Mine water micro-sand separation system and method based on dynamic filtration algorithm

Abstract

The present invention discloses a system and method for separating micro-sand from mine water based on a dynamic filtering algorithm, which specifically relates to the field of mine water treatment and is used to solve the problem of efficient separation of micro-sand particles. The system collects turbulence intensity and flow velocity data in real time and identifies high turbulence areas in combination with fast Fourier transform technology. The Kalman filter algorithm dynamically updates the turbulence intensity prediction model, accurately locates the resuspension area, and reduces the impact of turbulence on particle sedimentation. The turbulence intensity-particle concentration coupling index and the critical flow velocity deviation factor are introduced to achieve real-time monitoring and adaptive adjustment of the flow field dynamics by the fuzzy control algorithm, and optimize the operation of the filter and the flow velocity regulating valve. Through CFD simulation and feedback control, the dynamic adjustment of the guide tube angle and insertion depth further optimizes the flow field structure and reduces the interference of turbulence on the sedimentation area. This significantly improves the micro-sand separation efficiency, enhances operational stability and adaptability, reduces energy consumption, and improves the overall mine water treatment performance.

Description

Technical Field

[0001] The present invention relates to the field of mine water treatment, and more specifically, to a mine water micro-sand separation system and method based on a dynamic filtration algorithm. Background Art

[0002] With the continuous increase in the depth and scale of coal mining, the technical difficulties faced by mine wastewater treatment are becoming increasingly prominent. Mine wastewater contains a large number of micro-sand particles. Due to their small particle size and light specific gravity, these particles are very easy to remain suspended under the disturbance of water flow and are difficult to effectively remove by traditional sedimentation or filtration methods. In the process of mine water treatment, the separation efficiency of micro-sand particles directly affects the overall effect of water quality treatment. Although in the initial sedimentation stage, the particles can gradually sink under the action of gravity, under the influence of local high turbulence intensity, these settled particles are easily drawn into the flow field again, causing the particles to re-suspension, thereby affecting the stability and efficiency of the entire sedimentation and separation process.

[0003] The turbulence disturbance effect is the main cause of the resuspension phenomenon. When the mine water flow rate is large, the turbulence intensity in the water body increases significantly, especially in the clarifier or cyclone separator. The local flow field turbulence will cause the particles in the sedimentation area to be affected by both the upwelling and the rotating flow, making it difficult to maintain a stable sedimentation state. The resuspended particles leave the sedimentation area with the disturbance of the fluid and re-enter the mainstream area of ​​the fluid, which weakens the overall separation efficiency and increases the burden of subsequent processing. The traditional clarifier design lacks effective control measures for particle sedimentation in a high turbulence environment, especially when the mine wastewater flow rate fluctuates greatly. It is difficult to quickly respond to flow field changes through physical structure adjustments, resulting in low sedimentation efficiency.

[0004] Most of the current micro-sand separation systems rely on static filtration and sedimentation methods, lacking dynamic adjustment and adaptive optimization capabilities. For complex working conditions with changing flow velocity and turbulence intensity, existing equipment cannot adjust the working state in real time, making it easy for micro-sand particles to re-suspend when the turbulence intensity exceeds the critical value. For this reason, there is an urgent need for a micro-sand separation system based on dynamic monitoring and intelligent control to achieve accurate monitoring and rapid response to turbulent disturbance effects, and ensure efficient sedimentation of particles in complex flow fields.

[0005] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a mine water micro-sand separation system and method based on a dynamic filtration algorithm. By real-time collection and analysis of turbulence intensity and flow velocity data, combined with Fourier transform technology, high turbulence areas are effectively identified, ensuring that the system has a high-precision response capability to turbulent disturbances in the flow field, thereby improving the separation efficiency of micro-sand particles. By dynamically updating the turbulence intensity prediction model through the Kalman filter algorithm, the system can accurately locate the resuspension area and reduce the interference of turbulence on the particle sedimentation process. The combination of the turbulence intensity-particle concentration coupling index and the critical flow velocity deviation factor realizes the real-time monitoring and analysis of key dynamic changes in the flow field by the fuzzy control algorithm, and optimizes the adaptive adjustment of the filter and the flow velocity regulating valve. The inclination angle and insertion depth of the guide tube are dynamically optimized through CFD simulation and feedback control mechanism, which further improves the stability of the flow field and reduces the influence of high turbulence areas on the sedimentation area. Through the synergistic effect of various technical means, the present invention effectively improves the sedimentation efficiency and separation effect of micro-sand particles, enhances the operating stability and adaptability of the system, reduces energy consumption and maintenance costs, and significantly improves the overall performance of mine water treatment to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] S1, through multi-point flow field sensors, real-time collection of flow velocity, turbulence intensity data and flow velocity gradient data in the clarifier;

[0009] S2, based on the real-time collected flow field data, multi-dimensional data fitting of turbulence intensity and velocity gradient is performed to build a turbulence disturbance prediction model; and the resuspension area is located according to the turbulence disturbance prediction model;

[0010] S3, based on the real-time data of turbulence intensity and flow rate deviation, calculate the turbulence intensity-particle concentration coupling index and critical flow rate deviation factor to comprehensively analyze and determine whether the filter opening rate and flow rate regulating valve need to be adjusted;

[0011] S4, using the turbulence intensity-particle concentration coupling index and the critical velocity deviation factor, combined with the CFD simulation results and turbulence energy distribution, adjust the guide tube angle and depth.

[0012] In a preferred embodiment, step S1 includes the following contents:

[0013] S1.1, a multi-point flow field sensor array is arranged at different locations in the clarifier, and each sensor is used to collect real-time flow velocity and turbulence intensity data;

[0014] S1.2, based on the velocity data at different locations, the velocity gradient is calculated using the spatial difference method; the velocity gradient of each area is obtained by first-order difference processing of the velocity data between adjacent sensors;

[0015] S1.3, using a fast Fourier transform algorithm, converting the time domain information collected by the multi-point flow field sensors into frequency domain information;

[0016] S1.4, extracting the spectrum amplitude and phase information of each frequency band from the converted frequency domain information;

[0017] S1.5, for signals in different frequency bands, the spectral energy density is calculated using the square of the amplitude;

[0018] S1.6, calibrate the high turbulence area through comprehensive analysis of spectral energy density, spatial positioning, time domain information and phase information. The specific process includes the following steps: First, for the frequency band where the energy density exceeds the critical value, use the triangulation positioning algorithm to determine the spatial position of the corresponding frequency band in the clarifier, and calculate the signal difference between adjacent sensors based on the spatial layout of the sensor array to locate the specific position of the high turbulence area; then, combine the time domain information and phase information, and determine the changing trend of turbulence intensity through fluctuation direction and phase difference analysis; use the spatial mapping method based on distance weighted interpolation to match the located high turbulence area signal with the actual flow field area of ​​the clarifier to generate a spatial distribution map of the high turbulence area.

[0019] In a preferred embodiment, step S2 includes the following contents:

[0020] S2.1, using standardization processing to quantify the velocity and turbulence intensity data of different frequency bands and spatial positions;

[0021] S2.2, based on the velocity gradient and turbulence intensity data extracted in step S1, a multidimensional linear regression algorithm is used to fit the data, and the multidimensional coupling relationship between turbulence intensity and velocity gradient is calculated; the fitting curve is optimized by the least squares method to form a correlation equation between the turbulence intensity change at each position in the flow field and the velocity gradient;

[0022] S2.3, using Kalman filter algorithm to process real-time data and build a turbulence disturbance prediction model;

[0023] S2.4, based on the prediction results output by the Kalman filter algorithm, the critical value of turbulence intensity is calculated using the fitting curve;

[0024] S2.5, track the changing trend of turbulence intensity in real time, and mark the turbulence intensity area exceeding the critical value of turbulence intensity as the resuspension area.

[0025] In a preferred embodiment, step S3 includes the following contents:

[0026] S3.1, based on the turbulence intensity critical value and spatial positioning result obtained in step S2, receiving the output data of the turbulence disturbance prediction model in real time, including turbulence intensity distribution, flow velocity gradient and calibrated high turbulence area;

[0027] S3.2, based on the current real-time collected turbulence intensity and particle concentration data, calculate the turbulence intensity-particle concentration coupling index;

[0028] Calculation process of turbulence intensity-particle concentration coupling index:

[0029] A1, real-time collection of turbulence intensity data and particle concentration data, recorded as the function of turbulence intensity and particle concentration ,in, Represents time;

[0030] A2, to evaluate the synchronization of particle concentration fluctuations with turbulence intensity, firstly, a cross-correlation analysis is performed on turbulence intensity and particle concentration, and their correlation at different time lags is calculated; the cross-correlation function is defined as: ;in, Indicates time lag, is the upper limit of the time integral;

[0031] A3, based on the turbulence intensity and particle concentration data collected by sensors at different locations, a spatial coupling relationship is constructed; the relationship between turbulence intensity and particle concentration in spatial distribution is described by the following fitting equation: ;in, represents the spatial coordinates, For the fitting coefficient, the least square method of sensor data is used to fit and solve;

[0032] A4, based on the fitting equation and cross-correlation analysis results, define the turbulence intensity-particle concentration coupling index for: ;in, is the spatial integration range of turbulence intensity.

[0033] In a preferred embodiment, step S3 further includes the following contents:

[0034] S3.3, calculating the critical flow rate deviation factor by analyzing the deviation between the current flow rate and the critical flow rate set by the system;

[0035] Processing process of critical velocity deviation factor:

[0036] B1, real-time acquisition of flow gradient data As a function of flow velocity over time and space, Indicates the position in space and time Flow rate;

[0037] B2, the relative difference method is used to calculate the deviation between the velocity gradient data collected in real time and the critical velocity; the relative difference method is defined as follows: ;in Indicates the relative flow velocity deviation at the current position and time. represents the critical flow velocity;

[0038] B3, evaluate the dynamic impact of velocity changes and calculate the temporal and spatial derivatives of the deviation, namely the temporal deviation derivative and the spatial deviation derivative ; respectively describe the changing trend of velocity deviation in time and space, the formula is as follows: ; ;

[0039] B4, combining velocity deviation and its temporal and spatial derivatives to construct the critical velocity deviation factor , defined as: ;in, is the spatial integration range of turbulence intensity, and Respectively Item and The weight coefficient of the item;

[0040] S3.4, using the real-time calculation results of the turbulence intensity-particle concentration coupling index and the critical flow velocity deviation factor as the input variables of the fuzzy control algorithm, analyzing the combination of the two variables through the fuzzy inference rule base to determine the filter opening rate and the adjustment timing of the flow rate regulating valve; when the combination of the turbulence intensity-particle concentration coupling index and the critical flow velocity deviation factor reaches the preset threshold, it is determined that the current turbulence intensity and flow velocity changes lead to particle resuspension or a decrease in capture efficiency, triggering the switching of the filtration mode;

[0041] S3.5, according to the reasoning results of the fuzzy control algorithm, automatically adjusts the filter aperture and the opening of the flow rate control valve; through the step adjustment mechanism, the filter aperture and valve opening are gradually adjusted to ensure that the particle capture efficiency can be optimized in real time.

[0042] In a preferred embodiment, step S4 includes the following contents:

[0043] S4.1, based on the turbulence intensity-particle concentration coupling index and critical velocity deviation factor obtained in step S3, the output data of the turbulence disturbance model is used as the initial basis for adjusting the angle and depth of the guide tube;

[0044] S4.2, using CFD tools, the fluid movement inside the clarifier is simulated based on RANS. The RANS equation is as follows: ;in, is the turbulent mean velocity, is the fluid density, is the dynamic viscosity, represents the average pressure, is the Reynolds stress term, and Respectively represent the fluid and The velocity component in the direction, and Respectively represent the fluid and spatial coordinates in direction;

[0045] S4.3, combined with CFD simulation results and turbulent energy distribution, the influence of high turbulent energy area on the settling area is simulated and analyzed to determine the inclination angle of the guide tube to suppress the transfer of turbulent energy to the settling area; the formula for adjusting the inclination angle is as follows: ;in, is the initial angle, is the turbulence energy, is the adjustment factor, is the rate of change of turbulent energy along the spatial coordinate;

[0046] S4.4, evaluate the distribution of turbulence intensity at different depths based on the simulation results, and dynamically adjust the insertion depth of the guide tube; the insertion depth adjustment formula is as follows: ;in, is the initial insertion depth, is the adjustment factor, It represents the rate of change of turbulent energy with depth.

[0047] The mine water micro-sand separation system based on dynamic filtration algorithm includes: flow field monitoring module, prediction model module, control algorithm module and diversion optimization module;

[0048] The flow field monitoring module collects the flow velocity, turbulence intensity data and flow velocity gradient data in the clarifier in real time through multi-point flow field sensors, converts them into frequency domain information using Fourier transform technology, extracts flow field fluctuation characteristics using fast Fourier transform algorithm, and identifies high turbulence areas; outputs turbulence intensity, flow velocity data, flow velocity gradient data and high turbulence areas to the prediction model module;

[0049] The prediction model module performs multi-dimensional data fitting of turbulence intensity and velocity gradient based on real-time collected flow field data to build a turbulence disturbance prediction model. The turbulence disturbance prediction model is dynamically updated through the Kalman filter algorithm, and combined with historical data and real-time feedback, the critical value of turbulence intensity is calculated to locate the resuspension area. The resuspension area position and the critical value of turbulence intensity are output to the control algorithm module.

[0050] The control algorithm module calculates the turbulence intensity-particle concentration coupling index and the critical velocity deviation factor based on the real-time data of turbulence intensity and velocity deviation. The real-time calculation results of the two indicators are used as the input of the fuzzy control algorithm to determine whether the filter opening rate and the flow rate regulating valve need to be adjusted. The turbulence intensity-particle concentration coupling index and the critical velocity deviation factor are sent to the diversion optimization module.

[0051] The diversion optimization module is based on the turbulence intensity-particle concentration coupling index and the critical velocity deviation factor, combined with CFD simulation results and turbulence energy distribution, to adjust the angle and depth of the diversion tube, optimize the flow field structure, and suppress disturbances in the sedimentation zone.

[0052] The technical effects and advantages of the mine water micro-sand separation system and method based on dynamic filtration algorithm of the present invention are as follows:

[0053] 1. Real-time collection of flow velocity and turbulence intensity data through multi-point sensors ensures comprehensive coverage of the flow field and timely data response. The time domain information is converted into frequency domain information through fast Fourier transform to ensure detailed analysis of the spectral characteristics of the turbulence signal and extract key parameters such as amplitude, phase and energy density. Combined with the comparison of spectral energy density with the preset threshold, high turbulence areas can be automatically identified to provide accurate data support for subsequent dynamic adjustment and model optimization. Through calibration and feature extraction of high turbulence areas, the system's response speed and judgment accuracy to turbulent disturbances are significantly improved, effectively improving the separation efficiency of micro-sand particles in mine water.

[0054] 2. The turbulence intensity prediction model is dynamically updated through the Kalman filter algorithm, the changes in turbulence intensity in the flow field are tracked in real time, and the critical turbulence area that may cause particle resuspension is effectively predicted. The multidimensional linear regression algorithm is used to accurately calculate the coupling relationship between the velocity gradient and the turbulence intensity to ensure that the prediction model can reflect the actual distribution of turbulent disturbances in the flow field. The critical value calculation mechanism of turbulence intensity can effectively identify high-risk turbulence areas, and the model parameters are continuously optimized through the least squares method to ensure the long-term stability and high accuracy of the model. Through this process, the system can accurately locate the resuspension area, thereby effectively improving the micro-sand separation efficiency in mine water treatment, reducing the negative impact of turbulence on particle sedimentation, and significantly enhancing the overall separation performance and operational stability of the system.

[0055] 3. By introducing the turbulence intensity-particle concentration coupling index and the critical flow velocity deviation factor into the fuzzy control algorithm, the key dynamic changes in the flow field can be monitored and analyzed in real time. The turbulence intensity-particle concentration coupling index can quantify the impact of turbulence on particle sedimentation and resuspension, and the critical flow velocity deviation factor reflects the degree of deviation between the current flow velocity and the critical flow velocity. The combination of the two can accurately determine whether the turbulent disturbance and flow velocity fluctuation in the flow field will affect the particle capture process. When the index combination reaches the threshold, the filter opening rate and flow rate regulating valve are automatically adjusted to ensure the optimization of particle capture effect in complex flow fields. The introduction of the fuzzy control algorithm ensures the adaptability of the system under different working conditions, reduces human intervention, improves the stability and efficiency of system operation, reduces energy consumption and maintenance costs, and improves the accuracy and efficiency of micro-sand particle separation.

[0056] 4. By dynamically adjusting the inclination angle and insertion depth of the guide tube, the distribution of turbulent energy can be optimized according to the real-time feedback of turbulence intensity, reducing the impact of high turbulent energy areas on the sedimentation area, and effectively preventing the occurrence of particle resuspension. CFD simulation and feedback control mechanism ensure high adaptability under complex flow fields, and can maintain the best working state of the guide tube under different working conditions, thereby improving the sedimentation efficiency of particles, improving separation performance, reducing energy consumption and improving the overall operating efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic flow chart of a method for separating fine sand from mine water based on a dynamic filtration algorithm of the present invention;

[0058] Figure 2 It is a schematic diagram of the process of the mine water micro-sand separation system based on the dynamic filtration algorithm of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Embodiment 1, Figure 1 The present invention provides a method for separating fine sand from mine water based on a dynamic filtration algorithm, comprising:

[0061] S1, through multi-point flow field sensors, real-time collection of flow velocity and turbulence intensity data and flow velocity gradient data in the clarifier; Fourier transform technology is used to convert sensor data into frequency domain information; the fast Fourier transform algorithm is used to analyze the frequency distribution of turbulence signals, extract flow field fluctuation characteristics, and identify high turbulence areas.

[0062] S2, based on the real-time collected flow field data, performs multi-dimensional data fitting of turbulence intensity and flow velocity gradient to build a turbulence disturbance prediction model; the turbulence disturbance prediction model is dynamically updated through the Kalman filter algorithm, combined with historical data and real-time feedback, to calculate the critical value of turbulence intensity and locate the resuspension area.

[0063] S3, based on the real-time data of turbulence intensity and flow velocity deviation, calculates the turbulence intensity-particle concentration coupling index and the critical flow velocity deviation factor; uses the real-time calculation results of the two indicators as the input of the fuzzy control algorithm to determine whether the filter opening rate and flow velocity regulating valve need to be adjusted.

[0064] S4, based on the turbulence intensity-particle concentration coupling index and the critical velocity deviation factor, combined with the CFD simulation results and turbulence energy distribution, adjust the angle and depth of the guide tube, optimize the flow field structure, and suppress the disturbance in the sedimentation zone.

[0065] In the field of mine water treatment, efficient separation of suspended particles is one of the important links to ensure water quality. With the intensification of water resource pollution, the removal of fine suspended particles in water bodies has become a key technical difficulty in research and application. These suspended particles usually have the characteristics of small particle size and light specific gravity, and are easily disturbed by water flow and remain suspended. Traditional static filtration and sedimentation methods are difficult to effectively remove such fine particles, especially when dealing with large flow and high turbulence intensity water bodies. The separation effect is unstable and the efficiency is low.

[0066] In a highly turbulent environment, fine particles in the water body are affected by flow velocity fluctuations and turbulence intensity, and it is often difficult to maintain a stable sedimentation path. Turbulent disturbances not only cause the particle sedimentation path to deviate, but also easily cause the resuspension of settled particles, affecting the overall separation efficiency. This problem is particularly evident in water treatment processes with large flow fluctuations and strong turbulent areas. The problem faced by water treatment equipment is how to monitor and effectively control the sedimentation path of particles in real time under complex flow field conditions to avoid the occurrence of resuspension.

[0067] To this end, modern water treatment technology has gradually introduced intelligent control and regulation methods, and dynamically adjusted variables such as water flow velocity and turbulence intensity through real-time data collection and analysis. The technical solution with particle separation as the core uses advanced data analysis methods to monitor the turbulent disturbance in the flow field in real time and accurately identify the key factors affecting particle sedimentation. Combined with computational fluid dynamics (CFD) technology, it can simulate and optimize the energy distribution of the flow field in detail, ensure the stability of the flow state during the separation process, and reduce the interference of turbulence on particle sedimentation.

[0068] In this context, dynamically adjusting the operating parameters of particle capture equipment has become the key to improving water treatment efficiency. Through multi-dimensional fitting and model construction of flow field data, the critical value of turbulence intensity can be predicted, and then it can be determined whether the equipment parameters need to be adjusted. For working conditions with large flow velocity fluctuations, the fuzzy control algorithm can be used to optimize the filtration parameters in real time to ensure that the particles maintain efficient sedimentation and separation effects under different conditions. In addition, the application of turbulence suppression devices further reduces the energy disturbance in the flow field and ensures the stable sedimentation of particles in a high turbulence environment.

[0069] In summary, based on advanced real-time monitoring, data analysis and flow field optimization technology, the separation of suspended particles in water treatment is gradually developing in the direction of automation and intelligence. Through the refined control of key variables, the resuspension phenomenon induced by turbulence can be effectively solved, and the particle separation efficiency under complex flow field conditions can be ensured, providing theoretical and technical support for efficient water treatment.

[0070] In the process of mine water treatment, real-time monitoring of turbulence intensity and flow rate is essential to ensure the effective separation of micro-sand particles. Especially in the clarifier, changes in local turbulence directly affect the sedimentation efficiency of particles, resulting in particle resuspension, which in turn reduces the separation effect. In order to address this problem, through the precise arrangement of multi-point flow field sensor arrays and data analysis technology, key dynamic information in the flow field, especially changes in turbulence intensity and flow rate, can be collected and processed in real time. The fast Fourier transform (FFT) technology is used to convert the time domain information in the flow field into the frequency domain, and the high turbulence area is identified through energy density calculation and analysis, providing data support for the optimization and adjustment of the system under high dynamic conditions. The implementation of this step ensures the accurate monitoring and feedback of turbulence changes in complex flow fields, thereby providing technical support for improving the efficiency of mine water treatment systems.

[0071] The working process of clarifier, filter and guide tube in mine water treatment system is as follows:

[0072] Initial treatment of the clarifier: Mine water enters the clarifier, and the water flow is initially guided by the guide tube inside. The guide tube controls the direction and speed distribution of the water flow by adjusting its inclination angle and insertion depth, ensuring that the water flow enters the sedimentation area of ​​the clarifier along the predetermined path. The main function of the guide tube is to optimize the flow field structure, ensure that the water flow in the clarifier does not produce violent disturbances, and ensure the concentration of micro-sand particles in the sedimentation area.

[0073] Fine treatment of the filter: After the initial treatment in the clarifier, the water flows into the filter. The filter contains a filter with adjustable opening rate and a flow rate regulating valve for further separation of residual particles. The opening rate is dynamically adjusted according to the real-time turbulence intensity and particle concentration data to ensure efficient interception of micro-sand particles. The flow rate regulating valve adjusts the speed of the water flow according to the system feedback, controls the rate at which water passes through the filter, and prevents the water flow rate from being too high and causing particle resuspension.

[0074] Dynamic adjustment of the guide tube: The angle and insertion depth of the guide tube are dynamically adjusted through CFD simulation and real-time feedback data to ensure that the impact of high turbulence areas is suppressed. The synergy between the guide tube and the clarifier ensures that the water flow is properly controlled before entering the filter, avoiding turbulent disturbances entering the filtration stage, thereby stabilizing the water flow and optimizing the working conditions of the filter.

[0075] The connection of the overall workflow: the clarifier is responsible for the initial sedimentation of mine water and the pre-separation of micro-sand particles, the filter performs fine filtration through dynamically adjusted opening rate and flow rate regulating valve, and the guide tube adjusts the water flow distribution and speed to prevent turbulent disturbance from affecting particle sedimentation and filtration process. All parts work closely together to achieve real-time adjustment and optimization through feedback mechanism to ensure efficient and stable operation of the entire system.

[0076] Step S1 includes the following contents:

[0077] S1.1, precisely arrange multi-point flow field sensor arrays at different locations in the clarifier to ensure coverage of the entire flow field area in the clarifier. Each sensor is used to collect real-time flow velocity and turbulence intensity data, and ensure the timeliness and spatial distribution uniformity of data collection. High-precision sensors are used to ensure sensitive capture of tiny turbulence changes.

[0078] S1.2, based on the velocity data at different locations, the velocity gradient is calculated using the spatial difference method. The velocity gradient of each area is obtained by first-order difference processing of the velocity data between adjacent sensors. The calculation results of the spatial difference method generate a real-time velocity gradient distribution map, showing the velocity changes in different spatial areas in the clarifier, providing necessary data support for subsequent turbulence prediction and dynamic adjustment.

[0079] S1.3, using the fast Fourier transform (FFT) algorithm, the time domain information collected by the multi-point flow field sensors is converted into frequency domain information. The data of each sensor is pre-processed and input into the FFT module to complete the conversion from time domain to frequency domain, ensuring that the turbulence signal in the entire flow field area can be presented in the frequency domain.

[0080] S1.4, extract the spectrum amplitude and phase information of each frequency band from the converted frequency domain information. The spectrum amplitude is used to characterize the energy intensity of the turbulence signal in different frequency bands, while the phase information is used to confirm the fluctuation direction and characteristics of the turbulence signal. By jointly analyzing the signal amplitude and phase information, the change of turbulence intensity in the flow field can be judged.

[0081] The signal amplitude represents the energy intensity of the turbulence signal in a certain frequency band. Specifically, the amplitude reflects the energy carried by the turbulence signal in this frequency band. The larger the amplitude, the higher the turbulence intensity in this frequency band. Therefore, the signal amplitude can be used to determine the turbulence energy distribution of the fluid in different frequency bands, thereby identifying which frequency bands have more significant turbulence phenomena.

[0082] Phase information represents the fluctuation characteristics of turbulence signals over time in different frequency bands. Specifically, it reflects the relative position or delay of turbulence signals in time and space. Phase information can be used to determine the propagation direction and speed of turbulence signals, as well as the synchronization and phase difference between signals in different frequency bands. By analyzing phase information, we can determine the propagation of turbulence signals at different positions and time points, and then understand the spatiotemporal distribution and change trend of turbulence fluctuations in the flow field, so as to better predict and analyze the dynamic characteristics of the fluid.

[0083] S1.5, for signals in different frequency bands, use the square of the amplitude to calculate their spectral energy density. The energy density of each frequency band represents the energy carried by the turbulence signal in that frequency band. By integrating the energy density, the distribution of turbulence energy in each frequency band is calculated, and the high-energy density area is extracted.

[0084] S1.6, compare the spectral energy density of each frequency band with the preset critical value, and automatically identify the frequency bands whose energy density exceeds the threshold. For the frequency bands whose spectral energy density exceeds the critical value, extract the corresponding spatial position data. The signal collected by each sensor has a spatial identification in the data processing module, and the area where the turbulence intensity changes significantly is determined through the spatial positioning system of the signal source. The specific location is determined by the spatial coordinates of each sensor and the collected signal strength. This process uses a three-dimensional spatial interpolation algorithm to cross-validate the points with the highest turbulence intensity based on multiple sensor data, and calibrate the high turbulence area in the entire flow field.

[0085] After determining the spatial position of the high turbulence frequency band, the changing trend of the turbulence signal is analyzed by combining the time domain information and phase information of the frequency band. The time domain information reflects the dynamic changes of the turbulence signal over time. By jointly analyzing the time domain and phase information, the area with the most significant change in turbulence intensity in the flow field is identified.

[0086] The high turbulence area is finally calibrated through spectrum energy density, spatial positioning, time domain information and phase analysis. The calibration process accurately matches the spatial position of the high turbulence frequency band with the specific area in the flow field based on the system algorithm, forming a spatial distribution map of the high turbulence area, ensuring that the area with significant changes in turbulence intensity in the flow field can be accurately identified.

[0087] Through comprehensive analysis of spectral energy density, spatial positioning, time domain information and phase information, the specific process of calibrating high turbulence areas includes the following steps: First, for the frequency band whose energy density exceeds the critical value, the triangulation positioning algorithm is used to determine the spatial position of the frequency band in the clarifier. Based on the spatial layout of the sensor array, the signal difference of adjacent sensors is calculated to accurately locate the specific position of the high turbulence area. Then, combining the time domain information and phase information, the change trend of turbulence intensity is further determined by analyzing the fluctuation direction and phase difference. The system algorithm adopts a spatial mapping method based on distance weighted interpolation to match the high turbulence area signal obtained by positioning with the actual flow field area of ​​the clarifier to generate a spatial distribution map of the high turbulence area. Finally, after the calibration process is completed, the results are transmitted to the central control system through a real-time feedback mechanism to ensure the recognition accuracy and timeliness of the high turbulence area, and provide data support for subsequent dynamic adjustments.

[0088] Among them, determining the changing trend of turbulence intensity includes the following:

[0089] S1.6.1, first, the signal strength value recorded by each sensor in the multi-point flow field sensor array in the clarifier is extracted. Each pair of adjacent sensors is compared based on these signal strengths to calculate the signal difference between them. For example, assuming that the signal strengths collected by two adjacent sensors are A and B, respectively, the signal difference is the absolute difference in strength between each other, that is, the absolute value of A minus B. In this way, the location where the signal change between adjacent sensors is most significant can be found, which provides a basis for the subsequent positioning of high turbulence areas.

[0090] S1.6.2, Next, the spatial distance between adjacent sensors needs to be calculated based on the fixed spatial coordinates of the sensors in the clarifier. The spatial distance between adjacent sensors is calculated using the three-dimensional coordinates of each sensor (i.e., the x, y, z position of the sensor). The spatial distance between two sensors is the square root of the sum of the squares of their position differences in three dimensions. This step helps to relate the signal differences to the actual physical distance between the sensors to determine the spatial location of the turbulent area.

[0091] S1.6.3, when performing triangulation, select three adjacent sensors and find the location where the signal change is most significant based on the signal differences between them. Using the triangular geometry method, use the spatial coordinates of the three sensors as vertices and calculate the area with the greatest signal strength based on the relationship between the signal differences. Use the centroid formula of the triangle to average the positions of the three points to determine the specific location in space of the area with the greatest turbulence intensity.

[0092] S1.6.4, each sensor records not only the signal strength, but also the phase information of the signal. By comparing the phases of adjacent sensors, the phase difference is calculated. The phase difference is the absolute value of the difference in the phase of the two sensor signals. This phase difference can reflect the direction of propagation of the turbulence signal. The larger the phase difference, the stronger the wave propagation along the line between the two sensors.

[0093] S1.6.5, the wave propagation direction of the turbulent fluctuation can be inferred through the phase difference. Then, the wave propagation speed of the turbulent fluctuation is calculated in combination with the time information recorded by the sensor, that is, the wave propagation speed of the fluctuation in the flow field is determined by dividing the spatial distance between the two sensors by the time difference of the wave propagation. By comparing the wave propagation direction and the wave propagation speed of the turbulent fluctuation, the area where the fluctuation is advancing is first locked through the wave propagation direction. Then, the wave propagation speed in the propagation direction is compared with the turbulence intensity of the area in the CFD simulation results. Areas with high turbulence intensity are usually accompanied by higher wave propagation speeds, and the overlap of the wave direction with the high turbulence area can be used to determine the source of the fluctuation.

[0094] S1.6.6, compare the wave propagation velocity with the velocity field in the high turbulence area in the CFD simulation to ensure that the wave propagation velocity is consistent with the velocity field in the high turbulence area obtained by simulation. For areas with higher turbulence intensity, there are usually higher wave propagation velocities, so matching the two can further accurately identify the areas in the flow field with the most significant turbulence intensity.

[0095] S1.6.7, Finally, the phase difference information is combined to analyze the change of phase in the time domain information. The trend of wave propagation direction and phase change is compared with the change of time domain information obtained in CFD simulation to ensure the phase consistency of the wave propagation direction and the turbulence signal in the simulated flow field. After the wave propagation direction and wave propagation velocity match, the time evolution trend of the turbulence intensity change in the flow field can be judged by the phase difference.

[0096] S1.6.8 Finally, the wave propagation direction, wave propagation speed, and phase change trends are combined with the turbulent energy distribution simulated by CFD, and the high turbulence area positioning provided by CFD is used to further refine and identify the change trend of the high turbulence area. Combined with historical data, the trend of turbulence intensity changes can be analyzed in real time, the evolution direction of turbulence can be predicted, and the key turbulent disturbance areas in the flow field can be identified. Through such matching and analysis, it is ensured that the system can accurately monitor the impact of turbulent disturbances on the sedimentation process and provide a basis for subsequent adjustments and optimizations.

[0097] S1.7, based on the results of energy density analysis, calibrate the high turbulence area. The location of the high turbulence area is based on the spectral energy accumulation results of the multi-band signal, and the area where the high-frequency energy is concentrated is combined with the fluid dynamics in the clarifier as the key area where the turbulence intensity changes significantly.

[0098] S1.8, the extracted high turbulence area information and its corresponding spectrum features are transmitted to the subsequent steps. The spectrum data after feature extraction is used to establish a turbulence disturbance prediction model to ensure the accurate connection between the spectrum features and the dynamic adjustment process.

[0099] The flow velocity and turbulence intensity data are collected in real time through multi-point sensors to ensure comprehensive coverage of the flow field and timely data response. The time domain information is converted into frequency domain information through fast Fourier transform to ensure detailed analysis of the spectral characteristics of the turbulence signal and extract key parameters such as amplitude, phase and energy density. Combined with the comparison of spectral energy density with the preset threshold, high turbulence areas can be automatically identified to provide accurate data support for subsequent dynamic adjustment and model optimization. Through the calibration and feature extraction of high turbulence areas, the system's response speed and judgment accuracy to turbulent disturbances are significantly improved, and the separation efficiency of micro-sand particles in mine water is effectively improved.

[0100] In the process of mine water treatment, the resuspension of micro-sand particles is a key issue affecting sedimentation efficiency. Changes in turbulence intensity directly affect the sedimentation path and stability of particles. Especially in high turbulence areas, settled micro-sand particles are easy to resuspend, resulting in a decrease in separation efficiency. In order to solve this problem, by constructing a turbulence disturbance prediction model, the system can monitor the turbulence changes in the flow field in real time, and dynamically update it through the Kalman filter algorithm combined with real-time data and historical data to accurately predict the critical value of turbulence intensity. The model also further calibrates the high turbulence area, thereby effectively locating the resuspension area. By using a multidimensional fitting algorithm and the least squares method, the model can be adaptively adjusted to ensure high accuracy and stability under complex working conditions. The model provides data support for the dynamic adjustment of the filtration system, improves the system's response speed and accuracy to turbulent disturbances, and ensures a more stable sedimentation process for particles.

[0101] Step S2 includes the following contents:

[0102] S2.1, based on the frequency domain information features obtained in step S1, including spectrum amplitude, phase information and spectrum energy density, these data are processed as initial input. Standardization processing technology is used to uniformly quantify the flow velocity and turbulence intensity data of different frequency bands and spatial positions to ensure that the model can accurately identify the changing trend and regional distribution of turbulence intensity. The input data is normalized using a data normalization algorithm to eliminate unit differences and ensure that the multi-dimensional coupling relationship between turbulence intensity and flow velocity gradient remains consistent in the model.

[0103] S2.2, based on the velocity gradient and turbulence intensity data extracted in step S1, a multidimensional linear regression algorithm is used to fit the data and calculate the multidimensional coupling relationship between turbulence intensity and velocity gradient. The model optimizes the fitting curve by the least squares method to ensure the fitting accuracy and form the correlation equation between the turbulence intensity change and velocity gradient at each position in the flow field. This fitting model is used to infer the changes in turbulent disturbances in different regions and provide a basis for subsequent model construction.

[0104] S2.3, the real-time data is processed by the Kalman filter algorithm, and the turbulence disturbance prediction model is constructed by combining the historical data and spectral features obtained in step S1. The specific process of constructing the turbulence disturbance prediction model is as follows: First, based on the historical data, spectral features, velocity gradient and turbulence intensity data obtained in step S1, these data are used as the initial input of the Kalman filter algorithm to initialize the model state variables. The Kalman filter algorithm performs iterative processing through two stages: prediction and update. In the prediction stage, the turbulence intensity distribution at the next moment is predicted based on the last state estimation result and the fitting equations of velocity gradient and turbulence intensity. The prediction results are updated by the state transfer equation, which depends on the coupling relationship between turbulence intensity and velocity gradient, as well as the noise covariance matrix extracted from historical data to ensure that the model maintains high accuracy in a noisy environment. The real-time input data corrects the prediction results through the Kalman gain. In the update stage, according to the difference between the actual observed value and the predicted value, the state variables are adjusted and the noise covariance matrix of the model is updated to gradually optimize the prediction accuracy of the model. As new data is continuously input, the Kalman filter algorithm continuously updates the state estimate and tracks changes in turbulence intensity in real time, ensuring that the model can adapt to complex operating conditions in a dynamic flow field and maintain high accuracy and stability in turbulence disturbance prediction.

[0105] S2.3.1, when constructing the turbulence disturbance prediction model, first use the historical data, spectral characteristics, velocity gradient and turbulence intensity data collected in step S1 as the initial input of the Kalman filter algorithm. Define the state variable as the turbulence intensity distribution , which is the prediction target of the model. Set the initial state covariance matrix , which represents the uncertainty of state estimation. The real-time data collected by the sensor is used as observation input and recorded as observation vector .

[0106] S2.3.2, in the prediction phase, based on the state estimate of the previous time step and the state transition matrix , predict the turbulence intensity distribution at the next moment , which is the prior estimate. This step is based on the fitting equation of velocity gradient and turbulence intensity, and is predicted by the following state transfer equation: ;in, is the control input, It is a control matrix used to adjust the turbulence intensity distribution by introducing external influences (such as changes in operating conditions).

[0107] Next, the predicted state covariance matrix ;in, is the process noise covariance matrix, which reflects the error accumulation due to the internal uncertainty of the system.

[0108] S2.3.3, in the update phase, the prior prediction result is corrected in combination with the sensor's observation vector. First, the Kalman gain is calculated , which is used to adjust the state estimate. The Kalman gain is calculated by the following formula: ;in, is the observation matrix, which transforms the state space into the observation space, represents the transposed matrix of the observation matrix, is the observation noise covariance matrix, representing the sensor measurement error.

[0109] The turbulence intensity distribution is corrected using the Kalman gain to obtain the turbulence intensity distribution: ;

[0110] That is, through the sensor's observation vector and the predicted observation value The difference between them is used to adjust the predicted value.

[0111] S2.3.4, update the state covariance matrix to reflect the corrected state uncertainty: ;

[0112] This process optimizes state estimation by gradually reducing uncertainty, allowing the prediction model to gradually adapt to real-time data changes.

[0113] S2.3.5, Kalman filter dynamically adjusts state variables and covariance matrix by combining historical data with real-time data. Historical data provides initial state estimation and system model parameters, while real-time data corrects the prediction results through observation vectors to ensure that the model can be adjusted in time according to the changing trend of the current turbulence intensity. With the input of new data at each time step, Kalman filter continuously optimizes the model state estimation to ensure high-precision prediction capability under complex working conditions.

[0114] S2.3.6, Kalman filter tracks the change of turbulence intensity in real time through the cycle iteration of prediction and update, and gradually reduces the prediction error. By adjusting the process noise covariance matrix and the observation noise covariance matrix, the filter can adaptively handle turbulence disturbances and data noise of different intensities, ensuring the long-term stability and high accuracy of the model.

[0115] Kalman filtering integrates historical data and real-time data, and uses two steps, prediction and update, to achieve accurate prediction of turbulence intensity distribution. This process enables the system to continuously track and adapt to the dynamic changes of turbulence intensity through the state transfer equation, calculation of Kalman gain, and update of covariance matrix, ensuring the accuracy and stability of the turbulence disturbance prediction model.

[0116] S2.4, based on the prediction results output by the Kalman filter algorithm, the critical value of turbulence intensity is calculated using the fitting curve. The specific process of calculating the critical value of turbulence intensity is as follows: the coupling relationship between velocity gradient and turbulence intensity is extracted through the fitting curve of turbulence intensity and velocity gradient, and the critical change point is identified. By comparing the critical state data under historical conditions, the critical conditions of turbulence intensity when micro-sand particles are re-suspended in the flow field in the past are analyzed, and the critical value of turbulence intensity that may cause particle re-suspension under the current conditions is calculated by combining the turbulence intensity and velocity gradient in the current real-time data. The calculation process also refers to the spectrum energy density, especially the frequency band where the energy density is higher than the set threshold, and the fluctuation amplitude of the spectrum energy density is compared and analyzed with the preset reference threshold to further accurately determine the critical point. Finally, the prediction results in the real-time data are dynamically compared with the historical data, and the changing trends of turbulence intensity and velocity gradient are comprehensively analyzed to determine the position of the critical point to ensure that the critical value can accurately reflect the high turbulence area in the flow field that may cause micro-sand particles to re-suspension.

[0117] S2.5, track the changing trend of turbulence intensity in real time, and mark the turbulence intensity area exceeding the critical value of turbulence intensity as the resuspension area.

[0118] S2.6, optimize and adjust the parameters of the turbulence disturbance prediction model by the least squares method. According to the size of the fitting error, iteratively adjust the fitting coefficients and state variables in the model to ensure that the model can be adaptively adjusted and consistent with the actual flow field conditions. This optimization process is automatically triggered after each new data input to ensure the long-term stability and prediction accuracy of the model.

[0119] The turbulence intensity prediction model is dynamically updated through the Kalman filter algorithm, the changes in turbulence intensity in the flow field are tracked in real time, and the critical turbulence area that may cause particle resuspension is effectively predicted. The multidimensional linear regression algorithm is used to accurately calculate the coupling relationship between the velocity gradient and the turbulence intensity to ensure that the prediction model can reflect the actual distribution of turbulent disturbances in the flow field. The critical value calculation mechanism of turbulence intensity can effectively identify high-risk turbulence areas, and the model parameters are continuously optimized through the least squares method to ensure the long-term stability and high accuracy of the model. Through this process, the system can accurately locate the resuspension area, thereby effectively improving the micro-sand separation efficiency in mine water treatment, reducing the negative impact of turbulence on particle sedimentation, and significantly enhancing the overall separation performance and operational stability of the system.

[0120] In the mine water treatment system, changes in turbulence intensity and flow velocity fluctuations are key factors affecting the capture efficiency of micro-sand particles. High turbulence intensity and flow velocity deviation will cause particle resuspension, significantly reducing the separation effect. Therefore, in response to these dynamic change factors, a control model based on the turbulence intensity-particle concentration coupling index and the critical flow velocity deviation factor was constructed. The model uses a fuzzy control algorithm to adjust the filter aperture and the opening of the flow rate regulating valve in real time to ensure that the system can automatically optimize the particle capture efficiency based on the real-time collected flow field data and historical operating conditions. By real-time monitoring of turbulence intensity and flow velocity changes, the system can determine whether the current flow field needs to adjust the filtration parameters, thereby realizing intelligent dynamic control and ensuring that the system always operates in the best state.

[0121] Step S3 includes the following contents:

[0122] S3.1, based on the turbulence intensity critical value and spatial positioning results obtained in step S2, receive the output data of the turbulence disturbance prediction model in real time, including turbulence intensity distribution, velocity gradient, and calibrated high turbulence areas. The output data of the turbulence disturbance prediction model is used as the initial input of the fuzzy control algorithm to ensure that accurate adjustments can be made according to the turbulence intensity change trend in the current flow field. In order to further optimize the system response, a dynamic fusion algorithm of historical data and real-time data is used to evaluate the impact of turbulence intensity changes on the particle capture process in real time.

[0123] S3.2, based on the current real-time turbulence intensity and particle concentration data collected, calculate the turbulence intensity-particle concentration coupling index (TCI). This index extracts the coupling characteristics of turbulence intensity and particle concentration by analyzing the temporal and spatial variation relationship between the two. The specific calculation process uses a cross-correlation algorithm to analyze the synchronization of particle concentration fluctuations with turbulence intensity, and combines multidimensional data fitting equations to obtain the TCI value. This index reflects the degree of influence of turbulence intensity on particle sedimentation and resuspension, ensuring that the system has dynamic adaptability when capturing micro-sand particles.

[0124] Calculation process of turbulence intensity-particle concentration coupling index:

[0125] A1, real-time collection of turbulence intensity data and particle concentration data, recorded as the function of turbulence intensity and particle concentration . Represents time. This data is collected from a multi-point sensor arrangement with a high temporal resolution, ensuring that the dynamic changes in turbulence intensity and particle concentration are captured.

[0126] A2, in order to evaluate the synchronization of particle concentration with turbulence intensity fluctuations, firstly, a cross-correlation analysis is performed on turbulence intensity and particle concentration, and their correlation at different time lags is calculated. The cross-correlation function is defined as: ;

[0127] in, Indicates time lag, is the upper limit of the time integral. This integral calculates the correlation between turbulence intensity and particle concentration at different time lags, by finding the maximum value , determine the optimal lag time between particle concentration changes and turbulence intensity fluctuations .

[0128] A3, based on the turbulence intensity and particle concentration data collected by sensors at different locations, a spatial coupling relationship is constructed. The relationship between turbulence intensity and particle concentration in spatial distribution can be described by the following fitting equation: ;

[0129] in, represents the spatial coordinates, The fitting coefficient is obtained by fitting the sensor data using the least square method. This equation describes the dependence of particle concentration at different spatial locations on turbulence intensity and its rate of change.

[0130] A4, based on the fitting equation and cross-correlation analysis results, define the turbulence intensity-particle concentration coupling index for: ;

[0131] in, is the spatial integration range of turbulence intensity. The first part represents the synchronization of turbulence intensity and particle concentration in time lag, and the second part represents the influence of spatial distribution of turbulence intensity on particle concentration.

[0132] Turbulence disturbance is one of the key factors affecting the sedimentation efficiency of micro-sand particles in mine water treatment systems. With the increase of turbulence intensity, particles tend to re-enter the suspended state from the sedimentation state, resulting in reduced separation efficiency. In a dynamic flow field, the relationship between turbulence intensity and particle concentration is complex and nonlinear, so an indicator that can quantify the effect of turbulence intensity on particle concentration is needed. By calculating the turbulence intensity-particle concentration coupling index (TCI), the degree of influence of turbulence disturbance on particle sedimentation and resuspension can be accurately evaluated, thereby providing a scientific basis for the system in real-time monitoring and regulation. Based on cross-correlation analysis and multidimensional fitting technology, this index combines the spatiotemporal changes of turbulence intensity with the dynamics of particle concentration, and can accurately reflect the actual impact of turbulence on particle movement in the flow field.

[0133] The turbulence intensity-particle concentration coupling index can effectively identify areas in the flow field where turbulence interferes with particle sedimentation by quantifying the coupling effect of turbulence on particle concentration. A higher turbulence intensity-particle concentration coupling index value means that the system can adjust the filtration parameters in time to prevent particle resuspension, thereby improving separation efficiency; a lower turbulence intensity-particle concentration coupling index value indicates that the sedimentation process is stable, the influence of turbulent disturbance on particle sedimentation is small, and the system can maintain the current operation mode. The introduction of the turbulence intensity-particle concentration coupling index provides a quantitative basis for the adaptive adjustment of the system, which helps to optimize the operation of the micro-sand separation system, improve the sedimentation effect, and reduce energy consumption and maintenance costs.

[0134] S3.3, by analyzing the deviation between the current flow rate and the critical flow rate set by the system, the critical flow rate deviation factor (CVDF) is calculated. Using real-time flow gradient data, the relative difference method is used to calculate the degree of deviation between the actual flow rate and the critical flow rate. CVDF dynamically adjusts the control accuracy of the flow rate by comparing historical flow field data with current flow field data to ensure the stability of the particle capture process in turbulent areas.

[0135] Processing of critical velocity deviation factor (CVDF):

[0136] B1, real-time acquisition of flow gradient data As a function of flow velocity over time and space, Indicates the position in space and time Critical flow rate Pre-set according to requirements, represents the flow rate threshold that can ensure that particles do not re-suspend. The deviation between the current flow rate and the critical flow rate needs to be calculated through this step to evaluate the capture efficiency.

[0137] B2, the relative difference method is used to calculate the deviation between the real-time collected velocity gradient data and the critical velocity. The relative difference method is defined as follows: ;

[0138] in Represents the relative velocity deviation at the current position and time. This formula can reflect the degree of change of the velocity at each spatial position of the current flow field relative to the critical value. By calculating the deviation of data at multiple positions, the overall deviation of the flow field can be determined.

[0139] B3, in order to evaluate the dynamic impact of velocity changes, the temporal and spatial derivatives of the deviation are calculated, namely, the temporal deviation derivative and the spatial deviation derivative The formulas for describing the changing trends of velocity deviation in time and space are as follows: ; ;

[0140] By calculating the time and space derivatives of the velocity deviation, the dynamic change characteristics of the flow velocity in the turbulent area are captured, providing more detailed information on the flow velocity fluctuation.

[0141] B4, combining velocity deviation and its temporal and spatial derivatives to construct the critical velocity deviation factor , defined as: ;

[0142] in, is the spatial integration range of turbulence intensity. The first part represents the synchronization of turbulence intensity and particle concentration in time lag. The second part represents the influence of spatial distribution of turbulence intensity on particle concentration. and Usually represents a weight coefficient.

[0143] :adjust The relative weight of the term (time derivative term). The time derivative term describes the rate of change of the flow velocity deviation over time, so Controls the impact of the time change rate on the entire system.

[0144] :adjust The relative weight of the term (spatial derivative term). The spatial derivative term describes the rate of change of the velocity deviation in space, so Controls the impact weight of the spatial change rate on the system.

[0145] The critical velocity deviation factor (CVDF) is used to indicate the degree of deviation of the current flow velocity from the critical flow velocity set by the system, reflecting the impact of the flow velocity fluctuation in the flow field on the particle capture process. The larger the critical velocity deviation factor, the greater the deviation between the current flow velocity and the critical flow velocity, the stronger the turbulence fluctuation in the flow field, the greater the interference in the particle capture process, and the risk of particle resuspension or reduced capture efficiency; the smaller the critical velocity deviation factor, the closer the current flow velocity is to the critical flow velocity, the smaller the flow field fluctuation, the more stable the particle capture process, the system does not need to be adjusted significantly, and the particle settling efficiency is higher.

[0146] In mine water treatment systems, the sedimentation and capture process of micro-sand particles is significantly affected by changes in flow velocity, especially in areas with high turbulence intensity. Fluctuations in flow velocity can easily cause particle resuspension, resulting in reduced separation efficiency. Therefore, in order to effectively monitor and adjust the flow field and ensure that the flow velocity in the system is maintained within an appropriate range, a critical flow velocity deviation factor is proposed. By calculating the deviation between the current flow velocity and the critical flow velocity set by the system, the critical flow velocity deviation factor can dynamically reflect the impact of flow velocity fluctuations on the particle capture process. This factor integrates the temporal and spatial variation characteristics of the flow velocity, provides a quantitative judgment basis for the system, and helps to adjust the working parameters of the filter in real time to avoid a decrease in particle capture efficiency due to excessive flow velocity deviation.

[0147] The introduction of the critical flow rate deviation factor enables the system to accurately monitor the difference between the current flow rate and the critical flow rate, and promptly identify the flow rate deviation problem in the flow field. When the critical flow rate deviation factor is large, the system can automatically adjust the filter opening rate and flow rate regulating valve to cope with the negative impact of flow rate fluctuations on particle capture, thereby preventing particle resuspension and improving separation efficiency; when the critical flow rate deviation factor is small, it indicates that the flow rate is close to the critical value, the capture process is stable, and the system does not need to make too many adjustments, thereby saving energy and operating costs. Through dynamic feedback and real-time control of the critical flow rate deviation factor, the operating efficiency of the system in complex flow fields has been significantly improved, ensuring the stability of particle sedimentation and capture processes.

[0148] S3.4, the real-time calculation results of the turbulence intensity-particle concentration coupling index and the critical flow rate deviation factor are used as input variables of the fuzzy control algorithm. The combination of the two variables is analyzed through the fuzzy inference rule base, and a specific threshold range is set to determine the filter opening rate and the adjustment timing of the flow rate regulating valve. When the combination of the turbulence intensity-particle concentration coupling index and the critical flow rate deviation factor reaches the preset threshold, it is judged that the current turbulence intensity and flow rate changes may cause particle resuspension or a decrease in capture efficiency, triggering the switching of the filtration mode.

[0149] S3.4.1, turbulence intensity-particle concentration coupling index and critical velocity deviation factor are used as two input variables of the fuzzy control algorithm. The turbulence intensity-particle concentration coupling index and the critical velocity deviation factor are fuzzified according to different value ranges, and each variable is divided into multiple fuzzy sets, usually including three fuzzy levels of "low", "medium" and "high". Specifically:

[0150] Fuzzification of turbulence intensity-particle concentration coupling index: The value range of turbulence intensity-particle concentration coupling index is divided into three fuzzy sets, namely low, medium and high. Its value range is set according to system experience and experimental data, reflecting the influence of turbulence on particle resuspension.

[0151] Fuzzification of critical flow rate deviation factor: The numerical range of the critical flow rate deviation factor is also divided into three fuzzy sets: low, medium, and high, which respectively reflect the degree of deviation of the current flow rate from the critical flow rate, representing the risk of changes in the system capture efficiency.

[0152] S3.4.2, the fuzzy inference rule base is the core of the fuzzy control algorithm, which is used to judge the system state and trigger corresponding actions based on the combination of the input variable turbulence intensity-particle concentration coupling index and the critical velocity deviation factor. The inference rule base is based on the "if-then" reasoning structure:

[0153] Combination of input conditions: The fuzzy sets of turbulence intensity-particle concentration coupling index and critical velocity deviation factor are combined to form multiple rules. For example, if the turbulence intensity-particle concentration coupling index is in the "high" fuzzy set and the critical velocity deviation factor is in the "high" fuzzy set, then the system should consider switching the filtering mode. These rules cover various combinations such as "low-low", "low-medium", "medium-high", etc.

[0154] Example of rule:

[0155] If the turbulence intensity-particle concentration coupling index is "high" and the critical flow rate deviation factor is "high", the filter opening rate needs to be reduced immediately and the flow rate regulating valve needs to reduce the flow rate;

[0156] If the turbulence intensity-particle concentration coupling index is "medium" and the critical velocity deviation factor is "medium", the system maintains the existing filtering mode unchanged;

[0157] If the turbulence intensity-particle concentration coupling index is "low" and the critical flow rate deviation factor is "high", the system needs to adjust the opening ratio appropriately, but the flow rate regulating valve remains unchanged;

[0158] If the turbulence intensity-particle concentration coupling index is "high" and the critical velocity deviation factor is "low", the filter opening ratio is adjusted to prevent the turbulence intensity from being too high and causing particle resuspension.

[0159] S3.4.3, according to the fuzzy inference rules, the fuzzy control algorithm maps the actually calculated turbulence intensity-particle concentration coupling index and the critical velocity deviation factor value to the corresponding fuzzy set, and judges the current system state according to the inference rule base:

[0160] Rule matching: When the combination of turbulence intensity-particle concentration coupling index and critical velocity deviation factor meets a certain inference rule, the corresponding control action is triggered. Each inference rule defines the specific action that the system needs to take, such as adjusting the filter opening rate or adjusting the flow rate control valve.

[0161] Calculation of fuzzy reasoning: Fuzzy reasoning uses the minimum-maximum reasoning method, that is, according to the fuzzy membership of the input variables, the membership of each rule is calculated, and the rule with the maximum value is taken as the output. Through the calculation of fuzzy membership, the results of multiple fuzzy rules are comprehensively processed to generate clear control output.

[0162] S3.4.4, the fuzzy reasoning results are converted into actual control signals through the defuzzification process. The defuzzification method usually adopts the weighted average method, which weights and averages the results of all rules to obtain the final control output. This output determines the adjustment range of the filter opening rate and the opening adjustment degree of the flow rate control valve.

[0163] The defuzzification process is achieved through the following steps:

[0164] Calculate the membership of each rule output;

[0165] Take the weighted average of each membership value to get the control amount of the system;

[0166] Apply the control quantity to the actual physical equipment to adjust the filter opening ratio or the flow rate control valve opening.

[0167] The fuzzy control algorithm uses the turbulence intensity-particle concentration coupling index and the critical velocity deviation factor as input variables, uses the "if-then" rule in the inference rule base to judge the system state, and makes real-time adjustments based on the actual turbulence intensity and velocity deviation. The results of fuzzy reasoning are converted into actual control actions through defuzzification, ensuring that the filter and regulating valve can respond accurately according to the real-time working conditions, thereby maintaining the stability and efficiency of the system.

[0168] S3.5, based on the inference results of the fuzzy control algorithm, automatically adjusts the filter aperture and the opening of the flow rate control valve. Through the step adjustment mechanism, the filter aperture and valve opening are gradually adjusted to ensure that the system can optimize the particle capture efficiency in real time. This process is continuously optimized through the feedback loop, real-time monitoring of the flow field state after the filter mode is switched, and fine-tuning of the filter parameters to ensure that the system maintains the best working state under different working conditions.

[0169] By introducing the turbulence intensity-particle concentration coupling index and the critical velocity deviation factor into the fuzzy control algorithm, key dynamic changes in the flow field can be monitored and analyzed in real time. The turbulence intensity-particle concentration coupling index can quantify the impact of turbulence on particle sedimentation and resuspension, and the critical velocity deviation factor reflects the degree of deviation between the current flow velocity and the critical flow velocity. The combination of the two can accurately determine whether turbulent disturbances and velocity fluctuations in the flow field will affect the particle capture process. When the index combination reaches the threshold, the filter opening rate and flow rate regulating valve are automatically adjusted to ensure the optimization of particle capture effect in complex flow fields. The introduction of the fuzzy control algorithm ensures the adaptability of the system under different working conditions, reduces human intervention, improves the stability and efficiency of system operation, reduces energy consumption and maintenance costs, and improves the accuracy and efficiency of micro-sand particle separation.

[0170] In the mine water treatment system, the change of turbulence intensity has a significant impact on the sedimentation efficiency of micro-sand particles, especially the disturbance of the high turbulence energy area to the sedimentation zone easily leads to particle resuspension, affecting the separation effect. In order to solve this problem, a dynamic adjustment mechanism of the guide tube is introduced based on the turbulence intensity-particle concentration coupling index (TCI) and the critical velocity deviation factor (CVDF). By real-time monitoring of the turbulence intensity distribution and velocity gradient, combined with computational fluid dynamics (CFD) simulation and Reynolds average equation (RANS) calculation, the angle and insertion depth of the guide tube are optimized to inhibit the transfer of turbulent energy to the sedimentation zone. This technology can dynamically adjust the structural parameters of the guide tube, effectively optimize the layered structure of the flow field in the clarifier, reduce the turbulent disturbance to the sedimentation zone, and improve the separation efficiency.

[0171] Step S4 includes the following contents:

[0172] S4.1, based on the turbulence intensity-particle concentration coupling index (TCI) and critical velocity deviation factor (CVDF) obtained in step S3, the output data of the turbulence disturbance model is used as the initial basis for adjusting the angle and depth of the guide tube. The real-time collected turbulence intensity distribution and velocity gradient data are input into the control system, and combined with the historical data and current state of the flow field, a preliminary assessment of the turbulence intensity change trend is made to determine whether there is a high turbulence area in the flow field that disturbs the sedimentation area.

[0173] S4.2, using computational fluid dynamics (CFD) tools, simulate the fluid movement inside the clarifier based on the Reynolds average equation (RANS). The RANS equation is as follows: ;

[0174] in, is the turbulent mean velocity, is the fluid density, is the dynamic viscosity, represents the average pressure, is the Reynolds stress term; and Respectively represent the fluid and The velocity component in the direction, usually and It can be 1, 2, or 3, representing the velocity components in the x (horizontal), y (vertical), and z (depth) directions, respectively; and Respectively represent the fluid and The spatial coordinates in the directions represent the locations where the velocity is measured in these directions. Similar to the definition of velocity components, and 1, 2, and 3 can be taken to correspond to the x, y, and z coordinate axes. By solving the RANS equation, the turbulent energy distribution in each area of ​​the clarifier is calculated, and the high turbulent energy areas in the flow field and their effects on the sedimentation area are identified.

[0175] The construction and settings of CFD are as follows:

[0176] S4.2.1, before CFD simulation, firstly carry out geometric modeling of the interior of the clarifier and mesh the model. The purpose of meshing is to discretize the entire simulation domain into multiple small units (grids) and obtain the solution of the entire flow field by solving equations on each grid. The specific steps are as follows:

[0177] Select the mesh type: According to the geometric characteristics of the clarifier, select the appropriate mesh type, which usually includes structured mesh (suitable for regular geometry) or unstructured mesh (suitable for complex geometry). The interior of the clarifier is mostly a regular cylindrical structure, so it is recommended to use a structured mesh.

[0178] Determine the mesh density: In key areas (such as around the guide tube or in high turbulence areas), use a higher density mesh to improve simulation accuracy. The mesh density can be appropriately reduced in the sedimentation area and fluid stratification area to reduce the amount of calculation. The mesh density can be controlled by the number of mesh cells.

[0179] Mesh quality assessment: After meshing, the mesh quality needs to be evaluated, including the shape, angle, distortion and other indicators of the mesh cells, to ensure that the mesh is suitable for the computational accuracy requirements of CFD simulation.

[0180] S4.2.2, The setting of boundary conditions determines the flow of fluid in the clarifier. Common boundary conditions include:

[0181] Inlet conditions: Set velocity boundary conditions for the clarifier inlet, that is, set the velocity distribution of the fluid entering the clarifier. If the inlet velocity is unknown, you can select the flow boundary condition to define the inlet fluid movement by setting the volume flow rate.

[0182] Outlet conditions: The outlet usually adopts pressure boundary conditions and is set to atmospheric pressure or appropriate back pressure to ensure that the fluid is discharged smoothly from the clarifier.

[0183] Wall conditions: The inner wall of the clarifier and the wall of the guide tube are usually set to no-slip boundary conditions, that is, the wall fluid velocity is zero, simulating the adhesion effect of the fluid on the solid surface.

[0184] Symmetry conditions: For clarifiers with symmetrical geometric structures, symmetry boundary conditions can be used to simplify the calculation area and reduce the calculation time.

[0185] S4.2.3, In CFD simulation, turbulence models are used to simulate turbulence phenomena in fluids. There are many common turbulence models, and choosing an appropriate turbulence model can improve simulation accuracy. Commonly used models include:

[0186] standard Model: It is suitable for most engineering application scenarios, has high computational efficiency, and is suitable for overall flow field simulation.

[0187] Model: The turbulence dissipation rate equation has been improved, which is suitable for simulating turbulent fields with vortex and backflow phenomena, and is suitable for complex turbulence phenomena in clarifiers.

[0188] Model: It performs well in the near-wall region and can accurately capture the turbulent separation region. It is suitable for simulating the turbulent energy distribution around the guide tube.

[0189] S4.2.4, CFD simulation also requires setting physical parameters to reflect the actual flow conditions in the clarifier:

[0190] Fluid Properties: Set the density and viscosity of the fluid. For mine water treatment, water is usually selected as the fluid, and the viscosity and density parameters are set according to the temperature and particle concentration.

[0191] Gravity effect: The sedimentation and turbulence of the fluid in the clarifier are affected by gravity, so the gravity item needs to be activated and the gravity acceleration value needs to be set.

[0192] S4.2.5, To ensure the accuracy of CFD simulation, reasonable convergence criteria need to be set:

[0193] Residual convergence criterion: The residual is usually set to The following indicates that the error of the numerical solution is small enough and the simulation results have reached a stable state.

[0194] Fluid parameter stability: During the iteration process, fluid parameters such as velocity, pressure, and turbulent energy are monitored to ensure that the changes in these physical quantities are within a stable range.

[0195] S4.2.6, after setting the grid, boundary conditions, turbulence model and physical parameters, start the CFD simulation. After the calculation is completed, the results are analyzed through post-processing tools, mainly including:

[0196] Velocity field distribution: Observe the changes in fluid velocity in the clarifier, especially the velocity distribution around the draft tube.

[0197] Turbulence energy distribution: By analyzing the turbulence energy distribution, high turbulence energy areas are identified and the impact of turbulence on the sedimentation area is determined.

[0198] Particle trajectory: The settling paths of microsand particles are analyzed to determine whether particles are disturbed and resuspended in areas of high turbulence.

[0199] CFD simulation can accurately simulate the fluid movement in the clarifier through reasonable grid division, boundary condition setting, turbulence model selection and physical parameter adjustment. By setting the convergence standard, the stability and accuracy of the simulation results are ensured, and the turbulent energy distribution and particle movement in the clarifier are finally analyzed to provide data support for optimizing the guide tube structure.

[0200] S4.3, combined with CFD simulation results and turbulent energy distribution, determine whether the inclination angle of the guide tube needs to be adjusted. By simulating and analyzing the impact of the high turbulent energy area on the sedimentation area, determine the optimal inclination angle of the guide tube to suppress the transfer of turbulent energy to the sedimentation area. The formula for adjusting the inclination angle is as follows: ;

[0201] in, is the initial angle, is the turbulence energy, is the adjustment factor, is the rate of change of turbulent energy along the spatial coordinate. The adjustment of the tilt angle makes the high turbulent energy area deflect away from the sedimentation area, reducing the disturbance of turbulence on particle sedimentation.

[0202] S4.4, evaluate the distribution of turbulence intensity at different depths based on the simulation results, and dynamically adjust the insertion depth of the guide tube. The insertion depth adjustment formula is as follows: ;

[0203] in, is the initial insertion depth, is the adjustment factor, The rate of change of turbulent energy with depth is shown. According to the distribution of turbulent energy at depth, the insertion depth of the guide tube is automatically optimized to place it in the most favorable position for particle sedimentation and reduce turbulent disturbance in the sedimentation area.

[0204] S4.5, after adjusting the angle and depth of the guide tube, the adjustment effect is further verified through CFD simulation to ensure that the layered structure of the flow field is optimized. By simulating the distribution of turbulent energy in the layered structure, the system controls the turbulent energy-intensive area to ensure that the turbulent energy diffuses to the non-sedimentation area, minimizes the disturbance in the sedimentation area, and improves the sedimentation efficiency of particles.

[0205] S4.6, the system continuously monitors the changes in turbulence intensity based on real-time flow field feedback and makes further fine-tuning through the feedback control mechanism. After each adjustment, the new flow field data is input into the CFD model for recalculation to ensure that the inclination angle and insertion depth of the guide tube always remain optimal under different working conditions. This process is optimized through multiple iterations to ensure the dynamic adaptability and stability of the system.

[0206] By dynamically adjusting the inclination angle and insertion depth of the guide tube, the distribution of turbulent energy can be optimized according to the real-time feedback of turbulence intensity, reducing the impact of high turbulent energy areas on the sedimentation area, and effectively preventing the occurrence of particle resuspension. CFD simulation and feedback control mechanism ensure high adaptability under complex flow fields, and can maintain the best working state of the guide tube under different working conditions, thereby improving the sedimentation efficiency of particles, improving separation performance, reducing energy consumption and improving the overall operating efficiency of the system.

[0207] Embodiment 2, Figure 2 The invention provides a mine water micro-sand separation system based on a dynamic filtration algorithm, comprising: a flow field monitoring module, a prediction model module, a control algorithm module and a diversion optimization module;

[0208] The flow field monitoring module collects the flow velocity, turbulence intensity data and flow velocity gradient data in the clarifier in real time through multi-point flow field sensors, converts them into frequency domain information using Fourier transform technology, extracts flow field fluctuation characteristics using fast Fourier transform algorithm, and identifies high turbulence areas; outputs turbulence intensity, flow velocity data, flow velocity gradient data and high turbulence areas to the prediction model module;

[0209] The prediction model module performs multi-dimensional data fitting of turbulence intensity and velocity gradient based on real-time collected flow field data to build a turbulence disturbance prediction model. The turbulence disturbance prediction model is dynamically updated through the Kalman filter algorithm, and combined with historical data and real-time feedback, the critical value of turbulence intensity is calculated to locate the resuspension area. The resuspension area position and the critical value of turbulence intensity are output to the control algorithm module.

[0210] The control algorithm module calculates the turbulence intensity-particle concentration coupling index and the critical velocity deviation factor based on the real-time data of turbulence intensity and velocity deviation. The real-time calculation results of the two indicators are used as the input of the fuzzy control algorithm to determine whether the filter opening rate and the flow rate regulating valve need to be adjusted. The turbulence intensity-particle concentration coupling index and the critical velocity deviation factor are sent to the diversion optimization module.

[0211] The diversion optimization module is based on the turbulence intensity-particle concentration coupling index and the critical velocity deviation factor, combined with CFD simulation results and turbulence energy distribution, to adjust the angle and depth of the diversion tube, optimize the flow field structure, and suppress disturbances in the sedimentation zone.

[0212] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0213] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0214] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0215] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for separating fine sand from mine water based on a dynamic filtration algorithm, characterized in that: Includes steps: S1, through multi-point flow field sensors, real-time collection of flow velocity, turbulence intensity data and flow velocity gradient data in the clarifier; S2, based on the real-time collected flow field data, multi-dimensional data fitting of turbulence intensity and velocity gradient is performed to build a turbulence disturbance prediction model; and the resuspension area is located according to the turbulence disturbance prediction model; S3, based on the real-time data of turbulence intensity and flow rate deviation, calculate the turbulence intensity-particle concentration coupling index and critical flow rate deviation factor to comprehensively analyze and determine whether the filter opening rate and flow rate regulating valve need to be adjusted; S4, using the turbulence intensity-particle concentration coupling index and critical velocity deviation factor, combined with CFD simulation results and turbulence energy distribution, adjust the guide tube angle and depth; Step S1 includes the following contents: S1.1, a multi-point flow field sensor array is arranged at different locations in the clarifier, and each sensor is used to collect real-time flow velocity and turbulence intensity data; S1.2, based on the velocity data at different locations, the velocity gradient is calculated using the spatial difference method; the velocity gradient of each area is obtained by first-order difference processing of the velocity data between adjacent sensors; S1.3, using a fast Fourier transform algorithm, converting the time domain information collected by the multi-point flow field sensors into frequency domain information; S1.4, extracting the spectrum amplitude and phase information of each frequency band from the converted frequency domain information; S1.5, for signals in different frequency bands, the spectral energy density is calculated using the square of the amplitude; S1.6, through the comprehensive analysis of spectral energy density, spatial positioning, time domain information and phase information, the high turbulence area is calibrated. The specific process includes the following steps: First, for the frequency band whose energy density exceeds the critical value, the triangulation positioning algorithm is used to determine the spatial position of the corresponding frequency band in the clarifier, and based on the spatial layout of the sensor array, the signal difference of adjacent sensors is calculated to locate the specific position of the high turbulence area; then, combined with the time domain information and phase information, the change trend of the turbulence intensity is determined by analyzing the fluctuation direction and phase difference; the spatial mapping method based on distance weighted interpolation is used to match the high turbulence area signal obtained by positioning with the actual flow field area of ​​the clarifier to generate a spatial distribution map of the high turbulence area; Step S2 includes the following contents: S2.1, using standardization processing to quantify the velocity and turbulence intensity data of different frequency bands and spatial positions; S2.2, based on the velocity gradient and turbulence intensity data extracted in step S1, a multidimensional linear regression algorithm is used to fit the data, and the multidimensional coupling relationship between turbulence intensity and velocity gradient is calculated; the fitting curve is optimized by the least squares method to form a correlation equation between the turbulence intensity change at each position in the flow field and the velocity gradient; S2.3, using Kalman filter algorithm to process real-time data and build a turbulence disturbance prediction model; S2.4, based on the prediction results output by the Kalman filter algorithm, the critical value of turbulence intensity is calculated using the fitting curve; S2.5, real-time tracking of the changing trend of turbulence intensity, and marking the turbulence intensity area exceeding the critical value of turbulence intensity as the resuspension area; Step S3 includes the following contents: S3.1, based on the turbulence intensity critical value and spatial positioning result obtained in step S2, receiving the output data of the turbulence disturbance prediction model in real time, including turbulence intensity distribution, flow velocity gradient and calibrated high turbulence area; S3.2, based on the current real-time collected turbulence intensity and particle concentration data, calculate the turbulence intensity-particle concentration coupling index; Calculation process of turbulence intensity-particle concentration coupling index: A1, real-time collection of turbulence intensity data and particle concentration data, recorded as the function of turbulence intensity and particle concentration ,in, Represents time; A2, to evaluate the synchronization of particle concentration fluctuations with turbulence intensity, firstly, a cross-correlation analysis is performed on turbulence intensity and particle concentration, and their correlation at different time lags is calculated; the cross-correlation function is defined as: ;in, Indicates time lag, is the upper limit of the time integral; A3, based on the turbulence intensity and particle concentration data collected by sensors at different locations, a spatial coupling relationship is constructed; the relationship between turbulence intensity and particle concentration in spatial distribution is described by the following fitting equation: ;in, represents the spatial coordinates, For the fitting coefficient, the least square method of sensor data is used to fit and solve; A4, based on the fitting equation and cross-correlation analysis results, define the turbulence intensity-particle concentration coupling index for: ;in, is the spatial integration range of turbulence intensity; Step S3 also includes the following: S3.3, calculating the critical flow rate deviation factor by analyzing the deviation between the current flow rate and the critical flow rate set by the system; Processing process of critical velocity deviation factor: B1, real-time acquisition of flow gradient data As a function of flow velocity over time and space, Indicates the position in space and time Flow rate; B2, the relative difference method is used to calculate the deviation between the velocity gradient data collected in real time and the critical velocity; the relative difference method is defined as follows: ;in Indicates the relative flow velocity deviation at the current position and time. represents the critical flow velocity; B3, evaluate the dynamic impact of flow velocity changes and calculate the temporal and spatial derivatives of the deviation, namely the temporal deviation derivative and the spatial deviation derivative ; respectively describe the changing trend of velocity deviation in time and space, the formula is as follows: ; B4, combining velocity deviation and its temporal and spatial derivatives to construct the critical velocity deviation factor , defined as: ;in, is the spatial integration range of turbulence intensity, and Respectively Item and The weight coefficient of the item; S3.4, using the real-time calculation results of the turbulence intensity-particle concentration coupling index and the critical flow velocity deviation factor as the input variables of the fuzzy control algorithm, analyzing the combination of the two variables through the fuzzy inference rule base to determine the filter opening rate and the adjustment timing of the flow rate regulating valve; when the combination of the turbulence intensity-particle concentration coupling index and the critical flow velocity deviation factor reaches the preset threshold, it is determined that the current turbulence intensity and flow velocity changes lead to particle resuspension or a decrease in capture efficiency, triggering the switching of the filtration mode; S3.5, automatically adjusts the filter aperture and the flow rate regulating valve opening according to the inference results of the fuzzy control algorithm; through the step adjustment mechanism, the filter aperture and valve opening are gradually adjusted to ensure that the particle capture efficiency can be optimized in real time; Step S4 includes the following contents: S4.1, based on the turbulence intensity-particle concentration coupling index and critical velocity deviation factor obtained in step S3, the output data of the turbulence disturbance model is used as the initial basis for adjusting the angle and depth of the guide tube; S4.2, using CFD tools, the fluid movement inside the clarifier is simulated based on RANS. The RANS equation is as follows: ;in, is the turbulent mean velocity, is the fluid density, is the dynamic viscosity, represents the average pressure, is the Reynolds stress term, and Respectively represent the fluid and The velocity component in the direction, and Respectively represent the fluid and spatial coordinates in direction; S4.3, combined with CFD simulation results and turbulent energy distribution, the influence of high turbulent energy area on the settling area is simulated and analyzed to determine the inclination angle of the guide tube to suppress the transfer of turbulent energy to the settling area; the formula for adjusting the inclination angle is as follows: ;in, is the initial angle, is the turbulence energy, is the adjustment factor, is the rate of change of turbulent energy along the spatial coordinate; S4.4, evaluate the distribution of turbulence intensity at different depths based on the simulation results, and dynamically adjust the insertion depth of the guide tube; the insertion depth adjustment formula is as follows: ;in, is the initial insertion depth, is the adjustment factor, It represents the rate of change of turbulent energy with depth.

2. A mine water micro-sand separation system based on a dynamic filtration algorithm, used to implement the mine water micro-sand separation method based on a dynamic filtration algorithm as claimed in claim 1, characterized in that: include: Flow field monitoring module, prediction model module, control algorithm module and diversion optimization module; The flow field monitoring module collects the flow velocity, turbulence intensity data and flow velocity gradient data in the clarifier in real time through multi-point flow field sensors, converts them into frequency domain information using Fourier transform technology, extracts flow field fluctuation characteristics using fast Fourier transform algorithm, and identifies high turbulence areas; outputs turbulence intensity, flow velocity data, flow velocity gradient data and high turbulence areas to the prediction model module; The prediction model module performs multi-dimensional data fitting of turbulence intensity and velocity gradient based on real-time collected flow field data to build a turbulence disturbance prediction model. The turbulence disturbance prediction model is dynamically updated through the Kalman filter algorithm, and combined with historical data and real-time feedback, the critical value of turbulence intensity is calculated to locate the resuspension area. The resuspension area position and the critical value of turbulence intensity are output to the control algorithm module. The control algorithm module calculates the turbulence intensity-particle concentration coupling index and the critical velocity deviation factor based on the real-time data of turbulence intensity and velocity deviation. The real-time calculation results of the two indicators are used as the input of the fuzzy control algorithm to determine whether the filter opening rate and the flow rate regulating valve need to be adjusted. The turbulence intensity-particle concentration coupling index and the critical velocity deviation factor are sent to the diversion optimization module. The diversion optimization module is based on the turbulence intensity-particle concentration coupling index and the critical velocity deviation factor, combined with CFD simulation results and turbulence energy distribution, to adjust the angle and depth of the diversion tube, optimize the flow field structure, and suppress disturbances in the sedimentation zone.

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