Monitoring and alarming optimization system for ore flotation extraction process

Abstract

The invention belongs to the technical field of ore flotation process monitoring and optimization, and particularly discloses and provides an ore flotation extraction process monitoring and alarm optimization system, which is characterized in that based on different adhesion effects of different ores in liquids with different chemical characteristics, the near-ball rate of ore particles influences the contact area and adhesion stability of the ore particles and bubbles, the floatable adhesion characteristic of the ore is accurately analyzed, the situation that the flotation effect does not conform to expectation can be found in time, the situation serves as a flotation operation reference optimization condition, and therefore the recovery rate of the target ore is increased; the actually detected stone grain content in each grade of near-ball rate interval is compared with the expected content, the influence condition of the grinding precision of the ore shape near-ball rate on the flotation effect is judged, and then alarming and optimization are conducted on the condition that the grinding precision is insufficient; the bubbling parameters of the flotation machine are regulated and controlled by detecting the liquid level data in the flotation machine, bubbles with better quality can be generated, and enough attachment carriers are provided for ore particles.

Description

Technical Field

[0001] The invention belongs to the technical field of ore flotation process monitoring and optimization, and relates to an ore flotation extraction process monitoring alarm optimization system. Background Art

[0002] In today's mineral resource development field, ore flotation extraction technology, as a key mineral processing method, is widely used in the sorting of various metal and non-metallic ores to obtain high-purity concentrate products to meet the strong demand of industrial production. However, with the increasing complexity and variability of ore resources, and the continuous increase in the requirements for concentrate quality and production efficiency, the traditional ore flotation extraction process is facing many severe challenges. In order to overcome the many drawbacks of the traditional ore flotation extraction process and meet the diverse needs of safe, efficient production and high-quality development, "an ore flotation extraction process monitoring alarm optimization system" came into being.

[0003] In the prior art, there are also some solutions related to ore flotation process monitoring, for example, the Chinese patent publication number CN104408724B discloses a method and system for monitoring and identifying the froth flotation level based on depth information, which builds a froth flotation condition monitoring system based on Kinect from the aspects of software and hardware; collects flotation froth color and depth data through Kinect sensor, extracts depth information and filters the depth data, aligns the color and depth data in time sequence and position and stores them accordingly; extracts the three-dimensional (with depth information) features of the foam such as color, area, volume, speed, and crushing rate by combining color data and depth data; monitors the level by analyzing the relationship between the current foam surface level features and the overflow tank edge height; and uses the improved k-means algorithm to cluster the foam image features to realize online identification of the flotation condition. Automatic control and optimized operation of flotation production are realized, and resource utilization is improved.

[0004] Although the above scheme proposes some solutions for monitoring the ore flotation process, it still has the following limitations: 1. There is a lack of feedback on the correlation between the shape of the ore particles and the froth flotation effect. The irregular shape or agglomeration of the ore particles will reduce their floatability in the flotation process. For example, when the content of high-near-ball stone particles is lower than expected, the irregular shape of the ore particles may lead to a reduction in the contact area with the bubbles, thereby reducing the flotation accuracy of the ore particles.

[0005] 2. The lack of analysis on the adaptability of different ore components to foam solutions with different chemical properties makes it impossible to specifically suppress gangue minerals or promote the flotation of target minerals during the separation process. Summary of the invention

[0006] In view of this, in order to solve the problems raised in the above background technology, a monitoring alarm optimization system for ore flotation extraction process is proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides an ore flotation extraction process monitoring alarm optimization system, the system comprising: an ore feed form detection module, used to detect the ore particle size distribution state, analyze the stone content proportion of each ore component in each grade of near-ball rate interval, and number each ore component as 1, 2,...c...,n.

[0008] The foam state evaluation module is used to detect the foam surface characteristics and liquid level chemical characteristics in the flotation machine, wherein the foam surface characteristics include foaming characteristics and stone particle characteristics, and then based on the foaming characteristics, the foaming effect index B in the flotation machine is evaluated.

[0009] The ore attachment form evaluation module is used to evaluate the floatable attachment amount Q of each ore component based on the liquid level chemical characteristics in the flotation machine, the stone content proportion of each ore component in each grade of near-ball rate range, and the frothing effect index in the flotation machine. c .

[0010] The ore feed shape alarm diagnosis module is used to analyze the distribution adaptability of the stone particles in the flotation machine based on the characteristics of the stone particles in the flotation machine, and perform stone floating alarm diagnosis, including alarm instructions and non-alarm instructions.

[0011] The ore feeding accuracy troubleshooting module is used to judge whether the corresponding grinding accuracy of the ore feeding form has a process impact on the ore flotation based on the alarm instructions, and to adjust the corresponding grinding accuracy of the ore feeding form of each ore component based on the existence of process impact.

[0012] The flotation parameter optimization module is used to detect the liquid level data in the flotation machine and adjust the associated parameters of the flotation machine based on the absence of process influence.

[0013] Compared with the prior art, the present invention has the following beneficial effects: (1) Based on the fact that different ores have different adhesion effects in liquids with different chemical characteristics and that the near-spherical rate of ore particles affects their contact area with bubbles and adhesion stability, the present invention accurately analyzes the floatable adhesion characteristics of ores, helps to promptly discover situations where the flotation effect does not meet expectations, and can provide a reference for optimizing flotation operating conditions, thereby improving the recovery rate of the target ore.

[0014] (2) The present invention compares the actual detected stone content in each grade of ball proximity ratio with its expected content to determine the influence of the grinding accuracy of the ore shape ball proximity ratio on the flotation effect, and then alarms and optimizes the conditions of insufficient grinding accuracy, which helps to take targeted measures to improve the flotation index.

[0015] (3) The present invention can accurately control the associated parameters of the flotation machine, such as the amount of frother and the amount of slurry input, by detecting the liquid level data in the flotation machine. Under different liquid level conditions, by adjusting the frother parameters, bubbles of better quality can be generated. The appropriate amount of bubble generation can provide sufficient attachment carriers for the ore particles. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 This is a schematic diagram of the connection of the system modules of the present invention.

[0018] Figure 2 This is a diagram showing the ore feed morphology of the present invention.

[0019] Figure 3 It is a schematic diagram of the flow of the stone floating alarm diagnosis of the present invention.

[0020] Figure numerals: 1. Each facet. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1 As shown, the present invention provides an ore flotation extraction process monitoring alarm optimization system, which includes: an ore feed morphology detection module, a foam state evaluation module, an ore attachment morphology evaluation module, an ore feed morphology alarm diagnosis module, an ore feed accuracy investigation module, and a flotation parameter optimization module.

[0023] The ore feeding morphology detection module is connected to the foam state assessment module, the foam state assessment module is connected to the ore attachment morphology assessment module, the ore attachment morphology assessment module is connected to the ore feeding morphology warning diagnosis module, the ore feeding morphology warning diagnosis module is connected to the ore feeding accuracy investigation module, and the ore feeding accuracy investigation module is connected to the flotation parameter optimization module.

[0024] See also Figure 2As shown, the ore feed form detection module is used to detect the ore particle size distribution state, analyze the stone content proportion of each ore component in each grade of near-spherical rate range, and number each ore component as 1, 2, ...c..., n.

[0025] The ore components may include but are not limited to sulfide ore, oxide ore, and carbonate ore.

[0026] The near-sphericity refers to the proportion of ore particles close to sphericity, which is detected based on the difference in the profile distribution length of the ore particles on different edges. In the flotation process, ore particles with a high near-sphericity usually have better fluidity and suspension, because spherical particles have relatively less resistance in the slurry and can more easily contact with flotation reagents and bubbles, thereby improving flotation efficiency.

[0027] In a preferred embodiment, the ore feed morphology detection module includes: detecting the content ratio of each ore component corresponding to the ore slurry exported from the ball mill through a particle size and shape analyzer, and the contour area A of each stone particle corresponding to each ore component on each edge surface. cpq .

[0028] Substitute the contour area of ​​each stone particle on each edge surface of each ore component as a data point into the standard deviation formula, and then use the calculation result of the standard deviation formula as the near-spherical ratio of the contour of each stone particle of each ore component. in It represents the average contour area of ​​the pth stone particle corresponding to the cth ore component on each facet, p represents the number of each stone particle, p=1,2,...a1, q represents the number of each facet, q=1,2,...a2, a2 ​​represents the number of facets.

[0029] Match the near-spherical rate of each stone particle contour of each ore component with the preset near-spherical rate range of each grade to obtain the corresponding near-spherical rate grade of each stone particle contour of each ore component, and calculate the proportion of the number of stones of each ore component in each grade near-spherical rate range, and multiply it with the content proportion of the corresponding ore component to obtain the proportion of stone content of each ore component in each grade near-spherical rate range.

[0030] The foam state evaluation module is used to detect the foam surface characteristics and liquid level chemical characteristics in the flotation machine, wherein the foam surface characteristics include foaming characteristics and stone particle characteristics, and then based on the foaming characteristics, the foaming effect index B in the flotation machine is evaluated.

[0031] In a preferred embodiment, the liquid level chemical characteristics are such as ion concentration and pH.

[0032] The foaming characteristics include bubble volume distribution data and bubble color distribution data.

[0033] The bubble volume distribution data include bubble density, bubble volume, and bubble rupture speed.

[0034] The bubble color distribution data include bubble color value and bubble glossiness.

[0035] The stone particle characteristics include the ore content attached to the bubbles and the ore agglomeration amount.

[0036] It should be noted that the ore agglomeration refers to the aggregation phenomenon of ore particles due to their small outline volume.

[0037] The liquid level chemical characteristics can be detected by an ion concentration detection instrument.

[0038] The bubble density and volume corresponding to the apparent characteristics of the foam in the flotation machine, and the content of ore attached to the bubbles and the amount of ore agglomeration corresponding to the characteristics of the stone particles can be detected by laser detection. The basic principle of the laser detection method is: use a laser beam to pass through a liquid containing bubbles. When the laser encounters bubbles, it will be scattered or refracted. By detecting these changes, the position and number of bubbles are determined, and then the bubble density is calculated; by analyzing the scattered light intensity, angle distribution and other characteristics when the laser is irradiated on the bubbles, and the degree of light scattering differences caused by the attachment of ore or ore agglomeration on the bubbles, the bubble volume, the content of ore attached to the bubbles and the amount of ore agglomeration can be inferred.

[0039] The bubble rupture speed, bubble color value, and bubble glossiness of the foam surface characteristics in the flotation machine can be detected by a high-speed camera. The basic principle is: the bubble rupture process is photographed by a high-speed camera at an extremely high frame rate, and the laser stroboscopic lighting technology is used to illuminate the bubbles at a specific moment to obtain a clear bubble image, and then the bubble color value and bubble glossiness are identified by color card comparison and glossiness comparison of the bubble color in the photographed image; by analyzing the continuously photographed images, the diameter or radius change of the bubble at different moments is measured, and then the volume change rate during the bubble rupture process is calculated, thereby obtaining the bubble rupture speed.

[0040] In a further preferred embodiment, the evaluation of the frothing effect index in the flotation machine includes: detecting the bubble volume distribution data and bubble color distribution data of the frothing characteristics of each frothing period in the flotation machine, taking them as element points, and constructing a bubble characteristic element list X in the flotation machine = (χ1 ti ,χ2 tj ), where χ1 ti , χ2 tjThey respectively represent the i-th element of the bubble volume distribution data and the j-th element of the bubble color distribution data corresponding to the bubble characteristics of the t-th bubble period in the flotation machine, t represents the number of each bubble period, t=1,2,...d, i represents the number of each element corresponding to the bubble volume distribution data, i=1,2,...g1, j represents the number of each element corresponding to the bubble color distribution data, j=1,2,...g2.

[0041] Obtain the standard bubble volume distribution data and standard bubble color distribution data of the preset standard bubble characteristics of the flotation machine in each bubble period, and use them as element points to construct a standard bubble characteristic element list Y=(y1 ti ,y2 tj ), where χ1 ti , χ2 tj They respectively represent the i-th element corresponding to the standard bubble characteristic and the j-th element corresponding to the standard stone characteristic of the preset standard apparent characteristic of the t-th bubble period in the flotation machine.

[0042] Evaluation index of frothing effect in flotation machine B=||XY|| F , where ||XY|| F It represents the Frobenius norm of the bubble feature element list in the flotation machine and the standard bubble feature element list, that is, the difference measure of the bubble feature in the flotation machine.

[0043] The Frobenius norm is a way to measure the differences between lists of elements.

[0044] The ore attachment form evaluation module is used to evaluate the floatable attachment amount Q of each ore component based on the liquid level chemical characteristics in the flotation machine, the stone content proportion of each ore component in each grade of near-ball rate interval, and the foaming effect index in the flotation machine. c .

[0045] In a preferred embodiment, the evaluation of the floatable attachment amount of each ore component includes: obtaining the chemical characteristics of the liquid level in the flotation machine, comparing it with the preset floatability coefficient of each ore component under the state of the chemical characteristics of the liquid level, and matching the floatability coefficient γ of each ore component under the state of the chemical characteristics of the liquid level in the flotation machine. c .

[0046] Extract the proportion of stone content of each ore component in each grade of near-ball ratio, and integrate it to obtain the comprehensive proportion of stone content of each ore component κ c .

[0047] Combined with the frothing effect index B in the flotation machine, evaluate the floatable attachment amount of each ore component Where Q' represents the corresponding floatable attachment amount of the preset unit buoyancy coefficient.

[0048] It should be noted that, since the floatability of different types of ores at different pH values ​​varies greatly, the chemical characteristics of the liquid level in the flotation machine have different effects on the floatability of each ore component, and the bubbling effect of the liquid level in the flotation machine also affects the floatability of the ore, which leads to differences in the amount of floatable attachment of different ore components at different levels with different bubbling effects. For example, calcite has better floatability in an alkaline environment, because under alkaline conditions, fatty acid collectors (such as oleic acid) can better adsorb on the surface of calcite, making its surface hydrophobic, which is conducive to attachment to bubbles in the flotation machine. For some sulfide ores, such as sphalerite, the floatability is higher in the pH range of weakly acidic to neutral. When the pH value is too high or too low, its floatability will decrease, because the change in pH will affect the chemical composition and charge state of the sphalerite surface, thereby affecting the adsorption of the collector.

[0049] The invention is based on the fact that different ores have different attachment effects in liquids with different chemical characteristics and that the near-spherical rate of ore particles affects their contact area with bubbles and attachment stability. The invention accurately analyzes the floatable attachment characteristics of the ore, helps to timely discover the situation that the flotation effect does not meet expectations, and can provide a reference for optimizing the flotation operating conditions, thereby improving the recovery rate of the target ore.

[0050] See also Figure 3 As shown, the ore feed shape alarm diagnosis module is used to analyze the stone distribution adaptation amount in the flotation machine based on the stone characteristics in the flotation machine, and perform stone floating alarm diagnosis, including alarm instructions and non-alarm instructions.

[0051] In a preferred embodiment, the analysis of the adaptation of the distribution of the stone particles in the flotation machine includes: detecting the characteristics of the stone particles in each bubbling period in the flotation machine, including the ore content and ore agglomeration amount corresponding to each bubbling period in the flotation machine, which are respectively recorded as Q1 t 、Q2 t , and obtain the preset appropriate amount of the corresponding elements of the stone particles in the flotation machine, which are recorded as Q10 and Q20 respectively.

[0052] Combined with the frothing effect index B in the flotation machine, analyze the adaptation of the stone distribution in the flotation machine

[0053] Where Q″ represents the preset distribution adaptation amount of the unit adhesion content assessment coefficient. It indicates the preset stone particle distribution corresponding to the adaptive compensation amount, and z1 and z2 respectively indicate the ore content attached to the bubbles and the ore agglomeration amount corresponding to the preset weight coefficients.

[0054] It should be noted that for the content of ore attached to the bubbles: when the content is low, a small amount of ore is attached to the bubbles, and the bubble resources in the flotation machine cannot be fully utilized. The stone particles are relatively small, the processing capacity of the flotation machine is not fully utilized, and the stone distribution adaptation is low; when the content is moderate, the amount of ore attached to the bubbles matches the carrying capacity of the bubbles, which can achieve a better flotation effect, the stone particles are distributed more evenly in the flotation machine, and the stone distribution adaptation reaches a better level; when the content is too high, too much ore is attached to the bubbles, which will slow down the rising speed of the bubbles and even cause the bubbles to burst, and the ore falls back into the slurry, causing uneven stone distribution and a decrease in the stone distribution adaptation.

[0055] Regarding the amount of ore agglomeration: when the amount of agglomeration is small, the ore exists in the form of single particles or small agglomerates, and the contact and attachment with the bubbles are relatively dispersed. The distribution of the stone particles in the flotation machine is relatively uniform, and the stone particle distribution adaptability can be maintained at a certain level; when the amount of agglomeration is moderate, proper ore agglomeration can increase the effective particle size of the particles, making it easier for them to attach to and float with bubbles, which is beneficial to improving the stone particle distribution adaptability, and can improve the flotation efficiency and concentrate grade to a certain extent; when the amount of ore agglomeration is too large, large agglomerates will be formed, resulting in uneven stone particle distribution, excessive concentration of stone particles in some areas, and less stone particles in other areas, thereby reducing the stone particle distribution adaptability in the flotation machine.

[0056] In a further preferred embodiment, the stone floating alarm diagnosis corresponding method is: the floatable attachment amount of each ore component is accumulated, and compared with the stone distribution adaptation amount in the flotation machine, to obtain the abnormal weight of the stone distribution in the flotation machine.

[0057] Diagnostic stone floating alarm command Where ab is the alarm instruction, un_ab is the non-alarm instruction, and U0 is the preset stone particle distribution abnormality weight parameter.

[0058] The present invention compares the actually detected stone content in each grade of ball proximity ratio interval with its expected content, judges the influence of the grinding accuracy of the ore shape ball proximity ratio on the flotation effect, and then alarms and optimizes the condition of insufficient grinding accuracy, which is helpful to take targeted measures to improve the flotation index.

[0059] The ore feed accuracy troubleshooting module is used to judge whether the grinding accuracy of the ore feed form has a process impact on ore flotation based on the alarm instruction, and to adjust the grinding accuracy of the ore feed form of each ore component based on the existence of the process impact.

[0060] In a preferred embodiment, the ore feeding accuracy checking module includes: extracting the specific gravity κ of the stone content of each ore component within each grade of near-ball ratio intervalcr , r represents the number of each level of ball proximity interval, r = 1, 2, ... f.

[0061] The particle size and shape analyzer is used to detect the stone content κ′ of each grade of near-ball ratio interval attached to the bubbles in the flotation machine. r , determine the influence of stone content in each grade of ball-nearness interval on flotation effect Compare it with the preset baseline influence ratio, screen out the ball proximity rate intervals of each level whose influence ratio is lower than the preset baseline influence ratio, record them as the ball proximity rate influence intervals, and identify their progressive regularity, including regularity and irregularity.

[0062] It should be noted that the progressive regularity evaluation index is: if each proximity rate influence interval is a proximity rate interval of continuous levels from small to large, then each proximity rate influence interval is regularly progressive; if there is a proximity rate interval in each proximity rate influence interval that does not belong to the level range of the maximum proximity rate influence interval and the minimum proximity rate influence interval, then each proximity rate influence interval is irregularly progressive.

[0063] When the influence intervals of each near-spherical rate are regular, it means that the corresponding grinding accuracy of the ore feed form has a process impact on the ore flotation. In this case, the corresponding grinding accuracy of the ore feed form of each ore component is adjusted to grind the ore feed form of each ore component into particles with a smaller volume or closer to a spherical volume.

[0064] When the influence intervals of each ball proximity ratio are irregular, it means that the corresponding grinding accuracy of the ore feed form has no process influence on the ore flotation, and the flotation parameter optimization module is executed.

[0065] The flotation parameter optimization module is used to detect the liquid level data in the flotation machine and adjust the associated parameters of the flotation machine based on the absence of process influence.

[0066] In a preferred embodiment, the liquid level data in the flotation machine includes liquid level height and foam height.

[0067] The liquid level height can be obtained by ultrasonic liquid level detection, and the foam height can be obtained by optical sensor detection.

[0068] The flotation machine related parameters include the amount of frother, the amount of slurry input, and the frother residence time.

[0069] In a further preferred embodiment, the control of the flotation machine associated parameters includes: obtaining the comprehensive flotation addition content of the ore from the ball mill, and then obtaining the liquid level adaptation height interval [h_l1, h_l2] in the flotation machine, and based on the liquid level height h_liquid, controlling the flotation machine liquid level height associated parameters Wherein J1 represents the slurry input amount, J2 represents the frother dosage, h_l1 and h_l2 represent the corresponding minimum boundary value and maximum boundary value of the liquid level adaptation height interval in the flotation machine, respectively. For example, when h_liquid<h_l1, the slurry input amount is increased; when h_liquid>h_l2, the frother dosage is increased.

[0070] Get the foam adaptation height range [h_f1, h_f2] in the flotation machine, and adjust the foam height related parameters of the flotation machine based on the foam height h_foam Wherein G1 represents the amount of foaming agent, G2 represents the foaming residence time, h_f1 and h_f2 represent the corresponding minimum boundary value and maximum boundary value of the foam adaptation height interval in the flotation machine respectively. For example, when h_foam<h_f1, the amount of foaming agent is increased; when h_foam>h_f2, the foaming residence time is increased.

[0071] The present invention can accurately adjust the associated parameters of the flotation machine, such as the amount of frother and the amount of pulp input, by detecting the liquid level data in the flotation machine. Under different liquid level conditions, bubbles with better quality can be generated by adjusting the frother parameters. The appropriate amount of bubble generation can provide sufficient attachment carriers for ore particles.

[0072] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A monitoring alarm optimization system for ore flotation extraction process, characterized in that: The system includes: The ore feed form detection module is used to detect the ore particle size distribution state, analyze the stone content proportion of each ore component in each grade of near-spherical rate range, and number each ore component. ; The foam state evaluation module is used to detect the foam surface characteristics and liquid level chemical characteristics in the flotation machine. The foam surface characteristics include foaming characteristics and stone particle characteristics. Based on the foaming characteristics, the foaming effect index in the flotation machine is evaluated. ; The ore attachment form evaluation module is used to evaluate the floatable attachment amount of each ore component based on the liquid level chemical characteristics in the flotation machine, the stone content proportion of each ore component in each grade of near-ball ratio range, and the frothing effect index in the flotation machine. ; Ore feed shape alarm diagnosis module, which is used to analyze the distribution adaptability of stone particles in the flotation machine based on the characteristics of the stone particles in the flotation machine, and perform stone floating alarm diagnosis, including alarm instructions and non-alarm instructions; The ore feed accuracy troubleshooting module is used to judge whether the grinding accuracy of the ore feed form has a process impact on ore flotation based on the alarm instructions, and to adjust the grinding accuracy of the ore feed form of each ore component based on the process impact; The flotation parameter optimization module is used to detect the liquid level data in the flotation machine and adjust the associated parameters of the flotation machine based on the absence of process influence.

2. The ore flotation extraction process monitoring alarm optimization system according to claim 1 is characterized in that: The ore feed shape detection module includes: detecting the content ratio of each ore component corresponding to the ore slurry exported from the ball mill through a particle size and shape analyzer, and the contour area of ​​each stone particle corresponding to each ore component on each edge surface; Substitute the contour area of ​​each stone particle corresponding to each ore component on each edge surface as a data point into the standard deviation formula, and then use the calculation result of the standard deviation formula as the near-spherical ratio of the contour of each stone particle of each ore component; Match the near-spherical rate of each stone particle contour of each ore component with the preset near-spherical rate range of each grade to obtain the corresponding near-spherical rate grade of each stone particle contour of each ore component, and calculate the proportion of the number of stones of each ore component in each grade near-spherical rate range, and multiply it with the content proportion of the corresponding ore component to obtain the proportion of stone content of each ore component in each grade near-spherical rate range.

3. The ore flotation extraction process monitoring alarm optimization system according to claim 1 is characterized in that: The liquid level chemical characteristics such as ion concentration, pH; The foaming characteristics include bubble volume distribution data and bubble color distribution data; The bubble volume distribution data such as bubble density, bubble volume, and bubble rupture speed; The bubble color distribution data such as bubble color value and bubble glossiness; The stone particle characteristics include the ore content attached to the bubbles and the ore agglomeration amount.

4. The ore flotation extraction process monitoring alarm optimization system according to claim 3 is characterized in that: The evaluation of the frothing effect index in the flotation machine includes: detecting the bubble volume distribution data and bubble color distribution data of the frothing characteristics of each frothing period in the flotation machine, taking them as element points, and constructing a bubble characteristic element list in the flotation machine. ,in Respectively represent the first The bubble volume distribution data of the bubble characteristics of the bubble period corresponds to the elements, bubble color distribution data corresponding to elements, Indicates the number of each bubbling period, , Indicates the number of each element corresponding to the bubble volume distribution data, , Indicates the number of each element corresponding to the bubble color distribution data, ; Obtain the standard bubble volume distribution data and standard bubble color distribution data of the preset standard bubble characteristics of the flotation machine in each bubble period, and use them as element points to construct a standard bubble characteristic element list in the flotation machine ,in Respectively represent the first The preset standard appearance characteristics of the foaming period belong to the standard foaming characteristics corresponding to the first Elements, standard stone characteristics corresponding to elements; Indicators for evaluating the foaming effect in flotation machines ,in It represents the Frobenius norm of the bubble feature element list in the flotation machine and the standard bubble feature element list, that is, the difference measure of the bubble feature in the flotation machine.

5. The ore flotation extraction process monitoring alarm optimization system according to claim 1 is characterized in that: The evaluation of the floatable attachment amount of each ore component includes: obtaining the chemical characteristics of the liquid level in the flotation machine, comparing it with the preset floatability coefficient of each ore component under the chemical characteristic state of the liquid level, and matching the floatability coefficient of each ore component under the chemical characteristic state of the liquid level in the flotation machine. ; Extract the proportion of stone content of each ore component within each grade of near-ball ratio, and integrate it to obtain the comprehensive proportion of stone content of each ore component. ; Combined with the frothing effect index in the flotation machine , evaluate the floatable amount of each ore component ,in Indicates the corresponding floatable attachment amount of the preset unit buoyancy coefficient.

6. The ore flotation extraction process monitoring alarm optimization system according to claim 4 is characterized in that: The analysis of the distribution adaptability of the stone particles in the flotation machine includes: detecting the characteristics of the stone particles in each bubbling period in the flotation machine, including the ore content and ore agglomeration amount attached to the bubbles in each bubbling period in the flotation machine, which are recorded as , and obtain the preset appropriate amount of the corresponding elements of the stone particles in the flotation machine, which are recorded as ; Combined with the frothing effect index in the flotation machine , analyze the adaptability of stone distribution in the flotation machine ,in It indicates the corresponding distribution adaptation of the preset unit adhesion content evaluation coefficient. Indicates the preset stone distribution corresponding to the compensation amount, They respectively represent the preset weight coefficients corresponding to the ore content attached to the bubbles and the ore agglomeration amount.

7. The ore flotation extraction process monitoring alarm optimization system according to claim 6 is characterized in that: The corresponding method of the stone floating alarm diagnosis is: the floatable attachment amount of each ore component is accumulated, and compared with the stone distribution adaptation amount in the flotation machine, the abnormal weight of the stone distribution in the flotation machine is obtained. ; Diagnostic stone floating alarm command ,in For alarm instructions, For non-alarm instructions, It is the preset stone distribution anomaly weight parameter.

8. The ore flotation extraction process monitoring alarm optimization system according to claim 1 is characterized in that: The ore feeding accuracy check module includes: extracting the stone content proportion of each ore component within each grade of near-ball rate range , Indicates the number of each level of ball proximity interval, ; The particle size and shape analyzer is used to detect the stone content of each level of ball-nearness interval attached to the bubbles in the flotation machine. , determine the influence of stone content in each grade of ball-nearness interval on flotation effect , compare it with the preset baseline influence ratio, screen out the ball proximity rate intervals of each level whose influence ratio is lower than the preset baseline influence ratio, record them as the ball proximity rate influence intervals, and identify their progressive regularity, including regularity and irregularity; When the influence intervals of each near-ball ratio are regular, it means that the grinding accuracy of the ore feed form has a process impact on the ore flotation, so the grinding accuracy of the ore feed form of each ore component is adjusted; When the influence intervals of each ball proximity ratio are irregular, it means that the corresponding grinding accuracy of the ore feed form has no process influence on the ore flotation, and the flotation parameter optimization module is executed.

9. The ore flotation extraction process monitoring alarm optimization system according to claim 1 is characterized in that: The liquid level data in the flotation machine includes liquid level height and foam height; The flotation machine related parameters include the amount of frother, the amount of slurry input, and the frother residence time.

10. The ore flotation extraction process monitoring alarm optimization system according to claim 9, characterized in that: The control of the flotation machine related parameters includes: obtaining the comprehensive added content of ore flotation from the ball mill, and then obtaining the liquid level adaptation height range in the flotation machine , based on the liquid level , regulating the flotation machine's liquid level related parameters ,in Indicates the slurry input amount, Indicates the amount of foaming agent used. They respectively represent the corresponding minimum boundary value and maximum boundary value of the liquid level adaptation height interval in the flotation machine; Get the foam adaptation height range in the flotation machine , based on foam height , regulating the froth height related parameters of the flotation machine ,in Indicates the amount of foaming agent used. Indicates how long the foam stays. They respectively represent the corresponding minimum boundary value and maximum boundary value of the foam adaptation height range in the flotation machine.

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