CN104390990B - Method for quantitatively analyzing mineral substances in coke - Google Patents

Abstract

The invention relates to a method for quantitatively analyzing mineral substances in coke. The method is characterized by comprising the steps of grinding a sample, analyzing the sample to obtain an XRD (X-ray diffraction) graph and a space structure of the sample, and analyzing the XRD graph of the sample to obtain a sample function model and a type of a single mineral substance in the sample; finding a space structure and a diffraction pattern of the single mineral substance in the sample, analyzing the diffraction pattern, establishing a function model of the single mineral substance, respectively fitting the space structure, the diffraction pattern and the function model of a plurality of single mineral substances to correspondently obtain a to-be-matched graph, a to-be-matched space structure and a to-be-matched function model, and respectively matching the to-be-matched graph, the to-be-matched space structure and the to-be-matched function model with the XRD graph, the function model and the space structure of the sample by adjusting the content of each single mineral substance to obtain the relative content of each single mineral substance. By adopting the method, the content of complicated mineral substances can be accurately quantified, and the analysis period is short. The method is convenient for the practical application.

Description

Quantitative analysis method for mineral substances in coal coke

Technical Field

The invention relates to the technical field of ferrous metallurgy and coal chemical industry, in particular to a quantitative analysis method for minerals in coal coke.

Background

Currently, 90% of the world's iron production comes from blast furnace ironmaking. Compared with the existing non-blast furnace ironmaking technology, blast furnace ironmaking has the characteristics of good technical and economic indexes, simple process, large production capacity and low energy consumption, and is predicted to be a main iron production process in a period of time in the future. The coke is an important raw fuel for blast furnace ironmaking, plays important roles of a heat source, a reducing agent, a stock column framework, molten iron carburization and the like, and has important influence on the blast furnace coke ratio, the production efficiency, the molten iron quality, the economic benefit and the like. The requirements for metallurgical properties of coke are becoming more stringent as the coal ratio is increasing and the load of coke on the blast furnace is becoming heavier due to the increase in the size of blast furnaces and the development of coal injection technology. With the shortage of high-quality coking coal resources, the economic and effective utilization of high-ash and low-quality coal becomes one of the important tasks for the development of the current coal coke industry.

The mineral substances in the high-ash inferior coal are complex and difficult to utilize. It has been found that the difference in mineral content changes the optical microscopic composition of the coke, the stacking height in the microcrystalline structure, and the stacking layer spacing, etc., resulting in changes in coke properties. The mineral compositions in coal are complex, dozens of minerals are detected at present, mainly clay minerals, carbonate, sulfide, phosphate and the like, and the content and occurrence state of various minerals are different due to the difference of coal metamorphism. The mineral contains various elements, and the properties of the mineral composed of the same element under different valence states are also greatly different. During the coal coking process, a plurality of mineral substances are mixed together, so that the quantitative analysis of the mineral substances is difficult and serious. The minerals have important influence on the migration of various elements in the coking process, part of the minerals are beneficial to removing harmful elements such as sulfur, phosphorus, selenium and the like in the coal, and the content, the type and the occurrence state of the minerals can regulate and control the removal capability. Because the mineral substances in the coal are very complex and interact with various elements, the mineral substance content after coking is difficult to analyze.

The existing quantitative analysis methods for minerals in coal coke include semi-quantitative analysis by XRD (X-ray diffraction) spectrum, combination of scanning electron microscope and energy spectrum analysis, chemical methods and the like, but the methods have a plurality of defects. The quantitative determination of the types of minerals by combining XRD semi-quantitative analysis, a scanning electron microscope and energy spectrum analysis is less, and the complex minerals in the coal coke cannot be clearly and accurately quantitatively analyzed. The chemical method is complex, the quantitative analysis period is long, and the method is not beneficial to practical application. Based on the above technical background, there is a need to provide a new method for quantitatively analyzing minerals in coal coke, which can accurately quantify the content of complex minerals, has a short analysis period, and is convenient for practical application.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides a quantitative analysis method capable of quantifying the content of various mineral substances in coal coke.

In order to solve the technical problems, the invention adopts the following technical scheme: a quantitative analysis method for mineral substances in coal coke is characterized by comprising the following steps:

1) selecting a sample, grinding the sample to ensure that the granularity of the sample is less than 74 mu m, and analyzing to obtain an XRD (X-ray diffraction) pattern of the sample; performing transmission electron microscope detection on the sample to obtain a spatial structure of the sample;

carrying out nonlinear analysis on an XRD (X-ray diffraction) spectrum of the sample to obtain a sample function model;

analyzing data in an XRD (X-ray diffraction) spectrum of the sample to obtain the type of a single mineral substance in the sample;

2) searching the spatial structure and the diffraction pattern of the single mineral substance in the sample obtained in the step 1) in an inorganic crystal structure database and a standard PDF card of XRD, carrying out nonlinear analysis on the diffraction pattern of the single mineral substance, and establishing a corresponding single mineral substance function model;

performing linear regression fitting on the diffraction patterns of a plurality of single minerals in the sample to obtain a pattern to be matched, wherein when the diffraction patterns of the plurality of single minerals are subjected to linear regression fitting, the content of each single mineral is the weight corresponding to the single mineral when the pattern to be matched is obtained by fitting;

fitting the space structures of a plurality of single minerals in the sample to obtain a space structure to be matched, wherein when the space structures of the plurality of single minerals are fitted, the content of each single mineral is the fitting coefficient corresponding to the single mineral when the space structures are fitted to obtain the space structure to be matched;

fitting the single mineral function models of a plurality of single minerals in the sample to obtain a function model to be matched, wherein when fitting the single mineral function models of the plurality of single minerals, the content of each single mineral is the fitting coefficient corresponding to the single mineral when fitting the single mineral to obtain the function model to be matched;

3) assigning a value to the content of each individual mineral in the sample;

4) matching the pattern to be matched with the XRD pattern of the sample, matching the spatial structure to be matched with the spatial structure of the sample, and matching the function model to be matched with the sample function model;

5) if the XRD pattern of the pattern to be matched and the sample, the spatial structure of the spatial structure to be matched and the spatial structure of the sample or the function model to be matched and the sample function model in the step 4) are not matched successfully, the content of each single mineral in the sample is assigned again, and then the step 4) is returned;

if the spectrum to be matched in the step 4) is successfully matched with the XRD spectrum of the sample, the spatial structure to be matched is successfully matched with the spatial structure of the sample or the function model to be matched is successfully matched with the sample function model, the corresponding content of each single mineral is the relative content of each single mineral in the sample.

As optimization, the method for matching the spectrum to be matched with the XRD spectrum of the sample in the step 4) comprises the following steps:

the first step is as follows: setting M points on the XRD spectrum of the to-be-matched spectrum or the sample, wherein the intensity value of the first point on the band-matched spectrum is as shown in formula (A):

$x_{\mathrm{\δ}}^{\′}=z_{\mathrm{\δ}}+{\mathrm{\Σ}}_i^N{g}_i{x}_{i\mathrm{\δ}},\mathrm{\δ}=1,2,3...M---\left(A\right);$

wherein, x' As the intensity value of the diffraction of the first point on the map to be matched, g_iContent of the i-th individual mineral, x_iIs the diffraction intensity value of the first point on the diffraction spectrum of the ith single mineral, z Background intensity for the first spot; n represents the number of types of single minerals in the sample; t is less than or equal to 0.3;

correlation coefficient R of a first point on a pattern to be matched and a corresponding first point on an XRD pattern of a sample As shown in formula (B):

$R_{\mathrm{\δ}}=\sqrt{{\mathrm{\Σg}}_i{\left(\right(x_{\mathrm{\δ}}-x_{\mathrm{\δ}}^{\′}\left)\right(x_{\mathrm{\δ}}-z_{\mathrm{\δ}})/x_{\mathrm{\δ}})}^2/{\mathrm{\Σg}}_i{(x_{\mathrm{\δ}}-z_{\mathrm{\δ}})}^2}---\left(B\right)$

wherein R is Correlation coefficient of a first point on the pattern to be matched with a first point on the XRD pattern of the sample, g_iContent of the i-th individual mineral, x Is the diffraction intensity value, x 'of the first point on the XRD spectrum of the sample' The diffraction intensity value of the corresponding point on the map to be matched is obtained;

secondly, calculating the correlation coefficient R of each point in the matching map and the corresponding point on the XRD map of the sample one by one When all R are If the number of the matched spectrums is less than or equal to T, successfully matching the to-be-matched spectrums with the XRD spectrums of the samples; otherwise, a mismatch is considered.

As an optimization, in step 2), the model H is a function of the individual minerals_i(t) the following:

H_i(t)＝∫P(t)I_k(t)dt，i＝1，2，3...N，(C)；

wherein,n represents the number of the single mineral species in the sample, P (t) in formula (C) is represented by formula (C1), I_k(t) is of formula (c 2):

P(t)＝ηL(t)+(1-η)G(t，)，(c1)；

in the formula (c1), in the formula,

$L(t,\mathrm{\Γ})=\frac{\mathrm{\Γ}}{2\mathrm{\π}}\frac{1}{{(\mathrm{\Γ}/2)}^2+r^2},---(c1-1);$

$G\left(\mathrm{\Γ}\right)=\frac{2\sqrt{ln2}}{\sqrt{\mathrm{\π}}\mathrm{\Γ}}\exp \[\frac{-4ln2}{{\mathrm{\Γ}}^2}(2{\mathrm{\θ}}_t-2\mathrm{\θ})\],---(c1-2);$

η＝1.36603(γ/)-0.47719(γ/)²+0.11116(γ/)³，(c1-3)；

$\mathrm{\Γ}=\sqrt[5]{{\mathrm{\Γ}}_g^5+2.69269{\mathrm{\Γ}}_g^4\mathrm{\γ}+2.42843{\mathrm{\Γ}}_g^3{\mathrm{\γ}}^2+4.471163{\mathrm{\Γ}}_g^2{\mathrm{\γ}}^3+0.07842{\mathrm{\Γ}}_g{\mathrm{\γ}}^4+{\mathrm{\γ}}^5},---(c1-3-1);$

${\mathrm{\Γ}}_g=2\sqrt{\left(2ln2\right)}\mathrm{\σ},---(c1-3-1-1);$

in the above formula, the width at half maximum, gamma is the Lorentz constant, t is the time of the diffraction point, 2 theta_tThe Bragg angle at the time t of the diffraction point is shown, and 2 theta is the Bragg angle;

$I_k\left(t\right)=\frac{{\mathrm{\α}}^3}{2}\[(1-R)\]t^2{e}^{-\mathrm{\α}t}+\frac{2\mathrm{\β}R}{{(\mathrm{\α}-\mathrm{\β})}^3}\{e^{-\mathrm{\β}t}-\[1+t(\mathrm{\α}-\mathrm{\β})+\frac{t^2}{2}{(\mathrm{\α}-\mathrm{\β})}^2\]e^{-\mathrm{\α}t}\}---\left(c2\right);$

wherein alpha is an exponential growth constant, beta is an exponential decay constant, R is a moderator temperature coefficient, and t is a diffraction point time.

As optimization, the function model to be matched in step 2) is represented by formula (D):

$M\left(t\right)=\underset{i=1}{\overset{N}{\Σ}}{g}_i{H}_i\left(T\right)---\left(D\right);$

wherein g is_iThe content of various single mineral substances; n represents the number of types of single minerals in the sample; h_i(t) is a single mineral function model.

Compared with the prior art, the invention has the following advantages:

1) the invention is based on the XRD map, and achieves the quantitative analysis of the mineral substances in the coal coke by constructing and fitting a plurality of function models without other experimental detection analysis.

2) The invention expands to various complex mineral substances by analyzing a single mineral substance, can accurately quantify 8 mineral substances, and has great innovation compared with the prior quantification technology.

3) For mineral substance quantification, the invention provides three ways of determining the mineral substance content in the coal coke by adopting map fitting, structure matching and function model fitting, and improves the accuracy of mineral substance quantification.

Drawings

FIG. 1 is a flowchart of a method for quantitatively analyzing minerals in a coal char according to the present invention.

FIG. 2a is an XRD pattern of southern China tung coal.

FIG. 2b is an XRD pattern of coke made from southern China coal.

Figure 2c is an XRD pattern of permanent magnet coal.

FIG. 2d is an XRD pattern of coke refined from permanent magnet coal.

Detailed Description

In order to make the technical problems, technical solutions and advantageous effects solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

A quantitative analysis method for minerals in coal coke comprises the following steps:

1) selecting samples (coal samples and coke samples), grinding the samples to ensure that the granularity of the samples is less than 74 mu m, and analyzing the samples by an X-ray diffractometer (XRD) to obtain an XRD (X-ray diffraction) pattern of the samples;

performing transmission electron microscope detection on the sample to obtain a spatial structure of the sample;

carrying out nonlinear analysis on an XRD (X-ray diffraction) spectrum of the sample to obtain a sample function model;

analyzing data in an XRD (X-ray diffraction) spectrum of the sample to obtain the type of a single mineral substance in the sample;

2) searching the spatial structure and the diffraction pattern of the single mineral substance in the sample obtained in the step 1) in an inorganic crystal structure database and a standard PDF card of XRD, carrying out nonlinear analysis on the diffraction pattern of the single mineral substance, establishing a corresponding single mineral substance function model, and analyzing the crystal orientation and various spatial properties of the single mineral substance;

performing linear regression fitting on the diffraction patterns of a plurality of single minerals in the sample to obtain a pattern to be matched, wherein when the diffraction patterns of the plurality of single minerals are subjected to linear regression fitting, the content of each single mineral is the weight corresponding to the single mineral when the pattern to be matched is obtained by fitting;

fitting the space structures of a plurality of single minerals in the sample to obtain a space structure to be matched, wherein when the space structures of the plurality of single minerals are fitted, the content of each single mineral is the fitting coefficient corresponding to the single mineral when the space structures are fitted to obtain the space structure to be matched;

fitting the single mineral function models of a plurality of single minerals in the sample to obtain a function model to be matched, wherein when fitting the single mineral function models of the plurality of single minerals, the content of each single mineral is the fitting coefficient corresponding to the single mineral when fitting the single mineral to obtain the function model to be matched;

3) assigning a value to the content of each individual mineral in the sample;

4) matching the to-be-matched spectrum with the XRD spectrum of the sample: in order to determine whether the spectrum to be matched is matched with the XRD spectrum of the sample, a correlation coefficient is introduced, and the smaller the correlation coefficient is, the more successful the matching is.

The method for matching the spectrum to be matched with the XRD spectrum of the sample comprises the following steps:

and performing linear regression fitting on the diffraction patterns of a plurality of single minerals in the sample to obtain a pattern to be matched, wherein the pattern to be matched is converted into the linear regression fitting of the diffraction intensities of all points on the diffraction patterns of the single minerals, and the pattern to be matched is the superposition of the diffraction patterns of the single minerals under different weights.

The first step is as follows: setting M points on the XRD spectrum of the to-be-matched spectrum or the sample, wherein the intensity value of the first point on the to-be-matched spectrum is as shown in formula (A):

$x_{\mathrm{\δ}}^{\′}=z_{\mathrm{\δ}}+{\mathrm{\Σ}}_i^N{g}_i{x}_{i\mathrm{\δ}},\mathrm{\δ}=1,2,3...M---\left(A\right);$

wherein, x' As the intensity value of the diffraction of the first point on the map to be matched, g_iContent of the i-th individual mineral, x_iIs the diffraction intensity value of the first point on the diffraction spectrum of the ith single mineral, z Background intensity for the first spot; n represents the number of types of single minerals in the sample; t is less than or equal to 0.3;

correlation coefficient R of a first point on a pattern to be matched and a first point on an XRD pattern of a sample As shown in formula (B):

$R_{\mathrm{\δ}}=\sqrt{{\mathrm{\Σg}}_i{\left(\right(x_{\mathrm{\δ}}-x_{\mathrm{\δ}}^{\′}\left)\right(x_{\mathrm{\δ}}-z_{\mathrm{\δ}})/x_{\mathrm{\δ}})}^2/{\mathrm{\Σg}}_i{(x_{\mathrm{\δ}}-z_{\mathrm{\δ}})}^2}---\left(B\right)$

wherein R is Correlation coefficient of a first point on the pattern to be matched with a first point on the XRD pattern of the sample, g_iContent of the i-th individual mineral, x Is the diffraction intensity value, x 'of the first point on the XRD spectrum of the sample' The diffraction intensity value of the corresponding point on the map to be matched is obtained;

secondly, calculating the correlation coefficient R of each point in the matching map and the corresponding point on the XRD map of the sample one by one When all R are If the number of the matched spectrums is less than or equal to T, successfully matching the to-be-matched spectrums with the XRD spectrums of the samples; otherwise, a mismatch is considered. In specific implementation, T may be 0.3 or a smaller value, and the smaller the value of T, the more successful the matching.

Matching the spatial structure to be matched with the spatial structure of the sample, wherein the matching means that the spatial structure to be matched is approximate to the spatial structure parameters of the sample, the approximation here is understood as the common meaning in the field, the function model to be matched is matched with the sample function model, the matching means that the function model to be matched is consistent with the function model of the sample, and the consistency here is understood as the common meaning in the field;

5) if the XRD pattern of the pattern to be matched and the sample, the spatial structure of the spatial structure to be matched and the spatial structure of the sample or the function model to be matched and the sample function model in the step 4) are not matched successfully, the content of each single mineral in the sample is assigned again, and then the step 4) is returned; if the spectrum to be matched in the step 4) is successfully matched with the XRD spectrum of the sample, the spatial structure to be matched is successfully matched with the spatial structure of the sample or the function model to be matched is successfully matched with the sample function model, the corresponding content of each single mineral is the relative content of each single mineral in the sample.

As an optimization, in step 2), the model H is a function of the individual minerals_i(t) the following:

H_i(t)＝∫P(t)I_k(t)dt，i＝1，2，3...N，(C)；

wherein N represents the number of single mineral in the sample, and P (t) in formula (C) is represented by formula (C1), I_k(t) is of formula (c 2):

P(t)＝ηL(t，)+(1-η)G(t，)，(c1)；

in the formula (c1), in the formula,

$L(t,\mathrm{\Γ})=\frac{\mathrm{\Γ}}{2\mathrm{\π}}\frac{1}{{(\mathrm{\Γ}/2)}^2+r^2},---(c1-1);$

$G\left(\mathrm{\Γ}\right)=\frac{2\sqrt{ln2}}{\sqrt{\mathrm{\π}}\mathrm{\Γ}}\exp \[\frac{-4ln2}{{\mathrm{\Γ}}^2}(2{\mathrm{\θ}}_t-2\mathrm{\θ})\],---(c1-2);$

η＝1.36603(γ/)-0.47719(γ/)²+0.11116(γ/)³，(c1-3)；

$\mathrm{\Γ}=\sqrt[5]{{\mathrm{\Γ}}_g^5+2.69269{\mathrm{\Γ}}_g^4\mathrm{\γ}+2.42843{\mathrm{\Γ}}_g^3{\mathrm{\γ}}^2+4.471163{\mathrm{\Γ}}_g^2{\mathrm{\γ}}^3+0.07842{\mathrm{\Γ}}_g{\mathrm{\γ}}^4+{\mathrm{\γ}}^5},---(c1-3-1);$

${\mathrm{\Γ}}_g=2\sqrt{\left(2ln2\right)}\mathrm{\σ},---(c1-3-1-1);$

in the above formula, the width at half maximum, gamma is the Lorentz constant, t is the time of the diffraction point, 2 theta_tThe Bragg angle at the time t of the diffraction point is shown, and 2 theta is the Bragg angle;

$I_k\left(t\right)=\frac{{\mathrm{\α}}^3}{2}\[(1-R)\]t^2{e}^{-\mathrm{\α}t}+\frac{2\mathrm{\β}R}{{(\mathrm{\α}-\mathrm{\β})}^3}\{e^{-\mathrm{\β}t}-\[1+t(\mathrm{\α}-\mathrm{\β})+\frac{t^2}{2}{(\mathrm{\α}-\mathrm{\β})}^2\]e^{-\mathrm{\α}t}\}$

$\left(c2\right);$

wherein alpha is an exponential growth constant, beta is an exponential decay constant, R is a moderator temperature coefficient, and t is a diffraction point time.

The function model to be matched in the step 2) is represented by the formula (D):

$M\left(t\right)=\underset{i=1}{\overset{N}{\Σ}}{g}_i{H}_i\left(T\right)---\left(D\right)$

wherein g is_iThe content of each single mineral substance.

The determination of the content of each individual mineral in the coal sample or the coke sample by the above method is described in detail by examples below.

The first embodiment is as follows:

1) selecting southern tung coal (NT), grinding to a particle size of less than 74 μm, and carrying out XRD analysis to obtain an XRD pattern of the coal sample as shown in figure 2 a. And (4) carrying out transmission electron microscope analysis on the southeast tree coal sample to obtain the spatial structure of the southeast tree coal. And carrying out nonlinear analysis on the XRD pattern of the southern tung coal to obtain a function model of the XRD pattern. The data in the XRD pattern are analyzed, and the types of the mineral substances in the southern tung coal are shown in the table 1.

TABLE 1 mineral species in Nantong coal

2) Searching the spatial structure and the diffraction pattern of the mineral obtained in the table 1 in an inorganic crystal structure database and a standard PDF card of XRD, carrying out function analysis on the pattern of the single mineral, and establishing a corresponding function model of the single mineral; the individual minerals were analyzed for crystal orientation and various spatial properties.

3) Performing linear regression fitting on XRD (X-ray diffraction) patterns of a plurality of single mineral substances in the southern paulownia coal to obtain a pattern to be matched, and matching the pattern to be matched with the XRD pattern of the southern paulownia coal;

fitting the spatial structures of a plurality of single mineral substances in the southern paulownia coal to obtain a spatial structure to be matched, and matching the spatial structure to be matched with the spatial structure of the southern paulownia coal;

fitting a single mineral function model of a plurality of single minerals in the southbound coal to obtain a function model to be matched, and matching the function model to be matched with the function model of the southbound coal;

the contents of a plurality of single mineral substances in the southbound coal are continuously adjusted until the to-be-matched spectrum, the to-be-matched spatial structure or the to-be-matched function model are respectively matched with the corresponding XRD spectrum, spatial structure and function model in the southbound coal, and the relative contents of various mineral substances in the southbound coal are obtained and are shown in table 2.

Table 2 quantitative analysis result (wt.%) of minerals in nantong coal

Example two:

1) selecting a coke sample (NTC) refined from Nantong coal, grinding the coke sample to a particle size of less than 74 mu m, and carrying out XRD analysis to obtain an XRD (X-ray diffraction) pattern of the coke sample as shown in figure 2 b. And (4) carrying out transmission electron microscope analysis on the coke refined from the southern tung coal to obtain the space structure of the coke. And carrying out nonlinear analysis on the coke XRD spectrum to obtain a function model of the XRD spectrum. The data in the XRD pattern were analyzed to obtain the types of minerals in the coke as shown in Table 3.

TABLE 3 mineral species in coke refined from Nantong coal

2) Searching the spatial structure and the diffraction pattern of the mineral substances obtained in the table 3 in an inorganic crystal structure database and a standard PDF card of XRD, carrying out function analysis on the patterns of the single mineral substances, and establishing a corresponding function model; the individual minerals were analyzed for crystal orientation and various spatial properties.

3) Performing linear regression fitting on XRD (X-ray diffraction) patterns of a plurality of single mineral substances in the coke refined from the southern tung coal to obtain a pattern to be matched, and matching the pattern to be matched with the XRD pattern of the coke refined from the southern tung coal;

fitting the spatial structures of a plurality of single mineral substances in the coke refined from the southbound coal to obtain a spatial structure to be matched, and matching the spatial structure to be matched with the spatial structure of the coke refined from the southbound coal;

fitting a single mineral function model of a plurality of single minerals in the coke refined from the southern tung coal to obtain a function model to be matched, and matching the function model to be matched with the function model of the coke refined from the southern tung coal;

the contents of a plurality of single mineral substances in the coke prepared from the southbound coal are continuously adjusted until the to-be-matched spectrum, the to-be-matched spatial structure or the to-be-matched function model are respectively matched with the corresponding XRD spectrum, spatial structure and function model in the coke refined from the southbound coal, and the relative contents of different mineral substances in the obtained coke are shown in Table 4.

Table 4 quantitative analysis result (wt.%) of mineral in coke made from southern paulownia coal

Example three:

1) selecting the permanently mixed coal (YH), grinding the permanently mixed coal (YH) to the particle size of less than 74 μm, and carrying out XRD analysis to obtain the XRD pattern of the permanently mixed coal as shown in figure 2 c. And (5) carrying out transmission electron microscope analysis on the permanently mixed coal sample to obtain the spatial structure of the permanently mixed coal. And carrying out nonlinear analysis on the XRD pattern of the permanent mixed coal to obtain a function model of the XRD pattern. The data in the XRD pattern were analyzed to obtain the types of minerals in the permanent magnet blended coal as shown in Table 5.

TABLE 5 mineral species in permanently blended coals

2) Searching the spatial structure and the diffraction pattern of the mineral substances obtained in the table 5 in an inorganic crystal structure database and a standard PDF card of XRD, carrying out function analysis on the patterns of the single mineral substances, and establishing a corresponding function model; the individual minerals were analyzed for crystal orientation and various spatial properties.

3) Performing linear regression fitting on XRD (X-ray diffraction) patterns of a plurality of single mineral substances in the permanent mixed coal to obtain a pattern to be matched, and matching the pattern to be matched with the XRD pattern of the permanent mixed coal;

fitting the spatial structures of a plurality of single mineral substances in the permanent mixed coal to obtain a spatial structure to be matched, and matching the spatial structure to be matched with the spatial structure of the permanent mixed coal;

fitting single mineral function models of a plurality of single minerals in the permanent mixed coal to obtain a function model to be matched, and matching the function model to be matched with the function model of the permanent mixed coal;

continuously adjusting the content of a plurality of single mineral matters in the permanently mixed coal until the pattern to be matched, the spatial structure to be matched or the function model to be matched are respectively matched with the corresponding XRD pattern, spatial structure and function model in the permanently mixed coal, and obtaining the relative content of different mineral matters in the permanently mixed coal as shown in table 6.

TABLE 6 quantitative analysis results (wt.%) of minerals in permanent coal blend

Example four:

1) selecting coke (YHC) refined from permanent mixed coal, grinding the coke to a particle size of less than 74 μm, and carrying out XRD analysis to obtain an XRD (X-ray diffraction) pattern of a coke sample as shown in figure 2 d. And (4) carrying out transmission electron microscope analysis on the coke refined from the permanent mixed coal to obtain the space structure of the coke. And carrying out nonlinear analysis on the actual XRD spectrum to obtain a function model of the XRD spectrum. The data in the XRD spectrum are analyzed, and the types of mineral substances in the coke refined by the permanent mixed coal are shown in the table 7.

TABLE 7 types of minerals in coke made from permanent coal blending

2) Searching the spatial structure and the diffraction pattern of the mineral obtained in the table 7 in an inorganic crystal structure database and a standard PDF card of XRD, carrying out function analysis on the pattern of a single mineral, and establishing a corresponding function model; the individual minerals were analyzed for crystal orientation and various spatial properties.

3) Performing linear regression fitting on XRD (X-ray diffraction) patterns of a plurality of single mineral substances in the coke refined from the permanently mixed coal to obtain a pattern to be matched, and matching the pattern to be matched with the XRD pattern of the coke refined from the permanently mixed coal;

fitting the spatial structure of a plurality of single mineral substances in the coke refined from the permanent mixed coal to obtain a spatial structure to be matched, and matching the spatial structure to be matched with the spatial structure of the coke refined from the permanent mixed coal;

fitting single mineral function models of a plurality of single minerals in the coke refined from the permanently mixed coal to obtain a function model to be matched, and matching the function model to be matched with the function model of the coke refined from the permanently mixed coal;

the contents of a plurality of single mineral substances in the coke refined from the permanently mixed coal are continuously adjusted until the pattern to be matched, the spatial structure to be matched or the function model to be matched are respectively matched with the corresponding XRD pattern, spatial structure and function model in the coke refined from the permanently mixed coal, and the relative contents of different mineral substances in the coke refined from the permanently mixed coal are comprehensively obtained and are shown in the table 8.

TABLE 8 quantitative analysis results (wt.%) of minerals in char made from permanent coal blending

Claims (3)

1. A quantitative analysis method for mineral substances in coal coke is characterized by comprising the following steps:

1) selecting a sample, grinding the sample to ensure that the granularity of the sample is less than 74 mu m, and analyzing to obtain an XRD (X-ray diffraction) pattern of the sample;

performing transmission electron microscope detection on the sample to obtain a spatial structure of the sample;

carrying out nonlinear analysis on an XRD (X-ray diffraction) spectrum of the sample to obtain a sample function model;

analyzing data in an XRD (X-ray diffraction) spectrum of the sample to obtain the type of a single mineral substance in the sample;

2) searching the spatial structure and the diffraction pattern of the single mineral substance in the sample obtained in the step 1) in an inorganic crystal structure database and a standard PDF card of XRD, carrying out nonlinear analysis on the diffraction pattern of the single mineral substance, and establishing a corresponding single mineral substance function model;

performing linear regression fitting on the diffraction patterns of a plurality of single minerals in the sample to obtain a pattern to be matched, wherein when the diffraction patterns of the plurality of single minerals are subjected to linear regression fitting, the content of each single mineral is the weight corresponding to the single mineral when the pattern to be matched is obtained by fitting;

fitting the space structures of a plurality of single minerals in the sample to obtain a space structure to be matched, wherein when the space structures of the plurality of single minerals are fitted, the content of each single mineral is the fitting coefficient corresponding to the single mineral when the space structures are fitted to obtain the space structure to be matched;

fitting the single mineral function models of a plurality of single minerals in the sample to obtain a function model to be matched, wherein when fitting the single mineral function models of the plurality of single minerals, the content of each single mineral is the fitting coefficient corresponding to the single mineral when fitting the single mineral to obtain the function model to be matched;

3) assigning a value to the content of each individual mineral in the sample;

4) matching the pattern to be matched with the XRD pattern of the sample, matching the spatial structure to be matched with the spatial structure of the sample, and matching the function model to be matched with the sample function model;

5) if the XRD pattern of the pattern to be matched and the sample, the spatial structure of the spatial structure to be matched and the spatial structure of the sample or the function model to be matched and the sample function model in the step 4) are not matched successfully, the content of each single mineral in the sample is assigned again, and then the step 4) is returned;

if the spectrum to be matched in the step 4) is successfully matched with the XRD spectrum of the sample, the spatial structure to be matched is successfully matched with the spatial structure of the sample, and the function model to be matched is successfully matched with the sample function model, the corresponding content of each single mineral is the relative content of each single mineral in the sample.

2. The method for quantitatively analyzing minerals in coal char according to claim 1, wherein the pattern to be matched in step 4) is matched with the XRD pattern of the sample by the following method:

the first step is as follows: setting M points on the XRD spectrum of the to-be-matched spectrum or the sample, wherein the intensity value of the first point on the to-be-matched spectrum is as shown in formula (A):

$x_{\mathrm{\δ}}^{\′}=z_{\mathrm{\δ}}+{\mathrm{\Σ}}_i^N{g}_i{x}_{i\mathrm{\δ}},\mathrm{\δ}=1,2,\mathrm{3...}M---\left(A\right);$

wherein, x' As the intensity value of the diffraction of the first point on the map to be matched, g_iContent of the i-th individual mineral, x_iIs the diffraction intensity value of the first point on the diffraction spectrum of the ith single mineral, z Background intensity for the first spot; n represents the number of types of single minerals in the sample; t is less than or equal to 0.3;

correlation coefficient R of a first point on a pattern to be matched and a corresponding first point on an XRD pattern of a sample As shown in formula (B):

$R_{\mathrm{\δ}}=\sqrt{{\mathrm{\Σg}}_i{\left(\right(x_{\mathrm{\δ}}-x_{\mathrm{\δ}}^{\′}\left)\right(x_{\mathrm{\δ}}-z_{\mathrm{\δ}})/x_{\mathrm{\δ}})}^2/{\mathrm{\Σg}}_i{(x_{\mathrm{\δ}}-z_{\mathrm{\δ}})}^2}---\left(B\right)$

wherein R is Correlation coefficient of a first point on the pattern to be matched with a first point on the XRD pattern of the sample, g_iContent of the i-th individual mineral, x Is the diffraction intensity value, x 'of the first point on the XRD spectrum of the sample' The diffraction intensity value of the corresponding point on the map to be matched is obtained;

secondly, calculating the correlation between each point in the matching map and the corresponding point on the XRD map of the sample one by oneNumber R When all R are If the number of the matched spectrums is less than or equal to T, successfully matching the to-be-matched spectrums with the XRD spectrums of the samples; otherwise, a mismatch is considered.

3. The method for quantitatively analyzing minerals in coal coke according to claim 1, wherein the function model to be matched in step 2) is represented by formula (D):

$M\left(t\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{g}_i{H}_i\left(t\right)---\left(D\right)$

wherein g is_iThe content of various single mineral substances; n represents the number of types of single minerals in the sample; h_i(t) is a single mineral function model.