CN117900151A - Strategic metal element enrichment method based on mineral X-ray response characteristics - Google Patents
Strategic metal element enrichment method based on mineral X-ray response characteristics Download PDFInfo
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- 229910052500 inorganic mineral Inorganic materials 0.000 title claims abstract description 186
- 239000011707 mineral Substances 0.000 title claims abstract description 186
- 229910052751 metal Inorganic materials 0.000 title claims abstract description 84
- 239000002184 metal Substances 0.000 title claims abstract description 76
- 238000000034 method Methods 0.000 title claims abstract description 38
- 230000004044 response Effects 0.000 title claims abstract description 19
- 239000012141 concentrate Substances 0.000 claims abstract description 31
- 238000005065 mining Methods 0.000 claims abstract description 25
- 239000000203 mixture Substances 0.000 claims abstract description 18
- 239000000126 substance Substances 0.000 claims abstract description 18
- 238000012360 testing method Methods 0.000 claims abstract description 17
- 238000011160 research Methods 0.000 claims abstract description 16
- 150000002739 metals Chemical class 0.000 claims abstract description 14
- 238000001514 detection method Methods 0.000 claims description 13
- 238000012706 support-vector machine Methods 0.000 claims description 11
- 238000012549 training Methods 0.000 claims description 8
- 238000002360 preparation method Methods 0.000 claims description 2
- 238000012545 processing Methods 0.000 claims description 2
- 238000011835 investigation Methods 0.000 claims 2
- 238000000926 separation method Methods 0.000 abstract description 12
- 239000003245 coal Substances 0.000 abstract description 11
- 230000000694 effects Effects 0.000 abstract description 4
- GYHNNYVSQQEPJS-UHFFFAOYSA-N Gallium Chemical compound [Ga] GYHNNYVSQQEPJS-UHFFFAOYSA-N 0.000 description 15
- 229910052733 gallium Inorganic materials 0.000 description 15
- 239000010453 quartz Substances 0.000 description 11
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 11
- 238000007664 blowing Methods 0.000 description 9
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 8
- 239000010433 feldspar Substances 0.000 description 8
- 229910052744 lithium Inorganic materials 0.000 description 8
- 239000000843 powder Substances 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- CNLWCVNCHLKFHK-UHFFFAOYSA-N aluminum;lithium;dioxido(oxo)silane Chemical compound [Li+].[Al+3].[O-][Si]([O-])=O.[O-][Si]([O-])=O CNLWCVNCHLKFHK-UHFFFAOYSA-N 0.000 description 7
- 239000002245 particle Substances 0.000 description 7
- 229910052642 spodumene Inorganic materials 0.000 description 7
- RTAQQCXQSZGOHL-UHFFFAOYSA-N Titanium Chemical compound [Ti] RTAQQCXQSZGOHL-UHFFFAOYSA-N 0.000 description 6
- 238000011161 development Methods 0.000 description 6
- NLYAJNPCOHFWQQ-UHFFFAOYSA-N kaolin Chemical compound O.O.O=[Al]O[Si](=O)O[Si](=O)O[Al]=O NLYAJNPCOHFWQQ-UHFFFAOYSA-N 0.000 description 6
- 229910052622 kaolinite Inorganic materials 0.000 description 6
- 238000005070 sampling Methods 0.000 description 6
- 229910052719 titanium Inorganic materials 0.000 description 6
- 239000010936 titanium Substances 0.000 description 6
- 229910001593 boehmite Inorganic materials 0.000 description 4
- 238000000227 grinding Methods 0.000 description 4
- FAHBNUUHRFUEAI-UHFFFAOYSA-M hydroxidooxidoaluminium Chemical compound O[Al]=O FAHBNUUHRFUEAI-UHFFFAOYSA-M 0.000 description 4
- SZVJSHCCFOBDDC-UHFFFAOYSA-N iron(II,III) oxide Inorganic materials O=[Fe]O[Fe]O[Fe]=O SZVJSHCCFOBDDC-UHFFFAOYSA-N 0.000 description 4
- YDZQQRWRVYGNER-UHFFFAOYSA-N iron;titanium;trihydrate Chemical compound O.O.O.[Ti].[Fe] YDZQQRWRVYGNER-UHFFFAOYSA-N 0.000 description 4
- 238000011084 recovery Methods 0.000 description 4
- 230000007123 defense Effects 0.000 description 3
- GUJOJGAPFQRJSV-UHFFFAOYSA-N dialuminum;dioxosilane;oxygen(2-);hydrate Chemical compound O.[O-2].[O-2].[O-2].[Al+3].[Al+3].O=[Si]=O.O=[Si]=O.O=[Si]=O.O=[Si]=O GUJOJGAPFQRJSV-UHFFFAOYSA-N 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- 230000005284 excitation Effects 0.000 description 3
- 229910052900 illite Inorganic materials 0.000 description 3
- 229910052901 montmorillonite Inorganic materials 0.000 description 3
- VGIBGUSAECPPNB-UHFFFAOYSA-L nonaaluminum;magnesium;tripotassium;1,3-dioxido-2,4,5-trioxa-1,3-disilabicyclo[1.1.1]pentane;iron(2+);oxygen(2-);fluoride;hydroxide Chemical compound [OH-].[O-2].[O-2].[O-2].[O-2].[O-2].[F-].[Mg+2].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[K+].[K+].[K+].[Fe+2].O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2 VGIBGUSAECPPNB-UHFFFAOYSA-L 0.000 description 3
- 230000000704 physical effect Effects 0.000 description 3
- 238000003825 pressing Methods 0.000 description 3
- 229910052683 pyrite Inorganic materials 0.000 description 3
- 239000011028 pyrite Substances 0.000 description 3
- NIFIFKQPDTWWGU-UHFFFAOYSA-N pyrite Chemical compound [Fe+2].[S-][S-] NIFIFKQPDTWWGU-UHFFFAOYSA-N 0.000 description 3
- 229910052611 pyroxene Inorganic materials 0.000 description 3
- 229910010413 TiO 2 Inorganic materials 0.000 description 2
- 229910001570 bauxite Inorganic materials 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 239000010445 mica Substances 0.000 description 2
- 229910052618 mica group Inorganic materials 0.000 description 2
- 229910052702 rhenium Inorganic materials 0.000 description 2
- WUAPFZMCVAUBPE-UHFFFAOYSA-N rhenium atom Chemical compound [Re] WUAPFZMCVAUBPE-UHFFFAOYSA-N 0.000 description 2
- 239000000956 alloy Substances 0.000 description 1
- 229910045601 alloy Inorganic materials 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000006555 catalytic reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000005191 phase separation Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000003466 welding Methods 0.000 description 1
Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/04—Sorting according to size
- B07C5/10—Sorting according to size measured by light-responsive means
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/34—Sorting according to other particular properties
- B07C5/342—Sorting according to other particular properties according to optical properties, e.g. colour
- B07C5/3425—Sorting according to other particular properties according to optical properties, e.g. colour of granular material, e.g. ore particles, grain
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- Analysing Materials By The Use Of Radiation (AREA)
Abstract
The invention discloses a strategic metal element enrichment method based on mineral X-ray response characteristics, and belongs to the technical field of coal-based strategic metal sorting pre-enrichment; solves the problems of poor sorting effect and low precision of the existing strategic metal occurrence phase enrichment method. The strategic metal element enrichment method of the invention comprises the following steps: step 1, determining a research area according to a target mining area, and clearly strategically controlling the composition and physicochemical properties of metal mineral substances; step 2, preparing a regular test sample with controllable thickness; step 3, establishing a single mineral thickness-gray scale characteristic identification library of the occurrence phase/non-occurrence phase; step 4, establishing a thickness-gray scale characteristic identification library of the mixed minerals of the occurrence phase and the non-occurrence phase; step5, solving a sorting threshold value of the intelligent gangue selector, and completing establishment of a gangue prediction model; and step 6, carrying out separation on actual minerals in a research area according to a separation threshold value, wherein the obtained concentrate is an occurrence phase of strategic metals, and completing pre-enrichment of the strategic metals. The method realizes the pre-enrichment of strategic metals.
Description
Technical Field
The invention relates to the technical field of coal-based strategic metal sorting pre-enrichment, in particular to a strategic metal element enrichment method based on mineral X-ray response characteristics.
Background
Mineral resources such as strategic metal lithium, gallium, titanium and the like play an important and irreplaceable role in guaranteeing national economic safety, national defense safety and sustainable healthy development of strategic emerging industries, and are the leading field of resource competition of all countries in the world. Among them, gallium (Ga) is an important metal element having strategic value, which is widely used and is involved in both the military and civil industries, and is called "food for electronic industry". The method is mainly applied to the fields of semiconductors, catalysis, medical treatment and the like, and simultaneously has new application and development in the fields of thermal interface materials, welding, optics, memory alloys and the like.
The primary mineral product extracted by gallium is bauxite, but with the vigorous development of the application field, the consumption of gallium resources is increased increasingly, the bauxite faces the problems of insufficient reserves, high exploitation cost and the like, and the searching of stable and reliable renewable source substances is the only way for solving the resource shortage. The coal gangue has low gallium content, but has a large reserve, and potential economic value is huge. The effective utilization of the gallium-rich gangue can not only reduce the landfill cost, but also relieve the problem of insufficient primary mineral reserves of gallium.
The gangue has complex chemical properties, multiple types of occurrence phases, non-occurrence phase embedding, wide granularity span, difficult coarse grain response identification and the like, so that the conventional pre-enrichment means has low occurrence phase separation efficiency and poor precision. The current mainstream intelligent gangue selector is mainly applied to identification among different minerals, and the influence of thickness on mineral imaging is weakened by adopting an R value method, but the effect is poor, and the identification precision is difficult to meet the sorting of multiple mineral phases and embedded complex minerals.
Disclosure of Invention
In view of the analysis, the invention aims to provide a strategic metal element enrichment method based on mineral X-ray response characteristics, which is used for solving the problems of poor separation effect and low precision of the existing strategic metal occurrence phase enrichment method.
The aim of the invention is mainly realized by the following technical scheme:
the invention provides a strategic metal element enrichment method based on mineral X-ray response characteristics, which comprises the following steps:
step 1, determining a research area according to a target mining area, and clearly strategically controlling the composition and physicochemical properties of metal mineral substances;
step 2, preparing a regular test sample with controllable thickness;
Step 3, identifying and detecting the test sample, and establishing an occurrence phase/non-occurrence phase single mineral thickness-gray feature identification library according to the detection result;
Step 4, preparing a mixed sample, detecting the mixed sample, and establishing an occurrence phase/non-occurrence phase mixed mineral thickness-gray scale characteristic identification library according to a detection result;
Step 5, building a support vector machine model by using Python, inputting the thickness-gray characteristics of the mixed minerals as a training set of the model, generating a sorting threshold value of the intelligent gangue selector, and completing building of a gangue prediction model;
And step 6, sorting actual minerals in the research area according to a sorting threshold value to obtain concentrate and tailings, wherein the concentrate is an occurrence phase of strategic metals, and pre-enrichment of the strategic metals is completed.
Further, in step 1, after the study area is determined, ores in the study area are randomly sampled, and physicochemical property analysis is performed to clarify strategic metal mineral substance composition and physicochemical properties.
Further, in step 2, a study sample is collected, and strategic metal occurrence/non-occurrence categories are determined by analyzing strategic metal mineral substance compositions and physicochemical properties.
Further, in step 2, the single minerals corresponding to the occurrence/non-occurrence are purchased according to the strategic metal mineral substance composition.
In step 2, the purchased single minerals are mixed according to the composition proportion of actual strategic metal mineral substances, and the mixture is pressed into tablets to prepare samples, so that regular test samples with controllable thickness are obtained.
Further, in step 2, the strategic metal-forming/non-forming minerals obtained by purchasing include both bulk and powdery ones, and the bulk strategic metal-forming/non-forming minerals subjected to identification detection are cut into regular samples of a fixed thickness by advance processing.
Further, in step 2, the identified and detected powdered strategic metal-volatile/non-volatile minerals are pressed into regular samples of fixed thickness by an infrared tablet press.
Further, in step 2, each mineral was pressure equalized to a fixed thickness of 10mm, 15mm, 20mm, 25mm, 30mm, 35mm, 40mm, 45mm, 50mm, 55mm, 60mm, 65mm, 70 mm.
Further, in step 3, the regular strategic metal mineral occurrence phase/non-occurrence phase minerals with controllable thickness obtained by the tabletting and sample preparation are identified and detected by using an intelligent gangue selector, and the mineral thickness characteristics and the mineral gray scale characteristics are obtained.
Further, in step 3, a thickness-gray characteristic curve of strategic metal mineral occurrence phase/non-occurrence phase is drawn according to the mineral thickness and gray characteristics detected by the intelligent gangue selector, a curve is fitted, and a single mineral thickness-gray characteristic identification library of occurrence phase/non-occurrence phase is established.
Compared with the prior art, the invention has at least one of the following beneficial effects:
(1) The method adopts the method of mineral thickness-gray scale correspondence to replace the existing R value method, establishes a mineral thickness-gray scale identification library, can effectively solve the imaging characteristic difference caused by the mineral thickness difference, and realizes the accurate identification of the minerals.
(2) The method of the invention firstly provides a novel method for sorting and pre-enriching strategic metal mineral products by using an intelligent gangue selector, and the method is complex in embedding and provides a plurality of types of occurrence phases.
In the invention, the technical schemes can be mutually combined to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the embodiments of the invention particularly pointed out in the written description and drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, like reference numerals being used to refer to like parts throughout the several views.
FIG. 1 is a process for establishing a sorting threshold of an intelligent gangue selector.
Detailed Description
The following detailed description of preferred embodiments of the invention is made in connection with the accompanying drawings, which form a part hereof, and together with the description of the embodiments of the invention, are used to explain the principles of the invention and are not intended to limit the scope of the invention.
Strategic metals are a large number of metals used in the military manufacturing industry and play a vital role in national defense construction. With the continuous development of global economy, strategic metals have become an important support for national security and economic development. Mineral resources such as strategic metal lithium, gallium, titanium and the like play an important and irreplaceable role in guaranteeing national economic safety, national defense safety and sustainable healthy development of strategic emerging industries, and are the leading field of resource competition of all countries in the world.
In order to pre-enrich strategic metals (including lithium, gallium, titanium and rhenium), the invention provides a strategic metal element enrichment method based on mineral X-ray response characteristics, which comprises the following steps:
step 1, determining a research area according to a target mining area, and clearly strategically controlling the composition and physicochemical properties of metal mineral substances;
In the step1, after the research area is determined, ores in the research area are randomly sampled, and physicochemical property analysis is performed to clarify strategic metal mineral substance composition and physicochemical properties.
Step 2, preparing a regular test sample with controllable thickness;
the process of preparing the test sample includes: collecting a research sample, determining strategic metal occurrence phase/non-occurrence phase categories by analyzing strategic metal mineral substance compositions and physicochemical properties, purchasing single minerals corresponding to the occurrence phase/non-occurrence phase according to the strategic metal mineral substance compositions, blending the purchased single minerals according to the actual strategic metal mineral substance composition proportion, and tabletting the sample by an infrared tabletting sample making machine to obtain a regular and thickness-controllable test sample;
In the step 2, the strategic metal occurrence phase/non-occurrence phase minerals obtained by purchasing are in a block shape and a powder shape, and the block-shaped strategic metal occurrence phase/non-occurrence phase minerals which are identified and detected are processed in advance and cut into regular samples with fixed thickness; the powder strategic metal pre-existing phase/non-pre-existing phase minerals for identification detection are pressed into regular samples with fixed thickness by an infrared tablet press, and each mineral is pressed into fixed thickness of 10mm, 15mm, 20mm, 25mm, 30mm, 35mm, 40mm, 45mm, 50mm, 55mm, 60mm, 65mm and 70 mm.
Step 3, identifying and detecting the test sample, and establishing an occurrence phase/non-occurrence phase single mineral thickness-gray feature identification library according to the detection result;
The specific process comprises the following steps: identifying and detecting regular strategic metal mineral occurrence phase/non-occurrence phase minerals with controllable thickness by using an intelligent gangue selector, wherein an X-ray sensor and a receiver of the intelligent gangue selector can automatically extract gray features of the detected minerals and output gray feature results, a laser sensor arranged in the intelligent gangue selector reads the thicknesses of the minerals, and draws thickness-gray feature curves of strategic metal mineral occurrence phase/non-occurrence phase according to the thicknesses and the gray features of the minerals detected by the intelligent gangue selector, and fits the curves to establish a single mineral thickness-gray feature identification library of occurrence phase/non-occurrence phase;
Step 4, preparing a mixed sample, detecting the mixed sample, and establishing an occurrence phase/non-occurrence phase mixed mineral thickness-gray scale characteristic identification library according to a detection result;
Grinding massive strategic metal occurrence phase/non-occurrence phase minerals into powder, wherein the particle size range of the powder is 0.125-0.5mm, using an infrared tabletting sample making machine to press the powder into mixed samples with different contents and different thicknesses according to the content ratio of each mineral component in the gangue, controlling the thickness of the samples to be 10mm, 15mm, 20mm, 25mm, 30mm, 35mm, 40mm, 45mm, 50mm, 55mm, 60mm, 65mm and 70mm, and aiming at simulating the actual gangue by using the prepared mixed minerals so as to obtain the X-ray image identification characteristics of the actual gangue under the condition of standard thickness and adjusting the content on the basis;
In the step 4, the prepared mixed sample is detected by using an intelligent gangue selector, the thickness and the gray scale of the characteristic value of the mineral image are extracted, a strategic metal mineral occurrence phase/non-occurrence phase mixed mineral thickness-gray scale characteristic curve is drawn, a curve is fitted, and an occurrence phase/non-occurrence phase mixed mineral thickness-gray scale characteristic recognition library is established.
Step 5, building a support vector machine model by using Python, inputting the thickness-gray characteristics of the mixed minerals as a training set of the model, generating a sorting threshold value of the intelligent gangue selector, and completing building of a gangue prediction model;
the specific process comprises the following steps: using Python to establish a support vector machine model, inputting the thickness-gray characteristics of the mixed minerals as a training set of the model, generating a sorting threshold value of an intelligent gangue selector by using the training set data as a basis, completing establishment of a gangue prediction model, dividing the minerals into two parts, and clearly judging which part is concentrate and which part is tailings according to the image characteristics of single minerals, wherein the sorting threshold value is shown in figure 1, and performing subsequent gangue sorting work;
And step 6, sorting actual minerals in the research area according to a sorting threshold value to obtain concentrate and tailings, wherein the concentrate is an occurrence phase of strategic metals, and pre-enrichment of the strategic metals is completed.
The specific process comprises the following steps: according to the separation threshold value of the intelligent gangue separator, the actual minerals in the research area are separated, the ores are crushed to 10-70mm through a crusher after being extracted and transported to the intelligent gangue separator through a conveying belt, the intelligent gangue separator recognizes strategic metal pre-existing phase minerals/non-pre-existing phase minerals through the separation threshold value given by the gangue prediction model, concentrate and tailings are obtained, automatic separation operation is completed, strategic metal grade in the concentrate is improved to 26ppm from 19ppm of raw ores, concentrate recovery rate exceeds 65%, and pre-enrichment is achieved.
It should be noted that the intelligent gangue selector adopted by the invention is provided with a high-precision laser sensor, the high-precision laser sensor can accurately read the thickness information of the ore, and the existing gangue selector can only read the gray characteristic value; in addition, the intelligent gangue separator provided by the invention comprises an identification module, and has the functions of: the defect that the traditional pseudo dual-energy X-ray identification module only processes the gray scale characteristics of minerals is changed, the thickness and the gray scale characteristics of the minerals are processed, the two characteristics are correspondingly analyzed, the accurate identification of the image characteristics of the minerals is realized, and the gangue sorting precision is improved; compared with the prior art, the intelligent gangue selector provided by the invention adopts the thickness-gray dual-characteristic sorting, so that the result is more accurate.
It should be emphasized that the current intelligent gangue selector eliminates the influence of thickness on mineral identification by means of dual-energy rays based on langer-beer law, but is difficult to realize true dual-energy X-ray transmission minerals in the aspect of industrial application, only pseudo dual-energy X-rays can be used, so that the influence of thickness can be weakened only but not eliminated, and the intelligent gangue selector is applied to mineral separation with large difference of gray characteristics of minerals, such as coal gangue separation and gold ore separation, but has good effects, such as gangue separation, in mineral separation with small difference of physical and chemical properties of minerals. According to the invention, the thickness and gray scale characteristics of a mineral image are acquired through an X-ray sensor and a laser sensor, an identification module of an intelligent gangue selector is improved, the thickness and gray scale characteristics are correspondingly analyzed, an image prediction model is trained by using a support vector machine, a mineral gray scale-thickness binary separation threshold is generated, and accurate identification of mineral components is realized.
The strategic metal element enrichment method based on the mineral X-ray response characteristics of the invention is applicable to strategic metal elements including lithium, gallium, titanium and rhenium.
Example 1
Firstly, determining that a research area is a mountain and western plain certain mining area, wherein strategic metal mineral products are mining area coal gangue, mainly sorting strategic metal is gallium, and detecting physicochemical properties of the mining area coal gangue, wherein main mineral components of the mining area coal gangue are clear to be kaolinite, boehmite, quartz, pyrite, illite and the like. Wherein the gallium element-bearing phase is mainly kaolinite and boehmite.
Secondly, raw ore such as kaolinite, boehmite, quartz, pyrite and illite is purchased, the raw ore is cut and processed into ores with regular thickness, the ore thickness is set to be 10-70mm, the ore thickness is respectively 10mm, 15mm, 20mm, 25mm, 30mm, 35mm, 40mm, 45mm, 50mm, 55mm, 60mm, 65mm and 70mm, an intelligent gangue selector is used for detecting the ores, the characteristic values of raw ore images, namely the ore thickness and gray scale, are extracted by the intelligent dry selector, wherein the ore thickness characteristic is obtained by a laser sensor, the ore gray scale characteristic is obtained by an X-ray excitation/receiving device, the ore characteristic data are fitted according to the ore thickness and gray scale characteristic, different ore characteristic value distribution rules are obtained, the characteristic value difference of the kaolinite images is the highest in gray scale, the gray scale values of other minerals are lower under the condition of the same thickness, and the characteristic value difference of the kaolinite and other minerals meets the intelligent gangue selector sorting standard.
Thirdly, identifying and detecting regular strategic metal mineral occurrence phase/non-occurrence phase minerals with controllable thickness obtained by tabletting samples by using an intelligent gangue selector, automatically extracting gray scale characteristics of the detected minerals by an X-ray sensor and a receiver of the intelligent gangue selector, outputting gray scale characteristic results, reading the thicknesses of the minerals by a laser sensor arranged in the intelligent gangue selector, drawing thickness-gray scale characteristic curves of strategic metal mineral occurrence phase/non-occurrence phase according to the thicknesses and the gray scale characteristics of the minerals detected by the intelligent gangue selector, fitting the curves, and establishing an occurrence phase/non-occurrence phase single mineral thickness-gray scale characteristic identification library;
Grinding ore raw ores such as kaolinite, boehmite, quartz, pyrite and illite into powder, controlling the particle size to be 0.125-0.5mm, controlling the particle size to be too large or too small, influencing the sample pressing result to generate errors, adjusting the average content of each mineral in the gangue in a mining area by taking the average content of each mineral as a reference, using an infrared tabletting and sampling machine to press mixed minerals with different contents, simulating mineral components of actual gangue, adjusting different thicknesses, keeping the infrared tabletting and sampling machine at 50 tons, keeping the pressure for 2 minutes, feeding the pressed simulated minerals into an intelligent gangue selector, and extracting the characteristic values and thickness characteristics of the simulated mixed mineral images;
Fifthly, dividing the extracted image features into two parts and inputting the two parts into a support vector machine prediction model, wherein the support vector machine takes 80% of data quantity as a training set and 20% of the data quantity as a test set, establishes an image prediction model, generates a binary sorting threshold value, and the test set detects that the prediction accuracy is 96.11%, proves the accuracy of the model, obtains the image prediction model aiming at the mineral in the mining area, and inputs the model and the binary sorting threshold value into an intelligent gangue sorter central database for sorting operation;
Crushing the coal gangue in the mining area to 10-70mm, namely 10mm, 15mm, 20mm, 25mm, 30mm, 35mm, 40mm, 45mm, 50mm, 55mm, 60mm, 65mm and 70mm respectively, conveying the crushed coal gangue to an intelligent gangue separator through a conveying belt, extracting mineral gray scale characteristics by an X-ray identification device of the intelligent gangue separator, extracting thickness characteristics by a laser sensor, transmitting data to a central processor, dividing the minerals into concentrates (strategic gallium-occurring phase minerals) and tailings (non-strategic gallium-occurring phase minerals) by the central processor according to an image prediction model, transmitting identification results to a blowing system, adopting different blowing measures for the concentrates and the tailings by the blowing system, and finally collecting the concentrates into concentrate tanks and the tailings into the tailings tanks.
Physical property detection of raw ore and concentrate shows that the average gallium content in the raw ore is 19.72ppm, the average gallium content in the concentrate after pre-enrichment is 26.2ppm, the concentrate recovery rate is higher than 65%, and the gallium element enrichment ratio is 1.33.
Example 2
The embodiment performs pre-enrichment on strategic metallic titanium, and the specific process is as follows:
firstly, determining that a research area is a certain mining area in Hebei, wherein strategic metal mineral products are ilmenite of the mining area, mainly sorting strategic metals into titanium, and detecting physicochemical properties of the mining area ore, wherein main mineral components of the mining area ore are clear such as feldspar, pyroxene, quartz, montmorillonite, titanomagnetite and the like.
Secondly, raw ore such as feldspar, pyroxene, quartz, montmorillonite and titano-magnetite is purchased, the raw ore is cut and processed into ore with regular thickness, the ore thickness is set to be 10-70mm, the ore thickness is respectively 10mm, 15mm, 20mm, 25mm, 30mm, 35mm, 40mm, 45mm, 50mm, 55mm, 60mm, 65mm and 70mm, an intelligent gangue separator is used for detecting the ore, the characteristic value of an image of the raw ore, namely the ore thickness and the gray scale, is extracted by the intelligent dry separator, wherein the ore thickness characteristic is obtained by a laser sensor, the ore gray scale characteristic is obtained by an X-ray excitation/receiving device, the characteristic data is fitted according to the ore thickness and the gray scale characteristic, the distribution rule of different ore characteristic values is obtained, the gray scale value of the titano-magnetite image is the lowest under the condition of the same thickness, the gray scale value of other minerals is higher, and the characteristic value difference of the titano-magnetite and other minerals meets the classification standard of the intelligent gangue separator.
Thirdly, identifying and detecting regular strategic metal mineral occurrence phase/non-occurrence phase minerals with controllable thickness obtained by tabletting samples by using an intelligent gangue selector, automatically extracting gray scale characteristics of the detected minerals by an X-ray sensor and a receiver of the intelligent gangue selector, outputting gray scale characteristic results, reading the thicknesses of the minerals by a laser sensor arranged in the intelligent gangue selector, drawing thickness-gray scale characteristic curves of strategic metal mineral occurrence phase/non-occurrence phase according to the thicknesses and the gray scale characteristics of the minerals detected by the intelligent gangue selector, fitting the curves, and establishing an occurrence phase/non-occurrence phase single mineral thickness-gray scale characteristic identification library;
Grinding ore raw ores such as feldspar, pyroxene, quartz, montmorillonite and titano-magnetite into powder, controlling the particle size to be 0.125-0.5mm, controlling the particle size to be too large or too small so as to influence the sample pressing result, generating errors, adjusting the average content of each mineral in the coal gangue in a mining area by taking the average content of each mineral as a reference, using an infrared tabletting and sampling machine to press mixed minerals with different contents, simulating mineral components of actual ilmenite, adjusting different thicknesses, keeping the infrared tabletting and sampling machine at 50 tons, keeping the pressure for 2 minutes, sending the pressed simulated minerals to an intelligent gangue selector, and extracting the characteristic values and thickness characteristics of the simulated mixed minerals;
Fifthly, dividing the extracted image features into two parts and inputting the two parts into a support vector machine prediction model, wherein the support vector machine takes 80% of data quantity as a training set and 20% of the data quantity as a test set, establishes an image prediction model, generates a binary sorting threshold value, and the test set detects that the prediction accuracy is 95.71%, proves the accuracy of the model, obtains the image prediction model aiming at the mineral in the mining area, and inputs the model and the binary sorting threshold value into an intelligent gangue sorter central database for sorting operation;
Step six, crushing the ore in the mining area to 10-70mm, namely 10mm, 15mm, 20mm, 25mm, 30mm, 35mm, 40mm, 45mm, 50mm, 55mm, 60mm, 65mm and 70mm respectively, conveying the ore to an intelligent gangue separator through a conveying belt, extracting the gray level characteristics of the ore by an X-ray identification device of the intelligent gangue separator, extracting the thickness characteristics by a laser sensor, transmitting data to a central processor, dividing the ore into concentrate (ilmenite) and tailings (feldspar and quartz) by the central processor according to an image prediction model, transmitting the identification result to a blowing system, and adopting different blowing measures on the concentrate and the tailings by the blowing system, and finally collecting the concentrate into a concentrate tank and the tailings into a tailings tank.
Physical property detection of the raw ore and the concentrate shows that the grade of TiO 2 in the raw ore is 1.87 percent, the grade of TFe is 8.53 percent, the grade of TiO 2 in the concentrate after pre-enrichment is 27.82 percent, the grade of TFe is 58.53 percent, and the concentrate recovery rate is higher than 70 percent.
Example 3
The embodiment performs pre-enrichment on strategic metallic lithium, and the specific process is as follows:
Firstly, determining a research area as a mining area of Sichuan ganzi, wherein the strategic metal mineral is spodumene of the mining area, the main strategic metal is lithium, and the ore of the mining area is adopted for physical and chemical property detection, so that the main mineral components of the mining area are clear to be feldspar, spodumene, quartz and the like.
Secondly, raw ore such as feldspar, spodumene, quartz, mica and green column is purchased, the raw ore is cut and processed into ores with regular thickness, the ore thickness is set to be 10-70mm, the ore thickness is respectively 10mm, 15mm, 20mm, 25mm, 30mm, 35mm, 40mm, 45mm, 50mm, 55mm, 60mm, 65mm and 70mm, an intelligent gangue selector is used for detecting the ores, the characteristic values of raw ore images, namely the ore thickness and gray scale, are extracted by the intelligent dry selector, wherein the ore thickness characteristics are obtained by a laser sensor, the ore gray scale characteristics are obtained by an X-ray excitation/receiving device, the characteristic data of the ores are fitted according to the ore thickness and gray scale characteristics, the distribution rule of the characteristic values of different ores is obtained, the image gray scale values of the spodumene are the lowest, the gray scale values of other minerals are all higher under the condition of the same thickness is found through research, and the characteristic value difference of the spodumene and other minerals meets the sorting standard of the intelligent gangue selector.
Thirdly, identifying and detecting regular strategic metal mineral occurrence phase/non-occurrence phase minerals with controllable thickness obtained by tabletting samples by using an intelligent gangue selector, automatically extracting gray scale characteristics of the detected minerals by an X-ray sensor and a receiver of the intelligent gangue selector, outputting gray scale characteristic results, reading the thicknesses of the minerals by a laser sensor arranged in the intelligent gangue selector, drawing thickness-gray scale characteristic curves of strategic metal mineral occurrence phase/non-occurrence phase according to the thicknesses and the gray scale characteristics of the minerals detected by the intelligent gangue selector, fitting the curves, and establishing an occurrence phase/non-occurrence phase single mineral thickness-gray scale characteristic identification library;
Grinding ore raw ores such as feldspar, spodumene, quartz mica and green column stone into powder, controlling the particle size to be 0.125-0.5mm, controlling the particle size to be too large or too small so as to influence the sample pressing result, generating errors, adjusting the average content of each mineral in the coal gangue in a mining area by taking the average content of each mineral as a reference, using an infrared tabletting sampling machine to press mixed minerals with different contents, simulating mineral components of actual ilmenite, adjusting different thicknesses, keeping the infrared tabletting sampling machine at 50 tons, keeping the pressure for 2 minutes, feeding the pressed simulated minerals to an intelligent gangue selector, and extracting image characteristic values and thickness characteristics of the simulated mixed minerals;
Fifthly, dividing the extracted image features into two parts and inputting the two parts into a support vector machine prediction model, wherein the support vector machine takes 80% of data quantity as a training set and 20% of data quantity as a test set, establishes an image prediction model, generates a binary sorting threshold value, and the test set detects that the prediction accuracy is 94.62%, proves the accuracy of the model, obtains the image prediction model aiming at the mineral in the mining area, and inputs the model and the binary sorting threshold value into an intelligent gangue sorter central database for sorting operation;
Step six, crushing the ore in the mining area to 10-70mm, namely 10mm, 15mm, 20mm, 25mm, 30mm, 35mm, 40mm, 45mm, 50mm, 55mm, 60mm, 65mm and 70mm respectively, conveying the ore to an intelligent gangue separator through a conveying belt, extracting the gray level characteristics of the minerals by an X-ray identification device of the intelligent gangue separator, extracting the thickness characteristics by a laser sensor, transmitting data to a central processor, dividing the minerals into concentrates (spodumene) and tailings (feldspar and quartz) by the central processor according to an image prediction model, transmitting the identification result to a blowing system, adopting different blowing measures on the concentrates and the tailings by the blowing system, and finally collecting the concentrates and the tailings into a tailings groove.
Physical property detection of raw ore and concentrate shows that the lithium content in the raw ore is 1.65%, the lithium content in the concentrate after pre-enrichment is 8.7%, and the concentrate recovery rate is higher than 65%.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. The strategic metal element enrichment method based on the mineral X-ray response characteristics is characterized by comprising the following steps of:
step 1, determining a research area according to a target mining area, and clearly strategically controlling the composition and physicochemical properties of metal mineral substances;
step 2, preparing a regular test sample with controllable thickness;
Step 3, identifying and detecting the test sample, and establishing an occurrence phase/non-occurrence phase single mineral thickness-gray feature identification library according to the detection result;
Step 4, preparing a mixed sample, detecting the mixed sample, and establishing an occurrence phase/non-occurrence phase mixed mineral thickness-gray scale characteristic identification library according to a detection result;
Step 5, building a support vector machine model by using Python, inputting the thickness-gray characteristics of the mixed minerals as a training set of the model, generating a sorting threshold value of the intelligent gangue selector, and completing building of a gangue prediction model;
And step 6, sorting actual minerals in the research area according to a sorting threshold value to obtain concentrate and tailings, wherein the concentrate is an occurrence phase of strategic metals, and pre-enrichment of the strategic metals is completed.
2. The method for enriching strategic metal elements based on the response characteristics of mineral X-rays according to claim 1, wherein in said step 1, after determining the investigation region, the ores of the investigation region are randomly sampled and subjected to physicochemical property analysis to clarify the strategic metal mineral composition and physicochemical properties.
3. The method for enriching strategic metal elements based on the characteristic of the X-ray response of minerals according to claim 1, wherein in said step 2, a study sample is collected and strategic metal occurrence/non-occurrence categories are determined by analyzing strategic metal mineral composition and physicochemical properties.
4. A strategic metallic element enrichment method based on mineral X-ray response characteristics according to claim 3, wherein in said step 2, single minerals corresponding to the occurrence/non-occurrence of the procurement are made up according to strategic metallic mineral substance composition.
5. The method for enriching strategic metal elements based on the characteristic of the response of mineral X-rays according to claim 4, wherein in the step 2, the purchased single mineral is mixed according to the composition ratio of actual strategic metal mineral substances, and the mixture is pressed into a sheet for sample preparation, so that a regular and thickness-controllable test sample is obtained.
6. The method for enriching strategic metal elements based on the characteristic of X-ray response of minerals according to claim 1, wherein in said step 2, the strategic metal-forming/non-forming minerals obtained by purchasing include both bulk and powdery ones, and the bulk strategic metal-forming/non-forming minerals subjected to identification detection are cut into regular samples of a fixed thickness by processing in advance.
7. The method for enriching a strategic metal element based on the characteristic of the X-ray response of minerals according to claim 6, wherein in the step 2, the identified and detected powdered strategic metal-occurring phase/non-occurring phase minerals are pressed into regular samples with fixed thickness by an infrared tablet press.
8. The method for strategic metal element enrichment based on mineral X-ray response signatures of claim 7, wherein in said step 2, each mineral is pressure equalized to a fixed thickness of 10mm, 15mm, 20mm, 25mm, 30mm, 35mm, 40mm, 45mm, 50mm, 55mm, 60mm, 65mm, 70 mm.
9. The method for enriching strategic metal elements based on the response characteristics of the mineral X-rays according to any one of claims 1 to 8, wherein in the step 3, the strategic metal mineral occurrence phase/non-occurrence phase minerals with controllable thickness are identified and detected by using an intelligent gangue selector to obtain the mineral thickness characteristics and the mineral gray scale characteristics.
10. The method for enriching strategic metal elements based on the X-ray response characteristics of minerals according to claim 9, wherein in the step 3, a thickness-gray characteristic curve of the strategic metal mineral occurrence/non-occurrence is drawn according to the thickness and gray characteristics of the minerals detected by the intelligent gangue selector, and a single mineral thickness-gray characteristic identification library of occurrence/non-occurrence is established by fitting the curve.
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