A rapid identification method for graphite minerals based on spectral feature comparison

Abstract

This invention discloses a rapid identification method for graphite minerals based on spectral feature comparison. The method includes: acquiring a sample to be tested; performing a diffraction experiment on the sample to obtain its diffraction pattern and determine the graphite crystal structure characteristics; performing elemental analysis on the sample to determine the elemental content of each element; analyzing the graphite crystal structure characteristics and elemental content of the sample to determine the graphitization index; determining whether the sample is graphite based on the graphitization index; and identifying the graphite type when the sample is graphite; and displaying the identification results and elemental content of the sample on a preset terminal. This invention is based on the combined analysis of XRD and single-wavelength XRF, using XRD diffraction and single-wavelength X-ray fluorescence (which can measure ultralight elements) to detect carbon content, and verifying the results through phase structure characteristics and elemental composition analysis, thereby improving the accuracy and efficiency of graphite mineral identification.

Description

Technical Field

[0001] This application relates to the field of mineral resource analysis technology, and more specifically, to a rapid identification method for graphite minerals based on spectral feature comparison. Background Technology

[0002] The identification of graphite minerals usually relies on chemical analysis or single spectroscopic techniques, but traditional methods have the following problems: (1) Time-consuming: chemical wet analysis requires destruction of the sample and the process is complicated; (2) Limitations of single technology: it is difficult to distinguish the similar diffraction characteristics of graphite and other layered silicate minerals (such as mica) using XRD alone. Ordinary XRD detection equipment cannot detect carbon content, and relying solely on XRF cannot obtain mineral crystal structure information; (3) Interference factors: associated minerals (such as pyrite and quartz) in natural ores are prone to misjudgment.

[0003] In the existing technology, there is no detection technology that combines XRD phase characteristics with single-wavelength XRF elemental quantitative data for rapid identification of graphite minerals. Summary of the Invention

[0004] In view of the above problems, the purpose of this invention is to provide a rapid identification method for graphite minerals based on spectral feature comparison. The method combines XRD and single-wavelength XRF analysis, using XRD diffraction and single-wavelength X-ray fluorescence (equipped with a silver target and special bent crystals) to detect carbon content. The method is further verified by analyzing phase structure characteristics and elemental composition, thereby improving the accuracy and efficiency of graphite mineral type determination.

[0005] The first aspect of this invention provides a rapid identification method for graphite minerals based on spectral feature comparison, comprising:

[0006] Obtain the sample to be tested;

[0007] A diffraction experiment is performed on the sample to be tested to obtain the diffraction pattern of the sample to be tested, and the graphite crystal structure characteristics of the sample to be tested are determined; the graphite crystal structure characteristics include the full width at half maximum (FWHM), interlayer spacing, and stacking height;

[0008] Elemental analysis is performed on the sample to determine the elemental content of each element in the sample; each element includes carbon (C) and other elements; the other elements include at least iron (Fe), silicon (Si), aluminum (Al), and sulfur (S);

[0009] The graphite crystal structure characteristics and elemental content of each element in the sample to be tested are analyzed to determine the graphitization index. The graphitization index is used to determine whether the sample to be tested is graphite. When the sample to be tested is graphite, the type of graphite is identified. The types of graphite include crystalline graphite and cryptocrystalline graphite.

[0010] The identification results of the sample to be tested and the element content of each element are displayed through a preset terminal.

[0011] In this scheme, the step of performing a diffraction experiment on the sample to be tested to obtain the diffraction pattern of the sample to be tested and to determine the graphite crystal structure characteristics of the sample to be tested includes:

[0012] X-ray diffraction analysis was performed on the sample under test using an X-ray diffractometer to obtain the diffraction pattern;

[0013] The diffraction pattern was analyzed to extract the characteristic peak position and full width at half maximum (FWHM) β of carbon element, and the interlayer spacing d(002) of carbon element was calculated; the characteristic peak position of carbon element is the 002 peak.

[0014] Calculate the stacking height L using the Scherrer formula. c (002);

[0015]

[0016] Where k1 is the influencing factor, λ is the wavelength of the X-ray, θ is the Bragg diffraction angle, and β is the full width at half maximum (FWHM) of the 002 peak.

[0017] In this scheme, the step of performing elemental analysis on the sample to be tested to determine the elemental content of each element in the sample includes:

[0018] Elemental analysis of the sample was performed using a single-wavelength X-ray fluorescence analyzer to obtain the X-ray fluorescence spectrum.

[0019] The characteristic peaks of each element are extracted from the X-ray fluorescence spectrum, and the characteristic peaks of each element are input into a preset element content identification model to determine the element content of each element.

[0020] In this scheme, the formula for calculating the graphitization index is as follows:

[0021]

[0022] Where P is the graphitization index, d(002) is the interlayer spacing of carbon elements, and L c (002) represents the stacking height of carbon elements, k i Q represents the influence coefficient of other element i. (i) Q' represents the element content of other element i. (i) Let be the threshold for the element content of other element i, and n be the total number of other element i, where 1 ≤ i ≤ n.

[0023] In this solution, determining whether the sample to be tested is graphite based on the graphitization index, and identifying the type of graphite when the sample to be tested is graphite, includes:

[0024] When P c <P<P d If the test sample is found to be graphite, then the test sample is determined to be graphite; otherwise, the test sample is determined to be non-graphite.

[0025] When P c <P≤P a At that time, the peak intensity ratio of the 002 peak and the 110 peak, and the morphology score M of the 002 peak are calculated to determine whether the sample to be tested is crystalline graphite.

[0026] When P a <P≤P b At that time, the sample to be tested is further identified;

[0027] When P b <P<P d At that time, it was determined that the sample to be tested was cryptocrystalline graphite;

[0028] Among them, P a P represents the maximum graphitization index of crystalline graphite. b P is the minimum graphitization index for cryptocrystalline graphite. c P is the minimum graphitization index of crystalline graphite. d It represents the maximum graphitization index of cryptocrystalline graphite.

[0029] In this scheme, the step of determining whether the sample to be tested is crystalline graphite by calculating the peak intensity ratio of the 002 peak and the 110 peak (OI) and the morphology score M of the 002 peak includes:

[0030] Calculate the peak intensity ratio (OI) of peaks 002 and 110 based on their peak heights.

[0031] The morphological score M of peak 002 is calculated based on the peak value, half-peak width, and the ratio of overlapping areas on both sides of the axis.

[0032]

[0033] Where H is the peak value of peak 002, β is the full width at half maximum (FWHM) of peak 002, and H... a The preset minimum peak value threshold for peak 002, β a The preset maximum half-width threshold for peak 002, S a The area of ​​the region to the left of the axis of peak 002, S b The area of ​​the right-hand region of peak 002, S ab k represents the overlapping area of ​​the regions on both sides of the 002 peak axis. H k β and k S These are the influence coefficients for the peak value, half-peak width, and overlapping area of ​​the regions on both sides of the axis, respectively, k. H +kβ +k S =1;

[0034] When OI≥OI' and M≥M', the sample to be tested is determined to be crystalline graphite; OI' is the minimum peak intensity ratio of the 002 peak and the 110 peak of crystalline graphite, and M' is the minimum morphology score of crystalline graphite.

[0035] Conversely, if the sample to be tested is determined to be other types of graphite.

[0036] In this scheme, when P a <P≤P b Then, further identification of the sample to be tested is performed, including:

[0037] The diffraction pattern is analyzed to extract sub-diffraction patterns of the characteristic peak positions of each other element;

[0038] The sub-diffraction patterns of the characteristic peaks of each of the other elements are compared with the standard patterns of graphite associated minerals to determine the mineral types of associated minerals present in the sample to be tested.

[0039] The mineral composition of the associated minerals in the sample to be tested is determined by analyzing the mineral types and elemental contents of other elements present in the sample.

[0040] The influence coefficients of other elements are adjusted based on the mineral types and proportions of associated minerals present in the sample to be tested, and the graphitization index P' is recalculated based on the adjusted influence coefficients of other elements.

[0041] When P'≤P a At that time, it was determined that the sample to be tested was crystalline graphite;

[0042] When P a '<P'≤P b At that time, it was determined that the sample to be tested was other types of graphite;

[0043] When P' > P b At that time, it was determined that the sample to be tested was cryptocrystalline graphite;

[0044] Among them, P a 'and P b 'The maximum graphitization index of crystalline graphite and the minimum graphitization index of cryptocrystalline graphite are determined when the mineral types and mineral proportions of associated minerals in the sample to be tested are known.'

[0045] Output the type of graphite, graphite percentage, and the types and percentages of associated minerals present in the sample to be tested.

[0046] In this scheme, the step of analyzing the associated mineral types and elemental contents of other elements present in the sample to determine the mineral proportion of associated minerals in the sample to be tested includes:

[0047] The elemental percentage (k) of element x within the associated mineral j is determined based on the mineral types of the associated mineral j. x(j) ;

[0048] The elemental proportion k of element x within the associated mineral j x(j) Input into the preset associated mineral proportion coefficient calculation model, combined with the elemental content Q of element x in the sample to be tested. (x) Analysis was conducted to determine the mineral proportion k of associated mineral j. j .

[0049] In this scheme, the adjustment of the influence coefficients of other elements based on the mineral types and proportions of associated minerals present in the sample to be tested includes:

[0050] The mineral type influence coefficient q is determined based on the mineral types of the associated mineral j. j ;

[0051] According to the mineral proportion k of the associated mineral j j Influence coefficient q of mineral type j Adjust the influence coefficients of other elements i to determine the adjusted influence coefficients k of other elements i. i ';

[0052]

[0053] Where m is the total quantity of associated mineral j.

[0054] This invention discloses a rapid identification method for graphite minerals based on spectral feature comparison. The method includes: acquiring a sample to be tested; performing a diffraction experiment on the sample to obtain its diffraction pattern and determine the graphite crystal structure characteristics; performing elemental analysis on the sample to determine the elemental content of each element; analyzing the graphite crystal structure characteristics and elemental content of the sample to determine the graphitization index; determining whether the sample is graphite based on the graphitization index; and identifying the graphite type when the sample is graphite; and displaying the identification results and elemental content of the sample on a preset terminal. This invention is based on the combined analysis of XRD and single-wavelength XRF, using XRD diffraction and single-wavelength X-ray fluorescence (which can measure ultralight elements) to detect carbon content, and verifying the results through phase structure characteristics and elemental composition analysis, thereby improving the accuracy and efficiency of graphite mineral identification. Attached Figure Description

[0055] Figure 1A flowchart of a rapid graphite mineral identification method based on spectral feature comparison provided by the present invention is shown;

[0056] Figure 2 A flowchart illustrating the method for determining the identification result of a sample to be tested provided by the present invention is shown;

[0057] Figure 3 A flowchart of the graphite type identification method provided by the present invention is shown. Detailed Implementation

[0058] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0059] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0060] Figure 1 The flowchart of a rapid graphite mineral identification method based on spectral feature comparison provided by the present invention is shown.

[0061] like Figure 1 As shown, this invention discloses a rapid identification method for graphite minerals based on spectral feature comparison, comprising:

[0062] S102, Obtain the sample to be tested;

[0063] S104. Diffraction experiments are performed on the sample to obtain the diffraction pattern of the sample and determine the graphite crystal structure characteristics of the sample. The graphite crystal structure characteristics include the full width at half maximum (FWHM), interlayer spacing, and stacking height.

[0064] S106, performs elemental analysis on the sample to determine the content of each element in the sample; each element includes carbon (C) and other elements; other elements include iron (Fe), silicon (Si), aluminum (Al), and sulfur (S);

[0065] S108. Analyze the graphite crystal structure characteristics and elemental content of the sample to be tested to determine the graphitization index. Determine whether the sample to be tested is graphite based on the graphitization index. When the sample to be tested is graphite, identify the type of graphite. Graphite types include crystalline graphite and cryptocrystalline graphite.

[0066] S110 displays the identification results of the sample to be tested and the element content of each element through a preset terminal.

[0067] According to an embodiment of the present invention, after collecting the sample to be tested from the mining area, necessary pretreatment is first performed, including crushing, grinding, and drying. The sample is ground to below 200 mesh, pressed into a pellet, and then subjected to XRD and single-wavelength XRF detection by an X-ray diffractometer and a single-wavelength X-ray fluorescence analyzer, respectively, to obtain the XRD diffraction pattern and XRF diffraction pattern of the sample. By analyzing the XRD diffraction pattern and XRF diffraction pattern of the sample, the graphite crystal structure characteristics and elemental composition characteristics (i.e., the elemental content of each element) of the sample are determined. The graphitization index P of the sample is calculated using the graphitization index calculation formula, and the graphitization index P of the sample is compared with the minimum graphitization index P of crystalline graphite preset by the system. c The maximum graphitization index P of cryptocrystalline graphite d A comparison is performed to determine whether the sample to be tested is graphite. When the sample to be tested is graphite, the maximum graphitization index P of crystalline graphite, which is preset in the system, is used. a The minimum graphitization index P of cryptocrystalline graphite b Analysis is performed to determine whether the sample is crystalline or cryptocrystalline graphite. When P c <P≤P a When P is high, the sample is more likely to be crystalline graphite. Further identification is performed by calculating the peak intensity ratio of the 002 peak and the 110 peak, and the morphology score M of the 002 peak, to determine whether the sample is crystalline graphite. b <P<P d When P is present, the sample to be tested is determined to be cryptocrystalline graphite; when P is present, the sample is determined to be cryptocrystalline graphite. a <P≤P b When it is impossible to accurately determine whether the sample is crystalline graphite or transitional graphite, the mineral types and proportions of associated minerals in the sample are predicted by analyzing the characteristic peak positions and elemental contents of other elements. The influence coefficients of other elements are adjusted, and the graphitization index is recalculated. The maximum graphitization index of crystalline graphite and the minimum graphitization index of cryptocrystalline graphite are adjusted based on the mineral types and proportions of associated minerals in the sample. Finally, the sample is determined to be crystalline or amorphous graphite based on the recalculated graphitization index and the adjusted maximum and minimum graphitization indices of crystalline and cryptocrystalline graphite.

[0068] Once the test is complete, the identification results of the sample to be tested (including whether it is graphite and the specific type of graphite) and the elemental content of each element are displayed on a preset terminal (such as a monitor, mobile phone, etc.) for relevant personnel to view.

[0069] According to an embodiment of the present invention, a diffraction experiment is performed on the sample to be tested to obtain the diffraction pattern of the sample to be tested, and the graphite crystal structure characteristics of the sample to be tested are determined, including:

[0070] X-ray diffraction analysis was performed on the sample under test using an X-ray diffractometer to obtain the diffraction pattern;

[0071] The diffraction pattern was analyzed to extract the characteristic peak position and full width at half maximum (FWHM) β of carbon element, and the interlayer spacing d(002) of carbon element was calculated; the characteristic peak position of carbon element is the 002 peak.

[0072] Calculate the stacking height L using the Scherrer formula. c (002);

[0073]

[0074] Where k1 is the influencing factor, λ is the wavelength of the X-ray, θ is the Bragg diffraction angle, and β is the full width at half maximum (FWHM) of the 002 peak.

[0075] It should be noted that, firstly, the scanning parameters are set (for example, using Cu or Ka radiation (λ = 0.154056 nm), with a scanning range of 5° to 70°). The sample to be tested is then scanned using the set scanning parameters to generate a diffraction pattern. The interlayer spacing d(002) is calculated by substituting the X-ray wavelength λ and the Bragg diffraction angle θ into the Bragg formula and the Scherrer formula, and the stacking height Lc(002) is calculated by substituting the X-ray wavelength λ and the Bragg diffraction angle θ into the Scherrer formula.

[0076] The specific value of the influencing factor k1 is set by those skilled in the art according to actual needs; k1 is typically set to 0.89.

[0077] The calculation method for the interlayer spacing d(002) is expressed by the formula:

[0078]

[0079] Where n is the system's preset diffraction order.

[0080] According to an embodiment of the present invention, elemental analysis is performed on a sample to be tested to determine the elemental content of each element in the sample, including:

[0081] Elemental analysis of the sample was performed using a single-wavelength X-ray fluorescence analyzer to obtain the X-ray fluorescence spectrum.

[0082] The characteristic peaks of each element are extracted from the X-ray fluorescence spectrum, and the characteristic peaks of each element are input into the preset element content recognition model to determine the element content of each element.

[0083] It should be noted that, compared to the full-spectrum matrix parameter fitting method, single-wavelength X-ray fluorescence detection can obtain the elemental content of some light elements that cannot be calculated without standardization by the full-spectrum matrix parameter fitting method, such as carbon. A single-wavelength X-ray excitation source (such as a Mo target, wavelength...) is used. The characteristic X-ray fluorescence in the sample was detected to obtain the X-ray fluorescence spectrum; the carbon element (C-Kα line, wavelength) was segmented from the X-ray fluorescence spectrum. The spectral images of elements such as Si-Kα and Al-Kα are input into a preset element content recognition model. The model compares them with sample images in the database and outputs the element content of the matching sample image, which is then determined as the element content of the input element.

[0084] The preset element content recognition model is trained by the system by collecting sample images of the characteristic peaks of each element at different element contents (obtained through networks and other means), and the sample images are stored in the system database.

[0085] According to an embodiment of the present invention, the formula for calculating the graphitization index is as follows:

[0086]

[0087] Where P is the graphitization index, d(002) is the interlayer spacing of carbon elements, and L c (002) represents the stacking height of carbon elements, k i Q represents the influence coefficient of other element i. (i) Q' represents the element content of other element i. (i) Let be the threshold for the element content of other element i, and n be the total number of other element i, where 1 ≤ i ≤ n.

[0088] It should be noted that the formation of graphite ore is often accompanied by the presence of various other minerals. These associated minerals mainly include quartz, feldspar, biotite, and metallic minerals such as gold, silver, iron, and tin. The graphitization index of the sample is calculated by the interlayer spacing, stacking height of carbon elements, and the elemental content of each element.

[0089] Figure 2 A flowchart of the method for determining the identification result of a sample to be tested provided by the present invention is shown.

[0090] like Figure 2 As shown in the embodiment of the present invention, determining whether a sample to be tested is graphite based on the graphitization index, and identifying the type of graphite when the sample to be tested is graphite, includes:

[0091] S202, when P c <P<P d If the result is true, the sample is determined to be graphite; otherwise, the sample is determined not to be graphite.

[0092] S204, when P c <P≤P aAt that time, the peak intensity ratio OI of the 002 peak and the 110 peak and the morphology score M of the 002 peak are calculated to determine whether the sample to be tested is crystalline graphite.

[0093] S206, when P a <P≤P b At that time, the sample to be tested is further identified;

[0094] S208, when P b <P<P d At that time, it was determined that the sample to be tested was cryptocrystalline graphite;

[0095] Among them, P a P represents the maximum graphitization index of crystalline graphite. b P is the minimum graphitization index for cryptocrystalline graphite. c P is the minimum graphitization index of crystalline graphite. d It represents the maximum graphitization index of cryptocrystalline graphite.

[0096] It should be noted that P a P b P c and P d The value of P is affected by the region. For example, when the area where the sample is collected contains metamorphic rocks with associated minerals (such as quartz and mica) that accompany graphite, the content of Si and Al elements will be higher. The content of P is influenced by the content of Si and Al elements. a P b P c and P d The value of P will be higher than the normal value. For example, based on the geological conditions of a certain region, P will be... a Set P to 1. b Set it to 3, and set P c Set P to 0.2. d Set to 5. Those skilled in the art can adjust P according to actual needs (using different detection equipment, samples from different collection areas, etc.). a P b P c and P d The value of is adjusted.

[0097] When P a <P≤P b At that time, the sample to be tested was affected by associated minerals, and it was impossible to accurately determine whether the sample was crystalline graphite or amorphous graphite. Further identification of the sample was required.

[0098] Figure 3 A flowchart of the graphite type identification method provided by the present invention is shown.

[0099] like Figure 3As shown in the embodiment of the present invention, determining whether the sample to be tested is crystalline graphite by calculating the peak intensity ratio of the 002 peak and the 110 peak, and the morphology score M of the 002 peak, includes:

[0100] S302, calculate the peak intensity ratio OI of peak 002 and peak 110 based on the peak heights of peak 002 and peak 110;

[0101] S304, calculate the morphological score M of peak 002 based on peak value, half-peak width, and the ratio of overlapping areas on both sides of the axis;

[0102]

[0103] Where H is the peak value of peak 002, β is the full width at half maximum (FWHM) of peak 002, and H... a The preset minimum peak value threshold for peak 002, β a The preset maximum half-width threshold for peak 002, S a The area of ​​the region to the left of the axis of peak 002, S b The area of ​​the right-hand region of peak 002, S ab k represents the overlapping area of ​​the regions on both sides of the 002 peak axis. H k β and k S These are the influence coefficients for the peak value, half-peak width, and overlapping area of ​​the regions on both sides of the axis, respectively, k. H +k β +k S =1;

[0104] S306, when OI≥OI' and M≥M', the sample to be tested is determined to be crystalline graphite; OI' is the minimum peak intensity ratio of the 002 peak and the 110 peak of crystalline graphite, and M' is the minimum morphology score of crystalline graphite;

[0105] S308, otherwise, it indicates that the sample to be tested is other types of graphite.

[0106] It should be noted that when P c <P≤P a When the sample is tested, it is highly likely to be crystalline graphite. Further identification is performed by calculating the peak intensity ratio OI of the 002 peak and the 110 peak and the morphology score M of the 002 peak to determine whether the sample is crystalline graphite. The interlayer spacing of ideal crystalline graphite is 0.3354 to 0.3360 nm. Crystalline graphite has a highly ordered layered structure, and d(002) is close to the ideal value (e.g., 0.3356 nm). The 002 peak is relatively sharp and symmetrical, and the half-maximum width is small. The d(002) of cryptocrystalline graphite or other graphites is affected by lattice distortion, resulting in poor symmetry of the 002 peak and a larger half-maximum width.

[0107] Establish a symmetry axis using the peak value of peak 002, dividing peak 002 into left and right regions. Rotate the right region along the symmetry axis by 180° so that it overlaps with the left region. Calculate the area of ​​the overlapping region and determine the overlapping area S of the two regions on both sides of the axis. ab The morphology score M of the 002 peak is calculated by combining the peak value H and the full width at half maximum (FWHM) β of the 002 peak. The peak heights of the 002 and 110 peaks are determined by the diffraction pattern, and the peak intensity ratio OI of the 002 and 110 peaks is determined by calculating the ratio of their peak heights. When both OI ≥ 1.5 and M ≥ 1 are satisfied, the sample is determined to be crystalline graphite; otherwise, it is other types of graphite.

[0108] Other types of graphite cannot be identified by the method used in this invention and require further testing using other methods.

[0109] Among them, H a and β a These are the preset minimum peak value threshold and preset maximum half-width threshold for the 002 peak, respectively, to meet the conditions for crystalline graphite. Their specific values ​​can be set by those skilled in the art according to actual needs. The initial value of the minimum peak intensity ratio OI' between the 002 and 110 peaks of crystalline graphite is 1.5, and the initial value of the minimum morphology score M' of crystalline graphite is 1. Those skilled in the art can adjust the specific values ​​of OI' and M' according to actual needs.

[0110] According to an embodiment of the present invention, when P a <P≤P b At that time, the sample to be tested is further identified, including:

[0111] Analyze the diffraction pattern and extract the sub-diffraction patterns of the characteristic peak positions of each other element;

[0112] The sub-diffraction patterns of the characteristic peaks of each other element are compared with the standard patterns of graphite associated minerals to determine the mineral types of associated minerals present in the sample to be tested.

[0113] The mineral composition of associated minerals in the sample is determined by analyzing the types of associated minerals and the elemental content of other elements present in the sample.

[0114] The influence coefficients of other elements are adjusted based on the types and proportions of associated minerals present in the sample to be tested, and the graphitization index P' is recalculated based on the adjusted influence coefficients of other elements.

[0115] When P'≤P a At that time, it was determined that the sample to be tested was crystalline graphite;

[0116] When P a '<P'≤Pb At that time, it was determined that the sample to be tested was another type of graphite;

[0117] When P' > P b At that time, it was determined that the sample to be tested was cryptocrystalline graphite;

[0118] Among them, P a 'and P b 'The maximum graphitization index of crystalline graphite and the minimum graphitization index of cryptocrystalline graphite are determined when the mineral types and mineral proportions of associated minerals in the sample to be tested are known.'

[0119] Output the type of graphite, graphite percentage, and the types and percentages of associated minerals present in the sample to be tested.

[0120] It should be noted that when P a <P≤P b In some cases, it is impossible to accurately determine whether the sample to be tested is crystalline or cryptocrystalline graphite, and further identification of the sample is required. By analyzing the XRF diffraction patterns obtained by a single-wavelength X-ray fluorescence analyzer, the sub-diffraction patterns of the characteristic peaks of other elements are compared with the standard patterns of graphite-associated minerals. When the sub-diffraction patterns of the characteristic peaks of other elements match the standard pattern of a certain graphite-associated mineral, it is determined that the sample to be tested contains that graphite-associated mineral. Based on the elemental content of other elements in the sample and the mineral types of the associated minerals, the mineral proportions of each associated mineral are calculated. The influence coefficients of other elements in the sample are adjusted according to the mineral types of the associated minerals, and the graphitization index P' is recalculated. The maximum graphitization index of crystalline graphite and the minimum graphitization index of cryptocrystalline graphite are adjusted according to the mineral types and proportions of the associated minerals in the sample to obtain the maximum graphitization index P' of crystalline graphite corresponding to the mineral types and proportions of the associated minerals in the sample. a 'and the minimum graphitization index P of cryptocrystalline graphite b Based on the recalculated graphitization index P' and the adjusted maximum graphitization index P of crystalline graphite. a 'and the minimum graphitization index P of cryptocrystalline graphite b 'Determine whether the sample to be tested is crystalline graphite or amorphous graphite. When P a '<P'≤P b If the sample is determined to be other types of graphite, further testing using other methods is required.

[0121] Among them, the type and content of associated minerals affect P a 'and P b Adjustments were made, and the database stores the maximum graphitization index P of crystalline graphite corresponding to different mineral proportions for each type of associated mineral. a'and the minimum graphitization index P of cryptocrystalline graphite b '.

[0122] According to embodiments of the present invention, the mineral proportion of associated minerals in the sample is determined by analyzing the mineral types and elemental contents of associated minerals present in the sample, including:

[0123] The elemental proportion (k) of element x within associated mineral j is determined based on the mineral types of associated mineral j. x(j) ;

[0124] The elemental proportion k of element x within the associated mineral j x(j) Input into the preset associated mineral proportion coefficient calculation model, combined with the elemental content Q of element x in the sample to be tested. (x) Analysis was conducted to determine the mineral proportion k of associated mineral j. j .

[0125] It should be noted that the mineral types and main components of the associated mineral j are used to determine the mineral types of each element within the associated mineral j. Taking mica as an example, the main component is KAl2(AlSi3O) 10 (OH)₂, in which the proportions of potassium (K), aluminum (Al), and silicon (Si) are respectively...

[0126] Let the mineral percentage of associated mineral j be k. j Thus, the elemental content of element x in the associated mineral j is determined to be k. j ×k x(j) Based on element type, the element content of the same element type in different associated minerals is accumulated. The calculation is performed using a preset associated mineral proportion coefficient calculation model, and the mineral proportion k of associated mineral j is adjusted. j The value of is chosen such that the cumulative value of the element content of each element x in different associated minerals satisfies the element content Q of element x in the corresponding sample to be tested. (x) Determine the mineral proportion k of associated mineral j. j The final value.

[0127] Meanwhile, the types and proportions of associated minerals present in the sample to be tested can be displayed through a preset terminal.

[0128] According to embodiments of the present invention, the influence coefficients of other elements are adjusted based on the mineral types and proportions of associated minerals present in the sample to be tested, including:

[0129] The mineral type influence coefficient q is determined based on the mineral types of the associated mineral j. j ;

[0130] Based on the mineral proportion k of associated mineral jj Influence coefficient q of mineral type j Adjust the influence coefficients of other elements i to determine the adjusted influence coefficients k of other elements i. i ';

[0131]

[0132] Where m is the total quantity of associated mineral j.

[0133] It should be noted that the mineral type influence coefficient is set by the system based on the probability of the presence of corresponding associated minerals in crystalline graphite. The mineral type influence coefficient q corresponds to the mineral type of different associated minerals j. j Different. For example, when the associated mineral is quartz or mica, the probability of the sample being tested being crystalline graphite is relatively high, and the corresponding mineral type has a higher influence coefficient.

[0134] All information (including but not limited to user equipment information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals (including but not limited to signals transmitted between user terminals and other devices) involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the "sample to be tested" and "standard spectrum of graphite associated minerals" involved in this disclosure were obtained with full authorization.

[0135] This invention discloses a rapid identification method for graphite minerals based on spectral feature comparison. The method includes: acquiring a sample to be tested; performing a diffraction experiment on the sample to obtain its diffraction pattern and determine the graphite crystal structure characteristics; performing elemental analysis on the sample to determine the elemental content of each element; analyzing the graphite crystal structure characteristics and elemental content of the sample to determine the graphitization index; determining whether the sample is graphite based on the graphitization index; and identifying the graphite type when the sample is graphite; and displaying the identification results and elemental content of the sample on a preset terminal. This invention is based on the combined analysis of XRD and single-wavelength XRF, using XRD diffraction and single-wavelength X-ray fluorescence (which can measure ultralight elements) to detect carbon content, and verifying the results through phase structure characteristics and elemental composition analysis, thereby improving the accuracy and efficiency of graphite mineral identification.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0137] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0138] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0139] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0140] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A rapid identification method for graphite minerals based on spectral feature comparison, characterized in that, include: Obtain the sample to be tested; A diffraction experiment was performed on the sample to be tested to obtain the diffraction pattern of the sample to be tested, and the graphite crystal structure characteristics of the sample to be tested were determined. The graphite crystal structure features include full width at half maximum (FWHM), interlayer spacing, and stacking height. Elemental analysis is performed on the sample to determine the elemental content of each element in the sample; each element includes carbon (C) and other elements; the other elements include at least iron (Fe), silicon (Si), aluminum (Al), and sulfur (S); The graphite crystal structure characteristics and elemental content of each element in the sample to be tested are analyzed to determine the graphitization index. The graphitization index is used to determine whether the sample to be tested is graphite. When the sample to be tested is graphite, the type of graphite is identified. The types of graphite include crystalline graphite and cryptocrystalline graphite. The identification results of the sample to be tested and the element content of each element are displayed through a preset terminal. The formula for calculating the graphitization index is as follows: ； Where P is the graphitization index, d(002) is the interlayer spacing of carbon elements, and L c (002) represents the stacking height of carbon elements, k i Q represents the influence coefficient of other element i. (i) Q' represents the element content of other element i. (i) Let be the threshold for the element content of other element i, and n be the total number of other element i, where 1 ≤ i ≤ n; The step of determining whether the sample to be tested is graphite based on the graphitization index, and identifying the type of graphite when the sample to be tested is graphite, includes: When P c <P<P d If the test sample is found to be graphite, then the test sample is determined to be graphite; otherwise, the test sample is determined to be non-graphite. When P c <P≤P a At that time, the peak intensity ratio of the 002 peak and the 110 peak, and the morphology score M of the 002 peak are calculated to determine whether the sample to be tested is crystalline graphite. When P a <P≤P b At that time, the sample to be tested is further identified; When P b <P<P d At that time, it was determined that the sample to be tested was cryptocrystalline graphite; Among them, P a P represents the maximum graphitization index of crystalline graphite. b P is the minimum graphitization index for cryptocrystalline graphite. c P is the minimum graphitization index of crystalline graphite. d It represents the maximum graphitization index of cryptocrystalline graphite.

2. The rapid identification method for graphite minerals based on spectral feature comparison according to claim 1, characterized in that, The process of performing a diffraction experiment on the sample to be tested to obtain the diffraction pattern of the sample and determine the graphite crystal structure characteristics of the sample includes: X-ray diffraction analysis was performed on the sample under test using an X-ray diffractometer to obtain the diffraction pattern; The diffraction pattern was analyzed to extract the characteristic peak position and full width at half maximum (FWHM) β of carbon element, and the interlayer spacing d(002) of carbon element was calculated; the characteristic peak position of carbon element is the 002 peak. Calculate the stacking height L using the Scherrer formula. c (002); ； Where k1 is the influencing factor, λ is the wavelength of the X-ray, θ is the Bragg diffraction angle, and β is the full width at half maximum (FWHM) of the 002 peak.

3. The rapid identification method for graphite minerals based on spectral feature comparison according to claim 1, characterized in that, The step of performing elemental analysis on the sample to be tested to determine the elemental content of each element in the sample includes: Elemental analysis of the sample was performed using a single-wavelength X-ray fluorescence analyzer to obtain the X-ray fluorescence spectrum. The characteristic peaks of each element are extracted from the X-ray fluorescence spectrum, and the characteristic peaks of each element are input into a preset element content identification model to determine the element content of each element.

4. The rapid identification method for graphite minerals based on spectral feature comparison according to claim 1, characterized in that, The step of determining whether the sample to be tested is crystalline graphite by calculating the peak intensity ratio of the 002 peak and the 110 peak, and the morphology score M of the 002 peak, includes: Calculate the peak intensity ratio (OI) of peaks 002 and 110 based on their peak heights. The morphological score M of peak 002 is calculated based on the peak value, half-peak width, and the ratio of overlapping areas on both sides of the axis. ； Where H is the peak value of peak 002, β is the full width at half maximum (FWHM) of peak 002, and H... a The preset minimum peak value threshold for peak 002, β a The preset maximum half-width threshold for peak 002, S a The area of ​​the region to the left of the axis of peak 002, S b The area of ​​the right-hand region of peak 002, S ab k represents the overlapping area of ​​the regions on both sides of the 002 peak axis. H k β and k S These are the influence coefficients for the peak value, half-peak width, and overlapping area of ​​the regions on both sides of the axis, respectively, k. H +k β +k S =1; When OI≥OI' and M≥M', the sample to be tested is determined to be crystalline graphite; OI' is the minimum peak intensity ratio of the 002 peak and the 110 peak of crystalline graphite, and M' is the minimum morphology score of crystalline graphite. Conversely, if the sample to be tested is determined to be other types of graphite.

5. The rapid identification method for graphite minerals based on spectral feature comparison according to claim 1, characterized in that, When P a <P≤P b Then, further identification of the sample to be tested is performed, including: The diffraction pattern is analyzed to extract sub-diffraction patterns of the characteristic peak positions of each other element; The sub-diffraction patterns of the characteristic peaks of each of the other elements are compared with the standard patterns of graphite associated minerals to determine the mineral types of associated minerals present in the sample to be tested. The mineral composition of the associated minerals in the sample to be tested is determined by analyzing the mineral types and elemental contents of other elements present in the sample. The influence coefficients of other elements are adjusted based on the mineral types and proportions of associated minerals present in the sample to be tested, and the graphitization index P' is recalculated based on the adjusted influence coefficients of other elements. When P'≤P a At that time, it was determined that the sample to be tested was crystalline graphite; When P a '<P'≤P b At that time, it was determined that the sample to be tested was other types of graphite; When P' > P b At that time, it was determined that the sample to be tested was cryptocrystalline graphite; Among them, P a 'and P b 'The maximum graphitization index of crystalline graphite and the minimum graphitization index of cryptocrystalline graphite are determined when the mineral types and mineral proportions of associated minerals in the sample to be tested are known.' Output the type of graphite, graphite percentage, and the types and percentages of associated minerals present in the sample to be tested.

6. The rapid identification method for graphite minerals based on spectral feature comparison according to claim 5, characterized in that, The step of analyzing the associated mineral types and elemental contents of other elements present in the sample to determine the mineral proportion of associated minerals in the sample includes: The elemental percentage (k) of element x within the associated mineral j is determined based on the mineral types of the associated mineral j. x(j) ; The elemental proportion k of element x within the associated mineral j x(j) Input into the preset associated mineral proportion coefficient calculation model, combined with the elemental content Q of element x in the sample to be tested. (x) Analysis was conducted to determine the mineral proportion k of associated mineral j. j .

7. The rapid identification method for graphite minerals based on spectral feature comparison according to claim 6, characterized in that, The adjustment of the influence coefficients of other elements based on the mineral types and proportions of associated minerals present in the sample to be tested includes: The mineral type influence coefficient q is determined based on the mineral types of the associated mineral j. j ; According to the mineral proportion k of the associated mineral j j Influence coefficient q of mineral type j Adjust the influence coefficients of other elements i to determine the adjusted influence coefficients k of other elements i. i '; ； Where m is the total quantity of associated mineral j.

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