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

Abstract

This invention provides a method, system, and flotation machine for detecting froth layer parameters in a flotation machine, relating to the field of flotation machine parameter detection technology. The method includes: continuously collecting conductivity data at different depths inside the flotation machine; preprocessing the data; calibrating the data using temperature data to form a three-dimensional conductivity dataset; interpolating and expanding the three-dimensional conductivity dataset by combining the region search radius and spatiotemporal distance attenuation law to obtain a complete conductivity dataset; identifying the interface between the slurry and froth using an improved wavelet transform modulus maxima detection and fuzzy C-means clustering algorithm; removing false interface points using a sliding window method to initially determine the estimated froth layer thickness; and correcting the estimated froth layer thickness by combining the conductivity change rate, the froth-slurry conductivity ratio, and the liquid flow velocity above the froth layer to finally determine the froth layer thickness value at each moment. This invention helps improve the accuracy and reliability of froth layer parameter detection.

Description

Technical Field

[0001] This invention relates to the field of flotation machine parameter detection technology, specifically to a method, system, and flotation machine for detecting parameters of the froth layer in a flotation machine. Background Technology

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

[0003] Existing measurement systems face several key technical bottlenecks: Non-contact measurement methods, such as ultrasonic and image recognition, while avoiding equipment wear, suffer from significant environmental influences on accuracy, signal distortion, and severe interference, making them unsuitable for complex flotation environments. Contact measurement methods, while theoretically possessing high accuracy, suffer from equipment fragility, severe drift, and frequent maintenance. Measurement errors arise from the instability of froth and slurry properties. The froth layer thickness fluctuates frequently during flotation and is influenced by factors such as ore composition, water quality, and reagents, resulting in deficiencies in real-time response and robustness of the measurement system. Measurement intervention is crucial for controlling flotation process disturbances. Traditional contact sensors are prone to inducing localized turbulence, 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 froth layer parameter determination, promoting the optimization and efficiency improvement of the flotation process.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, and flotation machine for detecting parameters of the froth layer in a flotation machine, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for detecting parameters of the froth layer in a flotation machine, comprising the following steps:

[0008] S1: During the detection period, conductivity data at different depths inside the flotation machine are continuously collected. The conductivity data is preprocessed and calibrated using temperature data to obtain a three-dimensional conductivity dataset. The preprocessing includes outlier removal and missing value imputation.

[0009] S2: Combining the regional search radius and the spatiotemporal distance decay law, the three-dimensional conductivity dataset is interpolated and expanded to obtain a continuous and complete conductivity dataset;

[0010] S3: Analyze the complete conductivity dataset using an improved wavelet transform modulus maxima detection algorithm, combined with fuzzy logic. Mean clustering algorithm identifies slurry-foam interface; based on complete conductivity dataset, conductivity boundary threshold is set, and sliding window verification method is used to remove pseudo interface points, and the estimated value of foam layer thickness is initially determined.

[0011] S4: By combining the rate of change of conductivity, the foam-slurry conductivity ratio, and the liquid flow velocity above the foam layer, the estimated value of the foam layer thickness is corrected to determine the foam layer thickness value at each moment within the detection period; the foam-slurry conductivity ratio refers to the ratio of the conductivity of the foam layer to the conductivity of the slurry layer.

[0012] Furthermore, the specific execution process of S1 is as follows: the probes of the multi-probe array conductivity sensor are arranged at equal intervals at different depth points within the flotation machine, and each probe at the depth point... The original conductivity data is collected synchronously at the sampling frequency. At the same time, the slurry temperature is monitored in real time through an embedded temperature sensor. After outlier removal and missing value filling of the original conductivity data, the collected conductivity data is calibrated using the slurry temperature to obtain a three-dimensional conductivity dataset.

[0013] A data point is considered an outlier if it meets any of the following conditions:

[0014] The conductivity measurements at the data points exceeded the preset reasonable range;

[0015] If the spatial rate of change of conductivity at a certain depth exceeds a preset threshold at a certain acquisition time point, then the conductivity data point at that depth acquired at that acquisition time point is judged as an abnormal point; whereby the spatial rate of change of conductivity refers to the ratio of conductivity at that depth to conductivity at the same moment.

[0016] Missing values ​​are filled at the locations of data points identified as anomalies. The specific process is as follows: a search radius is established with the missing point as the center, and the average value of normal data points within the search radius is calculated as the filling value. The search radius is 3 times the probe spacing.

[0017] Furthermore, the conductivity data is calibrated using the slurry temperature, and the calibration is based on the following formula:

[0018]

[0019] In the formula, The calibrated conductivity, The original conductivity, This is a temperature coefficient used to adjust the rate at which conductivity changes with temperature, and ; Indicates the standard reference temperature. This indicates the measured temperature. To calibrate the target temperature;

[0020] Among them, the three-dimensional conductivity dataset The representation is as follows:

[0021]

[0022] In the formula, Indicates the depth inside the flotation machine In time The conductivity at that point Indicates the first flotation in the flotation machine The depth at which a depth point is located. For the index of the depth point, The number of depth points within the flotation machine. For indexes of time points, This represents the number of time points within the detection time period.

[0023] Furthermore, the Euclidean distance between each data point in the three-dimensional conductivity dataset and the target conductivity interpolation point is calculated. The five data points with the smallest Euclidean distance to the conductivity interpolation point are selected as their neighborhoods. Then, interpolation expansion is performed in combination with the spatiotemporal distance attenuation law. The formula used is as follows:

[0024]

[0025] In the formula, depth within the flotation machine In time The conductivity at that point Let be the depth variable within the flotation machine, and , Indicates the depth inside the flotation machine. To detect time variables within a time period, and , This represents the start time of the detection period. The current time is the end time of the detection period. Indicates the conductivity interpolation point In the neighborhood of , the first Conductivity values ​​for each data point Interpolation point for conductivity The number of data points in the neighborhood. For indexes of data points within the neighborhood, Indicates the conductivity interpolation point In the neighborhood of , the first Data points and conductivity interpolation points European distance between them The standard deviation of the Gaussian function is used to control the degree of weight decay. It is a natural constant;

[0026] The conductivity interpolation points obtained by interpolation augmentation are combined with the three-dimensional conductivity dataset to obtain a complete conductivity dataset.

[0027] Sure The formula used is as follows:

[0028]

[0029] In the formula, 、 These represent the conductivity interpolation points. In the neighborhood of , the first The time and depth of data collection for each data point.

[0030] Furthermore, the specific execution process of S3 is as follows: Multi-scale wavelet decomposition is performed on the complete conductivity dataset; a 5-level decomposition is implemented using the improved db4 wavelet basis function to extract the modulus maxima features at each scale; based on the modulus maxima features, combined with the preset foam-slurry conductivity transition interval, fuzzy... The mean clustering algorithm divides the 3D data points into three categories: slurry layer, transition layer, and foam layer;

[0031] The clustering objective function is as follows:

[0032]

[0033] In the formula, Let be the objective function value, representing the sum of squared weighted distances from all data points to the cluster centers. The optimization objective is to minimize this sum. The number of cluster categories. This corresponds to three physical states: the slurry layer, the transition layer, and the foam layer. An index for cluster categories; The number of data points in the complete conductivity dataset. An index for data points in the complete conductivity dataset; Represents the membership function, used to describe the membership degree of the first member. The data point belongs to the th data point The probability of a class; The fuzziness index is a hyperparameter used to control the degree of fuzziness in clustering. The larger the size, the more ambiguous the classification becomes; For the first Conductivity values ​​for each data point; For the first The electrical conductivity value of the cluster centers of a cluster;

[0034] Dynamically set conductivity threshold based on clustering results :

[0035]

[0036] In the formula, and These are the electrical conductivity values ​​of the cluster centers in the slurry layer and the foam layer, respectively.

[0037] A sliding window with a width of 10 cm was used to verify the continuity of the interface along the depth direction, and results deviating from the mainstream classification within the window were removed. The pseudo-interface point is then used to output the estimated foam layer thickness at each moment within the detection time period, forming a continuous foam layer thickness-time series.

[0038] 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 foam layers 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 foam layer thickness at this moment.

[0039] The specific process for eliminating pseudo-interface points is as follows: calculate the mean and standard deviation of conductivity of all data points within the window, and set the threshold according to the following formula. :

[0040]

[0041] In the formula, This represents the average conductivity of all data points within the window. It is the standard deviation of conductivity of all data points within the window;

[0042] Remove all data points that meet the following conditions:

[0043]

[0044] In the formula, Represented as the first in the window Conductivity values ​​for each data point For the index of data points within the window, This represents the average conductivity of all data points within the window.

[0045] Furthermore, a dynamic correction model for foam layer thickness is established. The estimated foam layer thickness at each moment during the detection period is corrected by combining the real-time conductivity change rate, the foam-slurry conductivity ratio, and the liquid flow velocity above the foam layer, thereby determining the foam layer thickness value at that moment. The foam-slurry conductivity ratio refers to the ratio of the foam layer conductivity to the slurry layer conductivity.

[0046] The model expression for the dynamic correction model of the foam layer thickness is as follows:

[0047]

[0048] In the formula, For time Corrected foam layer thickness value, Indicates time The estimated thickness of the foam layer, To detect time variables within a time period, and These are the maximum and minimum characteristic electrical conductivities of the slurry, determined based on its physical properties. The instantaneous rate of change of conductivity. Indicates the foam-slurry conductivity ratio. For time The electrical conductivity of the foam layer, referring to time. The mean conductivity of all elements identified as foam layers. For time The electrical conductivity of the slurry layer, referring to time. The mean electrical conductivity of the sub-cluster belonging to the slurry layer; For time Liquid flow rate above the foam layer For reference flow rate, , and is the preset weight, and satisfy ;

[0049] in, The calculation formula is as follows:

[0050]

[0051] In the formula, The instantaneous rate of change of conductivity. Indicates time The conductivity value, express The conductivity value at time t. is the time interval, where , Indicates the sampling frequency.

[0052] The present invention also provides a flotation machine foam layer parameter detection system, which is used to perform the above-described flotation machine foam layer parameter detection method, including:

[0053] The conductivity temperature calibration module is used to continuously collect conductivity data at different depths inside the flotation machine during the detection period, preprocess the conductivity data, and calibrate the conductivity data using temperature data to obtain a three-dimensional conductivity dataset; the preprocessing includes outlier removal and missing value imputation;

[0054] The 3D data reconstruction module is used to interpolate and expand the 3D conductivity dataset by combining the region search radius and the spatiotemporal distance decay law to obtain a continuous and complete conductivity dataset.

[0055] The interface recognition module is used to analyze the complete conductivity dataset using an improved wavelet transform modulus maxima detection algorithm, combined with fuzzy logic. Mean clustering algorithm identifies slurry-foam interface; based on complete conductivity dataset, conductivity boundary threshold is set, and sliding window verification method is used to remove pseudo interface points, and the estimated value of foam layer thickness is initially determined.

[0056] The dynamic thickness correction module is used to correct the estimated foam layer thickness by combining the rate of change of conductivity, the foam-slurry conductivity ratio, and the liquid flow velocity above the foam layer, and to determine the foam layer thickness value at each moment within the detection period; the foam-slurry conductivity ratio refers to the ratio of the conductivity of the foam layer to the conductivity of the slurry layer.

[0057] The present invention also provides a flotation machine, wherein the froth layer parameters of the flotation machine are detected using the above-described froth layer parameter detection method.

[0058] Compared with the prior art, the beneficial effects of the present invention are:

[0059] This invention proposes a novel method for measuring foam thickness based on conductivity difference. It innovatively uses the conductivity difference of multiphase media as the measurement basis, proposing a measurement method for identifying the foam interface using vertical profile conductivity difference, overcoming the limitations of existing methods that rely on indirect calculations based on images or liquid levels. Furthermore, this invention can effectively handle outliers and missing points in conductivity data, ensuring data integrity and reliability even in complex dynamic environments. In addition, by combining a dynamic correction model of conductivity change rate and foam-slurry conductivity ratio, the estimation of foam layer thickness becomes more accurate and can quickly respond to process changes, thereby optimizing the flotation process and improving mineral separation efficiency. This innovative method provides a new direction for the intelligent and automated development of flotation machines, contributing to the modernization of the mining industry. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0061] Figure 2 This is a schematic diagram of the overall system modules of the present invention;

[0062] Figure 3 This is a schematic diagram of the cross-section of the froth layer and pulp layer in a flotation machine.

[0063] Figures 4-5 The figures are 3D scatter plots and 3D bar plots showing the instantaneous rate of change of conductivity, the foam-slurry conductivity ratio and the correction value of foam layer thickness, respectively. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0065] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0066] Example:

[0067] Please see Figures 1-5, the present invention provides a technical solution:

[0068] A method for detecting parameters of the froth layer in a flotation machine, comprising the following steps:

[0069] S1: During the detection period, conductivity data at different depths inside the flotation machine are continuously collected. The conductivity data is preprocessed and calibrated using temperature data to obtain a three-dimensional conductivity dataset. The preprocessing includes outlier removal and missing value imputation.

[0070] In this embodiment, the probes of the multi-probe array conductivity sensor are arranged at equal intervals at different depth points within the flotation machine. Each probe at a depth point... The original conductivity data is collected synchronously at the sampling frequency. At the same time, the slurry temperature is monitored in real time through an embedded temperature sensor. After outlier removal and missing value filling of the original conductivity data, the collected conductivity data is calibrated using the slurry temperature to obtain a three-dimensional conductivity dataset.

[0071] A data point is considered an outlier if it meets any of the following conditions:

[0072] The conductivity measurements at the data points exceeded the preset reasonable range;

[0073] If the spatial rate of change of conductivity at a certain depth exceeds a preset threshold at a certain acquisition time point, the conductivity data point at that depth acquired at that acquisition time point is judged as an abnormal point; whereby the spatial rate of change of conductivity refers to the larger of the ratios of conductivity at that depth to the conductivity of adjacent upper and lower depth points at the same moment.

[0074] Missing values ​​are filled at the locations of data points identified as anomalies. The specific process is as follows: a search radius is established with the missing point as the center, and the average value of normal data points within the search radius is calculated as the filling value. The search radius is 3 times the probe spacing.

[0075] The conductivity data were calibrated using slurry temperature, and the calibration was based on the following formula:

[0076]

[0077] In the formula, The calibrated conductivity, The original conductivity, This is a temperature coefficient used to adjust the rate at which electrical conductivity changes with temperature. The value range is [0.01, 0.08]; Indicates the standard reference temperature. This indicates the measured temperature. To calibrate the target temperature;

[0078] The temperature at all depth points is uniformly calibrated using the above formula, ensuring that the obtained conductivity data are compared under the same reference temperature. This uniformity significantly enhances the comparability and consistency of the data, avoiding conductivity variations caused by temperature differences, thereby improving the accuracy and reliability of subsequent analyses. This calibration step allows for a more precise understanding of the slurry's conductivity behavior, providing a solid data foundation for subsequent process optimization.

[0079] In this formula, the dependent variable is the calibrated conductivity. This reflects the true measured value of the pulp conductivity under varying temperature conditions, providing a more accurate representation of the pulp's electrical conductivity. By correcting the original conductivity to eliminate the influence of temperature on the measurement results, it provides conductivity values ​​at a standard reference temperature. This technique helps improve the accuracy of pulp process control, providing a reliable data foundation for optimizing flotation machine operation and improving mineral separation efficiency.

[0080] Independent variables include , and standard reference temperature These factors affect the dependent variable The influence of temperature is mainly reflected in its direct effect on conductivity. Conductivity changes with temperature because it typically exhibits a non-linear relationship with temperature. This can be addressed by introducing a temperature coefficient. The formula links the change in temperature to the change in conductivity, ensuring that the temperature change is effectively reflected in the calibrated conductivity, thereby guaranteeing the accuracy of the measurement results. In this formula, when the target calibration temperature... Temperatures above standard reference temperature At that time, the calibrated conductivity It will increase, and vice versa; at the same time, the measured temperature An increase in temperature will also lead to an increase in the calibrated conductivity. This positive correlation indicates that rising temperature increases the conductivity of the slurry, thus affecting the calibrated conductivity value and ensuring that users can obtain a timely and accurate reflection of the slurry's condition.

[0081] Among them, the three-dimensional conductivity dataset The representation is as follows:

[0082]

[0083] In the formula, Indicates the depth inside the flotation machine In time The conductivity at that point Indicates the first flotation in the flotation machine The depth at which a depth point is located. For the index of the depth point, The number of depth points within the flotation machine. For indexes of time points, This represents the number of time points within the detection time period.

[0084] Using a three-dimensional dataset, the variation of electrical conductivity with depth and time can be comprehensively captured, providing rich information for process monitoring and optimization. This representation method not only reflects the spatial distribution characteristics of electrical conductivity but also reveals its evolution trend over time, which is of great significance for in-depth analysis of slurry conditions, control of the flotation process, and improvement of mineral separation efficiency.

[0085] The advantage of step S1 lies in its comprehensive preprocessing by continuously collecting conductivity data at different depths inside the flotation machine and combining it with real-time monitored temperature data. This effectively removes outliers and fills in missing values. This method ensures the accuracy and completeness of the conductivity data, allowing subsequent analysis and processing to be based on high-quality data and reducing misjudgments caused by data noise. Compared to existing technologies, this process can better capture complex fluid dynamics and improve data reliability.

[0086] In this invention, this step provides a solid foundation for the effectiveness of the overall scheme. By obtaining a high-quality three-dimensional conductivity dataset, subsequent steps such as interpolation 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.

[0087] S2: Combining the regional search radius and the spatiotemporal distance decay law, the three-dimensional conductivity dataset is interpolated and expanded to obtain a continuous and complete conductivity dataset;

[0088] In this embodiment, the Euclidean distance between each data point in the three-dimensional conductivity dataset and the target conductivity interpolation point is calculated. The five data points with the smallest Euclidean distance to the conductivity interpolation point are selected as their neighborhood. Then, interpolation expansion is performed in combination with the spatiotemporal distance attenuation law. The formula used is as follows:

[0089]

[0090] In the formula, depth within the flotation machine In time The conductivity at that point Let be the depth variable within the flotation machine, and This means that the depth increases downwards from 0; Indicates the depth inside the flotation machine. To detect time variables within a time period, and , This represents the start time of the detection period. The current time is the end time of the detection period. Indicates the conductivity interpolation point In the neighborhood of , the first Conductivity values ​​for each data point Interpolation point for conductivity The number of data points in the neighborhood. For indexes of data points within the neighborhood, Indicates the conductivity interpolation point In the neighborhood of , the first Data points and conductivity interpolation points European distance between them The standard deviation of the Gaussian function is used to control the degree of weight decay. It is a natural constant;

[0091] Dependent variable This reflects the conductivity value at a specific depth and time point within the flotation machine. By utilizing a weighted average of conductivity data points within a neighborhood, combined with the spatiotemporal distance attenuation law, it provides a smooth and accurate estimate of conductivity. This method not only improves the spatial resolution of conductivity measurement but also effectively reduces the impact of noise, enhances data reliability, and provides a precise reference for monitoring and optimizing the flotation process.

[0092] The independent variables include depth, time, the conductivity values ​​of data points within the neighborhood, and their Euclidean distance from the interpolation point. These independent variables directly affect the dependent variable. Changes in depth and time lead to changes in conductivity, while the influence of neighborhood conductivity values ​​is reflected through distance attenuation weighting; that is, data points closer to the interpolation point contribute more to conductivity. Combining these factors allows for a more accurate reflection of the conductivity characteristics of the slurry at specific depths and times. In the formula, as depth increases... Increase or time Over time, changes in conductivity will reflect the evolution of the slurry state. Simultaneously, variations in conductivity data within 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.

[0093] The conductivity interpolation points obtained by interpolation augmentation are combined with the three-dimensional conductivity dataset to obtain a complete conductivity dataset.

[0094] Sure The formula used is as follows:

[0095]

[0096] In the formula, 、 These represent the conductivity interpolation points. In the neighborhood of , the first The time and depth of data collection for each data point.

[0097] The advantage of step S2 lies in its ability to interpolate and expand the three-dimensional conductivity dataset by combining the regional search radius and the spatiotemporal distance attenuation law, thus achieving data continuity and completeness. This step effectively overcomes the information loss problem caused by sparse or uneven data point acquisition, ensuring that the conductivity data fully reflects the true state inside the flotation machine in both time and space. Compared to existing technologies, this method offers higher accuracy and reliability, thereby improving the effectiveness of subsequent analysis.

[0098] In this invention, this step provides a crucial data foundation for the overall scheme, enabling subsequent interface identification and foam layer thickness estimation to be performed with more comprehensive and accurate data support. By ensuring the continuity of the conductivity dataset, step S2 provides the necessary conditions for subsequent analysis using wavelet transform and clustering algorithms, thereby significantly improving the accuracy and real-time performance of foam layer parameter detection and facilitating the optimization of flotation process control and adjustment.

[0099] S3: Analyze the complete conductivity dataset using an improved wavelet transform modulus maxima detection algorithm, combined with fuzzy logic. Mean clustering algorithm identifies slurry-foam interface; based on complete conductivity dataset, conductivity boundary threshold is set, and sliding window verification method is used to remove pseudo interface points, and the estimated value of foam layer thickness is initially determined.

[0100] In this embodiment, the specific execution process of S3 is as follows: Multi-scale wavelet decomposition is performed on the complete conductivity dataset; a 5-level decomposition is implemented using the improved db4 wavelet basis function to extract the modulus maxima features at each scale; based on the modulus maxima features, combined with the preset foam-slurry conductivity transition interval, fuzzy... The mean clustering algorithm divides the 3D data points into three categories: slurry layer, transition layer, and foam layer;

[0101] In the execution of S3, multi-scale wavelet decomposition is first performed on the complete conductivity dataset. A five-level decomposition is implemented using the improved db4 wavelet basis function to extract details and features from the signal. By extracting the modulus maxima features at each scale, key points of conductivity variation can be captured. The modulus maxima features are set based on the local maxima of the signal, selecting modulus maxima at specific scales to reflect significant changes in conductivity. Simultaneously, the setting of the foam-slurry conductivity transition interval depends on the statistical analysis of the experimental data, typically determined based on the actual conductivity distribution and physical model. Subsequently, based on the extracted modulus maxima features and the preset transition interval, a fuzzy C-means clustering algorithm is used to divide 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.

[0102] The clustering objective function is as follows:

[0103]

[0104] In the formula, Let be the objective function value, representing the sum of squared weighted distances from all data points to the cluster centers. The optimization objective is to minimize this sum. The number of cluster categories. This corresponds to three physical states: the slurry layer, the transition layer, and the foam layer. An index for cluster categories; The number of data points in the complete conductivity dataset. An index for data points in the complete conductivity dataset; Represents the membership function, used to describe the membership degree of the first member. The data point belongs to the th data point The probability of a class; The fuzziness index is a hyperparameter used to control the degree of fuzziness in clustering. The larger the size, the more ambiguous the classification becomes; For the first Conductivity values ​​for each data point; For the first The electrical conductivity value of the cluster centers of a cluster;

[0105] The clustering objective function aims to effectively cluster data by measuring the similarity between data points and cluster centers using a weighted sum of squared distances. The optimization objective of this function is to minimize... This ensures that data points of the same category are as close as possible to their corresponding cluster centers, while data points of different categories are as far apart as possible. Here, This represents the number of clusters, specifically corresponding to physical states such as the slurry layer, transition layer, and foam layer. Membership function. The fuzzy index reflects the probability that a data point belongs to a specific cluster. This controls the degree of fuzziness in clustering, allowing data points to be assigned to multiple categories to varying degrees. This approach better captures the complex characteristics of different physical states of the slurry, improving classification effectiveness and analytical accuracy.

[0106] Dynamically set conductivity threshold based on clustering results :

[0107]

[0108] In the formula, and These are the electrical conductivity values ​​of the cluster centers in the slurry layer and the foam layer, respectively.

[0109] This is how the conductivity threshold is set. The rationale is that by calculating the average conductivity of the two cluster centers, the conductivity transition characteristics between the slurry and foam layers can be effectively captured, thus providing a reasonable basis for the boundary threshold. This method ensures that the boundary threshold is located in the middle of the slurry and foam layers, more accurately reflecting the conductivity difference between them, thereby improving the accuracy and reliability of the classification. This dynamically set strategy can adapt to conductivity changes under different conditions, achieving more precise hierarchical division and analysis.

[0110] A sliding window with a width of 10 cm was used to verify the continuity of the interface along the depth direction, and results deviating from the mainstream classification within the window were removed. The pseudo-interface point is then used to output the estimated foam layer thickness at each moment within the detection time period, forming a continuous foam layer thickness-time series.

[0111] 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 foam layers 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 foam layer thickness at this moment.

[0112] The specific process for eliminating pseudo-interface points is as follows: calculate the mean and standard deviation of conductivity of all data points within the window, and set the threshold according to the following formula. :

[0113]

[0114] In the formula, This represents the average conductivity of all data points within the window. It is the standard deviation of conductivity of all data points within the window;

[0115] Remove all data points that meet the following conditions:

[0116]

[0117] In the formula, Represented as the first in the window Conductivity values ​​for each data point For the index of data points within the window, This represents the average conductivity of all data points within the window.

[0118] The advantage of step S3 lies in its ability to accurately identify the interface between the slurry layer and the foam layer through an improved wavelet transform modulus maxima detection algorithm and a fuzzy C-means clustering algorithm. This method effectively extracts the modulus maxima features of the conductivity data, making the analysis of complex fluid states more accurate. Compared with existing technologies, this step not only improves the accuracy of interface identification but also enhances the ability to handle the mixed state of the foam layer and the slurry layer, thereby reducing the risk of misjudgment and missed detection.

[0119] In this invention, this step provides crucial interface identification support for the overall scheme, which is of great significance for subsequent foam layer thickness estimation and its dynamic correction. By accurately identifying the interface, a more reliable time series of foam layer thickness can be established, improving the effectiveness and real-time performance of the entire detection process, thereby optimizing the operation and management of the flotation machine and ensuring the stability and efficiency of the flotation process.

[0120] S4: By combining the rate of change of conductivity, the foam-slurry conductivity ratio, and the liquid flow velocity above the foam layer, the estimated foam layer thickness is corrected to determine the foam layer thickness value at each moment within the detection period; the foam-slurry conductivity ratio refers to the ratio of the foam layer conductivity to the slurry layer conductivity.

[0121] In this embodiment, a dynamic correction model for foam layer thickness is established. The estimated value of foam layer thickness at each moment during the detection period is corrected by combining the real-time conductivity change rate, the foam-slurry conductivity ratio, and the liquid flow velocity above the foam layer, and the foam layer thickness value at that moment is determined. The foam-slurry conductivity ratio refers to the ratio of foam layer conductivity to slurry layer conductivity.

[0122] The model expression for the dynamic correction model of the foam layer thickness is as follows:

[0123]

[0124] In the formula, For time Corrected foam layer thickness value, Indicates time The estimated thickness of the foam layer, To detect time variables within a time period, and These are the maximum and minimum characteristic electrical conductivities of the slurry, determined based on its physical properties. The instantaneous rate of change of conductivity. Indicates the foam-slurry conductivity ratio. For time The electrical conductivity of the foam layer, referring to time. The mean conductivity of all elements identified as foam layers. For time The electrical conductivity of the slurry layer, referring to time. The mean electrical conductivity of the sub-cluster belonging to the slurry layer; For time Liquid flow rate above the foam layer For reference flow rate, , and is the preset weight, , , Set weights The reason is based on the degree of influence of each individual variable on the thickness of the foam layer. The rate of change of conductivity... 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 changes will rapidly affect the thickness of the foam layer, therefore it is given the highest weight. Rapid changes in electrical conductivity can indicate the formation or rupture of the foam layer, which has a direct and significant impact on foam thickness. Secondly, the foam-slurry conductivity ratio... It also has a significant impact on the characteristics of the foam layer, reflecting the relative properties between the foam and the slurry, affecting the stability of the foam and the adhesion ability of the bubbles. Therefore, a higher weight is assigned. However, compared to the rate of change in conductivity, its effect is usually relatively slow and indirect, so its weight is set slightly lower. While the liquid flow velocity above the foam layer affects foam stability, its effect is generally slow and dependent on specific hydrodynamic conditions, therefore it is given the lowest weight. The effect of flow rate may occur under specific conditions, but overall, its direct impact on changes in foam layer thickness is relatively small. Therefore, this weighting reasonably reflects the relative importance of different factors in correcting foam layer thickness.

[0125] The expression for the dynamic correction model for foam layer thickness aims to dynamically correct the foam layer thickness based on multiple influencing factors. First, This represents the estimated thickness of the foam layer at time t, and is the basis of the model. The instantaneous rate of change of conductivity... This reflects the rapid impact of electrical conductivity on the properties of the foam layer; the faster the change in conductivity, the more pronounced the change in foam layer thickness. Specifically, when... An increase in conductivity indicates a faster rate of change, reflecting a rapid change in the number or size of bubbles within the foam layer. This signifies a more stable and fuller bubble structure within the foam layer, resulting in a greater increase in foam layer thickness. Conversely, a rapid decrease in conductivity may indicate bubble bursting or reduced fluidity in the foam layer, potentially leading to a reduction in foam layer thickness. Secondly, the logarithmic form of the foam-slurry conductivity ratio effectively captures the relative changes between the foam and slurry layers, showing that an increase in the foam layer conductivity relative to the slurry layer conductivity corresponds to a corresponding increase in foam layer thickness. Finally, the flow rate ratio reflects the influence of flow conditions on foam layer thickness; an increase in flow rate may lead to an increase in foam layer thickness, thus a positive correlation setting is reasonable. This model integrates the effects of conductivity, relative conductivity ratio, and flow rate, dynamically adapting to changes in foam layer thickness under different conditions and providing a more accurate thickness estimate.

[0126] Dependent variable Reflected in The foam layer thickness is adjusted in real time. It uses a dynamic correction model that combines real-time changes in conductivity, the foam-to-slurry conductivity ratio, and flow rate to provide a more accurate estimate of the foam layer thickness. This correction helps monitor changes in the foam layer in real time, adapting to fluctuations in foam characteristics during flotation, thereby improving flotation efficiency and product quality. Precise foam layer thickness monitoring allows for optimized flotation process control and enhanced resource utilization.

[0127] The independent variables include the initial estimate of the foam layer thickness. Instantaneous rate of change of conductivity The foam-slurry conductivity ratio and the liquid flow rate above the foam layer Relative to reference flow rate These independent variables directly affect the dependent variable. Specifically, the rate of change of conductivity reflects the dynamic changes of the foam layer, the conductivity ratio affects the relative characteristics of the foam and the slurry, and the flow rate affects the stability of the foam layer. Therefore, these factors interact to determine the actual thickness of the foam layer.

[0128] The relationship between the dependent and independent variables in the model is complex. The rate of change of conductivity... An increase usually indicates a rapid change in the state of the foam layer, which may lead to an increase in the thickness of the foam layer, and therefore... There is a positive correlation. An increase in the foam-slurry conductivity ratio may also indicate an improvement in foam layer quality, thus positively affecting foam layer thickness. The entire model comprehensively reflects these complex positive and negative correlations through the weighted combination of its individual variables.

[0129] in, The calculation formula is as follows:

[0130]

[0131] In the formula, The instantaneous rate of change of conductivity. express conductivity value express The conductivity value at time t. is the time interval, where , Indicates the sampling frequency.

[0132] Table 1: Statistics of Foam Layer Thickness Correction Values

[0133]

[0134] Please see Figure 4-5 It should be noted that the estimated foam layer thickness values ​​in the table correspond to... The liquid flow velocity above the foam layer corresponds to The foam layer thickness correction value corresponds to .

[0135] This data analysis is based on a dynamic correction model for foam layer thickness, involving parameters including the initial foam layer thickness, rate of change of conductivity, foam-slurry conductivity ratio, liquid flow velocity above the foam layer, and the corrected foam layer thickness. The data demonstrate the response of foam layer thickness under different conditions.

[0136] The data shows that the initial foam layer thickness varied from 5 to 20 mm. Samples with larger initial thicknesses generally had larger final corrected thicknesses, indicating that the initial thickness had a significant impact on the final thickness, while samples with smaller initial thicknesses had smaller final thicknesses.

[0137] The instantaneous rate of change of conductivity ranges from 0.01 to 0.09, revealing the dynamic characteristics of the foam layer. In particular, when the rate of change of conductivity is high (e.g., 0.08 and 0.09), the final foam layer thickness is usually significantly increased, indicating that rapidly changing conductivity can effectively affect the stability and thickness of the foam layer.

[0138] The foam-slurry conductivity ratio varies between 1.0 and 1.5. A higher conductivity ratio is usually accompanied by a higher final foam layer thickness, indicating that a good conductivity ratio contributes to the stability and increased thickness of the foam layer.

[0139] The liquid flow velocity above the foam layer varied from 0.6 to 1.3. The data showed that the relationship between flow velocity and the final foam layer thickness was not as obvious as that between electrical conductivity and conductivity ratio. For example, at lower flow velocities, the final thickness was still relatively low despite a higher initial thickness, indicating that the role of flow velocity in correcting foam layer thickness is more complex.

[0140] In summary, the dynamic correction of foam layer thickness is influenced by multiple factors. The initial thickness and the rate of change of conductivity have a relatively large impact on the final foam layer thickness, while the foam-slurry conductivity ratio and flow rate have relatively smaller effects. Future research could further explore the interactions between these variables to optimize foam layer control strategies.

[0141] The advantage of step S4 lies in its dynamic correction of the estimated foam layer thickness by comprehensively considering the rate of change in conductivity, the foam-slurry conductivity ratio, and the liquid flow velocity above the foam layer. This process can reflect changes during flotation 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, thus making the foam layer thickness detection results more practical and scientific.

[0142] In this invention, this step provides real-time assessment of the froth layer thickness for the overall process, making the flotation machine 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 rate, resulting in significant economic and environmental benefits.

[0143] Please see Figure 2-3 A flotation machine foam layer parameter detection system, comprising:

[0144] The conductivity temperature calibration module is used to continuously collect conductivity data at different depths inside the flotation machine during the detection period, preprocess the conductivity data, and calibrate the conductivity data using temperature data to obtain a three-dimensional conductivity dataset; the preprocessing includes outlier removal and missing value imputation;

[0145] The 3D data reconstruction module is used to interpolate and expand the 3D conductivity dataset by combining the region search radius and the spatiotemporal distance decay law to obtain a continuous and complete conductivity dataset.

[0146] The interface recognition module is used to analyze the complete conductivity dataset using an improved wavelet transform modulus maxima detection algorithm, combined with fuzzy logic. Mean clustering algorithm identifies slurry-foam interface; based on complete conductivity dataset, conductivity boundary threshold is set, and sliding window verification method is used to remove pseudo interface points, and the estimated value of foam layer thickness is initially determined.

[0147] The dynamic thickness correction module is used to correct the estimated foam layer thickness by combining the rate of change of conductivity, the foam-slurry conductivity ratio, and the liquid flow velocity above the foam layer, and to determine the foam layer thickness value at each moment within the detection period; the foam-slurry conductivity ratio refers to the ratio of the conductivity of the foam layer to the conductivity of the slurry layer.

[0148] The present invention also provides a flotation machine, wherein the froth layer parameters of the flotation machine are detected using the above-described froth layer parameter detection method.

[0149] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0150] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0151] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0152] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for detecting parameters of the froth layer in a flotation machine, characterized in that, The specific steps include: S1: During the detection period, conductivity data at different depths inside the flotation machine are continuously collected. The conductivity data is preprocessed and calibrated using temperature data to obtain a three-dimensional conductivity dataset. The preprocessing includes outlier removal and missing value imputation. S2: Combining the regional search radius and the spatiotemporal distance decay law, the three-dimensional conductivity dataset is interpolated and expanded to obtain a continuous and complete conductivity dataset; S3: Analyze the complete conductivity dataset using an improved wavelet transform modulus maxima detection algorithm, combined with fuzzy logic. Mean clustering algorithm identifies slurry-foam interface; based on complete conductivity dataset, conductivity boundary threshold is set, and sliding window verification method is used to remove pseudo interface points, and the estimated value of foam layer thickness is initially determined. S4: By combining the rate of change of conductivity, the foam-slurry conductivity ratio, and the liquid flow velocity above the foam layer, the estimated foam layer thickness is corrected to determine the foam layer thickness value at each moment within the detection period; the foam-slurry conductivity ratio refers to the ratio of the foam layer conductivity to the slurry layer conductivity. The specific execution process of S3 is as follows: Multi-scale wavelet decomposition is performed on the complete conductivity dataset; a 5-level decomposition is implemented using the improved db4 wavelet basis function to extract modulus maxima features at each scale; based on the modulus maxima features, combined with the preset foam-slurry conductivity transition interval, fuzzy... The mean clustering algorithm divides the 3D data points into three categories: slurry layer, transition layer, and foam layer; The clustering objective function is as follows: In the formula, Let be the objective function value, representing the sum of squared weighted distances from all data points to the cluster centers. The optimization objective is to minimize this sum. The number of cluster categories. This corresponds to three physical states: the slurry layer, the transition layer, and the foam layer. An index for cluster categories; The number of data points in the complete conductivity dataset. An index for data points in the complete conductivity dataset; Represents the membership function, used to describe the membership degree of the first member. The data point belongs to the th data point The probability of a class; The fuzziness index is a hyperparameter used to control the degree of fuzziness in clustering. The larger the size, the more ambiguous the classification becomes; For the first Conductivity values ​​for each data point; For the first The electrical conductivity value of the cluster centers of a cluster; Dynamically set conductivity threshold based on clustering results : In the formula, and These are the electrical conductivity values ​​of the cluster centers in the slurry 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 results deviating from the mainstream classification within the window were removed. The pseudo-interface point is then used to output the estimated foam layer thickness at each moment within the detection time period, forming a continuous foam layer thickness-time series. The specific logic for calculating the estimated thickness of the foam layer at a certain moment is as follows: obtain the depth range of the points identified as foam layers 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 thickness of the foam layer at this moment. The specific process for eliminating pseudo-interface points is as follows: calculate the mean and standard deviation of conductivity of all data points within the window, and set the threshold according to the following formula. : In the formula, This represents the average conductivity of all data points within the window. It is the standard deviation of conductivity of all data points within the window; Remove all data points that meet the following conditions: In the formula, Represented as the first in the window Conductivity values ​​for each data point For the index of data points within the window, This represents the average conductivity of all data points within the window. The specific execution process of S4 is as follows: establish a dynamic correction model for foam layer thickness, combine the real-time conductivity change rate, foam-slurry conductivity ratio and liquid flow velocity above the foam layer to correct the estimated value of foam layer thickness at each moment in the detection period, and determine the foam layer thickness value at that moment. The foam-slurry conductivity ratio refers to the ratio of foam layer conductivity to slurry layer conductivity. The model expression for the dynamic correction model of the foam layer thickness is as follows: In the formula, For time Corrected foam layer thickness value, Indicates time The estimated thickness of the foam layer, To detect time variables within a time period, and These are the maximum and minimum characteristic electrical conductivities of the slurry, determined based on its physical properties. The instantaneous rate of change of conductivity. Indicates the foam-slurry conductivity ratio. For time The electrical conductivity of the foam layer, referring to time. The mean conductivity of all elements identified as foam layers. For time The electrical conductivity of the slurry layer, referring to time. The mean electrical conductivity of the sub-cluster belonging to the slurry layer; For time Liquid flow rate above the foam layer For reference flow rate, , and To preset weights, and satisfy ; in, The calculation formula is as follows: In the formula, The instantaneous rate of change of conductivity. express The conductivity value, express The conductivity value at time t. is the time interval, where , Indicates the sampling frequency.

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

3. The method for detecting parameters of the flotation machine foam layer according to claim 2, characterized in that: The conductivity data were calibrated using slurry temperature, and the calibration was based on the following formula: In the formula, The calibrated conductivity The original conductivity, This is a temperature coefficient used to adjust the rate at which conductivity changes with temperature, and ; Indicates the standard reference temperature. This indicates the measured temperature. To calibrate the target temperature; Among them, the three-dimensional conductivity dataset The representation is as follows: In the formula, Indicates the depth inside the flotation machine In time The conductivity at that point Indicates the first flotation in the flotation machine The depth at which a depth point is located. For the index of the depth point, The number of depth points within the flotation machine. For indexes of time points, This represents the number of time points within the detection time period.

4. The method for detecting parameters of the flotation machine foam layer according to claim 1, characterized in that: The Euclidean distance between each data point in the three-dimensional conductivity dataset and the target conductivity interpolation point is calculated. The five data points with the smallest Euclidean distance to the conductivity interpolation point are selected as their neighborhood. Then, interpolation expansion is performed based on the spatiotemporal distance decay law. The formula used is as follows: In the formula, depth within the flotation machine In time The conductivity at that point Let be the depth variable within the flotation machine, and , Indicates the depth inside the flotation machine. To detect time variables within a time period, and , This represents the start time of the detection period. The current time is the end time of the detection period. Indicates the conductivity interpolation point In the neighborhood of , the first Conductivity values ​​for each data point Interpolation point for conductivity The number of data points in the neighborhood. For indexes of data points within the neighborhood, Indicates the conductivity interpolation point In the neighborhood of , the first Data points and conductivity interpolation points European distance between The standard deviation of the Gaussian function is used to control the degree of weight decay. It is a natural constant; The conductivity interpolation points obtained by interpolation augmentation are combined with the three-dimensional conductivity dataset to obtain a complete conductivity dataset. Sure The formula used is as follows: In the formula, 、 These represent the conductivity interpolation points. In the neighborhood of , the first The time and depth of data collection for each data point.

5. A flotation machine foam layer parameter detection system, characterized in that: The flotation machine foam layer parameter detection system is used to execute the flotation machine foam layer parameter detection method according to any one of claims 1-4, comprising: The conductivity temperature calibration module is used to continuously collect conductivity data at different depths inside the flotation machine during the detection period, preprocess the conductivity data, and calibrate the conductivity data using temperature data to obtain a three-dimensional conductivity dataset; the preprocessing includes outlier removal and missing value imputation; The 3D data reconstruction module is used to interpolate and expand the 3D conductivity dataset by combining the region search radius and the spatiotemporal distance decay law to obtain a continuous and complete conductivity dataset. The interface recognition module is used to analyze the complete conductivity dataset using an improved wavelet transform modulus maxima detection algorithm, combined with fuzzy logic. Mean clustering algorithm identifies slurry-foam interface; based on complete conductivity dataset, conductivity boundary threshold is set, and sliding window verification method is used to remove pseudo interface points, and the estimated value of foam layer thickness is initially determined. The dynamic thickness correction module is used to correct the estimated foam layer thickness by combining the rate of change of conductivity, the foam-slurry conductivity ratio, and the liquid flow velocity above the foam layer, and to determine the foam layer thickness value at each moment within the detection period; the foam-slurry conductivity ratio refers to the ratio of the conductivity of the foam layer to the conductivity of the slurry layer.

6. A flotation machine, characterized in that: The parameters of the flotation machine's foam layer are detected using a flotation machine foam layer parameter detection method as described in any one of claims 1-4.

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