Method for determining silver mineral morphology

Abstract

The application provides a method for determining silver mineral morphology, and belongs to the technical field of process mineralogy. The method comprises the following steps: silver ore sample preparation, first automatic mineralogical analysis, layer-by-layer stripping and repeated analysis, material composition determination and coefficient calculation, comprehensive morphology parameter calculation, and morphology statistics. Through the strategy of combining "surface layer-by-layer analysis-three-dimensional parameter reconstruction" and "process product analysis-distribution behavior analysis", the application not only realizes accurate quantification of two-dimensional / three-dimensional morphology (such as length-width ratio, thickness and complexity) of silver minerals, but also synchronously reveals the process mineralogical characteristics of different types of silver minerals, thereby providing comprehensive and accurate data support for the optimization of the crushing particle size of silver ore, the selection of the separation process and the evaluation of the process, and overcoming the shortcomings of single morphology information and insufficient correlation with process behavior in the traditional method.

Description

Technical Field

[0001] This invention relates to the field of process mineralogy, and in particular to a method for determining the morphology of silver minerals. Background Technology

[0002] The beneficiation and recovery efficiency of silver ore directly depends on the process mineralogical characteristics of the silver minerals, especially key parameters such as the initial unliberated morphology and grain size. These characteristics affect the behavior of the target minerals during grinding and separation. Accurate measurement of these parameters is a crucial prerequisite for optimizing the crushing and grinding process and separation technology.

[0003] Currently, automated mineral analysis systems based on scanning electron microscopy (such as MLA, TIMA, AMICS, and related software) have become the mainstream technology for quantitative mineral analysis. However, this technology essentially only performs statistical analysis on a two-dimensional cross-section of a polished surface, and has the following inherent limitations:

[0004] First, these automated analysis systems cannot directly obtain the true three-dimensional morphology of mineral particles (such as thickness, volume, etc.), and two-dimensional cross-sectional parameters may produce some data errors, especially for rare and precious target minerals such as silver with low content.

[0005] Secondly, the morphological judgment is based on a single cross-section, which cannot distinguish the difference between "surface dissociation" and "internal intergrowth". Therefore, it cannot provide effective data support for the judgment of the morphology of mineral aggregates.

[0006] Furthermore, the analytical results are mostly static statistical descriptions, making it difficult to establish a direct, quantitative causal relationship with the actual mineral sorting behavior. Traditional methods often conduct mineralogical parameter determination and sorting experiments independently, resulting in weak data correlation and an inability to clearly identify "which form of which mineral" is the main factor affecting the recovery rate.

[0007] Therefore, a comprehensive analytical method is needed to characterize the three-dimensional morphological features of silver minerals and directly correlate them with sorting performance, in order to bridge the technical gap between microscopic characterization and macroscopic process decision-making. Summary of the Invention

[0008] To address the shortcomings of existing technologies, the present invention aims to provide a systematic, objective, and accurate method for determining the morphology of silver minerals. This method combines a strategy of "surface layer-by-layer analysis - three-dimensional parameter reconstruction" with "process product analysis - distribution behavior analysis," achieving not only precise quantification of the two-dimensional / three-dimensional morphology of silver minerals (such as aspect ratio, thickness, and complexity), but also simultaneously revealing the process mineralogical characteristics of different types of silver minerals. This provides comprehensive and accurate data support for optimizing silver ore particle size, selecting sorting processes, and evaluating processes, overcoming the shortcomings of traditional methods that lack sufficient morphological information and correlation with process behavior.

[0009] To achieve the above objectives, the present invention provides a method for determining the morphology of silver minerals, comprising the following steps:

[0010] S1. Take the silver ore sample to be tested, cut and solidify it, then grind and polish the surface to be observed and spray carbon to obtain the initial observation sample, which is denoted as sample Ai1, where i is the sample number.

[0011] S2, Perform automated mineralogical analysis on sample Ai1 to obtain parameters of silver mineral particles; the parameters include the content hm.n, area Sn1, perimeter Ln1, and particle size dn of silver mineral particles; where n is the particle number and m is a natural number greater than or equal to 1, representing different types of silver minerals;

[0012] S3, the surface of sample Ai1 is finely ground to remove a material layer with a thickness of 0.01~0.05 mm, followed by polishing and carbon spraying to obtain a new observation surface sample Ai2; sample Ai2 is subjected to automatic mineralogical analysis to obtain the corresponding parameters of the same batch of silver mineral particles in step S2 on the new cross section; the parameters include the area Sn2 of the silver mineral particles.

[0013] S4, the surface of sample Ai2 is finely ground to remove a material layer with a thickness of 0.01~0.05 mm, followed by polishing and carbon spraying to obtain a new observation surface sample Ai3; sample Ai3 is subjected to automatic mineralogical analysis to obtain the corresponding parameters of the same batch of silver mineral particles in step S2 on the new cross section; the parameters include the area Sn3 of the silver mineral particles.

[0014] S5, take 1.0~3.0 kg of the silver ore to be tested, grind it, and control the grinding fineness to be -0.074 mm, accounting for 65~90%;

[0015] The ground sample was subjected to gravity separation with a gravity separation yield of t, yielding gravity concentrate J1 and gravity tailings W1.

[0016] Take the whole sample J1 and prepare sample J11 for automated mineralogical analysis.

[0017] Take 3.0~5.0 g of sample W1 and prepare sample w11 for automated mineralogical analysis;

[0018] Automated mineralogical analysis was performed on samples J11 and w11, and the silver mineral composition was determined to be Jm and Wm, respectively. The silver content Jm' of different types of silver minerals was also calculated.

[0019] S6, Calculate the metal distribution coefficient Km for different types of silver minerals:

[0020] Km=(t Jm Jm'+(1-t) Wm Jm') / (t) ∑Jm Jm'+(1-t)∑Wm Jm');

[0021] S7, denote the longest chord length passing through the centroid of the cross section of sample Ai1 as an, and the longest chord length perpendicular to an as bn; calculate the aspect ratio Fn1 of the cross section, Fn1=an / bn;

[0022] S8, Calculate the equivalent thickness parameter Fn2:

[0023] When Sn3=0, Fn2=Sn2 dn / (Sn1 bn);

[0024] When Sn3 > 0, Fn2 = Sn3 dn / (Sn1 bn);

[0025] S9. Determine the three-dimensional morphology of silver mineral grains according to the judgment conditions shown in the table below.

[0026]

[0027] S10, the relative content of each form of silver mineral in silver minerals is calculated using the following formula:

[0028] Hx=∑Km hmx, where Hx is the percentage content of silver mineral morphology in the sample; x is 1, 2, 3, 4, 5, representing particulate, granular, flaky, rod-shaped, and special irregular morphology, respectively;

[0029] Where hmx represents the relative content of the m-th silver mineral in the x-th form, and hmx is obtained by summing the relative contents of the m-th silver mineral in the x-th form.

[0030] Furthermore, the aforementioned special irregular shape refers to a tree-like or concave shape, which is determined by visual observation.

[0031] Furthermore, when the morphological characteristics are determined to be granular, flaky, or rod-shaped, the morphological complexity index Fn3 of the particles is calculated: Fn3 = (Ln1) 2 / (4π × Sn1);

[0032] When 1≤Fn3<10.0, the prefix for the morphological characteristics of the particles is round or spherical;

[0033] When 10.0 ≤ Fn3 < 25, the prefix of the particle morphological characteristics is angle;

[0034] When 25.0 ≤ Fn3, the prefix of the particle morphology is branch.

[0035] Furthermore, in step S5, the reselection yield t ranges from 0.1% to 0.5%.

[0036] Furthermore, m = 1, 2, 3, 4 respectively represent the types of silver minerals: argentite, argyrite, brookite, and silver-bearing chalcopyrite.

[0037] Furthermore, in step S5, Jm' is the average value of the silver content measurement results of five or more silver minerals.

[0038] Furthermore, the area Sni and perimeter Lni of the silver mineral particles were automatically measured using the Maps Min analytical instrument, where i is the sample number.

[0039] Furthermore, the grain size dn is the maximum chord length of the silver mineral cross section in a fixed random direction.

[0040] Furthermore, before step S5, the fine grinding, polishing, and carbon spraying processes in step S4 are repeated, and the newly obtained observation surface sample is subjected to automatic mineralogical analysis to obtain the corresponding parameters of the same batch of silver mineral particles on the new cross section at least once.

[0041] Furthermore, in step S5, the re-selection refers to re-selection using a Nelson centrifugal concentrator.

[0042] The beneficial effects of this invention are:

[0043] The method for determining the morphology of silver minerals provided in this application uses the "precise layer-by-layer grinding-in-situ repeated analysis" technique to perform more than three automatic mineralogical analyses on the same area, tracking the parameter changes of mineral particles on a continuous cross section, and realizing the quantitative characterization of the evolution of thickness, morphology and degree of liberation from two-dimensional cross section to three-dimensional cross section, breaking through the limitations of traditional single-section observation.

[0044] This application combines three-dimensional morphological analysis with parallel reseparation experiments. By calculating the "metal distribution coefficient (Km)" of different silver minerals, it directly reveals the enrichment or loss patterns of minerals of specific morphology or type during sorting, and establishes a quantitative correlation model of "morphology-sorting behavior", so that morphological parameters have clear process guidance significance.

[0045] This application systematically integrates in-situ three-dimensional reconstruction, quantitative analysis of material composition, and sorting response analysis to form a complete diagnostic system from microscopic morphology to macroscopic sorting behavior. It can provide accurate and predictable data support for grinding particle size optimization and sorting process selection, and realizes the integration and upgrading of process mineralogical analysis. Attached Figure Description

[0046] Figure 1 The image shows a color diagram of the MLA mineral analysis of the silver ore determined in Example 1. In the diagram, 1, 2, 3, 4, 5, and 6 refer to quartz, rhodochrosite, rhodochrosite iron, pyrite, hematite, and siderite, respectively.

[0047] Figure 2 The image shows a scanning electron microscope backscattering image of silver ore measured in Example 1. In the image, 1, 2, and 3 refer to measurement points 1, 2, and 3, respectively.

[0048] Figure 3 The image shows the elemental surface distribution of silver ore determined by scanning electron microscopy (SEM) in Example 1; where red represents argentite and green represents pyrite.

[0049] Figure 4 The second image shows the elemental surface distribution of silver ore determined by scanning electron microscopy (SEM) in Example 1; where red represents dark red silver ore, blue represents rhodochrosite, and green represents pyrite.

[0050] Figure 5 The image shows the elemental distribution of silver ore as determined by scanning electron microscopy (SEM) in Example 1; where red represents brittle silver ore, blue represents galena, purple represents manganese minerals, and green represents gangue minerals. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] It should also be noted that, in order to avoid obscuring the present invention with unnecessary details, only the structures and / or processing steps closely related to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0053] Additionally, it should be noted that the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0054] Please see Figures 1 to 5 As shown, the present invention provides a method for determining the morphology of silver minerals, comprising the following steps:

[0055] S1. Take the silver ore sample to be tested, cut and solidify it, then grind and polish the surface to be observed and spray carbon to obtain the initial observation sample, denoted as sample Ai1, where i is the sample number.

[0056] Specifically, representative silver ore samples are selected, with initial three-dimensional dimensions (length × width × height) not less than 10.0 cm. The samples are cut into uniform sheet-like pieces using a cutting device, with a length and width of 2.5–3.0 cm and a thickness of 0.5–1.0 cm. The cut samples are placed in a specialized sample preparation mold, and epoxy resin and curing agent are mixed at a volume ratio of 1:(0.3–1.0), with the resin layer height controlled at 1.0–1.5 cm. Subsequently, ultrasonic vibration is applied for 6.0–12.0 min to remove internal air bubbles, followed by accelerated curing in a constant temperature environment of 45–65℃.

[0057] The surface of the solidified body to be observed undergoes fine surface treatment: first, demolding; then, sequentially, coarse grinding (using 200-500 grit abrasive), fine grinding (500-1000 grit abrasive), high-precision grinding (1000-1500 grit abrasive), and final polishing (using 3-1μm diamond or silica polishing agent). After each step, the surface must be inspected under an optical microscope to ensure that the target mineral particles are fully exposed and that there are no obvious scratches on the surface.

[0058] Finally, a carbon film with a thickness of 10-30 nm was uniformly sprayed onto the sample surface using a multifunctional ion sputtering coating instrument to enhance its conductivity, thus obtaining the initial observation sample, labeled Ai1.

[0059] S2, perform automated mineralogical analysis on sample Ai1 to obtain parameters of silver mineral grains.

[0060] The parameters include the content of silver mineral particles hm.n, area Sn1, perimeter Ln1, and particle size dn; where n is the particle number and m is a natural number greater than or equal to 1, representing different types of silver minerals.

[0061] hm.n refers to the relative percentage content of the nth particle (belonging to the mth type of silver mineral).

[0062] m = 1, 2, 3, 4 respectively represent the types of silver minerals: argentite, argyrite, brittle silver, and silver-bearing chalcopyrite.

[0063] The area Sni and perimeter Lni of the silver mineral particles were automatically measured using an analytical instrument (Maps Min). For example, Sn1, Sn2, Sn3, Ln1, Ln2, and Ln3 were all automatically measured using the analytical instrument.

[0064] The grain size dn is the maximum chord length of the silver mineral cross section in a fixed random direction.

[0065] S3. Finely grind the surface of sample Ai1 (e.g., using 1000-mesh abrasive) to remove a material layer with a thickness of 0.01~0.05mm. Then polish and carbon spray the surface to obtain a new observation surface sample Ai2. Perform automated mineralogical analysis on sample Ai2 to obtain the corresponding parameters of the same batch of silver mineral particles from step S2 on the new cross section.

[0066] The parameters include the area Sn2 of the silver mineral grains.

[0067] S4. The surface of sample Ai2 is finely ground to remove a material layer with a thickness of 0.01~0.05 mm. It is then polished and carbon-sprayed to obtain a new observation surface sample Ai3. Automated mineralogical analysis is performed on sample Ai3 to obtain the corresponding parameters of the same batch of silver mineral particles from step S2 on the new cross-section. The parameters include the area Sn3 of the silver mineral particles.

[0068] In this application, data is provided for three-dimensional morphology reconstruction by tracking the changes of the same particle on a continuous cross section.

[0069] It should be understood that the fine grinding, polishing, and carbon spraying processes in step S4 can be repeated at least once, and the newly obtained observation surface sample can be subjected to automated mineralogical analysis to obtain the corresponding parameters of the same batch of silver mineral particles on the new cross section, in order to provide more three-dimensional morphological reconstruction data.

[0070] S5, take 1.0~3.0 kg of the silver ore to be tested, grind it, and control the grinding fineness to be -0.074 mm, accounting for 65~90%;

[0071] The ground sample was subjected to gravity separation with a gravity separation yield of t, yielding gravity separation concentrate J1 and gravity separation tailings W1; wherein the gravity separation yield t ranged from 0.1% to 0.5%.

[0072] Gravity separation refers to gravity separation performed using a Nelson centrifugal concentrator.

[0073] Take the whole sample J1 to prepare automated mineralogical analysis sample J11; take 3.0~5.0 g of sample W1 to prepare automated mineralogical analysis sample w11. The sample preparation process includes rolling, mixing, ultrasonic vibration for 30 min, curing followed by polishing, and carbon spraying.

[0074] Automated mineralogical analysis was performed on samples J11 and w11, and the silver mineral composition was determined to be Jm and Wm, respectively. The silver content Jm' of different types of silver minerals was calculated. Jm' is the average value of the silver content measurement results of 5 or more silver minerals.

[0075] S6, Calculate the metal distribution coefficient Km for different types of silver minerals:

[0076] Km=(t Jm Jm'+(1-t) Wm Jm') / (t) ∑Jm Jm'+(1-t)∑Wm Jm').

[0077] Then, based on the multi-layer cross-sectional data obtained in the aforementioned steps, the key parameters of the morphology and spatial occurrence of the traceable silver mineral grains are calculated, as follows:

[0078] First, the silver mineral particles are visually inspected to determine whether they have a special irregular shape. If so, they are determined to have a special irregular shape, which refers to irregular shapes such as dendritic or concave shapes.

[0079] If not, determine whether bn is less than or equal to 0.010 mm; when bn ≤ 0.010 mm, determine that the silver mineral morphology is microparticle-like.

[0080] When bn > 0.010 mm, continue with the following steps:

[0081] S7, denote the longest chord length passing through the centroid of the cross section of sample Ai1 as an, and the longest chord length perpendicular to an as bn; calculate the aspect ratio Fn1 of the cross section, Fn1=an / bn; Fn1 is used to characterize the ductility of the particle cross section.

[0082] S8, Calculate the equivalent thickness parameter Fn2:

[0083] When Sn3=0, Fn2=Sn2 dn / (Sn1 bn);

[0084] When Sn3 > 0, Fn2 = Sn3 dn / (Sn1 bn).

[0085] By combining the apparent particle size dn and bn of the first layer (sample Ai1), a model can be established to estimate the approximate thickness of the particles. The size of the particles in the third-dimensional direction can be estimated by using the change in the continuous cross-sectional area, so as to determine whether the morphological characteristics are granular, plate-like or rod-like.

[0086] S9. Determine the three-dimensional morphology of silver mineral grains according to the judgment conditions shown in the table below.

[0087]

[0088] Furthermore, when the morphological characteristics are determined to be granular, flaky, or rod-shaped, the morphological complexity index Fn3 of the particles can be calculated: Fn3 = (Ln1) 2 / (4π × Sn1); The larger the Fn3 value, the more irregular the particle boundaries and the more complex the morphology. The following prefixes are recommended for description:

[0089] When 1≤Fn3<10.0, the prefix for the morphological characteristics of the particles is round or spherical;

[0090] When 10.0 ≤ Fn3 < 25, the prefix of the particle morphological characteristics is angle;

[0091] When 25.0 ≤ Fn3, the prefix of the particle morphology is branch.

[0092] S10, the relative content of each three-dimensional form of silver mineral in silver minerals is calculated using the following formula:

[0093] Hx=∑Km hmx, where Hx is the percentage content of silver mineral morphology in the sample; x is 1, 2, 3, 4, 5, representing particulate, granular, flaky, rod-shaped, and special irregular morphology, respectively;

[0094] Where hmx represents the relative content of the m-th silver mineral in the x-th form, and hmx is obtained by summing the relative contents of the m-th silver mineral in the x-th form.

[0095] The method for determining the morphology of silver minerals provided by the present invention will be described below with reference to specific embodiments.

[0096] Example 1

[0097] This embodiment provides a method for determining the morphology of silver minerals, specifically for determining the morphology of silver minerals in a silver-bearing deposit in Heilongjiang Province.

[0098] like Figure 1-5 As shown, the main metallic sulfide in this silver-bearing polymetallic ore is pyrite, followed by galena, with other metallic sulfides present in smaller quantities. The main metallic oxide is magnetite, followed by hematite. The gangue minerals are primarily quartz, followed by rhodochrosite, rhodochrosite-iron ore, siderite, and dolomite, with other gangue minerals present in smaller quantities. Silver, lead, and manganese minerals can be recovered from the ore, and the processing type is a medium-sulfide manganese, lead, and zinc polymetallic silver-bearing ore. Among the silver-bearing minerals are argentite, argentite, argentite, and argentite-bearing chalcopyrite.

[0099] The data from the three measuring points are as follows:

[0100] Measurement point 1: Pyrite: S: 50.94%; Fe: 49.06%;

[0101] Point 2: Deep red silver ore: Ag: 51.30%; S: 24.72%; Fe: 4.24%; Sb: 19.74%;

[0102] Point 3: Argentite: Ag: 88.17%; S: 10.39%; Fe: 1.44%.

[0103] As can be seen, pyrite encases argentite and argyrite.

[0104] The specific procedure for determining the morphology of silver minerals is as follows:

[0105] Silver ore sampling:

[0106] Representative silver ore samples were selected, with initial three-dimensional dimensions (length × width × height) not less than 10.0 cm. The samples were cut into uniform sheet-like pieces, each 2.5 cm long and wide, and 0.5 cm thick. The cut samples were placed in a specialized mold, and epoxy resin and curing agent were mixed at a volume ratio of 1:0.5, with the resin layer height controlled at 1.30 cm. The samples were then subjected to ultrasonic vibration for 10.0 min to remove internal air bubbles, and finally placed in a constant temperature environment of 45℃ to accelerate curing.

[0107] The surface of the solidified body to be observed undergoes fine surface treatment: first, demolding, followed by coarse grinding (using 200-mesh abrasive), fine grinding (using 500-mesh abrasive), high-precision grinding (using 1000-mesh abrasive), and final polishing (using 3μm diamond polishing compound). After each step, the surface is inspected under an optical microscope to ensure that the target mineral particles are fully exposed and that there are no obvious scratches on the surface.

[0108] Finally, a 10 nm thick carbon film was uniformly sprayed onto the sample surface using a multifunctional ion sputtering coating instrument to enhance its conductivity, thus obtaining the initial observation sample, labeled Ai1, where i is the sample number.

[0109] First automated mineralogical analysis:

[0110] Automated mineralogical analysis was performed on sample Ai1 to obtain relevant parameters of silver minerals. Among them, the measured silver mineral particle content hm.n (the relative percentage content of the nth particle belonging to the mth silver mineral), area Sn1 (n is 1, 2, 3, 4, 5... representing the silver mineral particle number), perimeter Ln1, and particle size dn are shown in Table 1 below.

[0111] Table 1 Results of the first automated mineralogical analysis of silver minerals

[0112]

[0113] Due to space limitations, only data from a portion of the samples are shown as examples.

[0114] Layer-by-layer peeling and repeated analysis (3D morphology reconstruction):

[0115] The surface of sample Ai1 was finely ground using 1000-mesh abrasive to remove a material layer with a thickness controlled at 0.03 mm. It was then polished and carbonized again to obtain a new observation surface sample Ai2.

[0116] Perform the same automated mineralogical analysis on Ai2 as on sample Ai1 to obtain the corresponding parameters of the same batch of silver mineral particles on the new cross section: obtain the area Sn2 of the silver mineral particles.

[0117] The grinding, grinding depth, polishing, carbon spraying, and analysis processes were repeated at least once to obtain the third observation surface sample Ai3 and its corresponding silver mineral particle area Sn3, as shown in Table 2. By tracking the changes of the same particle on continuous cross sections, data was provided for three-dimensional morphology reconstruction.

[0118] Table 2 Results of the second and third automated mineralogical analyses of silver minerals

[0119]

[0120] Due to space limitations, only data from a portion of the samples are shown as examples.

[0121] Material composition determination and coefficient calculation:

[0122] Take 1.0 kg of the sample to be tested and grind it to a fineness of -0.074 mm with a content of 70%. Then, perform gravity separation on the ground sample (using a Nelson equipment) with a gravity separation yield of t=0.20%. The gravity concentrate and gravity tailings are obtained and are denoted as J1 and W1, respectively.

[0123] Take the whole sample J1 and 3.0~5.0 g of sample W1, and prepare automated mineralogical analysis samples respectively, namely, rolling, mixing, ultrasonic vibration for 30 min, curing and polishing, and carbon spraying treatment to obtain samples J11 and w11.

[0124] Automated mineralogical analysis was performed on samples J11 and w11 to determine the silver mineral composition as Jm and Wm respectively (m is 1, 2, 3... representing the types of silver minerals respectively), and the silver content Jm' of different types of silver minerals was calculated. The data are detailed in Table 3.

[0125] Table 3. Calculation results of relative silver mineral content and silver metal distribution rate.

[0126]

[0127] Calculate the metal distribution coefficient Km for different types of silver minerals:

[0128] Km=(t Jm Jm'+(1-t) Wm Jm') / (t) ∑Jm Jm'+(1-t)∑Wm Jm').

[0129] The calculated data are detailed in Table 4.

[0130] Table 4

[0131]

[0132] Example calculations are as follows:

[0133] K1=(t Jm Jm'+(1-t) Wm Jm') / (t) ∑Jm Jm'+(1-t)∑Wm Jm') = (0.20%) 0.653% 88.24% + (1 - 0.20%) 0.036% 88.24%

[0134] / (0.20%) 0.653% 88.24% + (1 - 0.20%) 0.036% 88.24% + ... 0.034% 3.05% = 60.17%.

[0135] Then, based on the multi-layer cross-sectional data obtained in the aforementioned steps, the key parameters of the morphology and spatial occurrence of the traceable silver mineral grains are calculated, as follows:

[0136] First, the silver mineral particles are visually inspected to determine whether they have a special irregular shape. If so, they are determined to have a special irregular shape, which refers to irregular shapes such as dendritic or concave shapes.

[0137] If not, determine whether bn is less than or equal to 0.010 mm; when bn ≤ 0.010 mm, determine that the silver mineral morphology is microparticle-like.

[0138] When bn > 0.010 mm, continue with the following steps:

[0139] S7. The longest chord length passing through the centroid of the cross section of sample Ai1 is denoted as an, and the longest chord length perpendicular to an is denoted as bn. Calculate the aspect ratio Fn1 of the cross section, Fn1=an / bn. See Table 5 for details.

[0140] S8, Calculate the equivalent thickness parameter Fn2:

[0141] When Sn3=0, Fn2=Sn2 dn / (Sn1 bn);

[0142] When Sn3 > 0, Fn2 = Sn3 dn / (Sn1 (bn), see Table 5 for details.

[0143] Further calculate the particle morphological complexity index Fn3: Fn3 = (Ln1) 2 / (4π × Sn1); The larger the Fn3 value, the more irregular the particle boundaries and the more complex the morphology. The following prefixes are recommended for description:

[0144] When 1≤Fn3<10.0, the prefix for the morphological characteristics of the particles is round or spherical;

[0145] When 10.0 ≤ Fn3 < 25, the prefix of the particle morphological characteristics is angle;

[0146] When 25.0 ≤ Fn3, the prefix of the particle morphology is branch.

[0147] Table 5. Calculation and analysis results of silver mineral morphology data

[0148]

[0149] Due to space limitations, only data from a portion of the samples are shown as examples.

[0150] S10, Calculate the relative content (hmx) of each three-dimensional form of silver mineral in silver minerals:

[0151] hmx represents the relative content of the m-th silver mineral in the x-th form. hmx is obtained by summing the relative contents of the m-th silver mineral in the x-th form. For details, please refer to Table 6.

[0152] For example, for particle 11, bn = 7.24 μm (Table 5), the corresponding h1.11 = 4.22 μm. 10 -6 (Table 1), this data is added to the argentite microparticle data.

[0153] Table 6. Measurement Results of Morphological Characteristics of Silver Minerals

[0154]

[0155] Hx=∑Km hmx, where x is 1, 2, 3, 4, and 5, representing microparticle, granular, flaky, rod-shaped, and special irregular morphology, respectively; the measurement results of the morphological characteristics of silver minerals are shown in Table 7.

[0156] Table 7. Measurement Results of Morphological Characteristics of Silver Minerals

[0157]

[0158] Example of the calculation process:

[0159] H1=60.17% 5.15% + 26.80% 10.44% + 11.11% 12.96% + 1.92% 1.58% = 7.367%.

[0160] H2=60.17% 38.22% + 26.80% 25.69% + 11.11% 51.73% + 1.92% 38.43% = 36.367%.

[0161] H3=60.17% 36.73% + 26.80% 46.57% + 11.11% 17.90% + 1.92% 28.94% = 37.126%.

[0162] H4=60.17% 8.50% + 26.80% 12.09% + 11.11% 6.87% + 1.92% 18.99% = 9.482%.

[0163] H5=60.17% 11.40% + 26.80% 5.21% + 11.11% 10.54% + 1.92% 12.06% = 9.658%.

[0164] This application employs a strategy combining "surface layer-by-layer analysis - three-dimensional parameter reconstruction" with "process product analysis - distribution behavior analysis," which not only achieves precise quantification of the two-dimensional / three-dimensional morphology of silver minerals (such as aspect ratio, thickness, and complexity), but also simultaneously reveals the process mineralogical characteristics of different types of silver minerals. This provides comprehensive and accurate data support for optimizing the particle size of silver ore, selecting sorting processes, and evaluating processes, overcoming the shortcomings of traditional methods that lack morphological information and sufficient correlation with process behavior.

[0165] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for determining the morphology of silver minerals, characterized in that, Includes the following steps: S1. Take the silver ore sample to be tested, cut and solidify it, then grind and polish the surface to be observed and spray carbon to obtain the initial observation sample, which is denoted as sample Ai1, where i is the sample number. S2, Perform automated mineralogical analysis on sample Ai1 to obtain parameters of silver mineral particles; the parameters include the content hm.n, area Sn1, perimeter Ln1, and particle size dn of silver mineral particles; where n is the particle number and m is a natural number greater than or equal to 1, representing different types of silver minerals; S3, the surface of sample Ai1 is finely ground to remove a material layer with a thickness of 0.01~0.05 mm, followed by polishing and carbon spraying to obtain a new observation surface sample Ai2; sample Ai2 is subjected to automatic mineralogical analysis to obtain the corresponding parameters of the same batch of silver mineral particles in step S2 on the new cross section; the parameters include the area Sn2 of the silver mineral particles. S4, the surface of sample Ai2 is finely ground to remove a material layer with a thickness of 0.01~0.05 mm, followed by polishing and carbon spraying to obtain a new observation surface sample Ai3; sample Ai3 is subjected to automatic mineralogical analysis to obtain the corresponding parameters of the same batch of silver mineral particles in step S2 on the new cross section; the parameters include the area Sn3 of the silver mineral particles. S5, take 1.0~3.0 kg of the silver ore to be tested, grind it, and control the grinding fineness to be -0.074 mm, accounting for 65~90%; The ground sample was subjected to gravity separation with a gravity separation yield of t, yielding gravity concentrate J1 and gravity tailings W1. Take the whole sample J1 and prepare sample J11 for automated mineralogical analysis. Take 3.0~5.0 g of sample W1 and prepare sample w11 for automated mineralogical analysis; Automated mineralogical analysis was performed on samples J11 and w11, and the silver mineral composition was determined to be Jm and Wm, respectively. The silver content Jm' of different types of silver minerals was also calculated. S6, Calculate the metal distribution coefficient Km for different types of silver minerals: Km=(t Jm Jm'+(1-t) Wm. Jm') / (t ∑Jm Jm'+(1-t)∑Wm Jm'); S7, denote the longest chord length passing through the centroid of the cross section of sample Ai1 as an, and the longest chord length perpendicular to an as bn; calculate the aspect ratio Fn1 of the cross section, Fn1=an / bn; S8, Calculate the equivalent thickness parameter Fn2: When Sn3=0, Fn2=Sn2 dn / (Sn1 bn); When Sn3 > 0, Fn2 = Sn3 dn / (Sn1 bn); S9. Determine the three-dimensional morphology of silver mineral grains according to the judgment conditions shown in the table below. S10, the relative content of each form of silver mineral in silver minerals is calculated using the following formula: Hx=∑Km hmx, where Hx is the percentage content of silver mineral morphology in the sample; x is 1, 2, 3, 4, 5, representing particulate, granular, flaky, rod-shaped, and special irregular morphology, respectively; Where hmx represents the relative content of the m-th silver mineral in the x-th form, and hmx is obtained by summing the relative contents of the m-th silver mineral in the x-th form.

2. The method for determining the morphology of silver minerals according to claim 1, characterized in that: The special irregular shape refers to a tree-like or concave shape, which is determined by visual observation.

3. The method for determining the morphology of silver minerals according to claim 1, characterized in that: When the morphological characteristics are determined to be granular, flaky, or rod-shaped, the morphological complexity index Fn3 is calculated: Fn3 = (Ln1) 2 / (4π × Sn1); When 1≤Fn3 <10.0, the prefix for the morphological characteristics of the particles is round or spherical; When 10.0 ≤ Fn3 < 25, the prefix of the particle morphological characteristics is angle; When 25.0 ≤ Fn3, the prefix of the particle morphology is branch.

4. The method for determining the morphology of silver minerals according to claim 1, characterized in that: In step S5, the reselection yield t ranges from 0.1% to 0.5%.

5. The method for determining the morphology of silver minerals according to claim 1, characterized in that: m = 1, 2, 3, 4 respectively represent the types of silver minerals: argentite, argyrite, brittle silver, and silver-bearing chalcopyrite.

6. The method for determining the morphology of silver minerals according to claim 1, characterized in that: In step S5, Jm' is the average value of the silver content measurements of five or more silver minerals.

7. The method for determining the morphology of silver minerals according to claim 1, characterized in that: The area Sni and perimeter Lni of the silver mineral particles were automatically measured using the Maps Min analytical instrument, where i is the sample number.

8. The method for determining the morphology of silver minerals according to claim 1, characterized in that: The grain size dn is the maximum chord length of the silver mineral cross section in a fixed random direction.

9. The method for determining the morphology of silver minerals according to claim 1, characterized in that: Before step S5, the fine grinding, polishing, and carbon spraying processes in step S4 are repeated, and the newly obtained observation surface sample is subjected to automatic mineralogical analysis to obtain the corresponding parameters of the same batch of silver mineral particles on the new cross section at least once.

10. The method for determining the morphology of silver minerals according to claim 1, characterized in that: In step S5, the re-selection refers to re-selection using a Nelson centrifugal concentrator.

Images