Metallurgical dust and sludge reduction roasting mechanism analysis method

Abstract

The invention relates to a metallurgical dust and sludge reduction roasting mechanism analysis method, and belongs to the field of metallurgical dust and sludge resource utilization research in the iron and steel industry. According to the method, a complete total cross-section Maps image of a metallized pellet is obtained through image splicing; maps images of all phases and pores are extracted by utilizing different colors of particles and pores of different phases, so that visual and visual analysis of the non-uniformity of the metallized pellets is realized; and through statistical analysis of phases and pores in different areas of the total cross-section Maps image of the metallized pellets, datamation analysis of the non-uniformity of the metallized pellets is realized. According to the method, the metallurgical dust sludge reduction roasting mechanism can be comprehensively revealed, and reliable guidance is provided for process optimization.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of metallurgical dust resource utilization research in the steel industry, and specifically relates to a metallurgical dust reduction roasting mechanism analysis method. BACKGROUND

[0002] Metallurgical dust is a solid waste generated during steel smelting, which poses a high environmental pollution risk, but also contains valuable components such as iron and zinc, and has high resource recycling value. Currently, the rotary hearth furnace process is the mainstream technology for treating metallurgical dust, and its technical core is to make metallurgical dust into green pellets, and then reduce and roast them at high temperature into metallized pellets. By taking advantage of the easy vaporization of zinc metal at high temperature, the separation and recovery of iron and zinc elements are achieved. However, the rotary hearth furnace process still has the following outstanding problems in practical application:

[0003] (1) Low production efficiency: The rotary hearth furnace technology relies on radiation heat transfer when treating metallurgical dust, resulting in low heat transfer efficiency, which leads to uneven heating of the pellets during the reduction roasting process. In order to ensure the reduction effect, thin material layer roasting is usually used in actual production, which limits the processing capacity of the rotary hearth furnace, thereby affecting the overall production efficiency.

[0004] (2) Uneven reduction degree: The reduction degree of the metallized pellets after reduction roasting is significantly different between the surface and the center, and between the upper and lower layers. Some metallized pellets are not fully roasted (i.e., the surface is fully reduced, but the center is not fully reduced), which makes the composition of the final metallized pellets fluctuate greatly, the metallization rate unstable, and the zinc removal rate difficult to consistently meet the standards.

[0005] Therefore, it is urgent to conduct in-depth analysis of the mechanism of the reduction roasting process of metallurgical dust to optimize process parameters and solve the problems of uneven reduction and low efficiency. The existing reduction roasting mechanism research of metallurgical dust mainly relies on two types of analysis methods:

[0006] Existing solution one: macroscopic analysis and detection method, that is, by analyzing the overall chemical composition and phase of the metallized pellet samples after roasting, the influence of roasting conditions (such as temperature, time, carbon content, etc.) on the reduction process is studied. Specifically, it includes:

[0007] Chemical composition analysis: Determine the iron content (TFe), zinc content, etc. of the metallized pellets as a whole, and calculate the metallization rate, zinc removal rate, etc. according to the chemical composition analysis results to evaluate the reduction effect.

[0008] XRD phase analysis (X-Ray Diffraction, XRD): Analyze the main phases of the metallized pellets (such as Fe2O3, Fe3O4, FeO, metallic iron, etc.), and study the phase transformation rules during the reduction process.

[0009] Document (Wei Xiujun et al., Removal of zinc, lead and alkali metals from zinc-containing dust and sludge. Metallurgical Energy, 2019. 38(01): p. 54-58.) used chemical composition analysis to study the effects of roasting temperature and time on the removal of lead and zinc and alkali metals from zinc-containing dust and sludge; Document (Shi Yanhong et al., Reduction roasting-magnetic separation test of zinc-containing metallurgical dust. Nonferrous Metal Engineering, 2024. 14(2): p. 136-141.) used chemical composition analysis and XRD phase analysis to study the effects of roasting temperature and time on zinc-iron separation and phase transformation during the roasting process of zinc-containing metallurgical dust.

[0010] Existing solution two: microscopic analysis method, which uses scanning electron microscopy (SEM) combined with energy dispersive spectrometer (EDS) and other microscopic characterization techniques to explore the element distribution, phase composition and microstructure changes in the local area of metallized pellets, in order to reveal the micro-mechanism of reduction reaction.

[0011] Document (Ren Xiaojian et al., Development and application of composite binder for iron-containing dust pelletizing. Steel Iron, 2024. 59(2): p. 164-172.) used SEM-EDS analysis to study the effects of composite binder on the microstructure and element distribution of metallized pellets after reduction roasting of metallurgical dust.

[0012] Although existing macroscopic and microscopic analysis methods are widely used in the study of metallurgical dust reduction roasting mechanism, they all have significant limitations:

[0013] Drawbacks of existing solution one: (1) Macroscopic analysis methods (such as chemical composition analysis and XRD phase analysis) can only characterize the overall average properties of metallized pellets, and cannot reflect the differences in reduction degree of different areas (such as surface and center, upper and lower) within the sample. For example, macroscopic analysis methods may show that the overall metallization rate of metallized pellets is high, but they cannot detect the unreacted FeO phase in the center area, leading to the "unburned" problem being covered up. (2) Macroscopic analysis cannot identify microstructure defects (such as uneven pore distribution, local unreacted nuclei, etc.), and cannot provide spatial resolution guidance for reduction roasting process optimization.

[0014] Disadvantages of the existing scheme two: (1) lack of representation, the analysis result is seriously dependent on the location and quantity of the selected micro area. For non-uniform metallurgical dust pellet, the analysis results of different micro areas may differ greatly (for example, the surface area may show completely reduced metallic iron, while the center area is still dominated by oxides), resulting in strong subjectivity and poor objectivity of the analysis conclusion. (2) unable to reflect the overall characteristics: micro analysis only focuses on local points or surfaces, and it is difficult to integrate the overall reduction behavior of the metallized pellet, and it is difficult to directly correlate the relationship between macro process parameters (such as roasting temperature, time) and global metallization rate, dezincification rate.

[0015] Therefore, the existing methods do not realize the organic combination of macro and micro analysis, resulting in lack of systematization for the optimization of metallurgical dust reduction roasting process. SUMMARY

[0016] The purpose of the present application is to provide a metallurgical dust reduction roasting mechanism analysis method to fully reveal the metallurgical dust reduction roasting mechanism and provide reliable guidance for process optimization.

[0017] To achieve the above-mentioned purpose, the technical scheme of the present application is: a metallurgical dust reduction roasting mechanism analysis method, comprising:

[0018] Obtain the complete cross-sectional Maps image of the metallized pellet by image stitching;

[0019] Extract the Maps image of each phase and pore by using the different colors of different phase particles and pores, and realize the intuitive visual analysis of the non-uniformity of the metallized pellet;

[0020] Statistical analysis of each phase and pore in different regions of the metallized pellet cross-sectional Maps image is carried out to realize the data analysis of the non-uniformity of the metallized pellet.

[0021] Further, before obtaining the complete cross-sectional Maps image of the metallized pellet by image stitching, the following pretreatment operations need to be performed:

[0022] Metallized pellet cold inlay and polishing: after the metallurgical dust reduction roasting, the metallized pellet is fixed by cold inlaying glue, and then polished by a polishing machine to the center section of the metallized pellet;

[0023] Phase confirmation: SEM-EDS element area scanning and point scanning analysis are carried out on the center section of the metallized pellet, and the phase category of the different color particles in the metallized pellet is determined according to the SEM-EDS analysis result.

[0024] Further, the complete cross-sectional Maps image of the metallized pellet is obtained by image stitching, and the specific implementation manner is:

[0025] The SEM images of the whole area of the center section of the metallized pellet are taken one by one by using a scanning electron microscope, and the single SEM images are spliced into a full-section Maps image of the metallized pellet.

[0026] Further, the adjacent single SEM images taken have an overlapping area of 5% to 15%.

[0027] Further, different phases and pores are distinguished by different colors, and before the Maps images of the phases and pores are extracted, the following preprocessing operations are performed:

[0028] Irregular edges of the full-section Maps image are cut off: according to the shape of the full-section Maps image of the metallized pellet, the irregular edges are cut off, and the full-section Maps image is cut into a regular image;

[0029] Interfering pores in the full-section Maps image are cut off: according to the shape, color and surrounding environment of the particles in the full-section Maps image, it is determined whether the particles are particles in the pores, and the particles in the pores in the full-section Maps image are cut off one by one.

[0030] Further, the rule for determining whether the particles are particles in the pores is: if the particles are in the pores, the particles are round in shape, gradually change in color and have black pores around; otherwise, the particles are flat in shape and uniform in color.

[0031] Further, the Maps images of the phases and pores are extracted by using different colors of the different phase particles and pores, and the specific implementation manner is:

[0032] Different color particles and black pores in the full-section Maps image of the metallized pellet are identified and extracted by using image recognition software, and then the Maps images of the different phases and pores extracted are marked with different colors according to the SEM-EDS phase analysis results, to obtain the Maps images of the phases and pores.

[0033] Further, the phases and pores in different regions of the full-section Maps image of the metallized pellet are statistically analyzed, and the specific implementation manner is:

[0034] According to the Maps images of the phases and pores extracted, the Maps images are divided into different analysis and statistical regions by using image editing software, specifically, the Maps images of the phases and pores are divided into three regions of upper, middle and lower parts, and two regions of inner part and outer circle; and the image recognition software is used to statistically calculate the area percentage of the phases and pores in the corresponding regions, so as to statistically analyze and data-display the change rules of the phases and pores in different regions of the metallized pellet.

[0035] Furthermore, the specific formulas for calculating the percentage of each phase and pore area in the corresponding region are as follows:

[0036] In a certain Maps image region, phase Fe, phase Fe x The statistical calculation formulas for O and the percentage of pore area are as follows:

[0037] A Fe= S Fe / S0×100%

[0038] A FexO= S FexO / S0×100%

[0039] A 孔隙= S 孔隙 / S0×100%

[0040] In the formula: A Fe A FexO A 孔隙 Fe and Fe x O and the percentage of pore area; S0 is the area of ​​the sample region; S Fe S represents the area occupied by Fe in the corresponding sample region. FexO For Fe in the corresponding sample region x Area occupied by O; S 孔隙 This represents the area occupied by pores in the corresponding sample region.

[0041] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor, wherein when the processor executes the computer program instructions, it can implement the steps of the method described above.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. This invention enables intuitive and visual analysis of the inhomogeneity of metallized pellets: By identifying and extracting metallic iron (Fe, bright white particles), floating matter (FexO, light gray particles), and pores (black areas) from the full-section Maps image of the metallized pellets into metallic iron (Fe) Maps images, floating matter (FexO) Maps images, and pore Maps images, the distribution characteristics of metallic iron (Fe), floating matter (FexO), and pores in different regions of the metallized pellets can be intuitively observed and analyzed (e.g., Figure 7 (As shown). In contrast, the existing macroscopic analysis and detection method of Scheme 1 cannot achieve visualization analysis; while the existing microscopic analysis and detection method of Scheme 2 can only observe the particle distribution and porosity of local micro-regions, and it is difficult to directly reflect the distribution characteristics and change patterns of different regions.

[0044] 2、The application can realize the data analysis of the non-uniformity of the metallized pellets: through the statistical analysis of the metallic iron (Fe), floating body (FexO) and pores in different regions of the metallized pellets, the data display of the distribution characteristics of each phase and pore in different regions can be realized (as shown in Figure 9 、 Figure 10 、 Figure 11 ). Compared with the prior art, the macroscopic analysis detection method (chemical composition analysis) of the prior art 1 can only reflect the data of the whole metallized pellets, and cannot analyze the distribution characteristics of different regions; and the microscopic analysis detection method of the prior art 2 can only reflect the element content data of the local micro area, and cannot reflect the phase distribution data, and cannot correlate the change rule of the global metallization rate and porosity.

[0045] 3、The mechanism analysis method adopted by the application is more objective, comprehensive and flexible: the full cross-section Maps image of the metallized pellets can reflect the overall distribution characteristics of each phase and pore, and can also reflect the distribution characteristics of any local region, can objectively and comprehensively reflect the non-uniformity of the metallized pellets, and can flexibly select a local image of appropriate size and appropriate region for targeted analysis. Compared with the prior art, the macroscopic analysis detection method of the prior art 1 can only reflect the overall situation of the metallized pellets, and cannot exhibit the non-uniformity characteristics; and the microscopic analysis detection method of the prior art 2 can only display the distribution of the independent regions of the metallized pellets, and lacks correlation between different independent regions, and the subjectivity of the selected region of the microscopic analysis is strong and the representativeness is insufficient.

[0046] 4、The application combines statistical analysis method, the analysis result is more reliable: the full cross-section Maps image of the metallized pellets is spliced by hundreds of SEM images, the application integrates and analyzes a large number of images, significantly enhances the statistical significance and reliability of the analysis result, and makes the analysis result more reliable. Compared with the prior art, the microscopic analysis detection method of the prior art 2 only uses single SEM image of individual digit for analysis, which is easy to cause deviation of the conclusion due to insufficient sample.

[0047] 5、The application improves the accuracy of the analysis result by removing the interference pores in the full cross-section Maps image of the metallized pellets: the pores in the full cross-section Maps image of the metallized pellets are divided into two categories, one is deep pore, and the color is black, which can be directly determined as pore region; the other is shallow pore, and the scanning electron microscope can capture the shallow particles in the pore, so that the color in the pore gradually changes, thereby interfering with the subsequent statistical analysis based on color recognition. The application analyzes the morphology, color and surrounding environment of the particles in the full cross-section Maps image, judges whether the particles are located in the pores (interference pores), and then removes the interference pores to improve the accuracy of the analysis result. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 For phase confirmation.

[0049] Figure 2 Full cross-section Maps image shooting and splicing.

[0050] Figure 3 Full cross-section Maps image edge cutting.

[0051] Figure 4 In-pore particles.

[0052] Figure 5 In-pore particle cutting.

[0053] Figure 6 Phase and pore recognition, extraction and marking.

[0054] Figure 7 Maps image recognition and extraction.

[0055] Figure 8 Maps image region division example.

[0056] Figure 9 Influence of roasting temperature on distribution of metallic iron (Fe) in each region of metallized pellets; wherein the upper region is a right diagonal line, the middle region is a square line, and the lower region is a left diagonal line; the full cross-section region is a black line, the inner region is a red line, and the outer ring region is a green line.

[0057] Figure 10 Influence of roasting temperature on distribution of floating body (Fe x O) in each region of metallized pellets; wherein the upper region is a right diagonal line, the middle region is a square line, and the lower region is a left diagonal line; the full cross-section region is a black line, the inner region is a red line, and the outer ring region is a green line.

[0058] Figure 11 Influence of roasting temperature on distribution of pores in each region of metallized pellets; wherein the upper region is a right diagonal line, the middle region is a square line, and the lower region is a left diagonal line; the full cross-section region is a black line, the inner region is a red line, and the outer ring region is a green line.

[0059] Figure 12 Mechanism analysis flowchart of the present application. DETAILED DESCRIPTION

[0060] The technical solutions of the present application will be specifically described below with reference to the accompanying drawings.

[0061] The present application provides a metallurgical dust and sludge reduction roasting mechanism analysis method, comprising:

[0062] Obtain the complete full-section Maps image of the metallized pellet by image stitching;

[0063] By using the color difference of different phase particles and pores, the Maps image of each phase and pore is extracted to realize intuitive visual analysis of the non-uniformity of the metallized pellet.

[0064] Statistical analysis is performed on each phase and pore in different regions of the full-section Maps image of the metallized pellet to realize data analysis of the non-uniformity of the metallized pellet.

[0065] The following is the specific implementation process of the present application.

[0066] As shown in Figure 12 , the metallurgical dust reduction roasting mechanism analysis method of the present application comprises the following steps:

[0067] 1. Cold inlaying and polishing of the metallized pellet: the metallized pellet after metallurgical dust reduction roasting is fixed by cold inlaying glue, and then polished by a polishing machine to the center section of the metallized pellet.

[0068] 2. Phase confirmation: SEM-EDS element area scanning and point scanning analysis are performed on the center section of the metallized pellet in step 1, and the phase category of the different color particles inside the metallized pellet is determined according to the SEM-EDS analysis results. As shown in Figure 1 , the SEM-EDS area scanning result shows that the bright white particles inside the metallized pellet are iron element aggregation areas, and the light gray particles are iron element and oxygen element coexistence areas; the SEM-EDS point scanning result shows that the bright white particles mainly contain Fe element with a mass fraction of 99%, and the light gray particles mainly contain O element and Fe element with a mass fraction of about 20% and 65% respectively (the atomic number ratio of O element and Fe element is about 1:1), and the bright white particles inside the metallized pellet are confirmed to be metallic iron (Fe) and the light gray particles are confirmed to be wustite (Fe x O) according to the SEM-EDS area scanning and point scanning results.

[0069] 3. Photograph and splice full-section Maps image: SEM images of all regions of the center section of the metallized pellet in step 1 are photographed one by one using a scanning electron microscope, and each single SEM image is spliced into a full-section Maps image of the metallized pellet (an image covering all regions of the center section of the metallized pellet, referred to as "Maps image"). As shown in Figure 2 , the adjacent single SEM images (each yellow square is an SEM image) are photographed with a 5% to 15% border area overlap to ensure the accuracy of the splicing of the full-section Maps image.

[0070] 4. Remove irregular edges and interfering pores from the full-section Maps image: Based on the shape of the full-section Maps image of the metallized pellets in step 3, remove irregular edges to cut the full-section Maps image into a regular image for easier subsequent statistical analysis, such as... Figure 3 As shown. Based on the morphology, color, and surrounding environment of the particles in the full-section Maps image from step 3, determine whether the particles are particles within the pores, such as... Figure 4 As shown, particles within the pores are rounded, have a gradient color, and are surrounded by black pores; particles outside the pores are flat and uniform in color. To avoid interference from particles within the pores in subsequent statistical analysis, interfering particles within the pores were removed one by one from the full-section Maps image, as shown below. Figure 5 As shown.

[0071] 5. Maps Image Recognition and Extraction: Using image recognition software (such as ImageJ, which can identify and extract different colored regions as independent images based on color differences within the image), the different colored particles and black pores in the full-section Maps image of the metallized pellets in step 4 are identified and extracted. Then, based on the SEM-EDS phase analysis results in step 2, the extracted Maps images of different phases and pores are marked with different colors for subsequent analysis and research. A schematic diagram of phase and pore identification, extraction, and marking is shown below. Figure 6 As shown, metallic (Fe) particles are identified, extracted, and labeled as green, while floating particles (Fe) are labeled as green. x O) Identify, extract, and mark as yellow; pores are identified, extracted, and marked as red. Figure 7 To identify and extract the full-section Maps images of metallized pellets into metallic iron (Fe) Maps images and floating bodies (Fe) images respectively. x O) Maps images and pore Maps images are used to intuitively show the spatial distribution patterns of different phases and pores.

[0072] 6. Maps Image Statistical Analysis: Based on the Maps images of each phase and pore size extracted in step 5, use image editing software (such as Photoshop) to divide each Maps image into different analytical and statistical regions, such as... Figure 7 As shown, the Maps images of each phase and pore can be divided into three regions: upper, middle, and lower, and two regions: inner and outer. Then, image recognition software (such as ImageJ) is used to statistically calculate the percentage of each phase and pore area in its corresponding region (the calculation formula is shown later in the paragraph). This allows for the statistical analysis and data-driven presentation of the variation patterns of each phase and pore in different regions of the metallized pellets. Figure 9 , Figure 10 , Figure 11 As shown, the data illustrates the concentrations of metallic iron (Fe) and floating bodies (Fe2+) in different regions of the metallized pellets at different reduction calcination temperatures.x O, the change rule of the pore with the calcination temperature.

[0073] Fe, Fe x O, the area percentage (area ratio) statistical calculation formula of the pore is respectively seen formula (1)~formula (3).

[0074] A Fe= S Fe / S0×100% (1)

[0075] A FexO= S FexO / S0×100% (2)

[0076] A 孔隙= S 孔隙 / S0×100% (3)

[0077] In the formula: A Fe , A FexO , A 孔隙 It is the area percentage of Fe, Fe x O and pore, %;S0 It is the area of sample area;S Fe It is the area of Fe in the sample area;S FexO It is the area of Fe x O in the sample area;S 孔隙 It is the area of pore in the sample area.

[0078] The application further provides a computer readable storage medium, which has computer program instructions capable of being run by a processor, when the processor runs the computer program instructions, the steps of the method described above can be realized.

[0079] The above is the preferred embodiment of the application, when the change made according to the technical scheme of the application, the function effect generated does not exceed the range of the technical scheme of the application, all belongs to the protection scope of the application.

Claims

1. A method for analyzing the reduction roasting mechanism of metallurgical dust and sludge, characterized in that, include: A complete full-section Maps image of the metallized pellets was obtained by image stitching; By utilizing the different colors of different phase particles and pores, Maps images of each phase and pore are extracted to achieve intuitive and visual analysis of the inhomogeneity of metallized pellets; Statistical analysis of phases and pores in different regions of full-section Maps images of metallized pellets is performed to achieve data-driven analysis of the inhomogeneity of metallized pellets.

2. The method for analyzing the reduction roasting mechanism of metallurgical dust and sludge according to claim 1, characterized in that, Before obtaining a complete full-section Maps image of the metallized pellets through image stitching, the following preprocessing operations need to be performed: Cold mounting and polishing of metallized pellets: The metallized pellets after reduction and calcination of metallurgical dust and sludge are mounted and fixed with cold mounting adhesive, and then polished to the center section of the metallized pellets with a polishing machine. Phase identification: SEM-EDS elemental surface scanning and point scanning analysis were performed on the central cross section of the metallized pellets. Based on the SEM-EDS analysis results, the phase categories of different colored particles inside the metallized pellets were determined.

3. The method for analyzing the reduction roasting mechanism of metallurgical dust and sludge according to claim 2, characterized in that, The complete full-section Maps image of the metallized pellet is obtained by image stitching. The specific implementation method is as follows: Scanning electron microscopes (SEM) were used to capture SEM images of the entire cross-section of the metallized pellet, and the individual SEM images were stitched together to form a full-section Maps image of the metallized pellet.

4. The method for analyzing the reduction roasting mechanism of metallurgical dust and sludge according to claim 3, characterized in that, Adjacent SEM images maintain a 5% to 15% overlap in boundary area.

5. The method for analyzing the reduction roasting mechanism of metallurgical dust and sludge according to claim 3, characterized in that, Before extracting Maps images of each phase and pore based on the different colors of particles and pores in different phases, the following preprocessing operations need to be performed: Remove irregular edges from the full-section Maps image: Based on the shape of the full-section Maps image of the metallized pellets, remove the irregular edges to cut the full-section Maps image into a regular image; Remove interfering pores in the full-section Maps image: Based on the morphology, color and surrounding environment of the particles in the full-section Maps image, determine whether the particles are particles within the pores, and remove the particles within the pores in the full-section Maps image one by one.

6. The method for analyzing the reduction roasting mechanism of metallurgical dust and sludge according to claim 5, characterized in that, The rule for determining whether a particle is a pore particle is as follows: if it is a pore particle, the particle is round in shape, has a gradual color change, and has black pores around it; otherwise, the particle is flat in shape and has a uniform color.

7. The method for analyzing the reduction roasting mechanism of metallurgical dust and sludge according to claim 5, characterized in that, By utilizing the different colors of particles and pores in different phases, Maps images of each phase and pore are extracted. The specific implementation method is as follows: Image recognition software was used to identify and extract particles of different colors and black pores in the full-section Maps image of the metallized pellets. Then, based on the SEM-EDS phase analysis results, the extracted Maps images of different phases and pores were marked with different colors to obtain Maps images of each phase and pore.

8. The method for analyzing the reduction roasting mechanism of metallurgical dust and sludge according to claim 7, characterized in that, Statistical analysis of phases and porosity in different regions of the full-section Maps image of metallized pellets is performed. The specific implementation method is as follows: Based on the extracted Maps images of each phase and pore, image editing software was used to divide each Maps image into different analytical and statistical regions. Specifically, the Maps images of each phase and pore were divided into three regions: upper, middle, and lower, and two regions: inner and outer. Then, image recognition software was used to statistically calculate the percentage of area occupied by each phase and pore in the corresponding region, so as to display the variation law of each phase and pore in different regions of the metallized pellets through statistical analysis.

9. The method for analyzing the reduction roasting mechanism of metallurgical dust and sludge according to claim 8, characterized in that, The specific formulas for calculating the percentage of area occupied by each phase and pores in the corresponding region are as follows: In a certain Maps image region, phase Fe, phase Fe x The statistical calculation formulas for O and the percentage of pore area are as follows: A Fe= S Fe / S0×100% A FexO= S FexO / S0×100% A 孔隙= S 孔隙 / S0×100% In the formula: A Fe A FexO A 孔隙 Fe and Fe x O and the percentage of pore area; S0 is the area of ​​the sample region; S Fe S represents the area occupied by Fe in the corresponding sample region. FexO For Fe in the corresponding sample region x Area occupied by O; S 孔隙 This represents the area occupied by pores in the corresponding sample region.

10. A computer-readable storage medium having stored thereon computer program instructions executable by a processor, wherein when the processor executes the computer program instructions, it is able to implement the steps of the method as described in any one of claims 1-9.

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