Representation of results of particle size analysis - Part 4: Characterization of a classification process
Some standard content:
KCS19.120
National Standard of the People's Republic of China
CB/T15445.4-2006/1S09276-4.2001Representation of particle size analysis-Part 4: Characterization of a classification provesIS092761:2001.IDT)
2006-02-05Promulgated
General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of ChinaAdministration of Standardization of the People's Republic of China
2006-08-01Implementation
1Scope
2.1Special terminology and symbols
GA/T15445.4—2006/ISO 927E-4:2001 Characterization of classification processes based on error-free classification and quality 3.1 Characterization of classification processes Balance of individual efficiency Definition of particle size Classification efficiency T (classification efficiency curve T(..), T:amP curve) Effect of systematic errors on the determination of classification efficiency 4.1 Abnormal classification process of classifier 4 Caused by control error 1. 4.3 Incomplete zoning of raw materials 4.4 Effect of raw material degradation in classifier Appendix A (informative) Effect of random non-errors on the evaluation of classification efficiency A.
A.2 or direct calculation
A.3 Establish the value of the distribution of the required cumulative error from the required cumulative error. See CB/T 15445.42006/1S0 9276-4; ... : Part 4: The proof of poor classification.
This part adopts the table of S925-20 test results and the following revisions compared with 5276.1: Use "this part represents the international standard" certification:
Exclude the preface of the relevant 1SO in the international standard: Add the preface of the relevant standard compilation instructions. Appendix A of this part is a supplementary appendix.
This part is proposed by the National Technical Committee for Standardization of Screen Particle Separation Methods. This part is under the jurisdiction of the National Technical Committee for Standardization of Screen Particle Separation Methods. This part was initiated by: Central Iron and Steel Research Institute. Mechanical Science Research Institute Shanghai Institute of Measurement and Testing Technology. The main person in charge of the part: Na Yi, Li Jianfeng, Yu Li, Wu Li, Sheng Keping: CB/T15445.4-200$/TS>9276-4.2001 Introduction
In the classification process used for particle size analysis, the quality of the material is divided into two levels, or the number of 7. , and the particle size standard (%) described by the state of the expected classification, at least divided into a fine component and a raw component, their quality, the effect of the market is 2. () Li. (), in the classification of the selected quantity category is expressed in the following standard meters, and the raw material, the fine component, the coarse component, the following bad f, C relatives are shown in Figure 1! 3H. 1()>mg.m,
Use service, [
Figure 1-hope classification formed by the distribution of components and when the twist component is more than one level, for example, the use of graded ejectors! To characterize the grading process, 0.1.2. etc. can be used to represent 3,1,1: For example, the second component is represented by: 0.1.2. etc. It contains larger particles than component 2. If the particles are of other shapes, the equivalent spherical diameter of the particles can be used to represent the total diameter according to the actual situation.
1 Scope
GB/I15445.4—2006/ISO 9276-4:20C1 Presentation of particle size analysis results
Part 4: Characterization of classification processes
The main purpose of this chapter is to provide a method for classifying processes using a meter to verify that the particle size is not a standard. Instead, a meter can be used to classify the process (e.g., air classification, centrifugal classification) or a classifying process (e.g., cyclone classification or hydraulic classification). The characterization of the classification process in Chapter 3 is based on the assumption that the initial classification curve and the overall quality are free of errors. Section 4 discusses the influence of systematic errors on classification efficiency and discusses the random errors in the actual classification process. Process table
2 Symbols
2.1 Special terms
Underlined terms are used in this section: cumulative distribution parameters derived from the line; F... a cumulative distribution is divided into the most stable quality and small error: small Jin inspection department:
=Q..—Q.
static and correct integral does not
start virtual classification of the total number of bottles or particles!
report distribution line:
Cumulative points are:
equal to the difference between the two cumulative distribution values, corresponding to the first, the largest interval: the relative distribution of the case, the heat difference:
-Stude nt country
classification efficiency;
total classification efficiency or separation efficiency:
classification efficiency industry line:
particle white will or equivalent spherical diameter:
analysis cutting short waste;
equal probability average scraping particle to be, the median of the efficiency curve of the classification line; the first: the point limit particle corresponding to the frequency interval is subject to: the first! The new interval is based on the virtual lower limit particle standard, the first: the width of the particle medical interval;
the block efficiency of the given distribution of the degree:
the record of the particle size distribution;
the slope angle, the sum of the gate weights;
the quality of the frequency distribution generation Balance error:
Variable:
Santa Clara precision measurement book source GB/T15445.4—2006/ISD9276-4:200 Relative registration:
2.2 Subscript
Variable:
The amount of particles that are detected after the test;
The following subscripts are used in the area:
Group (the second subscript after the first?)
E oblique group thickness second subscript "
Upper limit particle size becomes:, particle size interval sequence effect:: frequency distribution type (general expression) Note: Example: 3 The type of inflammation is body rent or original. Original powder or granules (subscript 2>3
when there is more than one coarse fraction, replace 5.
when entering a classification process:
when there is more than one coarse fraction, replace:
when there is more than one coarse fraction, replace:
Demonstration of classification process based on error-free distribution curve and quality level 3
3.1 Characterization of classification process by using the density distribution curve In a classification process, a given raw material (subscript 3) is classified into two parts, namely, the fine fraction The weighted frequency distribution curve of the raw materials and the fine components and the cake components under the ideal separation conditions is fy
. The weighted frequency distribution curve of the fine components and the cake components under the ideal separation conditions is respectively expressed as the amount of the coarse components and the fine components. , represents the percentage of paper particles, and represents The sum of the number of inspections is about 1, which represents the mass fraction of fine particles and the percentage of the number of particles in question. In actual work, there is a single particle size range. In the construction of particles, there may be not only the same component, but also the same component. The frequency distribution curve of the subdivided components overlaps in this particle size range. The intersection of the two points in Figure 3 shows a cut size, which is called the equal probability particle size. [See 3. 3.2]: GB/T15445.4—2006/1509276-42001Se
In the actual classification of raw materials, the weighted distribution of fine and coarse components is less than 2 in the rough component. And the fine component should be checked for errors in the classification. 3.2 Balance of quality and number
3.2.1 The balance of quality and number of particles within the range of x to x is required for the classification.The material with the mass variable efficiency as the subject is divided into the most efficient or the most effective component and the mass, or the period of time, and we get: -
represents the relative amount of fine components, and represents the sum of the components. In the range of 2, the function 3.3 and v are represented by the weighted elastic distribution curve of the fine components () and the torsion coefficient distribution of the components respectively. The product of the limit variable distribution of the raw material under the load) is 13.2.2. The balance of mass and quantity exists in the good material within the range of tensile strength from x to x+x. The kidney grains of this kind can be divided into the mixed grain or the raw material mixed grain during the rice separation process: the total amount of grains with a particle size of dQ in the raw material is thus divided into two parts Q( and Q: aar.(x) ...rdQ.(r)I,dQ...t.) Replace the equation
..rg-.r(b +rrtj
in order to read the graph correctly, it is necessary to use (6) or. When drawing, it should be recognized that there are three variables in (6) that can be selected. For example, if two frequency components exist (), the relative amount of the fine components is determined, and () and are determined accordingly
3.2.3 From to, the quality and quantity balance within the medical standard is used to judge the difference between (6):: get Q: +Q)
3.2.4 Indirect calculation of and*
In many practical situations, \ and \ cannot be calculated by the flash mass and mass flow rate, because these batches are difficult to obtain in practice, or even impossible to obtain. However, if the material sample provided is representative and its fine components and their distribution have been determined, then by inverting (3) and (6) or (7) we can calculate and*. Substituting (3) into (6) and (7), we have:
rf) n.()
—qr,ibzxZ.net
4)-(a)
If the distribution of the rate) and (6) formulas and () formulas are not conditional. The and will be bands and have nothing to do with the particle size.
3.3 Cutting particle size, is the meaning of
3.3. 1 Overview
In principle, the cut particle size can be estimated by any method between the fine component and the fixed component. As long as the uniform diameter shape in the area where the distribution of the fine component and the fixed component is mutually negative, the two definitions used in this cut control are described in 3.3.3 and 3.3.3. 3.3.2 Median of the classification efficiency curve - equal probability cut particle size, median diameter of the classification efficiency In Figure 3, the weighted regularity distribution of the fine component and the fixed component converges at a certain particle size. This curve is the equal regularity cut efficiency diameter x, as defined in 3.4, which is the median of the classification efficiency curve.. = x(T = 0. 5)
is different from other particle sizes. Particles of this particle size have equal probability of being classified in the fine component or the coarse component. In Figure 3, the length of the vertical dashed line from the intersection point of the weighted fine component and the coarse component is equal to the vertical line from this point to the weighted distribution curve of the raw material. Therefore, the particle size of
is the same as that of the raw material. The probability of being in the fine component and the coarse component is equal to the probability of being in the coarse component. That is, 3.3.3 Analysis of cutting particle size \
For the user, it is like using an analytical type classification buffer (such as a single-stage air flow stop) as shown in Figure 1. For example, the material is still or for the shot. Except for the material supplied to the classifier, after the classification process is completed, in most cases we can only take the pressure of the component, and the basis of the fine component can be calculated based on the \, and the benefit of the \. The relative mass of the fine components determined by the experiment is <: r/m>, which is taken as the relative mass of a certain size in the raw material. A particle size is also defined according to this size. This particle size is called the analytical particle size, which is usually defined as: 2. = v = Q(,
For a given raw material (or raw material with different degrees of separation), the analytical particle size produced by the relative amount of the fine components is shown in the figure. Definition of the cutting particle size
GR/T15445.4—2006/1S0 9276-4:2001 Substituting formula (11) for formula (7), it can be seen that, in terms of particle size, the fine fraction and the annual fraction contain equal amounts of misclassified substances. That is, the relative amount of coarse particles in the fine fraction L-Q () is equal to the relative amount of fine particles in the annual fraction Q (r,). In Figure 6, if A1 and A2 are equal, then there is no one component and the product A. represents the total area of the v-th part 3.4 classification efficiency 1 and the classification efficiency T(x), (Tromp curve) To describe the classification efficiency, the classification efficiency curve () is derived from the measurement classification curve in Figure 3. For a specific particle size, the classification efficiency (or the strength of the particle size) is the ratio of the amount of particles of the same size present in the crude fraction to the amount present in the raw material. The classification efficiency can be evaluated by () as follows,
hge）
.o..Q.)
Q(a, -r.(a-.
- 12 :
If the particle size is plotted in order to obtain the grading efficiency curve (as shown in Figure 5, the grading efficiency curve should start from the beginning and remain at 1. In practice, this may not be the case. See Chapter 4 for details. o.T5
3.5 Measurement of Sharpness
3.5.1 The smaller the overlap between the boundary and the boundary, or the less the amount of material being graded, the higher the quality of the grading process, or the better the sharpness of the grading process. In order to accurately represent the scalability or unsatisfactoriness of a grading process, many parameters can be used. In many cases, to fully describe the grading process, it is necessary to use a series of parameters, or even a combination of parameters. These parameters are only effective when the grading process is obtained. It is of little significance in the process. It is worth noting that most of the parameters obtained are partial extraction or can only quantify part of the information obtained from the classification efficiency curve. The three groups of parameters described in 3.2 and 3.5.3 include all the majority of the particle characteristic parameters obtained in the previous work. The difference (or ratio) of the particle size characteristic values obtained on the classification efficiency curve is used. 5, which represents the value of the particle size corresponding to the classification efficiency curve. For example, the following formula can be used to distinguish the difference between the particle size characteristics at the center of the classification efficiency curve: 2xm
(13)
B/T15495.4—2006/I509276-4:2001 or the formula () and (1) invert the ratio at the center of the classification efficiency curve. 3.5.3 Most of the following numbers derived from the cumulative distribution curve can be directly calculated from the cumulative distribution curve () and the total () component efficiency and true value, without the need for graded efficiency curves. As shown in the figure, the principle of two-zone division is to use a frequency-variable industry line, three-A small blood product. With this in mind, the following special gold number is given in turn, the relative number of fine particles in the raw material 2 and the coarse component and the red component below or above the medium or above the medium, or if we say their proportion, the frequency distribution of the fine particles in the raw material is defined by the following 6 areas:
A, = Q., (..)
In the raw material:
A. - 1-Q...)
In the group:
A,- .Ll- Q.tu]
Group:
4: = .0.(r)
In the fine component calibration, the total amount is A. Platform, the mask is related to the starting point, and the double north side can also form some other related factors, and the quick addition: the corresponding original scientific method can be carried out for the light-guard grade operation. The recovery rate of fine particles is 4:-2.Q..5x)
Relative to the coarse particles in the raw materials, after classification, the recovery rate of coarse particles is GB/T15445.4—2006/1S09276-420c1(21)
1 -(2)
3.5.4 Total classification or high efficiency T.
The total classification or separation efficiency needs to be evaluated - generally the performance of a small system such as a gas separator. It is related to the relative amount of particles that have been defined. The classification efficiency curve T () and the frequency of the raw material can be drawn as follows: T = v
fr(r)g..(r)dt
The influence of systematic errors on the determination of the classification efficiency curve 4.1.
Relative to the systematic increase of the classification efficiency curve, the following factors may cause: systematic analysis errors caused by sampling and sub-sampling: a.
The superposition of continuous and abnormal classification processes in the classifier: the particles of the special grating cannot be dispersed and are transferred to the annual components: the material that is not separated by the household is too high.
If the sampling and sub-sampling of the raw materials, secondary components and coarse components are already very fine, then the first systematic error listed above may not be caused.
4.2 Re-analytical errors caused by abnormal classification processes in the classifier are shown in the figure? As shown in the figure, in the small particle size area, the classification efficiency mainly decreases. When the classification efficiency reaches a certain value, its end is parallel to the coordinate system. This is probably the result of a non-normal classification process superimposed on the classification process. The influence of the classification process within the classification process on 1(x) is 2(=
. In this case, some elements in the raw materials enter the coarse classification through the graded method. Through the formula <24), a modified fractional efficiency curve () can be obtained.
At this time, 1) is still used to represent the fractional efficiency of the case, but by introducing (), the description of the classification efficiency curve can be simplified and some technical estimates can be given. F-
GB/T15445、42006/1S09276-2:20014.3 Incomplete dispersion of raw materials
If the raw materials are not completely separated into fine particles before entering the grading zone, they will be classified into the torsion components: when the raw materials are subjected to particle size change analysis, the fine components and phase components are better classified (processed) than the coarse particles (i.e., those particles that are regarded as phase components during grading) will be classified, and the grading efficiency curve will be raised in the fine particle area, as shown in Figure 8. 4.4 The influence of raw material crushing in the classifier on the classification efficiency caused by incomplete classification in the classification process
If there is powder degradation in the raw material classification process, that is, the classification is excessive at a certain point. When the grinding machine is used, this point will stop "the powder will stop at the same time: the new material will be produced continuously with light, and the problem will disappear within the same amount of time, and the quality of the material will be almost balanced. In the idle record A, a new variable is derived). It refers to whether the measured value is random or instrument-systematic, and its derivation is as follows: ()+ = 1- )
If the sum of the weighted distributions of the samples is equal to the weighted distribution of the raw materials, then (equals. Equivalent! 1(2)
If the deviations of the samples are of a random nature, it means that () is a natural error. When proposing the efficiency curve of the sample, the difference should be analyzed multiple times according to the characteristics described in the previous A. If the system deviates from 1, it may be that a slight error occurred in the classifier. In this case, it is not recommended to use this type of equipment for particle size analysis unless the operating system is carefully modified to avoid powder production.
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