Lead zinc ore preferential flotation method

Abstract

The invention discloses a lead-zinc ore preferential flotation method, which relates to the field of mineral processing, and comprises the following steps: S1, pretreating lead-zinc ore to remove impurities in the lead-zinc ore to obtain ore pulp; s2, a lead preferential flotation reagent is added into the ore pulp, lead concentrate and zinc concentrate are obtained, and the adding amount and adding time of the flotation reagent are adjusted through an intelligent control system; s3, according to the ore property and the flotation effect, operating parameters of a flotation machine are intelligently adjusted; and S4, a dynamic simulation model of the flotation process is established, and the flotation process is optimized in real time through a model prediction control algorithm in combination with real-time monitoring data. Through intelligent control and dynamic simulation model optimization, the flotation efficiency and selectivity of the lead-zinc ore are improved, the problems of low flotation efficiency and poor selectivity in the prior art are solved, and the recovery rate and economic benefits of the lead-zinc ore are improved.

Description

Technical Field

[0001] The invention relates to the field of mineral processing, and in particular to a lead-zinc ore preferential flotation method. Background Art

[0002] With the development of mineral processing technology, the flotation technology of lead-zinc ore has become relatively mature, but there are still some technical challenges. In this field, the closest technology may include the use of specific flotation reagents, adjustment of flotation process parameters, and optimization of flotation equipment. Although the existing lead-zinc ore flotation technology has achieved certain results, the following problems still exist: 1. Selectivity problem: In the flotation process, the selective separation of lead and zinc minerals is still a challenge, especially in complex ores. 2. Environmental impact: Traditional flotation reagents may cause pollution to the environment, and more environmentally friendly flotation reagents need to be developed. 3. Cost efficiency: Improving flotation efficiency and reducing costs are important goals of the lead-zinc ore processing industry.

[0003] In addition, the changes in ore properties and the dynamic changes of other factors in the flotation process of lead-zinc ore cause the problems of low flotation efficiency and poor selectivity in the existing flotation process. The present invention provides a lead-zinc ore preferential flotation method to solve the problems existing in the prior art. Summary of the invention

[0004] A brief summary of one or more aspects is given below to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceived aspects, and is neither intended to identify the key or critical elements of all aspects nor to define the scope of any or all aspects. Its only purpose is to give some concepts of one or more aspects in a simplified form as a prelude to a more detailed description that will be given later.

[0005] In order to overcome the above-mentioned defects, the purpose of the present invention is to provide a lead-zinc ore preferential flotation method, which improves the flotation efficiency and selectivity of lead-zinc ore through intelligent control and dynamic simulation model optimization, solves the problems of low flotation efficiency and poor selectivity in the prior art, and improves the recovery rate and economic benefit of lead-zinc ore.

[0006] According to one aspect of the present invention, a method for preferential flotation of lead-zinc ore is provided, characterized in that it comprises the following steps:

[0007] S1. Pretreating the lead-zinc ore to remove impurities in the lead-zinc ore to obtain ore pulp;

[0008] S2, adding lead priority flotation reagent to the ore pulp to obtain lead concentrate and zinc concentrate, and using the intelligent control system to adjust the amount and time of flotation reagent addition;

[0009] S3. Intelligently adjust the operating parameters of the flotation machine according to the ore properties and flotation effect;

[0010] S4. Establish a dynamic simulation model of the flotation process, combine it with real-time monitoring data, and use the model predictive control algorithm to optimize the flotation process in real time.

[0011] In one embodiment, step S1 further includes:

[0012] S11, coarse crushing, medium crushing and fine crushing of lead-zinc ore;

[0013] S12, grinding the lead-zinc ore to obtain ore pulp;

[0014] S13, controlling the concentration of the ground slurry to between 20-40%;

[0015] S14. Control the pH value of the ground slurry between 8 and 10.

[0016] In one embodiment, step S12 further includes classifying the slurry into a coarse particle size and a fine particle size. The coarse particle size continues to be ground, and the fine particle size enters the next step of the beneficiation process.

[0017] In one embodiment, the lead preferential flotation reagent in step S2 includes yellow medicine, black medicine and frother, and the mass ratio thereof is 3:2:1.

[0018] In one embodiment, the intelligent control system in step S2 includes at least one sensor for detecting the concentration and pH value of the flotation reagent.

[0019] In one embodiment, step S3 further includes:

[0020] S31. Analyze the properties of the lead-zinc ore to determine the mineral type, ore particle size distribution, surface wettability, surface potential and ore grade of the lead-zinc ore;

[0021] S32. Evaluation indicators of flotation effect include concentrate grade, tailings grade and recovery rate;

[0022] S33. A strategy for intelligently adjusting the operating parameters of the flotation machine based on the evaluation index of the flotation effect. The operating parameters of the flotation machine include stirring speed, aeration volume, slurry level, reagent addition type and reagent addition amount.

[0023] In one embodiment, the real-time monitoring of data in step S4 includes using image recognition technology to monitor changes in the foam layer.

[0024] In one embodiment, step S4 further includes:

[0025] S41. Establishing a physical model of the flotation process based on measured data;

[0026] S42, determining parameters of a dynamic simulation model based on the physical model;

[0027] S43. Utilize a model predictive control algorithm to optimize the flotation process in real time.

[0028] In one embodiment, the parameters of the dynamic simulation model in step S42 include: density, hardness, and surface properties of mineral particles; adsorption rate constant, desorption rate constant, and reaction equilibrium constant of flotation reagents; and viscosity and density of slurry.

[0029] In one embodiment, step S43 further includes:

[0030] S431. Constructing a prediction model for a model predictive control algorithm based on a dynamic simulation model;

[0031] S432, setting the objective function of the model predictive control algorithm according to the objectives and requirements of flotation production;

[0032] S433, setting the threshold range of each parameter in the prediction model;

[0033] S434, updating the prediction model according to the current state measurement value, and performing prediction and optimization calculation again to adapt to the dynamic changes and uncertainties of the flotation process.

[0034] The beneficial effects of the present invention are as follows: 1. Through the pretreatment step, impurities in the lead-zinc ore are effectively removed, and the flotation efficiency of the ore is improved. 2. The application of the intelligent control system makes the addition of flotation reagents more accurate, and improves the flotation selectivity of the lead-zinc ore. 3. The use of the dynamic simulation model and the model predictive control algorithm realizes the real-time optimization of the flotation process, and improves the recovery rate and economic benefits of the lead-zinc ore. 4. Through real-time monitoring and optimization, the energy consumption and reagent consumption in the flotation process are reduced, the production cost is reduced, and the environmental pollution is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above features and advantages of the present invention can be better understood after reading the detailed description of the embodiments disclosed in the present invention in conjunction with the following drawings.

[0036] Figure 1 It is a schematic flow diagram of a lead-zinc ore preferential flotation method according to one embodiment of the present invention;

[0037] Figure 2 It is a partial flow diagram of a lead-zinc ore preferential flotation method in an embodiment according to one aspect of the present invention;

[0038] Figure 3 It is a partial flow diagram of a lead-zinc ore preferential flotation method in an embodiment according to one aspect of the present invention;

[0039] Figure 4 It is a partial flow diagram of a lead-zinc ore preferential flotation method in an embodiment according to one aspect of the present invention;

[0040] Figure 5 It is a partial flow diagram of a lead-zinc ore preferential flotation method in an embodiment according to one aspect of the present invention. DETAILED DESCRIPTION

[0041] The following description is given to enable those skilled in the art to implement and use the invention and incorporate it into a specific application context. Various modifications, as well as various uses in different applications will be readily apparent to those skilled in the art, and the general principles defined herein are applicable to a wide range of embodiments. Thus, the present invention is not limited to the embodiments given herein, but should be granted the broadest scope consistent with the principles and novel features disclosed herein.

[0042] In the following detailed description, many specific details are set forth to provide a more thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the practice of the present invention need not be limited to these specific details. In other words, known structures and devices are shown in block diagram form without detailed display to avoid obscuring the present invention.

[0043] Unless otherwise expressly stated, all features disclosed in this specification (including any accompanying claims, abstracts and drawings) may be replaced by alternative features that achieve the same, equivalent or similar purpose. Therefore, unless expressly stated otherwise, each feature disclosed is only an example of a group of equivalent or similar features.

[0044] Note that, where used, the symbols left, right, front, back, top, bottom, forward, reverse, clockwise and counterclockwise are used only for convenience and do not imply any specific fixed direction. In fact, they are used to reflect the relative position and / or direction between the various parts of the object. In addition, the terms "first" and "second" are used only for descriptive purposes and should not be understood as indicating or implying relative importance.

[0045] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "connected", "connected", and "cross-connected" should be understood in a broad sense, for example, it can be directly connected, or indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0046] Note that, in the case of use, further, preferably, further and more preferably are simple beginnings for explaining another embodiment based on the previous embodiment, and the content of further, preferably, further or more preferably followed by the above embodiment is combined with the previous embodiment as a complete composition of another embodiment. Several further, preferably, further or more preferably settings followed by the same embodiment can be arbitrarily combined to form another embodiment.

[0047] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. Note that the aspects described below in conjunction with the accompanying drawings and specific embodiments are only exemplary and should not be construed as limiting the scope of protection of the present invention in any way.

[0048] According to one aspect of the present invention, there is provided a Figure 1 A schematic diagram of a process for preferential flotation of lead-zinc ore in one embodiment is shown. The preferential flotation method of lead-zinc ore includes: Figure 1 Steps S1 to S4 are shown.

[0049] Wherein, step S1 is: pre-treating the lead-zinc ore to remove impurities in the lead-zinc ore to obtain ore pulp.

[0050] Understandably, Figure 2 A partial schematic diagram of a process for preferential flotation of lead-zinc ore in one embodiment is shown, wherein step S1 further comprises: Figure 2 Steps S11 to S14 are shown.

[0051] Wherein, step S11 is: coarse crushing, medium crushing and fine crushing of the lead-zinc ore.

[0052] First, the lead-zinc ore is coarsely crushed, and the lead-zinc ore with a particle size of 150-1000mm is crushed to 100-300mm by squeezing and crushing the ore. The coarsely crushed ore is further crushed to a finer particle size, which can be selected according to the required product particle size. Furthermore, a jaw crusher can be used to coarsely crush the lead-zinc ore.

[0053] In the processing of lead and zinc ore, the particle size of the ore after secondary crushing can reach 30-100mm. Furthermore, a cone crusher can be used for secondary crushing.

[0054] Finally, the ore is ground to a particle size of 0.074-0.4 mm, which is beneficial for subsequent flotation and other mineral processing processes. Further, a ball mill can be used for fine crushing.

[0055] Step S12 is: grinding the lead-zinc ore to obtain ore pulp.

[0056] It is understandable that the lead-zinc ore can be washed before grinding. For lead-zinc ores, especially those mined in open pits, a large amount of clay impurities are often attached to the surface, which will affect the subsequent mineral processing effect. Washing can remove impurities such as clay and silt on the surface of the ore.

[0057] Furthermore, the ore and water can be put into the drum washing machine together. As the drum rotates, the ore collides and rubs against each other in the water, and impurities such as clay are dissolved or washed away by the water. A trough washing machine can also be used to separate impurities from the ore through the flow and stirring of the slurry in the trough. The advantage of the trough washing machine is that it has a good washing effect and is more suitable for some difficult-to-wash ores.

[0058] Preferably, step S12 also includes classifying the slurry, dividing the slurry into two parts: a coarse particle size and a fine particle size. The coarse particle size continues to be ground, and the fine particle size enters the next beneficiation process. Classification is to classify the crushed ore according to the particle size and remove the oversized or undersized particles that do not meet the requirements. In the pretreatment of lead-zinc ore, screening can ensure that the ore entering the next process has a uniform particle size, which is conducive to improving the beneficiation efficiency.

[0059] Step S12 may also include magnetic separation. For lead-zinc ore containing a small amount of magnetite impurities, after magnetic separation, the removal rate of magnetic impurities can reach more than 90%, thereby improving the grade of the lead-zinc ore.

[0060] It can be understood that ore grade refers to the content of useful components in the ore, which is the content of lead and zinc in lead-zinc ore. Ore grade is an important indicator to measure the quality and economic value of ore. The higher the grade, the greater the mining and processing value of the ore.

[0061] Step S13 is: controlling the concentration of the ground slurry to be between 20-40%.

[0062] It can be understood that the lead-zinc ore is placed in a stirring tank, and a proper amount of water and a regulator are added to form a slurry by stirring so that the ore is evenly dispersed in the water. The concentration of the slurry is controlled between 20% and 40%. The regulator can be lime or sodium carbonate.

[0063] Step S14 is: controlling the pH value of the ground slurry to be between 8 and 10.

[0064] The pH value of the slurry can be accurately measured with a pH meter. Insert the electrode of the pH meter into the slurry, making sure that the electrode is completely immersed and does not touch hard objects such as the stirring device to avoid damage to the electrode. Wait for the pH meter reading to stabilize and then record the value. Before measurement, the pH meter needs to be calibrated, usually using a standard buffer solution according to the steps in the instrument manual.

[0065] When the pH value in the ore pulp is less than 8, alkaline substances are added to increase the pH value of the ore pulp, and when the pH value in the ore pulp is greater than 10, acidic substances are added to reduce the pH value of the ore pulp. The alkaline substance may be lime or sodium hydroxide, and the acidic substance may be sulfuric acid or hydrochloric acid.

[0066] Through the above pretreatment steps, impurities in the lead-zinc ore are effectively removed, and the flotation efficiency of the ore is improved.

[0067] Step S2 is: adding lead priority flotation reagent to the ore pulp to obtain lead concentrate and zinc concentrate, and using the intelligent control system to adjust the addition amount and addition time of the flotation reagent.

[0068] It can be understood that the preferred flotation agents include collectors and frothers. Common lead collectors are xanthate and black powder. Xanthate can form insoluble metal xanthate with lead ions in the pulp, making its surface hydrophobic and easy to attach to bubbles; black powder has relatively good selectivity and has advantages for the flotation of lead in complex polymetallic ores. The frother can produce stable foam in the pulp, so that the lead mineral particles wrapped by the collector adhere to the foam and are brought to the surface of the pulp. Pine alcohol oil can be used. The dosage and performance of the frother directly affect the stability of the foam and the flotation effect.

[0069] The intelligent control system monitors various parameters in the flotation process in real time. The intelligent control system intelligently adjusts the time of adding roughing agents according to factors such as pulp flow and ore particle size. The intelligent control system intelligently adjusts the time of adding agents by real-time monitoring the grade of the selected foam product. At the same time, combined with the real-time monitoring of the grade and recovery rate of the lead concentrate, the time of adding agents is dynamically optimized, making the addition of flotation agents more accurate, improving the flotation selectivity of lead-zinc ore, and ensuring the efficiency and stability of the flotation process.

[0070] Step S3 is: intelligently adjusting the operating parameters of the flotation machine according to the ore properties and flotation effect.

[0071] Understandably, Figure 3 A partial schematic diagram of a process for preferential flotation of lead-zinc ore in one embodiment is shown, wherein step S3 further comprises: Figure 3 Steps S31 to S33 are shown.

[0072] Wherein, step S31 is: analyzing the properties of the lead-zinc ore to determine the mineral type, ore particle size distribution, wettability of the lead-zinc ore surface, surface potential of the lead-zinc ore, and ore grade of the lead-zinc ore;

[0073] The type of mineral can be determined by microscopic identification, X-ray diffraction analysis and chemical analysis. The particle size distribution of the ore can be determined by screening or laser particle size analyzer. The wettability of the lead-zinc ore surface can be determined by contact angle measurement or flotation test. The surface potential of the lead-zinc ore can be measured by electrophoresis or potentiometric titration. The grade of the ore can be determined by titration, atomic absorption spectroscopy or X-ray fluorescence spectroscopy.

[0074] Step S32 is: the evaluation indexes of flotation effect include concentrate grade, tailings grade and recovery rate;

[0075] Concentrate grade refers to the content of target metal (lead or zinc) in the concentrate, usually expressed as mass fraction (%), and is calculated by dividing the mass of the target metal in the concentrate by the total mass of the concentrate and then multiplying by 100%. Concentrate grade is a key indicator for measuring concentrate quality. High-grade concentrate has many advantages in the subsequent smelting process, such as reducing the smelting processing volume, reducing energy consumption, and improving smelting efficiency.

[0076] Tailings grade refers to the content of target metal (lead or zinc) remaining in the tailings, which is also expressed as mass fraction (%). The calculation method is to divide the mass of target metal in the tailings by the total mass of the tailings, and then multiply by 100%. Tailings grade reflects the loss of useful minerals in the tailings. The lower the tailings grade, the higher the recovery rate of useful minerals in the flotation process and the more fully the resources are utilized.

[0077] Recovery rate refers to the ratio of the mass of target metal recovered in the concentrate to the mass of target metal in the original ore. The calculation formula is:

[0078]

[0079] Recovery rate is a comprehensive indicator to measure the efficiency of mineral processing and the degree of resource recovery of mineral processing plants. A high recovery rate means that more useful minerals are recovered from the original ore, which is particularly important when resources are becoming increasingly scarce.

[0080] Step S33 is: intelligently adjusting the strategy of the flotation machine operating parameters based on the evaluation index of the flotation effect. The flotation machine operating parameters include stirring speed, aeration volume, slurry level, reagent addition type and reagent addition amount.

[0081] The current strategy is adjusted based on the concentrate grade, tailings grade and recovery rate. The intelligent control system can achieve automatic adjustment through the stirring motor power sensor and concentrate grade online analyzer installed in the flotation machine.

[0082] Utilizing the gas flow meter and foam layer stability monitoring device installed at the bottom of the flotation machine, the intelligent control system can monitor the aeration volume and foam status in real time, and automatically adjust the aeration volume based on the concentrate grade feedback information.

[0083] The intelligent control system monitors the slurry level in real time through the liquid level sensor, and automatically controls the slurry level by adjusting the opening of the tailings discharge valve or the opening of the water replenishment valve in combination with the concentrate grade data.

[0084] The intelligent control system uses reagent flow sensors and concentrate composition online detection equipment to adjust the type and amount of reagents added in real time according to the concentrate grade.

[0085] Step S4 is: establishing a dynamic simulation model of the flotation process, combining real-time monitoring data, and using a model predictive control algorithm to optimize the flotation process in real time.

[0086] Understandably, Figure 4 A partial schematic diagram of a process for preferential flotation of lead-zinc ore in one embodiment is shown, wherein step S4 further comprises: Figure 4 Steps S41 to S43 are shown.

[0087] Wherein, step S41 is: establishing a physical model of the flotation process based on measured data;

[0088] First, collect flotation parameters, i.e., stirring speed, aeration volume, slurry level, reagent addition type and amount, and other flotation machine operating parameters, collect ore property data, i.e., ore mineral type, particle size distribution, surface wettability, surface potential, and other properties, and collect re-selection effect data, i.e., concentrate grade, tailings grade, and recovery rate. Then, record the parameter values ​​at different time points over a fixed period to form time series data to capture the dynamic changes of parameters during the flotation process, which is used to analyze the impact of parameter changes on flotation effects.

[0089] Step S42 is: determining parameters of the dynamic simulation model based on the physical model;

[0090] Based on the mass conservation, momentum conservation and energy conservation equations, the flotation kinetic parameters, reagent action parameters and bubble-mineral interaction parameters of useful minerals are determined by fitting experimental data.

[0091] Step S43 is: using a model predictive control algorithm to optimize the flotation process in real time.

[0092] Understandably, Figure 5 A partial schematic diagram of a process for preferential flotation of lead-zinc ore in one embodiment is shown, wherein step S43 further includes: Figure 5 Steps S431 to S434 are shown.

[0093] Wherein, step S431 is: constructing a prediction model for a model predictive control algorithm based on a dynamic simulation model;

[0094] According to the characteristics of the flotation process, a structure based on the state space model can be selected. The state space model consists of state equations and output equations, which can well describe the dynamic characteristics of the system. In the flotation process, the state variables can include the concentration of useful minerals and gangue minerals in the pulp, the concentration of reagents in the pulp, the size and quantity distribution of bubbles, etc.; the output variables are flotation effect indicators, namely, concentrate grade, tailings grade and recovery rate.

[0095] According to the actual operation cycle and data collection frequency of the flotation process, the time scale of the prediction model is determined, and a suitable discretization method, such as the Euler method or the Runge-Kutta method, is used to transform the continuous dynamic simulation model into a discrete prediction model.

[0096] The previously collected measured data of the flotation process are used to estimate the parameters in the prediction model. The parameter estimation method takes into account the uncertainty of the model. Since there are various uncertain factors in the flotation process, such as fluctuations in ore properties and changes in equipment performance, these uncertainties need to be considered in the parameter estimation process. The Bayesian estimation method can be used to treat the parameters as random variables with a prior probability distribution, and the probability distribution of the parameters can be updated in combination with the measured data to obtain more accurate parameter estimates.

[0097] Step S432 is: setting the objective function of the model predictive control algorithm according to the goals and requirements of flotation production;

[0098] The components of the objective function are: concentrate grade related terms, recovery rate related terms and cost related terms.

[0099] Assume the lead concentrate grade is C 11 , zinc concentrate grade is C 12 The target concentrate grades are C 21 and C 22 . Construct the objective function term related to concentrate grade: J1=ω1[(C 11 -C 21 ) 2 +(C 12 -C 22 ) 2 ω1 is a weight coefficient used to adjust the importance of concentrate grade in the objective function. The purpose of this term is to make the concentrate grade as close to the target grade as possible. By minimizing this term, the flotation process is driven towards improving the concentrate grade.

[0100] Assume the lead recovery rate is R 11 The zinc recovery rate is R 12 The target recovery rates are R 21 and R 22 . Construct the objective function term related to recovery rate: J2=ω2[(R 11 -R 21 )2 +(R 12 -R 22 ) 2 ω2 is the weight coefficient. The function of this item is to encourage the improvement of the recovery rate of useful minerals, so that the flotation process can effectively recover the lead and zinc in the ore while improving the grade of the concentrate.

[0101] Consider the reagent cost, energy cost and equipment maintenance cost. Let the reagent cost be M1, the energy cost be M2, and the equipment maintenance cost be M3. The goal is to minimize the total cost. The cost-related objective function term can be constructed: J3 = ω3 (M1 + M2 + M3). Among them, ω3 is the weight coefficient. These costs can be reduced by reasonably controlling the operating parameters of the flotation machine, such as the amount of reagent added, the stirring speed and the amount of aeration.

[0102] Combining the above items, the concentrate grade, recovery rate and cost are taken as three independent objective functions J1, J2 and J3, and then a set of non-inferior solutions is found through a multi-objective optimization algorithm, that is, a set of relatively better flotation machine operating parameter combinations after weighing different objectives. This method can more comprehensively consider the complex relationship between multiple objectives of flotation production and provide more options for production decisions.

[0103] Step S433 is: setting the threshold range of each parameter in the prediction model;

[0104] Step S434 is: updating the prediction model according to the current state measurement value, and performing prediction and optimization calculation again to adapt to the dynamic changes and uncertainties of the flotation process.

[0105] Based on the newly collected current state measurements, the key parameters involved in the prediction model are re-evaluated. Due to the uncertainty of the flotation process, such as fluctuations in ore properties and changes in equipment performance, these factors are fully considered when updating the parameters.

[0106] Input the latest real-time data and the re-estimated and adjusted model parameters into the updated prediction model. Ensure that each data and parameter corresponds to the correct variable position so that the prediction model can calculate based on the current accurate information. Use the updated prediction model to predict the flotation effect in the future according to its set state equation and output equation. At the same time, continuously monitor the adjusted flotation process, repeat the above update, prediction and optimization process, form a closed-loop control, constantly adapt to the dynamic changes and uncertainties of the flotation process, and ensure that flotation production always develops towards the goal of high efficiency and high quality.

[0107] The present invention realizes real-time optimization of the flotation process and improves the recovery rate and economic benefit of the lead-zinc ore by using a dynamic simulation model and a model prediction control algorithm.

[0108] The foregoing description is provided to enable any person skilled in the art to practice the various aspects described herein. However, it should be understood that the scope of protection of the present invention shall be subject to the appended claims and shall not be limited to the specific structures and components of the above-described embodiments. Those skilled in the art may make various changes and modifications to the embodiments within the spirit and scope of the present invention, and these changes and modifications also fall within the scope of protection of the present invention.

Claims

1. A method for preferential flotation of lead-zinc ore, characterized in that: The following steps are involved: S1, pretreating the lead-zinc ore to remove impurities in the lead-zinc ore to obtain ore pulp; S2, adding a lead-preferential flotation agent to the slurry to obtain a lead concentrate and a zinc concentrate, and adjusting the amount and time of addition of the flotation agent by using an intelligent control system; S3. Intelligently adjust the operating parameters of the flotation machine according to the ore properties and flotation effect; S4. Establish a dynamic simulation model of the flotation process, combine it with real-time monitoring data, and use the model predictive control algorithm to optimize the flotation process in real time.

2. The lead-zinc ore preferential flotation method according to claim 1, characterized in that: Step S1 also includes: S11, performing coarse crushing, medium crushing and fine crushing on the lead-zinc ore; S12, grinding the lead-zinc ore to obtain ore pulp; S13, controlling the concentration of the ground slurry to between 20-40%; S14. Control the pH value of the ground slurry between 8 and 10.

3. The lead-zinc ore preferential flotation method according to claim 2, characterized in that: Step S12 also includes classifying the slurry into two parts: a coarse particle size and a fine particle size. The coarse particle size continues to be ground, while the fine particle size enters the next step of the mineral processing process.

4. The lead-zinc ore preferential flotation method according to claim 1, characterized in that: The lead preferential flotation reagents in step S2 include yellow medicine, black medicine and frother, and the mass ratio thereof is 3:2:

1.

5. The lead-zinc ore preferential flotation method according to claim 1, characterized in that: The intelligent control system in step S2 includes at least one sensor for detecting the concentration and pH value of the flotation reagent.

6. The lead-zinc ore preferential flotation method according to claim 1, characterized in that: Step S3 also includes: S31, analyzing the properties of the lead-zinc ore to determine the mineral type, ore particle size distribution, wettability of the lead-zinc ore surface, surface potential of the lead-zinc ore, and ore grade of the lead-zinc ore; S32, the evaluation indexes of the flotation effect include concentrate grade, tailings grade and recovery rate; S33, a strategy for intelligently adjusting the operating parameters of the flotation machine based on the evaluation index of the flotation effect, wherein the operating parameters of the flotation machine include stirring speed, aeration volume, slurry level, type of reagent addition, and amount of reagent addition.

7. The lead-zinc ore preferential flotation method according to claim 1, characterized in that: The real-time monitoring of data in step S4 includes using image recognition technology to monitor changes in the foam layer.

8. The lead-zinc ore preferential flotation method according to claim 1, characterized in that: Step S4 also includes: S41. Establishing a physical model of the flotation process based on measured data; S42, determining parameters of the dynamic simulation model based on the physical model; S43. Utilize a model predictive control algorithm to optimize the flotation process in real time.

9. The lead-zinc ore preferential flotation method according to claim 8, characterized in that: The parameters of the dynamic simulation model in step S42 include: density, hardness, and surface properties of mineral particles; adsorption rate constant, desorption rate constant, and reaction equilibrium constant of flotation reagents; and viscosity and density of slurry.

10. The lead-zinc ore preferential flotation method according to claim 8, characterized in that: Step S43 also includes: S431, constructing a prediction model for the model predictive control algorithm based on the dynamic simulation model; S432, setting the objective function of the model predictive control algorithm according to the goals and requirements of flotation production; S433, setting the threshold range of each parameter in the prediction model; S434, updating the prediction model according to the current state measurement value, and performing prediction and optimization calculation again to adapt to the dynamic changes and uncertainties of the flotation process.

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