Analysis method of in-situ leaching process

Abstract

The invention provides an analysis method of an in-situ leaching process, and belongs to the technical field of process mineralogy. According to the method, multi-scale penetration power parameter determination (ore structure and ore structure) is carried out by combining mineralogy characteristic digital analysis (mineral leaching property and mineral granularity characteristics), and finally quantitative indexes of the in-situ leaching process are determined. According to the method, automatic mineralogy analysis and seepage mechanics depth are coupled, closed-loop prediction of'mineral composition-leaching kinetics-economic indexes' is quantitatively analyzed, and a new analysis method is provided for low-grade resource development. Compared with a traditional trial and error method, related data of other metallurgical processes does not need to be determined through complex tests, the theoretical recovery rate can be rapidly measured, the index prediction efficiency is improved, and a basis can be provided for subsequent production and design optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of process mineralogy, and particularly relates to an analysis method for in-situ leaching process. Background Art

[0002] In mineral resources, low-grade resources account for a high proportion and large total amount, and have significant potential economic value. However, due to their low grade, complex mineral composition, special occurrence conditions, etc., traditional mining methods are difficult to achieve economical and effective development and utilization. As a new type of green mining method, the in-situ leaching technology has significant advantages such as no need for ore mining, in-situ selective leaching, and low capital 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 usually includes copper minerals such as chalcopyrite and chalcocite, and other metal sulfides such as pyrite and magnetite, as well as gangue minerals such as quartz and calcite. The diversity of such mineralogical characteristics leads to significant differences in the leaching behavior of different ores during the in-situ leaching process, specifically manifested as changes in key indicators such as leaching rate and final recovery rate. Therefore, in-depth study of the relationship between ore properties and leaching behavior, and optimization of process technical conditions to obtain the best recovery rate, not only has important guiding significance for process flow design, but also is the key basis for scientifically evaluating the developability of mineral resources.

[0004] Currently, the in-situ leaching process lacks a systematic and effective recovery rate prediction method. Conventional indoor column leaching tests or scale-up tests have the following limitations: one is the long test cycle and high cost; the second is that process parameters are difficult to optimize comprehensively; the third is that test results often cannot reflect the best leaching performance of actual ore bodies; the fourth is that the obtained data is difficult to be directly used for resource development feasibility assessment. These limitations seriously restrict the efficient evaluation and development process of low-grade mineral resources. Summary of the Invention

[0005] In view of the technical problems existing in the background art, the present application provides an analysis method for in-situ leaching process, aiming to solve the problems that the existing analysis methods for in-situ leaching process have a long test cycle, high cost, unrepresentative process parameters, and are difficult to be directly used for resource development feasibility assessment.

[0006] The embodiments of the present application provide an analysis method for in-situ leaching process, including the following steps: S1. Determine the leachable coefficient Grind the sample to be tested; Make an automatic mineralogical analysis sample from the ground sample to obtain sample a; Automatically analyze the sample a, and it is found through analysis that the content of the main target mineral containing a certain target element in the sample a is bi, and the content of the certain target element in the main target mineral is bi'; i is the type of mineral containing a certain target element, i = 1, 2,......, n; Use the single mineral or ore leaching method to confirm the leaching coefficient ci of the main target mineral; Calculate the leaching coefficient A of the sample to be tested, then A = ∑(bi * bi' * ci) / ∑(bi * bi'); S2. Determine the structural influence coefficient Perform the first cut on the sample to be tested to obtain the first cut sample block; Statistically analyze the structural characteristics of the cut surface of the first cut sample block, and count the number gf of occurrences of each type of structure; f is different structural types, f = 1, 2,......, m; Calculate the structural influence coefficient B of the sample to be tested, then B = ∑(df * gf / ∑gf); where df is the influence factor one; S3. Determine the texture influence coefficient Perform the second cut on the sample to be tested to obtain the second cut sample block; Make an automatically mineralogical analysis sample from the second cut sample block to obtain sample h; Perform an automatically mineralogical analysis on the sample h, statistically analyze the texture characteristics of the main target mineral containing a certain target element in the cut surface of the second cut sample block, and count the number ej of occurrences of each type of texture; j is the texture type of the main target mineral containing a certain target element, j = 1, 2,......, N; Calculate the texture influence coefficient C of the sample to be tested, then C = ∑dj * ej / 100; where dj is the influence factor two; S4. Determine the particle size influence coefficient Statistically analyze the number pi of the main target mineral containing a certain target element with a particle size above level k and the number pq of other minerals with a particle size above level k 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; 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; 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 measured, where W = A * B * C * D. In the technical solution of the embodiment of the present application, combined with the digital analysis of mineralogical characteristics (mineral leachability, mineral particle size characteristics), the multi-scale seepage dynamic parameters are measured (ore structure, ore texture), and finally the quantitative indexes of the in-situ leaching process are determined. The present invention deeply couples automatic mineralogical analysis with seepage mechanics, quantitatively analyzes the closed-loop prediction of "mineral composition - leaching kinetics - economic indexes", and provides a new analysis method for the development of low-grade resources. Compared with the traditional trial-and-error method, it is not necessary to determine the relevant data of other metallurgical processes through complex experiments, can quickly measure the theoretical recovery rate, improves the efficiency of index prediction, and can provide a basis for subsequent production and design optimization.

[0007] In some embodiments, in step S1, the mass of the sample to be measured is 1 - 3 kg; the grinding fineness is that the content of -0.074 mm is 60 - 80%.

[0008] In this embodiment, the raw ore sample with a specific fineness is obtained through grinding for subsequent processing.

[0009] In some embodiments, in step S2, the first influencing factor is determined according to the structure types shown in the following table;

[0010] In this embodiment, the structure influence coefficient is corrected through the first structure influencing factor, that is, the influence brought by different internal structures of minerals.

[0011] In some embodiments, in step S2, the three-dimensional size of the first cut sample block is greater than 20 cm.

[0012] In this embodiment, a larger cut surface is obtained through cutting, so that more different structures are exposed on the cut surface.

[0013] In some embodiments, in step S3, the second influencing factor is determined according to the structure types shown in the following table;

[0014] In this embodiment, the structure influence coefficient is corrected through the second structure influencing factor, that is, the influence brought by the forms in which different minerals exist in the ore.

[0015] In some embodiments, in step S3, the maximum horizontal size of the second cut sample block is less than the inner diameter of the mold for automatic mineralogical analysis sample preparation, and the thickness of the second cut sample block is 0.3 - 1.0 cm.

[0016] In this embodiment, a sample block of a specific size is cut so that it can be placed into the mold for sample preparation of automatic mineralogical analysis, facilitating subsequent sample preparation.

[0017] In some embodiments, in step S4, the other minerals are the minerals other than the main target mineral containing a certain target element that affect the leaching of the target mineral.

[0018] In this embodiment, not only the particle size of the target mineral itself affects the leaching effect, but also the particle size of the other minerals around the target mineral affects the leaching effect of the target mineral.

[0019] In some embodiments, in step S4, k is the particle size boundary corresponding to the main target mineral containing a certain target element; the particle size boundary k is the weighted average of the particle sizes of the main target mineral containing a certain target element according to its content.

[0020] In this embodiment, the particle sizes of the target mineral and the minerals around it have different effects on the leaching effect of the target mineral, so a suitable particle size boundary k is constructed.

[0021] The above description is only an overview of the technical solution of the present application. In order to be able to more clearly understand the technical means of the present application, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are specifically given below. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solution of the present application, the drawings used in the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a schematic flow chart of the analysis method for the in-situ leaching process of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The embodiments of the technical solution of the present application will be described in detail below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, so they are only examples and cannot be used to limit the protection scope of the present application.

[0025] References to "embodiments" in this application mean that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appearing at various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0026] To solve the problems of long test cycle, high cost, unrepresentative process parameters in the existing in-situ leaching process analysis method, and difficulty in directly applying it to the feasibility assessment of resource development, this application provides an analysis method for in-situ leaching process. This application combines digital analysis of mineralogical characteristics (mineral leachability, mineral particle size characteristics), conducts multi-scale permeability dynamic parameter determination (ore structure, ore texture), and finally determines the quantitative indicators of the in-situ leaching process. The present invention deeply couples automatic mineralogical analysis with seepage mechanics, quantitatively analyzes the closed-loop prediction of "mineral composition - leaching kinetics - economic indicators", and provides a new analysis method for the development of low-grade resources. Compared with the traditional trial-and-error method, it does not require complex experiments to determine the relevant data of other metallurgical processes, can quickly measure the theoretical recovery rate, improves the efficiency of index prediction, and can provide a basis for subsequent production and design optimization.

[0027] An embodiment of this application provides an analysis method for in-situ leaching process, including the following steps: S1. Determine the leachability coefficient Grind the sample to be tested; Make an automatic mineralogical analysis sample from the ground sample to obtain sample a; Conduct automatic mineralogical analysis on the sample a, and analyze that the content of the main target mineral containing a certain target element in the sample a is bi, and the content of the certain target element in the main target mineral is bi'; i is the type of mineral containing a certain target element, i = 1, 2,..., n; Use the single mineral or ore leaching method to confirm the leachability coefficient ci of the main target mineral; Calculate the leachability coefficient A of the sample to be tested, then A = ∑(bi * bi' * ci) / ∑(bi * bi'); S2. Determine the structure influence coefficient Conduct the first cutting on the sample to be tested to obtain the first cut sample block; Statistically analyze the structure characteristics of the cutting surface of the first cut sample block, and count the number gf of each type of structure that appears; 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); where df is influence factor one; S3. Determine the structure influence coefficient Perform a second cut on the sample to be measured to obtain the second cut sample block; Make an automatic mineralogical analysis sample from the second cut sample block to obtain sample h; Perform an automatic mineralogical analysis on the sample h, statistically analyze the structural characteristics of the main target minerals containing a certain target element in the cut surface of the second cut sample block, and count the number ej of each type of structure that appears; j is the structural type of the main target minerals containing a certain target element, j = 1, 2,..., N; Calculate the structure influence coefficient C of the sample to be measured, then C = ∑dj*ej / 100; where dj is the influence factor two; S4. Determine the particle size influence coefficient Statistically analyze the number pi of the main target minerals containing a certain target element with a particle size above level k and the number pq of other minerals with a particle size above level k in the cut surface of the second cut sample block; where i is the type of minerals containing a certain 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 main target minerals containing a certain 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 measured, then D = m1*m2; S5. Calculate the recovery rate W of the target element in the sample to be measured, then W = A*B*C*D. In the technical solution of the embodiment of the present application, combined with digital analysis of mineralogical characteristics (mineral leachability, mineral particle size characteristics), multi-scale infiltration dynamic parameters are measured (ore structure, ore texture), and finally the quantitative indexes of in-situ leaching process are determined. The present invention deeply couples automatic mineralogical analysis with seepage mechanics, quantitatively analyzes the closed-loop prediction of "mineral composition - leaching kinetics - economic indexes", and provides a new analysis method for the development of low-grade resources. Compared with the traditional trial-and-error method, it does not need to determine the relevant data of other metallurgical processes through complex experiments, can quickly measure the theoretical recovery rate, improves the efficiency of index prediction, and can provide a basis for subsequent production and design optimization.

[0028] Further, in some embodiments, in step S1, the mass of the sample to be measured is 1 - 3 kg; the grinding fineness is that the content of -0.074 mm is 60 - 80%.

[0029] In the technical solution of the embodiment of the present application, the original ore sample with a specific fineness is obtained through grinding for subsequent processing.

[0030] Further, in some embodiments, in step S1, the single mineral leaching method includes the following steps: taking a sample to be tested and crushing it to a particle size less than 5 mm, enriching it, and manually sorting the single mineral particles into monomeric mineral particles to obtain target mineral particles; coating epoxy resin glue in a sample preparation mold, adding the target mineral particles, and after curing and grinding treatment, obtaining a primary sample; cutting the primary sample, placing the grinding surface downward together with three wear-resistant alloys at the bottom of the sample preparation mold, injecting epoxy resin glue to cure and form a composite sample; then finely grinding and polishing until it is flush with the surface of the wear-resistant alloy, after carbon spraying treatment, performing the first automatic mineralogical analysis to measure the target mineral area Si; i is 1, 2, 3..., representing the types of target minerals; after the analysis is completed, cleaning the surface of the sample, performing leaching treatment to obtain a treated sample; coating epoxy resin glue in a sample preparation mold, adding the treated sample, injecting epoxy resin glue to cure, finely grinding and polishing until it is flush with the surface of the wear-resistant alloy, and after carbon spraying treatment, performing the second automatic mineralogical analysis to measure the remaining target mineral area Si'; calculating the leachability index Ci of the target mineral, where Ci = 1 - Si' / Si.

[0031] Further, in some embodiments, in step S1, the ore leaching method includes the following steps: taking a sample to be tested and grinding it to a fineness of -0.074 mm with 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 curing treatment, obtaining an automatic mineralogical analysis sample; then performing automatic mineralogical analysis to measure the target mineral content h ir and the target element content O i ; Calculating the average content h of the target mineral i , h i =∑h ir / r; i is 1, 2, 3..., representing the types of target minerals; r is I, II, III..., representing the sample numbers; Taking the pretreated sample and adding a reagent for leaching reaction, after solid-liquid separation, obtaining a mineral residue, calculating the mass ratio of the mineral residue to the pretreated sample before the reaction, denoted as t; mixing the mineral residue with graphite to prepare an automatic mineralogical analysis sample, and performing automatic mineralogical analysis to measure the remaining target mineral content h ir ’ and the remaining target element content O i ’; Calculating the average content h of the remaining target mineral i ’, h i ’=∑h ir ’ / r; Calculating the mineral leaching coefficient Ci, Ci = 1 - t * h i ’ * O i’ / (h i *O i ); Further, in some embodiments, in step S2, the influence factor 1 is determined according to the structural types shown in the following table;

[0032] In the technical solution of the embodiment of the present application, by constructing the influence factor 1, that is, the influence brought by different internal structures of minerals, the structural influence coefficient is corrected.

[0033] Further, in some embodiments, in step S2, the three-dimensional size of the first cutting sample block is greater than 20 cm.

[0034] In the technical solution of the embodiment of the present application, a larger cutting surface is obtained through cutting, so that more different structures are exposed on the cutting surface.

[0035] Further, in some embodiments, in step S3, the influence factor 2 is determined according to the structural types shown in the following table;

[0036] In the technical solution of the embodiment of the present application, by constructing the influence factor 2, that is, the influence brought by the forms of different minerals existing in the ore, the structural influence coefficient is corrected.

[0037] Further, in some embodiments, in step S3, the maximum horizontal size of the second cutting sample block is smaller than the inner diameter of the mold for preparing samples for automated mineralogical analysis, and the thickness of the second cutting sample block is 0.3 - 1.0 cm.

[0038] In the technical solution of the embodiment of the present application, by cutting sample blocks of specific sizes, they can be placed into the mold for preparing samples for automated mineralogical analysis, facilitating subsequent sample preparation.

[0039] Further, in some embodiments, in step S4, the other minerals are minerals other than the main target minerals containing a certain target element that affect the leaching of the target minerals.

[0040] In the technical solution of the embodiment of the present application, not only the particle size of the target minerals themselves affects the leaching effect, but also the particle sizes of the other minerals around the target minerals equally affect the leaching effect of the target minerals.

[0041] Further, in some embodiments, in step S4, the k is the particle size boundary corresponding to the main target minerals containing a certain target element; the particle size boundary k is the weighted average of the particle sizes of the main target minerals containing a certain target element according to their contents.

[0042] In the technical solution of the embodiment of the present application, the particle sizes of the target mineral and the minerals around it will have different effects on the leaching effect of the target mineral, so a suitable particle size boundary k is constructed.

[0043] Some specific embodiments are listed below. It should be noted that the embodiments described below are exemplary and are only used to explain the present application and should not be construed as a limitation of the present application. For those without specific technologies or conditions indicated in the embodiments, they shall be carried out according to the technologies or conditions described in the literature in the field or according to the product specifications. For reagents or instruments without the manufacturer indicated, they are all conventional products that can be obtained through commercial purchase.

[0044] Example 1 This embodiment provides an analysis method for in-situ leaching process, as Figure 1 shown, specifically including the following steps: (1) Weigh 1.5 kg of the sample to be tested, grind the sample to be tested, and obtain the raw ore sample with a grinding fineness of 70% for the content of -0.074 mm; make an automatic mineralogical analysis sample from the raw ore sample to obtain sample a; conduct an 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 certain target element in the main target mineral is bi'; i is the type of mineral containing a certain target element (i is 1, 2, 3, representing chalcopyrite, bornite, and cupric oxide mineral respectively), and the specific test results are shown in Table 1.

[0045] Table 1 Test results of bi and bi' in the sample Select 2 kg of the sample to be tested, grind it, and the grinding fineness is 85% for the content of -0.074 mm; then carry out washing and desliming treatment, specifically: add water to the ground sample according to a solid-liquid ratio of 1:2, stir for 2 min, stand for 2 min, extract 200 ml of the upper liquid, and repeat 3 times; take samples from the deslimed sample, take 3 g for each sample, mix in graphite particles with a particle size of 10 μm, and the volume ratio of the sample sampling volume to graphite is 1:1; add 10 ml of epoxy resin glue to the mixture, stir evenly at 15°C, then add epoxy resin glue until the total height of the sample is 1.0 cm, and then carry out vacuum pumping and cure at 25°C to make 5 automatic mineralogical analysis samples, and then conduct automatic mineralogical analysis respectively to obtain the main target mineral content h ir and the target element content O i (i is 1, 2, 3, representing chalcopyrite, bornite, and cupric oxide mineral respectively), and calculate the average content h of the main target mineral i, specifically shown in Table 2; Take 500 g of the sample after desliming, add the reagent to be used in production (sulfuric acid, 2 mol / L, solid-liquid ratio 1:4), stir and react for 12 h, calculate the mass ratio of the mineral residue obtained after the reaction to the sample before the reaction, denoted as t, t = 426.72 / 500.00 = 85.34%; Take the mineral residue and graphite and mix them to prepare an automated mineralogy analysis sample, with the method being the same as above, and then conduct automated mineralogy analysis. The content of the main target mineral obtained from the analysis is b in ’, and the content of the main target element is h in ’, calculate the average content O of the main target mineral i ’, see Table 2 for details.

[0046] Table 2 Measurement results of the content of the main purpose minerals Calculate the leaching coefficient Ci = 1 - t * h i ’ * O i ’ / (h i * O i ); C1 = 1 - 85.34% * 4.01% * 34.53% / (4.22% * 34.55%) = 18.95%; C2 = 1 - 85.34% * 1.21% * 63.22% / (2.25% * 63.23%) = 54.11%; C3 = 1 - 85.34% * 1.52% * 57.45% / (15.37% * 57.44%) = 91.56%.

[0047] Calculate the leaching 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%%.

[0048] (2) Conduct the first cutting on the sample to be tested to obtain cutting sample block 1, and the three-dimensional size of cutting sample block 1 is 25 cm; Statistically analyze the structural characteristics of the cutting surface of cutting sample block 1, and count the number gf of occurrences of each type of structure; f is different structural types, f = 1, 2,..., m, specifically shown in Table 3; Table 3 Detection results of the main structural types and their quantities Calculate the structural influence coefficient B of the sample to be measured, then B = ∑(df * gf / ∑gf) = 0.9480.

[0049] (3)Perform a second cut on the sample to be measured to obtain the second cut sample block, ensuring that the second cut sample block can be placed into the sample preparation mold; Make an automatic mineralogy analysis sample from the second cut sample block, then conduct an automatic mineralogy analysis, statistically analyze the structural characteristics of the main target minerals containing a certain target element in the cut surface of the second cut sample block, and count the number ej of occurrences of each type of structure; j is the structural type of the main target minerals containing a certain target element, j = 1, 2,..., N, as shown in Table 4 specifically.

[0050] Table 4 Detection results of the structural characteristics and their quantities of the main target minerals containing a certain target element Calculate the structural influence coefficient C of the sample to be measured, then C = ∑dj * ej / 100 = 0.9334.

[0051] (4)Statistically analyze the number pi of the main target minerals containing a certain target element with a particle size above level k and the number pq of other minerals with a particle size above level k in the cut surface of the second cut sample block; where, i is the type of minerals containing a certain target element, i = 1, 2,..., n; q is the type of other minerals, q = 1, 2,..., M, as shown in Table 5 specifically; where, k is taken as 0.037 mm.

[0052] Table 5 Detection results of the main mineral particle sizes Calculate the particle size influence coefficient m1 of the main target minerals containing a certain target element and the particle size influence coefficient m2 of other minerals, then m1 = 1 - ∏pi = 1 - 42.88% * 20.03% * 84.34% = 0.9276, m2 = 1 - ∏pq = 1 - 59.66% * 60.61% * 34.78% = 0.8742.

[0053] Calculate the particle size influence coefficient D of the sample to be measured, then D = m1 * m2 = 0.9276 * 0.8742 = 0.8109.

[0054] (5)Calculate the recovery rate W of the target element in the sample to be measured, then W = A * B * C * D = 77.97% * 0.9480 * 0.9334 * 0.8109 = 55.95%.

[0055] In summary, the present application provides an analysis method for in-situ leaching process. By combining digital analysis of mineralogical characteristics (mineral leachability, mineral particle size characteristics), multi-scale permeability dynamic parameter determination (ore structure, ore texture) is carried out, and finally the quantitative indexes of in-situ leaching process are determined. The present invention deeply couples automatic mineralogical analysis with seepage mechanics, and quantitatively analyzes the closed-loop prediction of "mineral composition - leaching kinetics - economic indexes", providing a new analysis method for the development of low-grade resources. Compared with the traditional trial-and-error method, it is not necessary to determine the relevant data of other metallurgical processes through complex experiments, and the theoretical recovery rate can be quickly measured, improving the efficiency of index prediction, and providing a basis for subsequent production and design optimization.

[0056] It should be noted that the present application is not limited to the above embodiments. The above embodiments are only examples, and embodiments having the same constitution and the same effect as the technical idea within the technical solution scope of the present application are all included in the technical scope of the present application. In addition, within the scope not departing from the gist of the present application, various modifications that can be conceived by those skilled in the art to the embodiments, and other ways constructed by combining some constituent elements of the embodiments are also included in the scope of the present application.

Claims

1. An analysis method for in-situ leaching process, characterized in that, It includes the following steps: S1. Determine the leaching coefficient Grind the sample to be measured; Make an automatic mineralogical analysis sample from the ground sample to obtain sample a; Conduct an automatic mineralogical analysis on the sample a, and analyze that the content of the main target mineral containing a certain target element in the sample a is bi, and the content of the certain target element in the main target mineral is bi'; i is the type of mineral containing a certain target element, i = 1, 2,..., n; Use the single mineral or ore leaching method to confirm the leaching coefficient ci of the main target mineral; Calculate the leaching coefficient A of the sample to be measured, then A = ∑(bi * bi' * ci) / ∑(bi * bi'); S2. Determine the structural influence coefficient Conduct the first cutting on the sample to be measured to obtain the first cut sample block; Statistically analyze the structural characteristics of the cutting surface of the first cut sample block, and count the number gf of various types of structures that appear; f is different structural types, f = 1, 2,..., m; Calculate the structural influence coefficient B of the sample to be measured, then B = ∑(df * gf / ∑gf); where df is the first influence factor; S3. Determine the texture influence coefficient Conduct the second cutting on the sample to be measured to obtain the second cut sample block; Make an automatic mineralogical analysis sample from the second cut sample block to obtain sample h; Conduct an automatic mineralogical analysis on the sample h, statistically analyze the texture characteristics of the main target mineral containing a certain target element in the cutting surface of the second cut sample block, and count the number ej of various types of textures that appear; j is the texture type of the main target mineral containing a certain target element, j = 1, 2,..., N; Calculate the texture influence coefficient C of the sample to be measured, then C = ∑dj * ej / 100; where dj is the second influence factor; S4. Determine the particle size influence coefficient Statistically analyze the number pi of the main target minerals containing a certain target element with a particle size above level k and the number pq of other minerals with a particle size above level k in the cutting 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; 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; Calculate the particle size influence coefficient D of the sample to be measured, then D = m1 * m2; S5. Calculate the recovery rate W of the target element in the sample to be measured, then W = A * B * C * D.

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

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

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

7. The analysis method of the in-situ leaching process according to claim 1, characterized in that, In step S4, the other minerals are minerals other than the main target mineral containing a certain target element that affect the leaching of the target mineral.

8. The analysis method of the in-situ leaching process according to claim 1, characterized in that, In step S4, k is the particle size limit corresponding to the main target mineral containing a certain target element; the particle size limit k is the weighted average of the particle sizes of the main target mineral containing a certain target element according to its content.

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