Flotation machine foam layer parameter detection method and system and flotation machine

Abstract

The invention provides a flotation machine foam layer parameter detection method and system and a flotation machine, and relates to the technical field of flotation machine parameter detection.The flotation machine foam layer parameter detection method comprises the steps that conductivity data of different depths in a flotation machine are continuously collected and preprocessed, then temperature data is used for calibration, and finally a three-dimensional conductivity data set is formed; carrying out interpolation expansion on the three-dimensional conductivity data set in combination with a region search radius and a space-time distance attenuation rule to obtain a complete conductivity data set; through an improved wavelet transform modulus maximum detection and fuzzy C-means clustering algorithm, identifying an interface of ore pulp and foam, adopting a sliding window method to remove false interface points, and preliminarily determining a thickness estimation value of a foam layer; and correcting the estimated value of the thickness of the foam layer by combining the conductivity change rate, the foam-ore pulp conductivity ratio and the flow rate of liquid above the foam layer, and finally determining the thickness value of the foam layer at each moment. According to the invention, the accuracy and reliability of foam layer parameter detection can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of flotation machine parameter detection, in particular to a flotation machine foam layer parameter detection method and system and a flotation machine. Background Art

[0002] Flotation is a widely used separation method in mineral processing. The thickness of the froth layer, a key indicator of the flotation process, directly affects recovery and concentration efficiency. Accurate, real-time measurement of flotation froth thickness is crucial for achieving intelligent and digital transformation of flotation processes. However, current mainstream measurement methods suffer from insufficient accuracy, poor stability, and limited environmental adaptability, making them unable to meet the high reliability, real-time performance, and adaptability requirements of intelligent equipment in the modern mining industry.

[0003] Existing measurement systems face several key technical bottlenecks: Non-contact measurement methods, such as ultrasound and image recognition, can avoid equipment wear and tear, but their accuracy is significantly affected by the environment, and signals are susceptible to distortion or interference, making them difficult to meet the practical needs of complex flotation environments. Contact measurement methods, while theoretically highly accurate, are subject to issues such as equipment fragility, significant drift, and frequent maintenance. Measurement errors are caused by unstable foam and slurry properties. The thickness of the foam layer fluctuates frequently during flotation and is affected by multiple factors, including ore composition, water quality, and reagents. This results in deficiencies in the measurement system's real-time response and robustness. Measurement intervention controls disturbances in the flotation process. Traditional contact sensors are prone to localized flow disturbances, especially in high-concentration, high-viscosity slurries, further exacerbating measurement errors. Therefore, a more advanced and comprehensive detection method is urgently needed to improve the accuracy and reliability of foam layer parameter measurement and promote optimization and efficiency improvements in flotation processes.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The object of the present invention is to provide a method and system for detecting parameters of a flotation machine foam layer and a flotation machine, so as to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for detecting parameters of a flotation machine foam layer, comprising the following steps: S1: During a detection period, continuously collect conductivity data at different depths inside the flotation machine, preprocess the conductivity data, and calibrate the conductivity data using temperature data to obtain a three-dimensional conductivity data set; the preprocessing includes outlier removal and missing value filling; S2: Combining the regional search radius and the spatiotemporal distance attenuation law, the three-dimensional conductivity dataset is interpolated and expanded to obtain a continuous and complete conductivity dataset; S3: Analyze the complete conductivity data set by improving the wavelet transform modulus maximum detection algorithm, combined with fuzzy The mean clustering algorithm identifies the slurry-foam interface. Based on the complete conductivity data set, a conductivity threshold is set, and a sliding window validation method is used to eliminate pseudo-interface points, thereby preliminarily estimating the foam layer thickness. S4: Based on the conductivity change rate, the foam-to-slurry conductivity ratio, and the liquid flow rate above the foam layer, the estimated value of the foam layer thickness is corrected to determine the foam layer thickness value at each moment during the detection time period; the foam-to-slurry conductivity ratio refers to the ratio of the foam layer conductivity to the slurry layer conductivity.

[0007] Furthermore, the specific execution process of S1 is: each probe of the multi-probe array conductivity sensor is evenly spaced at different depth points in the flotation machine, and each probe is at a depth point with a The raw conductivity data is collected synchronously with the sampling frequency of 100 MHz. At the same time, the slurry temperature is monitored in real time through the embedded temperature sensor. After outliers are removed and missing values ​​are filled in the raw conductivity data, the collected conductivity data is calibrated using the slurry temperature to obtain a three-dimensional conductivity data set. When a data point meets any of the following conditions, it is considered an outlier: The conductivity measurement value of the data point exceeds the preset reasonable range; If the conductivity spatial variation rate at a certain depth at a certain acquisition time point exceeds a preset threshold, the conductivity data point at that depth acquired at that acquisition time point is determined to be an outlier. The conductivity spatial variation rate refers to the ratio of the conductivity at that depth to the conductivity at the adjacent depth at the same moment. Missing values ​​are filled at the data locations determined to be abnormal points. The specific process is: with the missing point as the center, an area search radius is established, and the average value of the normal data points within the area search radius is calculated as the filling value. The area search radius is 3 times the probe spacing.

[0008] Furthermore, the conductivity data is calibrated using the slurry temperature. The calibration formula is as follows: ; Where, is the conductivity after calibration, is the original conductivity, is the temperature coefficient, which adjusts the rate at which conductivity changes with temperature, and ; Indicates the standard reference temperature, Indicates the measured temperature. is the calibration target temperature; Among them, the three-dimensional conductivity data set is represented as follows: ; Where, Indicates the depth inside the flotation machine In time The conductivity under Indicates the first The depth of the depth point, is the index of the depth point, is the number of depth points in the flotation machine, is the index of the time point, is the number of time points in the detection period.

[0009] Furthermore, the Euclidean distance between each data point in the three-dimensional conductivity dataset and the target conductivity interpolation point is calculated, and the five data points with the smallest Euclidean distance to the conductivity interpolation point are selected as its neighborhood. Then, the interpolation expansion is performed in combination with the spatiotemporal distance decay law. The formula is as follows: ; Where, Depth of flotation cell In time The conductivity under is the depth variable in the flotation machine, and , Indicates the depth inside the flotation machine, is the time variable within the detection period, and , is the starting time of the detection period, is the current time, which is the end time of the detection period. Indicates conductivity interpolation points In the neighborhood of The conductivity value of each data point, conductivity interpolation points The number of data points in the neighborhood of is the index of the data point in the neighborhood, Indicates conductivity interpolation points In the neighborhood of Data points and conductivity interpolation points The Euclidean distance between is the standard deviation of the Gaussian function, which is used to control the degree of weight attenuation. is a natural constant; The conductivity interpolation points obtained by interpolation expansion are combined with the three-dimensional conductivity data set to obtain a complete conductivity data set; Sure The formula is as follows: ; Where, 、 Represent the conductivity interpolation points In the neighborhood of The acquisition time and depth of each data point.

[0010] Furthermore, the specific execution process of S3 is as follows: multi-scale wavelet decomposition is performed on the complete conductivity data set, and a 5-layer decomposition is implemented using the improved db4 wavelet basis function to extract the modulus maximum features at each scale; based on the modulus maximum features, combined with the preset foam-slurry conductivity transition range, the fuzzy The mean clustering algorithm divides the three-dimensional data points into three categories: pulp layer, transition layer and foam layer; Among them, the clustering objective function is as follows: ; Where, is the objective function value, which is used to characterize the weighted sum of the distances from all data points to the cluster center. The optimization goal is to minimize it. is the number of cluster categories, , corresponding to the three physical states of slurry layer, transition layer and foam layer, is the index of the cluster category; is the number of data points in the complete conductivity data set, is the index of the data point in the complete conductivity data set; Represents the membership function, which is used to express the The data point belongs to class probability; is the fuzzy index, a hyperparameter used to control the degree of clustering fuzziness. The larger the classification, the more ambiguous it is; For the The conductivity value of each data point; For the The conductivity value of the cluster center of the cluster; Dynamically set conductivity threshold based on clustering results : ; Where, and are the conductivity values ​​of the cluster centers of the pulp layer and the foam layer, respectively; A sliding window with a width of 10 cm was used to verify the continuity of the interface along the depth direction, and the results of the mainstream classification that deviated from the window by more than The pseudo interface point is then output, and the estimated value of the foam layer thickness at each moment in the detection time period is output to form a continuous foam layer thickness-time series; The specific logic for calculating the estimated value of the foam layer thickness at a certain moment is as follows: obtain the depth range of the points identified as the foam layer in the cluster at this moment, find the maximum and minimum depth values ​​in the foam layer cluster, and use the difference between the maximum and minimum depth values ​​as the estimated value of the foam layer thickness at that moment.

[0011] The specific process of eliminating pseudo-interface points is as follows: calculate the conductivity mean and standard deviation of all data points in the window, and set the threshold according to the following formula: : ; Where, is the mean conductivity of all data points in the window, is the standard deviation of the conductivity of all data points within the window; Eliminate all data points that meet the following conditions: ; Where, Indicates the first The conductivity value of each data point, is the index of the data point in the window, is the mean conductivity of all data points within the window.

[0012] Furthermore, a dynamic correction model for the foam layer thickness is established. The estimated foam layer thickness at each moment in the detection period is corrected by combining the real-time conductivity change rate, the foam-to-slurry conductivity ratio, and the liquid flow rate above the foam layer to determine the foam layer thickness value at that moment. The foam-to-slurry conductivity ratio refers to the ratio of the foam layer conductivity to the slurry layer conductivity. Among them, the model expression of the dynamic correction model of the foam layer thickness is as follows: ; Where, For time Corrected foam layer thickness value, Indicates time Estimated foam layer thickness, is the time variable within the detection period, and are the maximum characteristic conductivity and minimum characteristic conductivity of the slurry, respectively, which are determined according to the physical properties of the slurry. is the instantaneous rate of change of conductivity, represents the foam-slurry conductivity ratio, For time The conductivity of the foam layer refers to the time The mean conductivity of all layers identified as foam layers, For time The conductivity of the slurry layer refers to the time The conductivity mean of the cluster belonging to the pulp layer; For time The liquid flow rate above the foam layer is is the reference flow rate, 、 and is the preset weight, , and satisfy ; in, The calculation formula is as follows: ; Where, is the instantaneous rate of change of conductivity, Indicates time The conductivity value, express The conductivity value at the time, is the time interval, where , Indicates the sampling frequency.

[0013] The present invention further provides a flotation machine foam layer parameter detection system, which is used to execute the above-mentioned flotation machine foam layer parameter detection method, and includes: The conductivity temperature measurement and calibration module is used to continuously collect conductivity data at different depths inside the flotation machine during a detection period, preprocess the conductivity data, and calibrate the conductivity data using temperature data to obtain a three-dimensional conductivity data set; the preprocessing includes outlier removal and missing value filling; The 3D data reconstruction module is used to interpolate and expand the 3D conductivity dataset by combining the regional search radius and the temporal and spatial distance attenuation law to obtain a continuous and complete conductivity dataset; Interface recognition module, used to analyze the complete conductivity data set by using the improved wavelet transform modulus maximum detection algorithm, combined with fuzzy The mean clustering algorithm identifies the slurry-foam interface. Based on the complete conductivity data set, a conductivity threshold is set, and a sliding window validation method is used to eliminate pseudo-interface points, thereby preliminarily estimating the foam layer thickness. The dynamic thickness correction module is used to correct the estimated foam layer thickness by combining the conductivity change rate, the foam-to-slurry conductivity ratio, and the liquid flow rate above the foam layer to determine the foam layer thickness value at each moment during the detection time period. The foam-to-slurry conductivity ratio refers to the ratio of the foam layer conductivity to the slurry layer conductivity.

[0014] The present invention also provides a flotation machine, wherein the foam layer parameter detection of the flotation machine is performed using the above-mentioned flotation machine foam layer parameter detection method.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The new foam thickness measurement method based on conductivity difference proposed in the present invention innovatively uses the conductivity difference of multiphase media as the measurement basis, and proposes a measurement method for identifying the foam interface by vertical profile conductivity difference, breaking through the limitations of the existing reliance on images or indirect inference of liquid levels. In addition, the present invention can effectively handle outliers and missing points in conductivity data, ensuring the integrity and reliability of the data in complex dynamic environments. In addition, the dynamic correction model of the conductivity change rate and the foam-to-pulp conductivity ratio is combined to make the estimation of the foam layer thickness more accurate and able to quickly respond to process changes, thereby optimizing the flotation process and improving the separation efficiency of minerals. This innovative method provides a new direction for the intelligence and automation of flotation machines, and contributes to the modernization of the mining industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the overall method flow of the present invention; Figure 2 This is a schematic diagram of the overall system module of the present invention; Figure 3 This is a cross-sectional diagram of the flotation machine's foam layer-slurry layer; Figure 4-Figure 5 They are 3D scatter plot and 3D bar graph of instantaneous rate of change of conductivity, foam-slurry conductivity ratio and foam layer thickness correction value respectively. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0019] Example: See also Figure 1-Figure 5 , the present invention provides a technical solution: A method for detecting parameters of a flotation machine foam layer, comprising the following steps: S1: During a detection period, continuously collect conductivity data at different depths inside the flotation machine, preprocess the conductivity data, and calibrate the conductivity data using temperature data to obtain a three-dimensional conductivity data set; the preprocessing includes outlier removal and missing value filling; In this embodiment, the probes of the multi-probe array conductivity sensor are arranged at equal intervals at different depths in the flotation machine. The raw conductivity data is collected synchronously with the sampling frequency of 100 MHz. At the same time, the slurry temperature is monitored in real time through the embedded temperature sensor. After outliers are removed and missing values ​​are filled in the raw conductivity data, the collected conductivity data is calibrated using the slurry temperature to obtain a three-dimensional conductivity data set. When a data point meets any of the following conditions, it is considered an outlier: The conductivity measurement value of the data point exceeds the preset reasonable range; If the conductivity spatial variation rate at a certain depth at a certain acquisition time point exceeds a preset threshold, the conductivity data point at that depth acquired at that acquisition time point is determined to be an outlier. The conductivity spatial variation rate refers to the larger ratio of the conductivity at that depth to the conductivity of the adjacent upper depth point and the conductivity of the adjacent lower depth point at the same time point. Missing values ​​are filled at the data locations determined to be abnormal points. The specific process is: with the missing point as the center, an area search radius is established, and the average value of the normal data points within the area search radius is calculated as the filling value. The area search radius is 3 times the probe spacing.

[0020] The conductivity data is calibrated using the slurry temperature. The calibration is based on the following formula: ; Where, is the conductivity after calibration, is the original conductivity, is the temperature coefficient, which is used to adjust the rate at which conductivity changes with temperature. The value range is [0.01,0.08]; Indicates the standard reference temperature, Indicates the measured temperature. is the calibration target temperature; By uniformly calibrating the temperature at all depths using the above formula, the resulting conductivity data can be compared at the same reference temperature. This uniformity significantly enhances data comparability and consistency, avoiding conductivity variations caused by temperature differences, thereby improving the accuracy and reliability of subsequent analysis. This calibration step provides a more precise understanding of the slurry's conductivity behavior, providing a solid data foundation for subsequent process optimization.

[0021] In this formula, the dependent variable is the calibrated conductivity , which reflects the true measured value of slurry conductivity under temperature fluctuations, more accurately representing the slurry's conductive properties. By correcting the raw conductivity to eliminate the influence of temperature on the measurement result, the conductivity value is provided at a standard reference temperature. This technical effect helps improve the accuracy of slurry process control and provides a reliable data foundation for optimizing flotation machine operation and improving mineral separation efficiency.

[0022] The independent variables include 、 and standard reference temperature , these factors have an impact on the dependent variable The main influence of temperature on conductivity is the direct effect of temperature on conductivity. As the temperature changes, the conductivity will also change, because conductivity usually has a nonlinear relationship with temperature. By introducing the temperature coefficient , the formula links the temperature change with the conductivity change, so that the temperature change can be effectively reflected in the conductivity after calibration, thereby ensuring the accuracy of the measurement results. In this formula, when the calibration target temperature Above standard reference temperature When the conductivity after calibration is will increase, otherwise it will decrease; at the same time, the measured temperature The increase in temperature will also lead to an increase in the calibrated conductivity. This positive correlation shows that the increase in temperature will increase the conductivity of the slurry, thereby affecting the calibrated conductivity value, ensuring that users can obtain a true reflection of the slurry state in a timely manner.

[0023] Among them, the three-dimensional conductivity data set is represented as follows: ; Where, Indicates the depth inside the flotation machine In time The conductivity under Indicates the first The depth of the depth point, is the index of the depth point, is the number of depth points in the flotation machine, is the index of the time point, is the number of time points in the detection period.

[0024] Using a three-dimensional dataset, we can comprehensively capture how conductivity changes with depth and time, providing rich information for process monitoring and optimization. This representation method not only reflects the spatial distribution of conductivity but also reveals its temporal evolution trend, which is of great significance for in-depth analysis of slurry conditions, controlling the flotation process, and improving mineral separation efficiency.

[0025] The advantage of step S1 lies in its comprehensive preprocessing by continuously collecting conductivity data at different depths within the flotation cell and combining it with real-time temperature data. This effectively removes outliers and fills in missing values. This approach ensures the accuracy and integrity of the conductivity data, enabling subsequent analysis and processing to be based on high-quality data and reducing misjudgments caused by data noise. Compared to existing technologies, this process better captures complex fluid dynamics and improves data reliability.

[0026] In this invention, this step provides a solid foundation for the effectiveness of the overall solution. By obtaining a high-quality 3D conductivity dataset, subsequent steps such as interpolation and expansion, interface identification, and foam layer thickness correction can be performed with more accurate data support, thereby improving the accuracy and real-time performance of the entire foam layer parameter detection process and facilitating effective monitoring and adjustment of the flotation process status.

[0027] S2: Combining the regional search radius and the spatiotemporal distance attenuation law, the three-dimensional conductivity dataset is interpolated and expanded to obtain a continuous and complete conductivity dataset; In this embodiment, the Euclidean distance between each data point in the three-dimensional conductivity dataset and the target conductivity interpolation point is calculated, and the five data points with the smallest Euclidean distance to the conductivity interpolation point are selected as its neighborhood. Then, interpolation expansion is performed in combination with the spatiotemporal distance decay law. The formula is as follows: ; Where, Depth of flotation cell In time The conductivity under is the depth variable in the flotation machine, and , which means the depth increases from 0 downwards; Indicates the depth inside the flotation machine, is the time variable within the detection period, and , is the starting time of the detection period, is the current time, which is the end time of the detection period. Indicates conductivity interpolation points In the neighborhood of The conductivity value of each data point, conductivity interpolation points The number of data points in the neighborhood of is the index of the data point in the neighborhood, Indicates conductivity interpolation points In the neighborhood of Data points and conductivity interpolation points The Euclidean distance between is the standard deviation of the Gaussian function, which is used to control the degree of weight attenuation. is a natural constant; Dependent variable This method reflects the conductivity value at a specific depth and time within the flotation cell. By utilizing a weighted average of conductivity data points within a neighborhood and incorporating the law of spatiotemporal distance decay, it provides a smooth and accurate estimate of conductivity. This method not only improves the spatial resolution of conductivity measurements but also effectively reduces the impact of noise, enhancing data reliability and providing a precise reference for monitoring and optimizing the flotation process.

[0028] The independent variables include depth, time, the conductivity value of the data points in the neighborhood, and their Euclidean distance from the interpolation point. These independent variables directly affect the dependent variable. Changes in depth and time will lead to changes in conductivity, and the influence of the conductivity value in the neighborhood is reflected by the distance attenuation weight, that is, the closer the data point is to the interpolation point, the greater its contribution to the conductivity. Combining these factors can more accurately reflect the conductivity characteristics of the slurry at a specific depth and time. In the formula, as the depth increases, the conductivity decreases. Increase or time As time goes by, the change of conductivity will reflect the evolution of the slurry state. At the same time, the change of conductivity data in the neighborhood and the distance from the interpolation point will also affect In particular, the introduction of the distance attenuation factor makes the data points closer to the interpolation point have a more significant impact on the final interpolation result, thereby enhancing the accuracy and rationality of the interpolation.

[0029] The conductivity interpolation points obtained by interpolation expansion are combined with the three-dimensional conductivity data set to obtain a complete conductivity data set; Sure The formula is as follows: ; Where, 、 Represent the conductivity interpolation points In the neighborhood of The acquisition time and depth of each data point.

[0030] The advantage of step S2 lies in interpolating and expanding the three-dimensional conductivity dataset by combining the regional search radius and the temporal and spatial distance decay law, ensuring data continuity and integrity. This step effectively overcomes the information loss caused by sparse or uneven data collection, ensuring that the conductivity data fully reflects the true internal conditions of the flotation machine in both time and space. Compared to existing technologies, this method provides higher accuracy and reliability, thereby improving the effectiveness of subsequent analysis.

[0031] In the present invention, this step provides a critical data foundation for the overall solution, enabling subsequent interface identification and froth layer thickness estimation to be performed with more comprehensive and accurate data support. By ensuring the continuity of the conductivity data set, step S2 provides the necessary conditions for subsequent analysis using wavelet transforms and clustering algorithms, significantly improving the accuracy and real-time performance of froth layer parameter detection, helping to optimize the control and regulation of the flotation process.

[0032] S3: Analyze the complete conductivity data set by improving the wavelet transform modulus maximum detection algorithm, combined with fuzzy The mean clustering algorithm identifies the slurry-foam interface. Based on the complete conductivity data set, a conductivity threshold is set, and a sliding window validation method is used to eliminate pseudo-interface points, thereby preliminarily estimating the foam layer thickness. In this embodiment, the specific execution process of S3 is as follows: multi-scale wavelet decomposition is performed on the complete conductivity data set, and a 5-layer decomposition is implemented using the improved db4 wavelet basis function to extract the modulus maximum features at each scale; based on the modulus maximum features, combined with the preset foam-slurry conductivity transition interval, the fuzzy The mean clustering algorithm divides the three-dimensional data points into three categories: pulp layer, transition layer and foam layer; During the S3 execution process, the complete conductivity dataset is first subjected to multiscale wavelet decomposition, using a modified db4 wavelet basis function to implement a five-layer decomposition to extract signal details and features. By extracting modulus maximum features at each scale, key points of conductivity variation can be captured. The modulus maximum features are set based on local maxima in the signal, selecting modulus maxima at specific scales to reflect significant conductivity changes. Furthermore, the foam-to-slurry conductivity transition interval is determined based on statistical analysis of experimental data and is typically determined based on the actual conductivity distribution and physical model. Based on the extracted modulus maximum features and the pre-defined transition interval, a fuzzy C-means clustering algorithm is then used to classify the three-dimensional data points into three categories: slurry layer, transition layer, and foam layer. This process effectively classifies the conductivity data, providing a clear hierarchical structure for subsequent analysis and processing.

[0033] Among them, the clustering objective function is as follows: ; Where, is the objective function value, which is used to characterize the weighted sum of the distances from all data points to the cluster center. The optimization goal is to minimize it. is the number of cluster categories, , corresponding to the three physical states of slurry layer, transition layer and foam layer, is the index of the cluster category; is the number of data points in the complete conductivity data set, is the index of the data point in the complete conductivity data set; Represents the membership function, which is used to express the The data point belongs to class probability; is the fuzzy index, a hyperparameter used to control the degree of clustering fuzziness. The larger the classification, the more ambiguous it is; For the The conductivity value of each data point; For the The conductivity value of the cluster center of the cluster; The clustering objective function is designed to measure the similarity between data points and cluster centers by weighted sum of squared distances, thereby achieving effective clustering of data. The optimization goal of this function is to minimize , so that the data points of the same category are as close as possible to their corresponding cluster centers, while the data points of different categories are as far away as possible. Here, Indicates the number of cluster categories, corresponding to physical states such as slurry layer, transition layer and foam layer. Reflects the probability that a data point belongs to a specific cluster, the fuzzy index The fuzziness of clustering is controlled, allowing data points to belong to multiple categories to varying degrees. In this way, the complex characteristics of different physical states of the slurry can be better captured, improving classification results and analysis accuracy.

[0034] Dynamically set conductivity threshold based on clustering results : ; Where, and are the conductivity values ​​of the cluster centers of the pulp layer and the foam layer, respectively; Set the conductivity threshold like this The reason for this is that calculating the mean conductivity of the two cluster centers effectively captures the conductivity transition characteristics between the slurry and foam layers, providing a reasonable basis for the demarcation threshold. This method ensures that the demarcation threshold is located midway between the slurry and foam layers, more accurately reflecting the conductivity difference between the two, thereby improving classification accuracy and reliability. This dynamic setting strategy can adapt to conductivity changes under different conditions, achieving more precise hierarchical classification and analysis.

[0035] A sliding window with a width of 10 cm was used to verify the continuity of the interface along the depth direction, and the results of the mainstream classification that deviated from the window by more than The pseudo interface point is then output, and the estimated value of the foam layer thickness at each moment in the detection time period is output to form a continuous foam layer thickness-time series; The specific logic for calculating the estimated value of the foam layer thickness at a certain moment is as follows: obtain the depth range of the points identified as the foam layer in the cluster at this moment, find the maximum and minimum depth values ​​in the foam layer cluster, and use the difference between the maximum and minimum depth values ​​as the estimated value of the foam layer thickness at that moment.

[0036] The specific process of eliminating pseudo-interface points is as follows: calculate the conductivity mean and standard deviation of all data points in the window, and set the threshold according to the following formula: : ; Where, is the mean conductivity of all data points in the window, is the standard deviation of the conductivity of all data points within the window; Eliminate all data points that meet the following conditions: ; Where, Indicates the first The conductivity value of each data point, is the index of the data point in the window, is the mean conductivity of all data points within the window.

[0037] The advantage of step S3 lies in its ability to accurately identify the interface between the slurry and foam layers through the use of an improved wavelet transform modulus maximum detection algorithm and fuzzy C-means clustering algorithm. This method effectively extracts the modulus maximum features of the conductivity data, enabling more accurate analysis of complex fluid states. Compared to existing technologies, this step not only improves the accuracy of interface identification but also enhances the ability to process the mixed state of the foam and slurry layers, thereby reducing the risk of misjudgment and missed detection.

[0038] In this invention, this step provides crucial interface identification support for the overall solution, which is crucial for subsequent froth layer thickness estimation and dynamic correction. Accurate interface identification enables the establishment of a more reliable froth layer thickness time series, improving the effectiveness and real-time performance of the entire detection process. This in turn optimizes flotation machine operation and management, ensuring the stability and efficiency of the flotation process.

[0039] S4: Based on the conductivity change rate, the foam-to-slurry conductivity ratio, and the liquid flow rate above the foam layer, the foam layer thickness estimate is corrected to determine the foam layer thickness value at each moment during the detection time period; the foam-to-slurry conductivity ratio refers to the ratio of the foam layer conductivity to the slurry layer conductivity; In this embodiment, a dynamic correction model for the foam layer thickness is established. The estimated foam layer thickness at each moment in the detection time period is corrected by combining the real-time conductivity change rate, the foam-to-slurry conductivity ratio, and the liquid flow rate above the foam layer to determine the foam layer thickness value at that moment. The foam-to-slurry conductivity ratio refers to the ratio of the foam layer conductivity to the slurry layer conductivity. Among them, the model expression of the dynamic correction model of the foam layer thickness is as follows: ; Where, For time Corrected foam layer thickness value, Indicates time Estimated foam layer thickness, is the time variable within the detection period, and are the maximum characteristic conductivity and minimum characteristic conductivity of the slurry, respectively, which are determined according to the physical properties of the slurry. is the instantaneous rate of change of conductivity, represents the foam-slurry conductivity ratio, For time The conductivity of the foam layer refers to the time The mean conductivity of all layers identified as foam layers, For time The conductivity of the slurry layer refers to the time The conductivity mean of the cluster belonging to the pulp layer; For time The liquid flow rate above the foam layer is is the reference flow rate, 、 and is the preset weight, , , ; Set weight The reason is based on the degree of influence of each variable on the thickness of the foam layer. It is a key indicator of the dynamic characteristics of the foam layer, directly reflecting the stability and flow state of the foam layer. Its change will quickly affect the thickness of the foam layer, so it is given the highest weight. The rapid change of conductivity may mean the formation or collapse of foam layer, which has a direct and significant impact on the foam thickness. It also has an important influence on the characteristics of the foam layer. It reflects the relative properties between the foam and the pulp, affecting the stability of the foam and the adhesion of the bubbles. Therefore, a higher weight is set. However, compared to the conductivity change rate, its influence is usually relatively slow and indirect, so the weight is set slightly lower. Although the liquid flow rate above the foam layer affects the stability of the foam, its influence is usually slow and depends on the specific fluid dynamic conditions, so it is given the lowest weight. The influence of flow rate may appear under certain conditions, but in general, its direct impact on the change of foam layer thickness is small. Therefore, this weight setting reasonably reflects the relative importance of different factors on the correction of foam layer thickness.

[0040] The expression of the dynamic correction model of foam layer thickness is intended to dynamically correct the foam layer thickness through multiple influencing factors. First, The instantaneous rate of change of conductivity is the estimated value of the foam layer thickness at time t, which is the basis of the model. This reflects the rapid effect of conductivity on the properties of the foam layer. The faster the conductivity changes, the more obvious the change in foam layer thickness will be. Specifically, when When it increases, it indicates that the rate of change of conductivity is accelerated, reflecting the rapid change of the number of bubbles or bubble size in the foam layer, which means that the bubble structure of the foam layer is more stable and full, resulting in the thickness of the foam layer. Increase. Conversely, when the conductivity drops rapidly, it may indicate that the bubbles in the foam layer have burst or the fluidity has weakened, and the thickness of the foam layer may decrease. Secondly, the logarithmic form of the foam-to-slurry conductivity ratio can effectively capture the relative changes between the foam and the slurry, indicating that when the conductivity of the foam layer increases relative to the conductivity of the slurry layer, the thickness of the foam layer will increase accordingly. Finally, the flow rate ratio reflects the impact of flow conditions on the thickness of the foam layer. An increase in flow rate may lead to an increase in the thickness of the foam layer, so the positive correlation setting is reasonable. This model combines the influence of factors such as conductivity, relative conductivity ratio and flow rate, and can dynamically adapt to changes in foam layer thickness under different conditions, providing more accurate thickness estimates.

[0041] Dependent variable Reflected in A constantly corrected froth layer thickness value. This provides a more accurate estimate of froth layer thickness using a dynamic correction model that incorporates real-time conductivity changes, the froth-to-pulp conductivity ratio, and flow rate. This correction helps monitor froth layer changes in real time and adapt to fluctuations in froth characteristics during flotation, thereby improving flotation efficiency and product quality. Accurate froth layer thickness monitoring optimizes flotation process control and enhances resource utilization.

[0042] Independent variables include an initial estimate of the foam layer thickness , instantaneous rate of change of conductivity , foam-slurry conductivity ratio and liquid flow rate above the foam layer Relative to reference flow rate These independent variables directly influence the dependent variables. Specifically, the conductivity change rate reflects the dynamics of the foam layer, the conductivity ratio influences the relative properties of the foam and slurry, and the flow rate influences the stability of the foam layer. Therefore, these factors interact to determine the actual thickness of the foam layer.

[0043] In the model, the relationship between the dependent variable and the independent variable is complex. When it increases, it usually means that the state of the foam layer is changing rapidly, which may lead to an increase in the thickness of the foam layer. There is a positive correlation. An increase in the foam-to-slurry conductivity ratio may also indicate an improvement in the quality of the foam layer, which in turn positively affects the thickness of the foam layer. The entire model comprehensively reflects these complex positive and negative correlations through the weighted combination of the individual variables.

[0044] in, The calculation formula is as follows: ; Where, is the instantaneous rate of change of conductivity, express Conductivity value, express The conductivity value at the time, is the time interval, where , Indicates the sampling frequency.

[0045] Table 1: Statistics of foam layer thickness correction values

[0046] See also Figure 4-5 It should be noted that the estimated value of the foam layer thickness in the table corresponds to , the liquid velocity above the foam layer corresponds to , the foam layer thickness correction value corresponds to .

[0047] This data analysis is based on a dynamic correction model for foam layer thickness. The parameters involved include initial foam layer thickness, conductivity change rate, foam-to-slurry conductivity ratio, liquid flow rate above the foam layer, and corrected foam layer thickness. The data demonstrates the response of foam layer thickness under different conditions.

[0048] From the data, we can see that the initial foam layer thickness varies from 5 to 20. Samples with larger initial thickness generally have higher final thickness after correction, indicating that the initial thickness has a significant impact on the final thickness, and samples with smaller initial thickness have lower final thickness.

[0049] The values ​​of the instantaneous rate of change of conductivity ranged from 0.01 to 0.09, demonstrating the dynamic nature of the foam layer. In particular, higher rates of change (such as 0.08 and 0.09) typically resulted in a significant increase in the final foam layer thickness, demonstrating that rapidly changing conductivity can significantly influence the stability and thickness of the foam layer.

[0050] The foam-to-slurry conductivity ratio ranges from 1.0 to 1.5. A higher conductivity ratio is usually associated with a higher final foam layer thickness, indicating that a good conductivity ratio contributes to the stability and thickness of the foam layer.

[0051] The liquid flow rate above the foam layer was varied from 0.6 to 1.3. The data showed that the relationship between flow rate and final foam layer thickness was not as clear as that between conductivity and conductivity ratio. For example, at lower flow rates, the final thickness was relatively low despite a higher initial thickness, indicating that flow rate plays a more complex role in modifying foam layer thickness.

[0052] Based on the above analysis, it can be concluded that the dynamic modification of foam layer thickness is affected by multiple factors. Initial thickness and conductivity change rate have a relatively large impact on the final foam layer thickness, while the foam-to-slurry conductivity ratio and flow rate have relatively small effects. Future research can further explore the interactions between these variables to optimize foam layer control strategies.

[0053] The advantage of step S4 is that it dynamically corrects the estimated foam layer thickness by comprehensively considering the conductivity change rate, the foam-to-pulp conductivity ratio, and the liquid flow rate above the foam layer. This process can reflect changes in the flotation process in real time, improving the accuracy and reliability of the foam layer thickness. Compared with existing technologies, this step utilizes multiple factors for correction, overcoming errors caused by a single parameter, thereby making the foam layer thickness measurement results more practical and scientific.

[0054] In this invention, this step provides real-time froth layer thickness assessment for the overall solution, making flotation machine operation more intelligent and adaptive. This dynamic correction mechanism not only improves the accuracy of froth layer parameter detection but also helps optimize the control strategy of the flotation process, thereby improving mineral separation efficiency and resource recovery, with significant economic and environmental benefits.

[0055] See also Figure 2-3 , a flotation machine foam layer parameter detection system, comprising: The conductivity temperature measurement and calibration module is used to continuously collect conductivity data at different depths inside the flotation machine during a detection period, preprocess the conductivity data, and calibrate the conductivity data using temperature data to obtain a three-dimensional conductivity data set; the preprocessing includes outlier removal and missing value filling; The 3D data reconstruction module is used to interpolate and expand the 3D conductivity dataset by combining the regional search radius and the temporal and spatial distance attenuation law to obtain a continuous and complete conductivity dataset; Interface recognition module, used to analyze the complete conductivity data set by using the improved wavelet transform modulus maximum detection algorithm, combined with fuzzy The mean clustering algorithm identifies the slurry-foam interface. Based on the complete conductivity data set, a conductivity threshold is set, and a sliding window validation method is used to eliminate pseudo-interface points, thereby preliminarily estimating the foam layer thickness. The dynamic thickness correction module is used to correct the estimated foam layer thickness by combining the conductivity change rate, the foam-to-slurry conductivity ratio, and the liquid flow rate above the foam layer to determine the foam layer thickness value at each moment during the detection time period. The foam-to-slurry conductivity ratio refers to the ratio of the foam layer conductivity to the slurry layer conductivity.

[0056] The present invention also provides a flotation machine, wherein the foam layer parameter detection of the flotation machine is performed using the above-mentioned flotation machine foam layer parameter detection method.

[0057] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0058] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0059] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0060] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for detecting parameters of a flotation machine foam layer, characterized in that: The specific steps include: S1: During a detection period, continuously collect conductivity data at different depths inside the flotation machine, preprocess the conductivity data, and calibrate the conductivity data using temperature data to obtain a three-dimensional conductivity data set; the preprocessing includes outlier removal and missing value filling; S2: Combining the regional search radius and the spatiotemporal distance attenuation law, the three-dimensional conductivity dataset is interpolated and expanded to obtain a continuous and complete conductivity dataset; S3: Analyze the complete conductivity data set by improving the wavelet transform modulus maximum detection algorithm, combined with fuzzy The mean clustering algorithm identifies the slurry-foam interface. Based on the complete conductivity data set, a conductivity threshold is set, and a sliding window validation method is used to eliminate pseudo-interface points, thereby preliminarily estimating the foam layer thickness. S4: Based on the conductivity change rate, the foam-to-slurry conductivity ratio, and the liquid flow rate above the foam layer, the estimated value of the foam layer thickness is corrected to determine the foam layer thickness value at each moment during the detection time period; the foam-to-slurry conductivity ratio refers to the ratio of the foam layer conductivity to the slurry layer conductivity.

2. A method for detecting parameters of a flotation machine foam layer according to claim 1, characterized in that: The specific execution process of S1 is: the probes of the multi-probe array conductivity sensor are arranged at equal intervals at different depths in the flotation machine, and each probe is at a depth point with The raw conductivity data is collected synchronously with the sampling frequency of 100 MHz. At the same time, the slurry temperature is monitored in real time through the embedded temperature sensor. After outliers are removed and missing values ​​are filled in the raw conductivity data, the collected conductivity data is calibrated using the slurry temperature to obtain a three-dimensional conductivity data set. When a data point meets any of the following conditions, it is considered an outlier: The conductivity measurement value of the data point exceeds the preset reasonable range; If the conductivity spatial variation rate at a certain depth at a certain acquisition time point exceeds a preset threshold, the conductivity data point at that depth acquired at that acquisition time point is determined to be an outlier. The conductivity spatial variation rate refers to the ratio of the conductivity at that depth to the conductivity at the adjacent depth at the same moment. Missing values ​​are filled at the data locations determined to be abnormal points. The specific process is: with the missing point as the center, an area search radius is established, and the average value of the normal data points within the area search radius is calculated as the filling value. The area search radius is 3 times the probe spacing.

3. A method for detecting parameters of a flotation machine foam layer according to claim 2, characterized in that: The conductivity data is calibrated using the slurry temperature. The calibration is based on the following formula: ； Where, is the conductivity after calibration, is the original conductivity, is the temperature coefficient, which adjusts the rate at which conductivity changes with temperature, and ; Indicates the standard reference temperature, Indicates the measured temperature. is the calibration target temperature; Among them, the three-dimensional conductivity data set is represented as follows: ； Where, Indicates the depth inside the flotation machine In time The conductivity under Indicates the first The depth of the depth point, is the index of the depth point, is the number of depth points in the flotation machine, is the index of the time point, is the number of time points in the detection period.

4. The method for detecting parameters of a flotation machine foam layer according to claim 1, wherein: The Euclidean distance between each data point in the three-dimensional conductivity dataset and the target conductivity interpolation point is calculated, and the five data points with the smallest Euclidean distance to the conductivity interpolation point are selected as its neighborhood. Then, the interpolation expansion is performed in combination with the spatiotemporal distance decay law. The formula is as follows: ； Where, Depth of flotation cell In time The conductivity under is the depth variable in the flotation machine, and , Indicates the depth inside the flotation machine, is the time variable within the detection period, and , is the starting time of the detection period, is the current time, which is the end time of the detection period. Indicates conductivity interpolation points In the neighborhood of The conductivity value of each data point, conductivity interpolation points The number of data points in the neighborhood of is the index of the data point in the neighborhood, Indicates conductivity interpolation points In the neighborhood of Data points and conductivity interpolation points The Euclidean distance between is the standard deviation of the Gaussian function, which is used to control the degree of weight attenuation. is a natural constant; The conductivity interpolation points obtained by interpolation expansion are combined with the three-dimensional conductivity data set to obtain a complete conductivity data set; Sure The formula is as follows: ； Where, 、 Represent the conductivity interpolation points In the neighborhood of The acquisition time and depth of each data point.

5. The method for detecting parameters of a flotation machine foam layer according to claim 1, wherein: The specific execution process of S3 is as follows: multi-scale wavelet decomposition is performed on the complete conductivity data set, and a 5-layer decomposition is implemented using the improved db4 wavelet basis function to extract the modulus maximum features at each scale; Based on the maximum modulus feature, combined with the preset foam-slurry conductivity transition range, the fuzzy The mean clustering algorithm divides the three-dimensional data points into three categories: pulp layer, transition layer and foam layer; Among them, the clustering objective function is as follows: ； Where, is the objective function value, which is used to characterize the weighted sum of the distances from all data points to the cluster center. The optimization goal is to minimize it. is the number of cluster categories, , corresponding to the three physical states of slurry layer, transition layer and foam layer, is the index of the cluster category; is the number of data points in the complete conductivity data set, is the index of the data point in the complete conductivity data set; Represents the membership function, which is used to express the The data point belongs to class probability; is the fuzzy index, a hyperparameter used to control the degree of clustering fuzziness. The larger the classification, the more ambiguous it is; For the The conductivity value of each data point; For the The conductivity value of the cluster center of the cluster; Dynamically set conductivity threshold based on clustering results : ； Where, and are the conductivity values ​​of the cluster centers of the pulp layer and the foam layer, respectively; A sliding window with a width of 10 cm was used to verify the continuity of the interface along the depth direction, and the results of the mainstream classification that deviated from the window by more than The pseudo interface point is then output, and the estimated value of the foam layer thickness at each moment in the detection time period is output to form a continuous foam layer thickness-time series; The specific logic for calculating the estimated foam layer thickness at a certain moment is as follows: obtain the depth range of the points identified as the foam layer in the cluster at that moment, find the maximum and minimum depth values ​​in the foam layer cluster, and use the difference between the maximum and minimum depth values ​​as the estimated foam layer thickness at that moment; The specific process of eliminating pseudo-interface points is as follows: calculate the conductivity mean and standard deviation of all data points in the window, and set the threshold according to the following formula: : ； Where, is the mean conductivity of all data points in the window, is the standard deviation of the conductivity of all data points within the window; Eliminate all data points that meet the following conditions: ； Where, Indicates the first The conductivity value of each data point, is the index of the data point in the window, is the mean conductivity of all data points within the window.

6. A method for detecting parameters of a flotation machine foam layer according to claim 5, characterized in that: A dynamic correction model for the foam layer thickness is established. The estimated foam layer thickness at each moment during the detection period is corrected based on the real-time conductivity change rate, the foam-to-slurry conductivity ratio, and the liquid flow rate above the foam layer to determine the foam layer thickness value at that moment. The foam-to-slurry conductivity ratio refers to the ratio of the foam layer conductivity to the slurry layer conductivity. Among them, the model expression of the dynamic correction model of the foam layer thickness is as follows: ； Where, For time Corrected foam layer thickness value, Indicates time Estimated foam layer thickness, is the time variable within the detection period, and are the maximum characteristic conductivity and minimum characteristic conductivity of the slurry, respectively, which are determined according to the physical properties of the slurry. is the instantaneous rate of change of conductivity, represents the foam-slurry conductivity ratio, For time The conductivity of the foam layer refers to the time The mean conductivity of all layers identified as foam layers, For time The conductivity of the slurry layer refers to the time The conductivity mean of the cluster belonging to the pulp layer; For time The liquid flow rate above the foam layer is is the reference flow rate, 、 and is the preset weight, , and satisfy ; in, The calculation formula is as follows: ； Where, is the instantaneous rate of change of conductivity, express The conductivity value, express The conductivity value at the time, is the time interval, where , Indicates the sampling frequency.

7. A flotation machine foam layer parameter detection system, characterized by: The flotation machine foam layer parameter detection system is used to execute the flotation machine foam layer parameter detection method according to claims 1 to 6, comprising: The conductivity temperature measurement and calibration module is used to continuously collect conductivity data at different depths inside the flotation machine during a detection period, preprocess the conductivity data, and calibrate the conductivity data using temperature data to obtain a three-dimensional conductivity data set; the preprocessing includes outlier removal and missing value filling; The 3D data reconstruction module is used to interpolate and expand the 3D conductivity dataset by combining the regional search radius and the temporal and spatial distance attenuation law to obtain a continuous and complete conductivity dataset; Interface recognition module, used to analyze the complete conductivity data set by using the improved wavelet transform modulus maximum detection algorithm, combined with fuzzy The mean clustering algorithm identifies the slurry-foam interface. Based on the complete conductivity data set, a conductivity threshold is set, and a sliding window validation method is used to eliminate pseudo-interface points, thereby preliminarily estimating the foam layer thickness. The dynamic thickness correction module is used to correct the estimated foam layer thickness by combining the conductivity change rate, the foam-to-slurry conductivity ratio, and the liquid flow rate above the foam layer to determine the foam layer thickness value at each moment during the detection time period. The foam-to-slurry conductivity ratio refers to the ratio of the foam layer conductivity to the slurry layer conductivity.

8. A flotation machine, characterized in that: The flotation machine's foam layer parameters are detected using a flotation machine's foam layer parameter detection method as described in claims 1-6.

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