CN102980902B - Visualization quantitative CT (Captive Test) characterization method for component distribution and physical structure of coal sample - Google Patents
Visualization quantitative CT (Captive Test) characterization method for component distribution and physical structure of coal sample Download PDFInfo
- Publication number
- CN102980902B CN102980902B CN 201210505069 CN201210505069A CN102980902B CN 102980902 B CN102980902 B CN 102980902B CN 201210505069 CN201210505069 CN 201210505069 CN 201210505069 A CN201210505069 A CN 201210505069A CN 102980902 B CN102980902 B CN 102980902B
- Authority
- CN
- China
- Prior art keywords
- coal
- ray
- mineral
- coal sample
- different
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Images
Landscapes
- Analysing Materials By The Use Of Radiation (AREA)
Abstract
The invention relates a visualization characterization technology of a coal sample, and in particular relates to a visualization quantitative CT (Captive Test) characterization method for component distribution and a physical structure of a coal sample. The visualization quantitative CT characterization method comprises the following steps of: sampling and pre-testing the coal sample; carrying out an X-ray absorption characteristic analysis on each component of a second coal sample; carrying out X-ray absorption characteristic analysis on residual minerals; carrying out a CT on the coal sample; establishing a mathematical model and carrying out a numerical analysis on CT slices; and carrying out visualization quantitative characterization. The CT characterization method provided by the invention adopts a plurality of CT data obtained by monochromatic X-ray experiment energy to carry out conjoint analysis, and detects the information of a component, dimension of which is smaller than CT resolution dimension, on a CT picture by establishing a data model on a single CT element, so that the visualization result of the component distribution characteristic and the physical structure ofthe coal sample is accurate.
Description
Technical field
The present invention relates to a kind of visual characterization technique of coal sample, specifically is that a kind of coal sample component distributes and the visual quantitative CT characterizing method of physical arrangement.
Background technology
The visual characterization technique of coal sample mainly comprises at present: scanning electron microscope method, X ray CT imaging method, nuclear magnetic resonance method etc.Said method respectively has relative merits when characterizing the three-dimensional structure of coal sample.
Scanning electron microscope method binding energy analysis of spectrum can provide comparatively accurate and high-resolution coal sample component two-dimensional distribution, but must carry out the physics section to the coal sample in obtaining the 3-D view process.The original physical arrangement of coal sample will be destroyed inevitably in slicing processes.
Nuclear magnetic resonance method can nondestructively obtain the tomograph of coal sample, but this method is remaining aspect the coal sample quantitatively characterizing in big difficulty.The NMR imaging method imaging needs the time long in addition, and at component composition and the structure of coal sample complexity, nuclear magnetic resonance is used for characterizing the distributed in three dimensions of coal sample mesoporosity more, and the distribution of mineral and the discriminating of component are seemed helpless.
The X ray CT scanning method has been widely used in medical science and investigation of materials field because it is quick, harmless, the outstanding advantage of three-dimensional visualization.The quantitative CT method that exists mainly contains dual energy method and carrying out image threshold segmentation method at present.Based on these two kinds of methods, numerous scholars also utilize X ray CT that coal sample physical arrangement has been carried out a large amount of signs.But the difficulty that still exists some to overcome at present.When coal sample scanning picture was analyzed, the problem of a key was exactly how to set up related between X-ray absorption coefficient that section after the reconstruct goes up each point and the scanning samples physical set branch.At first, because different mineral constituent may have similar absorption coefficient, component in the CT section that these absorption coefficients are close distinguishes very difficult mutually.Secondly, the resolution of CT device has seriously restricted the application of CT technology at the visual representational field of coal sample component.There are many holes less than CT resolution or mineral grain in the coal sample, this makes and may comprise various ingredients in each pixel size on the CT reformatted slices, make the gray-scale value of this point represent the average X-ray absorption coefficient of these components, this certainly will add to the difficulties just so-called " partial volume effect " for obtaining material component according to CT gradation of image value.On the other hand, though can reduce the influence of partial volume effect to a certain extent by the resolution that improves CT, along with the raising of resolution, sample size can fall sharply, and has a strong impact on the statistical representativeness of sample.
Summary of the invention
The present invention provides a kind of coal sample component to distribute and the visual quantitative CT characterizing method of physical arrangement for the defective that the visual characterization technique that remedies existing coal sample exists.
The present invention is achieved by the following technical solutions: the coal sample component distributes and the visual quantitative CT characterizing method of physical arrangement, comprise the steps: sampling and the pretest of the first step, coal sample: the first coal sample for coal analysis, overall porosity test is chosen in the adjacent area cutting on the coal sample, and the second coal sample that is used for the CT experiment, sampling will guarantee that sample (the first coal sample and the second coal sample) thing phase composition is even; Means obtain the first coal sample ature of coal test data by experiment, described ature of coal test data comprises ash content in the coal sample, ash content composition and ash content component content, organic element composition and content, overall porosity, infer the first coal sample component, each component volume fraction according to the ature of coal test data, described component is made of hole, matrix of coal and different minerals composition; The X ray absorption characteristic of second step, second each component of coal sample is analyzed: according to the ratio of the X ray uptake of each mineral and matrix of coal in the second coal sample under formula (1) the calculating different x-ray energy,
In the formula (1)
Represent different mineral;
mRepresent matrix of coal;
Represent the X ray energy;
With
Represent mineral respectively
With matrix of coal at energy be
The X ray linear absorption coefficient of keV;
With
Represent mineral respectively
Volume fraction with matrix of coal; Neglect ratio and be less than or equal to the mineral of CT experimental noise level, remainder mineral; Described remainder mineral constitute with reference to mineral and all the other mineral by one; The 3rd step, remainder mineral X ray absorption characteristic are analyzed: remainder mineral X ray absorption characteristic is analyzed: calculate each all the other mineral under the different x-ray energy and ratio with reference to the X ray linear absorption coefficient of mineral according to formula (2),
In the formula (2)
Getting all the other different mineral of different value representative at energy is
The X ray linear absorption coefficient of keV;
Representative with reference to mineral at energy is
The X ray linear absorption coefficient of keV; Make under the different x-ray energy each all the other mineral and X ray linear absorption coefficient ratio curve with reference to mineral, the remainder mineral are divided into groups, the mineral that X ray linear absorption coefficient curve is parallel to each other are made as a group, hole is made as a hole group separately, matrix of coal is made as a matrix of coal group separately, and the grouping of matrix of coal group and remainder mineral has constituted non-hole group; The CT experiment of the 4th step, coal sample: find out the X ray linear absorption coefficient curve least parallel energy section each other between the different groups, in above-mentioned energy section, choose
Individual X ray experiment energy carries out the CT experiment respectively, obtains a plurality of projection images, this X ray experiment energy number
Equal the grouping number of non-hole group, the imaging resolution of CT experiment projection image is
aAbove-mentioned projection image is carried out CT section reconstruct, bright background and dark background in the restructuring procedure in the deduction projection image, the Pixel Dimensions of every CT section is
c*
d, the minimum distinguishable unit of CT section is of a size of
a*
a*
aThe 5th goes on foot, sets up mathematical model, numerical analysis is carried out in section to CT: owing to distribute and can not observe directly from the CT section less than the component of CT resolution dimensions, in order to make the numerical analysis result comprise the component less than the CT resolution dimensions, set up mathematical model: sample by N (
N=
c*
d*
) individual simple cubic lattice formation, wherein
Be the number of plies of CT section, the size of each simple cubic lattice equals the size of the minimum distinguishable unit of CT section, for being in the position
(
) the simple cubic lattice located, set up following objective function (3):
Wherein
(=1,2....
) different value corresponding different X ray experiment energy respectively;
Be X ray experiment energy number;
(=0,1,2 ....
) respectively corresponding different grouping of different value, wherein
=0 corresponding hole group;
Expression the
Divide into groups in the individual simple cubic lattice
Volume fraction;
The expression grouping
The chemical potential parameter;
The expression grouping
X ray linear absorption coefficient under the corresponding experiment energy,
Equal grouping
In the X ray linear absorption coefficient of each component multiply by corresponding volume fraction, add up summation and obtain total value, again divided by grouping
Total volume fraction;
(
=1,2,3 ...,
) expression experiment obtain the
Individual simple cubic lattice exists
X ray linear absorption coefficient under the corresponding experiment energy; Chemical potential parameter with non-hole group
All be made as 0, utilize computer programming, adjust the chemical potential parameter of hole group
P (0), adjust each simple cubic lattice
The volume fraction of middle different grouping makes objective function (3) formula obtain minimum value, the volume fraction assembly average that the volume fraction of respectively dividing into groups in the result of calculation must satisfy constraint condition (4) and all simple cubic lattice mesoporosity groups equals the overall porosity test result of coal sample
Try to achieve the volume fraction of respectively dividing into groups in each simple cubic lattice in the CT section; The 6th step, quantitative visualization characterize: set different colours and represent different groupings, the intensity of color is directly proportional with this volume fraction that is grouped in this letter cubic lattice of calculating gained, based on the volume fraction ratio of different grouping in each simple cubic lattice, utilize different colours and different color intensities on all simple cubic lattices, to show all groupings or specific cluster, make coal maceral distribution and physical arrangement obtain quantitative visualization and characterize.
The ash content of coal sample, ash content composition and content, organic element composition and content obtain by laboratory facilities such as technical analysis, ultimate analysis, ash analysiss in the first step, and overall porosity obtains by laboratory facilities such as true apparent relative density methods.Mineral constituent kind and content are to derive according to the element corresponding relation according to common mineral species in the coal and coal sample ash content and ash content composition to obtain in the coal sample.Coal sample matrix of coal molecular formula is to write out according to the organic element ratio.Above-mentioned laboratory facilities and derivation are the conventional use means in this area.
The X ray absorption characteristic of second each component of coal sample is analyzed in second step, is that the component analysis result according to the first coal sample carries out.The first coal sample and the second coal sample are chosen from the adjacent area of same coal, and guarantee that the phase composition of sample thing evenly is in order to reduce the error that the coal anisotropy is brought.
The CT experimental noise is caused by CT experimental provision and experiment parameter, and CT experimental provision and experiment parameter difference, CT experimental noise level are just different.
First (in the bracket) in objective function (3) formula expression the
The X ray uptake that individual simple cubic lattice place calculates according to Beer law and experiment obtain the difference between the X ray uptake.According to Beer law, the ideal situation of this difference should be zero, however since the existence of experimental error, the error that component information preanalysis exists, and this difference is in most cases non-vanishing.In order to make CT picture numerical result more near the true distribution of component in the sample, second form with the chemical potential parameter of dividing into groups in (3) formula is added in the objective function.The chemical potential of each grouping is as adjustable parameter, make the measurement result of CT result and other laboratory facilities match by the chemical potential parameter of regulating different grouping, thereby make the truth of CT graphical analysis result and sample component distribution and physical arrangement approach more.
CT characterizing method of the present invention adopts the CT data aggregate that obtains under a plurality of homogeneous X-ray experiment energy to analyze, realization is to differentiation and the discriminating of different component in the CT section, by setting up mathematical model in single CT volume elements, weakened in the coal sample characterization process high request to CT resolution, detect on the CT picture less than the component information of CT resolution dimensions, make that the component distribution characteristics of coal sample and physical arrangement visualization result are more accurate.Content of the present invention characterizes the appearance of new method to the coal material, the foundation of gas transmission theory model in the coal, and development of clean coal, coal-bed gas exploitation rate and carbon dioxide are sealed the raising with efficiency of displacement up for safekeeping, have great science and technology and economic implications.
Description of drawings
Fig. 1 is smalite, quartz, illite, chlorite, TiO under the different x-ray energy
2Ratio curve with the X ray uptake of matrix of coal.
Fig. 2 is MnO under the different x-ray energy
2, plagioclase, rauhkalk, pyrite and matrix of coal the ratio curve of X ray uptake.
Fig. 3 is quartz, illite, chlorite, TiO under the different x-ray energy
2Ratio curve with kaolinic X ray uptake.
Fig. 4 is the CT section after wherein reconstruct.
Fig. 5 calculates the distribution plan of all groupings after setting up mathematical model for the CT section.
Fig. 6 is the distribution plan of smalite group among the Fig. 4 of back as calculated.
Fig. 7 is the distribution plan of chlorite group among the Fig. 4 of back as calculated.
Fig. 8 is the three-dimensional structure that all groupings of back coal sample one sub regions as calculated all show.
Fig. 9 be as calculated the back Fig. 8 in except matrix of coal other the grouping all the demonstration three-dimensional structures.
Embodiment
Characterizing method of the present invention can characterize all coals such as brown coal, bituminous coal, stone coal.Be that the present invention will be described in detail for embodiment with a kind of stone coal below.This embodiment just sets forth the present invention for further, but does not limit the scope that the present invention protects.
The coal sample component distributes and the visual quantitative CT characterizing method of physical arrangement, comprise the steps: sampling and the pretest of the first step, coal sample: the first coal sample for coal analysis, overall porosity test is chosen in the adjacent area cutting on the coal sample, and the second coal sample that is used for the CT experiment, sampling will guarantee that the phase composition of sample thing is even; Means obtain the first coal sample ature of coal test data by experiment, described ature of coal test data comprises ash content in the coal sample, ash content composition and ash content component content, organic element composition and content, overall porosity, infer the first coal sample component, each component volume fraction according to the ature of coal test data, described component is made of hole, matrix of coal and different minerals composition;
Carry out means of testing such as coal proximate analysis, ultimate analysis, ash analysis and true apparent relative density method, wherein the technical analysis of the first coal sample and results of elemental analyses see Table 1, and the overall porosity that true apparent relative density method obtains is 4.5%.
The table 1 first coal sample analysis result
When derivation coal sample component and content, it according to condition is: A) mineral are to exist with mineral forms common in the coal in the coal sample; B) metallic ion in the ash analysis comes from the metallic element in the coal, does not consider the volatilization of metallic element in the ash analysis process; C) the total volume of coal sample equals the volume sum of mineral, matrix of coal and hole; D) molecular formula of matrix of coal is to provide by each constituent content ratio.
According to condition, again according to common mineral forms in the coal in table 1 and the table 2, deriving obtains component and the content of the coal sample that present embodiment will characterize, and sees Table 3 based on above-mentioned.
Common mineral forms in table 2 coal
Each component and volume fraction in the table 3 coal sample
According to the requirement of CT device sample platform, the second coal sample that is used for the CT experiment need grind into suitable shape, guarantees that sample surfaces does not have sharp-pointed corner angle, and under the used X ray energy of experiment the X ray transmissivity of sample at 30%-70%.To grind into diameter 5mm with the abrasive paper for metallograph folk prescription to craft, the cylinder of high 10mm for the second coal sample of CT experiment in the present embodiment.Except cylindric, the second coal sample also can grind into other shapes that meets the requirement of CT device sample platform.
The X ray absorption characteristic of second step, second each component of coal sample is analyzed: according to the ratio of the X ray uptake of each mineral and matrix of coal in the second coal sample under formula (1) the calculating different x-ray energy,
In the formula (1)
Represent different mineral;
mRepresent matrix of coal;
Represent the X ray energy;
With
Represent mineral respectively
With matrix of coal at energy be
The X ray linear absorption coefficient of keV;
With
Represent mineral respectively
Volume fraction with matrix of coal;
Neglect ratio and be less than or equal to the mineral of CT experimental noise level, remainder mineral; Described remainder mineral constitute with reference to mineral and all the other mineral by one.
Fig. 1 and Fig. 2 are the images that obtains according to formula (1).As seen from Figure 2, compare MnO with matrix of coal
2, the X ray uptake that causes of plagioclase, rauhkalk, pyrite seldom.The CT experimental noise level of considering the CT experimental provision that adopts in this enforcement is 1%, these components (MnO
2, plagioclase, rauhkalk, pyrite) be difficult to be detected.So these components will be left in the basket in the CT in later stage analyzes.So the remainder mineral are smalite, illite, quartz, chlorite, TiO
2In the 3rd step of present embodiment, having chosen smalite is with reference to mineral (can also adopt other remainder mineral as the reference mineral).
The 3rd step, remainder mineral X ray absorption characteristic are analyzed: calculate each all the other mineral under the different x-ray energy and ratio with reference to the X ray linear absorption coefficient of mineral according to formula (2),
(2)
In the formula (2)
Getting all the other different mineral of different value representative at energy is
The X ray linear absorption coefficient of keV;
Representative with reference to mineral at energy is
The X ray linear absorption coefficient of keV; Make under the different x-ray energy each all the other mineral and X ray linear absorption coefficient ratio curve with reference to mineral, the remainder mineral are divided into groups, the mineral that X ray linear absorption coefficient curve is parallel to each other are made as a group, hole is made as a hole group separately, matrix of coal is made as a matrix of coal group separately, and the grouping of matrix of coal group and remainder mineral has constituted non-hole group.
Fig. 3 is quartz, illite, chlorite, TiO under the different x-ray energy
2With kaolinic X ray linear absorption coefficient ratio curve.See that from Fig. 3 the curve of smalite, illite and quartz is almost parallel, the linear absorption coefficient of meaning smalite, illite and quartz is identical with X ray energy variation rule.And chlorite and TiO
2The linear absorption coefficient curve almost parallel, both have similar X ray absorption characteristic.
Therefore, the component in the coal sample is divided into following four groups:
(1) hole;
(2) matrix of coal;
(3) smalite, illite, quartz;
(4) chlorite, TiO
2
The CT experiment of the 4th step, coal sample: find out the X ray linear absorption coefficient curve least parallel energy section each other between the different groups, in above-mentioned energy section, choose
Individual X ray experiment energy carries out the CT experiment respectively, obtains a plurality of projection images, this X ray experiment energy number
Equal the grouping number of non-hole group, the imaging resolution of CT experiment projection image is
aAbove-mentioned projection image is carried out CT section reconstruct, bright background and dark background in the restructuring procedure in the deduction projection image, the Pixel Dimensions of every CT section is
c*
d, the minimum distinguishable unit of CT section is of a size of
a*
a*
a
During concrete enforcement, the CT experiment of carrying out in the 4th step is maybe can provide the device of good homogeneous X-ray to carry out in synchrotron radiation.CT experiment of the present invention is to obtain at SSRF synchrotron radiation line station, and its imaging resolution is
a=3.7
, before and after imaging, all gather dark background and bright background.Choose 14keV, 18keV, three (identical with non-hole grouping number in the sample) X ray experiment energy of 30keV, under above-mentioned three X ray experiment energy, gather 1080 projection images respectively.
All projection images are carried out CT section reconstruct, deducted bright background and the dark background in the projection image in the restructuring procedure.The Pixel Dimensions of every CT section is 1519(after the reconstruct
c) * 1519(
d).Wherein Fig. 4 is seen in the section of the CT after reconstruct, and white portion is represented mineral among the figure, and gray area is represented matrix of coal.
The 5th goes on foot, sets up mathematical model, numerical analysis is carried out in section to CT: owing to distribute and can not observe directly from the CT section less than the component of CT resolution dimensions, in order to make the numerical analysis result comprise the component less than the CT resolution dimensions, set up mathematical model: sample by N (
N=
c*
d*
) individual simple cubic lattice formation, wherein
Be the number of plies of CT section, the size of each simple cubic lattice equals the size of the minimum distinguishable unit of CT section, for being in the position
(
) the simple cubic lattice located, set up following objective function (3):
Wherein
(=1,2....
) different value corresponding different X ray experiment energy respectively;
Be X ray experiment energy number;
(=0,1,2 ....
) respectively corresponding different grouping of different value, wherein
=0 corresponding hole group;
Expression the
Divide into groups in the individual simple cubic lattice
Volume fraction;
The expression grouping
The chemical potential parameter;
The expression grouping
X ray linear absorption coefficient under the corresponding experiment energy,
Equal grouping
In the X ray linear absorption coefficient of each component multiply by corresponding volume fraction, add up summation and obtain total value, again divided by grouping
Total volume fraction (weighted mean value-based algorithm);
(
=1,2,3 ...,
) expression experiment obtain the
Individual simple cubic lattice exists
X ray linear absorption coefficient under the corresponding experiment energy; Chemical potential parameter with non-hole group
All be made as 0, utilize computer programming, adjust the chemical potential parameter of hole group
P (0), adjust each simple cubic lattice
The volume fraction of middle different grouping makes objective function (3) formula obtain minimum value, the volume fraction assembly average that the volume fraction of respectively dividing into groups in the result of calculation must satisfy constraint condition (4) and all simple cubic lattice mesoporosity groups equals the overall porosity test result of coal sample
Try to achieve the volume fraction of respectively dividing into groups in each simple cubic lattice in the CT section.
Wherein
N=
c*
d*
For
, the monomer elemental size is 3.7 * 3.7 * 3.7 in each CT section
In the objective function (3),
=3;
The respectively corresponding X ray experiment of the different values of (=1,2,3) energy 14,18,30 keV;
(=0,1,2,3) are corresponding hole group, matrix of coal group, smalite group and chlorite group respectively.
Non-hole group
(=1,2,3) X ray linear absorption coefficient (weighted mean value-based algorithm) under three experiment energy is got following value:
Cm
-1
After the machine programming is calculated as calculated, the chemical potential parameter of non-hole group
All be made as 0, when the chemical potential parameter of hole group
P (0)Elect as at 0.0012 o'clock, the assembly average of all simple cubic lattice hole group volume fractions is identical with the overall porosity test result of coal sample, the volume fraction value of different grouping all makes objective function (3) formula obtain minimum value in the simple cubic lattice of all that calculate, and satisfies following constraint condition
The 6th step, quantitative visualization characterize: set different colours and represent different groupings, the intensity of color is directly proportional with this volume fraction that is grouped in this letter cubic lattice of calculating gained, based on the volume fraction ratio of different grouping in each simple cubic lattice, utilize different colours and different color intensities on all simple cubic lattices, to show all groupings or specific cluster, make coal maceral distribution and physical arrangement obtain quantitative visualization and characterize.
Fig. 5 (the examination as to substances data is with reference to coloured picture 5), Fig. 6, Fig. 7 are the numerical analysis result's of CT section shown in Figure 4 image shows.Fig. 5 medium green colour specification matrix of coal group, red expression hole group, blue expression smalite group, purple is represented the chlorite group.The coexistence of colored expression different grouping, this place's matrix of coal group of yellow expression and the coexistence of hole group.The intensity of each color of different pixels place is directly proportional with the volume fraction that its representative is grouped in this place.Because the volume fraction difference of different grouping, the grouping color of some volume fraction decided advantage can be covered the less grouping color of volume fraction.Can't see the chlorite group of tangible purple representative in Fig. 5, is because the volume fraction of this grouping is very little, so covered by the color of other groupings.
Only show the smalite group (specific cluster) in the CT section shown in Figure 4 among Fig. 6, other groupings do not show.
Fig. 7 has only shown the chlorite group (specific cluster) in the CT section shown in Figure 4, and other groupings do not show.
Fig. 8 (the examination as to substances data is with reference to coloured picture 8) has shown the three-dimensional structure that coal sample one sub regions calculates.This subregion is that the same area selected pixels is of a size of 208 * 208 subregion in continuous 99 layers of CT section.This subregion is owing to the various color intensities that are grouped in each point among the figure are directly proportional with the volume fraction that this is grouped in this place, so the less grouping of some volume fraction may be covered by the higher grouping of volume fraction on image.
In order more clearly to show the distribution characteristics of coal mesoporosity group and other two kinds of mineral groupings, Fig. 9 (examination as to substances reference coloured picture 9) has only shown hole group and other the two kinds of mineral groupings in the coal, and the matrix of coal group does not show.Can see that in Fig. 9 hole group and other two kinds of mineral groupings are interspersed.Some local chlorite group and the disperse of hole group are distributed in the smalite group as can be seen among Fig. 9.It is colored to see among Fig. 9 that some zone exists, and this explanation has the phenomenon of many grouping coexistences at these aspects.
Claims (2)
1. the coal sample component distributes and the visual quantitative CT characterizing method of physical arrangement, it is characterized in that, comprises the steps:
The sampling of the first step, coal sample and pretest: the first coal sample for coal analysis, overall porosity test is chosen in the adjacent area cutting on the coal sample, and the second coal sample that is used for the CT experiment, and sampling will guarantee that the phase composition of sample thing is even; Means obtain the first coal sample ature of coal test data by experiment, described ature of coal test data comprises ash content in the coal sample, ash content composition and ash content component content, organic element composition and content, overall porosity, infer the first coal sample component, each component volume fraction according to the ature of coal test data, described component is made of hole, matrix of coal and different minerals composition;
The X ray absorption characteristic of second step, second each component of coal sample is analyzed: according to the ratio of the X ray uptake of each mineral and matrix of coal in the second coal sample under formula (1) the calculating different x-ray energy,
（1）
In the formula (1)
Represent different mineral;
mRepresent matrix of coal;
Represent the X ray energy;
With
Represent mineral respectively
With matrix of coal at energy be
The X ray linear absorption coefficient of keV;
With
Represent mineral respectively
Volume fraction with matrix of coal;
Neglect ratio and be less than or equal to the mineral of CT experimental noise level, remainder mineral; Described remainder mineral constitute with reference to mineral and all the other mineral by one;
The 3rd step, remainder mineral X ray absorption characteristic are analyzed: remainder mineral X ray absorption characteristic is analyzed: calculate each all the other mineral under the different x-ray energy and ratio with reference to the X ray linear absorption coefficient of mineral according to formula (2),
In the formula (2)
Getting all the other different mineral of different value representative at energy is
The X ray linear absorption coefficient of keV;
Representative with reference to mineral at energy is
The X ray linear absorption coefficient of keV; Make under the different x-ray energy each all the other mineral and X ray linear absorption coefficient ratio curve with reference to mineral, the remainder mineral are divided into groups, the mineral that X ray linear absorption coefficient curve is parallel to each other are made as a group, hole is made as a hole group separately, matrix of coal is made as a matrix of coal group separately, and the grouping of matrix of coal group and remainder mineral has constituted non-hole group;
The CT experiment of the 4th step, coal sample: find out the X ray linear absorption coefficient curve least parallel energy section each other between the different groups, in above-mentioned energy section, choose
Individual X ray experiment energy carries out the CT experiment respectively, obtains a plurality of projection images, this X ray experiment energy number
Equal the grouping number of non-hole group, the imaging resolution of CT experiment projection image is
aAbove-mentioned projection image is carried out CT section reconstruct, bright background and dark background in the restructuring procedure in the deduction projection image, the Pixel Dimensions of every CT section is
c*
d, the minimum distinguishable unit of CT section is of a size of
a*
a*
a
The 5th goes on foot, sets up mathematical model, numerical analysis is carried out in section to CT: owing to distribute and can not observe directly from the CT section less than the component of CT resolution dimensions, in order to make the numerical analysis result comprise the component less than the CT resolution dimensions, set up mathematical model: sample by N (
N=
c*
d*
) individual simple cubic lattice formation, wherein
Be the number of plies of CT section, the size of each simple cubic lattice equals the size of the minimum distinguishable unit of CT section, for being in the position
(
) the simple cubic lattice located, set up following objective function (3):
Wherein
(=1,2....
) different value corresponding different X ray experiment energy respectively;
Be X ray experiment energy number;
(=0,1,2 ....
) respectively corresponding different grouping of different value, wherein
=0 corresponding hole group;
Expression the
Divide into groups in the individual simple cubic lattice
Volume fraction;
The expression grouping
The chemical potential parameter;
The expression grouping
X ray linear absorption coefficient under the corresponding experiment energy,
Equal grouping
In the X ray linear absorption coefficient of each component multiply by corresponding volume fraction, add up summation and obtain total value, again divided by grouping
Total volume fraction;
(
=1,2,3 ...,
) expression experiment obtain the
Individual simple cubic lattice exists
X ray linear absorption coefficient under the corresponding experiment energy; Chemical potential parameter with non-hole group
All be made as 0, utilize computer programming, adjust the chemical potential parameter of hole group
P (0), adjust each simple cubic lattice
The volume fraction of middle different grouping makes objective function (3) formula obtain minimum value, the volume fraction assembly average that the volume fraction of respectively dividing into groups in the result of calculation must satisfy constraint condition (4) and all simple cubic lattice mesoporosity groups equals the overall porosity test result of coal sample
Try to achieve the volume fraction of respectively dividing into groups in each simple cubic lattice in the CT section;
The 6th step, quantitative visualization characterize: set different colours and represent different groupings, the intensity of color is directly proportional with this volume fraction that is grouped in this letter cubic lattice of calculating gained, based on the volume fraction ratio of different grouping in each simple cubic lattice, utilize different colours and different color intensities on all simple cubic lattices, to show all groupings or specific cluster, make coal maceral distribution and physical arrangement obtain quantitative visualization and characterize.
2. coal sample component according to claim 1 distributes and the visual quantitative CT characterizing method of physical arrangement, it is characterized in that, the CT experiment of carrying out in the 4th step is maybe can provide the device of good homogeneous X-ray to carry out in synchrotron radiation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201210505069 CN102980902B (en) | 2012-12-03 | 2012-12-03 | Visualization quantitative CT (Captive Test) characterization method for component distribution and physical structure of coal sample |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201210505069 CN102980902B (en) | 2012-12-03 | 2012-12-03 | Visualization quantitative CT (Captive Test) characterization method for component distribution and physical structure of coal sample |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102980902A CN102980902A (en) | 2013-03-20 |
| CN102980902B true CN102980902B (en) | 2013-09-04 |
Family
ID=47855122
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201210505069 Expired - Fee Related CN102980902B (en) | 2012-12-03 | 2012-12-03 | Visualization quantitative CT (Captive Test) characterization method for component distribution and physical structure of coal sample |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102980902B (en) |
Families Citing this family (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2835817B1 (en) * | 2013-08-09 | 2017-12-20 | Carl Zeiss Microscopy Ltd. | Method for semi-automated particle analysis using a charged particle beam |
| CN103529012B (en) * | 2013-09-18 | 2016-01-06 | 武汉钢铁(集团)公司 | A kind of Raman spectrum quantitative being applicable to carbon source in blast furnace dust |
| CN103616494A (en) * | 2013-11-28 | 2014-03-05 | 中国石油天然气集团公司 | Method and system for simulating pore space fluid distribution of core |
| CN104297272B (en) * | 2014-11-11 | 2016-09-28 | 山西大学 | A kind of CT characterizing method of coal directly-liquefied residue sample |
| CN104390990B (en) * | 2014-11-18 | 2017-01-25 | 重庆大学 | Method for quantitatively analyzing mineral substances in coke |
| CN107192652B (en) * | 2017-06-23 | 2019-11-12 | 太原理工大学 | A kind of method of quantitatively characterizing coal three-dimensional opening and closing pore structure |
| CN110376347B (en) * | 2019-07-12 | 2022-10-04 | 太原理工大学 | Coal research model and construction method thereof |
| CN111855711B (en) * | 2020-09-10 | 2021-01-15 | 天津美腾科技股份有限公司 | Lump coal quality detection method and system |
| CN112697994B (en) * | 2020-12-11 | 2022-09-23 | 山西大学 | Unmanned intervention rapid coal quality intelligent detection metering system |
| CN112834535B (en) * | 2020-12-30 | 2022-05-31 | 山西大学 | Three-dimensional visual quantitative CT detection method for film morphology of film-coated sample |
| CN114397315B (en) * | 2022-01-11 | 2024-05-03 | 中国矿业大学 | Method for influencing three-dimensional morphological characteristics of coal crushed products by grinding medium |
| CN114235862B (en) * | 2022-02-21 | 2022-05-06 | 中国科学院地质与地球物理研究所 | Evaluation method for hydrocarbon source rock based on dual-energy CT |
| CN114359569B (en) | 2022-03-09 | 2022-06-03 | 中国科学院地质与地球物理研究所 | Rock bedding recognition method, device, equipment and storage medium |
| CN116912458A (en) * | 2023-09-05 | 2023-10-20 | 煤炭科学研究总院有限公司 | Finite element model generation method and device for coal rock and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1996031770A1 (en) * | 1995-04-07 | 1996-10-10 | Technological Resources Pty. Limited | A method and an apparatus for analysing a material |
| JP2005338011A (en) * | 2004-05-31 | 2005-12-08 | Jfe Steel Kk | Analytical method and quality control method for coal |
| CN101639434A (en) * | 2009-08-27 | 2010-02-03 | 太原理工大学 | Method for analyzing pore structure of solid material based on microscopic image |
| CN102135508A (en) * | 2011-01-25 | 2011-07-27 | 唐山建龙实业有限公司 | Method for testing chemical constituents in coal or coke ash by X-ray fluorescence spectrum analysis method |
| US8068579B1 (en) * | 2008-04-09 | 2011-11-29 | Xradia, Inc. | Process for examining mineral samples with X-ray microscope and projection systems |
-
2012
- 2012-12-03 CN CN 201210505069 patent/CN102980902B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1996031770A1 (en) * | 1995-04-07 | 1996-10-10 | Technological Resources Pty. Limited | A method and an apparatus for analysing a material |
| JP2005338011A (en) * | 2004-05-31 | 2005-12-08 | Jfe Steel Kk | Analytical method and quality control method for coal |
| US8068579B1 (en) * | 2008-04-09 | 2011-11-29 | Xradia, Inc. | Process for examining mineral samples with X-ray microscope and projection systems |
| CN101639434A (en) * | 2009-08-27 | 2010-02-03 | 太原理工大学 | Method for analyzing pore structure of solid material based on microscopic image |
| CN102135508A (en) * | 2011-01-25 | 2011-07-27 | 唐山建龙实业有限公司 | Method for testing chemical constituents in coal or coke ash by X-ray fluorescence spectrum analysis method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102980902A (en) | 2013-03-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102980902B (en) | Visualization quantitative CT (Captive Test) characterization method for component distribution and physical structure of coal sample | |
| Wu et al. | Effects of micropores on geometric, topological and transport properties of pore systems for low-permeability porous media | |
| Buscombe | Transferable wavelet method for grain‐size distribution from images of sediment surfaces and thin sections, and other natural granular patterns | |
| Zelelew et al. | An improved image processing technique for asphalt concrete X-ray CT images | |
| Zhang et al. | Adobe photoshop quantification (PSQ) rather than point-counting: A rapid and precise method for quantifying rock textural data and porosities | |
| Chen et al. | Fractal and multifractal analysis of different hydraulic flow units based on micro-CT images | |
| Wu et al. | Analyses of representative elementary volume for coal using X-ray μ-CT and FIB-SEM and its application in permeability predication model | |
| Clerke et al. | Application of Thomeer Hyperbolas to decode the pore systems, facies and reservoir properties of the Upper Jurassic Arab D Limestone, Ghawar field, Saudi Arabia: A “Rosetta Stone” approach | |
| Bruno et al. | Image analysis for detecting aggregate gradation in asphalt mixture from planar images | |
| Biswal et al. | Three-dimensional local porosity analysis of porous media | |
| Youssef et al. | High resolution CT and pore-network models to assess petrophysical properties of homogeneous and heterogeneous carbonates | |
| Lieb-Lappen et al. | Metrics for interpreting the microstructure of sea ice using X-ray micro-computed tomography | |
| Jean et al. | A multiscale microstructure model of carbon black distribution in rubber | |
| Chun et al. | Morphology, lacunarity and entropy of intra-aggregate pores: Aggregate size and soil management effects | |
| Tafesse et al. | Evaluation of image analysis methods used for quantification of particle angularity | |
| Elliot et al. | A comparison of optical and X-ray CT technique for void analysis in soil thin section | |
| Bendle et al. | A comparison of micro-CT and thin section analysis of Lateglacial glaciolacustrine varves from Glen Roy, Scotland | |
| Walls et al. | Shale reservoir properties from digital rock physics | |
| Garboczi et al. | The 3-D shape of blasted and crushed rocks: From 20 μm to 38 mm | |
| Li et al. | A new method for evaluating the pore structure complexity of digital rocks based on the relative value of fractal dimension | |
| Nohl et al. | Revealing the genesis of limestone-marl alternations: a taphonomic approach | |
| Pires et al. | Porosity distribution by computed tomography and its importance to characterize soil clod samples | |
| Vadood et al. | Introducing a simple method to determine aggregate gradation of hot mix asphalt using image processing | |
| Zhou et al. | Multifractal analyses of grayscale and binary soil thin section images | |
| Martínez et al. | Lacunarity of soil macropore space arrangement of CT images: effect of soil management and depth |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20130904 Termination date: 20191203 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |