Dynamic control method for potassium salt flotation reagent dosage

Abstract

The invention provides a sylvite flotation reagent dosage dynamic control method. The sylvite flotation reagent dosage dynamic control method comprises the following steps that at least one ore pulp parameter and at least one foam image characteristic parameter of a sylvite flotation system are collected in real time; dynamically calculating a flotation reagent dynamic response coefficient required at the current moment based on the collected real-time parameters; according to the dynamic response coefficient of the flotation reagent, the adding amount of the flotation reagent is adjusted in real time; according to the method, the multi-dimensional key parameters are collected in real time, the dynamic response model integrating multi-parameter coupling, historical efficiency accumulation and process time delay correction is constructed, accurate and real-time adjustment of the drug dosage is achieved, meanwhile, the method has the model self-adaptive calibration capability, and the problems that in the prior art, control precision is low, and adaptability is poor are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic flotation control, and particularly relates to a dynamic control method for the dosage of a potassium salt flotation reagent. BACKGROUND

[0002] As a core raw material for the production of agricultural potassium fertilizer, the flotation process of potassium salt is a key link to improve the grade and recovery rate of potassium salt. In the early stage, the state of the ore pulp and the characteristics of the foam were often judged by the operator according to experience, and the dosage of the reagent was manually adjusted, which was low in efficiency and poor in stability. From the end of the 20th century to the beginning of the 21st century, a semi-automatic control system appeared, which triggered the switching of the reagent dosage through fixed parameter thresholds, but could not adapt to the dynamic changes of the nature of the raw ore and the working condition of the ore pulp. In recent years, with the progress of sensor technology and control algorithm, intelligent flotation control has become a development trend, and the core demand is to realize the real-time matching of the reagent dosage and the flotation process. However, the current technology has many problems. Firstly, the traditional fixed reagent dosage mode ignores dynamic factors such as fluctuations in the grade of the raw ore and changes in the pH value of the ore pulp, resulting in excessive waste or insufficient dosage of the reagent, reducing the flotation efficiency. Secondly, the existing semi-automatic control only considers individual parameters such as the concentration or flow rate of the ore pulp, without considering the coupling effect of multiple parameters, resulting in insufficient control accuracy. Thirdly, the time delay characteristics of the flotation process are not fully considered, resulting in a lag in the control response and causing the foam state to be out of control. Fourthly, the cumulative effect of historical flotation efficiency data is not included in the control logic, which is prone to misadjustment due to single parameter fluctuations. Fifthly, the model parameters lack a self-adaptive calibration mechanism, and after long-term operation, control deviations may occur due to factors such as equipment aging and reagent performance degradation, requiring frequent manual calibration, resulting in high maintenance costs. These problems directly lead to low potassium salt flotation recovery rate and large reagent consumption, restricting the quality improvement, efficiency increase and green development of the potassium salt mining industry. Therefore, there is an urgent need in the art for a dynamic control method for the dosage of a potassium salt flotation reagent to solve the above problems. SUMMARY

[0003] The present application provides a dynamic control method for the dosage of a potassium salt flotation reagent, which aims to collect multiple key parameters in real time, build a dynamic response model that integrates multiple parameter coupling, historical efficiency accumulation and process time delay correction, and realize precise and real-time adjustment of the reagent dosage. At the same time, it has a model self-adaptive calibration capability, solving the problems of low control accuracy and poor adaptability of the existing technology.

[0004] The present application provides a dynamic control method for the dosage of a potassium salt flotation reagent, which includes the following steps: S1, at least one ore pulp parameter and at least one foam image feature parameter of a potassium salt flotation system are collected in real time; S2, based on the real-time parameters collected in step S1, the dynamic response coefficient of the flotation reagent required at the current time is dynamically calculated; S3, adjust the amount of flotation reagent added in real time according to the dynamic response coefficient of the flotation reagent.

[0005] Compared with the prior art, the beneficial effects of this application are as follows: 1. This application can significantly improve the potassium salt recovery rate and stabilize the concentrate grade by real-time collection of key parameters such as pulp pH, potassium ion concentration and foam stability, combined with multi-parameter coupling effect and dynamic response model, to accurately match reagent dosage and flotation conditions. 2. This application avoids excessive waste of reagents by correcting the residual concentration of the collector and limiting the threshold. Compared with traditional methods, it can reduce the amount of reagent used, reduce production costs and environmental pressure. 3. This application introduces a time lag correction factor to compensate for the inherent delay in the flotation process, and combines it with the historical efficiency cumulative response factor to avoid misadjustment caused by single parameter fluctuations, and can stably cope with the dynamic changes in the properties of the raw ore and the working conditions of the slurry. 4. The dynamic weighting coefficients of this application enable adaptive switching between immediate response and historical cumulative effect. The model adaptive calibration mechanism does not require frequent manual intervention, reducing the labor intensity and maintenance cost of operators. 5. This application can be directly integrated into existing flotation automation systems without large-scale equipment modifications. It is compatible with different potash minerals and flotation processes, providing reliable technical support for the intelligent upgrading of the potash beneficiation industry and helping to achieve the development goals of improving quality and efficiency, and green and low-carbon development.

[0006] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0007] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention, but do not constitute a limitation thereof; in the drawings: Figure 1 This is a flowchart illustrating a method for dynamically controlling the dosage of potassium salt flotation reagents provided by the present invention. Detailed Implementation

[0008] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention. Example 1:

[0009] This invention provides a method for dynamically controlling the dosage of potassium salt flotation reagents. Please refer to [link / reference]. Figure 1 This includes the following steps: S1, Real-time acquisition of at least one slurry parameter and at least one foam image feature parameter of the potassium salt flotation system; S2, based on the real-time parameters collected in step S1, dynamically calculate the dynamic response coefficient of the flotation reagent required at the current moment; S3 adjusts the amount of flotation reagent added in real time based on the dynamic response coefficient of the flotation reagent.

[0010] Specifically, this embodiment collects key parameters that reflect the state of the slurry and the characteristics of flotation foam in real time during the flotation process. Based on these parameters, a dynamic response model is constructed to calculate the dynamic response coefficient of the reagent. Then, the reagent addition is adjusted in real time according to the coefficient to achieve dynamic adaptation of reagent dosage. This solves the problem that the traditional fixed dosage cannot match the dynamic changes in the flotation process, thereby improving flotation efficiency and potassium salt recovery rate.

[0011] In one embodiment, in step S1, the slurry parameters include slurry pH value and potassium ion concentration; the foam image feature parameters include foam stability.

[0012] Specifically, the pH value of the pulp directly affects the surface charge of minerals and the effectiveness of reagents, and is a core acid-base environment parameter in the flotation process. The potassium ion concentration reflects the content level of the target mineral in the pulp and is directly related to the reagent demand. Foam stability is a direct representation of the flotation effect, and abnormal stability will directly affect the mineral separation efficiency. The above parameters are all key parameters that can accurately reflect the flotation state and can be obtained in real time through existing sensors or image analysis technology, providing basic data support for subsequent reagent dosage calculation.

[0013] In one embodiment, in step S2, the dynamic response coefficient of the flotation reagent is... Obtained through calculation using a dynamic response model; The dynamic response model is used to comprehensively consider the deviation between real-time parameters and preset baseline states, the cumulative effect of historical flotation efficiency, the multi-parameter coupling effect, and the time delay characteristics of the flotation process.

[0014] Specifically, the dynamic response model is the core of achieving dynamic control of reagent dosage. The deviation between real-time parameters and preset baseline states directly reflects the difference between the current flotation state and the ideal state. The cumulative effect of historical flotation efficiency can avoid misadjustment of reagent dosage caused by single parameter fluctuations. The multi-parameter coupling effect can reflect the mutual influence between various parameters (such as the synergistic effect of pH value and potassium ion concentration on reagent action). The time lag characteristic correction of the flotation process can compensate for the control deviation caused by delays in pulp transportation, reagent reaction and other links. Through multi-dimensional consideration, the accuracy and rationality of dynamic response coefficient calculation are ensured.

[0015] In one implementation, the calculation process of the dynamic response model includes: S21, Calculate the instantaneous response factor at the current moment. The calculation formula is: ; In the formula, This represents the real-time potassium ion concentration. Compared with the preset reference potassium ion concentration The absolute deviation; This represents the real-time foam stability. Foam stability compared to preset benchmark The absolute deviation; This represents the real-time pH value of the slurry. pH value of the preset reference slurry The absolute deviation; Based on the weighting coefficient, The coupling weight coefficients satisfy the following conditions: The basic contribution weights of potassium ion concentration, foam stability, and pH value, as well as the coupling contribution weights of the three, are respectively characterized. S22, Calculate the cumulative response factor considering historical efficiency. The calculation formula is: ; In the formula, This is a preset backtracking time window used to limit the time range of historical data involved in the calculation; For a historic moment The corresponding flotation efficiency parameter is either concentrate grade or potassium recovery rate. The preset target flotation efficiency parameter corresponds to the target concentrate grade or target potassium recovery rate. The attenuation coefficient characterizes the rate at which historical data diminishes in relation to the calculation results at the current moment. >0; The gradient weight coefficient is used to adjust the contribution of the historical efficiency deviation trend to the cumulative response factor.

[0016] Specifically, in the immediate response factor In the calculation formula, This is real-time data on potassium ion concentration in slurry acquired using a potassium ion selective electrode. The baseline potassium ion concentration is determined based on the statistical analysis of historical best flotation conditions. This is real-time data on foam stability extracted using a high-definition image acquisition device. The benchmark foam stability under the historically optimal flotation conditions; The pH value of the slurry is collected in real time by a pH sensor. This is the baseline pH value under the historically optimal flotation conditions. The weighting coefficients are determined based on flotation test data and production practice statistics. Their values ​​range from 0 to 1 and satisfy the following conditions: It can be manually adjusted according to different types of potash ore and flotation processes; In this formula, The 1.2-power nonlinear mapping is adopted because the effect of potassium ion concentration deviation on reagent demand increases nonlinearly; the larger the concentration deviation, the higher the sensitivity of reagent dosage adjustment needs to be. By mapping the foam stability deviation to the [0,1] interval using a sine function, the characteristics of the agent response being smooth when the foam stability is within a reasonable range and enhanced when it exceeds the reasonable range are simulated. The exponential decay function is adopted because the pH value has little effect on the reagent when it is near the reference value, and the effect increases in a decaying manner as the deviation increases, which is consistent with the law of pH value's influence on flotation. This is a coupling term between potassium ion concentration deviation and foam stability deviation, characterizing the synergistic effect of the two on the agent response; The pH deviation is mapped to the (0,1) interval to smoothly adjust the contribution strength of the coupling term and avoid abrupt changes in the coupling effect; This instant response factor The calculation formula uses the basic term (the first three terms of the numerator divided by) ) characterizes the fundamental effect of a single parameter deviation on the agent response, through coupling terms ( The multi-parameter synergistic effect is characterized by multiplying the product of the coupling term and the tanh function. The comprehensive calculation yields an instantaneous response factor that reflects the current flotation state's demand for reagents. In cumulative response factor In the calculation formula, The backtracking time window is set according to the flotation process cycle, and is usually set to 30~120 minutes to ensure that sufficient historical data is covered and outdated information is not included. For a historic moment Data on concentrate grade or potassium recovery rate obtained through laboratory testing. The target concentrate grade (e.g., 35%~45%) or target potassium recovery rate (e.g., 85%~95%) set according to production indicators are obtained through conventional means and will not be described in detail here. The attenuation coefficient ranges from 0.01 to 0.1 and is determined by fitting historical data to ensure that recent data has a higher impact weight and long-term data has a lower impact weight. The gradient weight coefficient has a value ranging from 0.2 to 0.8, and is determined based on the sensitivity of flotation efficiency fluctuations. In this formula, This is the squared term of the historical efficiency deviation, used to amplify the impact of larger efficiency deviations on the cumulative response, highlighting the need for reagent adjustment under inefficient operating conditions; This is an exponential decay term used to reduce the weight of historical data, making the data conform to patterns that are more relevant to recent data. The first derivative of the historical relative efficiency deviation characterizes the changing trend of the efficiency deviation (increasing or decreasing), and is used to predict the development direction of the flotation state. This is a normalization term used to eliminate the influence of the time window length on the cumulative results, ensuring that the cumulative response factors under different time windows are comparable. The cumulative response factor The calculation formula obtains a cumulative response factor that reflects the cumulative impact of historical flotation effects by superimposing the weighted cumulative term of historical efficiency deviation and the efficiency deviation trend term, and then normalizing it, thus avoiding short-sighted adjustments based solely on the current state.

[0017] In one implementation, the dynamic response coefficient of the flotation reagent By comprehensively considering immediate response factors and cumulative response factor The calculation formula is as follows: ; In the formula, For dynamic comprehensive weighting coefficients, satisfying Its dynamic adjustment formula is: Through immediate response factors The weights of both are dynamically allocated based on changes in the system. This is the comprehensive deviation amplification factor, used to enhance the influence of the comprehensive deviation of multiple parameters on the response coefficient; In the formula, The multi-dimensional time delay correction factor is calculated using the following formula: ; in, The time-delay sensitivity coefficient is used to adjust the contribution of the potassium ion concentration deviation rate and the foam stability deviation rate to the time-delay correction. This represents the rate of change of the absolute deviation of potassium ion concentration over time. This represents the rate of change of the absolute deviation of foam stability over time. The preset base time delay is used to match the inherent delay characteristics of the flotation process.

[0018] Specifically, the dynamic response coefficient of flotation reagents In the calculation formula, , These are dynamic weighting coefficients, requiring no additional setting, and are determined by the instantaneous response factor. Automatic adjustment; The comprehensive deviation amplification factor, ranging from 0.3 to 1.0, is determined based on the sensitivity of the influence of multi-parameter deviations on reagent adjustment, and is determined through conventional experimental methods or experience; multi-dimensional time lag correction factor. In the calculation formula, The time delay sensitivity coefficient ranges from 0.1 to 0.5. It is determined through time delay testing experiments in the flotation process, which is a routine experiment and will not be described in detail here. The base time delay, ranging from 5 to 15 minutes, corresponds to the typical delay time for slurry transport and reagent-mineral reaction. The dynamic response coefficient of the flotation reagent In the calculation formula, This is a dynamic weighting of immediate response and historical cumulative response. In the dynamic adjustment formula, when When the value is relatively small (the current state is close to the ideal state). Approaching 1, As the value approaches 1, the weights shift towards immediate response; when... When the value is large (the current state deviates from the ideal state). Decrease reduce, The weighting is increased, shifting towards historical cumulative responses to achieve adaptive switching of adjustment priorities; As a comprehensive deviation amplification term, the influence of the comprehensive deviation of multiple parameters on the response coefficient is smoothly amplified by the square root function, so as to avoid over-adjustment caused by excessive deviation of a single parameter; middle, The rate of change of potassium ion concentration deviation is mapped to the (-1,1) interval to correct the time lag effect caused by rapid changes in potassium ion concentration. It is a Sigmoid function that maps the rate of change of foam stability deviation to the (0,1) interval to simulate the time-delay cumulative effect caused by the change of foam state. The larger the rate of change, the higher the correction strength. This formula integrates the real-time state and historical cumulative effect through a dynamic weighting term, strengthens the influence of multi-parameter synergistic deviation through a comprehensive deviation amplification term, and compensates for the inherent delay of the flotation process through a multi-dimensional time delay correction factor. Finally, it obtains a dynamic response coefficient that can comprehensively reflect the current flotation state, historical effects, and process delay, providing a precise basis for adjusting reagent dosage.

[0019] In one implementation, step S3 specifically includes: Based on the dynamic response coefficient of flotation reagents Compared with the preset baseline dosage Combined with real-time slurry flow The dynamic correction is calculated using the following formula to determine the actual dosage of the drug at the current moment. : ; In the formula, For real-time slurry flow rate, Based on the slurry flow rate, The flow correction index is used to characterize the degree of influence of changes in slurry flow rate on reagent dosage. This represents the real-time residual concentration of the collector. Compared with the preset benchmark collector residual concentration The absolute deviation; This is a correction factor for the residual concentration of the collector, used to dynamically adjust the amount of agent added based on the remaining amount of collector, to avoid excessive or insufficient agent. The calculated actual dosage of the drug The signal is converted into a standard control signal and output to the drug addition actuator. The drug addition amount is adjusted in real time by regulating the speed of the metering pump in the actuator.

[0020] Specifically, The baseline reagent addition rate is determined based on the reagent dosage under the historical best flotation conditions, and the unit is kg / h; This refers to slurry flow data collected in real time via a flow sensor. The reference slurry flow rate under design conditions, in m³ / s. 3 / h; The flow correction index, ranging from 0.8 to 1.2, was determined through correlation tests between slurry flow rate and reagent dosage. When =1, the reagent dosage is linearly proportional to the slurry flow rate; This refers to the residual collector concentration in the slurry, which is collected in real time by a collector concentration detector. The baseline collector residual concentration required to maintain normal flotation, expressed in mg / L; The residual concentration correction factor for the collector is 0.5 to 1.5, and its value is determined based on the collector's efficiency and attenuation characteristics. The standard control signal adopts the industrially common 4-20mA current signal, and the agent addition actuator is a variable frequency metering pump.

[0021] In actual dosage In the calculation formula, The basic dosage of the reagent is based on the dynamic response coefficient. A positive value indicates that the dosage of the medicine needs to be increased, while a negative value indicates that the dosage of the medicine needs to be decreased. This is a slurry flow rate correction term, used to take into account the impact of changes in slurry flow rate on reagent concentration. When the flow rate increases, the reagent dosage is increased accordingly to ensure a stable reagent concentration in the slurry. This is a collector residual concentration correction term. When the collector residual concentration is higher than the benchmark value, the correction term is less than 1, reducing the amount of reagent added. When the residual concentration is lower than the benchmark value, the correction term is greater than 1, increasing the amount of reagent added to avoid reagent waste or insufficient dosage. This formula is based on the benchmark reagent dosage and combines dynamic response coefficients, changes in slurry flow rate, and collector residual concentration to obtain the actual reagent dosage through multi-dimensional correction. Then, the metering pump speed is adjusted through a standard control signal to achieve precise and real-time adjustment of the reagent dosage.

[0022] In one implementation, step S3 further includes a dosage threshold limiting step and a smooth transition step: The threshold limiting step includes: calculating the actual amount of drug added. Set upper and lower thresholds, i.e. ;in, This refers to the minimum allowable dosage of the reagent, set according to process requirements. This is the maximum allowable dosage of the reagent set according to process requirements; if Then As the actual amount added; if Then As the actual amount added; The smoothing transition step includes: using a first-order inertial filtering algorithm to smooth the adjusted dosage of the medicine, the filtering formula being as follows: ,in These are the filter coefficients. This is the smoothed dosage of the drug at the current moment. This is the amount of reagent added after smoothing out the previous moment, to avoid sudden changes in reagent dosage from impacting the flotation process.

[0023] Specifically, filter coefficients The value ranges from 0.1 to 0.3. The smaller the value, the more obvious the smoothing effect. The specific value should be selected based on routine experiments or experience.

[0024] In one embodiment, the method further includes step S4: based on a fixed period or preset triggering conditions, and based on recent flotation effect data, optimize and calibrate the preset baseline state parameters, weight coefficients and correction coefficients in the dynamic response model to ensure that the model adapts to the dynamic changes in the flotation process.

[0025] S41, Preset calibration trigger condition: When the relative deviation between the average flotation efficiency and the target flotation efficiency over N consecutive cycles exceeds a preset threshold, or when the fluctuation range of flotation efficiency in a single cycle exceeds a preset maximum fluctuation range threshold, the calibration procedure is initiated. S42, Calibration data selection: Collect full data of the flotation process within the most recent M cycles, including real-time slurry parameters, froth image feature parameters, reagent addition amount, slurry flow rate and corresponding flotation efficiency data, and M≥3N; S43, Parameter calibration: The objective function is to minimize the mean square error between the flotation efficiency and the target efficiency corresponding to the reagent dosage predicted by the gradient descent method. The preset baseline state parameters, weighting coefficients and time delay correction coefficients in the dynamic response model are then finely adjusted synchronously. S44, Calibration Constraints: All calibration parameters must be within a preset physical reasonable range to avoid parameter values ​​that exceed common process knowledge.

[0026] Specifically, N is the period count threshold, which takes the value of 3 to 5 periods (each period is 1 to 2 hours), with a preset threshold of 3% to 5% and a maximum fluctuation threshold of 5% to 8%; M is the data sample size threshold, where M ≥ 3N ensures sufficient effective data collection to cover different flotation states. Gradient descent is a commonly used method and will not be elaborated further; the calibration parameters include preset baseline state parameters ( ), weighting coefficients ( Time delay correction factor () By fine-tuning these parameters, the objective function is minimized, thus achieving a match between the model and the actual process.

[0027] In one implementation, a distributed acquisition system is used in step S1 to achieve real-time parameter acquisition; The distributed acquisition system includes a slurry parameter sensor group, a high-definition image acquisition device, a data preprocessing unit, and a real-time communication module; The pulp parameter sensor group includes a pH sensor, a potassium ion selective electrode, and a collector concentration detector, which are used to collect the pulp pH value and potassium ion concentration, respectively; the high-definition image acquisition device is equipped with an industrial camera and image preprocessing algorithm to acquire real-time images of foam on the surface of the flotation cell and extract foam stability. The data preprocessing unit filters and reduces noise on the collected raw parameters; the real-time communication module uploads the preprocessed parameters in real time to calculate the dynamic response coefficient of the flotation reagents. Specifically, the aforementioned sensor devices are all commonly used devices in this field, and can be replaced with other devices with the same function based on the usage conditions. This embodiment does not limit them.

[0028] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamically controlling the dosage of potassium salt flotation reagents, characterized in that, Includes the following steps: S1, Real-time acquisition of at least one slurry parameter and at least one foam image feature parameter of the potassium salt flotation system; S2, based on the real-time parameters collected in step S1, dynamically calculate the dynamic response coefficient of the flotation reagent required at the current moment; S3, adjust the amount of flotation reagent added in real time according to the dynamic response coefficient of the flotation reagent.

2. The method for dynamically controlling the dosage of potassium salt flotation reagents according to claim 1, characterized in that, In step S1, the slurry parameters include slurry pH value and potassium ion concentration; the foam image feature parameters include foam stability.

3. The method for dynamically controlling the dosage of potassium salt flotation reagents according to claim 1, characterized in that, In step S2, the dynamic response coefficient of the flotation reagent Obtained through calculation using a dynamic response model; The dynamic response model is used to comprehensively consider the deviation between real-time parameters and preset benchmark states, the cumulative effect of historical flotation efficiency, the multi-parameter coupling effect, and the time delay characteristics of the flotation process.

4. The method for dynamically controlling the dosage of potassium salt flotation reagents according to claim 3, characterized in that, The calculation process of the dynamic response model includes: S21, Calculate the instantaneous response factor at the current moment. The calculation formula is: ； In the formula, This represents the real-time potassium ion concentration. Compared with the preset reference potassium ion concentration The absolute deviation; This represents the real-time foam stability. Foam stability compared to preset benchmark The absolute deviation; This represents the real-time pH value of the slurry. pH value of the preset reference slurry The absolute deviation; Based on the weighting coefficient, The coupling weight coefficients satisfy the following conditions: The basic contribution weights of potassium ion concentration, foam stability, and pH value, as well as the coupling contribution weights of the three, are respectively characterized. S22, Calculate the cumulative response factor considering historical efficiency. The calculation formula is: ； In the formula, This is a preset backtracking time window used to limit the time range of historical data involved in the calculation; For a historic moment The corresponding flotation efficiency parameter is the concentrate grade or potassium recovery rate; The preset target flotation efficiency parameter corresponds to the target concentrate grade or target potassium recovery rate. The attenuation coefficient characterizes the rate at which historical data diminishes in relation to the calculation results at the current moment. >0; The gradient weight coefficient is used to adjust the contribution of the historical efficiency deviation trend to the cumulative response factor.

5. The method for dynamically controlling the dosage of potassium salt flotation reagents according to claim 4, characterized in that, In step S2, the dynamic response coefficient of the flotation reagent By comprehensively considering immediate response factors and cumulative response factor The calculation formula is as follows: ； In the formula, , For dynamic comprehensive weighting coefficients, satisfying Its dynamic adjustment formula is: , Through immediate response factors The weights of both are dynamically allocated based on changes in the system. This is the comprehensive deviation amplification factor, used to enhance the influence of the comprehensive deviation of multiple parameters on the response coefficient; In the formula, The multi-dimensional time delay correction factor is calculated using the following formula: ； in, , The time-delay sensitivity coefficient is used to adjust the contribution of the potassium ion concentration deviation rate and the foam stability deviation rate to the time-delay correction. This represents the rate of change of the absolute deviation of potassium ion concentration over time. This represents the rate of change of the absolute deviation of foam stability over time. The preset base time delay is used to match the inherent delay characteristics of the flotation process.

6. The method for dynamically controlling the dosage of potassium salt flotation reagents according to claim 1, characterized in that, The specific steps of step S3 include: Based on the dynamic response coefficient of the flotation reagent Compared with the preset baseline dosage Combined with real-time slurry flow The dynamic correction is calculated using the following formula to determine the actual dosage of the drug at the current moment. : ； In the formula, For real-time slurry flow rate, Based on the slurry flow rate, The flow correction index is used to characterize the degree of influence of changes in slurry flow rate on reagent dosage. This represents the real-time residual concentration of the collector. Compared with the preset benchmark collector residual concentration The absolute deviation; This is a correction factor for the residual concentration of the collector, used to dynamically adjust the amount of agent added based on the remaining amount of collector, to avoid excessive or insufficient agent. The calculated actual dosage of the drug The signal is converted into a standard control signal and output to the drug addition actuator. The drug addition amount is adjusted in real time by regulating the speed of the metering pump in the actuator.

7. The method for dynamically controlling the dosage of potassium salt flotation reagents according to claim 6, characterized in that, Step S3 also includes a dosage threshold limiting step and a smooth transition step: The threshold limiting step includes: calculating the actual dosage of the drug. Set upper and lower thresholds, i.e. ;in, This refers to the minimum allowable dosage of the reagent, set according to process requirements. This is the maximum allowable dosage of the reagent set according to process requirements; if Then As the actual amount added; if Then As the actual amount added; The smoothing transition step includes: smoothing the adjusted dosage of the medicine using a first-order inertial filtering algorithm, the filtering formula being: ,in These are the filter coefficients. This is the smoothed dosage of the drug at the current moment. This is the amount of reagent added after smoothing out the previous moment, to avoid sudden changes in reagent dosage from impacting the flotation process.

8. The method for dynamically controlling the dosage of potassium salt flotation reagents according to claim 1, characterized in that, It also includes step S4: Based on a fixed period or preset triggering conditions and recent flotation effect data, optimize and calibrate the preset benchmark state parameters, weight coefficients and correction coefficients in the dynamic response model to ensure that the model adapts to the dynamic changes in the flotation process.

9. The method for dynamically controlling the dosage of potassium salt flotation reagents according to claim 8, characterized in that, The implementation of step S4 includes: S41, Preset calibration trigger condition: When the relative deviation between the average flotation efficiency and the target flotation efficiency over N consecutive cycles exceeds a preset threshold, or when the fluctuation range of flotation efficiency in a single cycle exceeds a preset maximum fluctuation range threshold, the calibration procedure is initiated. S42, Calibration data selection: Collect full data of the flotation process within the most recent M cycles, including real-time slurry parameters, froth image feature parameters, reagent addition amount, slurry flow rate and corresponding flotation efficiency data, and M≥3N; S43, Parameter calibration: The objective function is to minimize the mean square error between the flotation efficiency and the target efficiency corresponding to the reagent dosage predicted by the gradient descent method. The preset baseline state parameters, weighting coefficients and time delay correction coefficients in the dynamic response model are then finely adjusted synchronously. S44, Calibration Constraints: All calibration parameters must be within a preset physical reasonable range to avoid parameter values ​​that exceed common process knowledge.

10. The method for dynamically controlling the dosage of potassium salt flotation reagents according to claim 1, characterized in that, In step S1, a distributed acquisition system is used to achieve real-time parameter acquisition; The distributed acquisition system includes a slurry parameter sensor group, a high-definition image acquisition device, a data preprocessing unit, and a real-time communication module; The pulp parameter sensor group includes a pH sensor, a potassium ion selective electrode, and a collector concentration detector, which are used to collect the pulp pH value and potassium ion concentration, respectively; the high-definition image acquisition device is equipped with an industrial camera and image preprocessing algorithm to acquire real-time images of foam on the surface of the flotation cell and extract foam stability. The data preprocessing unit performs filtering and noise reduction on the collected raw parameters; The real-time communication module uploads the preprocessed parameters in real time to calculate the dynamic response coefficient of the flotation reagent.

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