Acid fracturing waste liquid induction electro-Fenton treatment analysis method

Abstract

The invention relates to the technical field of oil and gas field wastewater treatment, and discloses an acid fracturing waste liquid induction electro-Fenton treatment analysis method. The method comprises the following steps: collecting multi-dimensional data such as a three-dimensional fluorescence spectrum, an ultraviolet-visible absorption spectrum and main ion concentration of the waste liquid; dividing an organic component reaction domain based on the three-dimensional fluorescence spectrum, and establishing a corresponding relation with ultraviolet absorption characteristics; and constructing an ion strength matrix by combining ion concentration data, and performing spatial registration with a reaction domain to form a waste liquid reaction activity spectrum. Specific waveform electric signals are applied through an electrochemical workstation, dynamic responses of the hydroxyl free radical generation rate and the organic matter degradation rate are monitored synchronously, accordingly, electric signal parameters are optimized, and a mapping relation library of the electric signals and a reaction activity spectrum is established. According to the method, the component characteristics and reaction activity of the organic matters in the waste liquid can be accurately identified, the dynamic self-adaptive regulation and control of the treatment process are realized, the pollutant degradation efficiency is improved, and the treatment cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field wastewater treatment technology, specifically to a method for the inductively coupled Fenton treatment and analysis of acid fracturing wastewater. Background Technology

[0002] Acid fracturing wastewater is a highly challenging organic wastewater generated during oil and gas field extraction, with an extremely complex composition. It contains various recalcitrant polycyclic aromatic hydrocarbons, surfactants, and high concentrations of inorganic salt ions. These components interact intricately, forming a stable complex pollution system. Conventional Fenton oxidation technology faces significant challenges in treating this type of wastewater. Existing technologies typically treat the wastewater as a homogeneous whole, assessing water quality and guiding treatment solely through macroscopic indicators such as chemical oxygen demand (COD) and total organic carbon (TOC). Treatment parameters are primarily determined empirically or optimized through static experiments targeting single pollutants.

[0003] This extensive treatment model has its flaws. Because it cannot identify the chemical characteristics of different organic components within the wastewater and their specific ionic microenvironment, the set reaction conditions cannot simultaneously and efficiently degrade all pollutants. While some easily degradable components are rapidly removed, some difficult-to-degrade components may remain due to insufficient hydroxyl radical attack. The inhibitory or promoting effect of high-concentration salt ions on free radical reactions cannot be accurately assessed and utilized. Throughout the reaction process, water quality changes dynamically, but the addition of Fenton's reagent and reaction conditions remain fixed, leading to unstable reaction efficiency, significant reagent waste, and treatment results that fall short of expectations.

[0004] Current technologies lack the means to perform detailed analysis of complex wastewater systems and cannot achieve real-time coupling between the treatment process and water quality changes. A key challenge in improving the efficiency and economic benefits of acid fracturing wastewater treatment lies in accurately characterizing the differences in reactivity of different organic components in the wastewater and their synergistic effects with the ionic environment, and establishing a dynamically adaptable treatment strategy accordingly. Summary of the Invention

[0005] The purpose of this invention is to provide a method for the inductively coupled Fenton method for treating and analyzing acid fracturing waste liquid, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a method for the inductively coupled Fenton treatment and analysis of acid fracturing waste liquid, the method comprising: Multidimensional physicochemical property data of acid fracturing waste liquid samples were collected. The multidimensional physicochemical property data included three-dimensional fluorescence spectral data, ultraviolet-visible absorption spectral data, and major ion concentration data. Based on the three-dimensional fluorescence spectral data, the reaction domains of organic components in the waste liquid were divided, and the correspondence between each reaction domain and the ultraviolet-visible absorption characteristics was established. Based on the main ion concentration data, an ion intensity matrix of the waste liquid system is constructed, and the ion intensity matrix is ​​spatially registered with each reaction domain to form a waste liquid reactivity spectrum. By applying specific waveform electrical signals to stimulate waste liquid samples using an electrochemical workstation, the dynamic responses of hydroxyl radical generation rate and organic matter degradation rate are monitored simultaneously. Based on the dynamic response, the electrical signal parameters are adjusted, and a mapping relationship library between the electrical signal parameters and the reactive activity spectrum of the waste liquid is established.

[0007] Preferably, the step of dividing the reaction domains of organic components in the waste liquid based on the three-dimensional fluorescence spectral data includes: Extracting the positions of fluorescence peaks and the distribution characteristics of fluorescence intensity from three-dimensional fluorescence spectra; Based on the position of the fluorescence peak, the spectral region is divided into a humic acid-like region and a fulvic acid-like region. Calculate the coefficient of variation of fluorescence intensity in each region, and determine the boundary range of each reaction domain based on the coefficient of variation.

[0008] Preferably, the step of spatially registering the ion intensity matrix with each reaction domain includes: calculating the concentration gradient distribution of the main ions in each reaction domain and establishing a spatial correspondence between the ion concentration gradient and the fluorescence intensity distribution; Discrete ion concentration data are converted into continuous spatial distribution data using an interpolation algorithm. The spatial distribution data is then superimposed with each reaction domain to generate a waste liquid reactivity map containing spatial coordinates and chemical properties.

[0009] Preferably, the step of applying a specific waveform electrical signal to stimulate the waste liquid sample via an electrochemical workstation includes: using a composite electrical signal combining a step wave, a triangular wave, and a pulse wave as the stimulation source; setting the frequency scanning range of the electrical signal from low frequency to high frequency; maintaining a constant current density at each frequency point for a specific stimulation duration; and recording the electrochemical impedance spectral characteristics of the waste liquid at each frequency point.

[0010] Preferably, the step of establishing a mapping relationship library between electrical signal parameters and waste liquid reactivity spectra includes: extracting characteristic parameters from the waste liquid reactivity spectra, including reaction domain area, ion concentration gradient, and spatial distribution uniformity; performing correlation analysis between the characteristic parameters and electrochemical impedance spectroscopy characteristics, and using a clustering algorithm to identify the corresponding patterns between characteristic parameter combinations and electrical signal parameters; and establishing a lookup table of characteristic parameter combinations and optimal electrical signal parameters for different waste liquid samples.

[0011] Preferably, the step of using a clustering algorithm to identify the correspondence between feature parameter combinations and electrical signal parameters includes: calculating the Euclidean distance between feature parameters of different waste liquid samples; dividing the waste liquid samples into multiple categories based on the Euclidean distance; analyzing the distribution pattern of electrical signal parameters in each category; and determining the optimal range of electrical signal parameters for each category.

[0012] Preferably, the calculation of the coefficient of variation of fluorescence intensity in each region includes: for each reaction region, statistically analyzing the fluorescence intensity values ​​of all data points, calculating the average and standard deviation of the fluorescence intensity values, and dividing the standard deviation by the average to obtain the coefficient of variation; the determination of the boundary range of each reaction domain based on the coefficient of variation includes: setting a threshold for the coefficient of variation, and when the coefficient of variation exceeds the threshold, expanding the boundary of the reaction domain until the coefficient of variation is lower than the threshold, and optimizing the boundary position through iterative calculation.

[0013] Preferably, the calculation of the concentration gradient distribution of major ions in each reaction domain includes: for each reaction domain, extracting the concentration data points of major ions in the domain, calculating the concentration change rate between adjacent data points, and generating a concentration gradient vector; the establishment of the spatial correspondence between the ion concentration gradient and the fluorescence intensity distribution includes: aligning the concentration gradient vector and the fluorescence intensity distribution data according to spatial coordinates, calculating the correlation coefficient between the gradient value of each coordinate point and the fluorescence intensity, and establishing a mapping table of spatial correspondence.

[0014] Preferably, the step of converting discrete ion concentration data into continuous spatial distribution data through an interpolation algorithm includes: using the Kriging interpolation algorithm, taking discrete ion concentration data points as input, calculating the spatial autocorrelation function, and generating a continuous ion concentration distribution surface; and adjusting the interpolation parameters to minimize the prediction error.

[0015] Preferably, the step of overlaying the spatial distribution data with each reaction domain includes: fusing the continuous ion concentration distribution surface with the reaction domain boundary data according to spatial coordinates, and assigning ion concentration values ​​and reaction domain attributes to each coordinate point; the step of generating a waste liquid reactivity spectrum containing spatial coordinates and chemical attributes includes: integrating spatial coordinates, ion concentrations, reaction domain attributes and fluorescence intensity data to form a multidimensional data structure.

[0016] Compared with the prior art, the beneficial effects of the present invention are: By collecting multi-dimensional data such as three-dimensional fluorescence spectroscopy, UV-Vis absorption spectroscopy, and major ion concentrations, organic components are divided into different reaction domains, and spatially registered with UV characteristics and ion intensity matrices to construct a wastewater reactivity map. This technology can accurately reveal the spatial distribution, chemical structure characteristics, and local microenvironment of different types of organic matter in wastewater. It achieves a transformation from macroscopic mixing indicators to microscopic component analysis of complex pollution systems, clearly identifying the specific composition and reactive sites of readily degradable and recalcitrant components. This provides unprecedentedly precise target identification for subsequent oxidation treatment, avoiding treatment blind spots caused by treating wastewater as a "black box."

[0017] By applying specific waveform electrical signals and simultaneously monitoring the dynamic responses of hydroxyl radical generation and organic matter degradation rates, a mapping library between electrical signal parameters and wastewater reactivity spectra is established. This technology enables the treatment process to be dynamically optimized based on real-time feedback electrochemical signals. For reactivity spectra with different characteristics, the most effective combination of electrical stimulation parameters is automatically matched and applied. This represents a leap from static setting to dynamic adaptation of treatment parameters, improving the effective utilization rate of free radicals. It ensures that the oxidation reaction always proceeds under optimal conditions, addressing dynamic changes in water quality during treatment and overcoming the shortcomings of rigid parameters and poor adaptability in traditional methods. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the working principle of the inductively coupled Fenton method for treating and analyzing acid fracturing waste liquid as described in this invention. Figure 2 This is a flowchart illustrating the process of dividing reaction domains based on three-dimensional fluorescence spectroscopy data. Figure 3 A flowchart for registering the ion intensity matrix with the reaction domain space; Figure 4 Cluster analysis diagram of waste liquid samples; Figure 5 This is a spatial distribution map of ion concentration gradients. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1This invention provides an inductively coupled Fenton method for the treatment and analysis of acid fracturing wastewater. The method includes: optimizing wastewater treatment through multi-dimensional data acquisition and analysis. Three-dimensional fluorescence spectral data, UV-Vis absorption spectral data, and major ion concentration data of acid fracturing wastewater samples are collected as multi-dimensional physicochemical property data. Based on the three-dimensional fluorescence spectral data, reaction domains of organic components in the wastewater are delineated, and a correspondence between each reaction domain and UV-Vis absorption characteristics is established. An ion intensity matrix of the wastewater system is constructed based on the major ion concentration data, and the ion intensity matrix is ​​spatially registered with each reaction domain to form a wastewater reactivity map. A specific waveform electrical signal is applied to the wastewater sample using an electrochemical workstation to stimulate it, and the dynamic response of hydroxyl radical generation rate and organic matter degradation rate is monitored simultaneously. The electrical signal parameters are adjusted based on the dynamic response, and a mapping relationship library between the electrical signal parameters and the wastewater reactivity map is established.

[0021] Example 1: See Figure 2 In practical implementation, when using the Fenton method for analyzing acid fracturing waste liquid, the processing of three-dimensional fluorescence spectral data begins with preprocessing the original spectral signal, including noise reduction and scattering correction. Extracting the fluorescence peak positions and intensity distribution characteristics from the three-dimensional fluorescence spectrum is accomplished by identifying local intensity maxima in the spectral matrix; the position of each fluorescence peak is determined by the coordinates of both the excitation and emission wavelengths. The fluorescence intensity distribution characteristics are obtained by calculating the intensity statistics for each wavelength pair across the entire spectral region. Dividing the spectral region into humic acid-like and fulvic acid-like regions based on the fluorescence peak positions is done using a standard fluorescence peak position database. Humic acid-like regions typically correspond to longer emission wavelengths, while fulvic acid-like regions correspond to shorter emission wavelengths.

[0022] In some embodiments, calculating the coefficient of variation (COP) of fluorescence intensity in each region involves fine-tuning each divided region. For each reaction region, the fluorescence intensity values ​​of all data points are statistically analyzed, and the arithmetic mean of these fluorescence intensity values ​​is calculated. The standard deviation of the fluorescence intensity values ​​is then calculated, and the COP is obtained by dividing the standard deviation by the mean. The formula for calculating the COP is:

[0023] in: Represents the coefficient of variation. The standard deviation of fluorescence intensity values, This represents the average fluorescence intensity value. The coefficient of variation threshold is set as a fixed value determined based on statistical analysis of a large number of historical samples. When the calculated coefficient of variation exceeds this threshold, the system automatically expands the boundary of the reaction domain along the spectral coordinate axis.

[0024] It is understandable that expanding the reaction domain boundaries until the coefficient of variation falls below a threshold is an iterative optimization process. In each iteration, the boundary is expanded outward by a preset wavelength step, and then the fluorescence intensity coefficient of variation in the new region is recalculated. This process is repeated until the coefficient of variation drops below the threshold. Optimizing the boundary position through iterative calculation means that after reaching the threshold requirement, fine-tuning is performed using even smaller step sizes to determine the optimal boundary coordinates, thereby ultimately determining the precise boundary range of each reaction domain.

[0025] Optionally, when dividing the humic acid-like and fulvic acid-like regions, fluorescence peak intensity ratio analysis is used to handle spectral overlap areas. When a fluorescence peak is located in a pre-defined boundary overlap region, the projection intensity of that peak on the characteristic excitation-emission matrices of the two standard regions is analyzed, and it is assigned to the reaction domain with the larger projection intensity. This assignment judgment is performed in conjunction with the subsequent coefficient of variation calculation to ensure the accuracy of reaction domain division. The entire division process is based entirely on the mathematical characteristics of three-dimensional fluorescence spectral data, without the need to introduce other physicochemical parameters.

[0026] In practice, the extraction of fluorescence intensity distribution features also includes generating fluorescence intensity contour maps. These contour maps reveal concentrated areas of fluorescence intensity and the direction of gradient changes; this spatial distribution information provides crucial visual assistance and verification for subsequent calculations of the coefficient of variation. The calculation process is implemented using a dedicated spectral processing software module, which integrates all the aforementioned computational logic and iterative control functions.

[0027] Example 2: See Figure 3 In practice, the quantitative analysis of ion behavior within the defined reaction domains, and the calculation of the concentration gradient distribution of major ions within each reaction domain, are performed for each spatial region determined by three-dimensional fluorescence spectroscopy. For each reaction domain, the concentration data points of major ions at all sampling points within the domain are extracted, and these data points correspond one-to-one with the spatial coordinates of the reaction domain. The concentration change rate between adjacent data points is calculated using the finite difference method. For any two adjacent sampling points, the concentration change rate is determined by the ratio of the concentration difference between the two points to the spatial distance, generating a concentration gradient vector from high concentration to low concentration. Establishing the spatial correspondence between ion concentration gradient and fluorescence intensity distribution requires spatial registration of the concentration gradient vector field and the fluorescence intensity distribution data. The concentration gradient vector and the fluorescence intensity distribution data are aligned using the same spatial coordinate grid, and the Pearson correlation coefficient between the gradient vector magnitude of each grid coordinate point and the fluorescence intensity value at that point is calculated, thereby establishing an ion concentration gradient-fluorescence intensity mapping table indexed by spatial coordinates.

[0028] In some embodiments, converting discrete ion concentration data into continuous spatial distribution data using an interpolation algorithm is a crucial step in constructing a complete spatial field. The Kriging interpolation algorithm is employed, using discrete ion concentration data points as input variables, and calculating the spatial autocorrelation function to describe the spatial dependency structure of the data points. The basic formula for Kriging interpolation is expressed as:

[0029] in: It is the point to be predicted The ion concentration value at that location, are known sample points The ion concentration value at that location, These are the weighting coefficients assigned to each known sample point. This is the number of known sample points used for interpolation. Weighting coefficients. The interpolation results were determined by solving the Kriging equations, which guarantee unbiasedness and minimum estimation variance. After generating a continuous ion concentration distribution surface, the range and sill values ​​in the interpolation parameters were adjusted using cross-validation to minimize prediction error.

[0030] It is understandable that overlaying spatial distribution data with each reaction domain constitutes a data fusion process. The continuous ion concentration distribution surface data generated by Kriging interpolation is fused with the reaction domain boundary data determined by fluorescence spectroscopy through coordinate matching. This simultaneously assigns each coordinate point in the spatial grid the interpolated ion concentration value and the reaction domain attribute value to which it belongs. Generating a waste liquid reactivity map containing both spatial coordinates and chemical attributes integrates all the above data layers into a multidimensional data structure. This structure includes the X and Y coordinates, ion concentration value, reaction domain classification code, and fluorescence intensity value for each spatial point.

[0031] Optionally, when performing the stimulation step at the electrochemical workstation, a signal generator needs to be pre-configured to apply a specific waveform electrical signal. A composite electrical signal combining stepped, triangular, and pulsed waves is used as the stimulation source, where the stepped wave provides the base voltage plateau, the triangular wave is used for linear scanning, and the pulsed wave is used to generate transient excitation. The frequency scanning range of the electrical signal is typically set from 0.1 Hz to 100 kHz, achieving coverage from low to high frequencies. A constant current density is maintained at each frequency point for a specific duration of stimulation, while the electrochemical impedance spectral characteristics of the waste liquid system at each frequency point, including impedance magnitude and phase angle, are recorded using the frequency response analyzer of the electrochemical workstation.

[0032] In practical implementation, the calculation of the ion concentration gradient vector can be further refined by refining the directional component. In a two-dimensional plane, the concentration gradient vector is decomposed into two components along the X and Y axes, thus more accurately describing the spatial variation trend of ion concentration. The spatial correspondence analysis between the concentration gradient vector and fluorescence intensity is not limited to scalar correlation but can also be extended to the spatial relationship analysis between the vector direction and the orientation of fluorescence intensity contour lines. The application sequence of the composite electrical signal can employ a step-wave stabilization system, followed by a triangular wave scan, and finally pulsed wave excitation to obtain richer electrochemical response information.

[0033] Example 3: In specific implementation, feature extraction is performed from the generated waste liquid reactivity spectrum. The extracted feature parameters include quantifying the reaction domain area, calculating the ion concentration gradient, and evaluating the spatial distribution uniformity. The reaction domain area is obtained by calculating the total number of pixels enclosed by each reaction domain in the excitation-emission wavelength plane. The ion concentration gradient is obtained by averaging the magnitudes of all concentration gradient vectors within the reaction domain. The spatial distribution uniformity is quantified by calculating the spatial information entropy of the fluorescence intensity values. The feature parameters are correlated with electrochemical impedance spectroscopy (EIS) features. The EIS features mainly select the charge transfer resistance and double-layer capacitance values ​​from the Nyquist plot. A quantitative relationship model between the feature parameters and the impedance feature parameters is established using a multiple linear regression method.

[0034] In some embodiments, identifying the correspondence between feature parameter combinations and electrical signal parameters using clustering algorithms requires constructing a feature matrix. The Euclidean distance between the feature parameters of different waste liquid samples is calculated using the following formula:

[0035] in: This represents the Euclidean distance between sample x and sample y. Indicates the number of feature parameters. and Let represent the standardized values ​​of the two samples at the k-th feature parameter. Based on the calculated Euclidean distance matrix, a hierarchical clustering algorithm is used to classify the waste liquid samples into multiple categories.

[0036] It is understandable that analyzing the distribution patterns of electrical signal parameters within each category is a deeper exploration of the clustering results. For each sample category formed by the clustering, the electrical signal parameters used in the electrochemical stimulation experiments for all samples within that category are statistically analyzed. These parameters include the frequency sweep range of the composite electrical signal, the step wave voltage amplitude, and the pulse wave duty cycle. By plotting the frequency distribution histograms of each electrical signal parameter, the central tendency and dispersion of the parameter values ​​are observed. The optimal range of electrical signal parameters for each category is determined based on the main peak region of the distribution histogram; the interval with the highest frequency of parameter values ​​is selected as the optimal range for that category.

[0037] Optionally, a database table structure is used to establish a lookup table for the characteristic parameter combinations and optimal electrical signal parameters of different waste liquid samples. The lookup table contains two main columns: a characteristic parameter field and an optimal electrical signal parameter field. The characteristic parameter field is further divided into three sub-columns: reaction domain area, ion concentration gradient, and spatial distribution uniformity. The optimal electrical signal parameter field is divided into four sub-columns: frequency range, voltage amplitude, current density, and waveform combination. Each record row corresponds to a waste liquid sample category, storing the typical characteristic parameter values ​​for that category and the optimal electrical signal parameter range determined by cluster analysis. This lookup table constitutes the core data storage structure of the mapping relationship database.

[0038] In practice, the standardization of feature parameters is a necessary step before cluster analysis. Z-score standardization is applied to three parameters with different dimensions: reaction domain area, ion concentration gradient, and spatial distribution uniformity, transforming them into dimensionless values ​​with a mean of 0 and a standard deviation of 1. Euclidean distance calculation is based on the standardized parameter values ​​to ensure that each feature has equal weight in the clustering. The number of clusters is optimized using the silhouette coefficient method, resulting in high cohesion and segregation of the sample classification results. Box plots can be used to identify outliers and ensure the representativeness of the optimal parameter range for the analysis of the distribution patterns of electrical signal parameters. After the mapping database is established, for a new waste liquid sample, only its feature parameters need to be extracted, and its category and corresponding optimal electrical signal parameters can be quickly determined by consulting a lookup table.

[0039] See Figure 4 This paper demonstrates the correlation between cluster analysis results of acid fracturing wastewater samples based on characteristic parameters and the organic matter degradation efficiency. The figure presents the distribution of different sample categories across two key characteristic dimensions: reaction domain area and ion concentration gradient, using a scatter plot format. The size of the dots reflects the spatial distribution uniformity, and the colors distinguish different cluster categories. Each cluster category is labeled with its typical combination of characteristic parameters and the corresponding organic matter degradation rate, clearly showing the impact of different feature combinations on the treatment effect. Larger dots represent samples with more uniform spatial distribution, which typically exhibit more stable treatment performance. The arrows provide degradation efficiency information for each category, offering an intuitive reference for optimizing electro-Fenton treatment parameters.

[0040] Example 4: In specific implementation, the concentration gradient distribution of major ions within each reaction domain is calculated. For each reaction domain divided by three-dimensional fluorescence spectroscopy, all major ion concentration data points within the domain are extracted. These data points have clear spatial coordinate information. The concentration change rate between adjacent data points is calculated by dividing the concentration difference between two points by the corresponding Euclidean distance, thereby generating a concentration gradient vector with magnitude and direction. Establishing the spatial correspondence between ion concentration gradient and fluorescence intensity distribution requires registering the concentration gradient vector field and fluorescence intensity distribution data using the same spatial coordinate grid. The Pearson correlation coefficient between the magnitude of the concentration gradient vector at each grid point and the fluorescence intensity value at that point is calculated, thus constructing a mapping table indexed by spatial coordinates. In specific implementation, the calculation of the concentration gradient vector can be extended to multi-dimensional space. For ion concentration data points within each reaction domain, the nearest neighbor pair is determined by constructing a Delaunay triangulation to ensure the accuracy of the concentration change rate calculation. The generation of the concentration gradient vector includes not only magnitude calculation but also the decomposition of the directional component to facilitate subsequent spatial correspondence analysis. After the triangulation is established, the nearest neighbor pairs for each data point are directly defined by the edges in the triangulation, which represent the spatial adjacency relationships between points. For each data point, the concentration change rate is calculated based on the concentration difference with its nearest neighbor and the Euclidean distance, generating a concentration gradient vector with magnitude and direction. The decomposition of the directional component is achieved by projecting the vector onto the spatial coordinate axes to support subsequent spatial correspondence analysis with the fluorescence intensity distribution.

[0041] In some embodiments, discrete ion concentration data is converted into continuous spatially distributed data using an interpolation algorithm. The Kriging interpolation algorithm is employed, taking discrete ion concentration data points as input and calculating a spatial autocorrelation function to quantify the spatial dependence of the data. This spatial autocorrelation function is expressed using a semivariance function, and its formula is defined as follows:

[0042] in: It is a distance vector The semivariance value at that point, The distance is Number of point pairs Spatial location The ion concentration value at that location, It is distance for After generating a continuous ion concentration distribution surface by calculating the ion concentration values ​​at the specified locations, interpolation parameters such as range, sill value, and nugget effect are adjusted to minimize prediction errors. In some embodiments, the Kriging interpolation process includes plotting the experimental semivariogram and fitting the theoretical model to ensure the accuracy of spatial predictions.

[0043] It is understandable that establishing the spatial correspondence between ion concentration gradient and fluorescence intensity distribution involves processing a large number of data points. Calculating the correlation coefficient for each coordinate point requires efficient algorithm optimization to avoid computational bottlenecks. The correlation coefficient is calculated using the Pearson formula, with a value range of [-1, 1], used to quantify the linear correlation strength between concentration gradient and fluorescence intensity. It is also understandable that the magnitude of the concentration gradient vector is calculated based on the Euclidean norm. In two-dimensional space, the magnitude is the square root of the sum of the squares of the vector components. The magnitude value reflects the magnitude of ion concentration variation and can reveal the intrinsic chemical relationship when correlated with fluorescence intensity values.

[0044] Optionally, the spatial correspondence mapping table can be stored in a structured table format for easy subsequent querying and analysis. Refer to Table 1, which shows the concentration gradient and fluorescence intensity data for some spatial coordinate points. Optionally, the construction of the mapping table may include time-series data for dynamic analysis of the concentration gradient's changing trend; however, this embodiment only considers static spatial distribution.

[0045] Table 1: Spatial mapping table of ion concentration gradient and fluorescence intensity

[0046] In practice, the implementation of the Kriging interpolation algorithm requires the support of specialized software, such as using a geographic information system database for spatial calculations. Preprocessing of discrete ion concentration data points includes outlier removal and data smoothing to ensure the reliability of the interpolation results.

[0047] See Figure 5 This figure illustrates the spatial distribution characteristics of ion concentration gradients in wastewater and their correspondence with fluorescence intensity distribution. The color depth represents the magnitude of the concentration gradient at different spatial locations, the size of the dots corresponds to the fluorescence intensity, and the red arrows indicate the direction and magnitude of the concentration gradient vector. The graph clearly reveals the spatial variation of ion concentration and its intrinsic relationship with the fluorescence properties of organic matter. Regions with larger concentration gradients typically correspond to stronger mass transfer processes, and these regions often exhibit higher reactivity in electro-Fenton treatment. The spatial variation of fluorescence intensity reflects the non-uniformity of organic pollutant distribution and shows a significant correlation with the concentration gradient distribution pattern. This analysis of spatial correspondence provides important evidence for understanding the microscopic reaction mechanisms of wastewater systems, and helps optimize electrode arrangement and reactor design, improving the generation efficiency of hydroxyl radicals and the degradation effect of organic matter.

[0048] Example 5: In specific implementation, the focus is on the overlay and fusion of spatial distribution data and reaction domains, and the generation of waste liquid reactivity maps. The fusion of the continuous ion concentration distribution surface and reaction domain boundary data according to spatial coordinates is achieved through a coordinate alignment algorithm. The continuous ion concentration distribution surface is grid data generated by Kriging interpolation, while the reaction domain boundary data is polygonal boundaries extracted from three-dimensional fluorescence spectroscopy analysis. The fusion process involves projecting both onto the same spatial coordinate system and assigning ion concentration values ​​and reaction domain attribute values ​​to each grid point or pixel. Generating waste liquid reactivity maps containing spatial coordinates and chemical attributes requires integrating multiple data sources. Spatial coordinate data, ion concentration data, reaction domain attribute data, and fluorescence intensity data are combined into a unified multidimensional data structure. This structure is typically stored in array or tensor form for easy subsequent analysis and querying.

[0049] In some embodiments, the spatial coordinate fusion process employs a point-by-point matching strategy. For each coordinate point (u, v) on the continuous ion concentration distribution surface, the corresponding spatial location is searched in the reaction domain boundary data to determine the reaction domain category to which the point belongs. The reaction domain category may include humic acid-like regions or fulvic acid-like regions, etc. Assigning ion concentration values ​​and reaction domain attributes to each coordinate point is accomplished by querying the ion concentration values ​​on the interpolation surface and combining them with the reaction domain classification results. The assignment logic can be represented as a mapping relationship: each point is assigned a triple. In some embodiments, the granularity of the coordinate points can be adjusted as needed, for example, using a high-resolution grid to improve the detail of the spectrum, but this increases the computational load.

[0050] It is understandable that the multidimensional data structure of the waste liquid reactivity spectrum integrates discrete and continuous data. Spatial coordinates are typically stored as two-dimensional floating-point numbers, ion concentration values ​​are floating-point numbers, reaction domain attributes are classified and encoded, and fluorescence intensity values ​​are floating-point numbers. The data integration formula can be expressed as:

[0051] in: Indicates the position of the waste liquid reactivity spectrum at the index. Data unit at the location, and These represent the horizontal and vertical coordinates of the spatial coordinates, respectively. Indicates ion concentration value. Indicates the reaction domain attribute value. This represents the fluorescence intensity value. This formula defines the five dimensions of information contained in each data unit, forming a structured dataset. It can be understood that the implementation of multidimensional data structures relies on arrays or database tables in the programming environment, where each dimension corresponds to a chemical or spatial property.

[0052] Optionally, the assignment of response domain attribute values ​​may involve fuzzy classification. When a coordinate point is near the boundary of a response domain, it can be assigned probability values ​​of multiple domain attributes instead of a single label to reflect the uncertainty of its attribution. The storage format of the multidimensional data structure can be HDF5 or NetCDF, supporting efficient read / write and parallel access.

[0053] In practical implementation, spatial coordinate fusion may encounter coordinate system mismatch issues. Affine transformations are needed to convert the continuous ion concentration distribution surface and reaction domain boundary data to the same coordinate system. Transformation parameters are determined through control point registration. In practice, after assigning attribute values ​​to each coordinate point, a visual representation of the waste liquid reactivity spectrum, such as a heatmap or 3D rendering, is generated to visually verify the data integration effect. The construction of multidimensional data structures is typically automated using scripts, reducing human intervention errors.

[0054] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for the inductively coupled Fenton method of acid fracturing waste liquid treatment and analysis, characterized in that, The method includes: Multidimensional physicochemical property data of acid fracturing waste liquid samples were collected. The multidimensional physicochemical property data included three-dimensional fluorescence spectral data, ultraviolet-visible absorption spectral data, and major ion concentration data. Based on the three-dimensional fluorescence spectral data, the reaction domains of organic components in the waste liquid were divided, and the correspondence between each reaction domain and the ultraviolet-visible absorption characteristics was established. Based on the main ion concentration data, an ion intensity matrix of the waste liquid system is constructed, and the ion intensity matrix is ​​spatially registered with each reaction domain to form a waste liquid reactivity spectrum. By applying specific waveform electrical signals to stimulate waste liquid samples using an electrochemical workstation, the dynamic responses of hydroxyl radical generation rate and organic matter degradation rate are monitored simultaneously. Based on the dynamic response, the electrical signal parameters are adjusted, and a mapping relationship library between the electrical signal parameters and the reactive activity spectrum of the waste liquid is established.

2. The method for inductively coupled Fenton analysis of acid fracturing waste liquid according to claim 1, characterized in that, The process of dividing the reaction domains of organic components in the waste liquid based on the three-dimensional fluorescence spectral data includes: Extracting the positions of fluorescence peaks and the distribution characteristics of fluorescence intensity from three-dimensional fluorescence spectra; Based on the position of the fluorescence peak, the spectral region is divided into a humic acid-like region and a fulvic acid-like region. Calculate the coefficient of variation of fluorescence intensity in each region, and determine the boundary range of each reaction domain based on the coefficient of variation.

3. The method for inductively coupled Fenton analysis of acid fracturing waste liquid according to claim 1, characterized in that, The step of spatially registering the ion intensity matrix with each reaction domain includes: calculating the concentration gradient distribution of the main ions in each reaction domain and establishing a spatial correspondence between the ion concentration gradient and the fluorescence intensity distribution; Discrete ion concentration data are converted into continuous spatial distribution data using an interpolation algorithm. The spatial distribution data is then superimposed with each reaction domain to generate a waste liquid reactivity map containing spatial coordinates and chemical properties.

4. The method for inductively coupled Fenton analysis of acid fracturing waste liquid according to claim 1, characterized in that, The process of applying a specific waveform electrical signal to stimulate the waste liquid sample via an electrochemical workstation includes: using a composite electrical signal combining a step wave, a triangular wave, and a pulse wave as the stimulation source; setting the frequency scanning range of the electrical signal from low frequency to high frequency; maintaining a constant current density at each frequency point for a specific stimulation duration; and recording the electrochemical impedance spectral characteristics of the waste liquid at each frequency point.

5. The method for inductively coupled Fenton analysis of acid fracturing waste liquid according to claim 1, characterized in that, The process of establishing a mapping database between electrical signal parameters and waste liquid reactivity spectra includes: extracting characteristic parameters from the waste liquid reactivity spectra, including reaction domain area, ion concentration gradient, and spatial distribution uniformity; performing correlation analysis between the characteristic parameters and electrochemical impedance spectroscopy characteristics; using a clustering algorithm to identify the corresponding patterns between characteristic parameter combinations and electrical signal parameters; and establishing a lookup table of characteristic parameter combinations and optimal electrical signal parameters for different waste liquid samples.

6. The method for inductively coupled Fenton analysis of acid fracturing waste liquid according to claim 5, characterized in that, The method of using clustering algorithms to identify the correspondence between feature parameter combinations and electrical signal parameters includes: calculating the Euclidean distance between feature parameters of different waste liquid samples; dividing the waste liquid samples into multiple categories based on the Euclidean distance; analyzing the distribution pattern of electrical signal parameters in each category; and determining the optimal range of electrical signal parameters for each category.

7. The method for inductively coupled Fenton analysis of acid fracturing waste liquid according to claim 2, characterized in that, The calculation of the coefficient of variation of fluorescence intensity in each region includes: for each reaction region, statistically analyzing the fluorescence intensity values ​​of all data points, calculating the average and standard deviation of the fluorescence intensity values, and dividing the standard deviation by the average to obtain the coefficient of variation; the determination of the boundary range of each reaction domain based on the coefficient of variation includes: setting a threshold for the coefficient of variation, and when the coefficient of variation exceeds the threshold, expanding the boundary of the reaction domain until the coefficient of variation is lower than the threshold, and optimizing the boundary position through iterative calculation.

8. The method for inductively coupled Fenton analysis of acid fracturing waste liquid according to claim 3, characterized in that, The calculation of the concentration gradient distribution of major ions in each reaction domain includes: for each reaction domain, extracting the concentration data points of major ions in the domain, calculating the concentration change rate between adjacent data points, and generating a concentration gradient vector; the establishment of the spatial correspondence between ion concentration gradient and fluorescence intensity distribution includes: aligning the concentration gradient vector and fluorescence intensity distribution data according to spatial coordinates, calculating the correlation coefficient between the gradient value of each coordinate point and the fluorescence intensity, and establishing a mapping table of spatial correspondence.

9. The method for inductively coupled Fenton analysis of acid fracturing waste liquid according to claim 3, characterized in that, The process of converting discrete ion concentration data into continuous spatial distribution data using an interpolation algorithm includes: employing the Kriging interpolation algorithm, taking discrete ion concentration data points as input, calculating the spatial autocorrelation function, and generating a continuous ion concentration distribution surface; and adjusting the interpolation parameters to minimize the prediction error.

10. The method for inductively coupled Fenton analysis of acid fracturing waste liquid according to claim 3, characterized in that, The step of overlaying the spatial distribution data with each reaction domain includes: fusing the continuous ion concentration distribution surface with the reaction domain boundary data according to spatial coordinates, and assigning ion concentration values ​​and reaction domain attributes to each coordinate point; the step of generating a waste liquid reactivity spectrum containing spatial coordinates and chemical attributes includes: integrating spatial coordinates, ion concentrations, reaction domain attributes and fluorescence intensity data to form a multidimensional data structure.

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