A fine grinding control method and control device for ore dressing

Abstract

The present application relates to the technical field of fine grinding of a grinding mill, and specifically to a fine grinding control method and control device for mineral processing, the method comprising: a collection step: after the grinding mill begins to operate stably, real-time collection of the weight proportion, average particle size, and classification efficiency of the minerals in different storage areas at each collection moment after the classifier has respectively sent different types of minerals into the corresponding storage areas; a processing step: reflecting the overall grinding efficiency of the grinding mill by analyzing the data characteristics collected at all collection moments for all types of minerals; and a control step: adaptively adjusting the current rotation speed of the grinding mill using the overall grinding efficiency of the grinding mill. The present application aims to adaptively adjust the rotation speed of the grinding mill by analyzing the overall grinding efficiency obtained, thereby achieving a more accurate rotation speed control strategy.

Description

Technical Field

[0001] The present application relates to the technical field of fine grinding of grinding mills, and in particular to a fine grinding control method and a control device thereof for ore dressing. Background Art

[0002] Fine grinding is a key step in mineral processing. With the decline in easily processed mineral resources and the increasing proportion of lean, fine, and mixed refractory ores, more sophisticated grinding technologies are needed to effectively separate minerals. Fine grinding technologies, such as stirred mills, have significantly improved grinding efficiency and the degree of monomer dissociation of target minerals, reducing production costs and improving economic benefits. Fine grinding can separate the useful components in the ore, creating conditions for subsequent operations and directly impacting the technical and economic indicators of the beneficiation plant. Appropriate grinding fineness can maximize processing capacity and improve production efficiency, while ensuring sufficient dissociation of minerals from gangue, which is conducive to improving metal recovery. Controlling grinding fineness is particularly critical for subsequent flotation operations, as it directly affects the flotation behavior of the mineral, including its surface properties and slurry properties. Therefore, fine grinding control not only affects beneficiation efficiency and costs, but also directly impacts the quality and economic benefits of the final product, making it an indispensable part of the beneficiation process.

[0003] During the mineral processing process, different minerals have different hardness, particle size and dissociation characteristics, so the suitable grinding mill speeds are also different. However, most existing mineral processing technologies rely on an initially set fixed speed. A single speed is difficult to adapt to the characteristics of all minerals, thus affecting the grinding efficiency and the dissociation degree of the minerals. This may not only lead to over-grinding or under-grinding of useful minerals, but also affect the subsequent flotation effect, reduce metal recovery rate, and increase energy consumption and production costs. Summary of the Invention

[0004] In order to solve the above technical problems, the present application provides a fine grinding control method and a control device for mineral processing, and the technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a fine grinding control method for mineral processing, the method comprising the following steps:

[0006] Collection step: After the grinding mill starts to run stably, the weight ratio, average particle size and classification efficiency of the minerals in different storage areas at each collection moment are collected in real time after the classifier sends different types of minerals to the corresponding storage areas;

[0007] Processing steps: By analyzing the data characteristics of all minerals collected at all collection times, the overall grinding efficiency of the mill is reflected; specifically:

[0008] S1, performing curve fitting on the weight percentage and average particle size of each mineral at any collection time and all previous collection times, analyzing the curvature characteristics of the corner points on the fitted curve and the range characteristics of the data around the corner points to determine the grinding fit of each mineral at any collection time;

[0009] S2, for each mineral, arrange the grinding fit calculated at the current and all previous collection moments in ascending order of time to obtain the current fit sequence of each mineral;

[0010] S3, calculate the similarity between the current fit sequence of each mineral and the classification efficiency sequence composed of the classification efficiencies at all collection moments at each collection moment; and use the similarity of each mineral at all collection moments as each row, and the similarity of different minerals at the same collection moment as each column to construct a trend change matrix;

[0011] S4, clustering the elements in the trend change matrix; determining the overall grinding efficiency of the grinding mill based on the proportion of mineral species in each cluster and the average distance between the center of each cluster and the centers of all other clusters in the clustering results;

[0012] Control steps: Adaptively adjust the current rotation speed of the grinding mill using the overall grinding efficiency of the grinding mill.

[0013] Preferably, in the collecting step, the time when the grinding mill starts to operate stably is one minute after the grinding mill starts to work.

[0014] Preferably, in step S1, the data around the corner point is all data within a preset time window on the fitting curve with the corner point as the center.

[0015] Preferably, in step S1, the grinding suitability of each mineral at any collection moment is recorded as A, and the calculation relationship is:

[0016] ; Wherein, exp represents the exponential function with the natural constant e as the base; Indicates the number of corner points on the i-th fitting curve; represents the curvature of the jth corner point on the i-th fitting curve; Represents the data range within the preset time window centered at the j-th corner point on the i-th fitting curve.

[0017] Preferably, in step S3, the similarity at each acquisition moment is obtained by calculating the similarity between the two sequences at each acquisition moment using a dynamic kernel correlation algorithm.

[0018] Preferably, in step S4, the clustering distance in the clustering process is the numerical difference and position difference between elements.

[0019] Preferably, in step S4, the calculation relationship of the overall grinding efficiency characteristic value of the grinding mill is:

[0020] Calculate the ratio of the mineral species proportion of each cluster and the average distance between the center of the cluster and the centers of all other clusters;

[0021] The average level of the ratio results of all clusters is used as the overall grinding efficiency characteristic value of the grinding mill.

[0022] Preferably, in the control step, the relationship for adaptively adjusting the current rotation speed of the grinding mill using the overall grinding efficiency of the grinding mill is:

[0023] ;in, is the current grinding mill adjusted speed, is the preset adjustment parameter, e is the natural constant, B is the overall grinding efficiency of the mill, and R is the current speed of the mill.

[0024] Preferably, the initial rotation speed of the grinding mill when it starts operating is a preset percentage of the critical rotation speed.

[0025] On the second aspect, another embodiment of the present application provides a fine grinding control device for mineral processing, which includes a mill and a classifier; the mill is used to control its rotation speed to grind the material and send it to the classifier from the mineral outlet of the mill; the classifier is used to divide the ground product into different types of minerals and send it to the corresponding storage area from the overflow port of the classifier, so that the screened minerals can be temporarily stored in specific areas for further processing or sorting, and the unqualified minerals are sent back to the mill from the sand return port of the classifier for re-grinding to ensure that the particle size of the grinding product meets the requirements of subsequent mineral processing operations.

[0026] This application has at least the following beneficial effects:

[0027] The present application proposes a fine grinding control method and a control device for mineral processing. In response to the problem of the grinding compatibility of the mill for different minerals, the corner point distribution characteristics on the curve after curve fitting are used to reflect the complexity and severity of the mineral distribution and particle size changes during the grinding process, thereby solving the problem of the compatibility between the preset parameters of the mill and the mineral grinding effect; in response to the problem of the overall grinding efficiency of the mill, a clustering method is used for the trend change matrix to reflect the distribution, change trend and influence of different mineral types on the classification efficiency, thereby eliminating the influence of single mineral characteristics on the overall grinding efficiency evaluation; by utilizing the overall grinding efficiency to adaptively adjust the speed of the mill, a more accurate speed control strategy is achieved to adapt to the characteristics of different minerals and improve the efficiency and economic benefits of the entire mineral processing process. The improved speed control strategy can dynamically adjust the operating parameters of the mill, optimize the grinding process, and enable the mill to better adapt to the physical characteristics of different minerals and achieve more effective grinding and separation. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A flowchart of a fine grinding control method for mineral processing provided in one embodiment of the present application;

[0029] Figure 2 A flow chart of a process for determining the overall grinding efficiency of a grinding mill provided in one embodiment of the present application. DETAILED DESCRIPTION

[0030] Example 1

[0031] During the mineral processing process, different minerals have different hardness, particle size and dissociation characteristics, so the suitable grinding mill speeds are also different. However, most existing mineral processing technologies rely on an initially set fixed speed. A single speed is difficult to adapt to the characteristics of all minerals, thus affecting the grinding efficiency and the dissociation degree of the minerals. This may not only lead to over-grinding or under-grinding of useful minerals, but also affect the subsequent flotation effect, reduce metal recovery rate, and increase energy consumption and production costs.

[0032] Based on this, this embodiment provides a step flow chart of a fine grinding control method for mineral processing, as shown in the attached figure. Figure 1 As shown, the method includes the following steps:

[0033] Collection steps: After the grinding mill starts to run stably, the weight proportion, average particle size and classification efficiency of the minerals in different storage areas at each collection moment are collected in real time after the classifier sends different types of minerals to the corresponding storage areas.

[0034] When the grinding mill starts to operate, the physical conditions such as temperature, humidity, friction coefficient, etc. inside the machine have not yet reached a stable state. These factors will affect the grinding effect and data accuracy. Therefore, relevant data will be collected in real time after the grinding mill starts to operate stably.

[0035] The time when the grinding mill starts to operate stably is set to one minute after the grinding mill starts to work in this embodiment, and can be set by the implementer.

[0036] In addition, in this embodiment, the time interval for collecting the above-mentioned relevant data is set to 1s, and the collected data is overall normalized. The normalization method adopts the maximum and minimum value normalization method. The maximum and minimum value normalization method is a well-known technology and will not be described in detail.

[0037] Processing steps: By analyzing the data characteristics collected at all collection times for all types of minerals, the overall grinding efficiency of the mill is reflected.

[0038] The weight proportion reflects the weight proportion of minerals in each storage area, that is, the ratio of the weight of minerals in each storage area to the total weight of minerals in all storage areas. By analyzing the changes in the proportion of different types of minerals, we can understand the distribution of minerals in the grinding process. If the proportion of a certain mineral increases, it may indicate that the grinding efficiency of the mill is higher for this mineral because more of the mineral is effectively separated. By monitoring the average particle size of different minerals, the grinding efficiency of the mill for different minerals can be evaluated.

[0039] If the average particle size of the target mineral after grinding is low, it means that the grinding effect of the mill on the target mineral is good. Conversely, if the average particle size of the target mineral after grinding is high, it means that the grinding effect of the mill on the target mineral is poor.

[0040] Compared to weight percentage and average particle size, classification efficiency reflects the classifier's ability to separate ore particles by size. High classification efficiency means more fine-grained minerals are effectively separated, and it is positively correlated with grinding efficiency. If grinding efficiency is low, mineral particles may not be fully dissociated, resulting in a decrease in classification efficiency. It is directly related to the performance and operational effectiveness of the classifier, indicating the classifier's ability to separate materials of the target particle size. It shows the classifier's separation effect on materials of a given particle size, that is, the degree of enrichment of fine-grained materials in the overflow. It is directly related to the ability to control the particle size distribution of the grinding product and is crucial to the overall grinding efficiency of subsequent mineral processing operations.

[0041] Based on the above analysis, it can be seen that the weight proportion of each mineral in the storage area, the average particle size and the classification efficiency directly reflect the overall grinding efficiency of the mill.

[0042] Accordingly, in this application, the overall grinding efficiency of the grinding mill is determined by a flow chart as shown in the attached figure. Figure 2 As shown, specifically:

[0043] S1. After performing curve fitting on the weight percentage and average particle size of each mineral at any collection moment and all previous collection moments, analyze the curvature characteristics of the corner points on the fitting curve and the range characteristics of the data around the corner points to determine the grinding fit of each mineral at any collection moment.

[0044] For each mineral, taking a random mineral as an example, in order to reflect the change of the mineral during grinding, all its weight proportions and all average particle sizes at any collection moment and all previous collection moments are sorted in the order of collection time and then curve fitting is performed.

[0045] The two fitted curves are then used as input, and the CSS (curvature scale space) algorithm is used to output the corner points of each fitted curve. The detected corner points represent the turning points of the weight proportion or average particle size change trend, that is, at these points, the distribution of minerals or grinding efficiency has changed significantly.

[0046] Among them, the least squares method and the CSS algorithm are well-known technologies and will not be described in detail. In other embodiments of the present application, the principal component analysis method can be used for curve fitting, and the corner point analysis algorithm can be used to obtain the corner points on the curve.

[0047] Furthermore, suppose that the i-th fitting curve has detected corner points, calculate the curvature of the j-th corner point on the i-th fitting curve And with each corner point as the center, a time window of preset length N is constructed. In this embodiment, N is 5. It should be noted that if the length of the time window exceeds the length of the fitting curve, mean padding is performed and the data range in each time window is calculated. Among them, mean padding is a well-known technology and will not be described in detail.

[0048] Based on the above analysis, the grinding suitability A of each mineral at any collection time is calculated. The specific calculation formula is: ; Wherein, exp represents the exponential function with the natural constant e as the base; Indicates the number of corner points on the i-th fitting curve; represents the curvature of the jth corner point on the i-th fitting curve; Represents the data range within the preset time window centered at the j-th corner point on the i-th fitting curve.

[0049] It should be understood that the number of corner points It reflects the number of turning points in the weight percentage or average particle size change trend of the mineral. The larger the value of , the more turning points there are in the changing trend, which means the greater the fluctuation in the grinding process, thus having a greater negative impact on the value of A. Describes the degree of curvature of the curve at the jth corner point on the i-th fitting curve of the mineral. The larger the curvature, the more severe the curve bends at that point. That is, during the grinding process, the greater the abnormal change in the weight proportion or average particle size of the mineral, the worse the grinding level of the mill for the mineral, and the smaller the A value. The size of represents the range of weight proportion or average particle size variation in the time period near the jth corner point on the i-th fitting curve of the mineral. This means that the change near the corner is more significant, which is consistent with Similarly, the smaller the A value.

[0050] A describes the complexity and severity of the mineral distribution and particle size changes during the grinding process, thereby characterizing the degree to which the mill meets the grinding requirements of the mineral. The larger the A value, the more stable the grinding process, that is, the more the grinding parameters of the mill meet the grinding conditions of the mineral.

[0051] S2: For each mineral, the grinding compatibility calculated at the current and all previous collection moments is arranged in ascending order of time to obtain the current compatibility sequence of each mineral.

[0052] Furthermore, in order to analyze the influence of the grinding effect of the grinding machine for each mineral on the classification efficiency, for each mineral, after the grinding machine starts to operate stably, the grinding fit calculated at the current and all previous collection moments are arranged in ascending chronological order to obtain the fit sequence of each mineral.

[0053] S3, calculate the similarity between the current fit sequence of each mineral and the classification efficiency sequence composed of the classification efficiency of all collection moments at each collection moment; and use the similarity of each mineral at all collection moments as each row, and the similarity of different minerals at the same collection moment as each column to construct a trend change matrix.

[0054] For each mineral, the classification efficiency at the current and all previous collection moments is organized into a classification efficiency sequence in the order of collection time.

[0055] Furthermore, the current compatibility sequence and classification efficiency sequence of each mineral are used as input, and the dynamic kernel correlation algorithm is used to calculate the similarity between the two sequences at each acquisition moment, wherein the dynamic kernel correlation algorithm is a well-known technology and will not be described in detail.

[0056] That is, at each collection moment, there is a similarity representing the historical trend similarity between the fit sequence and the classification efficiency sequence at the selected moment, and the similarity of each mineral at all collection moments is taken as each row, and the similarity of different minerals at the same collection moment is taken as each column to construct a trend change matrix.

[0057] S4, clustering the elements in the trend change matrix; determining the overall grinding efficiency of the grinding mill based on the proportion of mineral species in each cluster and the average distance between the center of each cluster and the centers of all other clusters in the clustering results.

[0058] To reveal the stability of the grinding process for different minerals, this example uses the trend change matrix as the input of the DBSCAN density clustering algorithm. The minimum number of points is set to 2, the neighborhood radius is set to 0.5, and the cluster distance is the numerical difference and position difference between elements. The DBSCAN density clustering algorithm is used to output each cluster of the clustered trend change matrix. The DBSCAN density clustering algorithm is a well-known technology and will not be described in detail.

[0059] Here, we take the clustering distance d between any two elements as an example: , where S represents the absolute value of the difference between any two elements, and W represents the position distance between any two elements.

[0060] For each cluster output, it represents the similar trend change of classification efficiency of different minerals. Assuming that there are P clusters in total, calculate the ratio between the mineral species in the p-th cluster and the total number of mineral species, which is the mineral species ratio of the p-th cluster. , and calculate the average distance between the pth cluster center and all other cluster centers .

[0061] Based on the above analysis, the overall grinding efficiency B of the grinding mill is calculated. The specific calculation formula is: , where P represents the number of clusters, represents the proportion of mineral species in the p-th cluster, It represents the average distance between the center of the p-th cluster and the centers of all other clusters.

[0062] It should be understood that It reflects the ratio of mineral species to the total mineral species in the p-th cluster, which can be understood as the richness of mineral species in the p-th cluster. The larger its value is, the more mineral species are contained in the cluster, which may indicate that the minerals in the cluster have a more comprehensive impact on the classification efficiency, that is, the greater the contribution to the overall grinding efficiency characteristic value, the larger B is. It is used to measure the degree of separation between clusters, that is, the distribution distance of clusters in the feature space. The larger the value, the more obvious the difference in positional characteristics between the cluster and other clusters. In other words, the greater the difference in the impact of the mineral types in the cluster on the classification efficiency compared with other clusters, the smaller the B value.

[0063] B, as the characteristic value of overall grinding efficiency, comprehensively considers the distribution, change trend and impact of different mineral types on classification efficiency to quantitatively evaluate the performance of the grinding mill. A larger B value indicates that the grinding mill can better adapt to the characteristics of different minerals and achieve more efficient grinding and separation.

[0064] Control steps: Adaptively adjust the current rotation speed of the grinding mill using the overall grinding efficiency of the grinding mill.

[0065] In this embodiment, the initial speed of the grinding mill is set to 80% of the critical speed. The critical speed refers to the speed at which the outermost grinding media rotates with the grinding cylinder without falling. In other embodiments, the initial speed of the grinding mill can be set by the implementer.

[0066] During the operation of the grinding mill, when the speed of the grinding mill exceeds the critical speed, the grinding media will rotate with the cylinder without falling, entering the so-called "centrifugal operation state". In this state, the grinding media has no impact effect and the grinding effect is also very small, causing the grinding effect to almost stop; if the speed of the grinding mill is too low, the grinding media will be lifted to a small height and they will move in a falling state. At this time, the impact force of the grinding media is very small, and it mainly relies on the grinding and stripping effect to crush the ore, resulting in poor grinding efficiency.

[0067] Based on this, the overall grinding efficiency of the mill is used to adaptively adjust the current speed of the mill. The specific adjustment relationship is: ;in, is the current grinding mill adjusted speed, is a preset adjustment parameter, and in this embodiment, its value is 0.25, which is used to prevent the adjusted grinding mill speed from exceeding the critical speed, resulting in reduced grinding effect; e is a natural constant, B is the overall grinding efficiency of the grinding mill, and R is the current grinding mill speed.

[0068] The current grinding mill speed is adaptively adjusted, and the operating parameters of the grinding mill can be dynamically adjusted to adapt to different working conditions and mineral characteristics, thereby optimizing the grinding process.

[0069] It should be understood that when the B value is larger, it indicates that the mill can better adapt to the characteristics of different minerals and achieve more effective grinding and separation. At this time, the current mill speed and operating parameters can match the physical properties of the minerals, and the adjustment degree of the current mill speed R is smaller. Infinitely close to the current speed R; the smaller the B value is, the less the speed and operating parameters of the grinding mill match the physical properties of the mineral, resulting in low grinding efficiency. At this time, the speed should be adjusted more sharply, and the adjusted speed should be limited to not exceed the critical speed by adjusting the parameters, and the speed should be increased on this basis to improve the grinding efficiency.

[0070] Based on the above adaptive modification of the speed, a more precise speed control strategy can be implemented to adapt to the characteristics of different minerals, improving the efficiency and economic benefits of the entire mineral processing process. The set speed is used to complete the grinding and mineral classification, thereby distinguishing different minerals and realizing the mineral processing operation.

[0071] Example 2

[0072] A fine grinding control device for mineral processing, comprising a mill and a classifier; the mill is used to control its rotational speed to grind the material and feed it into the classifier from the mill's mineral outlet; the classifier is used to separate the ground product into different types of minerals and feed the material from the classifier overflow port into corresponding storage areas, allowing the screened minerals to be temporarily stored in specific areas for further processing or sorting, and returning unqualified minerals from the classifier's sand return port to the mill for re-grinding to ensure that the particle size of the ground product meets the requirements of subsequent mineral processing operations. The classifier uses gravity classification to classify the ground product by balancing the buoyancy and gravity of the ground product in a liquid medium. By utilizing the differences in ore particle density and adjusting the medium concentration and flow rate, the settling velocities of ore particles of different densities are different, thereby achieving classification between different minerals.

[0073] Among them, each storage area is equipped with a weighing sensor to detect the weight of the minerals stored in the storage area, and use the weight of the minerals stored in all storage areas at the same time to determine the weight share of each storage area at each moment; the overflow port and the sand return port of the classifier are equipped with screening analysis equipment to calculate the classification efficiency of the storage area at each moment through the classification efficiency calculation method; the overflow port of the classifier is equipped with a laser particle size analyzer to collect the average particle size of the storage area.

[0074] It should be noted that the gravity classification method and the classification efficiency calculation method are both well-known technologies and will not be described in detail.

[0075] The above technical features constitute the best embodiment of the present application, which has strong adaptability and optimal implementation effect. Non-essential technical features can be added or removed according to actual needs to meet the requirements of different situations.

Claims

1. A fine grinding control method for mineral processing, characterized in that: The method includes: Collection step: After the grinding mill starts to run stably, the weight ratio, average particle size and classification efficiency of the minerals in different storage areas at each collection moment are collected in real time after the classifier sends different types of minerals to the corresponding storage areas; Processing steps: By analyzing the data characteristics of all minerals collected at all collection times, the overall grinding efficiency of the mill is reflected; specifically: S1, performing curve fitting on the weight percentage and average particle size of each mineral at any collection time and all previous collection times, analyzing the curvature characteristics of the corner points on the fitted curve and the range characteristics of the data around the corner points to determine the grinding fit of each mineral at any collection time; S2, for each mineral, arrange the grinding fit calculated at the current and all previous collection moments in ascending order of time to obtain the current fit sequence of each mineral; S3, calculate the similarity between the current fit sequence of each mineral and the classification efficiency sequence composed of the classification efficiencies at all collection moments at each collection moment; and use the similarity of each mineral at all collection moments as each row, and the similarity of different minerals at the same collection moment as each column to construct a trend change matrix; S4, clustering the elements in the trend change matrix; determining the overall grinding efficiency of the grinding mill based on the proportion of mineral species in each cluster and the average distance between the center of each cluster and the centers of all other clusters in the clustering results; Control steps: Use the overall grinding efficiency of the mill to adaptively adjust the current speed of the mill. The relationship is: ;in, is the current grinding mill adjusted speed, is the preset adjustment parameter, e is the natural constant, B is the overall grinding efficiency of the mill, and R is the current speed of the mill.

2. A fine grinding control method for mineral processing according to claim 1, characterized in that: In the acquisition step, the time when the grinding mill starts to operate stably is one minute after the grinding mill starts to work.

3. A fine grinding control method for mineral processing according to claim 1, characterized in that: In step S1 , the data around the corner point is all data within a preset time window on the fitting curve with the corner point as the center.

4. A fine grinding control method for mineral processing according to claim 3, characterized in that: In step S1, the grinding suitability of each mineral at any collection time is recorded as A, and the calculation relationship is: ; Wherein, exp represents the exponential function with the natural constant e as the base; Indicates the number of corner points on the i-th fitting curve; represents the curvature of the jth corner point on the i-th fitting curve; Represents the data range within the preset time window centered at the j-th corner point on the i-th fitting curve.

5. A fine grinding control method for mineral processing according to claim 1, characterized in that: In step S3, the similarity at each acquisition moment is obtained by calculating the similarity between the two sequences at each acquisition moment using a dynamic kernel correlation algorithm.

6. A fine grinding control method for mineral processing according to claim 1, characterized in that: In step S4, the clustering distance in the clustering process is the numerical difference and position difference between elements.

7. A fine grinding control method for mineral processing according to claim 6, characterized in that: In step S4, the calculation relationship of the overall grinding efficiency characteristic value of the grinding mill is: Calculate the ratio of the mineral species proportion of each cluster and the average distance between the center of the cluster and the centers of all other clusters; The average level of the ratio results of all clusters is used as the overall grinding efficiency characteristic value of the grinding mill.

8. A fine grinding control method for mineral processing according to claim 1, characterized in that: The initial speed at the start of the mill operation is a preset percentage of the critical speed.

9. A fine grinding control device for mineral processing, which implements a fine grinding control method for mineral processing according to any one of claims 1 to 8, characterized in that: The device includes a grinding mill and a classifier; the grinding mill is used to control its rotation speed to grind the material and send it to the classifier from the mineral outlet of the grinding mill; the classifier is used to separate the ground products into different types of minerals and send them to the corresponding storage area from the overflow port of the classifier, so that the screened minerals can be temporarily stored in specific areas for further processing or sorting, and the unqualified minerals are sent back to the grinding mill from the sand return port of the classifier for re-grinding to ensure that the particle size of the grinding products meets the requirements of subsequent mineral processing operations.

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