Analytical methods for in-situ leaching processes

Abstract

The application provides an analysis method of an in-situ leaching process, and belongs to the technical field of process mineralogy. The application combines digital analysis of mineralogy characteristics (mineral leachability, mineral particle size characteristics) to determine multi-scale seepage dynamic parameters (ore structure, ore structure), and finally determines the quantitative indexes of the in-situ leaching process. The application deeply couples automatic mineralogy analysis and seepage mechanics, quantitatively analyzes the closed-loop prediction of "mineral composition-leaching kinetics-economic index", and provides a new analysis method for low-grade resource development. Compared with the traditional trial-and-error method, the application can quickly measure the theoretical recovery rate without determining the related data of other metallurgical processes through complex experiments, improves the efficiency of index prediction, and can provide a basis for subsequent production and design optimization.

Description

Technical Field

[0001] This invention relates to the field of process mineralogy, specifically to an analytical method for in-situ leaching processes. Background Technology

[0002] Low-grade mineral resources constitute a large proportion of the total mineral resources and have significant potential economic value. However, due to their low grade, complex mineral composition, and special occurrence conditions, traditional mining methods are insufficient for their economical and efficient development and utilization. In-situ leaching technology, as a new type of green mining method, has significant advantages such as eliminating the need for ore mining, selective leaching in situ, and low infrastructure investment, providing an innovative solution for the efficient development of low-grade mineral resources.

[0003] In practical applications, the mineral composition of ore bodies is highly complex. Taking copper deposits as an example, their mineral composition typically includes copper minerals such as chalcopyrite and chalcocite, as well as other metallic sulfides such as pyrite and magnetite, and gangue minerals such as quartz and calcite. This diversity of mineralogical characteristics leads to significant differences in the leaching behavior of different ores during in-situ leaching, specifically manifested in variations in key indicators such as leaching rate and final recovery rate. Therefore, in-depth research into the relationship between ore properties and leaching behavior, and optimization of process technology conditions to achieve the best recovery rate, is not only of great guiding significance for process flow design, but also a key basis for scientifically evaluating the exploitability of mineral resources.

[0004] Currently, there is a lack of systematic and effective methods for predicting the recovery rate of in-situ leaching processes. Conventional indoor column leaching tests or scale-up tests have the following limitations: first, they are time-consuming and costly; second, process parameters are difficult to optimize comprehensively; third, the test results often fail to reflect the optimal leaching performance of the actual ore body; and fourth, the obtained data is difficult to directly use for resource development feasibility assessment. These limitations severely restrict the efficient evaluation and development of low-grade mineral resources. Summary of the Invention

[0005] In view of the technical problems existing in the background art, this application provides an analytical method for in-situ leaching process, which aims to solve the problems that the existing analytical methods for in-situ leaching process have long testing cycles, high costs, unrepresentative process parameters, and are difficult to use directly for resource development feasibility assessment.

[0006] This application provides an analytical method for in-situ leaching processes, comprising the following steps:

[0007] S1. Determine the immersion coefficient

[0008] Grind the sample to be tested;

[0009] The ground sample was used to prepare an automated mineralogical analysis sample, resulting in sample a;

[0010] The sample a is subjected to automated mineralogical analysis, and the content of the main target mineral containing a certain target element in the sample a is bi, and the content of the target element in the main target mineral is bi'; i is the type of mineral containing a certain target element, i=1, 2, ..., n;

[0011] The leaching coefficient ci of the main target mineral was determined using a single mineral or ore leaching method.

[0012] Calculate the impregnation coefficient A of the sample to be tested, then A = ∑(bi*bi'*ci) / ∑(bi*bi');

[0013] S2. Determine the structural influence coefficient

[0014] The sample to be tested is first cut to obtain cut sample block one;

[0015] The structural characteristics of the cut surface of the cut sample block 1 are statistically analyzed, and the number of each type of structure gf is counted; f represents different structural types, f=1, 2, ..., m;

[0016] Calculate the construction influence coefficient B of the sample to be tested, then B = ∑(df*gf / ∑gf); where df is the influence factor one;

[0017] S3. Determine the structural influence coefficient

[0018] The sample to be tested is cut a second time to obtain a second cut sample block;

[0019] The cut sample block 2 was used to prepare an automated mineralogical analysis sample, resulting in sample h.

[0020] Automated mineralogical analysis was performed on the sample h, and the structural characteristics of the main target mineral containing a certain target element in the cut surface of the cut sample block two were statistically analyzed. The number of each type of structure ej was counted; j is the structural type of the main target mineral containing a certain target element, j=1, 2, ..., N;

[0021] Calculate the structural influence coefficient C of the sample to be tested, then C = ∑dj*ej / 100; where dj is the second influence factor.

[0022] S4. Determine the particle size influence coefficient

[0023] The statistical analysis of the cut surface of the second cut sample block shows the number of main target minerals containing a certain target element with a particle size of k or higher (pi) and the number of other minerals with a particle size of k or higher (pq); where i is the type of mineral containing a certain target element, i = 1, 2, ..., n; q is the type of other minerals, q = 1, 2, ..., M;

[0024] Calculate the particle size influence coefficient m1 of the main target mineral containing a certain target element and the particle size influence coefficient m2 of other minerals, then m1=1-∏pi, m2=1-∏pq;

[0025] Calculate the particle size influence coefficient D of the sample to be tested, then D = m1 * m2;

[0026] S5. Calculate the recovery rate W of the target element in the sample to be tested, then W = A*B*C*D. In the technical solution of this application embodiment, combined with digital analysis of mineralogical characteristics (mineral leaching ability, mineral grain size characteristics), multi-scale permeability dynamic parameters (ore structure, ore texture) are determined, ultimately determining the quantitative indicators of the in-situ leaching process. This invention deeply couples automated mineralogical analysis with permeation mechanics, quantitatively analyzing the closed-loop prediction of "mineral composition-leaching kinetics-economic indicators," providing a new analytical method for low-grade resource development. Compared to traditional trial-and-error methods, it does not require complex experiments to determine relevant data for other metallurgical processes, can quickly measure the theoretical recovery rate, improves the efficiency of indicator prediction, and can provide a basis for subsequent production and design optimization.

[0027] In some embodiments, in step S1, the mass of the sample to be tested is 1~3kg; the grinding fineness is -0.074mm and the content is 60~80%.

[0028] In this embodiment, a raw ore sample of a specific fineness is obtained through grinding for subsequent processing.

[0029] In some embodiments, in step S2, the influence factor is determined according to the construction type shown in the table below;

[0030]

[0031]

[0032] In this embodiment, the structural influence coefficient is corrected by constructing an influence factor one, namely the influence brought about by different internal structures of minerals.

[0033] In some embodiments, in step S2, the three-dimensional dimensions of the first cutting sample block are greater than 20 cm.

[0034] In this embodiment, a larger cut surface is obtained by cutting, which exposes more different structures.

[0035] In some embodiments, in step S3, the influence factor two is determined according to the construction type shown in the table below;

[0036]

[0037] In this embodiment, the structural influence coefficient is corrected by constructing an influence factor two, namely the influence of the different forms of minerals in the ore.

[0038] In some embodiments, in step S3, the maximum horizontal dimension of the second cut sample block is smaller than the inner diameter of the mold for automatic mineralogy analysis sample preparation, and the thickness of the second cut sample block is 0.3~1.0 cm.

[0039] In this embodiment, sample blocks of a specific size are cut so that they can be placed into an automated mineralogical analysis sample preparation mold, facilitating subsequent sample preparation.

[0040] In some embodiments, in step S4, the other minerals are other minerals that affect the leaching of the target mineral, besides the main target mineral containing a certain target element.

[0041] In this embodiment, not only the particle size of the target mineral itself affects the leaching effect, but the particle size of other minerals surrounding the target mineral also affects the leaching effect of the target mineral.

[0042] In some embodiments, in step S4, k is the grain size limit of the main target mineral containing a certain target element; the grain size limit k is the weighted average of the grain size of the main target mineral containing a certain target element according to its content.

[0043] In this embodiment, the particle size of the target mineral and the surrounding minerals will have different effects on the leaching effect of the target mineral, so a suitable particle size limit k is constructed.

[0044] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0045] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in this application will be briefly described below. Obviously, the drawings described below are merely some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort.

[0046] Figure 1 This is a flowchart illustrating the analytical method of the in-situ leaching process in this application. Detailed Implementation

[0047] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0048] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0049] To address the problems of long testing cycles, high costs, and unrepresentative process parameters in existing in-situ leaching processes, which hinder their direct application in resource development feasibility assessments, this application provides an analytical method for in-situ leaching processes. This method combines digital analysis of mineralogical characteristics (mineral leaching ability, mineral grain size characteristics) with multi-scale determination of permeability dynamic parameters (ore structure, ore texture), ultimately determining the quantitative indicators of the in-situ leaching process. This invention deeply couples automated mineralogical analysis with permeation mechanics, quantitatively analyzing the closed-loop prediction of "mineral composition-leaching kinetics-economic indicators," providing a new analytical method for low-grade resource development. Compared to traditional trial-and-error methods, it eliminates the need for complex experiments to determine relevant data from other metallurgical processes, enabling rapid measurement of theoretical recovery rates, improving the efficiency of indicator prediction, and providing a basis for subsequent production and design optimization.

[0050] This application provides an analytical method for in-situ leaching processes, comprising the following steps:

[0051] S1. Determine the immersion coefficient

[0052] Grind the sample to be tested;

[0053] The ground sample was used to prepare an automated mineralogical analysis sample, resulting in sample a;

[0054] The sample a is subjected to automated mineralogical analysis, and the content of the main target mineral containing a certain target element in the sample a is bi, and the content of the target element in the main target mineral is bi'; i is the type of mineral containing a certain target element, i=1, 2, ..., n;

[0055] The leaching coefficient ci of the main target mineral was determined using a single mineral or ore leaching method.

[0056] Calculate the impregnation coefficient A of the sample to be tested, then A = ∑(bi*bi'*ci) / ∑(bi*bi');

[0057] S2. Determine the structural influence coefficient

[0058] The sample to be tested is first cut to obtain cut sample block one;

[0059] The structural characteristics of the cut surface of the cut sample block 1 are statistically analyzed, and the number of each type of structure gf is counted; f represents different structural types, f=1, 2, ..., m;

[0060] Calculate the construction influence coefficient B of the sample to be tested, then B = ∑(df*gf / ∑gf); where df is the influence factor one;

[0061] S3. Determine the structural influence coefficient

[0062] The sample to be tested is cut a second time to obtain a second cut sample block;

[0063] The cut sample block 2 was used to prepare an automated mineralogical analysis sample, resulting in sample h.

[0064] Automated mineralogical analysis was performed on the sample h, and the structural characteristics of the main target mineral containing a certain target element in the cut surface of the cut sample block two were statistically analyzed. The number of each type of structure ej was counted; j is the structural type of the main target mineral containing a certain target element, j=1, 2, ..., N;

[0065] Calculate the structural influence coefficient C of the sample to be tested, then C = ∑dj*ej / 100; where dj is the second influence factor.

[0066] S4. Determine the particle size influence coefficient

[0067] The statistical analysis of the cut surface of the second cut sample block shows the number of main target minerals containing a certain target element with a particle size of k or higher (pi) and the number of other minerals with a particle size of k or higher (pq); where i is the type of mineral containing a certain target element, i = 1, 2, ..., n; q is the type of other minerals, q = 1, 2, ..., M;

[0068] Calculate the particle size influence coefficient m1 of the main target mineral containing a certain target element and the particle size influence coefficient m2 of other minerals, then m1=1-∏pi, m2=1-∏pq;

[0069] Calculate the particle size influence coefficient D of the sample to be tested, then D = m1 * m2;

[0070] S5. Calculate the recovery rate W of the target element in the sample to be tested, then W = A*B*C*D. In the technical solution of this application embodiment, combined with digital analysis of mineralogical characteristics (mineral leaching ability, mineral grain size characteristics), multi-scale permeability dynamic parameters (ore structure, ore texture) are determined, ultimately determining the quantitative indicators of the in-situ leaching process. This invention deeply couples automated mineralogical analysis with permeation mechanics, quantitatively analyzing the closed-loop prediction of "mineral composition-leaching kinetics-economic indicators," providing a new analytical method for low-grade resource development. Compared to traditional trial-and-error methods, it does not require complex experiments to determine relevant data for other metallurgical processes, can quickly measure the theoretical recovery rate, improves the efficiency of indicator prediction, and can provide a basis for subsequent production and design optimization.

[0071] Furthermore, in some embodiments, in step S1, the mass of the sample to be tested is 1~3 kg; the grinding fineness is -0.074 mm and the content is 60~80%.

[0072] In the technical solution of this application embodiment, a raw ore sample of a specific fineness is obtained by grinding for subsequent processing.

[0073] Further, in some embodiments, in step S1, the single-mineral leaching method includes the following steps: taking the sample to be tested, crushing it to a particle size of less than 5 mm, enriching it, and manually sorting the single mineral particles into individual mineral particles to obtain target mineral particles; coating the sample preparation mold with epoxy resin, adding the target mineral particles, and then curing and grinding it to obtain a primary sample; cutting the primary sample, placing it with the ground surface facing down along with three wear-resistant alloy pieces at the bottom of the sample preparation mold, injecting epoxy resin to cure and form a composite sample; and then finely grinding and polishing it to the same consistency as the wear-resistant alloy. After the alloy surface is flush with the surface and carbonized, the first automated mineralogical analysis is performed to measure the area Si of the target mineral; i is 1, 2, 3..., representing the type of target mineral. After the analysis, the sample surface is cleaned and leaching is performed to obtain the treated sample. Epoxy resin is coated onto the sample preparation mold, the treated sample is added, the epoxy resin is injected and cured, and the sample is finely ground and polished until it is flush with the surface of the wear-resistant alloy. After carbonization, the second automated mineralogical analysis is performed to measure the area Si' of the residual target mineral. The leaching index Ci of the target mineral is calculated, where Ci = 1 - Si' / Si.

[0074] Further, in some embodiments, in step S1, the ore leaching method includes the following steps: taking the sample to be tested, grinding it to a fineness of -0.074 mm and a content of 85~95%, and after washing and desliming, obtaining a pretreated sample; mixing the pretreated sample with graphite to obtain a mixture, and after solidification treatment, obtaining an automated mineralogical analysis sample; then performing automated mineralogical analysis to measure the content h of the target mineral. ir and the content of target element Oi ;

[0075] Calculate the average content h of the target mineral i h i =∑h ir / r;

[0076] i is 1, 2, 3..., representing the type of target mineral;

[0077] r represents I, II, III..., indicating the sample number;

[0078] A pretreated sample was leached with a reagent, followed by solid-liquid separation to obtain mineral residue. The mass ratio of the mineral residue to the pretreated sample was calculated and denoted as t. The mineral residue was then mixed with graphite to prepare an automated mineralogical analysis sample, which was then subjected to automated mineralogical analysis to measure the residual target mineral content h. ir 'and residual target element content O i ';

[0079] Calculate the average content h of residual target minerals i ',h i '=∑h ir ' / r;

[0080] Calculate the mineral leachability coefficient Ci, Ci = 1 - t*h i '*O i ' / (h i *O i );

[0081] Furthermore, in some embodiments, in step S2, the influence factor is determined according to the construction type shown in the table below;

[0082]

[0083]

[0084] In the technical solution of this application embodiment, the structural influence coefficient is corrected by constructing an influence factor one, namely the influence brought about by different internal structures of minerals.

[0085] Furthermore, in some embodiments, in step S2, the three-dimensional dimensions of the first cutting sample block are greater than 20 cm.

[0086] In the technical solution of this application embodiment, a larger cutting surface is obtained by cutting, so that more different structures are exposed on the cutting surface.

[0087] Furthermore, in some embodiments, in step S3, the influence factor two is determined according to the construction type shown in the table below;

[0088]

[0089] In the technical solution of this application embodiment, the structural influence coefficient is corrected by constructing an influence factor two, namely the influence brought about by the different forms of minerals in the ore.

[0090] Furthermore, in some embodiments, in step S3, the maximum horizontal dimension of the second cut sample block is smaller than the inner diameter of the mold for automated mineralogy analysis sample preparation, and the thickness of the second cut sample block is 0.3~1.0 cm.

[0091] In the technical solution of this application embodiment, sample blocks of a specific size are cut so that they can be placed into an automated mineralogy analysis sample preparation mold, which facilitates subsequent sample preparation.

[0092] Furthermore, in some embodiments, in step S4, the other minerals are other minerals that affect the leaching of the target mineral besides the main target mineral containing a certain target element.

[0093] In the technical solutions of this application embodiment, not only the particle size of the target mineral itself affects the leaching effect, but the particle size of other minerals around the target mineral also affects the leaching effect of the target mineral.

[0094] Furthermore, in some embodiments, in step S4, k is the grain size limit of the main target mineral containing a certain target element; the grain size limit k is the weighted average of the grain size of the main target mineral containing a certain target element according to its content.

[0095] In the technical solution of this application embodiment, the particle size of the target mineral and the surrounding minerals will have different effects on the leaching effect of the target mineral, so a suitable particle size limit k is constructed.

[0096] The following are some specific embodiments. It should be noted that the embodiments described below are exemplary and are only used to explain this application, and should not be construed as limiting this application. Where specific techniques or conditions are not specified in the embodiments, they shall be performed in accordance with the techniques or conditions described in the literature in this field or according to the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be obtained commercially.

[0097] Example 1

[0098] This embodiment provides an analytical method for in-situ leaching processes, such as... Figure 1 As shown, the specific steps include the following:

[0099] (1) Weigh 1.5 kg of the sample to be tested and grind the sample to be tested. The grinding fineness is -0.074 mm and the content is 70% to obtain the raw ore sample. Prepare an automatic mineralogical analysis sample from the raw ore sample to obtain sample a. Perform automatic mineralogical analysis on sample a and analyze that the content of the main target mineral containing a certain target element in sample a is bi, and the content of the target element in the main target mineral is bi'. i is the mineral type containing a certain target element (i is 1, 2, 3, representing chalcopyrite, bornite, and copper oxide minerals, respectively). The specific test results are shown in Table 1.

[0100] Table 1 Test results of bi and bi' in the samples

[0101]

[0102] A 2kg sample was selected and ground to a fineness of -0.074mm with a content of 85%. Then, a washing and desliming process was performed: water was added to the ground sample at a solid-liquid ratio of 1:2, stirred for 2 minutes, allowed to stand for 2 minutes, and 200ml of the supernatant was extracted. This process was repeated three times. 3g of each deslimed sample was taken and mixed with 10μm graphite particles, with a sample volume to graphite volume ratio of 1:1. 10ml of epoxy resin was added to the mixture, stirred evenly at 15℃, and then more epoxy resin was added until the total sample height was 1.0cm. Vacuum was then applied, and the mixture was cured at 25℃ to prepare five automated mineralogical analysis samples. Automated mineralogical analysis was then performed on each sample to obtain the content h of the main target mineral. ir and the content of target element O i (i = 1, 2, 3, representing chalcopyrite, bornite, and copper oxide minerals respectively), calculate the average content h of the main target minerals. i See Table 2 for details; Take 500g of the deslimed sample, add the reagent to be used in production (sulfuric acid, 2mol / L, solid-liquid ratio 1:4), stir and react for 12h, calculate the mass ratio of the mineral residue obtained after the reaction to the sample before the reaction, and record it as t, t=426.72 / 500.00=85.34%; Take the mineral residue and mix it with graphite to prepare an automated mineralogical analysis sample, and then perform automated mineralogical analysis. The analysis shows that the content of the main target mineral is b. in The content of the main target element is h. in 'Calculate the average content of the main target minerals O' i See Table 2 for details.

[0103] Table 2. Measurement results of the main target mineral content

[0104]

[0105] Calculate the impregnation coefficient Ci = 1 - t * h i '*Oi ' / (h i *O i ) ;

[0106] C1=1-85.34%*4.01%*34.53% / (4.22%*34.55%)=18.95%;

[0107] C2=1-85.34%*1.21%*63.22% / (2.25%*63.23%)=54.11%;

[0108] C3=1-85.34%*1.52%*57.45% / (15.37%*57.44%)=91.56%.

[0109] Calculate the leachability coefficient A of the sample to be tested, then A = ∑(bi*bi'*ci) / ∑(bi*bi') = (4.22%*34.55%*18.95%+2.25%*63.23%*54.11%+15.37%*57.44%*91.56% / (4.22%*34.55%+2.25%*63.23%+15.37%*57.44%) = 77.97%.

[0110] (2) The sample to be tested is cut into the first cut to obtain the first cut sample block. The three-dimensional dimensions of the first cut sample block are 25cm.

[0111] The structural characteristics of the cut surface of the cutting sample block 1 were statistically analyzed, and the number of each type of structure gf was counted; f represents different structural types, f=1, 2, ..., m, as shown in Table 3.

[0112] Table 3. Detection Results of Main Structure Types and Their Quantities

[0113]

[0114]

[0115] Calculate the structural influence coefficient B of the sample to be tested, then B = ∑(df*gf / ∑gf) = 0.9480.

[0116] (3) The sample to be tested is cut a second time to obtain a second cut sample block, ensuring that the second cut sample block can be placed into the sample preparation mold;

[0117] Prepare an automated mineralogical analysis sample from the cut sample block 2, and then perform automated mineralogical analysis. Statistically analyze the structural characteristics of the main target mineral containing a certain target element in the cut surface of the cut sample block 2, and count the number of each type of structure ej; j is the structural type of the main target mineral containing a certain target element, j=1, 2, ..., N, as shown in Table 4.

[0118] Table 4. Structural characteristics and quantity detection results of major target minerals containing a certain target element.

[0119]

[0120] Calculate the structural influence coefficient C of the sample to be tested, then C = ∑dj*ej / 100 = 0.9334.

[0121] (4) Statistical analysis of the number of main target minerals containing a certain target element with a particle size of k or above, pi, and the number of other minerals with a particle size of k or above, pq in the cut surface of the second cut sample block; where i is the type of mineral containing a certain target element, i=1, 2, ..., n; q is the type of other minerals, q=1, 2, ..., M, see Table 5 for details; where k is 0.037 mm.

[0122] Table 5. Results of particle size analysis of major minerals

[0123]

[0124] Calculate the particle size influence coefficient m1 of the main target mineral containing a certain target element and the particle size influence coefficient m2 of other minerals. Then m1=1-πpi=1-42.88%*20.03%*84.34%=0.9276, m2=1-πpq=1-59.66%*60.61%*34.78%=0.8742.

[0125] Calculate the particle size influence coefficient D of the sample to be tested, then D=m1*m2=0.9276*0.8742=0.8109.

[0126] (5) Calculate the recovery rate W of the target element in the sample to be tested, then W=A*B*C*D=77.97%*0.9480*0.9334*0.8109=55.95%.

[0127] In summary, this application provides an analytical method for in-situ leaching processes. It combines digital analysis of mineralogical characteristics (mineral leaching ability, mineral grain size characteristics) with multi-scale determination of permeability dynamic parameters (ore structure, ore texture), ultimately determining the quantitative indicators of the in-situ leaching process. This invention deeply couples automated mineralogical analysis with flow mechanics, quantitatively analyzing the closed-loop prediction of "mineral composition-leaching kinetics-economic indicators," providing a new analytical method for low-grade resource development. Compared to traditional trial-and-error methods, it eliminates the need for complex experiments to determine relevant data from other metallurgical processes, enabling rapid measurement of theoretical recovery rates, improving the efficiency of indicator prediction, and providing a basis for subsequent production and design optimization.

[0128] It should be noted that this application is not limited to the above-described embodiments. The above embodiments are merely examples, and any embodiments with the same structure and effect as the technical concept within the scope of this application are included in the technical scope of this application. Furthermore, various modifications that can be conceived by those skilled in the art to the embodiments, and other ways of constructing by combining some of the constituent elements of the embodiments, without departing from the spirit of this application, are also included in the scope of this application.

Claims

1. An analytical method for an in-situ leaching process, characterized in that, It comprises the following steps: S1. Determine the leachable coefficient Grind the sample to be tested; Prepare an automatic mineralogical analysis sample of the ground sample to obtain sample a; Perform automatic mineralogical analysis on the sample a, and analyze the content of the target mineral containing a target element in the sample a to be bi, and the content of the target element in the target mineral to be bi'; i is the type of mineral containing a target element, i = 1, 2,..., n; Use single mineral or ore leaching method to confirm the leachable coefficient ci of the target mineral; Calculate the leachable coefficient A of the sample to be tested, then A = ∑(bi*bi'*ci) / ∑(bi*bi'); S2. Determine the structure influence coefficient Perform first cutting on the sample to be tested to obtain cutting block one; Statistically analyze the structure characteristics of the cutting surface of the cutting block one, and count the number gf of each type of structure; f is different structure types, f = 1, 2,..., m; Calculate the structure influence coefficient B of the sample to be tested, then B = ∑(df*gf / ∑gf); wherein df is the influence factor one; S3. Determine the structure influence coefficient Perform second cutting on the sample to be tested to obtain cutting block two; Prepare an automatic mineralogical analysis sample of the cutting block two to obtain sample h; Perform automatic mineralogical analysis on the sample h, and statistically analyze the structure characteristics of the target mineral containing a target element in the cutting surface of the cutting block two, and count the number ej of each type of structure; j is the structure type of the target mineral containing a target element, j = 1, 2,..., N; Calculate the structure influence coefficient C of the sample to be tested, then C = ∑dj*ej / 100; wherein dj is the influence factor two; S4. Determine the particle size influence coefficient Statistically analyze the number pi of the particle size of the target mineral containing a target element in the cutting surface of the cutting block two and the number pq of the particle size of other minerals above k level; wherein i is the type of mineral containing a target element, i = 1, 2,..., n; q is the type of other minerals, q = 1, 2,..., M; Calculate the particle size influence coefficient m1 of the target mineral containing a target element and the particle size influence coefficient m2 of other minerals, then m1 = 1-∏pi, m2 = 1-∏pq; Calculate the particle size influence coefficient D of the sample to be tested, then D = m1*m2; S5. Calculate the recovery rate W of the target element in the sample to be tested, then W = A*B*C*D.

2. The analytical method for in-situ leaching process according to claim 1, characterized in that, In step S1, the mass of the sample to be tested is 1-3 kg; the grinding fineness is 60-80% of -0.074 mm content.

3. The analytical method for in-situ leach process according to claim 1, characterized in that, In step S2, the influence factor one is determined according to the structure types shown in the following table; 。 4. The analytical method for in-situ leach process according to claim 1, characterized in that, In step S2, the three-dimensional size of the cutting block one is greater than 20 cm.

5. The analytical method for in-situ leach process according to claim 1, characterized in that, In step S3, the influence factor two is determined according to the structure types shown in the following table; 。 6. The analytical method for in-situ leach process according to claim 1, characterized in that, In step S3, the horizontal maximum size of the cutting block two is less than the inner diameter of the mold for automatic mineralogical analysis sample preparation, and the thickness of the cutting block two is 0.3-1.0 cm.

7. The analytical method for in-situ leach process according to claim 1, characterized in that, The other minerals in step S4 are minerals other than the target minerals containing the target element, which affect the leaching of the target minerals.

8. The analytical method for in-situ leach process according to claim 1, characterized in that, In step S4, k is the particle size limit corresponding to the target minerals containing the target element; the particle size limit k is the weighted average of the particle sizes of the target minerals containing the target element according to their contents.

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