Quantitative screening method for minerals mainly influencing ore selectivity

Abstract

The invention discloses a quantitative screening method for main influence minerals of ore selectability. The quantitative screening method comprises the specific steps that S1, ore in a mining area is collected, and mineral components contained in raw ore and the proportion of the mineral components are determined; s2, preliminarily screening influencing minerals influencing the mineral separation result, and searching corresponding pure mineral samples with high crystallinity and high purity as added minerals; s3, preparing artificial mixed ore in a corresponding proportion; and S4, carrying out a mineral separation experiment, and quantitatively screening main influencing minerals and proportions thereof. According to the method, the raw ore and the pure ore are used for manufacturing the artificial mixed ore, so that an indoor experiment is more suitable for actual production, meanwhile, the quantitative and controllable purposes are achieved, and a new thought is provided for screening main influencing minerals influencing the mineral separation experiment; experiments are carried out by adopting an orthogonal experiment design method, the experiment frequency is reduced, the time and the cost are saved, and meanwhile the purpose of quantitatively screening main influencing minerals is achieved through manually controllable experiment samples.

Description

Technical Field

[0001] The invention relates to the technical field of mineral screening, and in particular to a quantitative screening method for minerals that mainly affect ore selectivity. Background Art

[0002] The non-ferrous metals industry involves many varieties, wide applications, and outstanding strategic value. It is an important support for the development of strategic emerging industries and national defense science and technology industries, and an important field for maintaining national resource security and industrial security. With the rapid development of the mining economy, resource reserves have decreased sharply. Therefore, it is necessary to strengthen the development and utilization of difficult-to-select ore resources.

[0003] For difficult-to-process ore resources, researchers often focus more on improving the development of mineral processing technology and reagents, and conduct a large amount of elemental testing and analysis on the ore. However, these works cannot be well applied to the geological level, resulting in difficulty in classifying and distinguishing ore in the upstream mining stage of mineral processing, causing waste of resources; therefore, further research is needed. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a quantitative screening method for the main influencing minerals of ore selectivity, quantitatively determine the main influencing minerals affecting the ore dressing effect according to the results of ore dressing experiments, so as to achieve the distinction of ore selectivity at the geological level.

[0005] The technical solution adopted by the present invention is:

[0006] A quantitative screening method for the main minerals affecting ore selectivity, the specific steps are:

[0007] S1. Collect ore from the mining area as raw ore samples, conduct whole-rock mineral component analysis on the raw ore samples, and determine the mineral components and their proportions contained in the raw ore;

[0008] S2. Preliminary screening of minerals that affect the beneficiation results and search for corresponding high crystallinity and high purity pure mineral samples as added minerals;

[0009] S3. Using raw ore samples and pure mineral samples as raw materials, introduce the orthogonal experimental method in the field of mathematical statistics, select a suitable orthogonal experimental table to determine the specific proportion and optimal combination of each influencing mineral, and configure the corresponding proportion of artificial mixed ore;

[0010] S4. Conduct mineral processing experiments, use mineral processing recovery rate and concentrate grade as evaluation indicators, adopt mathematical and statistical methods for analysis, and quantitatively screen the main influencing minerals and their proportions.

[0011] Preferably, in step S1, the raw ore sample refers to the ore in actual mining production, including but not limited to separate sampling or mixed averaging of different areas in the mining area; the raw ore sample is subjected to whole-rock mineral component analysis, which requires that the raw ore sample must be highly evenly mixed, and the whole-rock mineral component analysis includes non-clay minerals and clay minerals.

[0012] Preferably, the method for preliminary screening of minerals that affect the mineral processing results in step S2 includes but is not limited to preliminary screening based on literature research, expert demonstration, experimental analysis, and production data; looking for corresponding high-crystallinity and high-purity pure mineral samples, requiring the mineral purity of the pure mineral samples to be above 98%, including but not limited to looking for highly crystallized minerals in the original mining area or looking for similar mineral substitutes in other areas.

[0013] Preferably, in step S3, using the raw ore sample and the pure mineral sample as raw materials means that the raw ore sample is used as a fixed component of the mixed ore, and the pure mineral sample that affects the mineral correspondence is used as an added component to prepare an artificial mixed ore.

[0014] Preferably, the orthogonal experimental method in the field of mathematical statistics is introduced in step S3, which refers to the use of the orthogonal experimental design method to optimize the experimental design scheme under multi-factor changes to reduce the number of experiments; specifically: taking the influencing mineral types screened in S2 as the number of experimental factors as columns, taking the content range of each influencing mineral in the mining area, and achieving the purpose of experimental screening of the main influencing minerals, dividing the number of levels not less than the number of experimental factors according to the content range as rows, and designing the horizontal factor table of the orthogonal experiment; selecting a smaller orthogonal experimental table based on the horizontal factor table to determine the specific proportion and optimal combination of each influencing mineral; and preparing an artificial mixed ore containing different influencing mineral contents according to the data in the orthogonal experimental table.

[0015] Preferably, in step S4, a mineral processing experiment is carried out, i.e., a consistent mineral processing experiment is carried out in strict accordance with the mineral processing process and relevant technical requirements; mathematical statistics methods are used for analysis, including but not limited to range analysis and correlation analysis; and the main influencing minerals and their proportions are quantitatively screened, i.e., the main influencing minerals are quantitatively screened by mathematical statistics methods, and the mineral content and the degree of influence thereof are determined.

[0016] Due to the adoption of the above-mentioned technical solution, the present invention has the following advantages:

[0017] The present invention utilizes raw ore and pure minerals to make artificial mixed ore, which not only makes indoor experiments more in line with actual production, but also achieves the purpose of quantitative control, and provides a new idea for screening the main influencing minerals that affect mineral processing experiments; the orthogonal experimental design method is used to conduct experiments, which reduces the number of experiments, saves time and cost, and realizes the purpose of quantitative screening of the main influencing minerals through artificially controllable experimental samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of a quantitative screening method for minerals that mainly affect ore selectivity in an embodiment of the present invention.

[0019] Figure 2 This is a thin section identification diagram of the alteration type of diopside in the embodiment of the present invention;

[0020] In the figure, 1- diopside, 2- chlorite, 3- carbonate mineral, 4- garnet.

[0021] Figure 3 Thin section identification diagram of the alteration type of garnet in the embodiment of the present invention;

[0022] In the figure: 1-garnet, 2-chlorite, 3-quartz.

[0023] Figure 4 It is a main effect diagram of the mean value of the mineral processing recovery rate of the embodiment of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further explained below in conjunction with the accompanying drawings and embodiments, which should not be used to limit the protection scope of the present invention. The purpose of disclosing the present invention is to protect all technical improvements within the scope of the present invention.

[0025] Combined with Figure 1-4 A quantitative screening method for the main minerals affecting ore selectivity is shown in the figure, and the specific steps are:

[0026] S1. Collect ores sampled separately or mixed and averaged from different areas of the mining area during mining production as raw ore samples, and conduct whole-rock mineral component analysis on the highly evenly mixed raw ore samples to determine the mineral components and their proportions contained in the raw ore; the whole-rock mineral component analysis includes clay minerals and non-clay minerals;

[0027] The ore in the mining area was sampled, and the whole-rock mineral test analysis results obtained experimentally are shown in Table 1.

[0028] Table 1 Main mineral composition and content of ore

[0029]

[0030] S2. Scanning electron microscopy, thin section identification, and X-ray diffraction experimental analysis show that the sample is strongly altered. The fine mud produced by the easily muddy minerals such as amphibole, chlorite, and diopside in the ore will have a certain impact on the mineral processing. The alteration types of amphibole, chlorite, and carbonatization of diopside are as follows: Figure 2 As shown in Figure 2, the chloritization alteration types of garnet are as follows: Figure 3As shown; combined with expert demonstration and production data, the main influencing mineral factors initially screened include chlorite, amphibole and diopside;

[0031] Look for corresponding high-crystallinity and high-purity chlorite, amphibole, diopside samples or similar mineral substitutes in the original mining area or other areas, and the mineral purity is required to be above 98%.

[0032] S3. Using raw ore samples and pure mineral samples as raw materials, taking the raw ore samples as a fixed component of the mixed ore, taking the influencing minerals, i.e. pure mineral samples as added components, and ball milling and mixing them evenly to prepare artificial mixed ore; using the orthogonal experimental design method in the field of mathematical statistics to optimize the experimental design scheme under multi-factor changes to reduce the number of experiments;

[0033] Taking the three influencing mineral types screened in S2 as the experimental factor number as the column, the content range of the three influencing minerals, and the purpose of experimental screening of the main influencing minerals, each factor is selected at four levels, and a set of three-factor four-level experiments is designed to prepare a level factor table, as shown in Table 2:

[0034] Table 2 Level factors of three-factor four-level orthogonal experimental design

[0035]

[0036] According to the characteristics of uniform dispersion, neatness and comparability of orthogonal array, the smaller L16 (4 5 ) to determine the specific proportion and optimal combination of each influencing mineral, and the experimental proportion of each component in the artificial mixed ore.

[0037] The total mass of the artificial mixed ore sample is fixed at 500g, and the mass of the original ore sample is fixed at 380g, accounting for 76% by mass; according to the proportion in the design table, the four levels of chlorite are 1.2%, 1.7%, 2.2%, and 2.7%, and its total mass in the 500g sample is 6.0g, 8.5g, 11.0g, and 13.5g; the mass of chlorite already contained in the original ore sample is 5.7g, so the mass of chlorite that needs to be added to the sample is: 0. 3g, 2.8g, 5.3g, 7.8g; the amphibole content that needs to be added is also divided into four levels: 0.212g, 6.712g, 14.212g, 21.712g; the diopside content that needs to be added is: 0.02g, 10.02g, 20.02g, 30.02g; at the same time, quartz that has no effect on the experimental results is selected as the counterweight, and it changes according to the changes in the quality of the minerals. The experimental proportions of each mineral are shown in Table 3.

[0038] Table 3: Orthogonal experiment of 3 factors and 4 levels mainly affecting ore selectivity

[0039] serial number Ore(g) Quartz(g) Chlorite(g) Amphibole(g) Diopside(g) 1 380 119.468 0.3 0.212 0.02 2 380 102.968 0.3 6.712 10.02 3 380 85.468 0.3 14.212 20.02 4 380 67.968 0.3 21.712 30.02 5 380 106.968 2.8 0.212 10.02 6 380 110.468 2.8 6.712 0.02 7 380 72.968 2.8 14.212 30.02 8 380 75.468 2.8 21.712 20.02 9 380 94.468 5.3 0.212 20.02 10 380 77.968 5.3 6.712 30.02 11 380 100.468 5.3 14.212 0.02 12 380 82.968 5.3 21.712 10.02 13 380 81.968 7.8 0.212 30.02 14 380 85.468 7.8 6.712 20.02 15 380 87.968 7.8 14.212 10.02 16 380 90.468 7.8 21.712 0.02

[0040] S4, the slurry ground in S3 was subjected to a consistent beneficiation experiment in strict accordance with the beneficiation process and relevant technical requirements. The experimental results are shown in Table 4; the beneficiation recovery rate and concentrate grade were used as evaluation indicators, and the range analysis and correlation analysis were carried out using mathematical statistics methods; the range analysis results are shown in Tables 5 and Figure 4 The results of correlation analysis are shown in Table 6.

[0041] Table 4: Consistent beneficiation test results

[0042]

[0043]

[0044] Table 5: Response table of mean recovery rate of mineral processing

[0045] level Chlorite Hornblende Diopside 1 83.92 80.58 81.45 2 82.58 81.6 80.92 3 79.2 80.67 80.88 4 77.4 80.25 79.85 Range R 6.52 1.35 1.6 Sorting 1 3 2

[0046] Note: The larger the range value R, the greater the impact of the factor on the index; on the contrary, if the range value R is smaller, it means that the impact of the factor on the experimental index is smaller.

[0047] Table 6: Correlation analysis results between mineral processing recovery rate and influencing factors

[0048] Chlorite Hornblende Diopside Correlation P 0.981 0.081 0.194 Sorting 1 3 2

[0049] Table 5 and Figure 4 The results show that the mineral processing recovery rate is negatively correlated with the contents of chlorite, amphibole and diopside as a whole, and the maximum range value R of chlorite is 0.0652, which has the greatest impact on the mineral processing recovery rate, followed by diopside and amphibole. The correlation analysis also shows the same law; the experiment shows that chlorite is the main influencing factor affecting mineral processing, and the mineral processing recovery rate decreases with the increase of chlorite content; in actual production, the chlorite content is used to determine the degree and range of ore difficulty, so as to realize the distinction of ore selectivity at the geological level. At the same time, the determination of the main influencing minerals also provides a direction for improving the mineral processing technology.

[0050] The parts not described in detail in this invention are prior art.

[0051] The embodiments selected herein for the purpose of disclosing the invention are currently considered to be suitable, but it should be understood that the invention is intended to include all changes and modifications of the embodiments that fall within the scope of the concept and invention.

Claims

1. A quantitative screening method for minerals that mainly affect ore selectivity, characterized in that: The specific steps are: S1. Collect ore from the mining area as raw ore samples, conduct whole-rock mineral component analysis on the raw ore samples, and determine the mineral components and their proportions contained in the raw ore; S2. Preliminary screening of minerals that affect the beneficiation results and search for corresponding high crystallinity and high purity pure mineral samples as added minerals; S3. Using raw ore samples and pure mineral samples as raw materials, introduce the orthogonal experimental method in the field of mathematical statistics, select a suitable orthogonal experimental table to determine the specific proportion and optimal combination of each influencing mineral, and configure the corresponding proportion of artificial mixed ore; S4. Conduct mineral processing experiments, use mineral processing recovery rate and concentrate grade as evaluation indicators, adopt mathematical and statistical methods for analysis, and quantitatively screen the main influencing minerals and their proportions.

2. The quantitative screening method for the minerals that mainly affect the selectivity of the ore according to claim 1 is characterized in that: In step S1, the raw ore sample refers to the ore in actual mining production, including but not limited to separate sampling or mixed averaging of different areas in the mining area; the raw ore sample is subjected to whole-rock mineral component analysis, which requires that the raw ore sample must be highly uniformly mixed, and the whole-rock mineral component analysis includes non-clay minerals and clay minerals.

3. The quantitative screening method for the minerals that mainly affect the selectivity of the ore according to claim 1 is characterized in that: The method for preliminary screening of minerals that affect the mineral processing results in step S2 includes but is not limited to preliminary screening based on literature research, expert demonstration, experimental analysis, and production data; finding corresponding high-crystallinity and high-purity pure mineral samples, requiring the mineral purity of the pure mineral samples to be above 98%, including but not limited to finding highly crystallized minerals in the original mining area or finding similar mineral substitutes in other areas.

4. The quantitative screening method for the minerals that mainly affect the selectivity of the ore according to claim 1 is characterized in that: In step S3, using the raw ore sample and the pure mineral sample as raw materials means that the raw ore sample is used as a fixed component of the mixed ore, and the pure mineral sample that affects the mineral correspondence is used as an added component to prepare an artificial mixed ore.

5. The quantitative screening method for the minerals that mainly affect the selectivity of the ore according to claim 4 is characterized in that: The orthogonal experiment method in the field of mathematical statistics is introduced in step S3, which refers to the use of the orthogonal experimental design method to optimize the experimental design scheme under multi-factor changes to reduce the number of experiments; specifically: taking the influencing mineral types screened in S2 as the experimental factor number as the column, taking the content range of each influencing mineral in the mining area, and the purpose of experimental screening of the main influencing minerals, dividing the number of levels that are not less than the number of experimental factors according to the content range as the row, and designing the horizontal factor table of the orthogonal experiment; selecting a smaller orthogonal experimental table based on the horizontal factor table to determine the specific proportion and optimal combination of each influencing mineral; and preparing an artificial mixed ore containing different influencing mineral contents according to the data in the orthogonal experimental table.

6. The quantitative screening method for the minerals that mainly affect the selectivity of the ore according to claim 1, characterized in that: In the step S4, a mineral processing experiment is carried out, i.e., a consistent mineral processing experiment is carried out in strict accordance with the mineral processing process and relevant technical requirements; mathematical statistics methods are used for analysis, including but not limited to range analysis and correlation analysis; and the main influencing minerals and their proportions are quantitatively screened, i.e., the main influencing minerals are quantitatively screened by mathematical statistics methods, and the mineral content and the degree of influence thereof are determined.

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