A method and system for predicting packed bed pressure drop and a packed bed partition structure

Abstract

The present invention belongs to the technical field of packed bed reactors, and discloses a method, system and packed bed partition structure for predicting packed bed pressure drop, wherein the method comprises: partitioning the packed bed, calculating the average void fraction of each region; obtaining the value of the packed bed void fraction distribution coefficient K according to the average void fraction of each region and the cross-sectional area ratio of each region; obtaining various parameters of the flow medium and calculating the particle shape coefficient δ, wherein the various parameters of the flow medium include the flow medium viscosity μ, the flow medium density ρ, and the fluid flow rate u; based on the distribution coefficient K, various parameters of the flow medium and the shape coefficient δ, the value of the packed bed pressure drop ΔP is calculated, thereby obtaining the final result of the prediction. The prediction method of the present invention takes into account the characteristic parameter of radial void fraction distribution in the packed bed, and develops a new packed bed pressure drop prediction correlation model, which has a significant improvement in the calculation accuracy compared with the existing pressure drop prediction model.

Description

Technical Field

[0001] The invention belongs to the technical field of packed bed reactors, and in particular relates to a packed bed pressure drop prediction method and system and a packed bed partition structure. Background Art

[0002] Pressure drop is a very important and must-obtain data for porous media packed beds. It is related to the design of packed beds and involves key issues such as the selection of utility equipment and energy consumption. Therefore, it has always attracted people's attention. The model method is one of the main directions for studying the pressure drop of packed beds. The model method can quickly predict the pressure drop of packed beds, thereby greatly saving costs and time. In the early days, Ergun et al. proposed an equation for the pressure drop of packed bed reactors, which has been generally recognized and widely used. However, the Ergun equation is based on the infinite bed and uniform porosity distribution. Because it does not take into account the influence of the side wall effect, it has no effect on small tube diameter ratios (small tube diameter ratio refers to the packed bed diameter D / filler particle diameter d p <10) The prediction deviation of packed bed pressure drop is large. Reichelt then corrected the packed bed pressure drop when the tube diameter is relatively small, and used parameters to take into account the effect of the tube diameter ratio on the packed bed pressure drop. In summary, although there are correlations that correct the side wall effect caused by the small tube diameter ratio, they have not deeply analyzed the real cause of the side wall effect in the packed bed - uneven radial porosity and flow velocity distribution, so their application scope is limited. Summary of the invention

[0003] In order to solve at least one problem in the background technology, the present invention provides a method and system for predicting the pressure drop of a packed bed and a packed bed partition structure.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for predicting packed bed pressure drop comprises the following steps:

[0006] Divide the packed bed into zones and calculate the average void fraction of each zone;

[0007] The value of the packed bed void ratio distribution coefficient K is obtained according to the average void ratio of each region and the cross-sectional area ratio of each region;

[0008] Obtain various parameters of the flow medium and calculate the particle shape coefficient δ, wherein the various parameters of the flow medium include flow medium viscosity μ, flow medium density ρ, and fluid flow velocity u;

[0009] Based on the distribution coefficient K, various parameters of the flow medium and the shape coefficient δ, the value of the packed bed pressure drop ΔP is calculated to obtain the final predicted result.

[0010] Preferably, the packed bed is divided into zones and the average void fraction of each zone is calculated, comprising the following steps:

[0011] The packed bed is divided into a wall area, a transition area and a central area from the tube wall inwards;

[0012] The corresponding average void fractions are obtained based on the wall region, transition region and central region.

[0013] Preferably, the average porosity of the wall region is 1 for:

[0014]

[0015] In the formula, r represents the radial position from the pipe wall, D represents the pipe diameter; d p is the particle diameter in the packed bed; ε(r) represents the radial porosity distribution function of the packed bed;

[0016] The average void fraction ε of the transition zone 2 for:

[0017]

[0018] Where, d p is the particle diameter in the packed bed;

[0019] The average void fraction ε of the central region 3 for:

[0020]

[0021] Preferably, the packed bed porosity distribution coefficient K is:

[0022]

[0023] In the formula, φ 1 is the ratio of the cross-sectional area of ​​the wall area to the total cross-sectional area of ​​the packed bed, ε 1 is the average porosity of the wall area, ε 2 is the average void fraction in the transition zone, ε 3 is the average void ratio in the central area.

[0024] Preferably, the particle shape coefficient δ is:

[0025] δ = surface area of ​​a sphere with the same volume as the non-spherical particle / surface area of ​​the non-spherical particle.

[0026] Preferably, the packed bed pressure drop ΔP is:

[0027]

[0028] Where ΔP is the packed bed pressure drop, L is the bed filling height, dp is the particle diameter of the packed bed, is the average porosity of the packed bed.

[0029] Preferably, the average porosity of the packed bed is for:

[0030]

[0031] In the formula, ε 1 is the average porosity of the wall area, ε 2 is the average void fraction in the transition zone, ε 3 is the average void fraction in the central area, ω 1 ,ω 2 ,ω 3 To calculate the coefficient, it is determined according to the area fraction of the wall area, transition area and central area respectively.

[0032] A packed bed partition structure, used in the above-mentioned packed bed pressure drop prediction method, comprising:

[0033] Packed bed, which is in the shape of a tube and filled with particles;

[0034] The packed bed is divided into a wall area, a transition area and a central area along the radial direction from the outside to the inside;

[0035] The wall area is from the wall to the distance d from the packed bed wall. p / 4 annular area, d p is the particle diameter of the filler;

[0036] The transition zone is d from the wall of the packed bed p / 4 to the distance d from the packed bed wall p the area where

[0037] The central area is d from the wall of the packed bed p to the area at the axis of the packed bed.

[0038] A system for predicting packed bed pressure drop, used in the above-mentioned method for predicting packed bed pressure drop, comprising:

[0039] Partitioning unit, used to partition the packed bed and calculate the average void fraction of each area;

[0040] A processing unit, used to obtain a value of a void ratio distribution coefficient K of the packed bed according to an average void ratio of each region and a proportion of a cross-sectional area of ​​each region;

[0041] An acquisition unit, used to acquire various parameters of the flow medium and calculate the particle shape coefficient δ, wherein the various parameters of the flow medium include the flow medium viscosity μ, the flow medium density ρ, and the fluid flow velocity u;

[0042] The calculation unit is used to calculate the value of the packed bed pressure drop ΔP based on the distribution coefficient K, various parameters of the flow medium and the shape coefficient δ, so as to obtain the predicted final result.

[0043] Preferably, the partition unit comprises:

[0044] A partitioning module is used to divide the packed bed into a wall area, a transition area and a central area in sequence from the tube wall inward;

[0045] The response module is used to obtain the corresponding average void fractions based on the wall area, the transition area and the central area.

[0046] Beneficial effects of the present invention:

[0047] 1. The prediction method of the present invention takes the characteristic parameter of radial void fraction distribution in the packed bed into consideration and develops a new packed bed pressure drop prediction correlation model, which has a significantly improved calculation accuracy compared with the existing pressure drop prediction model.

[0048] 2. The pressure drop prediction data obtained by the prediction method of the present invention shows a high degree of consistency with the existing experimental data, and has significantly improved accuracy compared with existing pressure drop prediction methods (such as the Ergun equation, the Reichelt equation, and the Foumeny equation), with a maximum deviation of only about 5% and an average deviation of only 3.9%.

[0049] Other features and advantages of the present invention will be described in the following description, and partly become obvious from the description, or be understood by implementing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 It is a flow chart of a method for predicting packed bed pressure drop of the present invention;

[0052] Figure 2 Schematic diagram of the experimental system for measuring the pressure drop of a packed bed;

[0053] Figure 3 It is a schematic diagram of the wall area, transition area and central area of ​​the packed bed in the prediction method of the present invention;

[0054] Figure 4The radial void fraction distribution and integral area diagram of each region (wall region, transition region and central region) in the prediction method of the present invention;

[0055] Figure 5 A deviation diagram of the prediction results obtained using the prediction method of the present invention;

[0056] Figure 6 is a deviation diagram of the prediction results obtained using the Ergun equation in the prior art;

[0057] Figure 7 is a deviation diagram of the prediction results obtained using the Foumeny equation in the prior art;

[0058] Figure 8 This is a deviation diagram of the prediction results obtained using the Reichelt equation in the prior art. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] A method for predicting the pressure drop of a packed bed, such as Figure 1 As shown, the following steps are included:

[0061] S1: Divide the packed bed into zones and calculate the average void fraction of each zone;

[0062] S2: The value of the porosity distribution coefficient K of the packed bed is obtained according to the average porosity of each region and the cross-sectional area ratio of each region;

[0063] S3: obtaining various parameters of the flow medium and calculating the particle shape coefficient δ, the various parameters of the flow medium include the flow medium viscosity μ, the flow medium density ρ, and the fluid flow velocity u;

[0064] S4: Based on the distribution coefficient K, various parameters of the flow medium and the shape coefficient δ, the value of the packed bed pressure drop ΔP is calculated to obtain the final result of the prediction.

[0065] It should be noted that in step S1, there are two ways to obtain the radial porosity distribution ε(r) of the packed bed in equations (1) to (3), among which, (1) for spherical particles: the Klerk model is recommended for obtaining ε(r), which is a damped oscillation function that combines cosine oscillation and exponential decay, and its accuracy has been widely verified. The Klerk model expression is as follows:

[0066]

[0067]

[0068]

[0069] In the formula, Z is the intermediate variable, ε b is the porosity of the bed without wall effect, which is a known quantity.

[0070] (2) For non-spherical particles: ε(r) can be obtained by generating a packed bed of non-spherical particles using a digital elevation model (DEM), and then using mapping tools (such as CAD tools) to obtain the radial porosity distribution of the packed bed.

[0071] Further, steps S1 to S5 are specifically described below, wherein step S1 includes the following steps:

[0072] S101: The packed bed is divided into a wall area, a transition area and a central area from the tube wall inward; specifically, Figure 3 It can be seen that the packed bed is divided into the wall area, transition area and central area from the tube wall to the inside. Figure 4 It can be seen that the wall area is from the wall to the packed bed wall. The annular area at The wall area is the main area where the wall effect occurs; the transition zone is the distance from the packed bed wall to the From the packed bed wall d p The area where The central area is d from the wall of the packed bed p to the area of ​​the packed bed axis, i.e. Where r represents the value range, D represents the pipe diameter; d p is the filling particle diameter.

[0073] S102: Obtain corresponding average porosity based on the wall area, transition area and central area respectively.

[0074] It should be noted that in order to facilitate the implementation of the prediction method, two assumptions are put forward:

[0075] (1) The entire packed bed is divided into three regions. Although the porosity in each region changes, considering that the variation of the porosity in each region is significantly lower than that of the entire packed bed, the porosity distribution characteristics in each region can be described by the average porosity. The average porosity value of each region can be calculated by formula (1)-formula (3), as follows:

[0076] Among them, the average void fraction of the wall area ε 1 for:

[0077]

[0078] In the formula, r represents the radial position from the pipe wall, D represents the pipe diameter; d p is the particle diameter in the packed bed; ε(r) represents the radial porosity distribution function of the packed bed;

[0079] The average void fraction ε in the transition zone 2 for:

[0080]

[0081] Average void ratio in the central area ε 3 for:

[0082]

[0083] Then the average porosity of the packed bed can be obtained

[0084]

[0085] In the formula, ε 1 is the average porosity of the wall area, ε 2 is the average void fraction in the transition zone, ε 3 is the average void fraction in the central area, ω 1 ,ω 2 ,ω 3 To calculate the coefficient, it is determined according to the area fraction of the wall area, transition area and central area respectively.

[0086] (2) The friction coefficient of the fluid in the wall area, transition area and internal area is equal. The fluid flows through the gaps between particles in the packed bed and contacts the particles in the packed bed. The friction coefficient is a function of the Reynolds number of the bed:

[0087] λ 1 =λ 2 =λ 3 =λ (5);

[0088] In the formula, λ 1 ,λ 2 ,λ 3 are the friction coefficients of the wall zone, transition zone and central zone respectively, and λ is the friction coefficient of the packed bed.

[0089]

[0090] Where Re' is the bed Reynolds number, k 1 and k 2 are the parameters to be fitted.

[0091] in addition,

[0092] Where ρ is the density of the flowing medium; u is the flow velocity of the fluid; a = (6 / δd p ) is the particle specific surface area; is the average porosity of the packed bed, and μ is the viscosity of the flowing medium.

[0093] Furthermore, in the process of calculating the particle shape coefficient δ, it is necessary to consider the volume of the spherical particles and the volume of the non-spherical particles as well as the surface areas of the two, so as to calculate δ, and the calculation formula is as follows:

[0094] δ = surface area of ​​a sphere with the same volume as the non-spherical particle / surface area of ​​the non-spherical particle.

[0095] Further, in step S2, obtaining the packed bed porosity distribution coefficient K requires the following steps:

[0096] S201: First, the pressure drop prediction calculation formulas (8) to (10) for each area of ​​the packed bed are listed, where:

[0097]

[0098] Where ΔP 1 is the pressure drop in the wall area, L is the bed filling height, u 1 is the fluid velocity in the wall region.

[0099]

[0100] Where ΔP 2 is the pressure drop in the transition zone, L is the bed filling height, u 2 is the fluid flow rate in the transition zone.

[0101]

[0102] Where ΔP 3 is the pressure drop in the central area, L is the bed filling height, u 3 is the fluid flow velocity in the central area.

[0103] S202: Further process formulas (8) to (10) to obtain formulas (11) to (13), where:

[0104]

[0105] In the formula, φ 1 It is the ratio of the cross-sectional area of ​​the wall region to the total cross-sectional area of ​​the packed bed and is a known quantity.

[0106]

[0107] In the formula, φ 2is the ratio of the cross-sectional area of ​​the transition zone to the total cross-sectional area of ​​the packed bed, which is a known quantity.

[0108]

[0109] In the formula, φ 3 It is the ratio of the cross-sectional area of ​​the central zone to the total cross-sectional area of ​​the packed bed and is a known quantity.

[0110] S203: Since the wall area, transition area and central area are connected in parallel, the pressure drop of the fluid passing through each area is equal, so it can be obtained:

[0111] ΔP 1 =ΔP 2 =ΔP 3 =ΔP (14);

[0112] Where ΔP is the packed bed pressure drop.

[0113] S204: Combining formulas (11) to (14), the relationship between the pressure drop of the packed bed and the pressure drop of each area is obtained through geometric conversion:

[0114]

[0115] From the law of conservation of mass, we know that:

[0116] u=u 1 φ 1 +u 2 φ 2 +u 3 φ 3 (16);

[0117] Then, by further processing equation (15) in combination with equation (16), we obtain:

[0118]

[0119] S205: Define the packed bed porosity distribution coefficient as K by equation (16), and obtain:

[0120]

[0121] In step S4, the distribution coefficient K, various parameters of the flow medium and the shape coefficient δ need to be substituted into the prediction calculation formula of the pressure drop of the packed bed, wherein the prediction calculation formula of the pressure drop of the packed bed is obtained by combining equations (6), (7), (15) and (16), as follows:

[0122]

[0123] In formula (18), the fitting parameter k is 1 and k 2 Fitting is performed to obtain the parameter k1 =5.75, k 2 =0.34, and its correlation coefficient R 2 =0.999, thus formula (19) is obtained, which is as follows:

[0124]

[0125] Where ΔP is the packed bed pressure drop, L is the bed filling height, d p is the particle diameter of filler 2, is the average porosity of the packed bed.

[0126] It should be noted that the parameter k to be fitted in the pressure drop prediction calculation formula of the packed bed in the present invention is 1 and k 2 It is the key parameter for accurate prediction in the pressure drop prediction calculation formula of the present invention. 1 =5.75, k 2 =0.34 is obtained by setting a small tube diameter-particle size ratio packed bed pressure drop experiment to fit k 1 and k 2 , the schematic diagram of the packed bed pressure drop measurement experimental system is shown in Figure 2 As shown in FIG. 1 , the pressure drop measurement experimental system includes particles as fillers, a packed bed, and a high-pressure fluid. The total height of the packed bed is 1000 mm, and there is a pressure measuring port at the top and bottom of the packed bed. Specifically, Figure 2 In the figure, T is the temperature measurement point, the dotted line is the pressure measurement line, Δp represents the pressure drop of the packed bed, and the high-pressure fluid uses the high-pressure characteristics of the fluid to compensate for the pressure drop of the fluid passing through the packed bed. The high-pressure fluid flows from the bottom of the packed bed and flows out from the top. In addition, Figure 2 The high-pressure fluid in the reactor is compressed air.

[0127] Experimental process: Figure 2 As shown, first, ABS resin particles with a particle size of 11.7 mm are slowly poured from the top of the packed bed and randomly stacked by free fall. Check that all pipeline connections are intact and the measuring equipment is normal, turn on the fan, and open the regulating valve after stabilization. The airflow enters the buffer tank after being pressurized by the blower, and then passes through the rotor flowmeter to enter the bottom of the packed bed, and then enters the packed bed through the distributor at the bottom and is discharged from the top. After the thermometer indication stabilizes, record the gas flow rate, the bed pressure drop of the packed bed, and the airflow temperature. The qualitative temperature of the air is the average value of the thermometer measurement data at the top and bottom of the packed bed. The gas flow rate is adjusted by adjusting the rotor flowmeter to change the inlet apparent flow rate, and the pressure drop of the packed bed at the corresponding flow rate is read after stabilization.

[0128] Based on the experimental data, the parameter k of the pressure drop correlation model (i.e., the pressure drop prediction calculation formula of the packed bed) derived by the present invention is calculated by data analysis and drawing software. 1 and k 2 Fitting is performed to obtain the parameter k 1 =5.75, k 2 =0.34, and its correlation coefficient R 2 =0.999.

[0129] A packed bed partition structure is used in the above-mentioned packed bed pressure drop prediction method, such as Figure 2 As shown, it includes: a packed bed, denoted as a, in a tubular shape, filled with a filler, denoted as b; and a combination of Figure 3 It can be seen that the packed bed is divided into the wall area (denoted as I), the transition area (denoted as II) and the central area (denoted as III) from the outside to the inside along the radial direction; the wall area is from the wall to the distance d from the wall of the packed bed. p / 4 annular area, d p is the particle diameter of filler 2; the transition zone is the distance d from the wall of the packed bed p / 4 to the distance d from the packed bed wall p The central area is the area d from the wall of the packed bed p to the area at the axis of the packed bed.

[0130] like Figure 4 As shown, the variation pattern of porosity of a typical packed bed layer and a typical schematic diagram of partitioning are shown.

[0131] In order to comprehensively compare and evaluate the accuracy of the pressure drop prediction method of the present invention, experimental data from publicly published literature are used for extrapolation evaluation, and a comparative analysis is performed with the existing packed bed pressure drop prediction method to comprehensively evaluate the prediction effect of the prediction method of the present invention. Figures 5 to 8 The relative deviations of various packed bed pressure drop prediction methods are shown. Figure 5 It shows that the prediction method of the present invention shows a high degree of consistency in the entire evaluation range, with a maximum deviation of about 5% and an average deviation of 3.9%. This prediction accuracy is reasonable and reliable and can be widely applied to fluids of different media.

[0132] like Figure 6 As shown, the prediction results of the Ergun equation show obvious positive deviations, with a maximum deviation of about 35% and an average deviation of 18.1%. Both the Foumeny equation and the Reichelt equation consider the influence of the wall effect of the small diameter-particle size ratio packed bed on the pressure drop, and their prediction accuracy should be improved compared with the Ergun equation. Figure 7 The maximum deviation of the prediction results of the Foumeny equation is about 30%, and the average deviation is 11.2%. Figure 8The maximum deviation of the predicted results of the Reichelt equation is about 25%, and the average deviation is 15.3%.

[0133] From the perspective of engineering design, the Reichelt equation has obvious positive and negative deviations in its prediction results, which increases the uncertainty of its prediction. Therefore, the Ergun equation and Foumeny equation are safer than the Reichelt equation in engineering applications. There will be redundancy in the design and selection of industrial equipment. If the pressure drop prediction is too large, it will lead to mismatch in the selection of supporting equipment, resulting in waste. If the prediction is too small, the packed bed cannot operate fully, the supporting equipment will be overloaded, and accidents will occur.

[0134] Compared with the above prediction model, the maximum deviation of the packed bed pressure drop prediction method of the present invention is reduced by 5-7 times, and the average deviation is reduced by 3-5 times. It has more advantages than the existing packed bed pressure drop method in terms of design safety assurance and cost control.

[0135] A system for predicting packed bed pressure drop, using the above-mentioned method for predicting packed bed pressure drop, comprises:

[0136] Partitioning unit, used to partition the packed bed and calculate the average void fraction of each area;

[0137] A processing unit, used to obtain a value of a void ratio distribution coefficient K of the packed bed according to an average void ratio of each region and a proportion of a cross-sectional area of ​​each region;

[0138] An acquisition unit is used to acquire various parameters of the flow medium and calculate the particle shape coefficient δ, wherein the various parameters of the flow medium include the flow medium viscosity μ, the flow medium density ρ, and the fluid flow velocity u;

[0139] The calculation unit is used to calculate the value of the packed bed pressure drop ΔP based on the distribution coefficient K, various parameters of the flow medium and the shape coefficient δ, so as to obtain the predicted final result.

[0140] Furthermore, the partition unit includes:

[0141] A partitioning module is used to divide the packed bed into a wall area, a transition area and a central area in sequence from the tube wall inward;

[0142] The response module is used to obtain the corresponding average void fractions based on the wall area, the transition area and the central area.

[0143] It should be noted that, for the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The various units and modules of the system of the present invention are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0144] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting packed bed pressure drop, It is characterized in that The following steps are involved: Divide the packed bed into zones and calculate the average void fraction of each zone, including: The packed bed is divided into a wall area, a transition area and a central area from the tube wall inwards; Based on the wall area, transition area and central area, the corresponding average void fractions are obtained respectively; The porosity distribution coefficient of the packed bed is obtained based on the average porosity of each area and the cross-sectional area ratio of each area. K The value of Obtain various parameters of the flow medium and calculate the particle shape factor The parameters of the flow medium include the viscosity of the flow medium , density of flowing medium , fluid flow rate ; Based on the distribution coefficient K、 Flow medium parameters and shape factor , the packed bed pressure drop is calculated The value of , thus obtaining the final result of the prediction; The packed bed pressure drop for: ； In the formula, is the packed bed pressure drop, L is the bed filling height, d p is the particle diameter of the packed bed, is the average porosity of the packed bed.

2. A method for predicting packed bed pressure drop according to claim 1, It is characterized in that The average void ratio of the wall area for: ； In the formula, represents the radial position from the pipe wall, Indicates the pipe diameter; d p is the particle diameter in the packed bed; represents the radial void fraction distribution function of the packed bed; The average void fraction of the transition zone for: ； In the formula, d p is the particle diameter in the packed bed; The average void ratio of the central area for: 。 3. The method for predicting packed bed pressure drop according to claim 1, It is characterized in that The packed bed void fraction distribution coefficient K for: ; In the formula, is the ratio of the cross-sectional area of ​​the wall area to the total cross-sectional area of ​​the packed bed, is the average void fraction of the wall area, is the average void fraction in the transition zone, is the average void ratio in the central area.

4. The method for predicting packed bed pressure drop according to claim 1, It is characterized in that Particle shape factor for: =Surface area of ​​a sphere with the same volume as the non-spherical particle / surface area of ​​the non-spherical particle.

5. The method for predicting packed bed pressure drop according to claim 1, It is characterized in that The average void fraction of the packed bed for: ； In the formula, is the average void fraction of the wall area, is the average void fraction in the transition zone, is the average void ratio in the central area, , , To calculate the coefficient, it is determined according to the area fraction of the wall area, transition area and central area respectively.

6. A packed bed partition structure, It is characterized in that The method for predicting the pressure drop of a packed bed according to any one of claims 1 to 5 comprises: Packed bed, which is in the shape of a tube and filled with particles; The packed bed is divided into a wall area, a transition area and a central area along the radial direction from the outside to the inside; The wall area is from the wall to the wall of the packed bed d p / 4 annular area, d p is the particle diameter of the filler; The transition zone is from the wall of the packed bed d p / 4 to the wall of packed bed the area where The central area is the distance from the packed bed wall to the d p to the area at the axis of the packed bed.

7. A system for predicting packed bed pressure drop, It is characterized in that The method for predicting the pressure drop of a packed bed according to any one of claims 1 to 5, characterized in that it comprises: Partitioning unit, used to partition the packed bed and calculate the average void fraction of each area; A processing unit is used to obtain the porosity distribution coefficient of the packed bed according to the average porosity of each area and the cross-sectional area ratio of each area. K The value of Acquisition unit, used to obtain various parameters of the flow medium and calculate the particle shape coefficient The parameters of the flow medium include the viscosity of the flow medium , density of flowing medium , fluid flow rate ; Calculation unit for distribution coefficient based K、 Flow medium parameters and shape factor , the packed bed pressure drop is calculated , thereby obtaining the final predicted result.

8. A system for predicting packed bed pressure drop according to claim 7, It is characterized in that The partition unit comprises: A partitioning module is used to divide the packed bed into a wall area, a transition area and a central area in sequence from the tube wall inward; The response module is used to obtain the corresponding average void fractions based on the wall area, the transition area and the central area.

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