It represents the molar concentration of adsorption.

[0166] The technical solution of the present invention is described in detail below through specific embodiments.

[0167] The simulation is performed through the following process and converges:

[0168] S1. Based on the lattice Boltzmann method, the fluid flow, mass transfer, and surface adsorption codes were written in C++ to read the input parameters, including the initialization velocity and concentration, adsorption parameters, CT scan slice images of biochar, image binarization processing, and the weights of the D2Q9 and D2Q5 model directions.

[0169] S2. Calculate the equilibrium distribution function, solve the flow and mass transfer evolution equations in the pore space, particle collision migration, and use the obtained velocity to drive the transfer of the volume concentration of the adsorbate.

[0170] S3. Calculate the interfacial Langmuir adsorption kinetics using Equation 1. After determining the adsorbate concentration at the matrix interface, update the interfacial adsorption capacity. Calculate the diffusion process of the adsorbate within the matrix using the homogeneous solid diffusion model.

[0171] S4. Calculate the density, velocity, concentration, adsorption amount and other macroscopic quantities of the fluid, and observe the change of the interface adsorption amount over time at different time steps until the adsorption saturation is reached. The program calculation ends after converging to a certain value.

[0172] In a preferred embodiment of the present invention, a C++ OpenCV library was deployed on a personal computer with a 14-core, 2.30GHz processor, 16.0GB of memory, and a 64-bit Windows operating system. Simulations generated two-dimensional time-varying distributions of velocity, concentration, and adsorption capacity, visually illustrating the microscopic flow and adsorption characteristics of gaseous toluene in biochar. The convergence criterion for the code was assumed to be:

[0173]

[0174] Among them, ξ can be any one of the fluid variables (u, v, C) at the non-initial moment; t represents the number of iterations; λ represents the total number of fluid calculation nodes; (i, j) represents the specific nodes that flow and perform calculations in the pores; and the β value is selected based on actual conditions.

[0175] Error(ξ) represents the convergence criterion of the computer code; represents the fluid variable at the node (i, j) at time t+δt; represents the fluid variable at the node (i, j) at time t;

[0176] represents the residual sum of fluid variables; Represents the moment square of the fluid variable at time t+δt.

[0177] The simulations revealed that velocity convergence was insufficient to achieve adsorption saturation, so the concentration convergence criterion was set to β = 10. Each simulation took approximately 55.0 hours. To ensure the connectivity of the computational domain, the left inlet and right outlet were each indented by 10 grid cells and designated as pores. The remaining regions were segmented into pores and matrix using the results of image binarization.

[0178] This example uses the Poiseuille flow of a two-dimensional plate, the surface adsorption reaction follows the Lévêsque solution of the Poiseuille flow mass transfer problem, and the verification of the adsorption mass transfer process within the particle to effectively simulate the flow, diffusion and surface adsorption process of gaseous toluene in biochar. The evolution of the flow velocity and streamlines of gaseous toluene in the pores of biochar over time is shown in the following table. Figure 5 As shown in the figure, the changes in the pore concentration of gaseous toluene adsorbed by biochar and the amount of biochar solid adsorption are shown in the figure. Figure 6 The kinetic results of the adsorption of gaseous toluene by biochar are shown in Figure 7 middle.

[0179] The simulation results show that the simulation results are reliable, efficient and robust, and can provide a theoretical basis for the adsorption application of large-particle biochar.

[0180] In summary, the embodiments of the present invention combine a number of advanced technologies such as CT imaging, image processing and the lattice Boltzmann method to form an innovative biochar research method. The method for preparing biochar in the embodiments of the present invention can be applied to a variety of fields, including environmental governance, wastewater treatment and soil improvement. The present invention accurately identifies and analyzes the pore structure of biochar through CT scanning and binarization image processing technology. The present invention uses LBM to perform flow and adsorption simulations, which can quickly obtain the adsorption performance data of biochar on a computer, reducing the time and cost required for actual experiments. This multidisciplinary cross-disciplinary technical application has promoted the performance research, rapid development and application promotion level of biochar, provided new ideas for subsequent technological development, and also provided new solutions for environmental protection, with significant social and economic value.

[0181] Obviously, the above embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

Claims

1. A method for simulating fluid flow and adsorption in biochar, characterized in that: The following steps are involved: Obtaining a computed tomography grayscale image of biochar, then performing image binarization processing to identify the pore and matrix distribution characteristics of the biochar, generating a binary image containing the matrix region and the pore region, and establishing a biochar pore geometry model; The biochar pore geometry model was matched to the computational domain of the lattice Boltzmann method, and the fluid characteristic parameters and boundary conditions were set to establish a lattice Boltzmann adsorption model for biochar convection-diffusion coupled surface adsorption. Based on the lattice Boltzmann adsorption model, the flow process of the fluid in the biochar pore geometry model and the adsorption process on the matrix surface are simulated. By changing the concentration of the fluid in the pores and the adsorption amount in the biochar matrix at different time steps, an adsorption kinetic model is obtained to characterize the flow and diffusion laws of the fluid in the biochar pores and the adsorption laws in the biochar matrix. A method for establishing a lattice Boltzmann adsorption model for biochar convection-diffusion coupled surface adsorption includes the following steps: According to the distribution characteristics of pores and matrix in the biochar pore geometry model, the biochar pore geometry model is divided into several grid units, each grid unit represents a grid node; Set the simulated fluid characteristic parameters and physical parameters and perform initialization processing; the fluid characteristic parameters include fluid density, viscosity, pore diffusion coefficient, inlet velocity and concentration, and matrix diffusion coefficient; the physical parameters include simulation domain, grid step and time step; Constructing a single relaxation time BGK collision operator model and calculating macroscopic quantities; the single relaxation time BGK collision operator model includes a lattice velocity model, an equilibrium distribution function, and an evolution equation of collision migration; the macroscopic quantities include density, velocity, concentration, and adsorption amount; Boundary conditions were set to establish a lattice Boltzmann adsorption model for biochar convection-diffusion coupled surface adsorption. The method for simulating the flow process of fluid in the biochar pore geometry model and the surface adsorption process is as follows: The lattice Boltzmann adsorption model is used to simulate the flow and mass transfer processes of the fluid in the biochar pore geometry model, and the flow field and concentration field in the biochar pores are calculated. The Langmuir adsorption kinetic model was used to analyze the adsorption amount and adsorption kinetics in the matrix region, and the concentration of the fluid in the pores and the changes in the adsorption amount in the biochar matrix were obtained.

2. The method for simulating fluid flow and adsorption in biochar according to claim 1, characterized in that: The method for image binarization is: The computed tomography grayscale images of biochar were binarized using Gaussian blur algorithm, fixed threshold segmentation, morphological operation and connected component analysis to identify the pore and matrix distribution characteristics of biochar. The grayscale value of the matrix area was defined as 0, and the grayscale value of the pore area was defined as 255 to generate a binary image containing the matrix area and the pore area.

3. The method for simulating fluid flow and adsorption in biochar according to claim 2, characterized in that: The method for image binarization of biochar computed tomography grayscale images using Gaussian blur algorithm, fixed threshold segmentation, morphological operation and connected component analysis is as follows: From the computed tomography grayscale image of biochar, the meta-scale region with the permeability equivalent to that of the biochar slice was screened out and segmented into multiple sub-regions; Gaussian blur algorithm is used to smooth each sub-region to suppress fine-grained noise and preserve the pore structure characteristics; The pixels below the threshold are set to RGB value 0, and the pixels above or equal to the threshold are set to RGB value 255, and the smoothed sub-region is segmented with a fixed threshold; Morphological operations are used to perform opening and closing operations on the sub-regions segmented by fixed thresholds to remove isolated noise points; Connected component analysis is performed on the sub-regions after the opening and closing operations, and they are filtered according to their area size. The sub-regions with an area larger than the set threshold are regarded as matrix regions, and a binary image containing matrix regions and pore regions is generated.

4. The method for simulating fluid flow and adsorption in biochar according to claim 1, characterized in that: The fluid is a volatile organic compound.

5. The method for simulating fluid flow and adsorption in biochar according to claim 1, wherein: The adsorption kinetics model that characterizes the flow and diffusion laws of the fluid in the biochar pores and the adsorption laws within the biochar matrix is ​​simulated and converged through the following process: Initialization parameters: set the initialization speed and concentration, adsorption parameters, and CT scan slice images of biochar, and perform binarization processing on the images to identify the pore structure of biochar; Select model: Use D2Q9 model and D2Q5 model to define the motion and interaction of fluid particles in different directions in two-dimensional space; Solve flow and mass transfer equations: Use the equilibrium distribution function to solve the flow and mass transfer evolution equations in the pore space. Use the resulting fluid particle velocity to drive the transfer of the adsorbate volume concentration to simulate the fluid particle collision and migration process. Calculate Langmuir adsorption kinetics: At the interface between the fluid and biochar, after determining the matrix interface adsorbate concentration, update the interface adsorption amount, and then calculate the adsorption process of the adsorbate using the Langmuir adsorption kinetics equation; Calculate the diffusion of adsorption within the matrix: Calculate the diffusion law of adsorption within the matrix based on the homogeneous solid diffusion model; Calculate macroscopic quantities: Calculate the macroscopic quantities of the fluid to observe the change pattern of the interface adsorption amount over time; the macroscopic quantities are the density, velocity, concentration and adsorption amount of the fluid; Reaching adsorption saturation: Continue calculating the macroscopic quantity until the interface adsorption reaches saturation, that is, the adsorption amount no longer changes significantly with time. At this time, the calculation converges to a certain value and the program ends.

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