A method for accurately distinguishing heavy rare earth minerals in Bayan Obo

Abstract

The present invention provides a method for accurately distinguishing heavy rare earth minerals in Bayan Obo, which includes collecting backscattered electron images of samples to be tested; using standard substances containing the same elements to quantitatively set the elements required for the samples to be tested; selecting image resolution, magnification and minimum collected particle pixels; optimizing images with grayscale filters; detecting particles and selecting grayscale thresholds for heavy rare earth minerals; optimizing images with different binary filters; scanning heavy rare earth mineral particles with two images; setting energy spectrum acquisition parameters; and setting classifiers according to the crystal chemical laws of yttrium niobium, yttrium niobium and pyrolytic ore. The present invention uses multiple grayscale and binary filters to optimize images and multi-level screening conditions to eliminate irrelevant particles, and uses multiple energy spectra and image acquisition to greatly shorten the location time and quantification of heavy rare earth minerals.

Description

Technical Field

[0001] The invention provides a method for accurately distinguishing heavy rare earth minerals in Bayan Obo, belonging to the technical field of mineral deposit separation. Background Art

[0002] Rare earth elements (REE) include: 15 lanthanide elements, yttrium (Y) and scandium (Sc), which are a group of elements with similar physical and chemical properties. According to the differences in physical and chemical properties, they can be divided into three categories: light, medium and heavy rare earths. Compared with light rare earths, heavy rare earths are relatively rare, but their application value in high-tech fields such as information, biology, new materials, new energy, and space is irreplaceable, and they are a type of resource with real strategic value in my country. Medium and heavy rare earths only account for 1% of the world's rare earth reserves. About 98% of the world's heavy rare earth resources come from granite weathering crust rare earth deposits in the Nanling region of my country, but the extraction of rare earths from such deposits is prone to cause environmental problems. The Bayan Obo deposit in Inner Mongolia, my country is the world's largest rare earth mine, and its rare earth reserves rank first in the world. It has always been considered to be the main producer of light rare earths, including monazite, fluorocarbon cerium, etc. Such minerals have been widely studied, but medium and heavy rare earths have not received effective attention. Many research reports show that Baiyunebo contains almost no heavy rare earth minerals, but Zhan (2023) found that 30% of the samples in Baiyunebo contain some heavy rare earth minerals to a greater or lesser extent. Although the medium and heavy rare earth distribution value of the Baiyunebo mine is relatively low, the total amount of rare earths is huge, so the medium and heavy rare earth resources should not be underestimated. Although with the increase in mining depth, the types, distribution, and distribution patterns of medium and heavy rare earth elements in the main and eastern mines of Baiyunebo need to be studied. The more important heavy rare earth minerals are brown yttrium niobium ore, niobium yttrium ore, and calcite, which are mainly heavy rare earth elements Y, with contents of 33.8%-36.2%, 14.9-28.6%, and 3.38-4.69%, respectively. These heavy rare earth minerals mainly exist in various ores in the form of isomorphous and fine inclusions. Where are these heavy rare earth minerals? What are they? The answers to these questions are important prerequisites for the sorting of heavy rare earth mineral deposits. Therefore, it is necessary to establish a technology for rapid differentiation and identification of heavy rare earth minerals.

[0003] At present, scientists mainly use polarizing microscope observation combined with electron probe analysis for classification. However, finely dispersed heavy rare earth minerals are difficult to find under polarizing microscope. Commercial automatic mineral analysis systems use point-by-point scanning, and it usually takes several days to analyze a thin slice. Summary of the invention

[0004] In view of the above problems, the present invention adopts multiple grayscale and binary image filters to optimize images and multi-level screening conditions to eliminate irrelevant particles, and adopts multiple energy spectra and image acquisition to greatly shorten the positioning and quantification time of heavy rare earth minerals.

[0005] The specific technical solution is:

[0006] A method for accurately distinguishing heavy rare earth minerals in Bayan Obo, comprising the following steps:

[0007] Step 1: Cut the collected Bayan Obo carbonate samples into reasonable sizes, grind, paste, polish several times, and make common geological slices or embed them in epoxy resin targets. The slices or resin targets are carbonized using a vacuum carbonizer.

[0008] Step 2: Select appropriate accelerating voltage, beam current and contrast to collect good backscattered electron images of the sample to be tested. Use standard materials containing the same elements to quantitatively set the elements required for the sample to be tested. Minerals with different chemical compositions have different contrasts in backscattered images, which can be quantitatively characterized by grayscale values, using grayscale-similar materials as standards. The search range of heavy rare earth minerals can be narrowed by the characteristic interval of grayscale values.

[0009] Step 3. Select the appropriate image resolution, magnification, and minimum particle pixel. Based on the minimum size of the mineral you want to search for, enable "Auxiliary magnification" to determine the minimum pixel and calculate the appropriate magnification. Use high-resolution backscatter images to resolve the grayscale value range of mineral particles, and then use the lowest magnification to minimize acquisition time.

[0010] Step 4: Grayscale filter optimization image. Or the quality of the acquired backscattered image is poor, and some particles may be missing. In these cases, it is necessary to use a grayscale filter to optimize the image. There are mainly three types of filters, namely smoothing filter, Gaussian smoothing filter, and median filter.

[0011] The difference between the three is that the smoothing filter averages the brightness levels of adjacent pixels, converting a noisy image with discontinuous features into a low-noise image with clear features. The Gaussian smoothing filter is similar to the smoothing filter, but it can remove noise while maintaining the details of the image, making it clearer and more natural. The median filter removes any abnormally bright or dark pixels from the image to achieve image smoothing.

[0012] Step 5: Detect particles and select grayscale threshold of heavy rare earth minerals. Steps 2-4 are all processing of the collected backscattered images, and are all for obtaining more accurate grayscale values ​​of heavy rare earth minerals.

[0013] Step 6. Use different binary filters to optimize the image. Sometimes, because the mineral particle image is close to the background value, or the edge of other particles is close to the gray value of the target heavy rare earth mineral particles, they are often identified as minerals. If this part is not removed, the morphological measurement of rich rare earth minerals will be inaccurate, which will greatly increase the acquisition time of the target particles. Therefore, binary image filters should be used to identify the characteristics of the target heavy rare earth mineral particles to the greatest extent and accurately. There are six main types of binary filters, namely expansion filter, corrosion filter, closing filter, opening filter, separation feature and hole filling.

[0014] The differences between the six are as follows: The dilation filter adds a layer of pixels around all features, thereby increasing the effective area. The erosion filter removes a layer of pixels from all features, thereby reducing their effective area. The closing filter fills any holes within the feature and eliminates small details by smoothing the outer boundary, which is equivalent to using a dilation filter followed by an erosion filter. The opening filter discards small features while retaining the largest feature with a shape similar to the original feature, which is equivalent to using an erosion filter followed by a dilation filter. Separating features erodes the edge of a feature and then dilates it without reorganizing adjacent features. Hole filling fills any part of an electronic image that is completely surrounded by pixels belonging to a feature.

[0015] Step 7: Use two images to scan the heavy rare earth mineral particles. The first high-speed scan (low dwell time) detects the approximate position of each heavy rare earth mineral particle. In order to more accurately detect the characteristics of possible target particles, the second slow scan (high dwell time) only scans the possible target particles within the gray value range detected by the first image.

[0016] Step 8. Set the spectrum acquisition parameters. When using the particle detection mode, the acceleration voltage required for the rare earth elements of heavy rare earth minerals is relatively high, and it is generally necessary to select an energy range of 0-20keV. For the acquisition mode, you can choose live time or count. In order to achieve rapid positioning of heavy rare earth minerals, you need to select a lower count (1000-3000) or a shorter live time (0.01-0.05s). Due to the complex chemical composition of heavy rare earth minerals, quantitative detection results are required to achieve accurate distinction. Therefore, it is necessary to perform secondary spectrum acquisition on the particles screened out at a low speed. This step requires filtering and screening according to the common rules of the three main heavy rare earth minerals, that is, containing Nb and Y, Nb2O5>0%, Y2O3>0%.

[0017] Step 9, set the classifier. According to the crystal chemical laws of yttrium ore, yttrium ore and pyroclastic stone, set their classifiers (here, yttrium ore only discusses the heavy rare earth element Y, and does not discuss other minerals that do not contain heavy rare earths such as cerium yttrium ore and neodymium yttrium ore. Pyroclastic stone also only discusses particles containing Y element. REE refers to rare earth elements).

[0018] Yttrium niobate: The general chemical formula is (Y,Fe,U)(Nb,Ta)O4, Y2O3>10%, TFeO>5%, Nb2O540~65%.

[0019] Brown yttrium niobium ore: The general formula is (Y,REE)NbO4, Y2O3+REE2O3>30%, TFeO<5%, Nb2O540~55%.

[0020] Easy-to-dissolve stone: general formula is (Y, Ln, Ca, Th)(Ti, Nb)2(O, OH)6, TiO2>10%, Nb2O535~50%, CaO+REE2O3>30%, Y2O3>0%.

[0021] The purpose of the present invention is to establish a quantitative analysis method suitable for the Bayan Obo deposit, which can quickly locate and distinguish heavy rare earth minerals, expand the source of heavy rare earth resource development in my country, and provide strong technical support for the search and location of heavy rare earth minerals.

[0022] The present invention realizes the rapid positioning and differentiation of heavy rare earth minerals, overcoming the shortcomings of the prior art such as small mineral particles, low grade, complex composition and co-occurrence relationships, time-consuming and labor-intensive, and lack of an effective database. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The comparison of recognition effects using different filters in the embodiment is shown in FIG. 1 , wherein:

[0024] (a) Backscatter image; (b) Dilation filter; (c) Filter off; (d) No filter processing; (e) Pore filling filter; (f) Erosion filter; (g) Filter on; (h) Accurate result;

[0025] Figure 2 The classification result of the single field of view using the classifier in the embodiment;

[0026] Figure 3 The classification results of multiple fields of view using a classifier in a larger area of ​​the embodiment;

[0027] Figure 4 is an X-ray energy spectrum of the brown yttrium niobium ore of the embodiment;

[0028] Figure 5 is the X-ray energy spectrum corresponding to the yttrium niobium ore of the embodiment;

[0029] Figure 6 The X-ray energy spectrum diagram corresponding to the calcite of the embodiment;

[0030] Figure 7 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0031] The following is a further description of the technical solution of the present invention in combination with specific samples, but it is not intended to limit the content of the present invention. As mentioned above, the present invention provides a quantitative analysis method for quickly locating and distinguishing heavy rare earth minerals, such as Figure 7 As shown, the method comprises the following steps:

[0032] (1) Albite, diopside, olivine, aluminum garnet, rutile, calcite, apatite, monazite, crocodite, yttrium aluminum garnet, NdF3, ThO2 and related rare earth elements such as niobium Si, Na, Ca, Mg, Fe, Ti, Mn, P, F, La, Ce, Nd, Pb, Y, Nb, Th, F were used for calibration. A potassium niobate sample was used as a grayscale standard material. The experiment was conducted on a Bayan Obo carbonate thin section sample with a slice size of 2cm×5cm using an accelerating voltage of 20kV and a beam current of 5nA to obtain a higher counting rate.

[0033] (2) Select photo pixels of 4096×3072, 60× magnification, and minimum collection particle size of 5 μm to collect backscattered photos of the sample to be tested, and select a Gaussian smoothing filter to eliminate noise.

[0034] (3) In step (1), the present embodiment adjusts the brightness and contrast to make the grayscale of KNbO3 at 20000. After collecting the backscattered image, it is determined that the heavy rare earth mineral is in the grayscale range of 21000-23000, and this value is fixed.

[0035] (4) Use different binary filters to optimize the image. Since the mineral particle image is close to the background value, or the edge of other particles is close to the gray value of the particle to be searched, they are often identified as minerals. If this part is not eliminated, the morphological measurement of rare earth-rich minerals will be inaccurate, which will greatly increase the acquisition time of the target particles. Therefore, a binary image filter should be used to accurately identify the characteristics of the target heavy rare earth mineral particles to the greatest extent. This embodiment tried several binary filters. For this sample, an open filter was selected, that is, corrosion followed by expansion, which is suitable for this sample. Figure 1 shown.

[0036] (5) Two images are used to scan the heavy rare earth mineral particles. In the first high-speed scan, this embodiment uses a dwell time of 2 μs to quickly scan the entire thin slice and detect the approximate position of each possible target particle in the grayscale range. In order to more accurately detect the characteristics of possible target particles, the second 4 μs scan is performed only on the possible target particles detected by the first image.

[0037] (6) Using the particle detection mode, the acceleration voltage required for the rare earth elements of heavy rare earth minerals is relatively high, and the energy range of 20keV is selected, and the live time is selected as 0.03s. The secondary energy spectrum acquisition time is selected as 1.97s, so that the target particle count can reach 200,000, and the common characteristics of the three main heavy rare earth minerals are defined: containing Nb and Y, Nb2O5>0%, Y2O3>0%.

[0038] (7) According to the crystal chemical laws of yttrium ore, yttrium columbite and pyroclastic stone, their classifiers are set (here, yttrium ore only discusses the one containing heavy rare earth element Y, and does not discuss other minerals such as cerium yttrium ore and neodymium yttrium ore that do not contain heavy rare earth elements. Pyroclastic stone also only discusses the particles containing Y element. REE refers to rare earth elements).

[0039] Yttrium niobate: The general chemical formula is (Y,Fe,U)(Nb,Ta)O4, Y2O3>10%, TFeO>5%, Nb2O540~65%.

[0040] Brown yttrium niobium ore: The general formula is (Y,REE)NbO4, Y2O3+REE2O3>30%, TFeO<5%, Nb2O540~55%.

[0041] Calcite: The general formula is (Y, Ln, Ca, Th)(Ti, Nb)2(O, OH)6, TiO2>10%, Nb2O535~50%, CaO+REE2O3>30%, Y2O3>0%.

[0042] Figures 4 to 6 These are the X-ray energy spectra corresponding to yttrium niobate, yttrium niobate, and calcite, respectively.

[0043] (8) According to the scale of the thin section, the magnification is consistent with (2), and the entire thin section is selected for experimental operation. At the end of the experiment, the collected data is classified using a classifier, and different mineral particles are colored, such as Figure 2 and Figure 3 shown.

Claims

1. A method for accurately distinguishing heavy rare earth minerals in Bayan Obo, characterized in that: The following steps are involved: Step 1: The collected Bayan Obo carbonate samples are cut, ground and polished multiple times to make common geological slices or embedded in epoxy resin targets; Carry out carbon coating treatment on the thin slice or resin target by using a vacuum carbon coating apparatus; Step 2: Select the accelerating voltage, beam current and contrast, collect the backscattered electron image of the sample to be tested; use the standard substance containing the same element to quantitatively set the elements required for the sample to be tested; Using gray-similar substances as standards, the gray-scale value interval of heavy rare earth minerals is determined, and the search range of heavy rare earth minerals is narrowed by the characteristic interval of gray-scale values; Step 3: Select image resolution, magnification, and particle pixels; search for the minimum size of heavy rare earth minerals as needed, determine the minimum pixel by enabling auxiliary magnification, and calculate the magnification; use high-resolution backscatter images to resolve the gray value range of mineral particles, and then use the lowest magnification to minimize the acquisition time; Step 4, grayscale filter optimization image; if the quality of the acquired backscattered image is poor, the grayscale filter is used to optimize the image; the grayscale filter is used to optimize the image, which includes three filters, namely, smoothing filter, Gaussian smoothing filter, and median filter; Step 5, detecting particulate matter and selecting a grayscale threshold of heavy rare earth minerals; Step 6: Use different binary filters to optimize the image; A binary image filter is used to remove edge features of target particles with similar grayscale and identify the morphological features of heavy rare earth mineral particles; The image is optimized using binary filters, which include 6 filters: dilation filter, erosion filter, closing filter, opening filter, separation feature and hole filling. Step 7, using two images to scan the heavy rare earth mineral particles; the first high-speed scan detects the approximate position of each heavy rare earth mineral particle; the second slow scan only scans the possible target particles detected in the first image within the set grayscale range; Step 8, set the spectrum acquisition parameters; filter and screen according to the common rules of three heavy rare earth minerals: yttrium ore, yttrium ore and pyrolyte, containing Nb and Y, Nb2O5>0%, Y2O3>0%; For the acquisition mode, select a low count of 1000 to 3000, or a short live time of 0.01 to 0.05 seconds; For the particles screened out at a low speed, a secondary energy spectrum acquisition is performed; Step 9, setting a classifier; setting a classifier according to the crystal chemical laws of yttrium ore, fernognathite and pyrolyte.

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