A method for quantitatively testing the content and embedded granularity of organic carbon in gold ore

Abstract

The application discloses a method for quantitatively testing the content and embedded granularity of organic carbon in gold ore, and belongs to the technical field of gold ore detection. The method comprises sample preparation and quantitative testing. The sample preparation uses palm wax as a cementing agent and barite as a standard sample, and the sample to be tested is prepared through melting, mixing, solidification, polishing and carbon spraying. The quantitative testing is based on an automatic process mineralogy testing system, the palm wax background is removed through background deduction, a spectrum library is established to identify organic carbon and barite particles, the area ratio of the two is measured, the mass ratio is converted in combination with the density ratio, the content of organic carbon in the raw ore is inversely calculated, and the granularity distribution of the organic carbon is counted. The application solves the problem that the prior art cannot simultaneously accurately quantify the content and granularity of organic carbon, has the advantages of high testing precision, high efficiency, comprehensive parameters and simple operation, and can provide key data for optimizing the cyanide leaching process of gold ore.

Description

Technical Field

[0001] This invention relates to the field of gold ore process mineralogy testing technology, and more specifically, to a method for quantitatively testing process mineralogy parameters such as organic carbon content and particle size in gold ore based on an automated process mineralogy testing system. Background Technology

[0002] In gold ore extraction processes, cyanide leaching is widely used in industrial production due to its mature technology and high gold recovery rate. However, the presence of organic carbon in gold ore can have a strong negative impact on cyanide leaching efficiency—organic carbon competes with gold-cyanide complexes for adsorption, leading to a significant reduction in gold leaching rate, and in severe cases, even preventing the leaching process from achieving the expected production targets. Therefore, accurately obtaining process mineralogical parameters such as organic carbon content and particle size distribution in gold ore is a core prerequisite for evaluating the ease of gold extraction from the ore and formulating a reasonable leaching process scheme.

[0003] Currently, the main testing methods for organic carbon in ores in the industry include chemical analysis methods (such as combustion-infrared absorption) and traditional microscopic observation methods. However, these methods have obvious limitations: although chemical analysis methods can quantify the organic carbon content, they cannot obtain key process parameters such as the particle size and spatial distribution of organic carbon, and the sample pretreatment process is complex and time-consuming; traditional microscopic observation methods rely on manual identification and particle size measurement of organic carbon particles, which is not only inefficient but also easily affected by the subjective factors of the operator, resulting in poor accuracy and repeatability of the measurement results. Especially for fine (often less than 10 μm) and unevenly distributed organic carbon particles, the error of this method is even more difficult to control.

[0004] In summary, existing technologies are insufficient to simultaneously meet the requirements for accurate and efficient quantitative testing of parameters such as organic carbon content and particle size in gold ore. There is an urgent need for a testing method that can balance accuracy, objectivity, and efficiency to address the current technical bottlenecks in the process mineralogical evaluation of gold ore. Summary of the Invention

[0005] The technical problem to be solved by this invention is to overcome the shortcomings of existing technologies that cannot accurately and efficiently measure the organic carbon content and particle size of gold ore simultaneously and quantitatively. This invention provides a method for quantitatively measuring the organic carbon content and particle size of gold ore. By optimizing the sample preparation process and testing parameters, the invention achieves simultaneous and accurate quantification of organic carbon content and particle size, providing reliable data support for the optimization of gold ore cyanide leaching processes.

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0007] A method for quantitatively determining the organic carbon content and particle size in gold ore includes the following steps:

[0008] (1) Sample preparation:

[0009] a. Preparation of binder: Use palm wax as binder and a bottomed copper ring as mold; put the palm wax into the copper ring mold and heat until the binder is completely melted;

[0010] b. Standard sample addition: Weigh out barite with a purity ≥99.5% and particle size consistent with the gold ore sample as a standard sample, and record its mass as m2. Weigh out gold ore powder sample without barite component, and record its mass as m1. Mix and grind evenly to obtain a mixed sample; the mass percentage of barite in the mixed sample is ω = m2 / (m1+m2)×100%.

[0011] c. Consolidation of the sample to be tested: The mixed sample is added to the molten cementitious liquid, stirred evenly, cooled and consolidated, demolded, and the sample block is polished. Then, vacuum carbon spraying is performed to form a conductive carbon film to obtain the sample to be tested.

[0012] (2) Determination of organic carbon content and calculation of particle size distribution:

[0013] a. Background subtraction: Place the sample to be tested in the automated process mineralogical testing system, set the scanning electron microscope parameters, start the background subtraction function, remove the background signal of the binder, so that the backscattered electron image retains only the ore particles;

[0014] b. Energy spectral library establishment: Collect standard spectra of organic carbon and barite standard samples, and establish a parameter calculation energy spectral library;

[0015] c. Mineral identification and particle size testing: The system automatically scans and compares energy spectra to identify organic carbon particles and calculate their particle size distribution;

[0016] d. Calculation of organic carbon content: Measure the area S of organic carbon. C The area S of barite Ba Combining the ω value from step (1) b, the organic carbon content is calculated using the following formula:

[0017] ω 原 =(R M ×ω) / (1+R M ); where R M =(S C / S Ba )×(ρ C / ρ Ba );

[0018] In the formula, ω 原 S represents the organic carbon content in a gold ore sample. CS represents the total area of ​​organic carbon particles within the scanned region; Ba ρ represents the total area of ​​barite standard particles within the scanned area; C Density representing organic carbon; ρ Ba This represents the density of barite.

[0019] Preferably, in step (1) c, the polishing process is carried out by polishing with 100 mesh, 400 mesh, 800 mesh, 1200 mesh and 2000 mesh silicon carbide sandpaper in sequence, and finally polished with 1μm diamond polishing paste.

[0020] Preferably, in step (1)c, the conditions for vacuum carbon spraying are: vacuum degree ≤ 5.0 × 10⁻⁶. -3 Pa, current 30-40mA, carbon film thickness 10-20nm.

[0021] Preferably, in step (1)b, both the gold ore sample and the barite standard sample pass through a 200-mesh standard sieve.

[0022] Preferably, in step (2)a, the scanning electron microscope parameters are: accelerating voltage 15-20kV, beam current 10-20nA, and working distance 10-15mm; the method for establishing the energy spectrum library in step (2)b is as follows: select organic carbon standard samples with a purity greater than or equal to 99.9% and barite standard samples, place them under a scanning electron microscope, collect the energy spectra of organic carbon and barite, and obtain the characteristic element peaks of the two substances; use the collected organic carbon energy spectrum as the standard spectrum, input it into the energy spectrum library of the automatic process mineralogy testing system, and establish a calculation model including parameters such as element atomic number, characteristic peak intensity, and conversion relationship between atomic percentage and mass percentage to obtain a standard energy spectrum library for mineral identification.

[0023] Preferably, in step (2)c, the area of ​​the scanning region is not less than 10 mm².

[0024] Preferably, in step (2)c, the particle size distribution includes the proportion of particles in different particle size ranges and the characteristic particle sizes of D10, D50 and D90.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. High testing accuracy: On the one hand, palm wax (chemical formula CH3(CH2)) is selected. （20-32） +COO(CH2) （20-32）+CH3) is used as a binder, and its average atomic number (approximately 2.2~2.6) is lower than that of organic carbon (atomic number 6, mainly because palm wax is a mixture of hydrocarbons with a lower density than organic carbon, resulting in a significant difference in backscattered electron signals). In BSE images, the brightness of organic carbon particles is significantly higher than that of the palm wax background, enabling accurate extraction of organic carbon particles and avoiding background interference. On the other hand, by adding barite standard samples and using the standard samples as quantitative benchmarks, the mass ratio is converted from the area ratio and density ratio, effectively solving the quantitative error problem caused by low organic carbon content and fine particle size. Experimental verification shows that the test error of organic carbon content can be controlled within ±0.05%, and the test error of particle size can be controlled within ±0.5μm.

[0027] 2. High testing efficiency: The sample preparation process can be streamlined through standardized operations, and the preparation time for a single sample can be controlled within 2 hours; the automated process mineralogical testing system can achieve full-area automatic scanning, automatic identification, and automatic calculation, and the testing time for a single sample (including scanning and data analysis) can be controlled within 1 hour. Compared with traditional chemical analysis methods (4-6 hours for a single sample) and manual microscopic observation methods (8-10 hours for a single sample), the efficiency is improved by 3-10 times.

[0028] 3. Comprehensive parameter acquisition: It can simultaneously quantify the organic carbon content (mass percentage) and the embedded particle size distribution (characteristic particle size, particle size range ratio, etc.). Compared with existing technologies that can only obtain a single parameter, it can provide more comprehensive process mineralogical data for optimizing the cyanide leaching process of gold ore. For example, if the test finds that the organic carbon is mainly embedded in fine particles (D50<5μm), a targeted "ultrafine grinding-selective decarburization" pretreatment process can be adopted to improve the subsequent gold leaching rate.

[0029] 4. Simple operation and strong objectivity: The sample preparation process does not require complex chemical reagents, and the operation is safe and environmentally friendly; the testing process is fully automated, without the need for manual intervention in identification and calculation, effectively avoiding the influence of subjective factors of operators on the test results, and the test repeatability is good (the relative standard deviation RSD of multiple tests on the same sample is <3%). Detailed Implementation

[0030] To make the technical solution of the present invention clearer and more explicit, the present invention will be described in detail below with reference to specific embodiments.

[0031] I. Overall Plan

[0032] This invention is based on an automated process mineralogical testing system (such as a scanning electron microscope-energy dispersive spectroscopy system, SEM-EDS), and includes the following steps:

[0033] Step 1: Sample Preparation

[0034] Sample preparation is fundamental to ensuring accurate test results. It requires the sequential steps of preparing the consolidating agent, adding the standard sample, and consolidating the sample to be tested. The specific procedures are as follows:

[0035] (I) Preparation of the binder: Palm wax is selected as the binder, and a bottomed copper ring is selected as the mold. The mold is a cylindrical structure with an inner diameter of 3cm and a height of 2cm. Ensure that the inner wall of the mold is smooth and free of impurities to avoid affecting the flatness of the sample after consolidation. After crushing the palm wax into particles with a particle size of less than 5mm, place them in the bottomed copper ring and place them on a constant temperature heating table (the temperature is set at 85-95℃, and the melting point of palm wax is about 80-85℃, which can ensure that the palm wax is completely melted and does not undergo thermal decomposition) until the palm wax is completely melted into a homogeneous liquid. During this process, it is necessary to gently stir with a glass rod to remove air bubbles from the liquid.

[0036] (ii) Standard sample addition: Barite (BaSO4) was selected as the standard sample. The barite must meet the following conditions: purity ≥ 99.5% (verified by X-ray diffraction analysis), particle size basically consistent with the particle size of the gold ore sample to be tested (both passed through a 200-mesh standard sieve, i.e., particle size ≤ 75 μm), and it was confirmed by testing that the original gold ore sample did not contain barite components, so as to avoid interference of the standard sample with the ore matrix.

[0037] Accurately weigh the gold ore powder sample to be tested (mass recorded as m1, accurate to 0.0001g) and the barite standard sample (mass recorded as m2, accurate to 0.0001g). Place both into an agate mortar and grind at a speed of 150-200 r / min for 5-10 min to ensure that the barite standard sample and the gold ore sample are mixed evenly. At this time, the mass percentage of barite in the mixed sample is ω=m2 / (m1+m2)×100%.

[0038] (III) Consolidation of the sample to be tested: Slowly add the mixed sample prepared in step 2 into the copper ring mold containing the molten palm wax in step 1, while gently stirring with a glass rod at a speed of 50-80 r / min for 3-5 min to ensure that the mixed sample is evenly dispersed in the palm wax liquid without agglomeration. After stirring, remove the mold from the constant temperature heating table and place it in a room temperature (20-25℃) environment to cool naturally for 20-30 min. After the palm wax has completely solidified, gently tap the outer wall of the copper ring mold with a demolding tool (such as a rubber hammer) to release the solidified sample block (composed of palm wax, gold ore sample and barite standard) from the mold.

[0039] The demolded sample block was sent to a polishing machine for polishing: First, coarse polishing was performed using 400-grit silicon carbide sandpaper (200 r / min, 5 min) to remove the uneven parts on the surface of the sample block; then fine polishing was performed using 800-grit, 1200-grit, and 2000-grit silicon carbide sandpaper in sequence (300 r / min for each grade, 3 min for each grade); finally, fine polishing was performed using diamond polishing paste (1 μm particle size) (400 r / min, 8-10 min) until the surface of the sample block was smooth, without scratches, protrusions, or depressions.

[0040] After polishing, the sample block is placed in a vacuum carbon spraying instrument. Under conditions of vacuum ≤5Pa and current 15-20mA, carbon is sprayed onto the surface of the sample block to form a conductive carbon film with a thickness of 10-20nm. This avoids charge accumulation during subsequent scanning electron microscopy observation, which would affect image quality. At this point, the test sample preparation is complete.

[0041] Step 2: Quantitative testing based on an automated process mineralogical testing system

[0042] The tests were conducted using an automated process mineralogical testing system equipped with an energy dispersive spectroscopy (EDS) instrument. The specific procedures are as follows:

[0043] (I) Background subtraction parameter settings: Fix the test sample prepared in step one on the sample stage and place it in the scanning electron microscope sample chamber. Set the scanning electron microscope working parameters: accelerating voltage 15-20kV, beam current 10-20nA, working distance 10-15mm. Activate the system's built-in background subtraction function. By adjusting the grayscale threshold (based on the difference in backscattered electron signals between palm wax and mineral particles, set the threshold to 80-120, the specific value needs to be adjusted according to the actual sample), the backscattered electron (BSE) image will only retain gold ore particles (containing organic carbon particles and barite standard particles), eliminating the background signal of the palm wax binder, ensuring that subsequent tests are only for the target particles.

[0044] (II) Establishment of Energy Dispersive Spectrum Library: Select pure organic carbon standard samples (purity ≥ 99.9%) and barite standard samples used in Step I, and place them under a scanning electron microscope. Under the above working parameters, collect energy dispersive spectra (EDS) of organic carbon and barite to obtain the characteristic elemental peaks of the two substances. Use the collected organic carbon EDS as the standard spectrum and input it into the EDS library of the automated process mineralogy testing system. Establish a parameter calculation model (including element atomic number, characteristic peak intensity, conversion relationship between atomic percentage and mass percentage, etc.) to form a standard EDS library that can be used for mineral identification.

[0045] (III) Mineral identification and particle size distribution test: Activate the system's automatic scanning function to perform a full-area scan of the sample surface (the scanning area must cover the center and edge areas of the sample block, with a total area of ​​not less than 10 mm² to ensure the representativeness of the test). The system will automatically collect BSE images and energy spectrum signals of all particles in the scanning area and compare the measured energy spectrum with the standard energy spectrum library established in step (II) (through peak matching analysis, a matching degree ≥90% is determined to be the target mineral), thereby accurately identifying organic carbon particles and barite standard particles.

[0046] For the identified organic carbon particles, the system will automatically calculate the equivalent particle size of each organic carbon particle based on the grayscale value and pixel size of the BSE image (the pixel size accuracy is ≤0.1μm through system calibration). The system will approximate the particle as a circle and use the diameter of the outer circle of the particle as the equivalent particle size. The system will also count the number and area ratio of organic carbon particles in different particle size ranges (such as 0-5μm, 5-10μm, 10-20μm, and above 20μm) and finally generate statistical data on the particle size distribution of organic carbon particles.

[0047] (iv) Calculation of organic carbon content: While identifying organic carbon particles and barite standard particles, the system will automatically measure the total area of ​​organic carbon particles in the scanned area (denoted as S). C ) and the total area of ​​the barite standard particles (denoted as S) Ba ).

[0048] The mass percentage of organic carbon in the mixed sample ω C :

[0049] ω C =(R M ×ω) / (1+R M ).

[0050] Since all the organic carbon in the mixed sample comes from the original gold ore sample (the barite standard does not contain carbon), the organic carbon content ω in the original gold ore sample is therefore... 原 The percentage of organic carbon in the mixed sample by mass ω C Equal, that is: ω 原 =ω C =(R M ×ω) / (1+R M ).

[0051] R M =(S C / S Ba )×(ρ C / ρ Ba Substituting into the above formula, we can obtain the S measured by the system. C S Ba and the known ρ C ρBa ω and ω are used to calculate the accurate content of organic carbon in the original gold ore sample.

[0052] II. Examples of Determination of Mineralogical Parameters of Organic Carbon Processes

[0053] Step 1: Prepare the test sample

[0054] 1.1 Preparation of the solidifying agent: Select palm wax with a purity ≥98% and crush it into particles with a diameter of approximately 3mm; select a copper ring mold with an inner diameter of 3cm and a height of 2cm, and wipe the inner wall of the mold with anhydrous ethanol to remove oil and impurities. Place the palm wax particles into the copper ring mold and heat it on a constant temperature heating table at 90℃, stirring continuously until the palm wax is completely melted (approximately 5 minutes), ensuring that there are no air bubbles in the liquid. Obtain the mold containing the solidifying agent and set it aside.

[0055] 1.2 Standard Sample Addition: Select a barite standard sample with a purity of 99.6% (barium sulfate mass percentage of 99.6%), sieve it through a 200-mesh standard sieve, and set it aside; weigh 1.0000g of the original gold ore powder sample (sieved through a 200-mesh standard sieve) m1, weigh 0.2000g of the barite standard sample m2, mix them, and put them into an agate mortar. Grind them at 180r / min for 8min to ensure uniform mixing. At this time, the mass percentage of barite in the mixed sample ω=0.2000 / (1.0000+0.2000)×100%≈16.67%.

[0056] 1.3 Consolidation of the test sample: The mixed sample was slowly added into the mold containing the consolidating agent prepared in step 1.1, and stirred at 60 r / min for 4 min. Then the mold was removed and cooled at room temperature of 23℃ for 25 min. After demolding, the sample block was obtained.

[0057] 1.4 Polishing of sample blocks: Polish the sample blocks sequentially with 100-grit, 400-grit, 800-grit, 1200-grit, and 2000-grit silicon carbide sandpaper, and finally polish with 1μm diamond polishing paste for 10 minutes until the surface is smooth.

[0058] 1.5 Carbon spraying of sample blocks: Place the polished sample blocks into a vacuum carbon spraying instrument, with a vacuum degree ≤ 5.0 × 10⁻⁶. -3 Carbon was sprayed for 20 seconds under the conditions of Pa and current of 30-40mA to form a conductive carbon film with a thickness of about 15nm, and the test sample was prepared.

[0059] Step 2: Quantitative Testing

[0060] 2.1 Equipment and parameter settings: A scanning electron microscope and energy dispersive spectrometer system were used, with the accelerating voltage set to 18kV, the beam current to 15nA, the working distance to 12mm, and the background grayscale subtraction threshold set to 100.

[0061] 2.2 Energy Dispersive Spectrum Library Establishment: Pure organic carbon standard samples (99.9% purity) and barite standard samples were selected and placed under a scanning electron microscope. Energy dispersive spectra (EDS) of organic carbon and barite were acquired under the working parameters described in step 2.1 to obtain the characteristic elemental peaks of the two substances (the characteristic peak of organic carbon is CK). α The energy is approximately 0.28 keV; the characteristic peaks of barite are Ba-L. α Energy approximately 4.47 keV; SK α Energy approximately 2.31 keV; OK α The energy level is approximately 0.52 keV. The collected organic carbon energy spectrum is used as a standard spectrum and input into the energy spectrum library of the automated process mineralogy testing system. A parameter calculation model is established (including element atomic number, characteristic peak intensity, conversion relationship between atomic percentage and mass percentage, etc.) to form a standard energy spectrum library that can be used for mineral identification.

[0062] Input the energy dispersive spectroscopy (EDS) spectra of two minerals. The measured spectra are compared with the standard spectra of these two minerals to identify which phases in the ore particles belong to these two minerals. Then, the parameter calculation is performed by testing the area ratio of the two minerals through an image analysis system and multiplying it by the specific gravity to obtain the weight ratio. This calculation process is built into the system itself.

[0063] 2.3 Determination and Calculation of Mineral Organic Carbon Content: The automatic scanning function was activated, with a scanning area of ​​12 mm². The system automatically identified organic carbon particles and barite particles, and the S content was measured. C =0.85mm², S Ba =5.20mm², R S =S C / S Ba =0.85 / 5.20≈0.163. Take ρ C =2.0g / cm³, ρ Ba =4.5g / cm³, then R M =0.163×(2.0 / 4.5)≈0.0724. Substituting into the formula ω 原 =(0.0724×16.67%) / (1+0.0724)≈1.12%.

[0064] Note: Organic carbon density ρ C =1.8-2.2 g / cm³, the average value of 2.0 g / cm³ is taken in this example; barite density ρ Ba =4.3-4.7 g / cm³, and the average value of 4.5 g / cm³ is taken in this example.

[0065] 2.4 Identification of particle size distribution of mineral organic carbon particles: The particle size distribution of organic carbon particles was statistically obtained as follows: 0-5μm accounts for 65%, 5-10μm accounts for 25%, 10-20μm accounts for 8%, and particles larger than 20μm account for 2%, with D50=4.8μm.

[0066] III. Verification Experiment Examples

[0067] To verify the accuracy of the method of the present invention, the organic carbon content of the same gold ore sample was tested using the combustion-infrared absorption method (national standard method GB / T19145-2003). The test result was 1.09%, and the relative error with the test result of the method of the present invention (1.12%) was 1.8%, which is less than the industry allowable error range of ±5%. The particle size of organic carbon was manually counted and measured using an image analyzer, and D50 was obtained as 4.7 μm. The relative error with the test result of the method of the present invention (4.8 μm) was 2.1%, which verified the accuracy and reliability of the method of the present invention.

Claims

1. A method for quantitatively determining the organic carbon content and particle size in gold ore, characterized in that... Includes the following steps: (1) Sample preparation: a. Preparation of binder: Use palm wax as binder and a bottomed copper ring as mold; put the palm wax into the copper ring mold and heat until the binder is completely melted; b. Standard sample addition: Weigh out barite with a purity ≥99.5% and particle size consistent with the gold ore sample as a standard sample, and record its mass as m2. Weigh out gold ore powder sample without barite component, and record its mass as m1. Mix and grind evenly to obtain a mixed sample; the mass percentage of barite in the mixed sample is ω = m2 / (m1+m2)×100%. c. Consolidation of the sample to be tested: The mixed sample is added to the molten cementitious liquid, stirred evenly, cooled and consolidated, demolded, and the sample block is polished. Then, vacuum carbon spraying is performed to form a conductive carbon film to obtain the sample to be tested. (2) Determination of organic carbon content and calculation of particle size distribution: a. Background subtraction: Place the sample to be tested in the automated process mineralogical testing system, set the scanning electron microscope parameters, start the background subtraction function, remove the background signal of the binder, so that the backscattered electron image retains only the ore particles; b. Energy spectral library establishment: Collect standard spectra of organic carbon and barite standard samples, and establish a parameter calculation energy spectral library; c. Mineral identification and particle size testing: The system automatically scans and compares energy spectra to identify organic carbon particles and calculate their particle size distribution; d. Calculation of organic carbon content: Measure the area S of organic carbon. C The area S of barite Ba Combining the ω value from step (1) b, the organic carbon content is calculated using the following formula: oh 原 =(R M ×ω) / (1+R M ); among them,R M =(S C / S Ba )×(ρ C / r Ba ); In the formula, ω 原 S represents the organic carbon content in a gold ore sample. C S represents the total area of ​​organic carbon particles within the scanned region; Ba ρ represents the total area of ​​barite standard particles within the scanned area; C Density representing organic carbon; ρ Ba This represents the density of barite.

2. The method according to claim 1, characterized in that: In step (1) c, the polishing process is carried out by polishing with 100-mesh, 400-mesh, 800-mesh, 1200-mesh and 2000-mesh silicon carbide sandpaper in sequence, and finally polished with 1μm diamond polishing paste.

3. The method according to claim 1, characterized in that: In step (1)c, the conditions for vacuum carbon spraying are: vacuum degree ≤ 5.0 × 10 -3 Pa, current 30-40mA, carbon film thickness 10-20nm.

4. The method according to claim 1, characterized in that: In step (1)b, both the gold ore sample and the barite standard sample passed through a 200-mesh standard sieve.

5. The method according to claim 1, characterized in that: In step (2)a, the scanning electron microscope parameters are: accelerating voltage 15-20kV, beam current 10-20nA, and working distance 10-15mm. In step (2)b, the method for establishing the energy spectrum library is as follows: select organic carbon standard samples with a purity greater than or equal to 99.9% and barite standard samples, place them under a scanning electron microscope, collect the energy spectra of organic carbon and barite, and obtain the characteristic element peaks of the two substances. Use the collected organic carbon energy spectrum as the standard spectrum, input it into the energy spectrum library of the automatic process mineralogy testing system, and establish a calculation model including parameters such as element atomic number, characteristic peak intensity, and conversion relationship between atomic percentage and mass percentage to obtain a standard energy spectrum library for mineral identification.

6. The method according to claim 1, characterized in that: In step (2) c, the area of ​​the scanning region shall not be less than 10 mm².

7. The method according to claim 1, characterized in that: In step (2)c, the particle size distribution includes the proportion of particles in different particle size ranges and the characteristic particle sizes of D10, D50 and D90.