Attenuation analysis method and attenuation analysis system for flue gas component filtration performance

Abstract

The present invention discloses an attenuation analysis method and an attenuation analysis system for the filtration performance of smoke components, and relates to the technical field of performance detection. The method includes: introducing a tracking medium to determine the non-targeted law of particles of mainstream smoke components; physically modeling the filter component to construct a fluid dynamics model; using the suction characteristics as simulation variables to perform dynamic simulation of smoke filtration, monitoring the first spatial cross section; and the second spatial cross section to determine the particle distribution characteristics of the mainstream smoke components during the test cycle; and constructing a filtration performance attenuation curve based on the particle distribution characteristics. The present invention solves the technical problem that the prior art lacks accurate analysis methods for the attenuation law of the filtration performance of smoke components, and thus it is difficult to effectively optimize the design and operation parameters of the filter component, and achieves the technical effect of comprehensively mastering the changing law of the filtration performance of smoke components under different conditions and effectively improving the accuracy and reliability of the performance evaluation of the smoke filtration system.

Description

Technical Field

[0001] The invention relates to the technical field of performance detection, and in particular to an attenuation analysis method and an attenuation analysis system for smoke component filtering performance. Background Art

[0002] In the field of flue gas treatment, accurate evaluation of the attenuation of the filtration performance of flue gas components is crucial for optimizing the filtration system. Traditional analysis methods can often only roughly evaluate the filtration performance, and lack in-depth exploration of the distribution and migration laws of particles in flue gas. For example, in previous studies, there was a lack of accurate analysis methods for the detailed movement characteristics of mainstream smoke components during the filtration process, including the distribution changes of particles in different spatial cross-sections and the performance evolution over the entire life cycle. It was difficult to effectively simulate the flow state of smoke under actual suction conditions, and it was impossible to accurately quantify the impact of different suction characteristics (such as suction pressure, suction speed, etc.) on filtration performance. This leads to a lack of scientific basis in the design and optimization of filter components, and it is impossible to adjust the filtration parameters in a targeted manner to adapt to different working conditions. At the same time, due to the lack of construction and analysis of the filtration performance attenuation curve, it is difficult to predict the performance change trend of the filter components during long-term use, and it is impossible to reasonably arrange maintenance and replacement plans, which affects the overall efficiency and reliability of the flue gas filtration system, increases operating costs, and is not conducive to ensuring the flue gas treatment effect to meet relevant requirements such as environmental protection.

[0003] The existing technology lacks accurate analysis methods for the attenuation law of the filtration performance of smoke components, which makes it difficult to effectively optimize the design and operating parameters of the filter components. Summary of the invention

[0004] The present application provides an attenuation analysis method and an attenuation analysis system for smoke component filtration performance, which are used to solve the technical problem that the prior art lacks accurate analysis means for the attenuation law of smoke component filtration performance, and thus it is difficult to effectively optimize the design and operating parameters of the filter assembly.

[0005] In view of the above problems, the present application provides an attenuation analysis method and an attenuation analysis system for smoke component filtration performance.

[0006] In a first aspect of the present application, a method for analyzing the attenuation of smoke component filtration performance is provided, the method comprising:

[0007] A tracking medium is introduced, and smoke flow tests and statistical analysis are performed through a smoking machine to determine the non-targeted law of particles of mainstream smoke components, wherein the non-targeted law of particles characterizes the distribution and migration law of particles, and the mainstream smoke components are classified as harmful components; physical modeling is performed on the filter component, and a fluid dynamic model is constructed in combination with the non-targeted law; the suction characteristics are used as simulation variables, and a dynamic simulation of smoke filtration is performed based on the fluid dynamic model to monitor the first spatial section and the second spatial section, and determine the particle distribution characteristics of the mainstream smoke components within the test period, wherein the first spatial section is the spatial section of the smoke particle generation position, and the second spatial section is the spatial section of the first position after filtration; based on the particle distribution characteristics, a filtration performance attenuation curve is constructed.

[0008] In a second aspect of the present application, a system for analyzing the attenuation of smoke component filtration performance is provided, the system comprising:

[0009] a particle non-targeting law determination module, the particle non-targeting law determination module is used to introduce a tracking medium, perform smoke flow testing and statistical analysis through a smoking machine, and determine the particle non-targeting law of mainstream smoke components, wherein the particle non-targeting law characterizes the particle distribution and migration law, and the mainstream smoke components are classified as harmful components; a fluid dynamic model construction module, the fluid dynamic model construction module is used to perform physical modeling of the filter component, and construct a fluid dynamic model in combination with the non-targeting law; a smoke filtration dynamic simulation module, the smoke filtration dynamic simulation module is used to use the suction characteristics as simulation variables, perform smoke filtration dynamic simulation based on the fluid dynamic model, and monitor the first spatial section; a particle distribution feature determination module, the particle distribution feature determination module is used to determine the particle distribution characteristics of the mainstream smoke components within the test period with the second spatial section, wherein the first spatial section is the spatial section of the smoke particle generation position, and the second spatial section is the spatial section of the first position after filtration; a filtration performance attenuation curve construction module, the filtration performance attenuation curve construction module constructs a filtration performance attenuation curve based on the particle distribution characteristics.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] By introducing tracking media, smoke flow testing and statistical analysis were performed through a smoking machine to determine the non-targeted laws of particles of mainstream smoke components; physical modeling of the filter components was performed, and a fluid dynamics model was constructed in combination with the non-targeted laws; the suction characteristics were used as simulation variables, and dynamic simulation of smoke filtration was performed based on the fluid dynamics model to monitor the first spatial section and the second spatial section to determine the particle distribution characteristics of the mainstream smoke components during the test period; based on the particle distribution characteristics, a filtration performance attenuation curve was constructed. The technical effect of fully understanding the changing laws of smoke component filtration performance under different conditions and effectively improving the accuracy and reliability of performance evaluation of smoke filtration systems was achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic flow chart of a method for analyzing the attenuation of smoke component filtration performance provided in an embodiment of the present application;

[0014] Figure 2 Schematic diagram of the structure of the attenuation analysis system for smoke component filtration performance provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: particle non-targeting law determination module 10, fluid dynamics model construction module 20, smoke filtration dynamic simulation module 30, particle distribution characteristic determination module 40, filtration performance attenuation curve construction module 50. DETAILED DESCRIPTION

[0016] The present application provides an attenuation analysis method and an attenuation analysis system for smoke component filtration performance, which is used to solve the technical problem that the prior art lacks accurate analysis means for the attenuation law of smoke component filtration performance, and thus it is difficult to effectively optimize the design and operating parameters of the filter assembly.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a method for analyzing the attenuation of smoke component filtration performance, the method comprising:

[0019] Step S100: introducing a tracking medium, performing smoke flow testing and statistical analysis through a smoking machine, and determining the particle non-targeting law of mainstream smoke components, wherein the particle non-targeting law characterizes the particle distribution and migration law, and the mainstream smoke components are classified as harmful components.

[0020] Specifically, firstly, a specific tracking medium is introduced. According to different harmful components, matching tracking media are selected. These tracking media have unique physical or chemical properties and can be closely combined with smoke particles, so as to facilitate subsequent monitoring and analysis. For example, for some heavy metal particle components, substances that can specifically adsorb the metal ions can be selected as tracking media; for organic harmful components, reagents that react with them in a specific chemical reaction and can produce detectable signals are selected as tracking media. The smoke flow test is carried out using a smoking machine. The smoking machine can simulate the smoking conditions in the actual smoking process, and make the smoke flow in a stable and adjustable state by accurately controlling parameters such as the smoking frequency and the smoking depth. During the test, with the help of detection equipment such as high-precision particle counters and spectrometers, the smoke particles marked by the tracking medium are monitored in real time, and the movement data of the particles at different times and different spatial positions are recorded, including the concentration change, movement speed, movement direction, and spatial distribution state of the particles. Finally, a comprehensive and in-depth statistical analysis is carried out on the massive particle movement data obtained, and cluster analysis is used to explore the laws hidden behind the data. By comprehensively considering various factors, such as the type, content, physical and chemical properties of smoke components and test environmental conditions, the overall movement characteristics of each mainstream smoke component are determined, so as to accurately grasp the non-targeted law of particles. This law can clearly reveal the distribution pattern of particles in the smoke flow and their migration trend over time and space, providing key basic data for subsequent model construction and performance analysis.

[0021] Step S200: Physically modeling the filter component and constructing a fluid dynamics model in combination with the non-targeted law.

[0022] Specifically, fine physical modeling is carried out for the filter component, based on a comprehensive analysis of the filter component, including accurate determination of its geometric structure parameters, such as using three-dimensional scanning technology to obtain its shape and size information, and using a porosity meter to determine the pore distribution, as well as in-depth analysis of the physical properties of the filter material, such as using professional instruments to measure the porosity and filter layer thickness, and using material analysis equipment to determine the material density, etc. Based on the basic principles of engineering mechanics and fluid mechanics, such as the Navier-Stokes equation, a basic physical model framework for the flow process of smoke inside the filter component is constructed. For example, for fibrous filter materials, microstructure analysis technology is used to clarify key parameters such as fiber arrangement, diameter size, and filling density, and the corresponding porous media flow model is constructed in the model based on this to accurately describe the flow state of smoke in the pores between fibers. Subsequently, the non-targeted laws of the particles of the determined mainstream smoke components are integrated into the physical model. Through in-depth quantitative analysis of the non-targeted laws of particles, they are converted into mathematical expressions that can be integrated into the fluid dynamics model. A term that reflects the additional resistance to smoke flow caused by uneven particle distribution is introduced into the momentum equation (such as calculating the local resistance coefficient based on the particle concentration distribution and adding it to the momentum equation). The mass change caused by particle migration is considered in the continuity equation (such as correcting the mass conservation term based on the particle migration rate). In this way, a fluid dynamics model that can accurately reflect the actual smoke flow characteristics is constructed, providing a solid theoretical foundation and precise calculation framework for subsequent dynamic simulation of smoke filtration.

[0023] Step S300: taking the suction characteristics as simulation variables, performing dynamic simulation of smoke filtration based on the fluid dynamics model, and monitoring the first spatial cross section.

[0024] Specifically, the suction characteristics are clearly used as the key simulation variables, and the dynamic simulation of smoke filtration is carried out comprehensively based on the fluid dynamics model constructed in the early stage. According to the suction characteristics, a series of different value combinations are carefully planned and set, which cover a variety of possible combinations of suction pressure and suction speed, so as to fully simulate various complex working conditions in actual operation. Subsequently, these carefully designed suction characteristic values ​​are substituted into the fluid dynamics model as input parameters, and the model equations are solved with the help of finite volume method, finite difference method, etc., so as to realistically simulate the dynamic filtration process of smoke inside the filter component. In the simulation process, the first spatial section (i.e., the spatial section where the smoke particles are initially generated) is monitored. By reasonably arranging a high-precision sensor network (such as a high-sensitivity particle concentration sensor, a multi-dimensional velocity sensor, etc.) on this section, detailed information of the smoke at the moment of generation can be captured in real time, including but not limited to the initial concentration distribution of particles, the size and direction distribution characteristics of the initial velocity, etc. In this process, we focus on the smoke distribution characteristics at the first spatial cross-section position. The main purpose is to obtain the change of the smoke distribution state at the initial generation position over time under different suction characteristics, so as to provide basic data for the subsequent comparative analysis of the smoke distribution characteristics at the cross-section after filtration, and then accurately evaluate the performance of the filter component under different working conditions.

[0025] Step S400: determining the particle distribution characteristics of the mainstream smoke component in the test period with the second spatial cross section, wherein the first spatial cross section is the spatial cross section of the smoke particle generation position, and the second spatial cross section is the spatial cross section of the first position after filtration.

[0026] Specifically, during the entire test cycle, the second spatial section (i.e., the spatial section at the first position after filtration) is continuously focused on. By deploying high-precision particle detection instruments (such as laser particle size analyzers, particle image velocimeters, etc.) on this section, relevant data of smoke particles after filtration are obtained in real time, including information such as the residual concentration, particle size distribution, speed size and direction of the particles. At the same time, these data are compared and analyzed with the data recorded at the corresponding test time of the first spatial section (the spatial section at the location where the smoke particles are generated). Considering the changes in the number, morphology, distribution range, etc. of particles between the two sections due to the combined influence of factors such as filtration, fluid dynamics, and the characteristics of the particles themselves when the smoke passes through the filter component. For example, comparing the reduction in particle concentration can reflect the interception efficiency of the filter component for particles of different particle sizes, and analyzing the changes in particle velocity can understand the influence of fluid resistance on particle movement during the filtration process. By collating and analyzing the particle data on the two sections under different test cycles and different simulation conditions, the particle distribution characteristics of the mainstream smoke components during the test cycle can be accurately determined, providing a key basis for evaluating the performance of the filter component.

[0027] Step S500: constructing a filtration performance attenuation curve based on the particle distribution characteristics.

[0028] Specifically, a filtration performance attenuation curve is constructed based on the determined particle distribution characteristics. First, the particle distribution characteristic data of the first cycle node is extracted, and the particle-related parameter difference between the first spatial section (the spatial section at the location where the smoke particles are generated) and the second spatial section (the spatial section at the first location after filtration) at the node is accurately measured, such as the particle concentration difference, the difference in the number of particles of a specific particle size, etc. These differences are converted into the first performance coefficient through the calculation formula, which can preliminarily characterize the filtration effect of the filter component in the initial stage. Then, for the entire life cycle of the filter component, the above difference measurement and performance coefficient calculation process are repeated one by one according to the set cycle node sequence to obtain a series of performance coefficients (Nth performance coefficient). These performance coefficients can reflect the performance status of the filter component at different stages of use. Finally, with the cycle node as the horizontal coordinate and the performance coefficient as the vertical coordinate, the performance coefficient corresponding to each cycle node is plotted as a data point, and the least squares method is used to connect these data points to construct a filtration performance attenuation curve. The slope of the curve can intuitively reflect the attenuation rate of filtration performance over time or usage cycle. By analyzing the curve shape, slope change and other characteristics, we can gain in-depth insight into the performance change trend of the filter component during long-term operation, providing important quantitative basis for evaluating the service life of the filter component, optimizing the filtration process and formulating maintenance strategies.

[0029] In a possible implementation, step S100 further includes:

[0030] Step S110: determining the mainstream smoke components, and configuring a tracking medium for each mainstream smoke component.

[0031] Step S120: Based on the smoking machine, the particle movement data of the tracking medium is monitored by controlling the pump suction parameters.

[0032] Step S130: Based on the particle motion data, statistical analysis is performed to mine the overall motion characteristics of each mainstream smoke component to determine the non-targeting law of the particles.

[0033] Specifically, first, use advanced component analysis technology (such as gas chromatography-mass spectrometry GC-MS, high-performance liquid chromatography HPLC, etc.) to conduct a comprehensive analysis of mainstream smoke and accurately identify the harmful components, which constitute the mainstream smoke components. For different types of harmful components, select tracking media that specifically bind to them or can effectively mark them. For example, for harmful components such as volatile organic compounds (VOCs), organic materials that have high adsorption and can produce fluorescent signals can be used as tracking media; for heavy metal particles, chemical reagents that can react with heavy metal ions and change color or generate electrical signals are selected as tracking media to ensure that each mainstream smoke component can be accurately marked, providing reliable identification for subsequent particle movement monitoring.

[0034] The core of using a smoking machine to conduct smoke flow testing is to simulate the real smoking scene by accurately controlling the pump suction parameters, and then monitor the particle motion data of the tracking medium. The pump system inside the smoking machine is a key component to achieve suction control. By adjusting the pump motor speed, valve opening and other parameters, the frequency, intensity and duration of each suction can be accurately controlled. For example, different combinations of suction frequencies (such as 5 times, 10 times, 15 times per minute, etc.) and suction intensities (expressed in suction pressure, such as 50Pa, 100Pa, 150Pa, etc.) are set to produce different flow states of the smoke flow. During the formation of the smoke flow, the introduced tracking medium is fully mixed with the smoke particles. At this time, a series of advanced monitoring instruments are used to obtain particle motion data. For example, a high-precision laser Doppler velocimeter (LDV) is used, which uses the laser Doppler effect to measure the velocity vector of the tracking medium particles in real time, including the magnitude and direction of the velocity, and can monitor the particle motion velocity accurately to the micron level. At the same time, combined with a high-resolution particle image velocimeter (PIV), by shooting a sequence of images of particle motion and using image analysis algorithms to calculate the position coordinates of particles at different times, the motion trajectory of particles can be determined. In addition, optical concentration sensors can be used to monitor the concentration changes of particles in the smoke flow in real time. These monitoring instruments work together to comprehensively and accurately record the motion data of tracking medium particles under different suction parameter settings, including the changes in the three-dimensional spatial position distribution of particles in the smoke flow over time, the dynamic changes in speed and direction, and the fluctuations in concentration, etc., providing a rich and reliable data source for subsequent in-depth analysis of particle motion characteristics and determination of non-targeted laws.

[0035] The particle motion data is preprocessed, including data cleaning to remove outliers and noise interference, and data normalization to make different parameters comparable. Then, cluster analysis algorithms (such as K-means clustering, hierarchical clustering, etc.) are used to divide the particles into different clusters according to the characteristics of particle movement speed, position distribution, etc. Each cluster represents a group of particles with similar movement patterns, thereby identifying the main movement trends of particles of different components in mainstream smoke. For example, particles of certain harmful components may tend to gather in specific areas of the smoke flow to form specific movement trajectory clusters. Then, dimensionality reduction techniques such as principal component analysis (PCA) are used to extract the key factors that play a dominant role in particle movement, simplify data representation, and highlight the main movement characteristics. For example, the key dimensions of the main movement direction and speed change of particles in space are determined to more clearly observe and understand the overall movement characteristics. At the same time, the time series analysis method (autoregressive moving average model ARIMA) is used to study the change law of particle motion parameters (such as speed, concentration, etc.) over time, and determine the migration speed and diffusion rate of particles in the smoke flow and the changes in the movement state at different time stages. Finally, based on the above analysis results, we can comprehensively describe the distribution pattern of particles in the smoke flow (such as concentration distribution in space, distribution of particle types in different regions, etc.), migration laws (such as diffusion direction over time, changes in migration speed under different conditions, etc.) and motion characteristics (such as typical speed ranges of particles with different components, motion trajectory morphology, etc.), so as to determine the non-targeting law of particles and provide a key theoretical basis for the subsequent construction of fluid dynamics models and filtration performance research.

[0036] In a possible implementation, step S200 further includes:

[0037] Step S210: determining a first smoke flow impact with respect to a filtration inertial resistance of the filter assembly, wherein the filtration inertial resistance is determined based on a porosity of the filter assembly and a filter layer thickness.

[0038] Step S220: Determine the second smoke flow impact based on the suction characteristics, where the suction characteristics at least include suction pressure and suction speed.

[0039] Step S230: Taking the first smoke flow influence as a quantitative constraint condition and the second smoke flow influence as a variable constraint condition, filtering simulation constraints are performed on the fluid dynamics model.

[0040] Specifically, the structural characteristics of the filter assembly are analyzed in depth, focusing on the two key parameters of porosity and filter layer thickness to determine the filtration inertial resistance, and then clarify the first flue gas flow impact. The porosity of the filter assembly is accurately measured using professional porosity measuring instruments (such as mercury intrusion meter, gas adsorption porosity analyzer, etc.), which reflects the size and distribution of the pore space inside the filter material. At the same time, the thickness of the filter layer is measured with the help of high-precision thickness measurement tools (such as micrometer, ultrasonic thickness gauge, etc.). Based on these measurement data, the filtration inertial resistance is calculated according to the porous media theory in fluid mechanics. According to Darcy's law and related correction formulas, combined with porosity and filter layer thickness, the obstruction to the flow of smoke caused by the structural characteristics of the filter material itself when the smoke passes through the filter assembly is calculated. This obstruction is the first flue gas flow impact. It serves as a relatively fixed quantitative constraint in the subsequent fluid dynamics model, reflecting the inherent influence of the filter assembly's own structure on the smoke flow.

[0041] The second flue gas flow influence is determined based on the suction characteristics. The suction pressure and suction speed in the suction characteristics are key factors. The suction pressure value under different working conditions is accurately measured by a pressure sensor. This pressure directly acts on the flue gas flow and affects its flow dynamics. At the same time, the suction speed is accurately measured using flow velocity measuring instruments (such as hot wire anemometers, pitot tubes, etc.). According to the principles of fluid mechanics, changes in suction pressure and suction speed will cause dynamic effects such as changes in the momentum of the flue gas flow and changes in streamline distribution. For example, higher suction pressure and speed will accelerate the flue gas flow, change its flow path and velocity distribution in the filter assembly, and thus produce the corresponding second flue gas flow influence. This influence is used as a variable constraint in the model, reflecting the dynamic influence of external suction operations on the flue gas flow.

[0042] The first smoke flow effect is taken as a quantitative constraint condition, and the second smoke flow effect is taken as a variable constraint condition to constrain the fluid dynamics model for filtering simulation. In the constructed fluid dynamics model, its control equations (such as the continuity equation, momentum equation, etc.) contain various physical quantities that describe the smoke flow. The parameters corresponding to the first smoke flow effect (such as the resistance coefficient calculated based on the filter inertial resistance, etc.) are substituted into the equation in the form of fixed values ​​to limit the model's consideration of the inherent influence of the filter component structure during the simulation process. At the same time, the parameters related to the second smoke flow effect (such as suction pressure, suction speed, etc.) are introduced into the equation as variables, so that the model can be dynamically simulated according to different suction conditions. In this way, the fluid dynamics model is more in line with the actual situation when simulating the smoke filtration process, the accuracy and reliability of the model are improved, and a scientific and reasonable theoretical framework is provided for the subsequent dynamic simulation of smoke filtration.

[0043] In a possible implementation, step S300 further includes:

[0044] Step S310: determining multiple types of suction characteristics for the simulation variables, wherein the multiple types of suction characteristics include characteristic combinations of multiple levels of suction pressure-suction speed.

[0045] Step S320: Based on the multiple types of suction characteristics, parallel simulation and sampling are performed under multiple test cycles to determine multiple groups of particle distribution characteristics.

[0046] Step S330: performing period node mapping and mean calculation on the multiple groups of particle distribution features as the particle distribution features within the test period.

[0047] Specifically, for the suction characteristics as simulation variables, the differences in smoking habits of different groups of people are fully considered, and multiple types of representative suction characteristics are determined. Among them, the characteristic combination of multi-level suction pressure-suction speed is the key part. Through the analysis and investigation of a large amount of actual smoking behavior data, different levels of suction pressure are set, such as from low pressure of 30-50Pa to medium pressure of 80-100Pa, and then to high pressure of 120-150Pa, etc., and the corresponding suction speeds are matched, such as low pressure with a lower suction speed of 0.3-0.5m / s, medium pressure is 0.6-0.8m / s, and high pressure is 0.9-1.2m / s and other combinations. These combinations are designed to cover various possible smoking conditions, from gentle to strong, to ensure that the simulation can fully reflect the diversity in actual use.

[0048] Based on the pre-set multiple types of suction characteristics, parallel simulation and sampling work is carried out in multiple test cycles to accurately determine multiple groups of particle distribution characteristics. For each type of suction characteristics, an independent simulation environment is carefully constructed to ensure that each simulation process is parallel and does not interfere with each other. At the start of each simulation, a specific suction pressure-suction speed combination is accurately input into the smoke filtration simulation system based on the fluid dynamics model, so that the simulation system runs according to the combination conditions and truly reproduces the dynamic filtration process of smoke in the filter component. During the simulation process, the sampling of particle distribution characteristics is carried out simultaneously. In the first spatial section (the spatial section where the smoke particles are generated), a monitoring sampling system composed of a high-resolution laser particle counter and a high-speed camera is used to capture the initial state of the particles at the moment of generation in real time, including the number of particles, concentration distribution, particle size range, and initial velocity vector information, providing source data for subsequent analysis. At the same time, in the second spatial section (the spatial section at the first position after filtration), equally high-precision particle detection equipment, such as a particle size analyzer and a particle concentration sensor based on optical principles, are deployed to obtain the residual conditions of particles after the filter component, including key parameters such as the change in particle concentration after filtration, the pass rate of particles of different particle sizes, and the degree of attenuation of particle velocity. By repeating the above simulation and sampling operations in multiple test cycles, a complete set of particle distribution characteristic data can be obtained for each type of suction feature, covering the changes in the particle state in the entire process from before the smoke enters the filter component to after filtration. In this way, after comprehensive simulation and sampling of multiple types of suction characteristics, multiple groups of particle distribution characteristics are finally determined. These data fully reflect the distribution characteristics of particles in the smoke filtration process under different suction conditions, providing rich and targeted data support for subsequent in-depth analysis of filtration performance.

[0049] The multiple groups of particle distribution characteristics obtained through multiple simulations are mapped to periodic nodes and the mean is calculated to obtain particle distribution characteristics that can represent the overall situation within the test cycle. First, the time period set for each simulation process is divided into several equally spaced periodic nodes. For example, a test cycle with a total duration of 10 minutes is divided into 10 periodic nodes at intervals of 1 minute. For multiple groups of particle distribution characteristics, each group of data is accurately mapped to these periodic nodes according to the corresponding simulation time sequence. In this way, at each periodic node, the particle distribution characteristic data from different simulation groups (corresponding to different suction characteristics) can be summarized. Then, the mean is calculated for multiple groups of data at each periodic node. For key parameters in the particle distribution characteristics, such as particle concentration, particle size distribution, etc., their average values ​​are calculated respectively. Taking particle concentration as an example, if at a certain periodic node, the particle concentration values ​​from three different simulations are 100 / cm 3 , 120 pieces / cm 3 and 80 pieces / cm3 , then the average particle concentration of this period node is (100+120+80) / 3=100 particles / cm 3 In this way, all relevant parameters at each cycle node are averaged to obtain a set of relatively stable particle distribution characteristic data at each cycle node that integrates the influence of different suction characteristics. This set of data can eliminate local fluctuations caused by differences in individual suction characteristics, thereby accurately characterizing the overall distribution characteristics of particles during the smoke filtration process during the test cycle, providing a reliable and representative data basis for the subsequent construction of the filtration performance attenuation curve, so as to more scientifically evaluate the long-term performance of the filter components under different working conditions.

[0050] In a possible implementation, step S320 further includes:

[0051] Step S321: setting a sampling interval according to the multiple types of puff characteristics, wherein the sampling interval is negatively correlated with the puff characteristics.

[0052] Step S322: sampling the first spatial cross section to determine a first distribution sample.

[0053] Step S323: sampling the second spatial cross section at the sampling interval to determine a second distribution sample.

[0054] Step S324: combining the first distribution sampling and the second distribution sampling to determine a set of particle distribution characteristics.

[0055] Specifically, the sampling interval is set according to the determined multiple types of suction characteristics, wherein the sampling interval is negatively correlated with the suction characteristics. When the suction pressure in the suction characteristics is greater and the suction speed is faster, the flow state of the smoke flow in the filter component will change significantly. The larger suction pressure provides a stronger driving force for the smoke flow, accelerating its flow, while the fast suction speed directly causes the smoke flow to pass through the filter component at a higher rate. In this case, the smoke particles are subject to a greater driving force and their movement speed is greatly increased. As the particle movement speed increases, the time required for the same batch of particles to reach the second spatial cross-section (the spatial cross-section of the smoke particle generation position) is shorter. In order to accurately capture the distribution changes of particles in this rapid transmission process, the sampling interval must be shortened. For example, under the conditions of high suction pressure and high suction speed, the sampling interval needs to be set within a very short range, such as 0.1-0.3 seconds. Such short-interval sampling can ensure that enough sample data points are obtained during the short process of particles quickly passing through the filter component, so as to accurately record the changes in key parameters such as concentration, particle size, and speed of the particles at different times, and then describe in detail the movement trajectory and distribution evolution of the particles under such high-intensity suction conditions. On the contrary, when the suction pressure is small and the suction speed is slow, the flow rate of the smoke flow is relatively low, and the particle movement speed also slows down. At this time, the time for the same batch of particles to reach the second spatial section from the starting position is longer, so the sampling interval can be appropriately extended, such as setting it to 1-2 seconds. By dynamically adjusting the sampling interval according to the suction characteristics, the most representative and accurate particle distribution data can be obtained at the most optimized sampling frequency under various suction conditions, providing a solid data foundation for the subsequent comprehensive evaluation of the performance of the filter component.

[0056] Comprehensive and detailed sampling work is carried out on the first spatial section (i.e., the spatial section where the smoke particles are generated) to determine the first distribution sampling. On this section, a sampling system consisting of a variety of high-precision instruments is deployed. First, a high-resolution laser particle counter is used, which can accurately measure the concentration of particles at this section. By emitting a laser beam and detecting the intensity and frequency changes of the scattered light, the number of particles of different sizes per unit volume is calculated, thereby obtaining detailed distribution information of the particle concentration, covering the concentration from tiny nano-scale particles to larger micron-scale particles. At the same time, a high-speed camera is equipped to capture images of the section at a very high frame rate. The camera can capture the instantaneous spatial distribution state of the particles, including intuitive information such as the aggregation area, dispersion degree, and approximate movement direction of the particles on a two-dimensional plane. In addition, optical sensors can be used to detect the color, gloss and other characteristics of the particles, which may be related to the composition or physical state of the particles, further enriching the description of the particles. During the entire test cycle, these instruments are continuously used to collect data according to the pre-set sampling time sequence. The particle concentration data measured by the laser particle counter at different times, a series of images taken by the high-speed camera, and the relevant characteristic data obtained by the optical sensor are integrated. The data are analyzed in time series to observe the fluctuation of particle concentration over time, the dynamic change trend of particles in spatial distribution, and the evolution law of characteristic parameters, so as to determine the first distribution sampling. This sampling result can accurately reflect the distribution characteristics of flue gas particles in the initial stage of generation, including the initial concentration distribution pattern of particles, the initial proportion relationship of particles of different particle sizes, and the initial aggregation or dispersion form in space. It provides important starting data for subsequent in-depth research on the migration and changes of particles in the flue gas filtration process.

[0057] According to the preset sampling interval, accurate sampling is carried out for the second spatial section (i.e., the spatial section at the first position after filtration) to determine the second distribution sampling. At this section, a series of advanced and adapted sampling equipment are carefully placed. First, it is equipped with a high-precision particle concentration sensor, which can keenly sense the residual concentration of particles in the flue gas after passing through the filter component. The sensor accurately calculates the number of particles per unit volume by measuring the signal changes passing through the flue gas flow in real time, thereby obtaining real-time data on the particle concentration at the section. At the same time, an advanced particle size analyzer is used to analyze the particle size of the filtered particles in detail using technologies such as laser diffraction and dynamic light scattering. The instrument can quickly and accurately determine the distribution of particles in different particle size ranges, determine the residual proportion of particles of different sizes and the changing trend of particle size distribution under filtration. In addition, a speed measurement device, such as a micro hot wire anemometer or a laser Doppler effect-based velocimeter, can be introduced to measure the movement speed of particles after filtration. This helps to understand the impact of the filtration process on the particle motion state, such as whether the particle speed is reduced or changed direction due to factors such as collision with the filter material. During the entire test process, these sampling devices are started synchronously for data collection in strict accordance with the established sampling interval. At each sampling moment, the concentration value measured by the particle concentration sensor, the particle size distribution data obtained by the particle size analyzer, and the particle speed information obtained by the speed measurement device are recorded. The data obtained at different sampling moments are systematically organized and analyzed to form a data set about the time series, which is the second distribution sampling. This sampling result can clearly show the changes in the distribution state of the flue gas particles at the second spatial cross section after the filter component acts, including the reduction in particle concentration, the filtration efficiency of particles of a specific particle size, and the degree of adjustment of the particle movement speed. Important information provides key data support for a comprehensive evaluation of the performance of the filter component.

[0058] The first distribution sampling and the second distribution sampling obtained previously are organically combined to determine a complete set of particle distribution characteristics. For the first distribution sampling, it contains the initial state information of the smoke particles at the generation position (the first spatial section), such as the initial particle concentration distribution accurately measured by the laser particle counter, covering the number of particles of different sizes at the section; the initial spatial distribution morphology of the particles reflected by the image taken by the high-speed camera, including the aggregation area, dispersion degree and overall distribution pattern of the particles; and the particle-related characteristics detected by the optical sensor, such as color, gloss and other information that may be related to the composition or physical state of the particles. The second distribution sampling provides the state change data of the particles at the first position (the second spatial section) after passing through the filter component. Among them, the residual particle concentration recorded by the high-precision particle concentration sensor intuitively shows the effect of the filtration process on the reduction of the number of particles; the particle size distribution after filtration obtained by the particle size analyzer clarifies the retention of particles of different sizes after filtration, which can be used to evaluate the interception ability of the filter component for particles of different sizes; the particle motion speed information obtained by the speed measurement device reflects the impact of the filtration process on the particle motion state, such as whether the particle speed changes due to factors such as collision with the filter material. By correlating and integrating these two parts of sampling data, a set of particle distribution characteristics is constructed. For example, by comparing the initial particle concentration in the first distribution sampling with the residual particle concentration in the second distribution sampling, the particle retention rate is calculated to measure the efficiency of the filter component in removing particles; the changes in particle size distribution before and after filtration are analyzed to determine the selective filtration effect of the filter component on particles in different particle size ranges; combined with the particle motion speed information, the change law of particle momentum during the filtration process and its impact on the overall distribution are explored. Combining these aspects of information, a set of comprehensive and detailed particle distribution characteristics is formed, which can accurately describe the distribution evolution of smoke particles from generation to filtration under specific suction characteristics and filtration conditions, providing a solid data foundation for in-depth research on the performance of filter components and further optimization of filtration processes.

[0059] In a possible implementation, step S330 further includes:

[0060] Step S331: mapping the multiple groups of particle distribution features to periodic nodes, and measuring the performance difference for the mapping group of the Nth periodic node.

[0061] Step S332: Based on the performance difference, determine the influence of the suction characteristics on the filtering performance.

[0062] Specifically, for multiple groups of particle distribution features obtained through multiple simulation experiments, we first perform precise mapping operations on the periodic nodes. Each group of particle distribution features is carefully divided into several equally spaced periodic nodes based on a pre-set time period to ensure that the data of different simulation groups (corresponding to different suction features) can be accurately aligned in the time dimension. Then, we focus on the Nth periodic node and extract the particle distribution data of all simulation groups at this specific periodic node to form a special mapping group. For this Nth periodic node mapping group, we conduct in-depth performance differential measurement work. Among them, the key performance parameters include particle concentration difference. By using high-precision particle concentration measuring instruments (such as laser particle counters), the initial particle concentration before filtration and the residual particle concentration after filtration at the Nth cycle node under different suction characteristics are obtained respectively, and the difference between the two is calculated. This difference can intuitively reflect the difference in particle removal effect of the filter component under different suction conditions at this cycle node; particle size distribution change difference. By using advanced particle size analysis technology (such as laser diffraction particle size analyzer), the change in the proportion of particles of different sizes before and after filtration at the Nth cycle node under different suction conditions is measured, so as to evaluate the difference in the selective filtration ability of the filter component for particles of various sizes; particle velocity change difference. With the help of a high-speed camera or a velocimeter based on the laser Doppler effect, the change in particle velocity at the Nth cycle node after filtration is recorded and analyzed, which helps to deeply understand the impact of suction characteristics on the movement state of particles in the filter component. By accurately measuring these key performance parameters, the difference in filtration performance caused by different suction characteristics at the Nth cycle node can be fully quantified, thus providing a solid data foundation for subsequent in-depth analysis of the relationship between suction characteristics and filtration performance, which helps to further optimize the performance and efficiency of the smoke filtration system.

[0063] A comprehensive and detailed comparative analysis of the performance difference data at different cycle nodes (especially the Nth cycle node) was conducted. Carefully observe the corresponding change patterns of the filtration performance difference (such as particle concentration difference, particle size distribution change difference, particle speed change difference, etc.) as the suction characteristics (such as suction pressure, suction speed, etc.) change. For example, if the particle concentration difference is found to increase continuously as the suction pressure gradually increases, this indicates that the filter component has a stronger ability to remove particles and a higher filtration efficiency under higher suction pressure; if the particle size distribution change difference shows that the filtration effect on small-sized particles is significantly enhanced, while the filtration effect on large-sized particles does not change significantly, this means that the change in suction pressure has an impact on the filtration selectivity of particles of different sizes, causing the filter component to be more inclined to intercept small-sized particles under high suction pressure. Then, using statistical analysis methods (such as multivariate linear regression analysis, correlation analysis, etc.), the suction characteristics are taken as independent variables and the filtration performance difference is taken as the dependent variable to construct a mathematical model between the two. The model accurately quantifies the quantitative influence relationship between the suction characteristics and the filtration performance, and determines the specific influence degree and direction of the change of each suction characteristic parameter (such as suction pressure, suction speed) on the various filtration performance indicators (such as particle removal rate, particle size selective filtration capacity, particle speed adjustment degree, etc.). The determination of this influence relationship can provide a key basis for the optimization design of the filter component, and adjust the suction conditions according to actual needs to achieve the best filtration effect; at the same time, it also provides a theoretical basis for predicting the performance of the filter component under different working conditions, which is helpful to evaluate and optimize the operating efficiency and reliability of the flue gas filtration system in various practical application scenarios in advance, and further improve the performance and stability of the entire filtration system. For example, if it is determined that the suction pressure is positively correlated with the particle removal rate, and the correlation coefficient is high, then in actual applications, the suction pressure can be appropriately increased to enhance the filtration effect, but at the same time, other effects that may be caused by excessive pressure (such as increased energy consumption, accelerated loss of filter materials, etc.) must be considered, so as to achieve precise regulation and optimization management of the filtration system.

[0064] In a possible implementation, step S500 further includes:

[0065] Step S510: determine the particle distribution characteristics of the first periodic node, measure the difference between the first spatial cross-section and the second spatial cross-section, and determine the first performance coefficient.

[0066] Step S520: for the entire life cycle of the filter component, the performance coefficient is measured node by node period by period to obtain the Nth performance coefficient.

[0067] Step S530: construct a filtering performance attenuation curve based on the first performance coefficient to the Nth performance coefficient, wherein the slope of the curve represents the attenuation rate.

[0068] Specifically, we must first determine the particle distribution characteristics of the first period node. In the first spatial section, that is, the spatial section where the smoke particles are generated, a high-resolution laser particle counter is used to accurately measure the particle concentration. The number and concentration distribution of particles of different sizes are calculated by detecting the changes in scattered light after the emitted laser beam interacts with the particles. At the same time, a high-speed camera shoots the section at a high frame rate to capture the instantaneous spatial distribution state of the particles, such as the aggregation area, dispersion degree and other intuitive information, as well as the approximate movement direction of the particles on the two-dimensional plane. In addition, optical sensors detect the color, gloss and other characteristics of the particles to assist in determining the composition or physical state of the particles. In the second spatial section, that is, the spatial section at the first position after filtration, the high-precision particle concentration sensor measures the signal changes passing through the flue gas flow in real time based on specific physical principles (such as electrical or optical principles), thereby accurately deriving the residual concentration of particles; advanced particle size analyzers use laser diffraction, dynamic light scattering and other technologies to carefully analyze the distribution of particles of different sizes after filtration, determine the residual proportion of particles of each size and the changing trend of particle size distribution; speed measurement devices (such as miniature hot wire anemometers or laser Doppler velocimeters) measure the movement speed of particles after filtration to understand the impact of the filtration process on the movement state of particles. Then, the difference between the first spatial section and the second spatial section is measured to calculate the particle concentration difference, that is, the initial particle concentration in the first spatial section minus the residual particle concentration in the second spatial section. This difference intuitively reflects the particle removal effect of the filter component; the particle size distribution change difference is obtained by comparing the proportional changes of particles of different sizes in the two sections before and after filtration, which can evaluate the selective filtration ability of the filter component for particles of different sizes; the particle velocity change difference is the initial particle velocity in the first spatial section minus the particle velocity after filtration in the second spatial section, which reflects the degree of change in the particle motion state during the filtration process. Finally, the first performance coefficient is determined based on these differential data. The weighted summation method is adopted to multiply the particle concentration difference by the corresponding weight coefficient a, the particle size distribution change difference by the weight coefficient b, and the particle velocity change difference by the weight coefficient c, and then add them together to obtain the first performance coefficient, for example, K1=a×ΔC+b×ΔD+c×ΔV, where ΔC is the particle concentration difference, ΔD is the particle size distribution change difference, and ΔV is the particle velocity change difference. The value of the weight coefficient can be determined according to the actual situation and the importance of each parameter on the filtration performance, so as to preliminarily quantify the filtration performance of the filter component in the initial stage of the test.

[0069] According to the pre-set periodic node division method, the entire process of the filter component from the beginning of use to the final failure or reaching the predetermined service life is carefully divided into multiple periodic nodes set at equal intervals or according to specific logic. After the first periodic node, each subsequent periodic node is entered in turn to measure the performance coefficient. At each periodic node, the measurement and analysis operations similar to the first periodic node are repeated. In the first spatial section corresponding to the periodic node (the spatial section where the flue gas particles are generated), a high-resolution laser particle counter is used to accurately measure the particle concentration distribution, a high-speed camera records the particle spatial distribution state and movement direction, and an optical sensor detects particle-related characteristics; in the second spatial section (the spatial section at the first position after filtration), the residual particle concentration is obtained by a high-precision particle concentration sensor, the particle size distribution change is analyzed by an advanced particle size analyzer, and the particle speed change is measured by a speed measurement device. Then, the difference between the first spatial section and the second spatial section at the periodic node is calculated, including the particle concentration difference (initial particle concentration minus residual particle concentration), the particle size distribution change difference (comparison of the change in the proportion of particles of different sizes before and after filtration), and the particle speed change difference (initial particle speed minus the particle speed after filtration). Based on these differential data, the performance coefficient of the cycle node is calculated using the same calculation formula as that for determining the first performance coefficient. The performance coefficient is measured cycle by cycle node until the last cycle node or the specified Nth cycle node in the entire life cycle is reached, thereby obtaining the Nth performance coefficient. These performance coefficient sequences comprehensively record the changes in the filtration performance of the filter component at different stages during the entire use process, providing a data basis for the subsequent construction of the filtration performance attenuation curve, which helps to deeply analyze the evolution of the performance of the filter component, and thus provides a key basis for optimizing the use, maintenance and replacement strategies of the filter component.

[0070] The construction of the filtration performance attenuation curve is based on the first performance coefficient to the Nth performance coefficient obtained previously. First, the periodic nodes within the test cycle are used as the horizontal coordinates. Usually, the periodic node numbers (such as 1, 2, 3...N) with equal time intervals or the actual use time (such as hours, days, etc.) can be selected as the scale of the horizontal coordinate. Then, the corresponding first performance coefficient is used as the starting point of the vertical coordinate, and the subsequent second performance coefficient, third performance coefficient, and Nth performance coefficient are used as the values ​​of the vertical coordinates in turn, and these coordinate points are plotted one by one in the plane rectangular coordinate system. Then, the least squares method is used to fit these discrete data points to obtain a continuous curve that can approximately reflect the trend of filtration performance with periodic nodes. This is the filtration performance attenuation curve. On this curve, the slope of the curve is of great significance, which characterizes the attenuation rate of filtration performance. The larger the slope, the faster the filtration performance decreases at this stage, which may mean that the filter component is under a large load during this period, the filter material is gradually blocked or the adsorption saturation is more serious; while the smaller the slope, the relatively slow attenuation of filtration performance. The filter component can still maintain a relatively good filtration effect at this stage. It may be that the self-cleaning ability of the filter component and the stability of the filter material are at work. Through the analysis of the filtration performance attenuation curve, we can intuitively understand the performance change law of the filter component throughout the test cycle or the life cycle. For example, predict the performance level of the filter component at a certain cycle node in the future, so as to provide a scientific basis for the reasonable arrangement of the maintenance and replacement time of the filter component. If it is found that the slope of the curve suddenly increases within a certain period of time, it is suggested that the filter component needs to be inspected or maintained in advance to ensure its continuous and stable filtration performance and ensure the normal operation of the entire flue gas filtration system.

[0071] Embodiment 2, based on the same inventive concept as the attenuation analysis method of smoke component filtration performance in the previous embodiment, Figure 2 As shown, the present application provides a system for analyzing the attenuation of smoke component filtration performance, and the system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0072] The particle non-targeting rule determination module 10 is used to introduce a tracking medium, perform smoke flow testing and statistical analysis through a smoking machine, and determine the particle non-targeting rule of mainstream smoke components, wherein the particle non-targeting rule characterizes the particle distribution and migration rule, and the mainstream smoke components are classified as harmful components.

[0073] The fluid dynamics model building module 20 is used to perform physical modeling on the filter component and to build a fluid dynamics model in combination with the non-targeted law.

[0074] The smoke filtration dynamic simulation module 30 is used to perform smoke filtration dynamic simulation based on the fluid dynamics model using the suction characteristics as simulation variables, and monitor the first spatial cross section.

[0075] The particle distribution characteristic determination module 40 is used to determine the particle distribution characteristics of the mainstream smoke component within the test period with the second spatial cross-section, wherein the first spatial cross-section is the spatial cross-section of the smoke particle generation position, and the second spatial cross-section is the spatial cross-section of the first position after filtration.

[0076] The filtering performance attenuation curve construction module 50 constructs a filtering performance attenuation curve based on the particle distribution characteristics.

[0077] Furthermore, the particle non-targeting regularity determination module 10 further includes:

[0078] The tracing medium configuration unit is used to determine the mainstream smoke components and configure the tracing medium for each mainstream smoke component.

[0079] A particle motion data monitoring unit is provided, wherein the particle motion data monitoring unit is based on the smoking machine and monitors the particle motion data of the tracking medium by controlling pump suction parameters.

[0080] A particle non-targeting law determining unit is configured to perform statistical analysis based on the particle motion data and to mine the overall motion characteristics of each mainstream smoke component to determine the particle non-targeting law.

[0081] Furthermore, the fluid dynamics model building module 20 also includes:

[0082] The first smoke flow impact determination unit is used to determine a first smoke flow impact with respect to a filtration inertial resistance of the filter assembly, wherein the filtration inertial resistance is determined based on a porosity of the filter assembly and a filter layer thickness.

[0083] The second smoke flow impact determining unit is configured to determine a second smoke flow impact based on a suction characteristic, wherein the suction characteristic at least includes a suction pressure and a suction speed.

[0084] A filtering simulation constraint unit is used to perform filtering simulation constraints on the fluid dynamics model by taking the first smoke flow influence as a quantitative constraint condition and taking the second smoke flow influence as a variable constraint condition.

[0085] Furthermore, the smoke filtration dynamic simulation module 30 also includes:

[0086] A multi-class suction feature determination unit is used to determine a multi-class suction feature for the simulation variable, wherein the multi-class suction feature includes a multi-level suction pressure-suction speed feature combination.

[0087] A unit for determining multiple groups of particle distribution characteristics is provided. The unit for determining multiple groups of particle distribution characteristics performs parallel simulation and sampling in multiple test cycles based on the multiple types of suction characteristics to determine multiple groups of particle distribution characteristics.

[0088] A mean calculation unit is used to perform period node mapping and mean calculation on the multiple groups of particle distribution characteristics as the particle distribution characteristics within a test period.

[0089] Furthermore, the multiple groups of particle distribution feature determination units also include:

[0090] A sampling interval setting unit is used to set a sampling interval according to the multiple types of puff characteristics, wherein the sampling interval is negatively correlated with the puff characteristics.

[0091] The first distribution sampling determination unit is used to sample the first spatial cross section to determine a first distribution sampling.

[0092] The second distribution sampling determination unit is used to sample the second spatial cross section at the sampling interval to determine the second distribution sampling.

[0093] A group of particle distribution feature determination units are provided, and the group of particle distribution feature determination units are used to combine the first distribution sampling and the second distribution sampling to determine a group of particle distribution features.

[0094] Furthermore, the mean value calculation unit further includes:

[0095] A performance difference measurement unit is used to map the multiple groups of particle distribution characteristics to periodic nodes, and to measure the performance difference for the mapping group of the Nth periodic node.

[0096] An influence relationship determination unit is configured to determine an influence relationship of the suction characteristic on the filtering performance based on the performance difference.

[0097] Furthermore, the filtering performance attenuation curve building module 50 also includes:

[0098] The first performance coefficient determination unit is used to determine the particle distribution characteristics of the first period node, measure the difference between the first spatial cross-section and the second spatial cross-section, and determine the first performance coefficient.

[0099] The Nth performance coefficient acquisition unit is used to measure the performance coefficient of each node period by period during the entire life cycle of the filtering component to obtain the Nth performance coefficient.

[0100] A filtering performance attenuation curve construction unit, wherein the filtering performance attenuation curve is used to construct a filtering performance attenuation curve according to the first performance coefficient to the Nth performance coefficient, wherein the slope of the curve represents the attenuation rate.

[0101] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0102] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0103] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for analyzing the attenuation of flue gas component filtration performance, characterized in that: The method comprises: Introducing a tracking medium, performing smoke flow testing and statistical analysis through a smoking machine, and determining the particle non-targeting law of mainstream smoke components, wherein the particle non-targeting law characterizes the particle distribution and migration law, and the mainstream smoke components are classified as harmful components; Physically modeling the filter component and constructing a fluid dynamics model in combination with the non-targeted law; Taking the suction characteristics as simulation variables, performing dynamic simulation of smoke filtration based on the fluid dynamics model, and monitoring the first spatial cross section; and a second spatial cross section, determining the particle distribution characteristics of the mainstream smoke component in the test period, wherein the first spatial cross section is the spatial cross section of the smoke particle generation position, and the second spatial cross section is the spatial cross section of the first position after filtration; Based on the particle distribution characteristics, construct a filtration performance attenuation curve; Wherein, constructing the fluid dynamics model comprises: Determining a first smoke flow impact with respect to a filtration inertial resistance of the filter assembly, wherein the filtration inertial resistance is determined based on a porosity of the filter assembly and a filter layer thickness; Determining a second smoke flow impact based on a puff characteristic, wherein the puff characteristic includes at least a puff pressure and a puff speed; Taking the first smoke flow influence as a quantitative constraint condition and taking the second smoke flow influence as a variable constraint condition, performing filtering simulation constraints on the fluid dynamics model; The dynamic simulation of smoke filtration based on the fluid dynamics model includes: For the simulation variables, determining multiple types of suction characteristics, wherein the multiple types of suction characteristics include characteristic combinations of multiple levels of suction pressure-suction speed; Based on the multiple types of suction characteristics, parallel simulation and sampling are performed under multiple test cycles to determine multiple groups of particle distribution characteristics; The multiple groups of particle distribution characteristics are mapped at periodic nodes and their averages are calculated as the particle distribution characteristics within a test period.

2. The attenuation analysis method of smoke component filtration performance according to claim 1, characterized in that: The non-targeted law of particles determining mainstream smoke components includes: Determining the mainstream smoke components, and configuring a tracking medium for each mainstream smoke component; Based on the smoking machine, monitoring the particle movement data of the tracking medium by controlling the pump suction parameters; Based on the particle motion data, statistical analysis is performed and the overall motion characteristics of each mainstream smoke component are mined to determine the non-targeting law of the particles.

3. The attenuation analysis method of smoke component filtration performance according to claim 1, characterized in that: Before performing parallel simulation and sampling under multiple test cycles, including: According to the multiple types of suction characteristics, a sampling interval is set, wherein the sampling interval is negatively correlated with the suction characteristics; Sampling the first spatial cross section to determine a first distribution sample; Sampling the second spatial cross section at the sampling interval to determine a second distribution sample; The first distribution sample is combined with the second distribution sample to determine a set of particle distribution characteristics.

4. The attenuation analysis method of smoke component filtration performance according to claim 1, characterized in that: The method comprises: Mapping the plurality of groups of particle distribution features to periodic nodes, and measuring the performance difference of the mapping group of the Nth periodic node; Based on the performance difference, the influence relationship of the suction characteristics on the filtering performance is determined.

5. The attenuation analysis method of smoke component filtration performance according to claim 1, characterized in that: Based on the particle distribution characteristics, a filtration performance attenuation curve is constructed, including: Determine the particle distribution characteristics of the first period node, measure the difference between the first spatial cross section and the second spatial cross section, and determine a first performance coefficient; For the entire life cycle of the filter component, the performance coefficient is measured at each cycle node to obtain the Nth performance coefficient; A filtering performance attenuation curve is constructed based on the first performance coefficient to the Nth performance coefficient, wherein the slope of the curve represents the attenuation rate.

6. A system for analyzing the attenuation of the filtration performance of smoke components, characterized in that: The system is used to implement the attenuation analysis method of smoke component filtration performance according to any one of claims 1 to 5, and the system comprises: A particle non-targeting rule determination module, which is used to introduce a tracking medium, perform smoke flow testing and statistical analysis through a smoking machine, and determine the particle non-targeting rule of mainstream smoke components, wherein the particle non-targeting rule represents the particle distribution and migration rule, and the mainstream smoke components are classified as harmful components; A fluid dynamics model building module, the fluid dynamics model building module is used to perform physical modeling on the filter component and build a fluid dynamics model in combination with the non-targeted law; A smoke filtration dynamic simulation module, the smoke filtration dynamic simulation module is used to perform a smoke filtration dynamic simulation based on the fluid dynamic model using the suction characteristics as simulation variables, and monitor the first spatial cross section; a particle distribution characteristic determination module, the particle distribution characteristic determination module is used to determine the particle distribution characteristics of the mainstream smoke component in the test period with the second spatial cross section, wherein the first spatial cross section is the spatial cross section of the smoke particle generation position, and the second spatial cross section is the spatial cross section of the first position after filtering; A filtering performance attenuation curve construction module is used to construct a filtering performance attenuation curve based on the particle distribution characteristics.

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