Method for predicting unsteady filtering resistance of dry dust removal system

Abstract

A method for predicting unsteady filtering resistance of a dry type dust removal system comprises the following steps: constructing a loading fiber layer filtering resistance model by utilizing the pressure loss of a fiber collecting body; constructing a relation model between the resistance on the cleaning fiber and the resistance acting on the loaded fiber trapping body; constructing a relation model among the diameter change rate of the fiber capturing body, the filtering efficiency and the filtering time parameter; the influence of particle deposition non-uniformity is eliminated through an integral method, and a loading fiber layer filtering resistance model is solved; according to the Darcy law, constructing a relation model among the filtering resistance, the dust layer thickness and the dust layer permeability coefficient when the dust-containing airflow flows through the dust layer; a dust layer filtering resistance model is constructed and solved through the equivalent relation between the deposited dust mass and the captured dust mass; and based on the fiber layer filtration resistance model and the dust layer filtration resistance model, obtaining an unsteady state filtration resistance prediction model of the dry dust removal system. According to the method, the accuracy and practicability of unsteady filtering resistance prediction can be improved.

Description

Technical Field

[0001] The invention relates to a method for predicting non-steady-state filtration resistance of a dry dust removal system, and belongs to the technical field of particle filtration resistance prediction. Background Art

[0002] With the acceleration of industrial production and urbanization, the problem of atmospheric particulate pollution has become increasingly prominent, posing a serious threat to the environment and human health. In order to effectively control particulate emissions, dry dust removal systems are widely used in power, metallurgy, chemical industry, building materials and other industries. Dry dust removal technology uses filter media to intercept and separate particulate matter. Its advantages are simple operation, convenient maintenance and no need to consume a lot of water resources. However, as the dry filtration dust removal process continues, particulate matter will gradually deposit on the surface of the filter medium and form a dust layer, resulting in a continuous increase in filtration resistance. In conventional industrial control of particulate emissions, the dry dust removal system has a large loading particle concentration and the loading particle concentration is in a dynamic change process, resulting in most of the dry dust removal systems being in a non-steady-state stage, and its filtration resistance does not change linearly, which greatly increases the difficulty of predicting filtration resistance.

[0003] Traditional filtration resistance prediction methods are usually based on steady-state assumptions, ignoring the dynamic changes of the particle deposition process, and it is difficult to accurately reflect the nonlinear growth characteristics of filtration resistance over time during operation. The limitations of this prediction method directly affect the operating efficiency and design optimization of the dust removal system. In addition, there are some filtration resistance models derived from theoretical models, such as using Darcy's law to describe the law of fluid movement in porous media. However, these models often fail to fully consider the complexity of particle deposition effects, especially the effects of dust layer thickness, filtration wind speed and filtration efficiency on filtration resistance. In recent years, with the advancement of computing technology and materials science, some emerging prediction methods and technologies (such as intelligent algorithms, machine learning, etc.) have been introduced into the field of filtration resistance research. However, these current methods have the problems of complex calculations and heavy reliance on actual operating data, which limits their wide application in the industrial field. Summary of the invention

[0004] In response to the problems existing in the above-mentioned prior art, the present invention provides a method for predicting the non-steady-state filtration resistance of a dry dust removal system. The method can comprehensively consider the influence of particle deposition and filtration parameters such as filtration wind speed, loaded dust concentration, and dust layer thickness, thereby improving the accuracy and practicality of the non-steady-state filtration resistance prediction, and can simplify the algorithm, reduce dependence on actual operating data, and expand its scope of application.

[0005] In order to achieve the above object, the present invention provides a method for predicting the non-steady-state filtration resistance of a dry dust removal system, comprising the following steps:

[0006] S1. By analogy with the calculation formula of filtration resistance of clean fiber layer, the filtration resistance model of loaded fiber layer is constructed by using the pressure loss of fiber collection body;

[0007] S2. constructing a relationship model between the resistance on the clean fiber and the resistance on the loaded fiber collection body through the change rate of the fiber collection body diameter;

[0008] S3, considering the change rate of the fiber collection group diameter as a function of the fiber layer thickness and the dust deposition mass, and constructing a relationship model between the change rate of the fiber collection group diameter and the filtration efficiency and filtration time parameters;

[0009] S4, based on the models constructed in S1, S2, and S3, the influence of particle deposition heterogeneity is eliminated by the integration method to solve the filtration resistance model of the loaded fiber layer;

[0010] S5. According to Darcy's law, a relationship model between the filtration resistance of the dust-laden airflow passing through the dust layer and the thickness and permeability coefficient of the dust layer is constructed;

[0011] S6. Based on S5 and in combination with the law of conservation of matter, the dust layer filtration resistance model is constructed and solved through the equivalent relationship between the mass of deposited dust and the mass of captured dust;

[0012] S7. Based on the fiber layer filtration resistance model and the dust layer filtration resistance model obtained by solving S4 and S6, a non-steady-state filtration resistance prediction model of the dry dust removal system is obtained.

[0013] Furthermore, the specific process of S1 is as follows:

[0014] S1.1. The pressure loss of the fiber layer in a clean state depends on the resistance of the fiber per unit length. The filtration resistance of the clean fiber layer is expressed as:

[0015] ΔP 0 =F l ·l f L(1);

[0016] Where ΔP 0 is the filtration resistance of the clean filter, F l is the resistance on the clean fiber, L is the thickness of the fiber layer, l f is the length of the fiber in the fiber layer per unit thickness, and its calculation formula is:

[0017]

[0018] Among them, ε f is the porosity of the fiber layer, d f is the fiber diameter;

[0019] S1.2, when the fiber layer filters the dusty airflow, dust accumulates on the fiber surface to form a new fiber collection group; as the loading continues, the new collection group will continue to capture particles and form a new fiber collection group again, and the cycle repeats; analogous to the calculation formula of the filtration resistance of the clean fiber layer, the filtration resistance of the loaded fiber layer is calculated by the resistance acting on the new fiber collection group, and the filtration resistance satisfies the following relationship:

[0020] ΔP f =F l ′ ·l f ·L(3);

[0021] Where ΔP f is the filtration resistance of the loaded fiber layer, F l ′ is the resistance acting on the loaded fiber collection group.

[0022] Furthermore, the specific process of S2 is:

[0023] The relationship between the resistance acting on the clean fiber and the resistance acting on the loaded fiber collection is expressed in the form of partial differential by the rate of change of the fiber collection diameter, which is expressed as:

[0024]

[0025] Among them, d f ′ is the diameter of the loaded fiber collection group.

[0026] Furthermore, the specific process of S3 is as follows:

[0027] According to the nonlinear relationship between the diameter of the fiber collection body and the fiber layer thickness L and the dust deposition mass m, a relationship model between the change rate of the fiber collection body diameter and filtration parameters such as filtration efficiency and filtration time is constructed, which is expressed as:

[0028]

[0029] The calculation formula for dust deposition mass m is:

[0030] m=ηn 0 U 0 At(6);

[0031] By combining formulas (5) and (6), we can obtain the relationship model between the change rate of fiber collection group diameter and filtration parameters such as filtration efficiency and filtration time:

[0032]

[0033] Where η is the filtration efficiency, n 0is the mass concentration of particles in the dust-laden airflow, A is the projected area of ​​the fiber filter material along the filtering airflow direction, U 0 is the filtering wind speed, t is the filtering time, k 1 、n 1 、n 2 , k 2 is a constant quantity.

[0034] Furthermore, the specific process of S4 is as follows:

[0035] Based on the calculation formula of the filtration resistance of the clean fiber layer and the model constructed by S1, S2, and S3, the microelement dL is taken along the thickness direction of the fiber layer, and the error caused by the unevenness of particle deposition in the filtration resistance calculation is eliminated by the integration method. The filtration resistance model of the loaded fiber layer is solved and expressed as:

[0036]

[0037] Formulas (1), (3), (4), and (7) are combined to solve the filtration resistance model of the loaded fiber layer, which is expressed as:

[0038]

[0039] Among them, C 1 , C 2 , α are intermediate variables, and their calculation formulas are:

[0040] C 1 =k 2 (10);

[0041]

[0042] α=n 1 (12).

[0043] Furthermore, the specific process of S5 is as follows:

[0044] According to Darcy's law, the dust layer filtration resistance is proportional to the thickness of the porous medium and the incoming flow velocity. A relationship model between the filtration resistance of the dust-laden airflow passing through the dust layer and the thickness of the dust layer and the permeability coefficient of the dust layer is constructed, which is expressed as:

[0045]

[0046] Among them, ΔP c is the filtration resistance of the dust layer, μ is the gas viscosity of the dust-laden airflow, ξ is the permeability coefficient of the dust layer, and H is the sum of the dust layer thickness and the fiber layer thickness.

[0047] Furthermore, the specific process of S6 is as follows:

[0048] S6.1. Based on S5 and the law of conservation of matter, the mass of dust deposited by the filter medium during time 0-t is equal to the sum of the mass of dust captured by the fiber layer and the mass of dust captured by the dust layer, expressed as:

[0049]

[0050] Among them, ε c is the porosity of the dust layer, given by Calculate, k 3 is the unknown quantity, ρ p is the density of the loaded dust;

[0051] S6.2. Based on the equivalent relationship between the dust mass deposited by the filter medium per unit time, the dust mass captured by the fiber layer, and the dust mass captured by the dust layer, a dust layer filtration resistance model is constructed and solved, which is expressed as:

[0052]

[0053] Among them, C 3 , C 4 , β are intermediate variables, and their calculation formulas are:

[0054]

[0055] β=2-n 3 (18);

[0056] Among them, κ and n 3 All are constant quantities.

[0057] Furthermore, the specific process of S7 is as follows:

[0058] S7.1. The non-steady-state filtration resistance of the dry dust removal system is expressed as the sum of the filtration resistance of the fiber layer and the filtration resistance of the dust layer as follows:

[0059] ΔP=ΔP f +ΔP c (19);

[0060] Among them, ΔP is the non-steady-state filtration resistance of the dry dust removal system;

[0061] S7.2. Based on the fiber layer filtration resistance model and dust layer filtration resistance model obtained by solving S4 and S6, the unsteady-state filtration resistance of the dry dust removal system is expressed as the sum of the fiber layer filtration resistance and the dust layer filtration resistance. The unsteady-state filtration resistance prediction model of the dry dust removal system is obtained, which is expressed as:

[0062]

[0063] Among them, ΔP is the non-steady-state filtration resistance of the dry dust removal system.

[0064] The present invention constructs a multi-level non-steady-state filtration resistance prediction model including a loaded fiber layer and a dust layer by comprehensively considering the particle deposition effect and combining the dynamic change characteristics of key parameters such as filtration time, wind speed, dust concentration and filtration efficiency. This model reflects the actual change process of filtration resistance under non-steady-state conditions and greatly improves the accuracy of filtration resistance prediction. Through the prediction results of the present invention, the filtration wind speed can be reasonably adjusted, the appropriate filter medium can be selected, and the dust removal equipment structure can be optimized, so as to achieve the low-resistance and high-efficiency operation goals of the dry dust removal system, which helps to extend the service life of the filter medium, reduce the frequency of system cleaning and maintenance, and significantly reduce operating costs. In addition, it can also reduce energy consumption and improve economic benefits. In addition, the algorithm of the present invention simplifies and reduces the dependence on actual operating data, expanding its scope of application. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a flow chart of the method of the present invention;

[0066] Figure 2 It is a schematic diagram of the relationship model constructed by S6 in the present invention;

[0067] Figure 3 It is a comparison chart between the actual test results and the model prediction results in the embodiment of the present invention. DETAILED DESCRIPTION

[0068] The present invention will be further described below in conjunction with the accompanying drawings.

[0069] like Figure 1 As shown, a method for predicting the non-steady-state filtration resistance of a dry dust removal system includes the following steps:

[0070] S1. By analogy with the calculation formula of filtration resistance of clean fiber layer, the filtration resistance model of loaded fiber layer is constructed by using the pressure loss of fiber collection body;

[0071] S2. constructing a relationship model between the resistance on the clean fiber and the resistance on the loaded fiber collection body through the change rate of the fiber collection body diameter;

[0072] S3, considering the change rate of the fiber collection group diameter as a function of the fiber layer thickness and the dust deposition mass, and constructing a relationship model between the change rate of the fiber collection group diameter and the filtration efficiency and filtration time parameters;

[0073] S4, based on the models constructed in S1, S2, and S3, the influence of particle deposition heterogeneity is eliminated by the integration method to solve the filtration resistance model of the loaded fiber layer;

[0074] S5. According to Darcy's law, a relationship model between the filtration resistance of the dust-laden airflow passing through the dust layer and the thickness and permeability coefficient of the dust layer is constructed;

[0075] S6, such as Figure 2 As shown, based on S5 and combined with the law of conservation of matter, the dust layer filtration resistance model is constructed and solved through the equivalent relationship between the mass of deposited dust and the mass of captured dust;

[0076] S7. Based on the fiber layer filtration resistance model and the dust layer filtration resistance model obtained by solving S4 and S6, a non-steady-state filtration resistance prediction model of the dry dust removal system is obtained.

[0077] In one embodiment of the present invention, the filtration resistance data of the bulk grain loading and unloading dust removal system of the ship unloader at Pier No. 2 of the Central Grain Reserve Direct Depot in Zhenjiang City, Jiangsu Province, China was selected, and the non-steady-state filtration resistance prediction method was verified by field measured data.

[0078] In this embodiment, the median particle size of the filtered dust is 19.27 μm, and a differential pressure gauge (model DP-CALC 5825, range -3735—3735 Pa, accuracy 0.1 Pa, TSI Inc., USA) is used to measure the pressure difference between the clean bin and the filter bin of the dust collector to represent the filtration resistance during the operation of the dust collector;

[0079] The filtration resistance data of the first two consecutive cycles in the positive sequence cleaning are selected for verification of the non-steady-state filtration resistance prediction method. The initial filtration resistances of the first two filtration cycles are 640.2Pa and 832.5Pa, respectively, which are used as input values ​​for the initial calculation value of the filtration resistance model.

[0080] The basic filtration parameter data such as dust median particle size, filtration wind speed, filtration area, dust concentration, etc. are substituted into the above-mentioned non-steady-state filtration resistance model for solution. The prediction results of the non-steady-state filtration resistance model are compared with the field measured data of filtration resistance to verify the accuracy of the non-steady-state filtration resistance prediction method for the dry dust removal system provided by the present invention.

[0081] The actual test results of the non-steady-state filtration resistance of the dry dust removal system and the non-steady-state filtration resistance results calculated by the prediction method are as follows: Figure 3 As shown. Figure 3 It can be seen that the actual test results of the non-steady-state filtration resistance in the two filtration cycles and the change trends of the calculation results of the prediction method maintain good consistency, and the model and test data are in good agreement.

[0082] In addition, five intergroup test results were selected at uniform time intervals in two filtering cycles and compared with the model prediction results, as shown in Table 1:

[0083] Table 1 Comparison between the actual test results and the model prediction results of the non-steady-state filtration resistance of the dry dust removal system

[0084]

[0085] It can be seen from Table 1 that in the 5 groups of verification data in the first filtering cycle, the maximum relative error between the model prediction results and the field measured results is -5.10%, the minimum relative error is 0.66%, and the average deviation between the model prediction results and the field measured results is 2.95%; in the 5 groups of verification data in the second filtering cycle, the maximum relative error between the model prediction results and the field measured results is -2.98%, the minimum relative error is -0.14%, and the average deviation between the model prediction results and the field measured results is 1.12%.

[0086] From the above data, it can be seen that the prediction method provided by the present invention can more accurately predict the changing trend of the filtration resistance during the actual operation of the dry dust removal system.

Claims

1. A method for predicting non-steady-state filtration resistance of a dry dust removal system, characterized in that: The following steps are involved: S1. By analogy with the calculation formula of filtration resistance of clean fiber layer, the filtration resistance model of loaded fiber layer is constructed by using the pressure loss of fiber collection body; S2. constructing a relationship model between the resistance on the clean fiber and the resistance on the loaded fiber collection body through the change rate of the fiber collection body diameter; S3, considering the change rate of the fiber collection group diameter as a function of the fiber layer thickness and the dust deposition mass, and constructing a relationship model between the change rate of the fiber collection group diameter and the filtration efficiency and filtration time parameters; S4, based on the models constructed in S1, S2, and S3, the influence of particle deposition heterogeneity is eliminated by the integration method to solve the filtration resistance model of the loaded fiber layer; S5. According to Darcy's law, a relationship model between the filtration resistance of the dust-laden airflow passing through the dust layer and the thickness and permeability coefficient of the dust layer is constructed; S6. Based on S5 and in combination with the law of conservation of matter, the dust layer filtration resistance model is constructed and solved through the equivalent relationship between the mass of deposited dust and the mass of captured dust; S7. Based on the fiber layer filtration resistance model and the dust layer filtration resistance model obtained by solving S4 and S6, a non-steady-state filtration resistance prediction model of the dry dust removal system is obtained.

2. The method for predicting the non-steady-state filtration resistance of a dry dust removal system according to claim 1 is characterized in that: The specific process of S1 is as follows: S1.

1. The pressure loss of the fiber layer in a clean state depends on the resistance of the fiber per unit length. The filtration resistance of the clean fiber layer is expressed as: ΔP0=F l ·l f ·L(1); Among them, ΔP0 is the filtration resistance of the clean filter, F l is the resistance on the clean fiber, L is the thickness of the fiber layer, l f is the length of the fiber in the fiber layer per unit thickness, and its calculation formula is: Among them, ε f is the porosity of the fiber layer, d f is the fiber diameter; S1.2, when the fiber layer filters the dusty airflow, dust accumulates on the fiber surface to form a new fiber collection group; as the loading continues, the new collection group will continue to capture particles and form a new fiber collection group again, and the cycle repeats; analogous to the calculation formula of the filtration resistance of the clean fiber layer, the filtration resistance of the loaded fiber layer is calculated by the resistance acting on the new fiber collection group, and the filtration resistance satisfies the following relationship: ΔP f =F l ′ ·l f ·L(3); Among them, ΔP f is the filtration resistance of the loaded fiber layer, F l ′ is the resistance acting on the loaded fiber collection group.

3. The method for predicting the non-steady-state filtration resistance of a dry dust removal system according to claim 2 is characterized in that: The specific process of S2 is: The relationship between the resistance acting on the clean fiber and the resistance acting on the loaded fiber collection is expressed in the form of partial differential by the rate of change of the fiber collection diameter, which is expressed as: Among them, d f ′ is the diameter of the loaded fiber collection group.

4. The method for predicting the non-steady-state filtration resistance of a dry dust removal system according to claim 3 is characterized in that: The specific process of S3 is as follows: According to the nonlinear relationship between the diameter of the fiber collection body and the fiber layer thickness L and the dust deposition mass m, a relationship model between the change rate of the fiber collection body diameter and filtration parameters such as filtration efficiency and filtration time is constructed, which is expressed as: The calculation formula for dust deposition mass m is: m = ηn0U0At (6); By combining formulas (5) and (6), we can obtain the relationship model between the change rate of fiber collection group diameter and filtration parameters such as filtration efficiency and filtration time: Among them, η is the filtration efficiency, n0 is the mass concentration of particles in the dust-laden airflow, A is the projected area of ​​the fiber filter material along the filtration airflow direction, U0 is the filtration wind speed, t is the filtration time, k1, n1, n2, k2 are constants.

5. The method for predicting the non-steady-state filtration resistance of a dry dust removal system according to claim 4, characterized in that: The specific process of S4 is as follows: Based on the calculation formula of the filtration resistance of the clean fiber layer and the model constructed by S1, S2, and S3, the microelement dL is taken along the thickness direction of the fiber layer, and the error caused by the unevenness of particle deposition in the filtration resistance calculation is eliminated by the integration method. The filtration resistance model of the loaded fiber layer is solved and expressed as: Formulas (1), (3), (4), and (7) are combined to solve the filtration resistance model of the loaded fiber layer, which is expressed as: Among them, C1, C2, and α are all intermediate variables, and their calculation formulas are: C1=k2(10); α＝n1(12)。 6. The method for predicting the non-steady-state filtration resistance of a dry dust removal system according to claim 5, characterized in that: The specific process of S5 is as follows: According to Darcy's law, the dust layer filtration resistance is proportional to the thickness of the porous medium and the incoming flow velocity. A relationship model between the filtration resistance of the dust-laden airflow passing through the dust layer and the thickness of the dust layer and the permeability coefficient of the dust layer is constructed, which is expressed as: Among them, ΔP c is the filtration resistance of the dust layer, μ is the gas viscosity of the dust-laden airflow, ξ is the permeability coefficient of the dust layer, and H is the sum of the dust layer thickness and the fiber layer thickness.

7. The method for predicting the non-steady-state filtration resistance of a dry dust removal system according to claim 6, characterized in that: The specific process of S6 is as follows: S6.

1. Based on S5 and the law of conservation of matter, the mass of dust deposited by the filter medium during time 0-t is equal to the sum of the mass of dust captured by the fiber layer and the mass of dust captured by the dust layer, expressed as: Among them, ε c is the porosity of the dust layer, given by Calculate, k3 is the unknown quantity, ρ p is the density of the loaded dust; S6.

2. Based on the equivalent relationship between the dust mass deposited by the filter medium per unit time, the dust mass captured by the fiber layer, and the dust mass captured by the dust layer, a dust layer filtration resistance model is constructed and solved, which is expressed as: Among them, C3, C4, and β are all intermediate variables, and their calculation formulas are: β＝2-n3(18)； Among them, k and n3 are both constants.

8. The method for predicting the non-steady-state filtration resistance of a dry dust removal system according to claim 7, characterized in that: The specific process of S7 is as follows: S7.

1. The non-steady-state filtration resistance of the dry dust removal system is expressed as the sum of the filtration resistance of the fiber layer and the filtration resistance of the dust layer as follows: ΔP=ΔP f +ΔP c (19); Among them, ΔP is the non-steady-state filtration resistance of the dry dust removal system; S7.

2. Based on the fiber layer filtration resistance model and dust layer filtration resistance model obtained by solving S4 and S6, the unsteady-state filtration resistance of the dry dust removal system is expressed as the sum of the fiber layer filtration resistance and the dust layer filtration resistance. The unsteady-state filtration resistance prediction model of the dry dust removal system is obtained, which is expressed as: Among them, ΔP is the non-steady-state filtration resistance of the dry dust removal system.

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