Method and system for on-line analysis of water quality in radioactive liquid waste treatment process

Abstract

The application provides an online water quality analysis method and system in a radioactive waste liquid treatment process, and relates to the technical field of online water quality monitoring.The method comprises the following steps: step 1, passing a clarified sample into an analysis pool, collecting Raman spectral characteristic peak data in real time through a plurality of groups of optical fiber Raman probe arrays arranged in the analysis pool along the flow direction of the waste liquid; performing principal component analysis on the Raman spectral characteristic peak data, extracting characteristic spectral vectors corresponding to main radioactive nuclides and interfering substances, and constructing a two-dimensional concentration distribution field according to the continuous change of the characteristic spectral vectors in the spatial position; and step 2, extracting an isochromatic line and performing discrete sampling on a specific interfering substance component in the two-dimensional concentration distribution field, calculating the minimum circumscribed circle of all sampling points, and obtaining the center coordinates and radius of the isochromatic line.The application realizes intelligent regulation and control of treatment process parameters and guarantees that the discharged water quality is stable and up to the standard.

Description

Technical Field

[0001] This invention relates to the field of online water quality monitoring technology, and in particular to online water quality analysis methods and systems in the treatment of radioactive waste liquid. Background Technology

[0002] In the treatment of radioactive waste liquid in the nuclear industry, the combined treatment process of evaporation concentration and reverse osmosis has become a routine technical means for reducing the volume of waste liquid and achieving compliant discharge. The existing online water quality analysis system for radioactive waste liquid treatment generally adopts a method of bypassing the evaporation concentration unit and the reverse osmosis unit, and directly passing the samples through total α and β radiation detectors, conductivity electrodes, pH electrodes, and turbidimeters for single-point in-situ detection after simple filtration. The evaporation concentration ratio and the reverse osmosis membrane flushing cycle are then adjusted manually or semi-automatically based on the detection values. The traditional single-point detection method does not take into account the actual working conditions of non-uniform spatial distribution of suspended solids, colloids and interfering components in the waste liquid. It cannot quantify the impact of the spatial distribution differences of interfering substances on the radiation attenuation of the detector window, electrode polarization, response delay and light scattering interference. As a result, key water quality parameters are prone to measurement drift and insufficient detection accuracy, making it difficult to ensure that the discharged water quality meets the standards and making it impossible to achieve intelligent control of the treatment process parameters. Summary of the Invention

[0003] This invention provides a method and system for online water quality analysis during the treatment of radioactive waste liquid, enabling intelligent control of treatment process parameters and ensuring stable compliance of discharged water quality.

[0004] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0005] Firstly, a method for online water quality analysis during the treatment of radioactive waste liquid, the method comprising:

[0006] Step 1: The clarified sample is introduced into the analysis cell. Multiple sets of fiber Raman probe arrays are arranged in the analysis cell along the flow direction of the waste liquid to collect Raman spectral characteristic peak data in real time. Principal component analysis is performed on the Raman spectral characteristic peak data to extract the characteristic spectral vectors corresponding to the main radionuclides and interfering substances. A two-dimensional concentration distribution field is constructed based on the continuous change of the characteristic spectral vectors in spatial position.

[0007] Step 2: For specific interfering components in the two-dimensional concentration distribution field, extract isoconcentration lines and perform discrete sampling, calculate the minimum circumcircle of all sampling points, and obtain the center coordinates and radius of the isoconcentration lines;

[0008] Step 3: Based on the positional deviation of the center coordinates relative to the center of the preset detection window and the ratio of the radius to the size of the detection window, and in conjunction with the pre-stored interference coefficient database, calculate the radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value, and light scattering interference suppression coefficient respectively.

[0009] Step 4: Using the radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value, and light scattering interference suppression coefficient, the initial water quality parameters of total α radioactivity, total β radioactivity, conductivity, pH value, and turbidity obtained in real time by the radiation detector, conductivity electrode, pH electrode, and turbidity meter are corrected to obtain the corrected real-time water quality parameters.

[0010] Step 5: Upload the corrected real-time water quality parameters to the distributed control unit, which will automatically adjust the evaporation concentration ratio and the reverse osmosis membrane flushing cycle, and determine whether the discharged water quality meets the discharge standards.

[0011] Furthermore, prior to step 1 above, the process includes: drawing closed bypass online analysis flow paths from the outlet of the evaporation and concentration unit and the discharge port to continuously introduce waste liquid samples; performing online ultrafiltration and ultrasonic disruption pretreatment on the introduced waste liquid samples to remove suspended solids and colloidal interferences from the waste liquid samples, thereby obtaining a clarified sample.

[0012] Furthermore, the clarified sample is introduced into the analysis cell, and Raman spectral characteristic peak data are acquired in real time through multiple fiber optic Raman probe arrays arranged along the flow direction of the waste liquid within the analysis cell. Principal component analysis is performed on the Raman spectral characteristic peak data to extract the characteristic spectral vectors corresponding to the main radionuclides and interfering substances. A two-dimensional concentration distribution field is constructed based on the continuous spatial variation of the characteristic spectral vectors, including:

[0013] Principal component decomposition was performed on the Raman spectral characteristic peak data collected by each fiber Raman probe array. Several principal components with a cumulative contribution rate exceeding a preset threshold were extracted from each fiber Raman probe array as the characteristic spectral vector at that position. The characteristic spectral vectors extracted from each fiber Raman probe array were arranged in order of the waste liquid flow direction to form a characteristic spectral vector sequence.

[0014] Spline interpolation is performed on the characteristic spectral vector sequence along the flow direction to obtain an axially continuous spectral characteristic distribution. Combined with the radial detection point coordinates of each fiber Raman probe array, and based on the spatial influence range of each radial detection point, the spectral characteristic values ​​at each axial position are spatially weighted on the cross section to obtain the spectral characteristic values ​​at each grid point on the cross section of the analysis cell. The similarity between the spectral characteristic values ​​of each grid point and the standard spectrum of the interfering substance is calculated. The concentration of the interfering substance at the grid point is determined based on the similarity value, thus forming a two-dimensional concentration distribution field of pollutants on the cross section of the analysis cell.

[0015] Furthermore, for specific interfering components in the two-dimensional concentration distribution field, isoconcentration lines are extracted and discretely sampled. The minimum circumcircle of all sampling points is calculated to obtain the center coordinates and radius of the isoconcentration lines, including:

[0016] For the target interfering component in the two-dimensional concentration distribution field, multiple concentration gradient values ​​are set, and isoconcentration lines corresponding to each concentration gradient value are extracted. Each isoconcentration line is composed of continuous spatial points with the same concentration value.

[0017] Each isoconcentration line is sampled discretely with equal arc length to obtain a sequence of boundary feature points uniformly distributed on the isoconcentration line. The sequence of boundary feature points contains the coordinate information of each feature point on the cross-section of the analysis cell.

[0018] For each isoconcentration line, the minimum circumcircle covering all feature points in the sequence is determined by gradually adjusting the center position and expanding the radius. The center coordinates and radius of the minimum circumcircle are recorded as the center coordinates and radius of the isoconcentration line.

[0019] Furthermore, based on the positional deviation of the center coordinates relative to the center of the preset detection window and the proportional relationship between the radius and the size of the detection window, and in conjunction with the pre-stored interference coefficient database, the radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value, and light scattering interference suppression coefficient are calculated, including:

[0020] Calculate the Euclidean distance between the center coordinates of each isoconcentration line and the center of the preset detection window. Divide the Euclidean distance by the diagonal length of the detection window for normalization to obtain the normalized positional deviation. Calculate the ratio between the radius of each isoconcentration line and the effective radius of the detection window to obtain the radial dimension ratio.

[0021] For each isoconcentration line, the normalized position deviation and radial size ratio are used as input parameters. The basic compensation coefficient group under the corresponding working condition is queried in the pre-stored interference coefficient database. The basic compensation coefficient group includes the basic radiation attenuation compensation coefficient of the radiation detector window, the basic polarization correction factor of the conductivity electrode, the basic response delay compensation value of the pH electrode, and the basic light scattering interference suppression coefficient of the turbidimeter.

[0022] The basic compensation coefficient group obtained from multiple isoconcentration lines corresponding to different concentration gradient values ​​is weighted and fused according to the concentration gradient value represented by each isoconcentration line. The isoconcentration line with a higher concentration gradient value has a larger weight coefficient. The comprehensive radiation attenuation compensation coefficient, comprehensive polarization correction factor, comprehensive response delay compensation value, and comprehensive light scattering interference suppression coefficient are calculated by weighted average and used as the final radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value, and light scattering interference suppression coefficient for correction.

[0023] Furthermore, step 4 above includes:

[0024] The initial data of total alpha radioactivity obtained in real time by the radiation detector is combined with the comprehensive radiation attenuation compensation coefficient to correct the initial data of total alpha radioactivity, and the corrected total alpha radioactivity is obtained.

[0025] The initial data of total β radioactivity obtained in real time by the radiation detector is combined with the comprehensive radiation attenuation compensation coefficient to correct the initial data of total β radioactivity, eliminate the negative measurement drift caused by detector window contamination, and obtain the corrected total β radioactivity.

[0026] The initial conductivity data obtained by real-time detection of the conductivity electrode is combined with the comprehensive polarization correction factor to correct the initial conductivity data, compensate for the response hysteresis and measurement deviation caused by electrode surface contamination, and obtain the corrected conductivity.

[0027] The initial pH value obtained by real-time detection of the pH electrode is combined with the comprehensive response delay compensation value to correct the initial pH value, compensate for the response delay and measurement drift caused by electrode surface contamination, and obtain the corrected pH value.

[0028] The initial turbidity data obtained from real-time detection by the turbidity meter is combined with the comprehensive light scattering interference suppression coefficient to correct the initial turbidity data, thus obtaining the corrected turbidity.

[0029] The corrected total alpha radioactivity, corrected total beta radioactivity, corrected conductivity, corrected pH value, and corrected turbidity together constitute the corrected real-time water quality parameters.

[0030] Furthermore, step 5 above includes:

[0031] The distributed control unit receives the corrected real-time water quality parameters and constructs a dual-objective optimization function with the optimization objectives of achieving compliant effluent quality and minimizing operating energy consumption. The compliant effluent quality is determined by comparing the corrected real-time water quality parameters with the preset effluent standard threshold, while the operating energy consumption is calculated based on the real-time operating parameters of the evaporation and concentration unit and the reverse osmosis membrane unit.

[0032] Based on a bi-objective optimization function, a population consisting of multiple candidate solutions is initialized. Each candidate solution corresponds to a set of parameter combinations for evaporation concentration ratio and reverse osmosis membrane flushing cycle. An iterative optimization method simulating the cooperation and competition mechanism of a biological community is adopted. The constructed bi-objective optimization function is used as the fitness evaluation basis. By calculating the fitness value of each candidate solution and simulating the information sharing and survival of the fittest process among individuals, the candidate solutions in the population are continuously updated until the preset number of iterations is reached. The final combination of evaporation concentration ratio and reverse osmosis membrane flushing cycle that makes the bi-objective optimization function work is obtained, which serves as the final control parameter combination.

[0033] The system sends control commands to the evaporation and concentration unit and the reverse osmosis membrane unit through the final control parameter combination, automatically adjusting the evaporation and concentration ratio and the reverse osmosis membrane flushing cycle. At the same time, the corrected real-time water quality parameters are compared with the preset discharge standard threshold. If all water quality parameters are less than or equal to the corresponding discharge standard threshold, the discharged water quality is determined to meet the discharge standard. Otherwise, an alarm signal is issued and an emergency treatment process is triggered.

[0034] Secondly, the online water quality analysis system during the radioactive waste treatment process includes:

[0035] The acquisition module is used to pass the clarified sample into the analysis cell and acquire Raman spectral characteristic peak data in real time through multiple sets of fiber Raman probe arrays arranged along the flow direction of the waste liquid in the analysis cell; principal component analysis is performed on the Raman spectral characteristic peak data to extract the characteristic spectral vectors corresponding to the main radionuclides and interfering substances, and a two-dimensional concentration distribution field is constructed based on the continuous change of the characteristic spectral vectors in spatial position.

[0036] The extraction module is used to extract isoconcentration lines and perform discrete sampling for specific interfering components in a two-dimensional concentration distribution field, calculate the minimum circumcircle of all sampling points, and obtain the center coordinates and radius of the isoconcentration lines.

[0037] The calculation module is used to calculate the radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value and light scattering interference suppression coefficient based on the positional deviation of the circle center coordinates relative to the center of the preset detection window and the ratio of the radius to the size of the detection window, combined with the pre-stored interference coefficient database.

[0038] The calibration module is used to correct the initial water quality parameters of total α radioactivity, total β radioactivity, conductivity, pH value and turbidity obtained in real time by the radiation detector, conductivity electrode, pH electrode and turbidity meter through the radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value and light scattering interference suppression coefficient, respectively, to obtain the corrected real-time water quality parameters.

[0039] The processing module is used to upload the corrected real-time water quality parameters to the distributed control unit, which automatically adjusts the evaporation concentration ratio and the reverse osmosis membrane flushing cycle, and determines whether the discharged water quality meets the discharge standards.

[0040] Thirdly, a computing device includes:

[0041] One or more processors;

[0042] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0043] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0044] The above-described solution of the present invention has at least the following beneficial effects:

[0045] By deploying multiple fiber optic Raman probe arrays along the wastewater flow direction to collect Raman spectral characteristic peak data, constructing a two-dimensional concentration distribution field through principal component analysis, extracting isoconcentration lines of interfering substances and calculating their minimum circumscribed circle geometric parameters, and calculating multiple compensation and correction parameters in conjunction with an interference coefficient database, these parameters are then used to correct initial water quality parameters such as total α and β radioactivity. Finally, the distributed control unit automatically adjusts process parameters based on the corrected parameters. This technical approach overcomes the technical problems of traditional single-point detection, which cannot quantify the impact of spatially non-uniform distribution of interfering substances on detection, leading to water quality parameter measurement drift, insufficient detection accuracy, and inability to achieve intelligent process control. Consequently, it achieves the technical effects of improving the accuracy and stability of online detection of radioactive wastewater, realizing intelligent control of treatment process parameters, and ensuring stable compliance of discharged water quality standards. Attached Figure Description

[0046] Figure 1 This is a schematic flowchart of the online water quality analysis method in the radioactive waste liquid treatment process provided by an embodiment of the present invention.

[0047] Figure 2 This is a schematic diagram of an online water quality analysis system provided in an embodiment of the present invention for the treatment of radioactive waste liquid. Detailed Implementation

[0048] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art.

[0049] like Figure 1 As shown, embodiments of the present invention propose an online water quality analysis method during the treatment of radioactive waste liquid, the method comprising the following steps:

[0050] Step 1: The clarified sample is introduced into the analysis cell. Multiple sets of fiber Raman probe arrays are arranged in the analysis cell along the flow direction of the waste liquid to collect Raman spectral characteristic peak data in real time. Principal component analysis is performed on the Raman spectral characteristic peak data to extract the characteristic spectral vectors corresponding to the main radionuclides and interfering substances. A two-dimensional concentration distribution field is constructed based on the continuous change of the characteristic spectral vectors in spatial position.

[0051] Step 2: For specific interfering components in the two-dimensional concentration distribution field, extract isoconcentration lines and perform discrete sampling, calculate the minimum circumcircle of all sampling points, and obtain the center coordinates and radius of the isoconcentration lines;

[0052] Step 3: Based on the positional deviation of the center coordinates relative to the center of the preset detection window and the ratio of the radius to the size of the detection window, and in conjunction with the pre-stored interference coefficient database, calculate the radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value, and light scattering interference suppression coefficient respectively.

[0053] Step 4: Using the radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value, and light scattering interference suppression coefficient, the initial water quality parameters of total α radioactivity, total β radioactivity, conductivity, pH value, and turbidity obtained in real time by the radiation detector, conductivity electrode, pH electrode, and turbidity meter are corrected to obtain the corrected real-time water quality parameters.

[0054] Step 5: Upload the corrected real-time water quality parameters to the distributed control unit, which will automatically adjust the evaporation concentration ratio and the reverse osmosis membrane flushing cycle, and determine whether the discharged water quality meets the discharge standards.

[0055] In this embodiment of the invention, multiple fiber optic Raman probe arrays are deployed along the flow direction of the waste liquid to collect Raman spectral characteristic peak data. A two-dimensional concentration distribution field is constructed through principal component analysis, isoconcentration lines of interfering substances are extracted, and the geometric parameters of the minimum circumcircle are calculated. Combined with the interference coefficient database, the radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value, and light scattering interference suppression coefficient are calculated. The above coefficients are used to correct the initial water quality parameters of total α and β radioactivity, conductivity, pH value, and turbidity. The distributed control unit automatically adjusts the evaporation concentration ratio and reverse osmosis membrane flushing cycle based on the corrected parameters and determines the effluent water quality. Therefore, this method overcomes the technical problems of traditional single-point detection, such as the inability to quantify the interference caused by the non-uniform spatial distribution of interfering substances, the easy measurement drift of water quality parameters, insufficient detection accuracy, and the inability to achieve intelligent control of the treatment process. Thus, it achieves the technical effects of improving the accuracy and stability of online detection of radioactive waste liquid, realizing intelligent optimization and control of treatment process parameters, and ensuring stable and compliant discharge of effluent water quality.

[0056] In a preferred embodiment of the present invention, the steps preceding step 1 include:

[0057] Step 01: Two completely sealed bypass online analysis flow paths are drawn from the outlet of the evaporation and concentration unit and the compliant discharge port, respectively, to continuously introduce waste liquid samples. The introduced waste liquid samples undergo online ultrafiltration and ultrasonic disruption pretreatment to remove suspended solids and colloidal interferences, obtaining clarified samples. Specifically, this includes: firstly, drawing two completely sealed bypass online analysis flow paths from the outlet of the evaporation and concentration unit and the compliant discharge port, respectively, continuously introducing radioactive waste liquid samples using the transport power of the flow paths. This ensures that the waste liquid samples reflect the true water quality status of the corresponding monitoring points in real time, eliminating the risk of radioactive leakage from open sampling. Subsequently, the continuously introduced waste liquid samples are first transported to the online ultrafiltration module, where the flow pressure of the waste liquid itself serves as the driving force, allowing the material to pass through the ultrafiltration membrane. The process involves sieving to separate large suspended particles in the waste liquid, allowing the liquid phase components to penetrate the ultrafiltration membrane while trapping suspended particles larger than the membrane pore size on the membrane surface. The pre-ultrafiltration waste liquid is then fed into an ultrasonic cavitation device. The high-frequency ultrasonic vibration generated by this device creates ultrasonic cavitation, breaking down residual colloidal particles and flocculent impurities into discrete microparticles, eliminating their adhesion and aggregation. Finally, the ultrasonically cleaved waste liquid is returned to the ultrafiltration module for deep purification, completely removing the broken-down microparticles. Through the synergistic treatment of online ultrafiltration and ultrasonic cavitation, suspended solids and colloidal interferences in the waste liquid sample are removed, ultimately yielding a clear sample that meets the requirements for subsequent testing.

[0058] In this embodiment of the invention, a closed bypass online analysis flow path is continuously introduced from the outlet of the evaporation and concentration unit and the discharge port to obtain a clear sample. The waste liquid sample is then pretreated by online ultrafiltration and ultrasonic disruption to remove suspended solids and colloidal interferences. This overcomes the technical problems of suspended solids and colloidal interferences in the waste liquid affecting the accuracy of subsequent spectral acquisition and water quality parameter detection, as well as the potential for radioactive contamination and safety hazards caused by non-closed sampling. This ensures the cleanliness and representativeness of the sample and eliminates interference from impurities in the early stages.

[0059] In a preferred embodiment of the present invention, step 1 above may include:

[0060] Step 1.1: Principal component decomposition is performed on the Raman spectral characteristic peak data acquired by each fiber Raman probe array. Several principal components with a cumulative contribution rate exceeding a preset threshold are extracted from each fiber Raman probe array as characteristic spectral vectors at that location. The characteristic spectral vectors extracted from each fiber Raman probe array are arranged in order of waste liquid flow direction to form a characteristic spectral vector sequence. Specifically, this includes: deploying multiple sets of fiber Raman probe arrays along the waste liquid flow direction inside the analysis cell; each set of fiber Raman probe arrays independently acquires Raman spectral characteristic peak data at its corresponding location; after data acquisition, principal component decomposition is performed on the Raman spectral characteristic peak data corresponding to each set of fiber Raman probe arrays, decomposing the original spectral data into multiple independent principal components; the variance contribution rate is calculated for each principal component obtained by decomposition, let the i-th... The variance of each principal component is The sum of the variances of all principal components is The formula for calculating the independent contribution rate of a single principal component is: The independent contribution rates of each principal component are summed sequentially in descending order of their independent contribution rates to obtain the cumulative contribution rate. Let the number of principal components selected be... The cumulative contribution rate is calculated using the formula: C = + +…+ Principal components whose cumulative contribution rate exceeds a preset threshold are extracted and used as the characteristic spectral vectors of the corresponding fiber Raman probe arrays. After all fiber Raman probe arrays have completed the extraction of characteristic spectral vectors, the characteristic spectral vectors are arranged in sequence according to the actual flow direction of the waste liquid in the analysis cell to form a complete characteristic spectral vector sequence.

[0061] Step 1.2 involves spline interpolation of the characteristic spectral vector sequence along the flow direction to obtain an axially continuous spectral feature distribution. Combining this with the radial detection point coordinates of each fiber Raman probe array, and based on the spatial influence range of each radial detection point, spatial weighting is applied to the spectral feature values ​​at each axial position on the cross-section. This yields the spectral feature values ​​at each grid point on the cross-section of the analytical cell. The similarity between the spectral feature values ​​at each grid point and the standard spectrum of the interfering substance is calculated. Based on the similarity value, the concentration of the interfering substance at each grid point is determined, thus forming a two-dimensional concentration distribution field of the pollutant on the cross-section of the analytical cell. Specifically, this includes: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] The spectral sequence is calculated using spline interpolation along the waste liquid flow direction. This interpolation completes the spectral characteristic data between adjacent fiber Raman probe arrays, resulting in a continuously distributed axial spectral characteristic distribution along the waste liquid flow direction. The cross-section of the analysis cell is divided into several uniform square grids according to a preset size. The center of each grid is used as the grid point for calculating the spectral characteristic value. Combining the radial detection point coordinates of each fiber Raman probe array, spatial weight allocation is calculated based on the spatial influence range of each radial detection point. First, the radial straight-line distance from any grid point to each radial detection point is calculated. Assuming the total number of radial detection points is m, the distance from the current grid point to the... The radial linear distance between each radial detection point is The distance inverse weighted method is used to calculate the corresponding grid point. Spatial influence weight of each radial detection point The calculation formula is: Then, the spectral characteristic value of each grid point is calculated, let the 1st grid point be... The spectral characteristic value of each radial detection point is S k The formula for calculating the spectral eigenvalue S of the grid points is: S = S1 +S2 +…+S m After calculating the spectral feature values ​​of all grid points, the cosine similarity method is used to calculate the similarity between the spectral feature value of each grid point and the standard spectrum of the interfering substance. The concentration of the interfering substance at the corresponding grid point is determined based on the calculated similarity value. The similarity value is positively correlated with the concentration of the interfering substance. The concentrations of the interfering substances at all grid points are integrated and mapped according to their spatial coordinates to form a two-dimensional concentration distribution field of pollutants on the cross-section of the analysis cell.

[0062] In this embodiment of the invention, principal component decomposition is performed on the Raman spectral characteristic peak data collected by each fiber Raman probe array, and principal components with a cumulative contribution rate exceeding a preset threshold are extracted as characteristic spectral vectors. These vectors are arranged according to the flow direction of the waste liquid to form a characteristic spectral vector sequence. Spline interpolation is then performed on this sequence to obtain an axial continuous spectral characteristic distribution. Spatial weight allocation is performed by combining the radial detection point coordinates and spatial influence range to obtain the spectral characteristic values ​​of each grid point. The concentration of the interfering substance is determined by calculating the similarity between the spectral characteristic values ​​of the grid points and the standard spectrum of the interfering substance, thereby forming a two-dimensional concentration distribution field. This technique overcomes the technical problems of traditional spectral analysis, such as the inability to extract effective spectral features, the difficulty in quantifying the spatial distribution of interfering substances, and the incomplete spectral data processing and discontinuous spatial distribution characterization, which leads to a lack of reliable data support for subsequent interference correction. This technique achieves the extraction of effective spectral features of radionuclides and interfering substances, and realizes the continuous characterization of the non-uniform spatial distribution of interfering substances on the cross-section of the analysis cell.

[0063] In a preferred embodiment of the present invention, step 2 above may include:

[0064] Step 2.1: For the target interfering component in the two-dimensional concentration distribution field, set multiple concentration gradient values ​​and extract the isoconcentration lines corresponding to each concentration gradient value. Each isoconcentration line consists of continuous spatial points with the same concentration value. Specifically, this includes: identifying the target interfering component in the two-dimensional concentration distribution field, which is the suspended matter, colloid, and related interfering components mentioned in the background art that can interfere with total α and β radiation detection, conductivity detection, etc.; combining the actual concentration range of the target interfering component in the radioactive waste liquid, setting multiple uniformly distributed concentration gradient values. The concentration gradient values ​​need to cover the lowest to highest concentration of the interfering component. The gradient interval is determined according to the detection accuracy requirements, usually 10% to 20% of the lowest concentration of the interfering component. Based on the constructed two-dimensional concentration distribution of the cross-section of the analysis cell... In this process, three non-collinear grid points on the plane containing the cross-section of the analytical cell are selected. The unit normal direction of the plane containing the cross-section of the analytical cell is determined based on the spatial coordinates of these three non-collinear grid points. Using this unit normal direction as a spatial reference, the spatial orientation of all grid points on the cross-section is uniformly calibrated. The concentration of interfering substances at all grid points whose spatial orientation has been calibrated is then traversed. All grid points whose concentration values ​​are equal to a certain preset concentration gradient value are connected sequentially according to their spatial coordinate positions to form a continuous closed set of spatial points. This set of spatial points is the isoconcentration line corresponding to the concentration gradient value. Each isoconcentration line is composed of continuous spatial points with the same concentration of interfering substances. Different concentration gradient values ​​correspond to different isoconcentration lines. Thus, the spatial distribution of the target interfering substances on the cross-section of the analytical cell is completely presented through multiple isoconcentration lines.

[0065] Step 2.2: Perform equal-arc-length discrete sampling on each isoconcentration line to obtain a uniformly distributed sequence of boundary feature points on the isoconcentration line. The boundary feature point sequence contains the coordinate information of each feature point on the cross-section of the analysis cell. Specifically, this includes performing equal-arc-length discrete sampling on each extracted isoconcentration line to transform continuous isoconcentration lines into uniformly distributed discrete boundary feature points. The specific sampling process is as follows:

[0066] Calculate the total arc length of each isoconcentration line, and divide the isoconcentration line into several small segments according to its curve direction. Let the coordinates of two adjacent spatial points be ( ). , )and( , The length of each tiny line segment is calculated using the formula: The total arc length of the isoconcentration line is obtained by successively adding the lengths of all the tiny line segments. The calculation formula is as follows: ,in To determine the total number of tiny line segments into which the isoconcentration line is divided, a preset number of sampling points N is set. The number of sampling points is determined based on the complexity of the isoconcentration line and the calculation accuracy, typically 30 to 50 sampling points are selected. The sampling interval is calculated using the following formula: =L÷N Starting from any point on the isoconcentration line, sampling points are selected sequentially along the curve of the isoconcentration line according to the sampling interval. The coordinate information of a spatial point is recorded at each sampling interval until the entire isoconcentration line is traversed. Finally, a sequence of boundary feature points uniformly distributed on the isoconcentration line is obtained. This sequence contains the x and y coordinates of each feature point on the cross-section of the analysis cell.

[0067] Step 2.3: For each isoconcentration line corresponding to the sequence of boundary feature points, determine the smallest circumcircle covering all feature points in the sequence by gradually adjusting the center position and expanding the radius. Record the center coordinates and radius of the smallest circumcircle as the center coordinates and radius of the isoconcentration line. Specifically, this includes: for each isoconcentration line corresponding to the sequence of boundary feature points, determining the smallest circumcircle covering all boundary feature points by gradually adjusting the center position and expanding the radius. The specific calculation process is as follows: Determine the initial center coordinates, assuming the total number of feature points in the boundary feature point sequence is... The coordinates of each feature point are (x i y i (i=1,2,…, The average x-coordinate of all feature points is taken as the x-coordinate of the initial circle center, and the average y-coordinate of all feature points is taken as the y-coordinate of the initial circle center. The calculation formula is as follows: , Calculate the initial radius, iterate through all feature points, and calculate the distance from each individual feature point to the initial center of the circle: The maximum value among all distances is selected as the initial radius r1.

[0068] Gradually adjust the center position and expand the radius range: Traverse each feature point in the boundary feature point sequence, and let the coordinates of the feature point be (x... p y p ), calculate its distance to the current center (x) c y c Distance: If the distance is greater than the current radius, it means the feature point is outside the current circle, and the center position needs to be adjusted. The formula for calculating the moving distance is: The formula for calculating the coordinates of the new center of the circle after the movement is: , Simultaneously update the radius, setting the new radius to the distance from the feature point to the new center. Repeat the above adjustment process, checking and adjusting the center and radius for each boundary feature point in turn, until the distance from all boundary feature points to the current center is less than or equal to the current radius. The circle at this point is the minimum circumcircle of the boundary feature point sequence. Record the center coordinates (x0, y0) and radius r2 of the minimum circumcircle, and use them as the center coordinates and radius of the corresponding isoconcentration line.

[0069] In this embodiment of the invention, multiple concentration gradient values ​​are set for the target interfering component in a two-dimensional concentration distribution field, and corresponding isoconcentration lines are extracted. Each isoconcentration line is sampled with equal arc length to obtain a uniformly distributed sequence of boundary feature points. Then, the minimum circumcircle is calculated for the sequence of boundary feature points, and the center coordinates and radius are recorded. Therefore, this method overcomes the technical problems of being unable to geometrically quantify the spatial distribution of interfering substances, being unable to extract the geometric features of the interfering distribution, resulting in a lack of reliable geometric basis and low calculation accuracy for subsequent compensation coefficient calculations. This method achieves the goal of obtaining the geometric feature parameters of the interfering substance distribution.

[0070] In a preferred embodiment of the present invention, step 3 above may include:

[0071] Step 3.1: Calculate the Euclidean distance between the center coordinates of each isoconcentration line and the center of the preset detection window. Normalize the Euclidean distance by dividing it by the diagonal length of the detection window to obtain the normalized positional deviation. Calculate the ratio between the radius of each isoconcentration line and the effective radius of the detection window to obtain the radial dimension ratio. Specifically, this includes: defining the basic parameters required for the calculation, setting the center coordinates of each isoconcentration line as (x0, y0), and the center coordinates of the preset detection window as (x0, y0). c y c The diagonal length of the detection window is D, the radius of the isoconcentration line is r, and the effective radius of the detection window is R.

[0072] First, calculate the Euclidean distance between the center coordinates of each isoconcentration line and the center of the preset detection window. The calculation formula is: This distance is used to characterize the spatial offset between the core distribution area of ​​the interfering object and the center of the detection window. The greater the distance, the more the distribution of the interfering object deviates from the core area of ​​the detection window, and the more uneven the interference effect on the detection.

[0073] Normalizing the Euclidean distance yields the normalized positional deviation. Normalization can eliminate calculation errors caused by differences in the size of the detection window and normalize the positional deviation. The value range is 0~1. The closer it is to 0, the closer the center of the isoconcentration line is to the center of the detection window, and the more concentrated the interference distribution is in the core area of ​​the detection window; The closer the value is to 1, the farther the center of the isoconcentration line is from the center of the detection window, and the more the interference distribution is biased towards the edge of the detection window.

[0074] Simultaneously, the radial dimension ratio k between the radius of each isoconcentration line and the effective radius of the detection window is calculated using the following formula: The radial dimension ratio k is used to characterize the degree of matching between the size of the interference distribution area and the effective detection area of ​​the detection window. The larger k is, the closer the interference distribution area is to or even exceeds the effective detection area of ​​the detection window, and the wider the interference range of the detection. The smaller k is, the smaller the interference distribution area is, and the more concentrated the interference range of the detection.

[0075] Step 3.2: For each isoconcentration line, using the normalized position deviation and radial dimension ratio as input parameters, query the pre-stored interference coefficient database for the corresponding basic compensation coefficient group under the operating condition. The basic compensation coefficient group includes the basic radiation attenuation compensation coefficient of the radiation detector window, the basic polarization correction factor of the conductivity electrode, the basic response delay compensation value of the pH electrode, and the basic light scattering interference suppression coefficient of the turbidimeter. Specifically, it includes: a pre-stored interference coefficient database, which was obtained through extensive experimental calibration in the early stage and stores different normalized position deviations. The system includes various basic compensation coefficient sets corresponding to different radial dimension ratios k, covering common operating condition combinations in radioactive waste treatment processes. This ensures the accuracy and relevance of the query results, and addresses the normalized positional deviation of each isoconcentration line calculated. The radial dimension ratio J and these two parameters are used as joint input parameters to query the pre-stored interference coefficient database and match the basic compensation coefficient group for the corresponding operating condition. This basic compensation coefficient group contains four core parameters, namely: the basic radiation attenuation compensation coefficient of the radiation detector window. The fundamental polarization correction factor of conductivity electrodes pH electrode baseline response delay compensation value The basic light scattering interference suppression coefficient of the turbidimeter ,in Representing the Each isoconcentration line corresponds to a specific interference distribution condition, and is used to specifically counteract the various interference effects under that condition.

[0076] Step 3.3: For the basic compensation coefficient group obtained from multiple isoconcentration lines corresponding to different concentration gradient values, a weighted fusion is performed according to the concentration gradient value represented by each isoconcentration line. The higher the concentration gradient value, the larger the weight coefficient corresponding to the isoconcentration line. The comprehensive radiation attenuation compensation coefficient, comprehensive polarization correction factor, comprehensive response delay compensation value, and comprehensive light scattering interference suppression coefficient are calculated by weighted averaging. These are used as the final radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value, and light scattering interference suppression coefficient for correction. Specifically, this includes: performing a weighted fusion calculation on the basic compensation coefficient group obtained from multiple isoconcentration lines corresponding to different concentration gradient values ​​to obtain the final comprehensive compensation correction coefficient used for correction, and setting the concentration gradient value corresponding to each isoconcentration line as... , Representing the The isoconcentration lines are used because the higher the concentration gradient value, the higher the concentration of interfering substances, and the greater the interference effect on the detection. Therefore, the weighting coefficient is set according to the concentration gradient value, and the higher the concentration gradient value, the larger the weighting coefficient.

[0077] Calculate the sum of the concentration gradient values ​​of all isoconcentration lines. The calculation formula is: Where n is the total number of isoconcentration lines; calculate the weighting coefficient w corresponding to each isoconcentration line. i The calculation formula is: The sum of the weighting coefficients of all isoconcentration lines is 1, that is... + +…+ =1, to ensure the rationality of the weighted fusion calculation, the weighted average of the four types of basic compensation coefficients is calculated to obtain the corresponding comprehensive compensation correction coefficient:

[0078] The comprehensive radiation attenuation compensation coefficient A is calculated using the following formula: ;

[0079] The comprehensive polarization correction factor B is calculated using the following formula: ;

[0080] The comprehensive response delay compensation value C is calculated using the following formula: ;

[0081] Overall light scattering interference suppression coefficient The calculation formula is: ;

[0082] After the calculation is completed, the comprehensive radiation attenuation compensation coefficient A, comprehensive polarization correction factor B, comprehensive response delay compensation value C, and comprehensive light scattering interference suppression coefficient will be included. As the final compensation correction coefficient used for water quality parameter correction, it ensures that the compensation coefficient matches the actual spatial distribution and concentration distribution of the interfering substances.

[0083] In this embodiment of the invention, the normalized positional deviation between the center coordinates of the isoconcentration lines and the center of the detection window is calculated, and the radial dimension ratio between the radius and the effective radius of the detection window is calculated. The normalized positional deviation and radial dimension ratio are used as input parameters to query the interference coefficient database to obtain the basic compensation coefficient set. The basic compensation coefficient sets corresponding to multiple isoconcentration lines are weighted and fused according to the concentration gradient value to obtain the comprehensive compensation correction coefficient. Therefore, this method overcomes the technical problems that the compensation coefficient cannot match the actual spatial distribution of the interfering substances, it is difficult to quantify the influence degree of interference at different locations and concentrations, and the single compensation parameter has low accuracy and poor specificity. Thus, it achieves the matching of the compensation correction coefficient with the actual working conditions and improves the calculation accuracy of various compensation and correction parameters.

[0084] In a preferred embodiment of the present invention, step 4 above may include:

[0085] Step 4.1: Combine the initial total alpha radioactivity data obtained from real-time detection by the radiation detector with the comprehensive radiation attenuation compensation coefficient to correct the initial total alpha radioactivity data, obtaining the corrected total alpha radioactivity. Specifically, this includes correcting the initial total alpha radioactivity data. The core purpose is to eliminate the measurement deviation caused by radiation attenuation at the radiation detector window. During the detection of radioactive waste liquid, suspended solids, colloids, and other interfering components in the waste liquid adhere to the surface of the radiation detector window, causing radiation attenuation at the detector window. This results in the initial total alpha radioactivity data detected by the radiation detector being less than the actual value of the total alpha radioactivity in the waste liquid, thus producing a measurement deviation. Based on the obtained... The comprehensive radiation attenuation compensation coefficient is used to correct the initial data of total alpha radioactivity. The specific correction process involves combining the initial data of total alpha radioactivity with the comprehensive radiation attenuation compensation coefficient and using a reasonable calculation method to compensate for the initial data and offset the effects of radiation attenuation. The comprehensive radiation attenuation compensation coefficient ranges from 0 to 1. The smaller the value of the coefficient, the more severe the radiation attenuation of the radiation detector window, and the greater the correction range of the initial data of total alpha radioactivity. Through this correction process, the corrected total alpha radioactivity is finally obtained. This value reflects the actual situation of total alpha radioactivity in radioactive waste liquid and eliminates the measurement deviation caused by radiation attenuation.

[0086] Step 4.2: Combine the initial total β radioactivity data obtained in real-time from the radiation detector with the comprehensive radiation attenuation compensation coefficient to correct the initial total β radioactivity data, eliminating the negative drift caused by detector window contamination, and obtaining the corrected total β radioactivity. Specifically, this includes correcting the initial total β radioactivity data, focusing on eliminating the negative drift caused by detector window contamination. The detection of total β radioactivity and the detection of total α radioactivity are affected by the same type of interference factor, namely radiation attenuation caused by contamination of the radiation detector window. Radiation attenuation will also cause a negative drift in the initial total β radioactivity data, meaning the detected value is less than the actual value and cannot accurately reflect the true state of total β radioactivity in the waste liquid. The same comprehensive radiation attenuation compensation coefficient is used to correct the initial total β radioactivity data. By combining the initial total β radioactivity data with the comprehensive radiation attenuation compensation coefficient for calculation, the negative drift of the initial data is compensated to offset the adverse effects of radiation attenuation, and finally the corrected total β radioactivity is obtained, ensuring the accuracy of the total β radioactivity detection data.

[0087] Step 4.3: Combine the initial conductivity data obtained by real-time detection of the conductivity electrode with the comprehensive polarization correction factor to correct the initial conductivity data, compensate for the response hysteresis and measurement deviation caused by electrode surface contamination, and obtain the corrected conductivity. Specifically, this includes correcting the initial conductivity data, mainly to compensate for the response hysteresis and measurement deviation caused by conductivity electrode surface contamination. During the detection process, the surface of the conductivity electrode can be contaminated by interfering components such as suspended solids and colloids in the waste liquid, resulting in electrode polarization. This polarization slows down the response speed of the conductivity electrode, causing response lag and inaccurate initial conductivity data, failing to reflect the actual electrolyte content in the waste liquid. To address this issue, a comprehensive polarization correction factor is used to correct the initial conductivity data. Specifically, the initial conductivity data is calculated by combining it with the comprehensive polarization correction factor to compensate for the response lag and measurement deviation caused by electrode polarization. The comprehensive polarization correction factor is obtained through prior experimental calibration based on the degree of electrode polarization, and its value is slightly greater than 1. This correction process effectively compensates for the deviation in the initial conductivity data, yielding a corrected conductivity value that reflects the actual electrolyte content in the radioactive waste liquid.

[0088] Step 4.4: Combine the initial pH data obtained from real-time detection by the pH electrode with the comprehensive response delay compensation value to correct the initial pH data. This corrects the response delay and measurement drift caused by electrode surface contamination, resulting in a corrected pH value. Specifically, this involves correcting the initial pH data, primarily to compensate for the response delay and measurement drift caused by pH electrode surface contamination. During long-term detection, interfering components from the wastewater can adhere to the surface of the pH electrode, causing a response delay. This leads to the initial pH data deviating from the actual acidity or alkalinity of the wastewater, resulting in measurement drift and affecting the accuracy of water quality assessment. The corrected pH value is obtained by combining the comprehensive response delay compensation value with the corrected response delay compensation value. The initial pH value is corrected by combining the initial pH value with the comprehensive response delay compensation value. This is done through addition and subtraction to compensate for deviations caused by response delay and measurement drift. The comprehensive response delay compensation value is the difference obtained from a large number of previous tests and calibrations. Its value can be positive or negative, and the specific value is determined according to the direction and degree of the pH electrode response delay. When the initial pH value is too low, the compensation value is positive, and when the initial pH value is too high, the compensation value is negative. Through this correction process, the influence of response delay and measurement drift is eliminated, and the corrected pH value is obtained, which accurately reflects the actual acidity and alkalinity of the radioactive waste liquid.

[0089] Step 4.5: Combine the initial turbidity data obtained from real-time detection by the turbidimeter with the comprehensive light scattering interference suppression coefficient to correct the initial turbidity data, obtaining the corrected turbidity. Specifically, this includes correcting the initial pH data. The core of this correction is to compensate for the response delay and measurement drift caused by surface contamination of the pH electrode. During long-term detection, interfering components from the waste liquid will adhere to the surface of the pH electrode, causing a response delay. This results in the initial pH data deviating from the actual acidity or alkalinity of the waste liquid, leading to measurement drift and affecting the accuracy of water quality assessment. The obtained comprehensive response delay compensation value is used to correct the initial pH data. The initial pH value is corrected by combining it with a comprehensive response delay compensation value. This is done through addition and subtraction to compensate for deviations caused by response delay and measurement drift. The comprehensive response delay compensation value is the difference obtained from extensive prior testing and calibration; its value can be positive or negative, depending on the direction and degree of the pH electrode's response delay. When the initial pH value is too low, the compensation value is positive; when the initial pH value is too high, the compensation value is negative. This correction process eliminates the effects of response delay and measurement drift, resulting in a corrected pH value that accurately reflects the actual acidity or alkalinity of the radioactive waste liquid.

[0090] Step 4.6: The corrected total alpha radioactivity, corrected total beta radioactivity, corrected conductivity, corrected pH value, and corrected turbidity are combined to form the corrected real-time water quality parameters. Specifically, this involves integrating all corrected water quality parameters to form a complete set of real-time water quality parameters. This set of real-time water quality parameters eliminates measurement drift and deviation caused by various interferences such as radiation attenuation, electrode polarization, response delay, and light scattering, improving detection accuracy and accurately reflecting the actual water quality state of radioactive waste liquid. This effectively solves the technical problem of insufficient detection accuracy of key water quality parameters in the background technology. Furthermore, this set of real-time water quality parameters...

[0091] In this embodiment of the invention, the initial detection data of total α radioactivity, total β radioactivity, conductivity, pH value and turbidity are specifically corrected by comprehensively considering the radiation attenuation compensation coefficient, comprehensive polarization correction factor, comprehensive response delay compensation value and comprehensive light scattering interference suppression coefficient, thus forming the corrected real-time water quality parameters. Therefore, this method overcomes the technical problems of negative measurement drift caused by detector window contamination, response lag, delay and measurement deviation caused by electrode surface contamination, and low accuracy and poor stability of water quality parameter detection caused by light scattering interference. In this way, it effectively eliminates the influence of various interferences and improves the accuracy of multi-water quality parameter detection.

[0092] In a preferred embodiment of the present invention, step 5 above may include:

[0093] Step 5.1: The distributed control unit receives the corrected real-time water quality parameters and constructs a dual-objective optimization function with the optimization objectives of achieving effluent quality compliance and minimizing operating energy consumption. Effluent quality compliance is determined by comparing the corrected real-time water quality parameters with a preset emission standard threshold. Operating energy consumption is calculated based on the real-time operating parameters of the evaporation and concentration unit and the reverse osmosis membrane unit. Specifically, the distributed control unit receives the corrected real-time water quality parameters and constructs a dual-objective optimization function with the optimization objectives of achieving effluent quality compliance and minimizing operating energy consumption, ensuring that the control process meets emission standards and achieves energy-saving operation. The corrected real-time water quality parameters are received in real-time, the data is verified to confirm that there are no missing or abnormal data, and the dual-objective optimization function is constructed. Let the dual-objective optimization function be F(X), where X is the control parameter vector, X = (k, t), k is the evaporation and concentration ratio, and t is the reverse osmosis membrane flushing cycle. The dual-objective optimization function is derived from the effluent quality compliance evaluation function. and operating energy consumption evaluation function The weighted composition is calculated using the following formula: ,in, To ensure that the discharged water meets the standards, As for the operating energy consumption weight, and + =1, based on the priority of nuclear industry waste liquid treatment. Value greater than ,generally =0.6~0.8, =0.2~0.4, to ensure that the discharge water quality meets the standards first.

[0094] Discharge water quality compliance evaluation function This method is used to determine whether the water quality meets the standards under the current control parameters. It is obtained by comparing the corrected real-time water quality parameters with the preset discharge standard threshold and then performing a normalized calculation. The value range is 0~1. =1 indicates that the water quality fully meets the standards. The smaller the value, the more serious the water quality exceedance and the worse the compliance. When all water quality parameters are less than or equal to the corresponding threshold, all items in parentheses are 0. =1, indicating that the water quality meets the standards.

[0095] Operating energy consumption evaluation function Used to calculate real-time operating energy consumption, the total system energy consumption is the sum of the energy consumption of the evaporation and concentration unit and the reverse osmosis membrane unit, calculated using the following formula: ,in, This represents the total operating energy consumption of the system. Energy consumption of the evaporation and concentration unit. Energy consumption of the reverse osmosis membrane unit;

[0096] Energy consumption of evaporation and concentration unit ,Right now For steam consumption rate, For runtime, The unit price of steam reflects the energy consumption of the evaporation and concentration process.

[0097] reverse osmosis membrane unit energy consumption , For operating power, For runtime, The electricity price reflects the energy consumption of the reverse osmosis membrane operation. To facilitate weighted integration with the compliance evaluation function, the total energy consumption E is normalized, resulting in the operating energy consumption evaluation function: ,in, To maximize energy consumption, To minimize possible energy consumption, The value ranges from 0 to 1. The larger the value, the lower the energy consumption and the better the energy-saving effect.

[0098] Step 5.2: Based on the bi-objective optimization function, initialize a population consisting of multiple candidate solutions. Each candidate solution corresponds to a set of parameter combinations for evaporation concentration ratio and reverse osmosis membrane flushing cycle. Employ an iterative optimization method simulating biological community cooperation and competition mechanisms. Using the constructed bi-objective optimization function as the fitness evaluation criterion, calculate the fitness value of each candidate solution and simulate information sharing and survival of the fittest among individuals to continuously update the candidate solutions in the population until a preset number of iterations is reached. This yields the final combination of evaporation concentration ratio and reverse osmosis membrane flushing cycle that enables the bi-objective optimization function to function optimally, serving as the final control parameter combination. Specifically, this includes: based on the constructed bi-objective optimization function, employing a simulated biological community cooperation and competition mechanism... The iterative optimization method based on the competition mechanism automatically solves for the optimal combination of process control parameters. Within a reasonable range of process values ​​for the evaporation concentration ratio (e.g., 1.2~3.0) and the reverse osmosis membrane flushing cycle (e.g., 30min~120min), multiple candidate solutions are randomly generated, consisting of different combinations of evaporation concentration ratios and reverse osmosis membrane flushing cycles. All candidate solutions together constitute the initial optimization population. Each candidate solution corresponds to a practically executable process control scheme. The calculation results of the bi-objective optimization function are used as the evaluation criteria for the quality of candidate solutions. The fitness value of each candidate solution in the population is calculated one by one. The higher the fitness value, the better the process parameter combination corresponding to the candidate solution can simultaneously meet the dual requirements of water quality compliance and minimum energy consumption.

[0099] During the iterative optimization process, the system simulates the survival of the fittest and information sharing mechanism of biological populations. First, it selects the top 20% of high-quality candidate solutions with the highest fitness values ​​in the population and retains them directly into the next generation. Then, it performs parameter crossover and parameter mutation operations on the retained high-quality candidate solutions. The crossover operation involves reasonably combining the evaporation concentration ratio and reverse osmosis membrane flushing cycle of different high-quality candidate solutions. The mutation operation involves making small-scale adjustments to the parameters of the candidate solutions within the allowable range of the process. New candidate solutions are generated through the above methods. The retained high-quality candidate solutions are combined with the newly generated candidate solutions to form a new generation of population, completing one iteration calculation. The iterative process of fitness calculation, retention of high-quality individuals, and crossover mutation to generate new individuals is repeated until the system reaches the preset total number of iterations. At this point, the candidate solution with the highest fitness value in the population is selected, along with the corresponding evaporation concentration ratio and reverse osmosis membrane flushing cycle.

[0100] Step 5.3: Send control commands to the evaporation and concentration unit and the reverse osmosis membrane unit through the final control parameter combination to automatically adjust the evaporation and concentration ratio and the reverse osmosis membrane flushing cycle. At the same time, compare the corrected real-time water quality parameters with the preset discharge standard threshold. If all water quality parameters are less than or equal to the corresponding discharge standard threshold, it is determined that the discharged water quality meets the discharge standard. Otherwise, an alarm signal is issued and an emergency handling process is triggered. Specifically, this includes: issuing automatic control commands to the evaporation and concentration unit and the reverse osmosis membrane unit according to the final control parameter combination obtained by iterative optimization; adjusting the operating ratio of the evaporation and concentration unit to the final value to ensure the waste liquid reduction treatment effect while avoiding excessive concentration and energy waste; and adjusting the flushing cycle of the reverse osmosis membrane unit to the final duration to remove impurities and interferences attached to the membrane surface in a timely manner, ensuring the reverse osmosis treatment effect, extending the service life of the membrane module, and avoiding the additional energy consumption caused by frequent flushing.

[0101] While issuing process control instructions, the distributed control unit continuously compares the corrected real-time water quality parameters with the preset discharge standard thresholds item by item. It sequentially checks whether the corrected total alpha radioactivity is less than or equal to the total alpha radioactivity discharge standard threshold, whether the corrected total beta radioactivity is less than or equal to the total beta radioactivity discharge standard threshold, whether the corrected conductivity is less than or equal to the conductivity discharge standard threshold, whether the corrected pH value is between the lower and upper limits of the pH discharge standard, and whether the corrected turbidity is less than or equal to the turbidity discharge standard threshold. If all water quality parameters meet the corresponding discharge standards... If the requirements are met, the current discharged water quality is determined to be in compliance with the standards, and the current final control parameters are maintained in a stable manner. If any water quality parameter exceeds the corresponding discharge standard threshold, the discharged water quality is immediately determined to be substandard, the distributed control unit automatically issues an audible and visual alarm signal, and simultaneously initiates a preset emergency treatment process, suspending the discharge of waste liquid, temporarily increasing the evaporation and concentration ratio, shortening the reverse osmosis membrane flushing cycle, and restarting the online water quality detection and parameter correction process until all water quality parameters meet the standards. Then, the alarm is turned off and normal operation is restored, thereby achieving intelligent control and safe and stable compliant discharge throughout the entire process of radioactive waste liquid treatment.

[0102] In this embodiment of the invention, a dual-objective optimization function is constructed with the optimization goals of achieving compliant effluent quality and minimizing operational energy consumption. The final combination of control parameters is obtained through an iterative optimization method simulating the cooperation and competition mechanism of biological communities. This method automatically adjusts the evaporation concentration ratio and the reverse osmosis membrane flushing cycle, and uses technical means to determine whether real-time water quality parameters meet standards, alarm when exceeding standards, and trigger emergency treatment procedures. Therefore, it overcomes the technical problems of traditional manual or semi-automatic control, such as low precision, inability to balance water quality compliance and operational energy consumption, poor process parameter matching, high operational energy consumption, and inability to promptly warn and handle water quality exceeding standards. This achieves intelligent optimization and control of radioactive waste liquid treatment process parameters, ensuring stable compliance of effluent quality while reducing operational energy consumption, extending the service life of reverse osmosis membranes, and improving automation level and operational safety.

[0103] like Figure 2 As shown, embodiments of the present invention also provide an online water quality analysis system for radioactive waste treatment, comprising:

[0104] The acquisition module is used to pass the clarified sample into the analysis cell and acquire Raman spectral characteristic peak data in real time through multiple sets of fiber Raman probe arrays arranged along the flow direction of the waste liquid in the analysis cell; principal component analysis is performed on the Raman spectral characteristic peak data to extract the characteristic spectral vectors corresponding to the main radionuclides and interfering substances, and a two-dimensional concentration distribution field is constructed based on the continuous change of the characteristic spectral vectors in spatial position.

[0105] The extraction module is used to extract isoconcentration lines and perform discrete sampling for specific interfering components in a two-dimensional concentration distribution field, calculate the minimum circumcircle of all sampling points, and obtain the center coordinates and radius of the isoconcentration lines.

[0106] The calculation module is used to calculate the radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value and light scattering interference suppression coefficient based on the positional deviation of the circle center coordinates relative to the center of the preset detection window and the ratio of the radius to the size of the detection window, combined with the pre-stored interference coefficient database.

[0107] The calibration module is used to correct the initial water quality parameters of total α radioactivity, total β radioactivity, conductivity, pH value and turbidity obtained in real time by the radiation detector, conductivity electrode, pH electrode and turbidity meter through the radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value and light scattering interference suppression coefficient, respectively, to obtain the corrected real-time water quality parameters.

[0108] The processing module is used to upload the corrected real-time water quality parameters to the distributed control unit, which automatically adjusts the evaporation concentration ratio and the reverse osmosis membrane flushing cycle, and determines whether the discharged water quality meets the discharge standards.

[0109] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0110] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0111] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0112] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for on-line analysis of water quality in a process for treatment of radioactive liquid waste, c h a r a c t e r i s e d in that The method includes: Step 1: The clarified sample is introduced into the analysis cell. Multiple sets of fiber Raman probe arrays are arranged in the analysis cell along the flow direction of the waste liquid to collect Raman spectral characteristic peak data in real time. Principal component analysis is performed on the Raman spectral characteristic peak data to extract the characteristic spectral vectors corresponding to the main radionuclides and interfering substances. A two-dimensional concentration distribution field is constructed based on the continuous change of the characteristic spectral vectors in spatial position. Step 2: Extract isoconcentration lines and perform discrete sampling for the target interference components in the two-dimensional concentration distribution field, calculate the minimum circumcircle of all sampling points, and obtain the center coordinates and radius of the isoconcentration lines. Step 3: Based on the positional deviation of the circle's center coordinates relative to the center of the preset detection window and the proportional relationship between the radius and the size of the detection window, and in conjunction with the pre-stored interference coefficient database, calculate the radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value, and light scattering interference suppression coefficient, including: Calculate the Euclidean distance between the center coordinates of each isoconcentration line and the center of the preset detection window. Divide the Euclidean distance by the diagonal length of the detection window for normalization to obtain the normalized positional deviation. Calculate the ratio between the radius of each isoconcentration line and the effective radius of the detection window to obtain the radial dimension ratio. For each isoconcentration line, the normalized position deviation and radial size ratio are used as input parameters. The basic compensation coefficient group under the corresponding working condition is queried in the pre-stored interference coefficient database. The basic compensation coefficient group includes the basic radiation attenuation compensation coefficient of the radiation detector window, the basic polarization correction factor of the conductivity electrode, the basic response delay compensation value of the pH electrode, and the basic light scattering interference suppression coefficient of the turbidimeter. The basic compensation coefficient group obtained from multiple isoconcentration lines corresponding to different concentration gradient values ​​is weighted and fused according to the concentration gradient value represented by each isoconcentration line. The higher the concentration gradient value, the larger the weight coefficient of the isoconcentration line. The comprehensive radiation attenuation compensation coefficient, comprehensive polarization correction factor, comprehensive response delay compensation value, and comprehensive light scattering interference suppression coefficient are calculated by weighted average. These are used as the final radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value, and light scattering interference suppression coefficient for correction. Step 4: The initial water quality parameters of total α radioactivity, total β radioactivity, conductivity, pH value and turbidity obtained in real time by the radiation detector, conductivity electrode, pH electrode and turbidity meter are corrected by the radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value and light scattering interference suppression coefficient, respectively, to obtain the corrected real-time water quality parameters. Step 5: Upload the corrected real-time water quality parameters to the distributed control unit (DCU). The DCU automatically adjusts the evaporation concentration ratio and reverse osmosis membrane flushing cycle, and determines whether the discharged water quality meets the discharge standards, including: The distributed control unit receives the corrected real-time water quality parameters and constructs a dual-objective optimization function with the optimization objectives of achieving compliant effluent quality and minimizing operating energy consumption. The compliant effluent quality is determined by comparing the corrected real-time water quality parameters with the preset effluent standard threshold, while the operating energy consumption is calculated based on the real-time operating parameters of the evaporation and concentration unit and the reverse osmosis membrane unit. Based on a bi-objective optimization function, a population consisting of multiple candidate solutions is initialized. Each candidate solution corresponds to a set of parameter combinations for evaporation concentration ratio and reverse osmosis membrane flushing cycle. An iterative optimization method simulating the cooperation and competition mechanism of a biological community is adopted. The constructed bi-objective optimization function is used as the fitness evaluation basis. By calculating the fitness value of each candidate solution and simulating the information sharing and survival of the fittest process among individuals, the candidate solutions in the population are continuously updated until the preset number of iterations is reached. The optimal combination of evaporation concentration ratio and reverse osmosis membrane flushing cycle that makes the bi-objective optimization function optimal is obtained as the final control parameter combination. The system sends control commands to the evaporation and concentration unit and the reverse osmosis membrane unit through the final control parameter combination, automatically adjusting the evaporation and concentration ratio and the reverse osmosis membrane flushing cycle. At the same time, the corrected real-time water quality parameters are compared with the preset discharge standard threshold. If all water quality parameters are less than or equal to the corresponding discharge standard threshold, the discharged water quality is determined to meet the discharge standard. Otherwise, an alarm signal is issued and an emergency treatment process is triggered.

2. The method for online water quality analysis during the treatment of radioactive waste liquid according to claim 1, characterized in that, Before step 1 above, the following steps are included: drawing closed bypass online analysis flow paths from the outlet of the evaporation and concentration unit and the discharge port to continuously introduce waste liquid samples; performing online ultrafiltration and ultrasonic disruption pretreatment on the introduced waste liquid samples to remove suspended solids and colloidal interferences from the waste liquid samples to obtain clarified samples.

3. The method for online water quality analysis during the treatment of radioactive waste liquid according to claim 2, characterized in that, Step 1: The clarified sample is introduced into the analysis cell. Multiple fiber optic Raman probe arrays arranged along the flow direction of the waste liquid within the analysis cell are used to acquire Raman spectral characteristic peak data in real time. Principal component analysis is performed on the Raman spectral characteristic peak data to extract the characteristic spectral vectors corresponding to the main radionuclides and interfering substances. A two-dimensional concentration distribution field is constructed based on the continuous spatial variation of the characteristic spectral vectors, including: Principal component decomposition was performed on the Raman spectral characteristic peak data collected by each fiber Raman probe array. Several principal components with a cumulative contribution rate exceeding a preset threshold were extracted from each fiber Raman probe array as the characteristic spectral vector at that position. The characteristic spectral vectors extracted from each fiber Raman probe array were arranged in order of the waste liquid flow direction to form a characteristic spectral vector sequence. Spline interpolation is performed on the characteristic spectral vector sequence along the flow direction to obtain an axially continuous spectral characteristic distribution. Combined with the radial detection point coordinates of each fiber Raman probe array, and based on the spatial influence range of each radial detection point, the spectral characteristic values ​​at each axial position are spatially weighted on the cross section to obtain the spectral characteristic values ​​at each grid point on the cross section of the analysis cell. The similarity between the spectral characteristic values ​​of each grid point and the standard spectrum of the interfering substance is calculated. The concentration of the interfering substance at the grid point is determined based on the similarity value, thus forming a two-dimensional concentration distribution field of the interfering substance on the cross section of the analysis cell.

4. The method for online water quality analysis during the treatment of radioactive waste liquid according to claim 3, characterized in that, Step 2: For the target interfering component in the two-dimensional concentration distribution field, extract isoconcentration lines and perform discrete sampling. Calculate the minimum circumcircle of all sampling points to obtain the center coordinates and radius of the isoconcentration lines, including: For the target interfering component in the two-dimensional concentration distribution field, multiple concentration gradient values ​​are set, and isoconcentration lines corresponding to each concentration gradient value are extracted. Each isoconcentration line is composed of continuous spatial points with the same concentration value. Each isoconcentration line is sampled discretely with equal arc length to obtain a sequence of boundary feature points uniformly distributed on the isoconcentration line. The sequence of boundary feature points contains the coordinate information of each feature point on the cross-section of the analysis cell. For each isoconcentration line, the minimum circumcircle covering all feature points in the sequence is determined by gradually adjusting the center position and expanding the radius. The center coordinates and radius of the minimum circumcircle are recorded as the center coordinates and radius of the isoconcentration line.

5. The method for online water quality analysis during the treatment of radioactive waste liquid according to claim 4, characterized in that, Step 4 above includes: The initial data of total alpha radioactivity obtained in real time by the radiation detector is combined with the radiation attenuation compensation coefficient to correct the initial data of total alpha radioactivity, and the corrected total alpha radioactivity is obtained. The initial data of total β radioactivity obtained in real time by the radiation detector is combined with the radiation attenuation compensation coefficient to correct the initial data of total β radioactivity, eliminate the negative measurement drift caused by detector window contamination, and obtain the corrected total β radioactivity. The initial conductivity data obtained by real-time detection of the conductivity electrode is combined with the polarization correction factor to correct the initial conductivity data, compensate for the response hysteresis and measurement deviation caused by electrode surface contamination, and obtain the corrected conductivity. The initial pH value obtained by real-time detection by the pH electrode is combined with the response delay compensation value to correct the initial pH value, thereby compensating for the response delay and measurement drift caused by electrode surface contamination, and obtaining the corrected pH value. The initial turbidity data obtained in real time by the turbidimeter is combined with the light scattering interference suppression coefficient to correct the initial turbidity data and obtain the corrected turbidity. The corrected total alpha radioactivity, corrected total beta radioactivity, corrected conductivity, corrected pH value, and corrected turbidity together constitute the corrected real-time water quality parameters.

6. An online water quality analysis system for radioactive waste liquid treatment, wherein the system implements the method as described in any one of claims 1 to 5, characterized in that, include: The acquisition module is used to pass the clarified sample into the analysis cell and acquire Raman spectral characteristic peak data in real time through multiple sets of fiber Raman probe arrays arranged along the flow direction of the waste liquid in the analysis cell; principal component analysis is performed on the Raman spectral characteristic peak data to extract the characteristic spectral vectors corresponding to the main radionuclides and interfering substances, and a two-dimensional concentration distribution field is constructed based on the continuous change of the characteristic spectral vectors in spatial position. The extraction module is used to extract isoconcentration lines and perform discrete sampling of target interference components in a two-dimensional concentration distribution field, calculate the minimum circumcircle of all sampling points, and obtain the center coordinates and radius of the isoconcentration lines. The calculation module is used to calculate the radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value and light scattering interference suppression coefficient based on the positional deviation of the circle center coordinates relative to the center of the preset detection window and the ratio of the radius to the size of the detection window, combined with the pre-stored interference coefficient database. The calibration module is used to correct the initial water quality parameters of total α radioactivity, total β radioactivity, conductivity, pH value and turbidity obtained in real time by the radiation detector, conductivity electrode, pH electrode and turbidity meter through the radiation attenuation compensation coefficient, polarization correction factor, response delay compensation value and light scattering interference suppression coefficient, respectively, so as to obtain the corrected real-time water quality parameters. The processing module is used to upload the corrected real-time water quality parameters to the distributed control unit, which automatically adjusts the evaporation concentration ratio and the reverse osmosis membrane flushing cycle, and determines whether the discharged water quality meets the discharge standards.

7. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 5.

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