Optimization method and system for tailing sand treatment process and storage medium

Abstract

The invention relates to the technical field of material detection and treatment, and discloses an optimization method and system for a tailing sand treatment process and a storage medium. The method comprises the following steps: analyzing and representing tailings sand components through X rays, and obtaining element composition and particle size parameters; treating the tailing sand by using an electromagnetic field system; adopting synchrotron radiation spectrum to determine chemical modification parameters for modification; small-angle X-ray scattering is adopted to determine nanometer doping parameters for doping; fiber reinforcement parameters are determined through tomography for reinforcement; foaming parameters are determined through ultra-fast X-ray scanning for foaming treatment, and a porous structure is obtained. According to the invention, the accurate regulation and control of the treatment process are realized, and the stability and controllability of the treatment effect are improved.

Description

Technical Field

[0001] The present application relates to the field of material detection and processing, and in particular to an optimization method, system and storage medium for a tailings sand processing process. Background Art

[0002] Tailings sand is a solid waste generated during the mining process. Its treatment and resource utilization have always been an important research topic in the field of environmental protection and materials science. At present, the treatment methods of tailings sand mainly include chemical modification, physical modification and composite modification. Chemical modification mainly uses surfactants, coupling agents, etc. to treat the surface of tailings sand to enhance its compatibility with other materials; physical modification includes mechanical crushing, heat treatment, electromagnetic treatment and other methods to improve the performance of tailings sand by changing its physical structure; composite modification combines a variety of modification methods, such as nanomaterial doping, fiber reinforcement, foaming treatment, etc., to obtain modified products with better comprehensive performance.

[0003] However, the existing tailings sand treatment methods have problems such as difficult to optimize process parameters and unstable treatment effects. The main manifestations are: due to the lack of real-time monitoring and precise control of the treatment process, the modification effect often relies on empirical judgment, resulting in large fluctuations in product quality; there is a lack of a systematic collaborative optimization mechanism between various modification methods, making it difficult to achieve the best composite modification effect; the key parameters of the treatment process lack a scientific basis for determination, resulting in waste of resources and increased costs. Especially in the multi-stage treatment process of tailings sand, due to the complex mutual influence between the various treatment steps, traditional single detection methods are difficult to achieve optimal control of the entire process. Summary of the invention

[0004] The present application provides an optimization method, system and storage medium for tailings sand treatment process, which are used to achieve precise regulation of the treatment process by establishing a systematic detection and analysis system, thereby improving the stability and controllability of the treatment effect.

[0005] In a first aspect, the present application provides an optimization method for a tailings sand treatment process, the optimization method for a tailings sand treatment process comprising: analyzing and processing the surface element content and particle size distribution of the tailings sand by a principal component analysis algorithm according to X-ray fluorescence spectrum data and laser particle size distribution data, to obtain element composition parameters and particle size distribution parameters;

[0006] According to the element composition parameters and the particle size distribution parameters, a three-dimensional electromagnetic field distribution detection system is used to collect magnetic field intensity data, electromagnetic field treatment parameters are determined through Fourier transform and wavelet analysis, and the tailings sand is subjected to electromagnetic field treatment to obtain electromagnetically treated tailings sand;

[0007] Based on the electromagnetically treated tailings sand, chemical bond change data are collected by synchrotron radiation X-ray absorption spectroscopy, chemical modification parameters are determined by least squares fitting, and the electromagnetically treated tailings sand is chemically modified to obtain chemically modified tailings sand;

[0008] According to the chemically modified tailings sand, small-angle X-ray scattering technology is used to collect nanoparticle distribution data, nano-doping parameters are determined by radial distribution function calculation, and nano-material doping is performed to obtain nano-doped tailings sand;

[0009] For the nano-doped tailings sand, three-dimensional fiber distribution data is collected by computer tomography, fiber reinforcement parameters are determined by Fourier transform and digital image correlation analysis, and fiber reinforcement treatment is performed to form fiber-reinforced tailings sand;

[0010] According to the fiber-reinforced tailings sand, ultra-fast X-ray scanning is used to collect bubble formation data, and foaming process parameters are determined through dynamic threshold segmentation and porosity calculation, and foaming treatment is performed to obtain porous tailings sand.

[0011] In a second aspect, the present application provides an optimization system for a tailings sand treatment process, the optimization system for a tailings sand treatment process comprising:

[0012] The acquisition module is used to analyze and process the surface element content and particle size distribution of the tailings sand according to the X-ray fluorescence spectrum data and the laser particle size distribution data through the principal component analysis algorithm to obtain the element composition parameters and particle size distribution parameters;

[0013] A processing module is used to collect magnetic field intensity data using a three-dimensional electromagnetic field distribution detection system according to the element composition parameters and the particle size distribution parameters, determine electromagnetic field processing parameters through Fourier transform and wavelet analysis, perform electromagnetic field processing on the tailings sand, and obtain electromagnetically processed tailings sand;

[0014] A modification module is used to collect chemical bond change data based on the electromagnetically treated tailings sand by using synchrotron radiation X-ray absorption spectroscopy, determine chemical modification parameters by least squares fitting, and chemically modify the electromagnetically treated tailings sand to obtain chemically modified tailings sand;

[0015] A calculation module is used to collect nanoparticle distribution data using small-angle X-ray scattering technology based on the chemically modified tailings sand, determine nano-doping parameters through radial distribution function calculation, perform nano-material doping, and obtain nano-doped tailings sand;

[0016] A scanning module is used for collecting three-dimensional fiber distribution data of the nano-doped tailings sand by computer tomography, determining fiber reinforcement parameters by Fourier transform and digital image correlation analysis, and performing fiber reinforcement processing to form fiber-reinforced tailings sand;

[0017] The foaming module is used to collect bubble formation data based on the fiber-reinforced tailings sand by ultra-fast X-ray scanning, determine foaming process parameters by dynamic threshold segmentation and porosity calculation, and perform foaming treatment to obtain porous tailings sand.

[0018] A third aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned optimization method for the tailings sand treatment process.

[0019] In the technical solution provided in this application, by adopting a characterization method combining X-ray fluorescence spectroscopy and laser particle size analysis, the accurate determination of the surface element content and particle size distribution of tailings sand is achieved, providing reliable basic data for subsequent processing; by using three-dimensional electromagnetic field distribution detection combined with Fourier transform and wavelet analysis, an optimization mechanism for electromagnetic field treatment parameters is established to ensure the uniformity and controllability of the electromagnetic treatment effect; by using synchrotron radiation X-ray absorption spectroscopy technology to monitor the chemical modification process in situ, the optimal modification parameters are determined by least squares fitting, and the accuracy of chemical modification is improved; by introducing small-angle X-ray scattering technology The technique and radial distribution function analysis were used to achieve quantitative characterization of the dispersion state of nanomaterials and ensure the uniformity of nano-doping; computed tomography combined with Fourier transform and digital image correlation analysis was used to establish a characterization method for fiber spatial distribution and optimize the fiber reinforcement effect; ultra-fast X-ray scanning technology combined with dynamic threshold segmentation and porosity calculation was used to achieve real-time monitoring of the foaming process, ensure the uniformity of the porous structure, and achieve precise control of the entire tailings sand treatment process, which significantly improved the stability and repeatability of the treatment effect and solved the problems of difficult parameter optimization and unstable effects in traditional treatment methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0021] Figure 1 A schematic diagram of an embodiment of an optimization method for a tailings sand treatment process in an embodiment of the present application;

[0022] Figure 2 It is a schematic diagram of an embodiment of an optimization system for tailings sand treatment process in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application embodiment provides an optimization method, system and storage medium for tailings sand treatment process. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than the content illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of the optimization method for the tailings sand treatment process in the embodiment of the present application includes:

[0025] Step S101, analyzing and processing the surface element content and particle size distribution of tailings sand by principal component analysis algorithm according to X-ray fluorescence spectrum data and laser particle size distribution data, to obtain element composition parameters and particle size distribution parameters;

[0026] Step S102: According to the element composition parameters and the particle size distribution parameters, a three-dimensional electromagnetic field distribution detection system is used to collect magnetic field intensity data, and electromagnetic field treatment parameters are determined by Fourier transform and wavelet analysis, and the tailings sand is subjected to electromagnetic field treatment to obtain electromagnetically treated tailings sand;

[0027] Step S103, based on the electromagnetic treatment of tailings sand, using synchrotron radiation X-ray absorption spectroscopy to collect chemical bond change data, determining chemical modification parameters by least squares fitting, chemically modifying the electromagnetic treatment tailings sand, and obtaining chemically modified tailings sand;

[0028] Step S104: collecting nanoparticle distribution data using small-angle X-ray scattering technology based on the chemically modified tailings sand, determining nano-doping parameters by radial distribution function calculation, performing nano-material doping, and obtaining nano-doped tailings sand;

[0029] Step S105: for the nano-doped tailings sand, three-dimensional fiber distribution data is collected by computer tomography, fiber reinforcement parameters are determined by Fourier transform and digital image correlation analysis, and fiber reinforcement treatment is performed to form fiber-reinforced tailings sand;

[0030] Step S106: Based on the fiber-reinforced tailings sand, ultra-fast X-ray scanning is used to collect bubble formation data, and foaming process parameters are determined through dynamic threshold segmentation and porosity calculation, and foaming treatment is performed to obtain porous tailings sand.

[0031] It is understandable that the execution subject of the present application may be an optimization system for the tailings sand treatment process, or a terminal or a server, which is not specifically limited here. The present application embodiment is described by taking the server as the execution subject as an example.

[0032] Specifically, X-ray fluorescence spectroscopy is used to quantitatively detect the elements on the surface of tailings sand. X-ray fluorescence spectroscopy uses the principle that a substance produces characteristic fluorescence radiation after being excited by X-rays, and the type and content of the elements are determined by analyzing the wavelength and intensity of the fluorescence radiation. When collecting X-ray fluorescence spectrum data, a scan is performed every 0.01nm within the wavelength range of 0.01-15nm, and the fluorescence intensity values ​​of silicon, aluminum, iron, calcium, and magnesium at each wavelength are recorded. The characteristic peaks of these elements appear at wavelengths of 0.71nm, 0.83nm, 0.19nm, 0.34nm, and 0.99nm, respectively. The content data of each element can be obtained by calculating the peak area of ​​the characteristic peaks. At the same time, laser particle size analysis technology is used to measure the particle size distribution of tailings sand. Laser particle size analysis is based on the principle of laser diffraction. When the laser is irradiated on the particles, a diffraction pattern related to the particle size is generated. The size distribution information of the particles can be obtained by analyzing this diffraction pattern. In this scheme, the laser particle size analyzer detects the tailings sand in the range of 0.1-1000μm, collects a data point every 0.5μm, and obtains the distribution data of tailings sand particles in different particle size ranges.

[0033] The collected element content data and particle size distribution data are processed using the principal component analysis algorithm. Principal component analysis is a statistical method that converts multidimensional data into low-dimensional data. The original data is projected onto a new set of orthogonal bases through linear transformation. This set of bases is arranged from large to small according to the data variance. In this scheme, the principal component analysis algorithm performs dimensionality reduction processing on the content data of five main elements and the distribution data of three particle size ranges (0.1-10μm, 10-100μm, 100-1000μm), and extracts the principal components with a contribution rate greater than 85% as the element composition parameters and particle size distribution parameters.

[0034] Based on the obtained elemental composition parameters and particle size distribution parameters, the electromagnetic field treatment process is optimized. Electromagnetic field treatment is a method of changing the arrangement and structure of tailings sand particles through the action of electromagnetic fields. In the present invention, a 24×24×24 dot matrix detector of a three-dimensional electromagnetic field distribution detection system is used to arrange measurement points every 0.5 cm in the x, y, and z directions to collect magnetic field intensity data. The collected magnetic field intensity data is processed by Fourier transform, the data in the spatial domain is converted to the frequency domain, and the contribution of different frequency components is analyzed. At the same time, the signal is multi-scale decomposed by the wavelet analysis method, and the frequency components with an energy share of more than 90% are selected. By analyzing the magnetic field spectrum data, combining the correlation between the elemental composition parameters and the particle size distribution parameters and the magnetic field intensity, the optimal electromagnetic field treatment parameters are determined. In the actual treatment process, the electromagnetic field intensity is controlled within the range of 0.4-0.6T, which is determined based on the content of magnetic elements such as iron and aluminum in the tailings sand and the particle size distribution characteristics. Through this electromagnetic field treatment, the rearrangement of charged particles in the tailings sand can be induced, affecting the growth orientation of the crystal.

[0035] The tailings sand treated with electromagnetic fields is characterized by synchrotron radiation X-ray absorption spectroscopy. Synchrotron radiation X-ray absorption spectroscopy is an effective means to study the local structure of materials. Chemical bond information is obtained by analyzing the energy changes when X-rays are absorbed by the material. In this scheme, the sample is scanned in the energy range of 5-30keV, and the absorption spectrum is recorded every 0.5eV. Focus on the absorption peaks of Si-O bonds at 1.84keV, Al-O bonds at 1.56keV, and Fe-O bonds at 7.11keV. The intensity changes of these absorption peaks directly reflect the changes in chemical bonds.

[0036] The chemical bond change data was fitted and analyzed by the least squares method to calculate the reactivity of different chemical bonds. The least squares method is a mathematical optimization method that finds the best fitting value of the data by minimizing the sum of squares of errors. Based on the fitting results, the addition ratio of chemical modifiers was determined, including 3-5% of silane coupling agent KH550, 1-2% of organic titanate TC-100, and 0.5-1% of sodium dodecylbenzene sulfonate. These modifiers react chemically with the active groups on the surface of tailings sand to form stable chemical bonds. For the chemically modified tailings sand, the dispersion state of nanomaterials was studied by small-angle X-ray scattering technology. Small-angle X-ray scattering is an effective method for observing nanoscale structures. The structural information of the material is obtained by analyzing the scattering behavior of X-rays in a small angle range. Scattering data was collected every 0.1nm-1 in the Q range of 0.1-10nm-1, and the spatial distribution of nanoparticles was calculated by radial distribution function conversion. After data analysis, the optimal particle size distribution of nano-aluminum oxide in the range of 20-40nm and nano-iron oxide in the range of 30-50nm, as well as their optimal ratio of 2:1 were determined.

[0037] In order to achieve the fiber reinforcement effect, computed tomography technology was used to perform three-dimensional imaging analysis on the nano-doped samples. During the scanning process, a tomographic image was collected every 1°, and a scan was performed every 5μm in the vertical direction. The collected image data was processed by Fourier transform to analyze the distribution characteristics of the fiber in the spatial frequency domain. The spatial orientation of basalt fiber and glass fiber was determined by digital image correlation analysis to optimize the fiber addition method. The process parameters were determined as follows: basalt fiber length of 10-15mm, glass fiber length of 6-8mm, mass ratio of 3:2, and total addition of 3-4%.

[0038] After the fiber reinforcement treatment is completed, the optimization of the foaming process is a key step in the preparation of porous tailings sand. Ultra-fast X-ray scanning technology is used to monitor the formation and evolution of bubbles in the foaming process in real time. During the scanning process, an ultra-high sampling frequency of 1000 frames per second is used, and the resolution of each frame image reaches 2048×2048 pixels, and the acquisition lasts for 10 minutes. This high-time resolution scanning method can capture the complete process of bubble formation, growth and rupture.

[0039] The collected bubble dynamic change data is segmented and the bubble area is identified by setting a threshold range of grayscale value 0-50. The selection of this grayscale value range is based on the difference in X-ray attenuation coefficient between the bubble and the matrix material. By calculating the area of ​​the bubble region in each frame of the image, the data of the bubble area changing over time is obtained. The area growth rate and rupture rate of the bubble can be obtained by performing difference calculation on the bubble area between adjacent frames. These data directly reflect the dynamic characteristics of the foaming process. The porosity change curve is calculated based on the bubble evolution data, and the 10-minute foaming process is divided into two stages (0-5 minutes and 5-10 minutes) for statistical analysis. Every 30 seconds is used as a time window to calculate the ratio of the total bubble area to the total sample area to obtain the porosity data in the time series. This segmented statistical method can reflect the dynamic change characteristics of the porosity during the foaming process.

[0040] The optimal foaming process parameters were determined by analyzing the porosity distribution data. These included 1.5-2% nitrogen generator addition, 1-1.5% sodium bicarbonate addition, and 0.3-0.5% sodium dodecyl sulfate addition. These parameters were determined based on the change of porosity over time and the stability of porosity in different time periods. The foaming treatment was carried out for 15-20 minutes at a temperature of 45-50°C to obtain a porous tailings sand material with a uniform pore structure. By precisely controlling and optimizing the process parameters of each treatment step, the multi-stage modification of tailings sand was successfully achieved. From the initial elemental composition and particle size distribution analysis, to electromagnetic field treatment, chemical modification, nanomaterial doping, fiber reinforcement, and finally foaming treatment, a complete treatment process system was formed. Advanced characterization methods and data analysis methods were used in each step to ensure the controllability and repeatability of the treatment effect.

[0041] In the examples of the present application, by adopting a characterization method combining X-ray fluorescence spectroscopy and laser particle size analysis, the precise determination of the surface element content and particle size distribution of the tailings sand is achieved, providing reliable basic data for subsequent processing; by using three-dimensional electromagnetic field distribution detection combined with Fourier transform and wavelet analysis, an optimization mechanism for electromagnetic field treatment parameters is established to ensure the uniformity and controllability of the electromagnetic treatment effect; by using synchrotron radiation X-ray absorption spectroscopy technology to monitor the chemical modification process in situ, the optimal modification parameters are determined by least squares fitting, and the accuracy of chemical modification is improved; by introducing small-angle X-ray scattering technology and Radial distribution function analysis realizes the quantitative characterization of the dispersion state of nanomaterials and ensures the uniformity of nano-doping; using computer tomography combined with Fourier transform and digital image correlation analysis, a characterization method for fiber spatial distribution was established to optimize the fiber reinforcement effect; ultra-fast X-ray scanning technology combined with dynamic threshold segmentation and porosity calculation was used to realize real-time monitoring of the foaming process, ensure the uniformity of the porous structure, and achieve precise control of the entire tailings sand treatment process, which significantly improves the stability and repeatability of the treatment effect and solves the problems of difficult parameter optimization and unstable effects in traditional treatment methods.

[0042] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0043] (1) When collecting X-ray fluorescence spectrum data, the surface of the tailings sand is scanned every 0.01 nm within the wavelength range of 0.01-15 nm, and the fluorescence intensity values ​​of silicon, aluminum, iron, calcium, and magnesium at each wavelength are recorded to form a fluorescence intensity data sequence;

[0044] (2) performing baseline correction and noise filtering on the fluorescence intensity data sequence, calculating the characteristic peak areas of silicon at a wavelength of 0.71 nm, aluminum at a wavelength of 0.83 nm, iron at a wavelength of 0.19 nm, calcium at a wavelength of 0.34 nm, and magnesium at a wavelength of 0.99 nm, and obtaining initial data on element content;

[0045] (3) Arranging the initial data of element content in the order of the mass numbers of silicon, aluminum, iron, calcium, and magnesium, and calculating the relative content percentages of these five elements respectively to obtain element content distribution data;

[0046] (4) Using a laser particle size analyzer to measure the particle size of the tailings sand in the range of 0.1-1000 μm, collecting a data point every 0.5 μm, and recording the number of tailings sand particles in each particle size interval to obtain initial particle size distribution data;

[0047] (5) normalizing the initial particle size distribution data, calculating the volume fractions of tailings sand particles in three particle size ranges of 0.1-10 μm, 10-100 μm, and 100-1000 μm, respectively, and obtaining volume distribution data;

[0048] (6) The relative content percentage values ​​of the five elements in the element content distribution data and the volume fraction values ​​of the three particle size intervals in the volume distribution data are input into the principal component analysis algorithm, and the principal components with a contribution rate greater than 85% are extracted respectively to obtain the element composition parameters and particle size distribution parameters.

[0049] Specifically, when atoms are irradiated by X-rays, the inner electrons are excited to transition to high energy levels or are ionized, and the outer electrons release characteristic fluorescent X-rays when filling the inner vacancies. The type and content of the element can be determined by measuring the wavelength and intensity of these characteristic X-rays. In the tailings sand processing process optimization method, the tailings sand surface is first scanned finely within the wavelength range of 0.01-15nm, and the scanning interval is set to 0.01nm. This high-precision scanning method ensures that the characteristic peaks of each element can be accurately captured. Silicon, aluminum, iron, calcium, and magnesium are the main components of tailings sand, and they produce characteristic fluorescence peaks at specific wavelength positions.

[0050] When processing the collected fluorescence intensity data sequence, baseline correction is first required to remove background signals caused by instrument drift, matrix effects and other factors. The baseline correction uses a polynomial fitting method to select a smooth area outside the characteristic peak as the baseline point, and the baseline curve of the entire spectrum is obtained by fitting. The noise filtering uses the Savitzky-Golay filtering algorithm, which smoothes the data by local polynomial fitting while maintaining the shape characteristics of the original signal. After baseline correction and noise filtering, the peak area of ​​the characteristic peak of each element is calculated. The characteristic peak of silicon is located at a wavelength of 0.71nm, aluminum at 0.83nm, iron at 0.19nm, calcium at 0.34nm, and magnesium at 0.99nm. The peak area is calculated using the trapezoidal integration method, and the integration range is the interval of the half-height width of the characteristic peak. After obtaining the initial data of the element content, the data needs to be arranged in order of the element mass number and the relative content is calculated. The element mass number represents the total number of protons and neutrons in the nucleus and is an important physical property of the element. In order of mass number from small to large, they are magnesium, aluminum, silicon, calcium, and iron. The relative content is calculated by dividing the content of each element by the sum of the contents of all measured elements to obtain a percentage value. This standardized processing method eliminates the impact of sample quantity differences and makes data from different batches comparable.

[0051] Laser particle size analysis is a particle size measurement method based on Mie scattering theory. When the laser beam is irradiated on the particles, scattered light will be generated. The intensity distribution of the scattered light is closely related to the particle size. In this scheme, a laser particle size analyzer is used to detect the particle size of tailings sand. The measurement range is 0.1-1000μm and the sampling interval is 0.5μm. This fine sampling method can accurately reflect the size distribution characteristics of tailings sand particles. For each particle size interval, the number of particles is recorded to form detailed particle size distribution data. The initial particle size distribution data is normalized to eliminate the influence of the total sample amount. The entire measurement range is divided into three main intervals: a fine particle interval of 0.1-10μm, a medium particle interval of 10-100μm, and a coarse particle interval of 100-1000μm. By calculating the proportion of the particle volume in each interval to the total volume, the standardized volume distribution data is obtained. The calculation of the volume fraction takes into account the spherical assumption of the particles, and the volume contribution of each interval is calculated using the number of particles and the corresponding particle size.

[0052] Finally, the principal component analysis algorithm is used to conduct a comprehensive analysis of the element content distribution data and volume distribution data. The core of principal component analysis is to convert correlated high-dimensional data into linearly independent low-dimensional data through orthogonal transformation. In this scheme, the input data includes the relative content percentage values ​​of five elements and the volume fraction values ​​of three particle size intervals, a total of eight variables. By calculating the eigenvalues ​​and eigenvectors of the data covariance matrix, sorting them according to the size of the eigenvalues, and selecting the eigenvectors with a cumulative contribution rate of more than 85% as the principal components, the element composition parameters and particle size distribution parameters after dimensionality reduction are obtained.

[0053] For example, a tailings sand sample was analyzed by X-ray fluorescence spectroscopy. After baseline correction and noise filtering, the fluorescence intensity values ​​recorded at each characteristic peak wavelength were calculated to be 4500 counts for silicon, 3200 counts for aluminum, 2800 counts for iron, 1500 counts for calcium, and 1000 counts for magnesium. The corresponding element content values ​​were converted by standard curve, arranged in order of mass number, and the relative content was calculated. At the same time, laser particle size analysis found that 15,000 particles were detected in the 0.1-10μm range, 25,000 particles in the 10-100μm range, and 5,000 particles in the 100-1000μm range. These particle data were converted into volume distributions and input into the principal component analysis algorithm together with the element content data. The main parameters that can characterize the sample characteristics were extracted through eigenvalue decomposition and contribution rate analysis.

[0054] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0055] (1) Using the 24×24×24 dot array detector of the three-dimensional electromagnetic field distribution detection system, the magnetic field intensity values ​​are collected at 0.5 cm intervals in the x, y, and z directions to obtain the original data of the magnetic field intensity;

[0056] (2) Using Fourier transform to transform the original data of magnetic field intensity in the x, y, and z directions respectively, and perform spectrum calculation every 0.1T in the range of 0-2T to obtain magnetic field spectrum data, where T is the unit of magnetic induction intensity;

[0057] (3) Perform wavelet analysis on the magnetic field spectrum data, calculate the coefficient values ​​at scales 1-8, select the frequency values ​​with energy accounting for more than 90%, and obtain the magnetic field main frequency data;

[0058] (4) Based on the numerical correlation between elemental composition parameters, particle size distribution parameters and magnetic field strength, a magnetic field strength threshold range of 0.4-0.6 T is determined by calculation to obtain magnetic field control data;

[0059] (5) Perform numerical comparative analysis on the magnetic field main frequency data and the magnetic field control data, select the most suitable frequency-intensity combination value, and obtain the electromagnetic field processing parameters;

[0060] (6) Based on the electromagnetic field treatment parameters, the tailings sand was subjected to electromagnetic field treatment at 25±2°C for 30 minutes to obtain electromagnetically treated tailings sand.

[0061] Specifically, the three-dimensional electromagnetic field distribution detection adopts a 24×24×24 dot matrix detector structure, which is equivalent to arranging 13824 detection points in a cubic space. These detection points are evenly distributed in the three spatial directions of x, y, and z, and the interval between adjacent points is 0.5 cm. Each detection point is equipped with a Hall element sensor to measure the magnetic field strength at that location. The Hall element is a magnetic field sensor based on the Hall effect principle. When a current-carrying conductor is in a magnetic field perpendicular to the direction of the current, a potential difference will be generated in a direction perpendicular to both the current and the magnetic field. This potential difference is proportional to the magnetic field strength. The collected raw data of the magnetic field strength is processed by Fourier transform. Fourier transform is a mathematical tool that converts time domain or spatial domain signals into frequency domain representation. In this scheme, the magnetic field strength data in the three directions of x, y, and z are subjected to three-dimensional Fourier transform respectively. Specifically, the data of each yz plane is first Fourier transformed in the x direction, then the obtained results are transformed in the y direction, and finally the transformation is completed in the z direction. In this way, the spectrum representation of the magnetic field distribution is obtained. In the magnetic field strength range of 0-2T, a sampling point was taken every 0.1T for spectrum analysis to obtain frequency components at different intensities.

[0062] Wavelet analysis is a time-frequency analysis method that uses wavelet basis functions of different scales to decompose the signal. In this scheme, the magnetic field spectrum data is decomposed by wavelets of 1-8 scales, and each scale corresponds to a different frequency range. Scale 1 corresponds to the highest frequency component, and scale 8 corresponds to the lowest frequency component. By calculating the wavelet coefficients at each scale and statistically analyzing the energy contribution of each scale, the frequency component with a cumulative energy share of more than 90% is selected as the main component. This method can effectively remove noise and retain the main features in the signal. There is an important correlation between the elemental composition parameters and the particle size distribution parameters and the magnetic field intensity. The higher the iron content, the stronger the material's response to the magnetic field; the smaller the particle size, the easier it is to rearrange under the action of the magnetic field. Based on the relative content of iron in the elemental composition parameters obtained previously, and the proportion of fine particles in the particle size distribution parameters, combined with the magnetization characteristics of the material, the optimal magnetic field intensity range of 0.4-0.6T was determined. This range can ensure sufficient magnetic field intensity without causing material agglomeration due to excessive intensity.

[0063] The magnetic field main frequency data reflects the response characteristics of the material under the action of magnetic fields of different frequencies, while the magnetic field control data gives the appropriate intensity range. By comparing and analyzing these two sets of data, the best combination within the main frequency data range and meeting the intensity requirements is found. This process is actually a multi-parameter optimization problem that requires considering both frequency and intensity factors. The selected electromagnetic field processing parameters must ensure that the strongest material response can be stimulated within a given intensity range.

[0064] It should be noted that in the embodiments of the present application, during the tailings sand treatment process, the range of 0.4-0.6T has a specific physical meaning: when the magnetic induction intensity reaches 0.4T, it is sufficient to affect the magnetic arrangement of the iron elements in the tailings sand and orient it; and when the magnetic induction intensity exceeds 0.6T, it will cause excessive aggregation of magnetic particles, which is not conducive to subsequent treatment; the magnetic field intensity in this range can achieve the regulation of its microstructure without destroying the basic structure of the tailings sand.

[0065] For example, a batch of tailings sand samples were measured for magnetic field strength using a 24×24×24 dot matrix detector, and a total of 13,824 data points were obtained. These data were first organized into a three-dimensional array according to the spatial coordinates, and then a three-dimensional Fourier transform was performed. The transformation results showed that there were multiple frequency components in the range of 0.4-0.6T. Subsequent wavelet analysis showed that the energy accounted for the highest proportion in the decomposition results of scales 3 and 4, and the frequencies corresponding to these two scales became the main frequency data. Combined with the 30% iron content and 65% fine particle content in the sample, it was determined that the alternating magnetic field corresponding to the main frequency was used for treatment at a magnetic field intensity of 0.5T. The entire treatment process was carried out at a constant temperature of 25±2℃ for 30 minutes, and electromagnetically treated tailings sand with an optimized microstructure was obtained.

[0066] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0067] (1) Performing energy scanning on the electromagnetically treated tailings sand, recording the X-ray absorption spectrum every 0.5 eV in the range of 5-30 keV, and obtaining the original absorption spectrum data;

[0068] (2) Based on the original absorption spectrum data, the absorption peak intensity values ​​of Si-O bond at 1.84keV, Al-O bond at 1.56keV, and Fe-O bond at 7.11keV are calculated within the energy range to obtain the chemical bond absorption data;

[0069] (3) Baseline correction is performed on the chemical bond absorption data, and background noise signals are eliminated, and the absorption curves of each chemical bond as it changes with energy are recorded to obtain chemical bond change data;

[0070] (4) Substituting the chemical bond change data into the least square method, calculating the reactivity values ​​of Si-O bonds, Al-O bonds, and Fe-O bonds, and obtaining chemical bond reaction data;

[0071] (5) Calculating the addition amount of silane coupling agent KH550 to be 3-5%, the addition amount of organic titanate TC-100 to be 1-2%, and the addition amount of sodium dodecylbenzene sulfonate to be 0.5-1% through chemical bond reaction data to obtain chemical modification parameters;

[0072] (6) According to the chemical modification parameters, the electromagnetic treated tailings sand is subjected to chemical modification treatment at 40-50° C. for 2-3 hours to obtain chemically modified tailings sand.

[0073] Specifically, in the tailings sand treatment process, when performing X-ray absorption spectroscopy analysis on the tailings sand after electromagnetic treatment, a fine scan is first performed within the energy range of 5-30keV. Among them, keV (kiloelectron volt) is a unit of energy, and 1keV is equal to the kinetic energy obtained by 1000 electrons at a voltage of 1 volt. The energy range of 5-30keV was chosen because this range covers the K absorption edge of elements such as Si, Al, and Fe. Recording the absorption spectrum every 0.5eV ensures sufficient energy resolution and can accurately capture subtle changes in chemical bonds. For the obtained absorption spectrum raw data, focus on the characteristic absorption peaks of Si-O bonds, Al-O bonds, and Fe-O bonds. These three chemical bonds are the most important types of chemical bonds in tailings sand, and produce characteristic absorption at different energy positions. The absorption peak of Si-O bond is at 1.84keV, which is caused by the transition of inner electrons of silicon atoms; the absorption peak of Al-O bond is at 1.56keV, reflecting the electronic structure characteristics of aluminum atoms; the absorption peak of Fe-O bond appears at 7.11keV, corresponding to the K-edge absorption of iron atoms. By measuring the intensity values ​​of these characteristic peaks, quantitative information of various chemical bonds can be obtained.

[0074] The processing of chemical bond absorption data requires baseline correction and noise elimination. The baseline correction uses a piecewise polynomial fitting method, selects the smooth areas on both sides of the characteristic peak as baseline points, and fits the baseline curve of the entire energy range. The elimination of background noise uses wavelet transform filtering, which can effectively retain the characteristic shape of the signal while removing high-frequency noise. After these data processing, clear chemical bond absorption curves are obtained, which accurately reflect the law of chemical bond changes with energy. The least squares method is an optimization algorithm used to find the best fitting parameters for data. In this scheme, the chemical bond change data is substituted into the least squares method to calculate the reactivity of each chemical bond. The calculation of the reaction activity value takes into account multiple parameters such as chemical bond absorption intensity, peak position and peak width. By establishing a mathematical model and solving the optimal solution, a quantitative indicator reflecting the reactivity of the chemical bond is obtained. The higher the reaction activity value, the easier it is for the chemical bond to participate in the chemical modification reaction.

[0075] Determining the amount of modifier added based on the chemical bond reaction data is the key to optimizing the modification effect. Silane coupling agent KH550 is an organosilicon compound containing amino and alkoxy groups. Its alkoxy group can undergo condensation reaction with the hydroxyl group on the surface of the tailings sand, and the amino group can react with other substances added later. Organic titanate TC-100 has a similar mechanism of action and can form coordination bonds on the surface of the tailings sand. Sodium dodecylbenzene sulfonate acts as a surfactant to improve the spreadability of the modifier on the surface of the tailings sand. The amount of these three modifiers added is calculated according to a certain proportional relationship based on the chemical bond reaction activity value.

[0076] For example, a batch of tailings sand samples that have been treated by electromagnetic radiation are first characterized by synchrotron radiation X-ray absorption spectroscopy. In the energy range of 5-30keV, a data point is recorded every 0.5eV to obtain an absorption spectrum. Through analysis, it is found that the absorption peak intensity of the Si-O bond at 1.84keV is relatively high, indicating that there are a large number of silicon-oxygen bonds available for reaction on the sample surface; the Al-O bond has a second absorption peak at 1.56keV; the Fe-O bond also shows obvious absorption characteristics at 7.11keV. After baseline correction and noise removal of these data, a clear chemical bond absorption curve is obtained. Substitute these curve data into the least squares calculation formula to solve the reactivity values ​​of the Si-O bond, Al-O bond and Fe-O bond. The results show that the Si-O bond has the highest reactivity, so when determining the modifier ratio, the addition amount of silane coupling agent KH550 is set at 4%, which can ensure that enough alkoxy groups react with surface hydroxyl groups. The addition amount of organic titanate TC-100 is set at 1.5%, mainly considering the low reactivity of Al-O bond and Fe-O bond, and this amount is enough to form a stable coordination bond. The addition amount of sodium dodecylbenzene sulfonate is set at 0.8%, which can ensure the uniform dispersion of the modifier without generating too much foam. Finally, the chemical modification treatment is carried out at a temperature of 45°C for 2.5 hours. The temperature is selected to accelerate the reaction rate while avoiding thermal decomposition of the modifier. The time is the optimal reaction cycle determined according to the reaction kinetics data.

[0077] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0078] (1) Using small angle X-ray scattering technology to measure the scattering intensity of chemically modified tailings sand, in the Q range of 0.1-10nm -1 Every 0.1nm -1 Collect a scattering point and obtain the original data of scattering intensity;

[0079] (2) performing background subtraction and normalization processing on the raw data of scattering intensity, calculating the scattering intensity corresponding to each scattering vector Q value, and obtaining scattering intensity distribution data;

[0080] (3) converting the scattering intensity distribution data through the radial distribution function, calculating the spatial distribution distance r value of nano-aluminum oxide and nano-iron oxide, and obtaining the nanoparticle distribution data;

[0081] (4) performing integrated statistics on the nanoparticle distribution data, calculating the particle number distribution of nano-aluminum oxide in the range of 20-40 nm and nano-iron oxide in the range of 30-50 nm, respectively, to obtain particle size statistics;

[0082] (5) Calculating the nano-doping parameters based on the particle size statistical data, the mass ratio of nano-aluminum oxide to nano-iron oxide is 2:1, and the total addition amount is 1-2%;

[0083] (6) The nano-doping parameters were introduced into an ultrasonic dispersion device, and the chemically modified tailings sand was doped with nanomaterials for 1 hour at a power of 300 W and a frequency of 40 kHz to obtain nano-doped tailings sand.

[0084] Specifically, small-angle X-ray scattering (SAXS) is an effective means to study nanoscale structures. The basic principle of this technology is that when X-rays pass through a sample, they produce small-angle scattering due to the uneven distribution of electron density. By analyzing the scattering pattern, the structural information of the material can be obtained. The scattering vector Q is a physical quantity that characterizes the scattering angle. Its unit is nm-1. The smaller the Q value, the smaller the scattering angle, which corresponds to the larger-scale structural features in the sample. In this scheme, a Q range of 0.1-10nm-1 is selected for measurement, and a scattering point is collected every 0.1nm-1. Such a sampling density is sufficient to capture the structural details of the nanoscale. For the processing of the raw data of the scattering intensity, background subtraction must be performed first. Background scattering mainly comes from air scattering and sample pool scattering. The scattering intensity of the empty sample pool and the pure solvent needs to be measured separately, and then subtracted from the total scattering intensity of the sample. Normalization processing is to standardize the scattering intensity according to the incident X-ray intensity and sample thickness, so that the data of different batches are comparable. After such processing of the scattering intensity corresponding to each scattering vector Q value, the obtained scattering intensity distribution data can truly reflect the structural characteristics of the sample.

[0085] The radial distribution function (PDF) is an important function that describes the spatial distribution of atoms in a substance. Fourier transform processing is required to convert the scattering intensity distribution data into the radial distribution function. This conversion process actually converts the scattering data in the reciprocal space into the distance distribution information in the real space. Through this conversion, the distribution distance r value of nano-aluminum oxide and nano-iron oxide particles in space can be directly obtained. The distribution distance r value reflects the relative position relationship between nanoparticles and is an important parameter for evaluating the dispersion state. The integral statistics of nanoparticle distribution data is an important data processing step. For nano-aluminum oxide, the focus is on the particle size distribution in the range of 20-40nm; for nano-iron oxide, the focus is on analyzing the distribution in the range of 30-50nm. The selection of these two ranges is based on the optimal working size of these nanomaterials in tailings sand modification. By integrating the distribution curve within a specific range, the number distribution of nanoparticles in each size range can be obtained.

[0086] The mass ratio of nano-alumina and nano-iron oxide is set to 2:1, which is determined based on their synergistic effect in tailings sand. Nano-alumina mainly provides a larger specific surface area and stronger surface activity, while nano-iron oxide mainly plays a role in enhancing magnetism and structural stability. The total addition amount is controlled within the range of 1-2%, which can ensure sufficient modification effect without causing agglomeration due to excessive addition. Ultrasonic dispersion is the key process to evenly disperse nanomaterials into tailings sand. The cavitation effect generated when ultrasonic waves propagate in the medium can effectively break the agglomeration of nanoparticles. The ultrasonic power of 300W and the frequency of 40kHz are optimized process parameters. This power can provide sufficient dispersion energy without causing the sample temperature to rise too quickly due to excessive power; the frequency of 40kHz can ensure good cavitation effect and dispersion uniformity.

[0087] For example, a batch of chemically modified tailings sand was subjected to small-angle X-ray scattering analysis, and a scattering intensity value was recorded every 0.1nm-1 in the Q range from 0.1 to 10nm-1. After deducting air scattering and sample pool scattering, and normalizing the incident light intensity and sample thickness, a pure scattering signal was obtained. These data were converted into radial distribution functions by Fourier transform, showing the spatial distribution characteristics of nanoparticles. The optimal addition ratio was determined by analyzing the distribution of nano-alumina in the range of 20-40nm and the distribution of nano-iron oxide in the range of 30-50nm. Dispersion treatment was carried out under ultrasonic conditions of 300W power and 40kHz frequency to obtain uniformly dispersed nano-doped tailings sand.

[0088] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0089] (1) Perform 360° circular scanning imaging on the nano-doped tailings sand, collect a tomographic image every 1°, and scan once every 5 μm in the vertical direction to obtain three-dimensional scanning raw data;

[0090] (2) Calibrate the grayscale value of the original data of the three-dimensional scanning, set the grayscale threshold ranges of 150-200 and 100-150 for basalt fiber and glass fiber, respectively, and obtain fiber identification data;

[0091] (3) Performing Fourier transform processing on the fiber identification data, calculating the amplitude distribution of basalt fiber and glass fiber in the spatial frequency domain, and obtaining spatial spectrum data;

[0092] (4) Inputting the spatial spectrum data into digital image correlation analysis, calculating the orientation angles of basalt fiber and glass fiber in the x, y, and z axes, and obtaining the three-dimensional distribution data of the fiber;

[0093] (5) Based on the three-dimensional fiber distribution data, the fiber reinforcement parameters are calculated as follows: the basalt fiber length is 10-15 mm, the glass fiber length is 6-8 mm, the mass ratio is 3:2, and the total addition amount is 3-4%;

[0094] (6) According to the fiber reinforcement parameters, the nano-doped tailings sand is stirred and mixed at a rotation speed of 150-200 r / min for 20-30 minutes to obtain fiber-reinforced tailings sand.

[0095] Specifically, 360° ring scanning imaging is an accurate 3D reconstruction method that records the projection information of the sample at 360 angles by collecting a tomographic image every 1°. At the same time, a scan is performed every 5μm in the vertical direction, and such a scanning interval is sufficient to capture the spatial distribution details of the fiber. Micron-level spatial resolution ensures accurate characterization of fiber morphology and position. Grayscale calibration is a key step in identifying different types of fibers. The grayscale value reflects the attenuation degree of the material to X-rays, and different materials will present different grayscale values ​​due to differences in density and composition. Basalt fiber has a strong attenuation of X-rays due to its higher density, and its grayscale value range is set between 150-200; glass fiber has a relatively low density, and its grayscale value range is set to 100-150. This grayscale threshold segmentation method based on material properties can accurately distinguish between the two types of fibers.

[0096] Fourier transform is an important mathematical tool for analyzing the spatial distribution of fibers. The fiber identification data is subjected to a three-dimensional Fourier transform to obtain the spectrum information in the spatial frequency domain. In the frequency domain, the orientation characteristics of the fiber are manifested as the enhancement of the frequency components in a specific direction. By analyzing the amplitude distribution of these frequency components, the main orientation of the fiber can be determined. Basalt fiber and glass fiber show different amplitude distribution characteristics in the spatial frequency domain, which are directly related to the spatial arrangement of the fiber. Digital image correlation analysis is an analysis method based on image feature matching. After the spatial spectrum data is input into the digital image correlation analysis, the orientation angle of the fiber in three-dimensional space is calculated by tracking the feature points in the image. Specifically, the angle between the fiber and the coordinate axis is calculated in the three directions of x, y, and z, respectively, to obtain the complete three-dimensional distribution information of the fiber. These angle data directly reflect the arrangement state of the fiber in space.

[0097] The determination of fiber reinforcement parameters is based on the optimization calculation of the three-dimensional distribution data of the fiber. The length of basalt fiber is selected in the range of 10-15mm because this length can provide sufficient reinforcement effect without being too long to be difficult to disperse; the length of glass fiber is selected in the range of 6-8mm, mainly considering its synergistic effect with basalt fiber. The mass ratio of the two fibers of 3:2 is determined based on their mechanical properties and spatial distribution characteristics. This ratio can give full play to the respective advantages of the two fibers. The total addition amount is controlled within the range of 3-4%, which is the balance point between ensuring the reinforcement effect and maintaining workability.

[0098] For example, a batch of nano-doped tailings sand samples was first CT scanned to obtain 360 tomographic images. After grayscale value calibration, these images clearly show the distribution of basalt fibers in the grayscale value range of 150-200 and the distribution of glass fibers in the grayscale value range of 100-150. Through Fourier transform processing, it was observed that basalt fibers showed strong radial characteristics in the frequency domain, while glass fibers showed a more uniform frequency distribution. Digital image correlation analysis showed that the two fibers formed a complementary network structure in space. Based on these analysis results, basalt fibers with a length of 12 mm and glass fibers with a length of 7 mm were selected and added in a mass ratio of 3:2, with a total addition of 3.5%. After stirring at a speed of 175 r / min for 25 minutes, fiber-reinforced tailings sand with uniform fiber distribution and reasonable orientation was obtained.

[0099] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0100] (1) Ultra-fast X-ray scanning of fiber-reinforced tailings sand at 1000 frames per second was performed to collect a 10-minute scanning sequence with a resolution of 2048 × 2048 pixels per frame to obtain bubble dynamic change data;

[0101] (2) segmenting the bubble dynamic change data, calculating the bubble area within the grayscale value range of 0-50 in each frame image, and obtaining the bubble area data;

[0102] (3) performing difference calculation on the bubble area data between adjacent frames, counting the bubble area growth rate and rupture rate, and obtaining the bubble evolution data;

[0103] (4) calculating the porosity change curve based on the bubble evolution data, and calculating the average porosity of 0-5 minutes and 5-10 minutes respectively to obtain the porosity distribution data;

[0104] (5) Calculating the addition amount of nitrogen generating agent 1.5-2%, sodium bicarbonate 1-1.5%, and sodium dodecyl sulfate 0.3-0.5% based on the porosity distribution data to obtain the foaming process parameters;

[0105] (6) Inputting the foaming process parameters into a closed stirring device, foaming the fiber-reinforced tailings sand for 15-20 minutes at a temperature of 45-50° C., to obtain porous tailings sand.

[0106] Specifically, the ultra-high temporal resolution scanning of 1000 frames per second can accurately capture the transient process of bubble formation, growth and rupture. Each frame of the image adopts a high spatial resolution of 2048×2048 pixels, ensuring the accurate recording of the bubble morphology. In the 10-minute scanning time, a total of 600,000 frames of images were collected to form a complete bubble evolution sequence. This high temporal and spatial resolution scanning method provides a reliable data basis for subsequent quantitative analysis. Image region segmentation is a key step in extracting bubble information from a complex background. Since the bubbles are filled with gas, the attenuation of X-rays is weak, which is manifested as a lower grayscale value in the image. The grayscale value range is set between 0-50, which is determined by the difference in attenuation coefficient between the bubble and the surrounding medium. The bubble area can be accurately identified by thresholding each frame of the image. After image segmentation, the connected region labeling algorithm is used to number the bubbles and calculate the area of ​​each bubble. This process processes 600,000 frames of images one by one to obtain bubble area data that changes with time.

[0107] The calculation of the difference in bubble area between adjacent frames reflects the dynamic evolution of bubbles. For each marked bubble, the area change between two adjacent frames is calculated. An increase in area indicates bubble growth, while a decrease or disappearance in area indicates bubble rupture. The bubble growth rate is obtained by calculating the ratio of the area growth of the bubble per unit time to the initial area; the bubble rupture rate is obtained by calculating the ratio of the number of ruptured bubbles per unit time to the total number of bubbles. These two parameters directly reflect the dynamic characteristics of the foaming process. The calculation of the porosity change curve converts the microscopic bubble evolution data into macroscopic structural parameters. The 10-minute foaming process is divided into two stages (0-5 minutes and 5-10 minutes), and the average porosity of each stage is calculated respectively. The calculation method is to divide the sum of the bubble areas in each frame of the image by the area of ​​the entire observation area to obtain the instantaneous porosity, and then average the porosity values ​​of each stage. This segmented statistical method can reflect the evolution law of the material structure during the foaming process.

[0108] The foaming process parameters are determined by optimization calculation based on porosity distribution data. Nitrogen generator is the main gas source, and its addition amount affects the bubble generation rate; sodium bicarbonate, as an auxiliary foaming agent, can provide stable gas release; sodium dodecyl sulfate, as a surfactant, reduces the surface tension of bubbles and improves foam stability. The addition amount of these three substances is determined according to the porosity change law: in the first 5 minutes, more gas sources are needed for the rapid increase of porosity, and good stability is needed in the last 5 minutes to maintain the pore structure.

[0109] For example, a batch of fiber-reinforced tailings sand samples recorded the entire process during the foaming process through ultra-fast X-ray scanning. The scanning sequence clearly shows the entire process from bubble formation to stabilization: in the first 5 minutes, a large number of small bubbles are formed rapidly, the average diameter gradually increases from the initial 10 microns, and the number of bubbles increases rapidly; after entering the 5-10 minute stage, the growth rate of the bubbles slows down significantly, and the phenomenon of small bubbles merging to form larger bubbles is observed, but the rupture rate remains at a low level. This evolution law indicates that it is necessary to provide sufficient gas source in the early stage, and it is necessary to pay attention to the stability of the foam in the later stage. Based on this, the addition amount of nitrogen generator is determined to be 1.8% to ensure sufficient gas generation; the addition amount of sodium bicarbonate is 1.2% to provide continuous gas supplementation; the addition amount of sodium dodecyl sulfate is 0.4% to maintain appropriate surface activity. These parameters are input into a closed stirring device, and the foaming treatment is carried out at 48°C for 18 minutes to obtain porous tailings sand with uniform pore size distribution and stable structure.

[0110] In a specific embodiment, the porosity change curve is calculated based on the bubble evolution data, and the average porosity of 0-5 minutes and 5-10 minutes is respectively counted to obtain the porosity distribution data, including:

[0111] (1) The bubble evolution data is segmented into time windows of 30 seconds each, and the ratio of the total bubble area to the total sample area is calculated for each segment to obtain the time series porosity data;

[0112] (2) The time series porosity data were sampled every 30 seconds in the interval of 0-5 minutes, and the porosity values ​​of 10 sampling points were accumulated and averaged to obtain the front-end porosity data;

[0113] (3) The time series porosity data were sampled every 30 seconds within the 5-10 minute interval, and the porosity values ​​of 10 sampling points were accumulated and averaged to obtain the latter-stage porosity data;

[0114] (4) curve fitting is performed on the porosity data of the first section and the porosity data of the second section, and the rate of change of the porosity in the two time periods is calculated to obtain the porosity dynamic data;

[0115] (5) Based on the porosity dynamic data, the porosity standard deviation of the two time periods of 0-5 minutes and 5-10 minutes is calculated to obtain the porosity distribution data.

[0116] Specifically, in the tailings sofa foaming process, 30 seconds is selected as the time window for data segmentation processing, which is the result of weighing the statistical significance of the data and the dynamic response characteristics. Every 30 seconds contains 30,000 frames of image data, which is enough to ensure the reliability of statistics and reflect the dynamic change characteristics of porosity. For the data in each time window, the porosity value at that time point is obtained by calculating the ratio of the total area of ​​bubbles to the total area of ​​the sample observation area, thereby constructing a complete time series porosity data. In the initial stage of 0-5 minutes, sampling is performed every 30 seconds, and a total of 10 sampling points are obtained. This sampling frequency can accurately capture the characteristics of rapid changes in porosity in the initial stage of foaming. The porosity values ​​of these 10 sampling points are accumulated and averaged, and the obtained front-end porosity data reflects the overall trend of the initial stage of foaming. Each sampling point contains statistical information in the 30-second time window before and after, so this average value is very representative.

[0117] For the subsequent stage of 5-10 minutes, the same sampling interval and data processing method were used. The porosity changes in this stage are relatively gentle, which mainly reflects the stability of the foam structure. Similarly, 10 sampling points were averaged to obtain the porosity data of the latter stage. The same sampling and statistical methods were used in the two stages to ensure the comparability of the data. Curve fitting is an important means to analyze the dynamic changes of porosity. Polynomial fitting was performed on the porosity data of the front section and the porosity data of the latter section respectively to obtain two smooth change curves. The rate of change of porosity was obtained by calculating the derivative of the curve at each time point. This rate of change directly reflects the kinetic characteristics of the foaming process: it usually appears as a large positive value in the first period, indicating that the porosity increases rapidly; it approaches zero in the latter period, indicating that the structure tends to be stable.

[0118] For example, the bubble evolution data of a batch of tailings sand samples during the foaming process was first divided into 20 30-second time windows. In each window, the ratio of bubble area to sample area was calculated by image analysis. For example, in the time window of 2 minutes and 30 seconds, 30,000 frames of images were processed, the total bubble area in each frame was calculated, and then divided by the area of ​​the sample observation area of ​​2048×2048 pixels to obtain the average porosity in these 30 seconds. Similarly, 10 porosity values ​​were obtained at 10 sampling time points from 0 to 5 minutes. After polynomial fitting, these data clearly showed that the porosity rose rapidly in the first 3 minutes, and then the growth rate gradually decreased. In the latter data of 5-10 minutes, the rate of change of porosity further decreased and tended to be stable. By calculating the standard deviation of the porosity values ​​in the two time periods, it was found that the standard deviation of the former period was significantly greater than that of the latter period. This difference quantitatively reflects the characteristics of the foaming process from dynamic development to stability.

[0119] The above describes the optimization method for the tailings sand treatment process in the embodiment of the present application. The following describes the optimization system for the tailings sand treatment process in the embodiment of the present application. Figure 2 In the embodiment of the present application, an optimization system for the tailings sand treatment process includes:

[0120] The acquisition module 201 is used to analyze and process the surface element content and particle size distribution of the tailings sand according to the X-ray fluorescence spectrum data and the laser particle size distribution data by using the principal component analysis algorithm to obtain the element composition parameters and particle size distribution parameters;

[0121] The processing module 202 is used to collect magnetic field intensity data using a three-dimensional electromagnetic field distribution detection system according to the element composition parameters and the particle size distribution parameters, determine electromagnetic field treatment parameters through Fourier transform and wavelet analysis, and perform electromagnetic field treatment on the tailings sand to obtain electromagnetically treated tailings sand;

[0122] A modification module 203 is used to collect chemical bond change data based on the electromagnetically treated tailings sand by using synchrotron radiation X-ray absorption spectroscopy, determine chemical modification parameters by least squares fitting, and chemically modify the electromagnetically treated tailings sand to obtain chemically modified tailings sand;

[0123] The calculation module 204 is used to collect nanoparticle distribution data using small-angle X-ray scattering technology according to the chemically modified tailings sand, determine nano-doping parameters through radial distribution function calculation, perform nano-material doping, and obtain nano-doped tailings sand;

[0124] The scanning module 205 is used to collect the three-dimensional fiber distribution data of the nano-doped tailings sand by computer tomography, determine the fiber reinforcement parameters by Fourier transform and digital image correlation analysis, perform fiber reinforcement treatment, and form fiber-reinforced tailings sand;

[0125] The foaming module 206 is used to collect bubble formation data based on the fiber-reinforced tailings sand by ultra-fast X-ray scanning, determine foaming process parameters by dynamic threshold segmentation and porosity calculation, and perform foaming treatment to obtain porous tailings sand.

[0126] Through the synergistic cooperation of the above-mentioned components, the precise determination of the surface element content and particle size distribution of tailings sand was achieved by adopting a characterization method combining X-ray fluorescence spectroscopy and laser particle size analysis, providing reliable basic data for subsequent treatment; an optimization mechanism for electromagnetic field treatment parameters was established by using three-dimensional electromagnetic field distribution detection combined with Fourier transform and wavelet analysis, ensuring the uniformity and controllability of the electromagnetic treatment effect; synchrotron radiation X-ray absorption spectroscopy was used to monitor the chemical modification process in situ, and the optimal modification parameters were determined by least squares fitting, thereby improving the accuracy of chemical modification; small-angle X-ray scattering was introduced to obtain the optimal modification parameters. The scattering technology and radial distribution function analysis were used to quantitatively characterize the dispersion state of nanomaterials and ensure the uniformity of nano-doping. Computer tomography, Fourier transform and digital image correlation analysis were used to establish a characterization method for fiber spatial distribution and optimize the fiber reinforcement effect. Ultra-fast X-ray scanning technology combined with dynamic threshold segmentation and porosity calculation were used to realize real-time monitoring of the foaming process, ensure the uniformity of the porous structure, and achieve precise control of the entire tailings sand treatment process, which significantly improved the stability and repeatability of the treatment effect and solved the problems of difficult parameter optimization and unstable effect in traditional treatment methods.

[0127] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein instructions are stored in the computer, and when the instructions are executed on a computer, the computer executes the steps of the optimization method for the tailings sand processing process.

[0128] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0129] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0130] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing tailings sand processing flow, characterized in that: The optimization method for tailings sand treatment process includes: According to the X-ray fluorescence spectrum data and laser particle size distribution data, the element content and particle size distribution of the tailings sand surface are analyzed and processed by the principal component analysis algorithm to obtain the element composition parameters and particle size distribution parameters; According to the element composition parameters and the particle size distribution parameters, a three-dimensional electromagnetic field distribution detection system is used to collect magnetic field intensity data, electromagnetic field treatment parameters are determined through Fourier transform and wavelet analysis, and the tailings sand is subjected to electromagnetic field treatment to obtain electromagnetically treated tailings sand; Based on the electromagnetically treated tailings sand, chemical bond change data are collected by synchrotron radiation X-ray absorption spectroscopy, chemical modification parameters are determined by least squares fitting, and the electromagnetically treated tailings sand is chemically modified to obtain chemically modified tailings sand; According to the chemically modified tailings sand, small-angle X-ray scattering technology is used to collect nanoparticle distribution data, nano-doping parameters are determined by radial distribution function calculation, and nano-material doping is performed to obtain nano-doped tailings sand; For the nano-doped tailings sand, three-dimensional fiber distribution data is collected by computer tomography, fiber reinforcement parameters are determined by Fourier transform and digital image correlation analysis, and fiber reinforcement treatment is performed to form fiber-reinforced tailings sand; According to the fiber-reinforced tailings sand, ultra-fast X-ray scanning is used to collect bubble formation data, and foaming process parameters are determined through dynamic threshold segmentation and porosity calculation, and foaming treatment is performed to obtain porous tailings sand.

2. The optimization method for tailings sand treatment process according to claim 1, characterized in that: According to the X-ray fluorescence spectrum data and laser particle size distribution data, the element content and particle size distribution of the tailings sand surface are analyzed and processed by the principal component analysis algorithm to obtain element composition parameters and particle size distribution parameters, including: When collecting X-ray fluorescence spectrum data, the surface of the tailings sand is scanned every 0.01nm within the wavelength range of 0.01-15nm, and the fluorescence intensity values ​​of silicon, aluminum, iron, calcium and magnesium at each wavelength are recorded to form a fluorescence intensity data sequence; Baseline correction and noise filtering are performed on the fluorescence intensity data sequence, characteristic peak areas of silicon at a wavelength of 0.71 nm, aluminum at a wavelength of 0.83 nm, iron at a wavelength of 0.19 nm, calcium at a wavelength of 0.34 nm, and magnesium at a wavelength of 0.99 nm are calculated, and initial data of element content are obtained; Arrange the initial element content data in the order of the mass numbers of silicon, aluminum, iron, calcium, and magnesium, and calculate the relative content percentages of the five elements to obtain element content distribution data; The tailings sand particle size was measured in the range of 0.1-1000 μm using a laser particle size analyzer. A data point was collected every 0.5 μm, and the number of tailings sand particles in each particle size interval was recorded to obtain the initial particle size distribution data. Normalizing the initial particle size distribution data, calculating the volume fractions of tailings sand particles in three particle size ranges of 0.1-10 μm, 10-100 μm, and 100-1000 μm, respectively, to obtain volume distribution data; The relative content percentage values ​​of the five elements in the element content distribution data and the volume fraction values ​​of the three particle size intervals in the volume distribution data are input into the principal component analysis algorithm, and the principal components with contribution rates greater than 85% are extracted respectively to obtain element composition parameters and particle size distribution parameters.

3. The optimization method for tailings sand treatment process according to claim 1, characterized in that: The method comprises: collecting magnetic field intensity data using a three-dimensional electromagnetic field distribution detection system according to the element composition parameters and the particle size distribution parameters, determining electromagnetic field treatment parameters through Fourier transform and wavelet analysis, and performing electromagnetic field treatment on the tailings sand to obtain electromagnetically treated tailings sand, including: The 24×24×24 dot matrix detector of the three-dimensional electromagnetic field distribution detection system is used to collect magnetic field intensity values ​​at 0.5 cm intervals in the x, y, and z directions to obtain the original data of magnetic field intensity; The original data of magnetic field intensity are transformed in the x, y and z directions respectively by Fourier transform, and the spectrum is calculated every 0.1T in the range of 0-2T to obtain the magnetic field spectrum data, wherein T is the unit of magnetic induction intensity; Perform wavelet analysis on the magnetic field spectrum data, calculate coefficient values ​​at scales 1-8, select frequency values ​​with energy accounting for more than 90%, and obtain magnetic field main frequency data; According to the numerical correlation between the element composition parameter, the particle size distribution parameter and the magnetic field strength, a magnetic field strength threshold range of 0.4-0.6T is determined by calculation to obtain magnetic field control data; Performing numerical comparison and analysis on the magnetic field main frequency data and the magnetic field control data, selecting the most suitable frequency-intensity combination value, and obtaining the electromagnetic field processing parameters; Based on the electromagnetic field treatment parameters, the tailings sand was subjected to electromagnetic field treatment for 30 minutes at 25±2° C. to obtain electromagnetically treated tailings sand.

4. The optimization method for tailings sand treatment process according to claim 1, characterized in that: The method comprises: collecting chemical bond change data based on the electromagnetically treated tailings sand by using synchrotron radiation X-ray absorption spectroscopy, determining chemical modification parameters by least squares fitting, chemically modifying the electromagnetically treated tailings sand, and obtaining chemically modified tailings sand, comprising: Performing energy scanning on the electromagnetically treated tailings sand, recording an X-ray absorption spectrum every 0.5 eV in the range of 5-30 keV, and obtaining raw absorption spectrum data; According to the absorption spectrum raw data, the absorption peak intensity values ​​of Si-O bond at 1.84keV, Al-O bond at 1.56keV, and Fe-O bond at 7.11keV are calculated within the energy range to obtain chemical bond absorption data; Baseline correction is performed on the chemical bond absorption data, and background noise signals are eliminated, and absorption curves of each chemical bond as it changes with energy are recorded to obtain chemical bond change data; Substituting the chemical bond change data into the least square method, calculating the reactivity values ​​of Si-O bonds, Al-O bonds, and Fe-O bonds, and obtaining chemical bond reaction data; The chemical modification parameters are obtained by calculating the addition amount of silane coupling agent KH550 to be 3-5%, the addition amount of organic titanate TC-100 to be 1-2%, and the addition amount of sodium dodecylbenzene sulfonate to be 0.5-1% through the chemical bond reaction data; The electromagnetic treated tailings sand is subjected to chemical modification treatment at 40-50° C. for 2-3 hours according to the chemical modification parameters to obtain chemically modified tailings sand.

5. The optimization method for tailings sand treatment process according to claim 1, characterized in that: The method comprises: collecting nanoparticle distribution data by using small-angle X-ray scattering technology according to the chemically modified tailings sand, determining nano-doping parameters by radial distribution function calculation, performing nano-material doping, and obtaining nano-doped tailings sand, including: The scattering intensity of the chemically modified tailings sand was measured using small angle X-ray scattering technology in the Q range of 0.1-10nm -1 Every 0.1nm -1 Collect a scattering point to obtain the original data of scattering intensity; Performing background subtraction and normalization processing on the scattering intensity raw data, calculating the scattering intensity corresponding to each scattering vector Q value, and obtaining scattering intensity distribution data; The scattering intensity distribution data is converted through a radial distribution function, and the spatial distribution distance r value of nano-aluminum oxide and nano-iron oxide is calculated to obtain nanoparticle distribution data; Performing integrated statistics on the nanoparticle distribution data, respectively calculating the particle number distribution of nano-aluminum oxide in the range of 20-40nm and nano-iron oxide in the range of 30-50nm, and obtaining particle size statistical data; According to the particle size statistical data, the mass ratio of nano-aluminum oxide to nano-iron oxide is calculated to be 2:1, and the total addition amount is 1-2%, thereby obtaining nano-doping parameters; The nano-doping parameters were introduced into an ultrasonic dispersion device, and the chemically modified tailings sand was doped with nano-materials for 1 hour under the conditions of a power of 300 W and a frequency of 40 kHz to obtain nano-doped tailings sand.

6. The optimization method for tailings sand treatment process according to claim 1, characterized in that: The method comprises: collecting three-dimensional fiber distribution data by computer tomography, determining fiber reinforcement parameters by Fourier transform and digital image correlation analysis, and performing fiber reinforcement treatment to form fiber-reinforced tailings sand by using the nano-doped tailings sand; and The nano-doped tailings sand is subjected to 360° circular scanning imaging, a tomographic image is collected at intervals of 1°, and a scan is performed at intervals of 5 μm in the vertical direction to obtain three-dimensional scanning raw data; The grayscale value of the three-dimensional scanning raw data is calibrated, and the grayscale threshold ranges of basalt fiber and glass fiber are set to 150-200 and 100-150 respectively to obtain fiber identification data; Performing Fourier transform processing on the fiber identification data, calculating the amplitude distribution of basalt fiber and glass fiber in the spatial frequency domain, and obtaining spatial spectrum data; Input the spatial spectrum data into digital image correlation analysis, calculate the orientation angles of basalt fiber and glass fiber on the x, y, and z axes, and obtain fiber three-dimensional distribution data; Based on the three-dimensional fiber distribution data, the fiber reinforcement parameters are calculated to be 10-15 mm in length of basalt fiber, 6-8 mm in length of glass fiber, 3:2 in mass ratio, and 3-4% in total addition amount; According to the fiber reinforcement parameters, the nano-doped tailings sand is stirred and mixed for 20-30 minutes at a rotation speed of 150-200 r / min to obtain fiber-reinforced tailings sand.

7. The optimization method for tailings sand treatment process according to claim 1, characterized in that: The fiber-reinforced tailings sand is subjected to ultrafast X-ray scanning to collect bubble formation data, and foaming process parameters are determined by dynamic threshold segmentation and porosity calculation to perform foaming treatment to obtain porous tailings sand, including: The fiber-reinforced tailings sand is subjected to ultra-fast X-ray scanning at 1000 frames per second, and a 10-minute scanning sequence is collected, with a resolution of 2048×2048 pixels per frame, to obtain bubble dynamic change data; The bubble dynamic change data is segmented into regions, and the bubble area within the gray value range of 0-50 in each frame of the image is calculated to obtain the bubble area data; Performing difference calculation on the bubble area data between adjacent frames, counting the bubble area growth rate and rupture rate, and obtaining bubble evolution data; Calculate the porosity change curve based on the bubble evolution data, and calculate the average porosity of 0-5 minutes and 5-10 minutes respectively to obtain porosity distribution data; According to the porosity distribution data, the addition amount of nitrogen generator is 1.5-2%, the addition amount of sodium bicarbonate is 1-1.5%, and the addition amount of sodium dodecyl sulfate is 0.3-0.5%, and the foaming process parameters are obtained; The foaming process parameters are input into a closed stirring device, and the fiber-reinforced tailings sand is foamed for 15-20 minutes at a temperature of 45-50° C. to obtain porous tailings sand.

8. The optimization method for tailings sand treatment process according to claim 7, characterized in that: The porosity change curve is calculated based on the bubble evolution data, and the average porosity of 0-5 minutes and 5-10 minutes is respectively counted to obtain the porosity distribution data, including: The bubble evolution data is segmented into time windows of 30 seconds each, and the ratio of the total area of ​​bubbles to the total area of ​​the sample is calculated for each segment of data to obtain time series porosity data; The time series porosity data is sampled every 30 seconds in the interval of 0-5 minutes, and the porosity values ​​of 10 sampling points are accumulated and averaged to obtain the front-end porosity data; The time series porosity data is sampled every 30 seconds within a period of 5-10 minutes, and the porosity values ​​of 10 sampling points are accumulated and averaged to obtain the latter-stage porosity data; Performing curve fitting on the porosity data of the front section and the porosity data of the back section, calculating the rate of change of porosity in two time periods, and obtaining porosity dynamic data; The porosity standard deviations of the two time periods of 0-5 minutes and 5-10 minutes are calculated based on the porosity dynamic data to obtain the porosity distribution data.

9. An optimization system for tailings sand treatment process, used to implement the optimization method for tailings sand treatment process as described in any one of claims 1 to 8, characterized in that: The optimization system for tailings sand treatment process includes: The acquisition module is used to analyze and process the surface element content and particle size distribution of the tailings sand according to the X-ray fluorescence spectrum data and the laser particle size distribution data through the principal component analysis algorithm to obtain the element composition parameters and particle size distribution parameters; A processing module is used to collect magnetic field intensity data using a three-dimensional electromagnetic field distribution detection system according to the element composition parameters and the particle size distribution parameters, determine electromagnetic field processing parameters through Fourier transform and wavelet analysis, perform electromagnetic field processing on the tailings sand, and obtain electromagnetically processed tailings sand; A modification module is used to collect chemical bond change data based on the electromagnetically treated tailings sand by using synchrotron radiation X-ray absorption spectroscopy, determine chemical modification parameters by least squares fitting, and chemically modify the electromagnetically treated tailings sand to obtain chemically modified tailings sand; A calculation module is used to collect nanoparticle distribution data using small-angle X-ray scattering technology based on the chemically modified tailings sand, determine nano-doping parameters through radial distribution function calculation, perform nano-material doping, and obtain nano-doped tailings sand; A scanning module is used for collecting three-dimensional fiber distribution data of the nano-doped tailings sand by computer tomography, determining fiber reinforcement parameters by Fourier transform and digital image correlation analysis, and performing fiber reinforcement processing to form fiber-reinforced tailings sand; The foaming module is used to collect bubble formation data based on the fiber-reinforced tailings sand by ultra-fast X-ray scanning, determine foaming process parameters by dynamic threshold segmentation and porosity calculation, and perform foaming treatment to obtain porous tailings sand.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the optimization method for the tailings sand treatment process as described in any one of claims 1-8 is implemented.

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