Method and device for establishing prediction model and method and device for predicting interception efficiency

Abstract

The invention provides a method and device for establishing a prediction model and a method and device for predicting interception efficiency, and relates to the field of cigarette performance analysis. The method for establishing the prediction model comprises the following steps: acquiring a mass transfer model and a heat transfer model of aerosol particles in a filter part of a cigarette; obtaining an interception model of the aerosol particles by the filtering part; and according to the mass transfer model, the heat transfer model and the interception model, establishing a prediction model of the interception efficiency of the filtering part. Therefore, the quality of the related smoke components in the smoke flowing out of the cigarette branches can be predicted more accurately, and the accuracy of the established interception efficiency prediction model is higher.

Description

Technical Field

[0001] The present disclosure relates to the field of cigarette performance analysis, and more specifically, to a method and device for establishing a prediction model, and a method and device for predicting interception efficiency. Background Art

[0002] During the process of smoking, cigarettes usually intercept smoke components, and consumers' smoking experience is usually closely related to the smoke flowing out of the cigarettes. Therefore, there is a need to analyze the efficiency of cigarettes in intercepting smoke components.

[0003] In the related art, the movement state of the fluid in the cigarette can be analyzed based on computational fluid dynamics (CFD) to predict the mass of relevant smoke components in the smoke flowing out of the cigarette, thereby predicting the interception efficiency of the cigarette for the relevant smoke components. Summary of the invention

[0004] A brief overview of the disclosure is given below in order to provide a basic understanding of some aspects of the disclosure. However, it should be understood that this overview is not an exhaustive overview of the disclosure. It is not intended to identify the key or important parts of the disclosure, nor is it intended to limit the scope of the disclosure. Its purpose is simply to give some concepts of the disclosure in a simplified form as a prelude to a more detailed description given later.

[0005] The inventors noticed that the interception efficiency predicted in the related art is not accurate. After analysis, it was found that the related art analyzed the motion state of the fluid in the cigarette based on computational fluid dynamics (CFD), but did not consider the microscopic behavior of the aerosol particles in the cigarette. The predicted mass of the relevant smoke components was quite different from the actual situation, which led to the low accuracy of the subsequent predicted interception efficiency.

[0006] In order to solve the above problems, the embodiments of the present disclosure propose the following solutions.

[0007] According to a first aspect of an embodiment of the present disclosure, a method for establishing a prediction model is provided, comprising:

[0008] Obtaining a mass transfer model and a heat transfer model of aerosol particles in the filter portion of a cigarette;

[0009] Obtaining a retention model of the aerosol particles by the filter unit;

[0010] A prediction model for the interception efficiency of the filtration unit is established based on the mass transfer model, the heat transfer model and the interception model.

[0011] In some embodiments, obtaining the interception model of the aerosol particles by the filter unit includes:

[0012] Obtaining a retention mechanism of the aerosol particles by the filter unit;

[0013] The interception rate is determined according to the interception mechanism, wherein the interception rate is used to reflect the change of the number of aerosol particles over time.

[0014] In some embodiments, the trapping mechanism includes at least one of a first trapping mechanism based on diffuse deposition, a second trapping mechanism based on direct interception, and a third trapping mechanism based on inertial impaction.

[0015] In some embodiments, determining the retention rate according to the retention mechanism comprises:

[0016] Determining a first retention rate, a second retention rate, and a third retention rate according to the first retention mechanism, the second retention mechanism, and the third retention mechanism, respectively;

[0017] The rejection rate is determined based on the first rejection rate, the second rejection rate, and the third rejection rate.

[0018] In some embodiments, the mass transfer model is based on the following equation:

[0019]

[0020] Among them, m p is the mass of aerosol particles, t is the time, N i is the mass transfer flux of the i-th component in the smoke generated by the cracking of tobacco shreds, M w,i is the molar mass of the i-th component, A p is the surface area of ​​aerosol particles.

[0021] In some embodiments, the heat transfer model is based on the following equation:

[0022]

[0023] Where H1=h*A p *(T ∞ -T p ), m p is the mass of aerosol particles, t is the time, C p is the specific heat capacity of aerosol particles, T p is the temperature of aerosol particles, h is the convective heat transfer coefficient, A p is the surface area of ​​aerosol particles, T ∞ is the temperature of the smoke generated by the decomposition of tobacco shreds, is the mass transfer rate of the i-th component in the flue gas, h fg,iis the phase change enthalpy of the i-th component, and N is the number of components in the flue gas.

[0024] According to a second aspect of an embodiment of the present disclosure, a method for predicting interception efficiency is provided, comprising:

[0025] Performing grid division according to the geometric structure of the cigarette to be tested, wherein the cigarette to be tested has a filter portion;

[0026] Acquiring characteristic parameters of the cigarette to be tested;

[0027] According to the prediction model established by the method described in any one of the above embodiments, the divided grids and the characteristic parameters, the interception efficiency of the filter part of the cigarette to be tested is predicted.

[0028] In some embodiments, obtaining the characteristic parameter includes:

[0029] Obtaining the substance content of at least one component in the smoke generated by decomposition of the tobacco shreds of the tested cigarette, and using it as a first boundary condition for the inlet substance content of the filter unit;

[0030] Obtaining the suction speed of the cigarette to be tested and using it as a second boundary condition for the inlet speed of the filter portion; and

[0031] Acquiring parameters related to the filling material of the filter portion;

[0032] The interception efficiency is determined according to the prediction model, the first boundary condition, the second boundary condition and parameters related to the filling material.

[0033] According to a third aspect of an embodiment of the present disclosure, there is provided an apparatus for establishing a prediction model, comprising a module configured to execute the method for establishing a prediction model of any one of the above embodiments.

[0034] According to a fourth aspect of an embodiment of the present disclosure, there is provided an apparatus for predicting interception efficiency, comprising a module configured to execute the method for predicting interception efficiency of any one of the above embodiments.

[0035] According to a fifth aspect of an embodiment of the present disclosure, an electronic device is provided, including a processor and a memory, wherein instructions are stored in the memory, and when the instructions are executed by the processor, a method of any one of the above embodiments is implemented.

[0036] According to a sixth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, comprising instructions, which, when executed by a processor, implement a method of any one of the above embodiments.

[0037] According to a seventh aspect of an embodiment of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method of any one of the above embodiments is implemented.

[0038] According to some embodiments of the present disclosure, through the mass transfer model and heat transfer model of aerosol particles, as well as the retention model of aerosol particles by the filter section, the process of nucleation, growth, and extinction (being retained in the filter section or leaving the filter section) of aerosol particles in the filter section of the cigarette can be simulated to simulate the life cycle of aerosol particles, thereby more accurately predicting the mass of relevant smoke components in the smoke flowing out of the cigarette, and the established prediction model of retention efficiency has a higher accuracy.

[0039] Other features and advantages of the present disclosure will become more apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The foregoing and other features and advantages of the present disclosure will become apparent from the following description of the embodiments of the present disclosure in conjunction with the accompanying drawings. The accompanying drawings are incorporated herein and form a part of the specification, and are further used to explain the principles of the present disclosure and enable those skilled in the art to make and use the present disclosure. Among them:

[0041] Figure 1 A schematic diagram of a process for establishing a prediction model according to some embodiments of the present disclosure is shown;

[0042] Figure 2 A schematic diagram of a process for obtaining a retention model of aerosol particles by a filter unit in a method for establishing a prediction model according to some embodiments of the present disclosure is shown;

[0043] Figure 3 A schematic flow chart of a method for predicting interception efficiency according to some embodiments of the present disclosure is shown;

[0044] Figure 4 A schematic structural diagram of a unitary acetate tow filter rod according to some embodiments of the present disclosure is shown;

[0045] Figure 5 A schematic structural diagram of a one-unit hollow filter rod according to some embodiments of the present disclosure is shown;

[0046] Figure 6 A schematic structural diagram of a one-yuan straight-line filter rod according to some embodiments of the present disclosure is shown;

[0047] Figure 7 A schematic structural diagram of a one-piece twisted filter rod according to some embodiments of the present disclosure is shown;

[0048] Figure 8A schematic structural diagram of a binary filter rod according to some embodiments of the present disclosure is shown;

[0049] Fig. 9 Shown are schematic structural diagrams of binary filter rods according to other embodiments of the present disclosure;

[0050] Fig.10 A schematic structural diagram of a ternary filter rod according to some embodiments of the present disclosure is shown;

[0051] Fig.11 A schematic diagram of the structure of an apparatus for establishing a prediction model according to some embodiments of the present disclosure is shown;

[0052] Fig.12 A schematic diagram of the structure of a device for predicting interception efficiency according to some embodiments of the present disclosure is shown;

[0053] Fig.13 A schematic diagram of the structure of an electronic device according to some embodiments of the present disclosure is shown;

[0054] Fig.14 A schematic diagram of the structure of a computer system that can implement some embodiments of the present disclosure is shown.

[0055] For ease of understanding, the position, size, range, etc. of each structure shown in the drawings and the like may not represent the actual position, size, range, etc. Therefore, the present disclosure is not limited to the position, size, range, etc. disclosed in the drawings and the like. DETAILED DESCRIPTION

[0056] Various exemplary embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure.

[0057] The following description of at least one exemplary embodiment is in fact merely illustrative and is in no way intended to limit the present disclosure and its application or use. That is, the structures and methods herein are shown in an exemplary manner to illustrate different embodiments of the structures and methods in the present disclosure. However, those skilled in the art will appreciate that they merely illustrate exemplary ways of the present disclosure that can be implemented, rather than exhaustive ways. In addition, the drawings need not be drawn to scale, and some features may be enlarged to illustrate the details of specific components.

[0058] In addition, technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0059] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0060] Figure 1 A flowchart of a method for establishing a prediction model according to some embodiments of the present disclosure is shown.

[0061] like Figure 1 As shown, the method for establishing a prediction model according to some embodiments of the present disclosure may include steps S110 to S130.

[0062] In step S110, a mass transfer model and a heat transfer model of aerosol particles in the filter portion of the cigarette are obtained.

[0063] The mass transfer model can reflect the change of the mass of aerosol particles over time, and the heat transfer model can reflect the change of the temperature of aerosol particles over time. The mass transfer model and heat transfer model of aerosol particles can be used to simulate the nucleation and growth process of aerosol particles in the filter part of the cigarette.

[0064] In step S120, a retention model of aerosol particles by the filter unit is obtained.

[0065] The interception model can reflect the change in the number of aerosol particles trapped in the filter over time. The interception model of aerosol particles by the filter can simulate the process of aerosol particles dying in the filter (for example, being trapped in the filter or leaving the filter), thereby simulating the life cycle of aerosol particles in the filter.

[0066] In step S130, a prediction model of the interception efficiency of the filtration unit is established based on the mass transfer model, the heat transfer model and the interception model.

[0067] According to some embodiments of the present disclosure, the life cycle of aerosol particles can be simulated through the mass transfer model and heat transfer model of aerosol particles and the interception model of aerosol particles by the filter part, and the change of the state of aerosol particles in the filter part of the cigarette within a unit time step can be analyzed, so as to more accurately predict the mass of relevant smoke components in the smoke flowing out of the cigarette, and the established prediction model of interception efficiency has higher accuracy.

[0068] In some embodiments, the aerosol particles may have correspondingly different mass transfer models for different components in the composition of smoke. The composition of smoke may include, for example, glycerin, propylene glycol, nicotine, and water.

[0069] The direction of mass transfer of aerosol particles is determined by evaporation and condensation, that is, the direction of mass transfer of aerosol particles is determined by the direction of heat transfer. For example, the mass transfer of aerosol particles can be determined by the heat transfer. The evaporation and condensation processes can be controlled by the law of diffusion. When the evaporation amount of a component is greater than the condensation amount, the mass of the component decreases; otherwise, the mass of the component increases.

[0070] In some embodiments, the mass transfer model can be based on the mass transfer equation Establish, where m p is the mass of aerosol particles, in units such as kg; t is the time, in units such as s; represents the derivative of mass with respect to time, where d is the differential operator; N i is the mass transfer flux of the i-th component in the smoke generated by the decomposition of tobacco shreds, in units such as kmol / m 2 *s -1 ;M w,i is the molar mass of the i-th component, in units such as kg / kmol; A p is the surface area of ​​the aerosol particles, in units such as m 2 .

[0071] Herein, the i-th component may be any component in smoke, for example, glycerol, propylene glycol, nicotine or water.

[0072] As some implementations, by calculating the mass transfer flux N i , combined with the given A p 、M w,i The mass transfer rate can be solved Among them, the mass transfer flux N i It can be calculated by the following formula:

[0073] N i =k c *(C i,s -C i,∞ ).

[0074] k c is the mass transfer coefficient, in units such as m / s; C i,s is the gas phase concentration of the i-th component on the surface of the aerosol particles, in kmol / m 3 ; C i,∞ is the gas phase concentration of the i-th component in the flue gas, in units such as kmol / m 3 .

[0075] As some implementations, C i,s and C i,∞ It can be obtained by the following particle phase gas state equation:

[0076]

[0077] T p is the temperature of the aerosol particles, in units such as K; T ∞ is the temperature of the flue gas, in units such as K; p sat is the aerosol particle at T p The saturated vapor pressure of the i-th component at the temperature, in kPa for example; p is the absolute pressure inside the filter where the aerosol particles are located, in kPa for example; R is the gas constant, for example 8.314 J / mol*m -1 ;X i is the mole fraction of the ith component.

[0078] As some implementations, the mass transfer coefficient k c It can be calculated from the Sherwood number Sh. Specifically, the mass transfer coefficient k can be obtained according to the following formula: c :

[0079]

[0080] d p is the diameter of the aerosol particles, in units such as m; D i,m is the diffusion coefficient of the i-th component in the smoke, the unit is, for example, m 2 / s, where D i,m It can be obtained according to the following formula:

[0081]

[0082] M i 、M x are the molar masses of the i-th component and the flue gas respectively; ∑v i ,∑v x are the molecular diffusion volumes of the i-th component and the smoke respectively; p is the total pressure of the smoke, for example, in Pa; T is the thermodynamic temperature of the smoke, for example, in K.

[0083] As some implementations, the Sherwood number Sh can be obtained according to the Reynolds number Re and the Schmidt number Sc. Specifically, it can be obtained according to the formula Get the Sherwood number Sh.

[0084] As some implementations, the Reynolds number Re and the Schmidt number Sc may be obtained by the following formulas:

[0085]

[0086] ρ is the smoke density, the unit is, for example, kg / m 3 ; and are the velocities of aerosol particles and smoke respectively, with units such as m / s; μ is the dynamic viscosity, with units such as kg / m*s.

[0087] The heat transfer of aerosol particles can be divided into the convection heat transfer between aerosol particles and flue gas phase H1 and the phase change heat transfer H2 caused by gas phase condensation mass transfer.

[0088] In some embodiments, the heat transfer model can be based on the heat transfer equation Establish, where m p is the mass of aerosol particles, in units such as kg; t is the time, in units such as s; c p is the specific heat capacity of aerosol particles, in units such as J / kg*K -1 ; T p is the temperature of aerosol particles, in K; represents the derivative of temperature with respect to time, where d is the differential operator.

[0089] As some implementations, H1 can be expressed by the formula H1=h*A p *(T ∞ -T p ) is obtained, where h is the convective heat transfer coefficient, the unit is, for example, W / m 2 *K -1 ; A p is the surface area of ​​aerosol particles, m 2 ; T ∞ is the temperature of the flue gas, the unit is, for example, K.

[0090] As some implementations, the convective heat transfer coefficient h can be calculated based on the Nusselt number Nu. Specifically, it can be calculated based on the formula The convective heat transfer coefficient h is obtained, where d p is the diameter of the aerosol particles, in units such as m; k is the thermal conductivity of the smoke, in units such as W / m*K -1 .

[0091] As some implementations, the Nusselt number Nu can be obtained according to the Reynolds number Re and the Prandtl number Pr. Specifically, it can be obtained according to the formula Get the Nusselt number Nu.

[0092] As some implementations, the Reynolds number Re and the Prandtl number Pr may be obtained by the following formulas:

[0093]

[0094] ρ is the smoke density, the unit is, for example, kg / m 3 ; and are the velocities of aerosol particles and smoke respectively, with units such as m / s; μ is the dynamic viscosity, with units such as kg / m*s.

[0095] As some implementations, H2 can be obtained by the following formula:

[0096]

[0097] is the mass transfer rate of the i-th component in the flue gas, in units of kg / s; h i,fg is the phase change enthalpy of the i-th component, and its unit is, for example, J / kg; N is the number of components in the smoke composition, wherein N may be the total number of all components in the smoke composition or the total number of components to be studied in the smoke composition. For example, when the components to be studied are glycerol, propylene glycol, nicotine and water, N is 4.

[0098] Figure 2 A schematic diagram of a process for obtaining a retention model of aerosol particles by a filter unit in a method for establishing a prediction model according to some embodiments of the present disclosure is shown.

[0099] like Figure 2 As shown, obtaining the interception model of the filter part for aerosol particles includes step S210 and step S220.

[0100] In step S210, the interception mechanism of the filter unit for aerosol particles is obtained.

[0101] In step S220, the interception rate is determined according to the interception mechanism, wherein the interception rate is used to reflect the change of the number of aerosol particles over time.

[0102] The interception mechanism of the filter for aerosol particles can be predetermined, and the corresponding interception rate can be determined based on the interception mechanism, so as to establish an interception model of the filter for aerosol particles, thereby simulating the interception process of aerosol particles in the filter.

[0103] In some embodiments, the trapping mechanism may include at least one of a first trapping mechanism based on diffuse deposition, a second trapping mechanism based on direct interception, and a third trapping mechanism based on inertial impaction.

[0104] For the first interception mechanism, aerosol particles may diffuse and deposit due to Brownian motion, and aerosol particles with smaller diameters may leave the streamline and thus collide with the filling material (eg, acetate fiber bundle) in the filter portion and be captured.

[0105] As some implementations, the first retention rate η1 determined based on the first retention mechanism can be calculated according to the formula: Sure.

[0106] In the above formula, ε is the porosity of the filter, Ku is the Kuwabara fluid dynamic factor, and Pe is the Peclet number. As some implementations, Among them, u g is the gas flow rate, d is the equivalent diameter of the filler material (e.g., acetate tow), D is the particle diffusion coefficient, and in, K B is the Boltzmann constant, C u is the Cunningham correction factor, T is the gas temperature, μ is the fluid viscosity, d P is the diameter of aerosol particles, and λ is the mean free path of gas.

[0107] For the second interception mechanism, aerosol particles with larger diameters may be directly intercepted by the filling material (e.g., acetate fiber bundle) in the filter portion. For example, when the radius of the aerosol particle is greater than the distance between the streamline of the aerosol particle and the surface of the acetate fiber bundle, it will be captured by the acetate fiber bundle.

[0108] As some implementations, the second retention rate η2 determined based on the second retention mechanism can be calculated according to the formula: Determine, where G is the ratio of the diameter of the aerosol particles to the diameter of the filler material (e.g., acetate tow).

[0109] For the third interception mechanism, aerosol particles with larger diameters have high kinetic energy at relatively high flow rates, and the particles tend to maintain their original direction of motion and then impact the filling material (e.g., acetate tow) in the filter portion and be captured.

[0110] As some implementations, the third interception rate η3 determined based on the third interception mechanism can be calculated according to the formula Determine, where Stk is the Stokes number, and J = [29.6-28(1-ε) 0.62 ]*G 2 -27.5*G 2.8 .

[0111] In some embodiments, the interception mechanism may include a plurality of interception mechanisms among the first interception mechanism, the second interception mechanism, and the third interception mechanism, such as any two or all of the interception mechanisms. In this way, the determined interception rate is more accurate, thereby further improving the accuracy of the prediction model of interception efficiency.

[0112] In some embodiments, determining the interception rate according to the interception mechanism may include: determining the first interception rate η1, the second interception rate η2 and the third interception rate η3 according to the first interception mechanism, the second interception mechanism and the third interception mechanism, respectively, and determining the interception rate η according to the first interception rate η1, the second interception rate η2 and the third interception rate η3. In this way, the pre-established interception model can integrate the above three interception mechanisms to more accurately simulate the interception process of aerosol particles in the filter part, thereby further improving the accuracy of the interception efficiency prediction model established based on the interception model.

[0113] In some implementations, the interception rate η can be calculated according to the formula η=1-_1-η1`_1-η2`_1-η3`.

[0114] In some embodiments, the interception rate η can be determined according to any two of the first interception rate η1, the second interception rate η2 and the third interception rate η3. In some implementations, the interception rate η can be determined according to the first interception rate η1 and the second interception rate η2, that is, the contribution rate of η3 is very small and can be ignored. Specifically, it can be calculated according to the formula η=1-_1-η1`_1-η2`.

[0115] According to some embodiments of the present disclosure, in combination with the mass transfer model, heat transfer model and interception model obtained above, it is possible to analyze the change of the state of aerosol particles in the filter part over time, for example, it is possible to analyze the change of the mass and quantity of aerosol particles in the filter part within a unit time step. In this way, in the subsequent process of solving the prediction model by dividing the grid, the change of the mass and quantity of aerosol particles in each grid within a unit time step can be solved. In addition, it is also possible to obtain the flow state of the fluid in each grid by solving the established prediction model, so as to combine the flow state of the fluid in the filter part of the cigarette and the state of the aerosol particles to predict the mass of the relevant smoke components in the smoke flowing out of the cigarette, thereby predicting the interception efficiency of the filter part for the relevant smoke components.

[0116] As some implementations, the established prediction model can determine the interception efficiency based on the following formula Among them, m i1 is the mass of the i-th component retained in the filtration section, m i2 is the mass of the i-th component flowing out of the filtration unit. As another implementation, the retention efficiency f of the filtration unit can also be determined based on the mass of the i-th component entering the filtration unit and the mass of the i-th component flowing out of the filtration unit. i .

[0117] In some embodiments, multiple user-defined memory areas (UDM, User-Defined Memory) can be used to store the mass of each component intercepted in the filter. In addition, in some embodiments, a particle tracking (Particle Tracks) module based on a discrete phase model (DPM) can be used in a CFD-based simulation analysis software (e.g., Ansys-Fluent software) to obtain the mass of each component flowing out of the filter. As some implementations, a particle tracking module can also be used to visualize the motion trajectory of aerosol particles and count the mass of aerosol particles at the outlet of the filter.

[0118] In some embodiments, corresponding user defined functions (UDF) can be written according to the mass transfer model, heat transfer model and interception model, respectively, and the UDFs corresponding to the mass transfer model, heat transfer model and interception model can be integrated into the CFD simulation software so that the model can be solved in combination with the divided grids to obtain the predicted results of the interception efficiency of the filtration unit.

[0119] In some embodiments, the filter portion may be a unitary filter rod or a multi-element filter rod, wherein the unitary filter rod includes a hollow filter rod, an acetate fiber bundle filter rod and a special-shaped filter rod, and the multi-element filter rod includes a binary filter rod formed by adding an acetate fiber bundle segment to the suction end of the unitary filter rod and a ternary filter rod formed by combining two different unitary filter rods and then adding an acetate fiber bundle segment to the suction end. As some implementations, the special-shaped filter rod may be a filter rod with a central opening, and according to the type of opening, the special-shaped filter rod may include a straight filter rod in the shape of a straight cylinder, a cross filter rod, a moon filter rod, and a five-star filter rod. In addition, the special-shaped filter rod may also include a twisted filter rod obtained by twisting a straight filter rod in the shape of a straight cylinder.

[0120] According to some embodiments of the present disclosure, the interception efficiency of a specified filter unit for a specified component in the composition of flue gas can be predicted based on the established prediction model, wherein the prediction model can be used for the interception efficiency of various unitary filter rods or multi-element filter rods, and in particular, can be analyzed for various special-shaped filter rods, so as to subsequently optimize the structure of the filter unit so that the designed filter unit can have the desired interception efficiency. According to some embodiments of the present disclosure, the independent design and development of diversified filter rods are promoted, and a more accurate interception simulation of flue gas is realized. The use of simulation not only greatly reduces the experimental research cost and trial and error cycle, but also can obtain parameters and phenomena that are difficult to obtain under traditional experimental conditions, such as the flow rate and temperature distribution of flue gas in the filter rod, the life cycle of aerosol particles, etc. Through simulation means, the dynamic characteristics of flue gas flow and its temperature change law can be directly observed, and then the interception behavior of key components in flue gas can be clarified, providing a basis for predicting the influence of different filter rod configurations on the interception effect of key components in flue gas.

[0121] According to another aspect of the present disclosure, a method for predicting interception efficiency is provided. Figure 3 As shown, the method for predicting interception efficiency may include step S310 and step S330.

[0122] In step S310, grid division is performed according to the geometric structure of the cigarette to be tested, wherein the cigarette to be tested has a filter portion.

[0123] In some embodiments, the filter portion of the cigarette to be tested can be gridded to divide the fluid domain in the filter portion into multiple grids, so as to solve the flow state of the fluid in each grid per unit time step based on computational fluid dynamics (CFD), thereby obtaining the flow state of the smoke in the filter portion of the cigarette to be tested.

[0124] As some implementations, the geometric structure of the cigarette to be tested may be drawn and meshed in CFD-based simulation analysis software (eg, Ansys-Fluent software).

[0125] In step S320, characteristic parameters of the cigarette to be tested are obtained.

[0126] In some embodiments, obtaining the characteristic parameter may include: obtaining the substance content of at least one component in the composition of smoke generated by decomposition of the tobacco shreds of the tested cigarette, and using it as the first boundary condition of the inlet substance content of the filter unit. For example, the substance content of glycerin, propylene glycol, nicotine, and water in the composition of smoke generated by the tobacco shreds of the tested cigarette may be obtained.

[0127] As some implementations, the Cambridge filter sampling method can be used to measure the substance content of the specified components in the composition of the smoke of the tobacco shreds of the tested cigarette. Specifically, a Cambridge filter can be set behind the paper tube, and suction can be carried out at a fixed flow rate (for example, 1m / s). The outlet substance content is intercepted by the Cambridge filter, and the filling material (for example, acetate fiber bundle) and the Cambridge filter in the filter part are placed in a fixed mass of isopropanol for extraction, and the substance content of the specified component in the isopropanol can be measured by, for example, chromatography and Karl Fischer titrator.

[0128] As some implementations, according to measurements, the substance contents of glycerin, propylene glycol, nicotine and water in the smoke are 16.37%, 0.7%, 0.9% and 8.08% respectively.

[0129] In some embodiments, obtaining characteristic parameters may include: obtaining the suction speed v of the cigarette to be tested and using it as the second boundary condition of the inlet speed of the filter portion.

[0130] As some implementations, the suction speed is calculated based on the formula v=1.191*sin(π / 2*t), where v is the suction speed and t is the suction time.

[0131] The puffing speed v can be determined by referring to the Canadian deep inhalation (HCI) mode. For example, the puffing behavior can be set as follows: the puffing duration is 2s, the puffing frequency is 30s, 12 puffs per cigarette, the puff volume per puff is 55mL, and the cross-sectional area of ​​the smoke entering the filter rod is 36.25mm 2 .

[0132] In some embodiments, obtaining characteristic parameters of the cigarette to be tested may include obtaining parameters related to the filling material of the filter portion. The filling material of the filter portion may be, for example, acetate fiber bundle.

[0133] The parameters related to the filling material of the filter part may include one or more of the porosity, characteristic size (e.g., equivalent diameter or equivalent radius), specific surface area, thermal conductivity, specific heat capacity, and density of the filling material. As some implementations, according to measurements, the porosity of the acetate fiber bundle is 0.9021, the characteristic size is 3.41*10 -5 m, specific surface area is 16808m 2 / m 3 , thermal conductivity is 0.095W / (m·℃), specific heat capacity is 1460J / kg*K -1 , density is 1300kg / m 3 .

[0134] In step S330, the interception efficiency of the filter portion of the cigarette to be tested is predicted based on the established prediction model, the divided grids and the characteristic parameters.

[0135] In some embodiments, the predicted interception efficiency can be determined based on the divided grid, the prediction model, the first boundary condition, the second boundary condition and the parameters related to the filling material. For example, the first boundary condition of the inlet material content of the above-mentioned filter unit, the second boundary condition of the inlet velocity of the filter unit, and the parameters related to the material filled in the filter unit (such as the porosity, characteristic size, specific surface area, thermal conductivity, specific heat capacity, density, etc. of the acetate fiber bundle) can be substituted into the prediction model established based on any of the above embodiments, and the prediction model is solved in combination with the divided grid to obtain the interception efficiency of the filter unit for the specified component in the smoke.

[0136] In some embodiments, the established prediction model can be verified. As some implementations, an experiment can be conducted to measure the interception efficiency of a specified filter unit for a specified component, and compared with the interception efficiency obtained based on the prediction model to determine whether the accuracy of the established prediction model meets expectations. For example, it can be determined whether the absolute value of the difference between the interception efficiency obtained based on the prediction model and the interception efficiency obtained by experimental measurement is less than a preset threshold.

[0137] As Figure 4 The one-piece acetate tow filter rod shown and Figure 5 The one-piece hollow filter rod shown is used as the filter part of the cigarette, wherein: Figure 4 The diameter of the single-element acetate tow filter rod shown is 7.2mm and the total length is 33mm. Figure 5 The one-piece hollow filter rod shown has an opening inner diameter of 4.2 mm.

[0138] The retention efficiencies of mono-acetate tow filter rod and mono-hollow filter rod for glycerin, propylene glycol, nicotine and water were measured experimentally respectively, and the simulated values ​​of the retention efficiencies of mono-acetate tow filter rod and mono-hollow filter rod for glycerin, propylene glycol, nicotine and water were obtained according to the prediction model established in the present disclosure, to form the following Tables 1 and 2.

[0139] Table 1 Experimental and simulated values ​​of the interception efficiency of mono-acetate fiber tow filter rod

[0140]

[0141] Table 2 Experimental and simulated values ​​of the interception efficiency of the monolayer hollow filter rod

[0142]

[0143] It can be seen from Tables 1 and 2 above that the interception efficiency of the mono-component acetate fiber tow filter rod and the mono-component hollow filter rod obtained based on the prediction model and the absolute error between the experimental measurement and the experimental measurement are less than 4%, that is, the prediction model disclosed in the present invention has a high accuracy. Subsequently, the prediction model can be applied to different filter rod structures (especially filter rods and multi-component filter rods with various special-shaped structures that are difficult to analyze in the related art) to simulate and solve their interception efficiency.

[0144] As some implementations, the prediction model can be applied to unidimensional shaped filter rods, e.g. Figure 6 The one-yuan straight filter rod and Figure 7 The one-dimensional twisted filter rod shown (for example, the twist ratio is set to 0.4) is used to predict the retention efficiency of the one-dimensional straight filter rod and the one-dimensional twisted filter rod, as shown in Table 3 below.

[0145] Table 3 Retention efficiency of one-dimensional straight and twisted filter rods

[0146]

[0147] It can be seen from Table 3 that the interception efficiency of the twisted filter rod is higher than that of the straight filter rod because the smoke path is lengthened.

[0148] As some implementations, the prediction model can also be applied to multivariate filter rods, such as Figure 8 The dual filter rod shown in the figure is formed by adding a acetate fiber bundle segment to the suction end of a one-piece straight filter rod. Fig. 9 The two-component filter rod shown in the figure is formed by adding a acetate fiber bundle segment to the suction end of the one-component straight-line twisted filter rod and Fig.10 The ternary filter rod formed by combining a one-piece straight filter rod and a one-piece twisted filter rod and then adding a acetate fiber bundle segment at the suction end is shown in Table 4 to predict the retention efficiency of the corresponding filter rod.

[0149] Table 4 Retention efficiency of multi-element filter rod

[0150]

[0151]

[0152] It can be seen from Table 4 that the interception efficiency of the ternary filter rod is higher than that of the binary filter rod.

[0153] According to another aspect of an embodiment of the present disclosure, a device for establishing a prediction model is provided, wherein the device may include a module configured to execute the method for establishing a prediction model described in any of the aforementioned embodiments of the present disclosure.

[0154] In some embodiments, Fig.11 As shown, the device 1100 for establishing a prediction model according to some embodiments of the present disclosure may include an acquisition module 1110 and an establishment module 1120. In some embodiments, the acquisition module 1110 may be configured to acquire a mass transfer model and a heat transfer model of aerosol particles in the filter unit. The acquisition module 1110 may also be configured to acquire a retention model of aerosol particles by the filter unit. The establishment module 1120 may be configured to establish a prediction model of the retention efficiency of the filter unit based on the mass transfer model, the heat transfer model, and the retention model.

[0155] According to another aspect of an embodiment of the present disclosure, a device for predicting interception efficiency is provided, wherein the device may include a module configured to execute the method for predicting interception efficiency described in any of the aforementioned embodiments of the present disclosure.

[0156] In some embodiments, Fig.12As shown, the device 1200 for predicting interception efficiency according to some embodiments of the present disclosure may include a division module 1210, an acquisition module 1220 and a prediction module 1230. In some embodiments, the division module 1110 may be configured to perform grid division according to the geometric structure of the cigarette to be tested, wherein the cigarette to be tested has a filter portion. The acquisition module 1220 may be configured to obtain characteristic parameters of the cigarette to be tested. The prediction module 1230 may be configured to predict the interception efficiency of the filter portion of the cigarette to be tested according to the prediction model established by the method for establishing a prediction model described in any of the aforementioned embodiments, the divided grids and the characteristic parameters.

[0157] In some embodiments, the means for predicting the retention efficiency comprises means for building a prediction model.

[0158] According to another aspect of the embodiments of the present disclosure, there is provided an electronic device 1300, such as Fig.13 As shown, the electronic device 1300 may include a processor 1310 and a memory 1320. The memory 1320 stores instructions, which, when executed by the processor 1310, implement the method described in any of the foregoing embodiments of the present disclosure.

[0159] The processor 1310 may be an integrated circuit chip having signal processing capabilities. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component to implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present disclosure. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc., and may be an X86 architecture or an ARM architecture, etc.

[0160] The memory 1320 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronously connected dynamic random access memory (SLDRAM), and direct memory bus random access memory (DRRAM). It should be noted that the memory described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0161] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which instructions are stored, and when the instructions are executed by a processor, the method described in any of the foregoing embodiments of the present disclosure can be implemented. The computer-readable storage medium of the present disclosure can be a non-transitory computer-readable storage medium.

[0162] The non-transitory computer-readable storage medium in the embodiments of the present disclosure may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory. It should be noted that the non-transitory computer-readable storage medium described herein is intended to include but is not limited to these and any other suitable types of memory.

[0163] According to yet another aspect of the present disclosure, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the method described in any of the foregoing embodiments of the present disclosure can be implemented.

[0164] The instructions may be any set of instructions to be executed directly by one or more processors, such as machine code, or any set of instructions to be executed indirectly, such as a script. The terms "instructions", "applications", "processes", "steps" and "programs" (including "computer programs") herein may be used interchangeably herein. The instructions may be stored in an object code format for direct processing by one or more processors, or as a script or collection of independent source code modules in any other computer language, including those interpreted on demand or compiled in advance. The instructions may include instructions that cause one or more processors, such as to act as the various neural networks herein. The functions, methods and routines of the instructions are explained in more detail elsewhere herein.

[0165] Fig.14A schematic block diagram of a computer system 1400 on which an embodiment of the present disclosure may be implemented is shown. The computer system 1400 includes a bus 1410 or other communication mechanism for transmitting information, and a processing device 1420 coupled to the bus 1410 for processing information. The computer system 1400 also includes a memory 1430 coupled to the bus 1410 for storing instructions to be executed by the processing device 1420, which may be a random access memory (RAM) or other dynamic storage device. The memory 1430 may also be used to store temporary variables or other intermediate information during the execution of instructions to be executed by the processing device 1420. The computer system 1400 may also include a read-only memory (ROM) 1440 or other static storage device coupled to the bus 1410 for storing static information and instructions for the processing device 1420. A storage device 1450 such as a magnetic disk or optical disk is provided and coupled to the bus 1410 for storing information and instructions. The computer system 1400 may be coupled to an output device 1460 via the bus 1410 for providing output to a user, such as, but not limited to, a display (such as a cathode ray tube (CRT) or a liquid crystal display (LCD)), a speaker, etc. An input device 1470 such as a keyboard, a mouse, a microphone, etc. is coupled to the bus 1410 for transmitting information and command selections to the processing device 1420. The computer system 1400 may perform embodiments of the present disclosure. Consistent with certain implementations of the present disclosure, the results are provided by the computer system 1400 in response to the processing device 1420 executing one or more sequences of one or more instructions contained in the memory 1430. Such instructions may be read into the memory 1430 from another computer-readable medium such as the storage device 1450. Execution of the sequence of instructions contained in the memory 1430 causes the processing device 1420 to perform the methods described herein. Alternatively, hard-wired circuitry may be used instead of or in combination with software instructions to implement the present teachings. Therefore, implementations of the present disclosure are not limited to any particular combination of hardware circuitry and software. In various embodiments, computer system 1400 can be connected to one or more other computer systems like computer system 1400 across a network via network interface 1480 to form a networked system. The network can include a private network or a public network such as the Internet. In a networked system, one or more computer systems can store data and supply data to other computer systems. The term "computer-readable medium" as used herein refers to any medium that participates in providing instructions to processing device 1420 for execution. Such media can take many forms, including but not limited to non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks such as storage device 1450. Volatile media include dynamic memories such as memory 1430.Transmission media include coaxial cables, copper wires, and optical fibers, including the wiring that contains bus 1410. Common forms of computer-readable media or computer program products include, for example, floppy disks, flexible disks, hard disks, tapes, or any other magnetic media, CD-ROMs, digital video disks (DVDs), Blu-ray disks, any other optical media, thumb drives, memory cards, RAMs, PROMs and EPROMs, flash EPROMs, any other memory chips or boxes, or any other tangible media from which a computer can read. Various forms of computer-readable media may be involved when carrying one or more sequences of one or more instructions to processing device 1420 for execution. For example, the instructions may initially be carried on a disk of a remote computer. The remote computer may load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 1400 may receive data on the telephone line and convert the data into an infrared signal using an infrared transmitter. An infrared detector coupled to bus 1410 may receive the data carried in the infrared signal and place the data on bus 1410. Bus 1410 carries the data to memory 1430, from which processing device 1420 retrieves and executes the instructions. Optionally, the instructions received by memory 1430 may be stored on storage device 1450 either before or after execution by processing device 1420.

[0166] According to various embodiments, instructions configured to be executed by the processing device 1420 to perform the method are stored on a computer readable medium. A computer readable medium may be a device that stores digital information. For example, a computer readable medium includes a compact disk read-only memory (CD-ROM) as known in the art for storing software. The computer readable medium is accessed by a processor adapted to execute the instructions configured to be executed.

[0167] So far, various embodiments of the present disclosure have been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical solution disclosed here.

[0168] In addition, the terms "first", "second" and the like may also be used herein for reference purposes only and are not intended to be limiting. For example, the terms "first", "second" and other such numerical terms referring to structures or elements do not imply a sequence or order unless the context clearly indicates otherwise.

[0169] It should also be understood that when the term “include / comprises” is used in this document, it indicates the presence of the specified features, integers, steps, operations, units and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, units and / or components and / or their combinations.

[0170] Those skilled in the art will appreciate that the boundaries between the above operations are merely illustrative. Multiple operations can be combined into a single operation, a single operation can be distributed in additional operations, and operations can be performed at least partially overlapping in time. Moreover, alternative embodiments may include multiple instances of a specific operation, and the order of operations may be changed in various other embodiments. However, other modifications, variations, and replacements are equally possible. Aspects and elements of all embodiments disclosed above may be combined in any manner and / or in combination with aspects or elements of other embodiments to provide multiple additional embodiments. Therefore, this specification and accompanying drawings should be considered illustrative, not restrictive.

[0171] Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. The various embodiments disclosed herein may be combined in any manner without departing from the spirit and scope of the present disclosure. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A method for establishing a prediction model, comprising: Obtaining a mass transfer model and a heat transfer model of aerosol particles in the filter portion of a cigarette; Obtaining a retention model of the aerosol particles by the filter unit; A prediction model for the interception efficiency of the filtration unit is established based on the mass transfer model, the heat transfer model and the interception model.

2. The method according to claim 1, wherein: Obtaining the interception model of the filter unit for the aerosol particles includes: Obtaining a retention mechanism of the aerosol particles by the filter unit; The interception rate is determined according to the interception mechanism, wherein the interception rate is used to reflect the change of the number of aerosol particles over time.

3. The method according to claim 2, wherein: The trapping mechanism includes at least one of a first trapping mechanism based on diffusion deposition, a second trapping mechanism based on direct interception, and a third trapping mechanism based on inertial collision.

4. The method according to claim 3, wherein: Determining the retention rate according to the retention mechanism includes: Determining a first retention rate, a second retention rate, and a third retention rate according to the first retention mechanism, the second retention mechanism, and the third retention mechanism, respectively; The rejection rate is determined based on the first rejection rate, the second rejection rate, and the third rejection rate.

5. The method according to any one of claims 1 to 4, wherein: The mass transfer model is based on the following equation: Among them, m p is the mass of aerosol particles, t is the time, N i is the mass transfer flux of the i-th component in the smoke generated by the cracking of tobacco shreds, N w,i is the molar mass of the i-th component, A p is the surface area of ​​aerosol particles.

6. The method according to any one of claims 1 to 4, wherein: The heat transfer model is based on the following equation: Where H1=h*A p *(T ∞ -T p ), m p is the mass of aerosol particles, t is the time, C p is the specific heat capacity of aerosol particles, T p is the temperature of aerosol particles, h is the convective heat transfer coefficient, A p is the surface area of ​​aerosol particles, T ∞ is the temperature of the smoke generated by the decomposition of tobacco shreds, is the mass transfer rate of the i-th component in the flue gas, h fg,i is the phase change enthalpy of the i-th component, and N is the number of components in the flue gas.

7. A method for predicting interception efficiency, comprising: Performing grid division according to the geometric structure of the cigarette to be tested, wherein the cigarette to be tested has a filter portion; Acquiring characteristic parameters of the cigarette to be tested; According to the prediction model established by the method according to any one of claims 1 to 6, the divided grids and the characteristic parameters, the interception efficiency of the filter portion of the cigarette to be tested is predicted.

8. The method according to claim 7, wherein: Acquiring the characteristic parameters includes: Obtaining the substance content of at least one component in the smoke generated by decomposition of the tobacco shreds of the tested cigarette, and using it as a first boundary condition for the inlet substance content of the filter unit; Obtaining the suction speed of the cigarette to be tested and using it as a second boundary condition for the inlet speed of the filter portion; and Acquiring parameters related to the filling material of the filter portion; The interception efficiency is determined according to the prediction model, the first boundary condition, the second boundary condition and parameters related to the filling material.

9. An apparatus for establishing a prediction model, comprising a module configured to execute the method according to any one of claims 1 to 6.

10. An apparatus for predicting interception efficiency, comprising a module configured to execute the method according to any one of claims 7 to 8.

11. An electronic device, comprising a processor and a memory, wherein the memory stores instructions, and when the instructions are executed by the processor, the method according to any one of claims 1 to 8 is implemented.

12. A computer-readable storage medium comprising instructions, which, when executed by a processor, implement the method of any one of claims 1 to 8.

13. A computer program product, comprising a computer program, which implements the method according to any one of claims 1 to 8 when the computer program is executed by a processor.

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