Method for calculating flotation rate of copper smelting slag

Abstract

The invention belongs to the relevant technical field of metallurgical mineral separation, and discloses a method for calculating the flotation rate of copper smelting slag, which comprises the following steps of: acquiring image data of various elements contained in a copper slag block by adopting an X-ray fluorescence spectrophotometer; extracting different color pixels from the image data of different elements, and obtaining an image in which only color pixels are reserved and a background region is completely removed; extracting a particle contour in the image, and generating a binary image of the particles and the separation background; calculating the area of each particle and carrying out classified statistics on all the particles; and dividing the image into uniform grid units, screening whether each particle of each grid unit has flotation property or not according to the area of the particle, and calculating to obtain a flotation rate result. Compared with the prior art, the calculation accuracy of the copper smelting slag flotation rate can be remarkably improved, and meanwhile the method has the advantages of being high in judgment efficiency, convenient to control, good in adaptability and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field related to metallurgical mineral processing, and more specifically, relates to a method for calculating the flotation rate of copper smelting slag. Background Art

[0002] Copper smelter slag, a byproduct of the copper smelting process, contains a variety of valuable metal elements, such as copper, iron, lead, and zinc. Recycling copper smelter slag through flotation not only improves resource utilization but also reduces environmental pollution. Accurately calculating the flotation efficiency of copper smelter slag is one of the keys to optimizing the flotation process.

[0003] A search revealed that existing methods for calculating the flotation efficiency of copper smelting slag primarily rely on chemical analysis and physical separation experiments. These processes are cumbersome and time-consuming, making it difficult to quickly and accurately assess flotation performance. Furthermore, due to the uneven distribution and complex morphology of particles in copper smelting slag, existing analytical methods cannot fully reflect the particle characteristics, directly impacting the optimization and evaluation of flotation processes.

[0004] Accordingly, many problems existing in the existing flotation rate calculation technology have become the main bottleneck restricting the efficient and high-quality recycling of copper smelting slag. This field urgently needs further research and improvement to better meet comprehensive needs. Summary of the Invention

[0005] In response to one or more of the above-mentioned deficiencies or needs in the prior art, the present invention provides a method for calculating the flotation rate of copper smelting slag. Based on the research mechanisms of image processing and grid analysis, the entire detection process, including image acquisition and processing, particle identification and statistics, grid division and screening, is redesigned and optimized. This method can significantly improve the accuracy of calculating the flotation rate of copper smelting slag. Furthermore, the method has the advantages of high determination efficiency, ease of control, and good adaptability. Therefore, the method is particularly suitable for application scenarios in which the flotation rate of copper smelting slag with various complex structures is calculated.

[0006] To achieve the above object, according to the present invention, a method for calculating the flotation rate of copper smelting slag is provided, characterized in that the method comprises the following steps: S1. Using an X-ray fluorescence spectrometer to obtain image data of various elements from a copper slag block as a test sample; S2. extracting different color pixels from the image data of different elements, and obtaining an image in which only the color pixels are retained and the background area is completely removed; S3. Grayscale and binarize the image obtained in step S2 to extract the particle outlines in the image and generate a binary image of the particles and the separated background. Next, the area of ​​each particle is calculated using a contour extraction method and converted to a circle to obtain the equivalent diameter of the particle. At the same time, all particles are classified and counted according to a preset particle diameter range. S4, dividing the binary image into uniform grid units according to a preset grid side length, and using the grid units as basic units for particle analysis; then, for each particle in each grid unit, screening whether it has flotation properties based on its area; S5. Screen out the particles with floatability in step S4, calculate the ratio between the number of the particles and the total number of particles, and thereby obtain the required flotation rate calculation result.

[0007] As a further preference of the present invention, in step S1, it is preferred to also include pretreatment of the test sample, which pretreatment includes the following processes: cleaning the copper slag block to remove surface dust and impurities, drying to eliminate moisture, and then polishing the test surface of the copper slag block to obtain a flat and smooth surface.

[0008] As a further preferred embodiment of the present invention, in step S1, the elements are preferably copper, iron, lead, and zinc.

[0009] As a further preference of the present invention, in step S2, different color pixel thresholds are preferably set, and then the color pixels in the image that meet the threshold conditions are extracted, while other color pixels and background areas are completely removed, thereby obtaining an image with a clear display of the target area.

[0010] As a further preferred embodiment of the present invention, in step S3, the area of ​​each particle is preferably recalculated and verified using a pixel point method.

[0011] As a further preferred embodiment of the present invention, in step S3, the diameter sizes and diameter distributions of all particles are preferably visualized and exported in the form of histograms.

[0012] As a further preference of the present invention, in step S4, it is preferred to screen whether the particles have floatation properties according to the following conditions: first, the actual area of ​​the particles exceeds two-thirds of the area of ​​the grid unit; second, the outline of the particles crosses the boundary of the grid unit; if the current particle meets any of the conditions, it is determined to have floatation properties.

[0013] In general, the above technical solutions conceived by the present invention have the following technical advantages compared with the existing technology: 1. The present application aims at the technical problem of the prior art flotation rate evaluation relying on chemical analysis, lengthy process and sample destruction, and constructs a collaborative computing system of "image acquisition-intelligent analysis-grid screening" in a targeted manner, and redesigns and optimizes the entire detection process including image acquisition and processing, particle identification and statistics, grid division and screening, etc., which can significantly improve the calculation accuracy and efficiency of copper smelting slag flotation rate; 2. The present application can realize non-destructive detection and dynamic characterization of copper slag particles by combining XRF (X-ray fluorescence spectrometer) element imaging technology with multi-threshold image processing algorithm. At the same time, by adopting a dual-mode particle statistical verification mechanism, the identification error of small particles can be greatly reduced by combining the collaborative calculation of the contour method and the pixel point method. 3. The present application also proposes a dynamic flotation determination model based on grid elements, which realizes the accurate classification of flotation particles through grid division and area / boundary double screening conditions, and significantly improves the determination efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a process flow diagram of a method for calculating the flotation rate of copper smelting slag according to a preferred embodiment of the present application; Figure 2 is a schematic diagram for more specifically illustrating the image processing process of the present application; Figure 3 is a schematic diagram for more specifically illustrating the particle identification and statistics of the present application. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme of the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0016] Figure 1 is a process flow diagram of a method for calculating the flotation rate of copper smelting slag according to a preferred embodiment of the present application, which will be described below with reference to Figure 1 and Figure 2 and Figure 3 to more specifically explain and illustrate the present application.

[0017] Step one, for the copper slag block as a test sample, an X-ray fluorescence spectrometer is used to obtain image data containing various elements.

[0018] More specifically, the following steps can be included in this step: ① Cleaning the copper slag sample: To prevent surface dust, impurities or other contaminants from affecting subsequent image analysis, the copper slag sample is first thoroughly cleaned.

[0019] ② Remove moisture: Cleaned copper slag samples usually contain a certain amount of moisture, so they must be dried to prevent moisture from interfering with reflection or light refraction during image acquisition, ensuring image clarity and consistency.

[0020] ③ Ensure that the sample surface is clean and dry: After cleaning and drying, it is also necessary to ensure that there is no moisture or contaminants remaining on the sample surface.

[0021] ④ XRF instrument collects elemental image data: The processed copper slag blocks are placed in an XRF (X-ray fluorescence spectrometer) instrument for analysis. The analysis of copper smelting slag focuses on the distribution of elements such as copper, iron, lead, and zinc. The image data of these elements provides an important basis for subsequent particle analysis.

[0022] Step 2: extract different color pixels from the image data of different elements, and obtain an image in which only the color pixels are retained and the background area is completely removed.

[0023] This step involves image processing. Image processing is the process of converting the raw collected image data into useful information, especially in analyzing the distribution of different elements and particle characteristics in copper smelting slag. The following are the main steps of image processing, such as Figure 2 As shown in the figure, the copper element (Cu) image is used as an example: ① Elemental Image Data Extraction: Different elements appear in different colors in an image. For example, copper typically appears red in an image. By setting a specific color channel threshold, you can extract the target element region from the image for subsequent analysis.

[0024] ② Set the color channel threshold: Taking copper as an example, in RGB mode, the red channel (R) is stronger. By setting the R channel threshold (for example, R ≥ 200), all red pixels that meet the criteria are extracted, indicating the copper area.

[0025] ③ Screening and removing non-target areas: For pixels with R values ​​lower than 200, set them to black (value 0), thereby removing noise and non-target areas in the image and retaining only the copper element area.

[0026] ④ Obtain the target image: After processing, only the red area (copper element) is retained in the image, and the background and other irrelevant areas are removed, ensuring that the target area is clearly visible, providing high-quality images for subsequent particle analysis and screening.

[0027] In step three, the obtained image is grayscaled and binarized to extract the particle contours in the image and generate a binary image of the particles and the separated background. Then, the contour extraction method is used to calculate the area of ​​each particle, and it is equivalent to a circle to obtain the equivalent diameter of the particle. At the same time, all particles are classified and counted according to the preset particle diameter range.

[0028] In this step, by extracting image data and identifying particle outlines, the particle morphology, size, and distribution can be accurately quantified. By calculating the area or equivalent diameter of the particles and classifying them into different particle size ranges, data support is provided for flotation performance evaluation. Figure 3 As shown in , the following is a more specific identification and statistics process: ① Image preprocessing and binarization: Read and grayscale the input image to remove color interference. Binarization converts the image to black and white, using two different thresholds to distinguish between background and particle areas, thereby improving particle recognition accuracy.

[0029] ② Contour extraction and particle area calculation: Contour extraction can be used to identify particle boundaries and calculate particle area. Particle size can be further quantified by treating particles as circular and calculating their equivalent circular diameter.

[0030] ③ Particle classification and statistics: Based on the preset particle size range (such as 0-30 μm, 30-43 μm, 75-200 μm, 200-1000 μm, >1000 μm), the particles are classified according to equivalent diameter and the number of particles of each type is counted to analyze the particle distribution.

[0031] ④ Pixel point method verification and small particle correction: The pixel point method can be used to correct the particle area, especially for small particles, by verifying the results of the contour method and eliminating errors to ensure statistical accuracy.

[0032] ⑤Result visualization and data export: Visualize the particle size distribution results in the form of a histogram and export statistical data (such as Excel files) for further analysis, providing a basis for flotation effect evaluation and process optimization.

[0033] Step 4: Divide the binary image into uniform grid units according to a preset grid side length, and use these as basic units for particle analysis; then screen each particle in each grid unit for flotation properties based on its area.

[0034] In this step, the image is evenly divided into a grid of a set size (preferably 43 μm side length), providing a standardized unit for particle area calculation and screening. Particles within each grid are screened for flotation potential based on their area and whether their outline crosses the grid boundary, effectively identifying particles with flotation potential. The following is the grid division and screening process: The first step is to divide the grid: The image is evenly divided into multiple small cells according to the set grid size (each grid cell has a side length of 43 μm). These grid cells provide the basic unit for particle analysis, facilitating more detailed analysis of particle distribution and characteristics.

[0035] The second is the screening of particles: First, within each grid cell, the area of ​​the particle is calculated (either using the contour method or the pixel method). Next, the particle is considered floatable based on two screening criteria: (i) the particle's actual area exceeds two-thirds of the grid cell area; and (ii) the particle's outline crosses the grid boundary.

[0036] Next comes the flotation determination: If a particle meets any of the above conditions, it is considered to be flotable. Otherwise, it is considered to be non-floatable. This screening method can effectively identify particles with flotation potential.

[0037] Step 5: Calculate the ratio between the number of the selected flotation particles and the total number of particles, thereby obtaining the required flotation rate calculation result.

[0038] More specifically, in this step, the flotation rate is an important indicator for evaluating the proportion of floatable particles in a particle, reflecting the recovery efficiency of the flotation process. In image processing, the flotation rate can be calculated by counting the number of particles that meet the flotation conditions, Nfloat, and the total number of all particles in the image, Ntotal. The calculation formula is:

[0039] Where R represents the flotation rate, expressed as a percentage (%). Nfloat represents the number of particles that meet the flotation criteria, that is, the total number of particles whose area exceeds two-thirds of the grid cell area or crosses the grid boundary. Ntotal represents the total number of all particles in the image, including both floatable and non-floatable particles. The flotation rate reflects the flotation distribution characteristics of the particles, helps determine the effectiveness and recovery efficiency of the flotation process, and provides data support for process optimization and resource utilization.

[0040] In summary, according to the copper smelting slag flotation rate calculation method of the present invention, a "image acquisition-intelligent analysis-grid screening" collaborative computing system is specifically constructed, and the entire detection process including image acquisition and processing, particle identification and statistics, grid division and screening, etc. is redesigned and optimized. Accordingly, the rapid determination of particle flotation properties and the efficient calculation of flotation rates can be achieved, providing technical support for flotation process optimization and comprehensive resource utilization, and thus has good practical value and application prospects.

[0041] It will be easily understood by those skilled in the art that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for calculating the flotation rate of copper smelting slag, characterized in that: The method comprises the following steps: S1. Using an X-ray fluorescence spectrometer to obtain image data of various elements from a copper slag block as a test sample; S2. extracting different color pixels from the image data of different elements, and obtaining an image in which only the color pixels are retained and the background area is completely removed; S3. Grayscale and binarize the image obtained in step S2 to extract the particle outlines in the image and generate a binary image of the particles and the separated background. Next, the area of ​​each particle is calculated using a contour extraction method and converted to a circle to obtain the equivalent diameter of the particle. At the same time, all particles are classified and counted according to a preset particle diameter range. S4, dividing the binary image into uniform grid units according to a preset grid side length, and using the grid units as basic units for particle analysis; then, for each particle in each grid unit, screening whether it has flotation properties based on its area; S5. Screen out the particles with floatability in step S4, calculate the ratio between the number of the particles and the total number of particles, and thereby obtain the required flotation rate calculation result.

2. The method according to claim 1, wherein In step S1, it is preferred to further include pretreatment of the test sample, which includes the following processes: cleaning the copper slag block to remove surface dust and impurities, drying to eliminate moisture, and then polishing the test surface of the copper slag block to obtain a flat and smooth surface.

3. The method according to claim 1 or 2, wherein: In step S1 , the elements are preferably copper, iron, lead, and zinc.

4. The method according to any one of claims 1 to 3, wherein: In step S2, different color pixel thresholds are preferably set, and then color pixels that meet the threshold conditions are extracted from the image, while other color pixels and background areas are completely removed, thereby obtaining an image with a clear display of the target area.

5. The method according to any one of claims 1 to 4, characterized in that In step S3, the area of ​​each particle is preferably recalculated and verified using a pixel point method.

6. The method according to any one of claims 1 to 5, wherein: In step S3 , the diameter sizes and diameter distributions of all particles are preferably visualized and exported in the form of histograms.

7. The method according to any one of claims 1 to 6, wherein: In step S4, it is preferred to screen whether the particles have flotation properties according to the following conditions: first, the actual area of ​​the particles exceeds two-thirds of the area of ​​the grid unit; second, the outline of the particles crosses the boundary of the grid unit; if the current particle meets any of the conditions, it is determined to have flotation properties.

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