Process control-based small-sized flotation machine liquid level automatic control method and system

Abstract

This invention relates to the field of automated control technology in mineral flotation, specifically to a method and system for automated liquid level control of a small flotation machine based on process control. The method includes: acquiring multi-source data such as images of the froth layer thickness, feed slurry flow rate and concentration, flotation reagent addition rate, and static pressure at the bottom of the tank; extracting features and recognizing contours from the froth layer images; calculating the solid material handling load based on feed parameters and performing correlation analysis to generate froth layer state assessment parameters; establishing dynamic correction coefficients for reagent efficacy based on reagent rate and froth texture complexity to adjust the liquid level control target value; and integrating multiple parameters to calculate and control the opening of the tailings valve or water supply valve. This invention enables quantitative characterization of the froth layer state and dynamic adaptation of reagent efficacy, allowing liquid level adjustment to conform to real-time operating conditions, improving control accuracy and adaptability to operating conditions.

Description

Technical Field

[0001] This invention relates to the field of automated control technology for mineral flotation, and in particular to a method and system for automated control of liquid level in a small flotation machine based on process control. Background Technology

[0002] Current automated level control in small-scale flotation machines primarily relies on the static pressure at the bottom of the tank. Feed slurry flow rate and concentration are monitored separately, without any linkage analysis with the froth layer condition. The froth layer condition is assessed visually without image feature extraction and contour recognition for digital processing. This makes it impossible to quantitatively obtain the average froth layer thickness, surface texture complexity, and the area ratio of stable to unstable regions. Furthermore, it fails to correlate solid material handling load with froth layer image data to create froth layer condition evaluation parameters. The flotation reagent addition rate is set to a constant value, without establishing a quantitative correlation with the froth layer surface texture complexity. The expected target value for level control remains fixed, lacking a dynamic adjustment mechanism.

[0003] A single static pressure parameter cannot fully characterize the real-time operating conditions within the flotation cell; manual observation of the froth layer is subject to subjective bias; the solid material handling load and froth layer operating status data are independent of each other; and the input dimension for level control is singular and disconnected from actual operating conditions. Real-time changes in reagent efficacy cannot be transmitted to the level control closed loop; fixed level target values ​​are difficult to adapt to fluctuations in the flotation process; and the adjustment of the tailings valve and make-up water valve openings lacks a basis for collaborative calculation using multi-source process data.

[0004] It is impossible to extract features and identify contours from foam layer thickness images and combine them with load data to generate foam layer status assessment parameters. It is also impossible to establish dynamic correction coefficients for agent efficacy based on agent addition rate and foam texture complexity to adjust liquid level control target values. Furthermore, it is impossible to integrate static pressure values, foam layer status assessment parameters, and corrected target values ​​to calculate actuator adjustment commands. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and to propose a method and system for automatic liquid level control of a small flotation machine based on process control.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for automated liquid level control of a small flotation machine based on process control, comprising:

[0007] Collect multi-source real-time process data of a small flotation machine in operation. The multi-source real-time process data includes images of the thickness of the froth layer in the flotation cell, the flow rate and concentration of the feed slurry, the addition rate of flotation reagents, and the static pressure value at the bottom of the cell.

[0008] Image feature extraction and contour recognition processing are performed on the foam layer thickness image to calculate the average thickness of the foam layer, surface texture complexity, and the area ratio of stable and unstable regions of the foam.

[0009] Based on the flow rate and concentration of the feed slurry, the solid material processing load per unit time is calculated, and the solid material processing load is correlated with the processing results of the foam layer thickness image to generate foam layer state evaluation parameters.

[0010] Based on the correspondence between the addition rate of the flotation reagent and the surface texture complexity of the foam layer, a dynamic correction coefficient for reagent effectiveness is established, and the expected target value of liquid level control is adjusted according to the dynamic correction coefficient for reagent effectiveness.

[0011] Based on the static pressure value at the bottom of the tank, the foam layer status evaluation parameters, and the adjusted expected target value for liquid level control, the adjustment command of the control actuator is calculated. The adjustment command is used to adjust the opening degree of the tailings valve or water supply valve of the flotation machine.

[0012] As a further aspect of the present invention, image feature extraction and contour recognition processing are performed on the foam layer thickness image to calculate the average thickness of the foam layer, surface texture complexity, and the area ratio of stable and unstable regions of the foam, including:

[0013] The image of the foam layer thickness is processed by grayscale conversion and edge enhancement to identify two continuous edge contour lines of the upper surface of the foam layer and the slurry-foam interface.

[0014] The average vertical distance between the upper surface contour line of the foam layer and the contour line of the slurry-foam interface is calculated as the average thickness of the foam layer.

[0015] Extract the image gray-level co-occurrence matrix within the area defined by the upper surface contour line of the foam layer, and calculate the contrast and entropy values ​​based on the gray-level co-occurrence matrix. Use the weighted sum of the contrast and entropy values ​​as the surface texture complexity.

[0016] Image stability analysis was performed on the region defined by the contour line on the upper surface of the foam layer. Sub-regions where the pixel gray value fluctuated less than the threshold in multiple consecutive frames of images were identified as stable regions, while sub-regions where the pixel gray value fluctuated continuously greater than the threshold were identified as unstable regions.

[0017] The total number of pixels in the stable region and the total number of pixels in the unstable region are statistically analyzed, and the proportions of the total number of pixels in the stable region and the total number of pixels in the unstable region to the total number of pixels in the area defined by the outline of the upper surface of the foam layer are calculated to obtain the area proportions of the stable region and the unstable region.

[0018] As a further aspect of the present invention, the solid material processing load per unit time is calculated by combining the flow rate and concentration of the feed slurry, including:

[0019] The flow rate signal of the feed slurry is filtered and smoothed to obtain a stable feed slurry volumetric flow rate value;

[0020] Obtain the online concentration value of the feed slurry and convert the online concentration value into a solid mass percentage;

[0021] The solid material processing load per unit time is calculated by multiplying the stable feed slurry volumetric flow rate by the solid mass percentage and then by the average solid density of the slurry.

[0022] As a further aspect of the present invention, the solid material processing load is correlated with the processing results of the foam layer thickness image to generate foam layer state assessment parameters, including:

[0023] Establish a correlation model between the historical time-series curve of the solid material processing load per unit time and the historical time-series curve of the average thickness of the foam layer, and calculate the expected foam layer thickness benchmark value corresponding to the current solid material processing load through the correlation model;

[0024] Calculate the deviation between the measured average thickness of the foam layer at the current moment and the expected foam layer thickness reference value, and calculate the percentage of the deviation relative to the expected foam layer thickness reference value to obtain the thickness deviation rate;

[0025] The thickness deviation rate, the surface texture complexity of the foam layer, and the area ratio of the unstable region of the foam are input into a pre-trained fuzzy comprehensive evaluation network. The fuzzy comprehensive evaluation network outputs a quantified state evaluation parameter of the foam layer, which is used to comprehensively characterize the stability and floatability of the foam layer.

[0026] As a further aspect of the present invention, based on the correspondence between the addition rate of the flotation reagent and the surface texture complexity of the foam layer, a dynamic correction coefficient for reagent effectiveness is established, and the expected target value of the liquid level control is adjusted according to the dynamic correction coefficient for reagent effectiveness, including:

[0027] Obtain the baseline addition rate of flotation reagents and the corresponding baseline value of foam layer surface texture complexity under standard operating conditions;

[0028] Calculate the ratio of the current flotation reagent addition rate to the reference addition rate of the flotation reagent to obtain the reagent addition rate ratio;

[0029] Calculate the ratio of the current foam layer surface texture complexity to the baseline value of the foam layer surface texture complexity to obtain the surface texture complexity ratio;

[0030] Dividing the drug addition rate ratio by the surface texture complexity ratio yields the drug efficacy dynamic correction coefficient. When the drug efficacy dynamic correction coefficient is greater than one, it indicates that the current drug efficacy is lower than the benchmark. When the drug efficacy dynamic correction coefficient is less than one, it indicates that the current drug efficacy is higher than the benchmark.

[0031] The initial liquid level control target value corresponding to the current operating condition is retrieved from the preset liquid level control target table. The initial liquid level control target value is then multiplied by the dynamic correction coefficient of the agent efficacy to obtain the adjusted expected liquid level control target value.

[0032] As a further aspect of the present invention, based on the static pressure value at the bottom of the tank, the foam layer state evaluation parameters, and the adjusted expected target value for liquid level control, the adjustment command of the control actuator is calculated, including:

[0033] The static pressure value at the bottom of the tank is converted from pressure to liquid level to obtain the actual liquid level height of the slurry in the flotation cell;

[0034] The difference between the actual liquid level height value and the adjusted expected target value for liquid level control is calculated to obtain the liquid level height deviation;

[0035] The liquid level height deviation, the foam layer state evaluation parameters, and the addition rate of the flotation reagent are used as input variables and input to a multivariable proportional-integral-derivative controller.

[0036] The multivariable proportional-integral-derivative controller calculates a first valve position adjustment amount for adjusting the opening of the tailings valve of the flotation machine, or a second valve position adjustment amount for adjusting the opening of the water supply valve of the flotation machine, according to the control rules set internally. The first valve position adjustment amount or the second valve position adjustment amount constitutes the adjustment command.

[0037] As a further aspect of the present invention, it also includes a dynamic feedforward correction step for the liquid level control target based on the state of the foam layer:

[0038] Monitor the changing trend of the foam layer state assessment parameters, and trigger the feedforward correction logic when the foam layer state assessment parameters continue to deteriorate within multiple consecutive control cycles.

[0039] When the feedforward correction logic is triggered, an instantaneous compensation value for the liquid level control target is calculated based on the deterioration rate of the foam layer state evaluation parameters and the deviation of the current liquid level height.

[0040] The instantaneous compensation value of the liquid level control target is superimposed with the adjusted expected liquid level control target value at the current moment to generate a corrected temporary liquid level control target.

[0041] In the next control cycle, the modified temporary liquid level control target is used to replace the original adjusted liquid level control expected target value, and is used as the calculation input for the multivariable proportional-integral-derivative controller.

[0042] As a further aspect of the present invention, it also includes a step of online self-tuning of the parameters of the multivariable proportional-integral-derivative controller:

[0043] Record the curve of the change in the actual liquid level height after each adjustment command is output by the multivariable proportional-integral-derivative controller;

[0044] From the curve of the change process of the actual liquid level height, the overshoot, settling time and steady-state error are extracted as control performance evaluation indicators.

[0045] Establish a gradient relationship model between the control performance evaluation index and the three parameters of the multivariable proportional-integral-derivative controller: the proportional coefficient, integral time, and derivative time.

[0046] Based on the gradient relationship model, with the optimization objective of minimizing the steady-state error and the overshoot, the gradient descent algorithm is used to iteratively update the proportional coefficient, the integral time, and the derivative time of the multivariable proportional-integral-derivative controller.

[0047] As a further aspect of the present invention, it also includes abnormal operating condition diagnosis and safety control steps:

[0048] The average thickness of the foam layer, the foam layer status evaluation parameters, the actual liquid level height, and the valve position feedback signal of the control actuator are monitored in real time.

[0049] Set a safety upper limit and a safety lower limit for the average thickness of the foam layer, set a deterioration threshold for the foam layer condition assessment parameters, and set an overflow protection limit and an empty tank protection limit for the actual liquid level height value;

[0050] When any monitored value exceeds its corresponding safety range or threshold, the abnormal operating condition type is determined.

[0051] According to the abnormal operating condition type, a corresponding emergency control command is matched from the preset safety control strategy library. The emergency control command takes priority over the adjustment command in the normal control cycle and is directly sent to the control execution mechanism, while simultaneously triggering an audible and visual alarm.

[0052] As a further aspect of the present invention, the present invention also includes a small-scale flotation machine liquid level automation control system based on process control. The system includes a central control unit, a data acquisition unit, and a control execution unit. The central control unit is configured to execute a computer program stored in a memory to implement all the steps of the small-scale flotation machine liquid level automation control method based on process control as described above.

[0053] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0054] Image feature extraction and contour recognition processing of foam layer thickness images can quantify the average foam layer thickness, surface texture complexity, and the area ratio of stable and unstable regions. Combined with the feed slurry flow rate and concentration, the solid material processing load per unit time is calculated. This load value is then directly correlated with the foam layer image processing results to form foam layer state assessment parameters. The foam layer state is transformed from manual qualitative observation to digital quantitative characterization. A direct correlation is established between the solid material processing load and the foam layer operating state. The reference basis for liquid level control is expanded from a single static pressure parameter to multi-dimensional process data coupling. The thickness distribution, texture characteristics, and regional stability differences of the foam layer are fully quantified and presented. The correspondence between load fluctuations and foam layer state changes is intuitively reflected. The basic data for liquid level control is kept synchronized with the actual operating status of the flotation machine in real time.

[0055] A dynamic correction coefficient for reagent efficacy is constructed based on the correlation between the flotation reagent addition rate and the complexity of the froth layer surface texture. This coefficient directly adjusts the expected target value of the liquid level control. Using the static pressure at the bottom of the tank, froth layer state evaluation parameters, and the adjusted expected target value of the liquid level control as input parameters, the adjustment command for the opening of the tailings valve or make-up water valve is calculated. The adaptation relationship between the reagent addition rate and the froth texture is transformed into a directly applicable quantitative correction parameter. The expected target value of the liquid level control is no longer a fixed setting mode and can be dynamically corrected in real time according to changes in reagent efficacy. The generation of the adjustment command integrates three core parameters: static pressure, froth state, and correction target value. The opening adjustment actions of the tailings valve and make-up water valve closely match the real-time operating conditions of the flotation process. The liquid level control logic forms a fully coordinated adaptation with the dynamic changes in reagent efficacy, froth layer state, and feed load. Attached Figure Description

[0056] Figure 1 This is a flowchart of the automated liquid level control method for a small flotation machine based on process control as described in this invention;

[0057] Figure 2 A flowchart for generating parameters for assessing the condition of the foam layer;

[0058] Figure 3 This is a time-series diagram showing the feed flow rate and concentration under flotation machine operating conditions.

[0059] Figure 4 A comparison chart of flotation machine liquid level control;

[0060] Figure 5 This is a chart analyzing the monitoring parameters for abnormal operating conditions of the flotation machine. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0062] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0063] See Figure 1 This invention provides an automated liquid level control method for a small-scale flotation machine based on process control. The method includes: acquiring multi-source real-time process data of the small-scale flotation machine during operation using deployed sensors and visual acquisition devices. This data includes images of the froth layer thickness in the flotation cell, the flow rate and concentration of the feed slurry, the addition rate of flotation reagents, and the static pressure at the bottom of the cell. Subsequently, image feature extraction and contour recognition processing are performed on the acquired froth layer thickness images to calculate the average thickness of the froth layer, surface texture complexity, and the area ratio of stable and unstable regions of the froth. Next, combined with the flow rate and concentration information of the feed slurry, the solid material handling load per unit time is calculated, and this solid material handling load is correlated with the processing results of the froth layer thickness images to generate a comprehensive froth layer state evaluation parameter. Simultaneously, based on the correspondence between the addition rate of flotation reagents and the surface texture complexity of the froth layer, a dynamic correction coefficient for reagent efficiency is established, and the expected target value for liquid level control is adjusted according to this dynamic correction coefficient. Finally, based on the static pressure value at the bottom of the tank, the aforementioned foam layer state evaluation parameters, and the adjusted expected target value for liquid level control, the control algorithm calculates the adjustment command for the control actuator. This adjustment command is used to drive the tailings valve or water supply valve of the flotation machine to adjust its opening degree in order to achieve stable and optimized control of the liquid level.

[0064] In one embodiment of the present invention, image feature extraction and contour recognition processing are performed on the foam layer thickness image to calculate the average thickness of the foam layer, surface texture complexity, and the area ratio of stable and unstable regions of the foam. The process is as follows: The acquired foam layer thickness image is subjected to grayscale conversion and edge enhancement processing to identify two continuous edge contour lines of the upper surface of the foam layer and the slurry-foam interface. The average vertical distance between the upper surface contour line of the foam layer and the slurry-foam interface contour line is calculated, and this average vertical distance is taken as the average thickness of the foam layer. The image grayscale co-occurrence matrix of the region defined by the upper surface contour line of the foam layer is extracted, and the contrast and entropy values ​​are calculated based on this grayscale co-occurrence matrix. The weighted sum of the contrast and entropy values ​​is taken as the surface texture complexity. Image stability analysis is performed on the region defined by the upper surface contour line of the foam layer. Sub-regions where the pixel grayscale value fluctuates less than a preset threshold in multiple consecutive frames of images are determined as stable regions, and sub-regions where the pixel grayscale value fluctuates continuously greater than a preset threshold are determined as unstable regions. The total number of pixels in the stable region and the total number of pixels in the unstable region are statistically analyzed. The proportions of the total number of pixels in the stable region and the total number of pixels in the unstable region to the total number of pixels in the area defined by the outline of the upper surface of the foam layer are calculated to obtain the area proportions of the stable region and the unstable region.

[0065] In specific implementation, image feature extraction and contour recognition processing are performed on the foam layer thickness image to calculate the average thickness of the foam layer, surface texture complexity, and the area ratio of stable and unstable regions of the foam. The processing involves the following operations: grayscale conversion and edge enhancement processing are performed on the foam layer thickness image; two continuous edge contour lines of the upper surface of the foam layer and the slurry-foam interface are identified; the average vertical distance between the upper surface contour line of the foam layer and the slurry-foam interface contour line is calculated, and the average vertical distance is used as the average thickness of the foam layer. In specific implementation, the image gray-level co-occurrence matrix of the region defined by the upper surface contour line of the foam layer is extracted; the contrast and entropy values ​​are calculated based on the image gray-level co-occurrence matrix; and the surface texture complexity is calculated by weighted sum of the contrast and entropy values. The formula for calculating the surface texture complexity is expressed as:

[0066]

[0067] Where: S represents surface texture complexity, C represents contrast, and E represents entropy. and It's the weighting coefficient, the weighting coefficient. and satisfy + =1. In some embodiments, the extraction of the image gray-level co-occurrence matrix employs standard image processing methods. In some embodiments, the calculation of contrast and entropy values ​​is based on the statistical characteristics of the image gray-level co-occurrence matrix. Optionally, image stability analysis is performed on the region defined by the contour line of the upper surface of the foam layer. Image stability analysis determines sub-regions where the pixel gray-level value fluctuation is less than a preset threshold in consecutive frames of images as stable regions. Optionally, image stability analysis determines sub-regions where the pixel gray-level value fluctuation is continuously greater than a preset threshold as unstable regions. It can be understood that by statistically summing the total number of pixels in stable regions and the total number of pixels in unstable regions, the proportion of stable region area is calculated as the ratio of the total number of pixels in stable regions to the total number of pixels in the region defined by the contour line of the upper surface of the foam layer. It can be understood that by calculating the proportion of unstable region area to the total number of pixels in unstable regions to the total number of pixels in the region defined by the contour line of the upper surface of the foam layer, the proportion of unstable region area is obtained.

[0068] In one embodiment of the present invention, the solid material handling load per unit time is calculated by combining the flow rate and concentration of the feed slurry. This is achieved by: filtering and smoothing the flow rate signal of the feed slurry to obtain a stable feed slurry volumetric flow rate value; acquiring the online concentration detection value of the feed slurry and converting this value into a solid mass percentage; multiplying the stable feed slurry volumetric flow rate value by the solid mass percentage, and then multiplying by the average solid density of the slurry to calculate the solid material handling load per unit time; and performing a correlation analysis between the solid material handling load and the processing results of the foam layer thickness image to generate foam layer state assessment parameters. The process includes: (See [reference]). Figure 2 A correlation model was established between the historical time-series curve of solid material processing load per unit time and the historical time-series curve of average foam layer thickness. This correlation model was used to calculate the expected foam layer thickness benchmark value corresponding to the current solid material processing load. The deviation between the measured average foam layer thickness at the current moment and the expected foam layer thickness benchmark value was calculated, and the percentage of this deviation relative to the expected foam layer thickness benchmark value was calculated to obtain the thickness deviation rate. The thickness deviation rate, the surface texture complexity of the foam layer, and the proportion of unstable regions in the foam were input into a pre-trained fuzzy comprehensive evaluation network. This fuzzy comprehensive evaluation network outputs a quantified foam layer state evaluation parameter, which is used to comprehensively characterize the stability and floatability of the foam layer.

[0069] In specific implementation, the processing flow for calculating the solid material handling load per unit time and generating foam layer state assessment parameters is as follows: The flow rate signal of the feed slurry is filtered and smoothed to obtain a stable feed slurry volumetric flow rate value; the online concentration detection value of the feed slurry is obtained and converted into a solid mass percentage. In some embodiments, the formula for calculating the solid material handling load is expressed as:

[0070]

[0071] Where: L represents the solid material processing load per unit time. This represents the stable volumetric flow rate of the feed slurry, where c represents the percentage of solids by mass. This represents the average solid density of the slurry. It can be understood that the smoothing of the feed slurry flow signal can be achieved using a moving average filter. In a specific implementation, a correlation model is established between the historical time-series curve of the solid material processing load per unit time and the historical time-series curve of the average thickness of the foam layer. The correlation model can be constructed using a linear regression method. The expected foam layer thickness benchmark value corresponding to the current solid material processing load is calculated using the correlation model. The deviation between the measured average thickness of the foam layer at the current moment and the expected foam layer thickness benchmark value is calculated, and the percentage of the deviation relative to the expected foam layer thickness benchmark value is used to obtain the thickness deviation rate. In some embodiments, the training data for the correlation model comes from the historical process data of the flotation machine during the stable operation phase. Optionally, the thickness deviation rate is calculated by dividing the difference between the measured average thickness of the foam layer and the expected foam layer thickness benchmark value by the expected foam layer thickness benchmark value. In a specific implementation, the thickness deviation rate, the surface texture complexity of the foam layer, and the proportion of the unstable area of ​​the foam are input into a pre-trained fuzzy comprehensive evaluation network. It can be understood that the input layer of the fuzzy comprehensive evaluation network contains three nodes: thickness deviation rate, surface texture complexity of the foam layer, and proportion of the unstable area of ​​the foam. Optionally, the fuzzy comprehensive evaluation network is trained with historical data, and its output layer outputs a quantified foam layer state evaluation parameter, which is used to comprehensively characterize the stability and floatability of the foam layer.

[0072] In one embodiment of the present invention, a dynamic correction coefficient for reagent efficiency is established based on the correspondence between the addition rate of flotation reagents and the surface texture complexity of the foam layer. The expected target value of the liquid level control is then adjusted according to this dynamic correction coefficient. The process is as follows: Obtain the baseline addition rate of flotation reagents and the corresponding baseline value of the surface texture complexity of the foam layer under standard operating conditions. Calculate the ratio of the current addition rate of flotation reagents to the baseline addition rate, obtaining the reagent addition rate ratio. Calculate the ratio of the current surface texture complexity of the foam layer to the baseline value, obtaining the surface texture complexity ratio. Divide the reagent addition rate ratio by the surface texture complexity ratio to obtain the dynamic correction coefficient for reagent efficiency. When the dynamic correction coefficient is greater than one, it indicates that the current reagent efficiency is lower than the baseline; when the dynamic correction coefficient is less than one, it indicates that the current reagent efficiency is higher than the baseline. The initial liquid level control target value corresponding to the current operating condition is retrieved from the preset liquid level control target table. This initial liquid level control target value is multiplied by the reagent efficiency dynamic correction coefficient to obtain the adjusted expected liquid level control target value. Based on the static pressure value at the bottom of the tank, the foam layer state evaluation parameters, and the adjusted expected liquid level control target value, the adjustment command of the control actuator is calculated. The process includes: performing pressure-level conversion calculation on the static pressure value at the bottom of the tank to obtain the actual liquid level height of the slurry in the flotation cell. The difference between the actual liquid level height value and the adjusted expected liquid level control target value is calculated to obtain the liquid level height deviation. The liquid level height deviation, the foam layer state evaluation parameters, and the addition rate of flotation reagents are used as input variables and input to a multivariable proportional-integral-derivative (PID) controller. According to its internally set control rules, the PID controller calculates a first valve position adjustment amount for adjusting the opening of the flotation machine tailings valve, or a second valve position adjustment amount for adjusting the opening of the flotation machine make-up water valve. The first or second valve position adjustment amount constitutes the adjustment command.

[0073] In specific implementation, the steps for establishing a dynamic correction coefficient for reagent efficacy and adjusting the expected target value of liquid level control based on the dynamic correction coefficient are as follows: Obtain the reference addition rate of flotation reagents under standard operating conditions and the corresponding reference value of foam layer surface texture complexity. Calculate the ratio of the current addition rate of flotation reagents to the reference addition rate of flotation reagents to obtain the reagent addition rate ratio. Calculate the ratio of the current foam layer surface texture complexity to the reference value of foam layer surface texture complexity to obtain the surface texture complexity ratio. In some embodiments, the reference addition rate of flotation reagents and the reference value of foam layer surface texture complexity under standard operating conditions are determined through long-term statistical data of the flotation machine under optimal production conditions. In specific implementation, the dynamic correction coefficient for reagent efficacy is obtained by dividing the reagent addition rate ratio by the surface texture complexity ratio. The formula for calculating the dynamic correction coefficient for reagent efficacy is expressed as:

[0074]

[0075] Where: K represents the dynamic correction coefficient for drug efficacy. This indicates the rate at which flotation reagents are added at the current moment. Indicates the baseline addition rate of flotation reagents. This represents the surface texture complexity of the foam layer at the current moment. This represents the baseline value for the surface texture complexity of the foam layer. When the dynamic correction coefficient for reagent efficacy is greater than one, it indicates that the current reagent efficacy is lower than the baseline; when the dynamic correction coefficient for reagent efficacy is less than one, it indicates that the current reagent efficacy is higher than the baseline. The initial liquid level control target value corresponding to the current operating condition is retrieved from the preset liquid level control target table. An example of the liquid level control target table includes the operating condition number, feed flow rate range, concentration range, and the corresponding initial liquid level control target value; see Table 1.

[0076] Table 1: Liquid Level Control Target Table

[0077] Operating condition number Feed flow rate range (m³ / h) Concentration range (%) Initial liquid level control target value (mm) GX-01 2.0-3.0 25-30 650 GX-02 3.0-4.0 30-35 670 GX-03 4.0-5.0 35-40 690

[0078] It is understandable that when querying the liquid level control target table, the corresponding operating condition number is matched based on the currently measured flow rate and concentration of the feed slurry. In specific implementation, the initial liquid level control target value obtained from the query is multiplied by the reagent efficiency dynamic correction coefficient to obtain the adjusted expected liquid level control target value. Optionally, the calculation of the adjusted expected liquid level control target value is an arithmetic multiplication operation. The process of calculating the adjustment command of the control actuator based on the static pressure value at the bottom of the tank, the foam layer state evaluation parameters, and the adjusted expected liquid level control target value is as follows: the pressure-level conversion calculation of the static pressure value at the bottom of the tank is performed to obtain the actual liquid level height value of the slurry in the flotation cell. The pressure-level conversion calculation is based on the principle of hydrostatics. In some embodiments, the difference between the actual liquid level height value and the adjusted expected liquid level control target value is calculated to obtain the liquid level height deviation. Optionally, the liquid level height deviation is the actual liquid level height value minus the adjusted expected liquid level control target value. In specific implementation, the liquid level height deviation, the foam layer state evaluation parameters, and the addition rate of the flotation reagent are input as input variables to a multivariable proportional-integral-derivative controller. It is understood that the multivariable proportional-integral-derivative (MIDR) controller has three input channels, receiving signals for liquid level height deviation, froth layer state assessment parameters, and flotation reagent addition rate. Based on its internally set control rules, the MIDR controller calculates either a first valve position adjustment amount for regulating the opening of the flotation machine tailings valve, or a second valve position adjustment amount for regulating the opening of the flotation machine make-up water valve. The first or second valve position adjustment amount constitutes the final control command issued to the actuator.

[0079] See Figure 3This is a time-series graph showing the feed flow rate and concentration under flotation machine operating conditions. It visually reflects the changes in the flow rate and concentration of the feed slurry over time during flotation production. The flow rate curve rises continuously from 0 to 4 hours, reaching a peak of 4.5 m³ / h in the 4th hour, then gradually declines, reaching a trough of 2.6 m³ / h in the 9th hour, before rising again. The concentration curve is highly synchronized with the flow rate curve, also reaching a peak of 39% in the 4th hour. The overall fluctuation trend is consistent with the flow rate, reflecting the strong correlation between the feed conditions. The synchronous changes in flow rate and concentration reflect the typical operating condition characteristic of "high flow rate corresponds to high concentration, and low flow rate corresponds to low concentration" in actual production. This data can be directly used to calculate the solid material handling load per unit time and serves as the basic input for subsequent froth layer status assessment and liquid level control.

[0080] In one embodiment of the present invention, the method further includes a dynamic feedforward correction step for the liquid level control target based on the foam layer state. The changing trend of the foam layer state evaluation parameters is monitored. When the foam layer state evaluation parameters continuously deteriorate over multiple consecutive control cycles, feedforward correction logic is triggered. When the feedforward correction logic is triggered, an instantaneous compensation value for the liquid level control target is calculated based on the deterioration rate of the foam layer state evaluation parameters and the deviation from the current liquid level height. This instantaneous compensation value for the liquid level control target is superimposed with the adjusted expected liquid level control target value at the current moment to generate a corrected temporary liquid level control target. In the next control cycle, the corrected temporary liquid level control target replaces the original adjusted expected liquid level control target value and is used as the calculation input for the multivariable proportional-integral-derivative (PID) controller. The method also includes a step of online self-tuning of the parameters of the PID controller. The change curve of the actual liquid level height value is recorded after each PID controller outputs an adjustment command. From the change curve of the actual liquid level height value, overshoot, settling time, and steady-state error are extracted as control performance evaluation indicators. A gradient relationship model is established between the control performance evaluation index and the three parameters of the multivariable proportional-integral-derivative (PID) controller: the proportional coefficient, integral time, and derivative time. Based on this gradient relationship model, with the optimization objective of minimizing steady-state error and overshoot, the gradient descent algorithm is used to iteratively update the proportional coefficient, integral time, and derivative time of the PLD controller.

[0081] In specific implementation, the execution flow of the dynamic feedforward correction step for level control based on the foam layer state is as follows: The changing trend of the foam layer state assessment parameters is monitored. When the foam layer state assessment parameters continuously deteriorate over multiple consecutive control cycles, the feedforward correction logic is triggered. The specific number of consecutive control cycles is configurable. The deterioration of the foam layer state assessment parameters is manifested by their values ​​monotonically changing towards a preset negative threshold. In some embodiments, one control cycle corresponds to the complete time interval between the multivariable proportional-integral-derivative controller completing one data acquisition, calculation, and output of an adjustment command. Optionally, the numerical changes of the foam layer state assessment parameters over three consecutive control cycles can be recorded as shown in the table below. When the value continuously increases and exceeds the set deterioration judgment threshold, it is determined to be continuous deterioration, and the feedforward correction logic is triggered (see Table 2).

[0082] Table 2: Continuous Monitoring Table of Foam Layer Condition Assessment Parameters

[0083] Control periodic sequence Foam layer condition assessment parameter values Does it trigger feedforward correction logic? n-2 0.65 no n-1 0.78 no n 0.92 yes

[0084] In practical implementation, when the feedforward correction logic is triggered, an instantaneous compensation value for the liquid level control target is calculated based on the deterioration rate of the foam layer state assessment parameters and the deviation of the current liquid level height. The calculation formula for the instantaneous compensation value of the liquid level control target is expressed as follows:

[0085]

[0086] in: This represents the instantaneous compensation value for the liquid level control target. This indicates the degradation rate of the parameters used to assess the condition of the foam layer. This represents the current liquid level height deviation, and α and β are feedforward correction coefficients. In some embodiments, the degradation rate of the foam layer condition assessment parameters... The slope is obtained by linearly fitting the parameter values ​​over the most recent control cycles. This can be understood as superimposing the calculated instantaneous compensation value of the liquid level control target with the adjusted expected liquid level control target value at the current moment to generate a corrected temporary liquid level control target. In the next control cycle, the corrected temporary liquid level control target replaces the original adjusted expected liquid level control target value for the calculation input of the multivariable proportional-integral-derivative (PID) controller. In specific implementation, the online self-tuning steps for the parameters of the PID controller include recording the change curve of the actual liquid level height value after each PID controller outputs an adjustment command. Overshoot, settling time, and steady-state error are extracted from the change curve of the actual liquid level height value as control performance evaluation indicators. Optionally, overshoot is defined as the percentage difference between the peak value of the response curve and the final steady-state value relative to the steady-state value; settling time is defined as the shortest time required for the response curve to enter and remain within an allowable error band near the final steady-state value; and steady-state error is defined as the residual difference between the actual liquid level height value and the adjusted expected liquid level control target value after the response tends to stabilize. In practical implementation, a gradient relationship model is established between the control performance evaluation index and the three parameters of the multivariable proportional-integral-derivative (MIDR) controller: the proportional gain, integral time, and derivative time. This model describes the quantitative impact of small changes in the proportional gain, integral time, and derivative time on overshoot, settling time, and steady-state error. Based on this gradient relationship model, with the optimization objective of minimizing steady-state error and overshoot, a gradient descent algorithm is used to iteratively update the proportional gain, integral time, and derivative time of the MIDR controller. The gradient descent algorithm determines the update direction and step size based on the partial derivative values ​​of the control performance evaluation index with respect to the controller parameters. It can be understood that the parameter update process occurs after each control cycle or every few control cycles to ensure that the parameters of the MIDR controller adapt to the changing dynamic characteristics of the flotation process.

[0087] See Figure 4This is a comparison chart of flotation machine level control, visually demonstrating the changes in the original target level, the corrected target level, and the actual level over time. It is crucial for evaluating the effectiveness of reagent efficacy correction and control. The original target level remained stable at 80cm throughout, serving as the control baseline. The corrected target level, from 0 to 9 seconds, matched the original target at 80cm. From 10 to 14 seconds, it temporarily rose to 85cm, indicating the triggering of reagent efficacy correction or feedforward compensation logic. After 15 seconds, it fell back to 80cm, restoring baseline control. From 0 to 6 seconds, the actual level remained consistently higher than the target value, reaching a maximum of 86.2cm, indicating significant overshoot. From 7 to 14 seconds, it rapidly declined, continuing to decrease even after the corrected target level was raised, reaching a low of 73.3cm, showing a large negative deviation. From 15 to 20 seconds, it gradually recovered, converging towards the 80cm target. The continued decline in the actual level during the target correction phase indicates a lag in the multivariable PID controller's response to target changes, or that the parameter tuning was not ideal.

[0088] In one embodiment of the present invention, the method further includes abnormal operating condition diagnosis and safety control steps. The average thickness of the foam layer, foam layer condition assessment parameters, actual liquid level height, and valve position feedback signals of the control actuator are monitored in real time. A safety upper limit and a safety lower limit are set for the average thickness of the foam layer, a deterioration threshold is set for the foam layer condition assessment parameters, and an overflow protection limit and an empty tank protection limit are set for the actual liquid level height. When any monitored value exceeds its corresponding safety range or threshold, the abnormal operating condition type is determined. Based on the determined abnormal operating condition type, a corresponding emergency control command is matched from a preset safety control strategy library. This emergency control command takes precedence over the adjustment commands in the normal control cycle and is directly issued to the control actuator, simultaneously triggering an audible and visual alarm.

[0089] In practical implementation, the execution logic of abnormal operating condition diagnosis and safety control steps involves real-time comparison of multiple monitoring signals with preset thresholds, real-time monitoring of the average thickness of the foam layer, foam layer state assessment parameters, actual liquid level height, and valve position feedback signals of the control actuator. A safety upper limit and a safety lower limit are set for the average thickness of the foam layer, a deterioration threshold is set for the foam layer state assessment parameters, and an overflow protection limit and an empty cell protection limit are set for the actual liquid level height. All these safety limits and thresholds are pre-configured and stored based on the design parameters of the flotation machine and empirical data from long-term safe operation. In some embodiments, the safety upper limit is the critical thickness to prevent foam from carrying minerals and overflowing, and the safety lower limit is the critical thickness to prevent the pulp surface from being exposed to air, leading to a sharp drop in flotation efficiency. In some embodiments, the deterioration threshold of the foam layer state assessment parameters is an empirical value; when the foam layer state assessment parameters exceed this value, the foam layer stability is considered to have severely deteriorated. Optionally, the overflow protection limit is slightly lower than the physical overflow weir height of the flotation cell, and the empty cell protection limit is set at a safe distance above the agitator impeller. In practical implementation, when any monitored value exceeds its corresponding safety range or threshold, the type of abnormal operating condition is determined. This determination is based on a comprehensive logical decision table, which defines the specific abnormal type corresponding to one or more monitored parameters exceeding their limits. For example, when the actual liquid level exceeds the overflow protection limit and the average thickness of the foam layer is below the lower safety limit, it might be classified as an abnormal condition of "thin foam layer but high slurry level." The logic for determining the type of abnormal operating condition can be formally expressed as:

[0090]

[0091] in: Indicates the type of abnormal operating condition that is ultimately triggered. This indicates that the average thickness of the foam layer has exceeded the limit. This indicates that the parameters for assessing the condition of the foam layer have exceeded their limits. This indicates that the actual liquid level height has exceeded the limit. This indicates an abnormal valve position feedback signal state. Function F represents the mapping relationship from the logical decision table to the specific abnormality type identifier. It can be understood that, based on the determined abnormal condition type, a corresponding emergency control command is matched from a preset safety control strategy library. Optionally, the safety control strategy library exists in the form of a database or configuration file, with each record associated with an abnormal condition type and a set of preset, highest-priority control actions. For example, for the abnormal condition type "actual liquid level exceeds the overflow protection limit," the matched emergency control command might be "immediately fully open the tailings valve and close the water supply valve." In practical implementation, the matched emergency control command takes precedence over the adjustment commands in the normal control cycle. The emergency control command is directly issued to the control actuator and simultaneously triggers an audible and visual alarm. It can be understood that the issuance of the emergency control command will interrupt the normal output of the multivariable proportional-integral-derivative controller until the abnormal condition is resolved and the system is confirmed by the operator or automatically reset, at which point control is returned to the normal automatic control cycle.

[0092] See Figure 5 This is a chart analyzing abnormal operating parameters of the flotation machine, used for real-time diagnosis of whether the operating conditions are within a safe range. The foam layer thickness is abnormal; the current value (~30) is significantly higher than the safe upper limit (~25), indicating that the foam layer is too thick, which may lead to decreased flotation efficiency and slurry entrainment. The actual liquid level is abnormal; the current value (~90) is higher than the safe upper limit (~85), indicating an overflow risk that requires immediate intervention. The foam layer status assessment parameters and valve position feedback signals are both within the safe range, indicating that foam stability and the actuator status have not yet deteriorated. Prioritize issuing emergency commands to close or reduce the opening of the water supply valve, while simultaneously increasing the tailings valve opening to quickly lower the liquid level. If the excessively thick foam layer persists, the reagent addition rate can be adjusted appropriately to suppress excessive foam generation. Check for sudden changes in feed flow rate / concentration and abnormal reagent efficacy to address the root cause of the problem.

[0093] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for automated level control of a small flotation machine based on process control, characterized in that, The method includes: Collect multi-source real-time process data of a small flotation machine in operation. The multi-source real-time process data includes images of the thickness of the froth layer in the flotation cell, the flow rate and concentration of the feed slurry, the addition rate of flotation reagents, and the static pressure value at the bottom of the cell. Image feature extraction and contour recognition processing are performed on the foam layer thickness image to calculate the average thickness of the foam layer, surface texture complexity, and the area ratio of stable and unstable regions of the foam. Based on the flow rate and concentration of the feed slurry, the solid material processing load per unit time is calculated, and the solid material processing load is correlated with the processing results of the foam layer thickness image to generate foam layer state evaluation parameters. Based on the correspondence between the addition rate of the flotation reagent and the surface texture complexity of the foam layer, a dynamic correction coefficient for reagent effectiveness is established, and the expected target value of liquid level control is adjusted according to the dynamic correction coefficient for reagent effectiveness. Based on the static pressure value at the bottom of the tank, the foam layer status evaluation parameters, and the adjusted expected target value for liquid level control, the adjustment command of the control actuator is calculated. The adjustment command is used to adjust the opening degree of the tailings valve or water supply valve of the flotation machine.

2. The method for automated level control of a small flotation machine based on process control according to claim 1, characterized in that, Image feature extraction and contour recognition are performed on the foam layer thickness image to calculate the average thickness of the foam layer, surface texture complexity, and the area ratio of stable and unstable regions of the foam, including: The image of the foam layer thickness is processed by grayscale conversion and edge enhancement to identify two continuous edge contour lines of the upper surface of the foam layer and the slurry-foam interface. The average vertical distance between the upper surface contour line of the foam layer and the contour line of the slurry-foam interface is calculated as the average thickness of the foam layer. Extract the image gray-level co-occurrence matrix within the area defined by the upper surface contour line of the foam layer, and calculate the contrast and entropy values ​​based on the gray-level co-occurrence matrix. Use the weighted sum of the contrast and entropy values ​​as the surface texture complexity. Image stability analysis was performed on the region defined by the contour line on the upper surface of the foam layer. Sub-regions where the pixel gray value fluctuated less than the threshold in multiple consecutive frames of images were identified as stable regions, while sub-regions where the pixel gray value fluctuated continuously greater than the threshold were identified as unstable regions. The total number of pixels in the stable region and the total number of pixels in the unstable region are statistically analyzed, and the proportions of the total number of pixels in the stable region and the total number of pixels in the unstable region to the total number of pixels in the area defined by the outline of the upper surface of the foam layer are calculated to obtain the area proportions of the stable region and the unstable region.

3. The method for automated level control of a small flotation machine based on process control according to claim 2, characterized in that, Based on the flow rate and concentration of the feed slurry, calculate the solid material handling load per unit time, including: The flow rate signal of the feed slurry is filtered and smoothed to obtain a stable feed slurry volumetric flow rate value; Obtain the online concentration value of the feed slurry and convert the online concentration value into a solid mass percentage; The solid material processing load per unit time is calculated by multiplying the stable feed slurry volumetric flow rate by the solid mass percentage and then by the average solid density of the slurry.

4. The method for automated level control of a small flotation machine based on process control according to claim 3, characterized in that, The solid material processing load is correlated with the processing results of the foam layer thickness image to generate foam layer condition assessment parameters, including: Establish a correlation model between the historical time-series curve of the solid material processing load per unit time and the historical time-series curve of the average thickness of the foam layer, and calculate the expected foam layer thickness benchmark value corresponding to the current solid material processing load through the correlation model; Calculate the deviation between the measured average thickness of the foam layer at the current moment and the expected foam layer thickness reference value, and calculate the percentage of the deviation relative to the expected foam layer thickness reference value to obtain the thickness deviation rate; The thickness deviation rate, the surface texture complexity of the foam layer, and the area ratio of the unstable region of the foam are input into a pre-trained fuzzy comprehensive evaluation network. The fuzzy comprehensive evaluation network outputs a quantified state evaluation parameter of the foam layer, which is used to comprehensively characterize the stability and floatability of the foam layer.

5. The method for automated level control of a small flotation machine based on process control according to claim 4, characterized in that, Based on the correlation between the addition rate of the flotation reagent and the surface texture complexity of the foam layer, a dynamic correction coefficient for reagent effectiveness is established, and the expected target value of the liquid level control is adjusted according to the dynamic correction coefficient for reagent effectiveness, including: Obtain the baseline addition rate of flotation reagents and the corresponding baseline value of foam layer surface texture complexity under standard operating conditions; Calculate the ratio of the current flotation reagent addition rate to the reference addition rate of the flotation reagent to obtain the reagent addition rate ratio; Calculate the ratio of the current foam layer surface texture complexity to the baseline value of the foam layer surface texture complexity to obtain the surface texture complexity ratio; Dividing the drug addition rate ratio by the surface texture complexity ratio yields the drug efficacy dynamic correction coefficient. When the drug efficacy dynamic correction coefficient is greater than one, it indicates that the current drug efficacy is lower than the benchmark. When the drug efficacy dynamic correction coefficient is less than one, it indicates that the current drug efficacy is higher than the benchmark. The initial liquid level control target value corresponding to the current operating condition is retrieved from the preset liquid level control target table. The initial liquid level control target value is then multiplied by the dynamic correction coefficient of the agent efficacy to obtain the adjusted expected liquid level control target value.

6. The method for automated level control of a small flotation machine based on process control according to claim 5, characterized in that, Based on the static pressure value at the bottom of the tank, the foam layer state assessment parameters, and the adjusted expected target value for liquid level control, the adjustment commands for the control actuator are calculated, including: The static pressure value at the bottom of the tank is converted from pressure to liquid level to obtain the actual liquid level height of the slurry in the flotation cell; The difference between the actual liquid level height value and the adjusted expected target value for liquid level control is calculated to obtain the liquid level height deviation; The liquid level height deviation, the foam layer state evaluation parameters, and the addition rate of the flotation reagent are used as input variables and input to a multivariable proportional-integral-derivative controller. The multivariable proportional-integral-derivative controller calculates a first valve position adjustment amount for adjusting the opening of the tailings valve of the flotation machine, or a second valve position adjustment amount for adjusting the opening of the water supply valve of the flotation machine, according to the control rules set internally. The first valve position adjustment amount or the second valve position adjustment amount constitutes the adjustment command.

7. The method for automated level control of a small flotation machine based on process control according to claim 6, characterized in that, It also includes a dynamic feedforward correction step for the liquid level control target based on the state of the foam layer: Monitor the changing trend of the foam layer state assessment parameters, and trigger the feedforward correction logic when the foam layer state assessment parameters continue to deteriorate within multiple consecutive control cycles. When the feedforward correction logic is triggered, an instantaneous compensation value for the liquid level control target is calculated based on the deterioration rate of the foam layer state evaluation parameters and the deviation of the current liquid level height. The instantaneous compensation value of the liquid level control target is superimposed with the adjusted expected liquid level control target value at the current moment to generate a corrected temporary liquid level control target. In the next control cycle, the modified temporary liquid level control target is used to replace the original adjusted liquid level control expected target value, and is used as the calculation input for the multivariable proportional-integral-derivative controller.

8. The method for automated level control of a small flotation machine based on process control according to claim 7, characterized in that, It also includes the step of online self-tuning of the parameters of the multivariable proportional-integral-derivative controller: Record the curve of the change in the actual liquid level height after each adjustment command is output by the multivariable proportional-integral-derivative controller; From the curve of the change process of the actual liquid level height, the overshoot, settling time and steady-state error are extracted as control performance evaluation indicators. Establish a gradient relationship model between the control performance evaluation index and the three parameters of the multivariable proportional-integral-derivative controller: the proportional coefficient, integral time, and derivative time. Based on the gradient relationship model, with the optimization objective of minimizing the steady-state error and the overshoot, the gradient descent algorithm is used to iteratively update the proportional coefficient, the integral time, and the derivative time of the multivariable proportional-integral-derivative controller.

9. The method for automated level control of a small flotation machine based on process control according to claim 8, characterized in that, It also includes abnormal operating condition diagnosis and safety control procedures: The average thickness of the foam layer, the foam layer status evaluation parameters, the actual liquid level height, and the valve position feedback signal of the control actuator are monitored in real time. Set a safety upper limit and a safety lower limit for the average thickness of the foam layer, set a deterioration threshold for the foam layer condition assessment parameters, and set an overflow protection limit and an empty tank protection limit for the actual liquid level height value; When any monitored value exceeds its corresponding safety range or threshold, the abnormal operating condition type is determined. According to the abnormal operating condition type, a corresponding emergency control command is matched from the preset safety control strategy library. The emergency control command takes priority over the adjustment command in the normal control cycle and is directly sent to the control execution mechanism, while simultaneously triggering an audible and visual alarm.

10. A small-scale flotation machine liquid level automation control system based on process control, characterized in that, It includes a central control unit, a data acquisition unit, and a control execution unit. The central control unit is configured to execute a computer program stored in a memory to implement all the steps of the process control-based automated level control method for a small flotation machine as described in any one of claims 1 to 9.

Images