System and method for testing and analyzing dust holding capacity of nano filter material
By adjusting the simulation parameters of the nanofiltration material through 3D modeling and experimental data matching analysis, the problem of insufficient accuracy in simulation calculation in existing technologies was solved, and efficient simulation testing of dust holding capacity was achieved, reducing R&D costs.
Patent Information
- Application Number
- CN202512008612.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-29
- Publication Date
- 2026-01-27
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The accuracy of existing technologies for simulating the dust holding capacity of nanofiltration materials is insufficient, leading to frequent experimental verification, which increases R&D costs and time, and makes it impossible to accurately capture the nonlinear change law of filtration resistance with the increase of dust holding capacity.
By acquiring the microstructure data of the nanofiltration material, a three-dimensional model is created to simulate the dust holding process of multi-particle-size dust load. Combined with experimental data, response matching analysis is performed to determine the simulation constraints and adjust the simulation parameters, generating simulated test results of dust holding capacity.
It improves the realism of simulation calculations, reduces the need for physical testing, lowers testing costs, and enables reliable prediction of actual dust holding capacity.
Smart Images
Figure CN121413518A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of nanofiltration material testing, and more particularly to a nanofiltration material dust holding capacity testing and analyzing system and method. BACKGROUND
[0002] Nanofiltration materials are widely used in air purification, medical protection and industrial dust removal due to their excellent filtration performance. The dust holding capacity, as a core indicator for measuring service life and filtration efficiency, directly affects the economy and reliability of the filtration system. Currently, the testing and analysis of this performance mainly relies on physical experiment methods. This process has inherent limitations such as long cycle, high cost and poor repeatability, which seriously restricts the research and development process and product iteration speed of new materials. Therefore, developing a testing and analyzing technology that can effectively evaluate the performance is particularly important for improving research efficiency and promoting industry progress.
[0003] In the prior art, the dust holding capacity evaluation method based on simulation calculation can partially replace experimental testing, but its accuracy is often limited by the oversimplification of the microstructure of the material and the actual dust holding process. In particular, existing simulation methods generally use a single particle size or a simplified dust distribution model, which fails to accurately reflect the deposition behavior of multi-scale and multi-component dust in the complex network of nanofibers in the actual environment. This idealized modeling leads to systematic deviations between simulation results and experimental data, making it impossible to accurately capture the nonlinear variation of filtration resistance with dust holding capacity. Due to the lack of simulation authenticity, a large number of repetitive experiments are still needed for verification and correction during the research and development process, significantly increasing time and resource consumption and weakening the cost advantage that should be brought by computational simulation. Therefore, how to improve the authenticity of nanofiltration material dust holding capacity simulation calculation and thus reduce the testing cost of nanofiltration materials has become a difficult problem faced by the industry. SUMMARY
[0004] The present application provides a nanofiltration material dust holding capacity testing and analyzing system and method, which can improve the authenticity of nanofiltration material dust holding capacity simulation calculation and thus reduce the testing cost of nanofiltration materials.
[0005] In a first aspect, the present application provides a nanofiltration material dust holding capacity testing and analyzing method, which comprises the following steps: Obtaining microstructure data of a nanofiltration material, performing three-dimensional modeling on the nanofiltration material through the microstructure data to obtain a three-dimensional digital model of the nanofiltration material; Performing dust holding process simulation of multi-particle size dust loading on the three-dimensional digital model to obtain a response curve of filtration resistance of the three-dimensional digital model within a dust holding simulation period; Obtaining experimental dust capacity data of the nanofiltration material, and performing response matching analysis on the dust capacity process simulation of the three-dimensional digital model through dust capacity gradients of the experimental dust capacity data at different time points to obtain a simulation matching degree of the dust capacity process simulation of the three-dimensional digital model; Determining simulation constraint conditions of the dust capacity process simulation of the three-dimensional digital model according to the simulation matching degree and a response curve of filtration resistance, and adjusting dust capacity simulation parameters of the three-dimensional digital model based on the simulation constraint conditions; Generating a simulation test result of the dust capacity of the nanofiltration material through the adjusted dust capacity simulation parameters.
[0006] In this embodiment, the three-dimensional modeling of the nanofiltration material through the microstructure data includes specifically: Preprocessing the microstructure data; Extracting pore boundary features and fiber geometric features of the nanofiltration material from the preprocessed microstructure data; Performing three-dimensional modeling of the nanofiltration material based on the pore boundary features and the fiber geometric features to obtain a three-dimensional digital model of the nanofiltration material.
[0007] In this embodiment, the dust capacity process simulation of the three-dimensional digital model under multi-particle size dust loading includes specifically: Setting multi-particle size dust distribution parameters and fluid boundary conditions of the three-dimensional digital model; Simulating a deposition process of multi-particle size dust in the three-dimensional digital model based on the multi-particle size dust distribution parameters and the fluid boundary conditions; Real-time calculating changes of filtration resistance of the three-dimensional digital model in the dust deposition process, and further generating a response curve of filtration resistance of the three-dimensional digital model within a dust capacity simulation period.
[0008] In this embodiment, the response matching analysis on the dust capacity process simulation of the three-dimensional digital model through dust capacity gradients of the experimental dust capacity data at different time points includes specifically: Determining dust capacity gradients of the experimental dust capacity data at different time points; Dividing different dust capacity feature stages according to the dust capacity gradients of the experimental dust capacity data at different time points; Extracting pore structure change features of each dust capacity feature stage from the dust capacity process simulation of the three-dimensional digital model; Determining dust capacity matching coefficients of each dust capacity feature stage through the pore structure change features of each dust capacity feature stage; The simulation matching degree of the dust-binding process simulation of the three-dimensional digital model is determined based on all dust-binding matching coefficients.
[0009] In this embodiment, the simulation constraints for simulating the dust-holding process of the three-dimensional digital model, determined based on the response curves of the simulation matching degree and the filtration resistance, specifically include: Fluctuation analysis was performed on the response curve of the filter resistance to obtain the resistance fluctuation characteristics; The constraint priority of the dust-binding simulation parameters in the three-dimensional digital model dust-binding process simulation is determined based on the resistance fluctuation characteristics and the simulation matching degree. The simulation constraints for simulating the dust-collecting process in the three-dimensional digital model are determined by the constraint priority of the dust-collecting simulation parameters.
[0010] In this embodiment, adjusting the dust-holding simulation parameters of the three-dimensional digital model based on the simulation constraints specifically includes: The dust-holding simulation parameters of the three-dimensional digital model are constrained and adjusted using the aforementioned simulation constraints. The feasibility of the dust holding capacity simulation parameters after constraint adjustment was verified, and the feasibility verification results were obtained. The parameter configuration of the three-dimensional digital model is updated based on the feasibility verification results.
[0011] In this embodiment, generating the simulated test results of the dust holding capacity of the nanofiltration material by adjusting the dust holding capacity simulation parameters specifically includes: The dust-holding process simulation is performed based on the adjusted dust-holding simulation parameters; Extract key indicators of dust holding capacity from the simulation results; Simulated test results of the dust holding capacity of the nanofiltration material were generated using key indicators of dust holding capacity.
[0012] In this embodiment, the simulation matching degree represents the degree of matching between the three-dimensional digital model simulation of the dust-bearing process and the actual dust-bearing process.
[0013] In this embodiment, the simulation constraints represent the conditions that constrain the dust-binding simulation parameters in the three-dimensional digital model dust-binding process simulation.
[0014] Secondly, this application provides a dust holding capacity testing and analysis system for nanofiltration materials, used to perform a method for testing and analyzing the dust holding capacity of nanofiltration materials, the testing and analysis system comprising: The model building module is used to acquire the microstructure data of the nanofiltration material, and to perform three-dimensional modeling of the nanofiltration material using the microstructure data to obtain a three-dimensional digital model of the nanofiltration material. The dust-holding process simulation module is used to simulate the dust-holding process of the three-dimensional digital model under multi-particle-size dust load, and obtain the response curve of the filtration resistance of the three-dimensional digital model within the dust-holding simulation period. The response matching analysis module is used to acquire experimental dust holding data of the nanofiltration material, and to perform response matching analysis on the dust holding process simulation of the three-dimensional digital model by using the dust holding gradient of the experimental dust holding data at different times, so as to obtain the simulation matching degree of the dust holding process simulation of the three-dimensional digital model. The constraint adjustment module is used to determine the simulation constraints of the dust-holding process simulation of the three-dimensional digital model based on the response curves of the simulation matching degree and the filter resistance, and to adjust the dust-holding simulation parameters of the three-dimensional digital model based on the simulation constraints. The simulation result generation module is used to generate simulation test results of the dust holding capacity of the nanofiltration material by adjusting the dust holding simulation parameters.
[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: Microstructure data of the nanofiltration material is acquired, and a three-dimensional model of the nanofiltration material is created using the microstructure data to obtain a three-dimensional digital model of the nanofiltration material. The dust-holding process of the three-dimensional digital model under multi-particle-size dust load is simulated to obtain the response curve of the filtration resistance of the three-dimensional digital model within the dust-holding simulation period. Experimental dust-holding data of the nanofiltration material is acquired, and the response matching analysis of the dust-holding gradient of the experimental dust-holding data at different times is performed on the dust-holding process simulation of the three-dimensional digital model to obtain the simulation matching degree of the three-dimensional digital model's dust-holding process simulation. Simulation constraints for the dust-holding process simulation of the three-dimensional digital model are determined based on the simulation matching degree and the response curve of the filtration resistance. The dust-holding simulation parameters of the three-dimensional digital model are adjusted based on the simulation constraints. Simulation test results of the dust-holding capacity of the nanofiltration material are generated using the adjusted dust-holding simulation parameters.
[0016] Therefore, this application demonstrates that the simulated dust holding capacity of the nanofiber filter material can be generated using adjusted dust holding simulation parameters. Firstly, by acquiring real microstructural data of the material for 3D modeling, this initial step overcomes the distortion of the complex nanofiber network structure by traditional simplified models, accurately restoring its pore distribution and spatial architecture, and avoiding systematic errors caused by inherent model distortion. Secondly, by simulating the dust holding process under multi-particle-size dust loads, the collaborative deposition behavior of multi-scale dust in the nanonetwork in the actual environment is reproduced, accurately capturing the nonlinear evolution law of filtration resistance with increasing dust holding capacity. This makes the simulated resistance response curve more valuable for practical reference and can effectively replace some time-consuming physical dust holding tests. Furthermore, an experimental data matching and analysis machine is introduced. By performing response matching analysis and quantifying the simulation matching degree between the simulation results and the experimental dust holding gradient, the consistency between the virtual simulation world and the real physical process is ensured, greatly improving the credibility of the simulation results as a basis for decision-making and reducing the need for comprehensive experimental verification due to simulation uncertainties. On this basis, the simulation constraints are determined by the simulation matching degree and the filter resistance response curve, and the dust holding simulation parameters are adjusted accordingly to form a closed-loop correction mechanism. This ensures that the model not only relies on theoretical construction but also on constraints provided by real experimental behavior, so that the simulation output gradually approaches the real dust holding law, further improving the authenticity. Finally, the adjusted dust holding simulation parameters are used to generate simulation test results, enabling the output results to replace part of the physical test while closely approximating the experimental trend, thus achieving a reliable prediction of the actual dust holding capacity.
[0017] In summary, the technical solution adopted in this application can improve the realism of the simulation calculation of dust holding capacity of nanofiltration materials, thereby reducing the testing cost of nanofiltration materials. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is an exemplary flowchart of a method for testing and analyzing the dust holding capacity of nanofiltration materials according to the present application; Figure 2 This is a flowchart illustrating the process of determining the simulation matching degree provided in this application; Figure 3 This is a flowchart illustrating the process of determining simulation constraints based on the information provided in this application; Figure 4This is a module structure diagram of a dust holding capacity testing and analysis system for nanofiltration materials provided in this application. Detailed Implementation
[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0021] This application provides a system and method for testing and analyzing the dust holding capacity of nanofiltration materials. The core of the system involves acquiring microstructure data of the nanofiltration material, creating a three-dimensional model of the nanofiltration material using this data, obtaining a three-dimensional digital model of the nanofiltration material, simulating the dust holding process of the three-dimensional digital model under multi-particle-size dust load, obtaining the response curve of the filtration resistance of the three-dimensional digital model within the dust holding simulation period, acquiring experimental dust holding data of the nanofiltration material, performing response matching analysis on the dust holding gradient of the experimental dust holding data at different times to obtain the simulation matching degree of the three-dimensional digital model's dust holding process simulation, determining the simulation constraints of the three-dimensional digital model's dust holding process simulation based on the simulation matching degree and the response curve of the filtration resistance, adjusting the dust holding simulation parameters of the three-dimensional digital model based on the simulation constraints, and generating the simulation test results of the dust holding capacity of the nanofiltration material using the adjusted dust holding simulation parameters.
[0022] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of a method for testing and analyzing the dust holding capacity of nanofiltration materials according to this embodiment of the present application. The testing and analysis method includes the following steps: In step S1, the microstructure data of the nanofiltration material is obtained, and the nanofiltration material is three-dimensionally modeled using the microstructure data to obtain a three-dimensional digital model of the nanofiltration material.
[0023] In practice, the microstructure data of nanofiltration materials can be obtained in the following way: First, the nanofiltration material sample is prepared, including cutting it to an appropriate size and spraying a conductive metal layer on its surface using an ion sputtering instrument; then, the sample is observed using a high-resolution scanning electron microscope, where the accelerating voltage is set to 1 to 5 kV and the working distance is 5 to 8 mm to obtain clear secondary electron images of the nanofibers and pore structure, thus obtaining a series of two-dimensional image sequences of continuous cross sections; for scenarios requiring internal three-dimensional structural information, an X-ray nanotomography system can be used to perform a 360-degree rotational scan of the nanofiltration material sample with a voxel resolution of less than 50 nanometers to obtain complete projection data; finally, the scanned image data is used as the microstructure data of the nanofiltration material.
[0024] In this embodiment, the three-dimensional digital model of the nanofiltration material obtained by performing three-dimensional modeling using the microstructure data can be achieved through the following steps: The microstructure data is preprocessed; The pore boundary features and fiber geometric features of the nanofiltration material were extracted from the preprocessed microstructure data. Based on the pore boundary features and fiber geometric features, a three-dimensional model of the nanofiltration material is performed to obtain a three-dimensional digital model of the nanofiltration material.
[0025] It should be noted that the pore boundary features described in this application represent the features of the contour and shape of the pores in the nanofiltration material; the fiber geometric features represent the features of the size, shape and spatial distribution of the fibers in the nanofiltration material; and the three-dimensional digital model represents a virtual three-dimensional representation model of the nanofiltration material.
[0026] In specific implementation, the preprocessing of the microstructure data can be achieved in the following way: First, noise filtering is performed on the image in the microstructure data using Gaussian filtering or median filtering algorithms to eliminate random noise and speckle noise introduced during image acquisition; then, contrast enhancement processing is performed on the denoised image using histogram equalization or contrast-limited adaptive histogram equalization to enhance the gray-level difference between fiber and pore boundary regions, improving the poor image quality caused by uneven illumination or sample surface undulations; finally, the processed image is uniformly scaled to a standard pixel size and stored in a universal image format to complete the standardization of image data, providing high-quality, distortion-free input data for subsequent feature extraction steps.
[0027] In specific implementation, the extraction of pore boundary features and fiber geometric features of the nanofiltration material from the preprocessed microstructure data can be achieved in the following manner: First, the Canny edge detection operator or the Sobel operator is used to perform boundary recognition on the preprocessed image to accurately delineate the contours of the pores and the edges of the fibers; then, based on the identified boundaries, the centerline of the fibers is extracted using the skeletonization algorithm in image morphology operations, and the area, perimeter, and equivalent diameter of each pore region are calculated using the region attribute measurement algorithm. At the same time, the diameter, orientation angle, and curvature of the fibers are measured based on the fiber centerline; finally, all extracted pore boundary feature data and fiber geometric feature data are stored as structured data lists, where the pore feature list contains the pore location coordinates and shape parameters, and the pore feature list is used as the pore boundary features of the nanofiltration material; the fiber feature list contains the fiber length, diameter, and spatial orientation vector, and the fiber feature list is used as the fiber geometric features of the nanofiltration material.
[0028] In specific implementation, the three-dimensional digital model of the nanofiltration material is obtained by performing three-dimensional modeling based on the pore boundary features and fiber geometric features. This can be achieved in the following way: First, based on the fiber geometric features, a three-dimensional fiber network skeleton conforming to statistical characteristics is generated in three-dimensional space using a random walk algorithm, with a step size of 50 nanometers and non-physical intersections between fibers are avoided; then, based on the pore boundary features, a three-dimensional geometric model of the pore space is constructed using the level set method, and the level set equation is solved using the narrowband method to improve computational efficiency; finally, the fiber network skeleton and the pore space are fused by Boolean subtraction, and a triangular mesh model is generated at a voxel resolution of 128×128×128 using the moving cube algorithm, and the mesh quality is optimized by three iterations of Laplacian smoothing, finally obtaining a three-dimensional digital model characterizing the microstructure of the nanofiltration material.
[0029] In step S2, the dust holding process of the three-dimensional digital model under multi-particle-size dust load is simulated to obtain the response curve of the filtration resistance of the three-dimensional digital model during the dust holding simulation period.
[0030] In this embodiment, the dust-holding process simulation of the three-dimensional digital model under multi-particle-size dust load, and the response curve of the filtration resistance of the three-dimensional digital model within the dust-holding simulation period, can be achieved by the following steps: Set the multi-particle-size dust distribution parameters and fluid boundary conditions of the three-dimensional digital model; The deposition process of multi-particle-size dust in the three-dimensional digital model is simulated based on the multi-particle-size dust distribution parameters and the fluid boundary conditions. The filtration resistance change of the three-dimensional digital model is calculated in real time during the dust deposition process, thereby generating the response curve of the filtration resistance of the three-dimensional digital model during the dust holding simulation cycle.
[0031] It should be noted that the response curve described in this application represents a curve that evaluates the resistance growth of the nanofiltration material during the dust-holding simulation process.
[0032] In specific implementation, the multi-particle size dust distribution parameters and fluid boundary conditions of the three-dimensional digital model can be set in the following way: First, the particle size distribution of the multi-particle size dust is defined using the Rosin-Rammler distribution model, with a characteristic particle size of 2 micrometers, a distribution index of 1.5, and a particle size range covering 0.1 micrometers to 10 micrometers; then, the inlet dust mass concentration is set to 10 mg / m³, and the particle density is set to 2500 kg / m³; simultaneously, the fluid boundary conditions are configured, with the inlet set as a velocity inlet boundary condition with a flow velocity of 0.1 m / s, and the outlet set as a pressure outlet boundary condition with a gauge pressure of 0 Pa. The fluid medium is air at 25°C, with a density of 1.185 kg / m³ and a dynamic viscosity of 1.831 × 10⁻⁶. -5 Pa·s; Finally, input all parameters into the preprocessing module of the computational fluid dynamics software to complete the initialization settings of the calculation conditions.
[0033] In specific implementation, the simulation of the deposition process of multi-particle-size dust in the three-dimensional digital model based on the multi-particle-size dust distribution parameters and the fluid boundary conditions can be achieved in the following way: The simulation is performed using a computational fluid dynamics and discrete element method coupled approach. First, the Navier-Stokes equations of the fluid phase are solved to obtain the flow field distribution. Then, the motion trajectory of the dust particles is tracked through the discrete phase model, considering the drag force, gravity, Brownian force, and Saffman lift acting on the particles. When the distance between the particle and the fiber surface is less than the critical capture distance, it is determined that the particle has deposited and the deposition position is updated. The calculation time step is set to 0.001 seconds, and the total simulation time is 100 seconds. The particle deposition position and quantity at each time step are recorded in real time to complete the simulation of the dynamic deposition process of dust in the three-dimensional digital model.
[0034] In specific implementation, the change in filtration resistance of the three-dimensional digital model is calculated in real time during the dust deposition process, and the response curve of the filtration resistance of the three-dimensional digital model in the dust holding simulation cycle is generated. This can be achieved in the following way: Based on Darcy's law, the inlet and outlet pressure difference data and flow rate data of the three-dimensional digital model are extracted at each calculation time step, and the instantaneous filtration resistance value is calculated in real time in combination with the thickness of the three-dimensional digital model; the filtration resistance data of all time steps are organized into a time series, and a continuous change curve is generated with time as the horizontal axis and filtration resistance as the vertical axis. The obtained change curve is used as the response curve of the filtration resistance of the three-dimensional digital model in the dust holding simulation cycle.
[0035] In step S3, experimental dust holding data of the nanofiltration material is obtained, and the dust holding gradient of the experimental dust holding data at different times is used to perform response matching analysis on the dust holding process simulation of the three-dimensional digital model to obtain the simulation matching degree of the dust holding process simulation of the three-dimensional digital model.
[0036] In practice, the experimental dust holding capacity data of the nanofiltration material can be obtained in the following way: First, the nanofiltration material sample is cut into standard test sizes and installed on a standard dust test bench that meets the requirements of ISO 16890 standard; then, A2 fine dust as specified in ISO 12103-1 standard is used as test dust, and dust is loaded onto the test sample at a constant mass concentration through a dust generator, while maintaining the test airflow velocity at 0.1 m / s, the test environment temperature at 23±2℃, and the relative humidity at 50±5%; during the test, the accumulated dust mass is recorded once per minute using a precision balance; finally, the collected dust mass data and corresponding timestamps are integrated into structured experimental data, and the obtained experimental data is used as the experimental dust holding capacity data of the nanofiltration material.
[0037] Preferably, in this embodiment, the simulation matching degree of the dust-holding process of the three-dimensional digital model is obtained by performing response matching analysis on the dust-holding gradient of the experimental dust-holding data at different times, and referring to... Figure 2 As shown in the figure, this is a flowchart illustrating the process of determining the simulation matching degree in some embodiments of this application. In this embodiment, the simulation matching degree can be determined by the following steps: In step S31, the dust holding gradient of the experimental dust holding data at different times is determined; In step S32, different dust-holding characteristic stages are divided according to the dust-holding gradient at different times based on the experimental dust-holding data; In step S33, the pore structure change characteristics of each dust-binding characteristic stage are extracted from the dust-binding process simulation of the three-dimensional digital model. In step S34, the dust-holding matching coefficient of each dust-holding characteristic stage is determined by the pore structure change characteristics of each dust-holding characteristic stage. In step S35, the simulation matching degree of the dust-binding process simulation of the three-dimensional digital model is determined based on all the dust-binding matching coefficients.
[0038] It should be noted that, in this application, the dust holding gradient represents the rate of change of dust volume per unit time; the dust holding characteristic stage represents a stage in the dust holding process with different filtration characteristics; the pore structure change characteristic represents a feature describing the changes in pore size, connectivity, and specific surface area during the dust holding simulation, used to evaluate the impact of dust deposition on the microstructure of the material; the dust holding matching coefficient represents a parameter measuring the degree of agreement between the local dust holding simulation within the dust holding characteristic stage; and the simulation matching degree represents a measure of the degree of agreement between the three-dimensional digital model dust holding process simulation and the actual dust holding process.
[0039] In specific implementation, the dust holding gradient of the experimental dust holding data at different times can be determined in the following way: First, the experimental dust holding data is smoothed by a sliding window, and the window size is set to 5 data points; then, the central difference method is used to calculate the dust holding gradient at each time point, that is, the difference in dust holding amount between adjacent time points is divided by the corresponding time interval to obtain the dust holding gradient of the experimental dust holding data at different times.
[0040] In specific implementation, the different dust-holding characteristic stages can be divided according to the dust-holding gradient at different times based on the experimental dust-holding data in the following way: First, calculate the cumulative change of the dust-holding gradient during the entire dust-holding process. When the cumulative gradient change reaches 25% of the total change, it is divided into the initial surface filtration stage, corresponding to the rapid decrease of the dust-holding gradient. When the cumulative gradient change reaches the range of 25% to 60%, it is divided into the transition stage, corresponding to the relatively stable dust-holding gradient. When the cumulative gradient change exceeds 60%, it is divided into the deep filtration stage, corresponding to the slow increase of the dust-holding gradient. Thus, different stages are obtained, and all of them are taken as dust-holding characteristic stages.
[0041] In specific implementation, the pore structure change characteristics of each dust-binding characteristic stage can be extracted from the dust-binding process simulation of the three-dimensional digital model in the following way: within each dust-binding characteristic stage of the dust-binding process simulation of the three-dimensional digital model, the pore structure parameters at the start and end times of each dust-binding characteristic stage are recorded, including the average pore size, pore network connectivity and specific surface area change rate; finally, the extracted pore structure parameters are classified and organized according to the characteristic stages to form the pore structure change characteristics of each dust-binding characteristic stage.
[0042] In practice, the dust-holding matching coefficients for each dust-holding characteristic stage can be determined by the following method based on the pore structure change characteristics of each dust-holding characteristic stage: First, establish a multiple regression model of experimental dust-holding gradient and pore structure parameters for each dust-holding characteristic stage, with the experimental dust-holding gradient as the dependent variable and the pore structure parameters as the independent variables; then, solve the regression coefficients using the least squares method, and calculate the determination coefficients of the multiple regression model; finally, multiply the absolute values of the determination coefficients and regression coefficients, and use the result as the dust-holding matching coefficient for the corresponding dust-holding characteristic stage, thus obtaining the dust-holding matching coefficients for each dust-holding characteristic stage.
[0043] In specific implementation, the simulation matching degree of the three-dimensional digital model dust-binding process simulation can be determined based on all dust-binding matching coefficients in the following manner: First, calculate the coefficient of variation of the dust-binding matching coefficients for each dust-binding characteristic stage. The coefficient of variation can be used to evaluate the consistency between stages by the ratio of the standard deviation to the average value of all dust-binding matching coefficients. When the coefficient of variation is less than 0.15, the arithmetic mean is directly used as the simulation matching degree of the three-dimensional digital model dust-binding process simulation. When the coefficient of variation is greater than or equal to 0.15, the geometric mean is used as the simulation matching degree of the three-dimensional digital model dust-binding process simulation. The threshold of 0.15 is the optimal segmentation point obtained based on statistical analysis of a large amount of experimental data, which can effectively distinguish between uniform matching and differential matching.
[0044] In step S4, the simulation constraints for simulating the dust-holding process of the three-dimensional digital model are determined based on the response curves of the simulation matching degree and the filtration resistance, and the dust-holding simulation parameters of the three-dimensional digital model are adjusted based on the simulation constraints.
[0045] Preferably, in this embodiment, the simulation constraints for simulating the dust-holding process of the three-dimensional digital model are determined based on the response curves of the simulation matching degree and the filtration resistance, with reference to... Figure 3 As shown in the figure, this is a flowchart illustrating the process of determining simulation constraints in some embodiments of this application. In this embodiment, the determination of simulation constraints can be achieved through the following steps: In step S41, fluctuation analysis is performed on the response curve of the filter resistance to obtain the resistance fluctuation characteristics; In step S42, the constraint priority of the dust-holding simulation parameters in the three-dimensional digital model dust-holding process simulation is determined based on the resistance fluctuation characteristics and the simulation matching degree. In step S43, the simulation constraints for simulating the dust-containing process of the three-dimensional digital model are determined by the constraint priority of the dust-containing simulation parameters.
[0046] It should be noted that the resistance fluctuation characteristics described in this application refer to the characteristics that describe the fluctuation characteristics of the filter resistance response curve; the dust holding simulation parameters refer to the key parameters that describe the dust holding performance in the dust holding process simulation; the constraint priority refers to the priority of the dust holding simulation parameters when adjusting them; and the simulation constraint conditions refer to the conditions that constrain the dust holding simulation parameters in the three-dimensional digital model dust holding process simulation.
[0047] In practice, fluctuation analysis of the response curve of the filter resistance can be performed to obtain the resistance fluctuation characteristics. This can be achieved in the following way: First, the response curve of the filter resistance is divided into multiple continuous sliding windows according to the time series, and each window contains resistance data of 20 consecutive sampling points. Then, the standard deviation of the resistance data in each window is calculated, and the standard deviations of all windows are sorted by value. Finally, the 90th percentile of the standard deviations of all windows is taken as the high fluctuation characteristic value, the 50th percentile as the medium fluctuation characteristic value, and the 30th percentile as the low fluctuation characteristic value. These three characteristic values together constitute the resistance fluctuation characteristics.
[0048] In specific implementation, the constraint priority of the dust-holding simulation parameters in the three-dimensional digital model dust-holding process simulation can be determined according to the resistance fluctuation characteristics and the simulation matching degree in the following manner: First, the high fluctuation characteristic value, medium fluctuation characteristic value, and low fluctuation characteristic value are multiplied by the simulation matching degree, and the multiplied values are used as the basic influence factors of the three different parameters of porosity, fiber diameter, and fiber surface energy in the dust-holding simulation parameters, respectively. Then, the basic influence factor of porosity is multiplied by a coefficient representing the importance of the flow path, the basic influence factor of fiber diameter is multiplied by a coefficient representing the importance of interception efficiency, and the basic influence factor of fiber surface energy is multiplied by a coefficient representing the importance of adhesion characteristics. Finally, the three adjusted influence factors are normalized and sorted, and the parameter with the largest value is determined as the highest constraint priority, the parameter with the second largest value is determined as the medium constraint priority, and the parameter with the smallest value is determined as the lowest constraint priority, thereby obtaining the constraint priority of the dust-holding simulation parameters in the three-dimensional digital model dust-holding process simulation.
[0049] It should be noted that the coefficient characterizing the importance of the flow path is determined by analyzing the influence of porosity changes on the filtration velocity in historical experimental data. Specifically, this is achieved by calculating the coefficient of variation of the filtration velocity under different porosities and then normalizing it. The coefficient characterizing the importance of interception efficiency is obtained based on the correlation analysis between fiber diameter and particle capture efficiency. By statistically analyzing the interception efficiency data of standard dust particles under different fiber diameters, a diameter-efficiency relationship curve is established, and the slope of the curve is taken as the coefficient after normalization. The coefficient characterizing the importance of adhesion characteristics comes from the correlation analysis between surface energy test data and dust shedding rate. By measuring the shedding rate of deposited dust under different surface energy conditions, the correlation coefficient between surface energy and shedding rate is calculated, and the obtained correlation coefficient is used as the coefficient.
[0050] In specific implementation, the simulation constraints for simulating the dust-binding process in the three-dimensional digital model can be determined by the constraint priority of the dust-binding simulation parameters in the following manner: First, the three different parameters of porosity, fiber diameter, and fiber surface energy in the dust-binding simulation parameters are divided into three levels according to the constraint priority. The parameter with the highest constraint priority is marked as Level 1 constraint, the second highest constraint priority is marked as Level 2 constraint, and the lowest constraint priority is marked as Level 3 constraint. Then, based on the distribution characteristics of historical experimental data of each parameter in the material property database, the allowable variation range of Level 1 constraint parameters is determined to be 5% above and below the median of the historical valid data, the allowable variation range of Level 2 constraint parameters is 10% above and below the median of the historical valid data, and the allowable variation range of Level 3 constraint parameters is 15% above and below the median of the historical valid data. Finally, the constraint levels and specific numerical ranges of the three parameters are integrated into standardized constraint conditions, and the obtained constraint conditions are used as the simulation constraints for simulating the dust-binding process in the three-dimensional digital model.
[0051] In this embodiment, adjusting the dust-holding simulation parameters of the three-dimensional digital model based on the simulation constraints can be achieved through the following steps: The dust-holding simulation parameters of the three-dimensional digital model are constrained and adjusted using the aforementioned simulation constraints. The feasibility of the dust holding capacity simulation parameters after constraint adjustment was verified, and the feasibility verification results were obtained. The parameter configuration of the three-dimensional digital model is updated based on the feasibility verification results.
[0052] In specific implementation, the constraint adjustment of the dust-holding simulation parameters of the three-dimensional digital model through the simulation constraints can be achieved in the following way: First, obtain the constraint range and constraint level of the three parameters—porosity, fiber diameter, and fiber surface energy—from the dust-holding simulation parameters within the simulation constraints; then, perform differentiated parameter adjustment according to the constraint level: for the parameters of the first-level constraints, a gradual adjustment is adopted, with each adjustment not exceeding 1% of the initial value, gradually approaching the optimal value within the constraint range; for the parameters of the second-level constraints, the response surface optimization method is adopted to establish the response relationship between the parameters and the simulation matching degree within the constraint range, and determine the optimal parameter value through interpolation calculation; for the parameters of the third-level constraints, the boundary correction method is adopted to directly adjust the parameter values that exceed the constraint range to the nearest boundary value.
[0053] In practice, the feasibility of the adjusted dust-holding simulation parameters is verified. The feasibility verification results can be obtained in the following way: First, parameter boundary verification is performed to check whether the three parameters of porosity, fiber diameter, and fiber surface energy in the adjusted dust-holding simulation parameters all meet the numerical range specified in the simulation constraints. Then, physical rationality verification is performed. Based on historical experimental data in the material property database, it is confirmed that the adjusted dust-holding simulation parameters conform to the reasonable distribution range of material physical properties. Finally, simulation stability verification is performed. A simplified dust-holding process simulation is run using the adjusted dust-holding simulation parameters to observe the convergence and stability of the filtration resistance response curve, ensuring that there are no abnormal fluctuations in the simulation process. The three verification results are then integrated to generate the feasibility verification results.
[0054] The parameter configuration of the 3D digital model can be updated based on the feasibility verification results in the following manner: First, select all parameters that have passed the verification from the feasibility verification results; then, update the verified parameter values to the dust-containing configuration file of the 3D digital model, replacing the original parameter settings; finally, generate a parameter update log to record the numerical changes, verification results, and update time before and after the dust-containing simulation parameter adjustment, thus completing the update process of the 3D digital model parameter configuration.
[0055] In step S5, the simulated test results of the dust holding capacity of the nanofiltration material are generated by adjusting the dust holding simulation parameters.
[0056] In this embodiment, the simulation test results of the dust holding capacity of the nanofiltration material can be generated by adjusting the dust holding capacity simulation parameters using the following steps: The dust-holding process simulation is performed based on the adjusted dust-holding simulation parameters; Extract key indicators of dust holding capacity from the simulation results; Simulated test results of the dust holding capacity of the nanofiltration material were generated using key indicators of dust holding capacity.
[0057] It should be noted that the key indicators of dust holding capacity mentioned in this application represent the core indicators for comprehensively evaluating the dust holding performance of nanofiltration materials; the simulation test results represent the simulation evaluation results of the dust holding capacity of nanofiltration materials.
[0058] In practice, the dust-binding process simulation based on the adjusted dust-binding simulation parameters can be implemented in the following way: First, the adjusted dust-binding simulation parameters are loaded into the three-dimensional digital model, and a multiphysics coupled simulation environment is set up. The Navier-Stokes equations for the fluid phase are solved using computational fluid dynamics, and the dust movement trajectory is tracked using the discrete element method for the particle phase. Then, the boundary parameters are set according to the standard test conditions, including an inlet flow velocity of 0.1 m / s, a dust concentration of 10 mg / m³, and a simulation duration of 600 seconds. Finally, the transient solver is started to execute the complete dust-binding process simulation.
[0059] In practice, the key indicators of dust holding capacity in the simulation results can be extracted in the following way: First, extract time series data from the simulation results, including cumulative dust holding capacity, instantaneous filtration efficiency, and pressure drop changes; then calculate key performance indicators, including maximum dust holding capacity (total amount of dust deposited per unit area), dust holding life (time required for pressure drop to reach twice the initial value), and dust holding efficiency curve (filtration efficiency changes at different times), and use the obtained key performance indicators as the key indicators of dust holding capacity.
[0060] In practice, the simulation test results of the dust holding capacity of the nanofiltration material can be generated by using key dust holding capacity indicators as follows: First, the extracted key dust holding capacity indicators are compared and verified with experimental data, and the relative error between the simulation results and the experimental data is calculated; then, a test report containing numerical results and visualization content is generated, specifically including the maximum dust holding capacity value, dust holding life curve, resistance growth curve, and three-dimensional dust distribution map; finally, a standardized test report document is output, and the obtained test report document is used as the simulation test result of the dust holding capacity of the nanofiltration material.
[0061] Therefore, this application demonstrates that the simulated dust holding capacity of the nanofiber filter material can be generated using adjusted dust holding simulation parameters. Firstly, by acquiring real microstructural data of the material for 3D modeling, this initial step overcomes the distortion of the complex nanofiber network structure by traditional simplified models, accurately restoring its pore distribution and spatial architecture, and avoiding systematic errors caused by inherent model distortion. Secondly, by simulating the dust holding process under multi-particle-size dust loads, the collaborative deposition behavior of multi-scale dust in the nanonetwork in the actual environment is reproduced, accurately capturing the nonlinear evolution law of filtration resistance with increasing dust holding capacity. This makes the simulated resistance response curve more valuable for practical reference and can effectively replace some time-consuming physical dust holding tests. Furthermore, an experimental data matching and analysis machine is introduced. By performing response matching analysis and quantifying the simulation matching degree between the simulation results and the experimental dust holding gradient, the consistency between the virtual simulation world and the real physical process is ensured, greatly improving the credibility of the simulation results as a basis for decision-making and reducing the need for comprehensive experimental verification due to simulation uncertainties. On this basis, the simulation constraints are determined by the simulation matching degree and the filter resistance response curve, and the dust holding simulation parameters are adjusted accordingly to form a closed-loop correction mechanism. This ensures that the model not only relies on theoretical construction but also on constraints provided by real experimental behavior, so that the simulation output gradually approaches the real dust holding law, further improving the authenticity. Finally, the adjusted dust holding simulation parameters are used to generate simulation test results, enabling the output results to replace part of the physical test while closely approximating the experimental trend, thus achieving a reliable prediction of the actual dust holding capacity.
[0062] In summary, the technical solution adopted in this application can improve the realism of the simulation calculation of dust holding capacity of nanofiltration materials, thereby reducing the testing cost of nanofiltration materials.
[0063] Example 2: This application provides a dust holding capacity testing and analysis system for nanofiltration materials, referring to... Figure 4 As shown in the figure, this is a modular structure diagram of a dust holding capacity testing and analysis system for nanofiltration materials according to this embodiment of the present application. The testing and analysis system includes: The model building module 100 is used to acquire the microstructure data of the nanofiltration material, and to perform three-dimensional modeling of the nanofiltration material using the microstructure data to obtain a three-dimensional digital model of the nanofiltration material. The dust-holding process simulation module 200 is used to simulate the dust-holding process of the three-dimensional digital model under multi-particle-size dust load, and obtain the response curve of the filtration resistance of the three-dimensional digital model within the dust-holding simulation period. The response matching analysis module 300 is used to acquire the experimental dust holding data of the nanofiltration material, and to perform response matching analysis on the dust holding process simulation of the three-dimensional digital model by using the dust holding gradient of the experimental dust holding data at different times, so as to obtain the simulation matching degree of the dust holding process simulation of the three-dimensional digital model. The constraint adjustment module 400 is used to determine the simulation constraints of the dust-holding process simulation of the three-dimensional digital model based on the response curves of the simulation matching degree and the filter resistance, and to adjust the dust-holding simulation parameters of the three-dimensional digital model based on the simulation constraints. The simulation result generation module 500 is used to generate simulation test results of the dust holding capacity of the nanofiltration material by adjusting the dust holding simulation parameters.
[0064] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0065] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compactdisc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.
[0066] It should also be noted that 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 process, method, article, or apparatus. Unless otherwise specified, 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 that element.
Claims
1. A method for testing and analyzing the dust holding capacity of nanofiltration materials, characterized in that, The test analysis method includes the following steps: The microstructure data of the nanofiltration material is obtained, and the nanofiltration material is three-dimensionally modeled using the microstructure data to obtain a three-dimensional digital model of the nanofiltration material. The dust-holding process of the three-dimensional digital model under multi-particle-size dust load was simulated to obtain the response curve of the filtration resistance of the three-dimensional digital model during the dust-holding simulation period. The experimental dust holding data of the nanofiltration material is obtained, and the dust holding gradient of the experimental dust holding data at different times is used to perform response matching analysis on the dust holding process simulation of the three-dimensional digital model to obtain the simulation matching degree of the dust holding process simulation of the three-dimensional digital model. The simulation constraints for simulating the dust-holding process of the three-dimensional digital model are determined based on the response curves of the simulation matching degree and the filtration resistance, and the dust-holding simulation parameters of the three-dimensional digital model are adjusted based on the simulation constraints. The simulated test results of the dust holding capacity of the nanofiltration material are generated by adjusting the dust holding capacity simulation parameters.
2. The method for testing and analyzing the dust holding capacity of nanofiltration materials as described in claim 1, characterized in that, The three-dimensional digital model of the nanofiltration material obtained by using the microstructure data specifically includes: The microstructure data is preprocessed; The pore boundary features and fiber geometric features of the nanofiltration material were extracted from the preprocessed microstructure data. Based on the pore boundary features and fiber geometric features, a three-dimensional model of the nanofiltration material is performed to obtain a three-dimensional digital model of the nanofiltration material.
3. The method for testing and analyzing the dust holding capacity of nanofiltration materials as described in claim 1, characterized in that, The dust-holding process of the three-dimensional digital model under multi-particle-size dust load was simulated, and the response curve of the filtration resistance of the three-dimensional digital model during the dust-holding simulation period was obtained, specifically including: Set the multi-particle-size dust distribution parameters and fluid boundary conditions of the three-dimensional digital model; The deposition process of multi-particle-size dust in the three-dimensional digital model is simulated based on the multi-particle-size dust distribution parameters and the fluid boundary conditions. The filtration resistance change of the three-dimensional digital model is calculated in real time during the dust deposition process, thereby generating the response curve of the filtration resistance of the three-dimensional digital model during the dust holding simulation cycle.
4. The method for testing and analyzing the dust holding capacity of nanofiltration materials as described in claim 1, characterized in that, By performing response matching analysis on the dust-binding gradient of the experimental dust-binding data at different times, the simulation matching degree of the dust-binding process simulation of the three-dimensional digital model is obtained, specifically including: Determine the dust holding gradient of the experimental dust holding data at different times; Based on the experimental dust holding data, different dust holding characteristic stages are divided at different times; The pore structure change characteristics of each dust-binding stage are extracted from the dust-binding process simulation of the three-dimensional digital model. The dust-holding matching coefficient for each dust-holding characteristic stage is determined by the pore structure change characteristics of each dust-holding characteristic stage. The simulation matching degree of the dust-binding process simulation of the three-dimensional digital model is determined based on all dust-binding matching coefficients.
5. The method for testing and analyzing the dust holding capacity of nanofiltration materials as described in claim 1, characterized in that, The simulation constraints for simulating the dust-collecting process in the three-dimensional digital model, determined based on the response curves of the simulation matching degree and filtration resistance, specifically include: Fluctuation analysis was performed on the response curve of the filter resistance to obtain the resistance fluctuation characteristics; The constraint priority of the dust-binding simulation parameters in the three-dimensional digital model dust-binding process simulation is determined based on the resistance fluctuation characteristics and the simulation matching degree. The simulation constraints for simulating the dust-collecting process in the three-dimensional digital model are determined by the constraint priority of the dust-collecting simulation parameters.
6. The method for testing and analyzing the dust holding capacity of nanofiltration materials as described in claim 1, characterized in that, Adjusting the dust-holding simulation parameters of the three-dimensional digital model based on the aforementioned simulation constraints specifically includes: The dust-holding simulation parameters of the three-dimensional digital model are constrained and adjusted using the aforementioned simulation constraints. The feasibility of the dust holding capacity simulation parameters after constraint adjustment was verified, and the feasibility verification results were obtained. The parameter configuration of the three-dimensional digital model is updated based on the feasibility verification results.
7. The method for testing and analyzing the dust holding capacity of nanofiltration materials as described in claim 1, characterized in that, The simulation test results of the dust holding capacity of the nanofiltration material generated by adjusting the dust holding simulation parameters specifically include: The dust-holding process simulation is performed based on the adjusted dust-holding simulation parameters; Extract key indicators of dust holding capacity from the simulation results; Simulated test results of the dust holding capacity of the nanofiltration material were generated using key indicators of dust holding capacity.
8. The method for testing and analyzing the dust holding capacity of nanofiltration materials as described in claim 1, characterized in that, The simulation matching degree represents the degree of matching between the three-dimensional digital model simulation of the dust-bearing process and the actual dust-bearing process.
9. The method for testing and analyzing the dust holding capacity of nanofiltration materials as described in claim 1, characterized in that, The simulation constraints represent the conditions that constrain the dust-binding simulation parameters in the three-dimensional digital model dust-binding process simulation.
10. A dust holding capacity testing and analysis system for nanofiltration materials, used to perform a dust holding capacity testing and analysis method for nanofiltration materials as described in any one of claims 1 to 9, characterized in that, The test analysis system includes: The model building module is used to acquire the microstructure data of the nanofiltration material, and to perform three-dimensional modeling of the nanofiltration material using the microstructure data to obtain a three-dimensional digital model of the nanofiltration material. The dust-holding process simulation module is used to simulate the dust-holding process of the three-dimensional digital model under multi-particle-size dust load, and obtain the response curve of the filtration resistance of the three-dimensional digital model within the dust-holding simulation period. The response matching analysis module is used to acquire experimental dust holding data of the nanofiltration material, and to perform response matching analysis on the dust holding process simulation of the three-dimensional digital model by using the dust holding gradient of the experimental dust holding data at different times, so as to obtain the simulation matching degree of the dust holding process simulation of the three-dimensional digital model. The constraint adjustment module is used to determine the simulation constraints of the dust-holding process simulation of the three-dimensional digital model based on the response curves of the simulation matching degree and the filter resistance, and to adjust the dust-holding simulation parameters of the three-dimensional digital model based on the simulation constraints. The simulation result generation module is used to generate simulation test results of the dust holding capacity of the nanofiltration material by adjusting the dust holding simulation parameters.