Method and system for removing COD from nanofiltration concentrated water

Abstract

The application relates to the technical field of water treatment, and discloses a COD removal method and system for nanofiltration concentrated water, which comprises the following steps: collecting full-waveband ultraviolet-to-visible absorption spectrum data, inorganic anion concentration data and reactor geometric parameters of the nanofiltration concentrated water; removing the interference spectrum of inorganic salts and identifying organic components through Gaussian deconvolution peak decomposition; evaluating a comprehensive halogenation risk index based on halogenation reaction activity, generating a multi-stage oxidation parameter sequence by using a risk perception oxidation mode decision model; calculating electrode surface focused ultrasonic control parameters through sound field finite element simulation; performing a staged electrocatalytic oxidation treatment, simultaneously performing partition current density monitoring and local pollution targeted cleaning, online monitoring trihalomethane concentration and dynamically adjusting anode potential. The application solves the problems of organic component identification deviation, chlorinated byproduct generation and electrode pollution in the treatment of high-salt and high-chlorine nanofiltration concentrated water.

Description

Technical Field

[0001] This invention relates to the field of water treatment technology, and more specifically, to a method and system for COD removal from nanofiltration concentrate. Background Technology

[0002] In nanofiltration concentrate COD removal processes, titanium-based nano-conductive ceramic anode electrocatalytic oxidation technology is widely used for the oxidative degradation of organic pollutants. Nanofiltration concentrate typically contains high concentrations of inorganic salts and chloride ions. Existing technologies use water quality parameters to set oxidation process parameters, apply a constant potential to the entire electrode for treatment, and regularly clean and maintain the electrode.

[0003] However, high-salt, high-chlorine nanofiltration concentrate treatment faces several related technical problems: First, high concentrations of inorganic anions generate strong absorption interference in the ultraviolet to visible wavelength range, leading to deviations in the identification of organic components based on spectral analysis and making it impossible to accurately distinguish the chlorination reaction tendencies of organic compounds with different structures; Second, the uniform oxidation parameter setting without distinguishing easily chlorinated components leads to the generation of a large number of chlorination byproducts, threatening the safety of the effluent; Third, large-area electrode surfaces are locally contaminated due to uneven distribution of influent, making it difficult for overall impedance monitoring to identify locally contaminated areas, and uniform cleaning results in energy waste and insufficient cleaning efficiency; Fourth, ultrasonic waves easily form standing waves in narrow electrode gaps, and the cavitation energy distribution is mismatched with the mass transfer requirements of the electrode surface, affecting oxidation efficiency. Summary of the Invention

[0004] This invention provides a method and system for COD removal from nanofiltration concentrate, which solves the technical problems in related technologies such as inaccurate identification of organic components, imprecise control of halogenated byproducts, low efficiency in cleaning electrode contamination, and insufficient optimization of oxidation parameters during nanofiltration concentrate treatment.

[0005] This invention discloses a method for COD removal from nanofiltration concentrate, comprising the following steps:

[0006] Collect full-band UV-Vis absorption spectrum data, inorganic anion concentration data, and chloride ion concentration data of nanofiltration concentrate, and obtain the electrode spacing value and cavity geometry parameters of the electrocatalytic reactor;

[0007] Based on the concentration data of each inorganic anion and the pre-stored standard molar absorption coefficient spectrum, the inorganic salt interference spectrum is calculated. The inorganic salt interference spectrum is subtracted from the original spectrum to obtain the net absorption spectrum of organic matter. The Gaussian deconvolution peak decomposition of the net absorption spectrum of organic matter is performed to output the type of each organic component and its relative content.

[0008] Based on the identified organic component types, the component and halogenation reactivity database is queried to calculate the comprehensive halogenation risk index. The comprehensive halogenation risk index and the chlorine-carbon molar ratio are then input into the risk-aware oxidation mode decision model to generate a multi-level oxidation parameter sequence.

[0009] The comprehensive halogenation risk index is calculated by summing the products of the relative content weights of each organic component type, the halogenation reaction rate constant, and the structure sensitivity factor; wherein the halogenation reaction rate constant and the structure sensitivity factor are obtained from a component and halogenation reactivity database.

[0010] The risk-aware oxidation mode decision model is constructed using a multilayer perceptron neural network. The input layer receives the normalized comprehensive halogenation risk index and the chlorine-carbon molar ratio, and the output layer outputs the anode potential value, current density target value, and processing time for each treatment level in the multi-level oxidation parameter sequence. In the multi-level oxidation parameter sequence, the treatment level for high halogenation risk components adopts a lower anode potential to suppress the indirect chlorination path.

[0011] The method for calculating the overlap between the antinode position and the surface of the titanium-based nano-conductive ceramic anode is as follows: divide the overlapping area between the antinode region and the surface of the titanium-based nano-conductive ceramic anode by the total area of ​​the surface of the titanium-based nano-conductive ceramic anode; wherein, the antinode region is defined as the spatial region where the sound pressure amplitude exceeds 90% of the maximum sound pressure amplitude;

[0012] Based on the electrode spacing, cavity geometric parameters and nanofiltration concentrate sound velocity, the standing wave mode spectrum at different ultrasonic frequencies is calculated by the acoustic field finite element simulation model. The ultrasonic frequency with the highest overlap between the antinode position and the surface of the titanium-based nano-conductive ceramic anode is selected to generate the focused ultrasonic control parameters of the electrode surface.

[0013] The ultrasonic transducer is driven by the focused ultrasonic control parameters on the electrode surface to establish a high-intensity cavitation zone on the surface of the titanium-based nano-conductive ceramic anode. At the same time, the titanium-based nano-conductive ceramic anode is controlled to perform graded electrocatalytic oxidation treatment according to the multi-level oxidation parameter sequence.

[0014] Furthermore, the calculation of the inorganic salt interference spectrum is based on Beer-Lambert's law, which involves summing the products of the standard molar absorption coefficient of each inorganic anion at each wavelength with the corresponding concentration and optical path length to obtain the inorganic salt interference absorbance at each wavelength.

[0015] Furthermore, the Gaussian deconvolution peak decomposition decomposes the net absorption spectrum of organic matter into a superposition of multiple Gaussian peaks. The center wavelength, peak width, and peak height of each Gaussian peak are matched with the organic matter type and the characteristic parameters in the absorption peak feature database to identify the organic matter component type. The integral area ratio of each Gaussian peak characterizes the relative content of the corresponding component.

[0016] Furthermore, the electrocatalytic oxidation process also includes: continuously collecting the local current density values ​​of each electrode zone to generate a current density distribution matrix, calculating the deviation rate between the local current density value of each electrode zone and the initial reference current density value, identifying locally contaminated zones with deviation rates exceeding a preset threshold, performing polarity reversal pulse cleaning only on the locally contaminated zones, and maintaining the anodic working state for other electrode zones; wherein, the deviation rate is the absolute value of the difference between the current current density of each electrode zone and the initial reference current density divided by the initial reference current density.

[0017] Furthermore, the specific operation of the polarity reversal pulse cleaning is as follows: the working polarity of the local contaminated zone is reversed from the anode to the cathode, a negative potential pulse is applied, and the organic deposit layer and oxide passivation layer on the surface of the titanium-based nano-conductive ceramic anode are removed through the mechanical stripping action generated by the cathode reduction reaction and hydrogen evolution. After the polarity reversal pulse cleaning is completed, the anode working state of the local contaminated zone is restored.

[0018] Furthermore, the electrocatalytic oxidation process also includes: continuously collecting trihalomethane concentration data in water using online ultraviolet absorption, comparing the measured trihalomethane concentration with the trihalomethane emission limit to calculate a safety margin value, and when the safety margin value is lower than the warning threshold, calculating the anode potential correction value using a constrained optimization algorithm and performing adjustment; wherein, the safety margin value is the difference between the trihalomethane emission limit and the measured trihalomethane concentration divided by the trihalomethane emission limit; the constrained optimization algorithm uses maximizing the COD removal rate as the objective function and the trihalomethane generation rate being lower than the safety constraint value as the constraint condition.

[0019] This invention provides a COD removal system for nanofiltration concentrate, comprising:

[0020] The water quality and parameter acquisition module is used to acquire full-band ultraviolet to visible absorption spectrum data, inorganic anion concentration data, and chloride ion concentration data of nanofiltration concentrate, and to obtain the electrode spacing value and cavity geometric parameters of the electrocatalytic reactor.

[0021] The organic component identification module is used to calculate the inorganic salt interference spectrum based on the concentration data of each inorganic anion and the pre-stored standard molar absorption coefficient spectrum, subtract the inorganic salt interference spectrum from the original spectrum to obtain the net absorption spectrum of organic matter, perform Gaussian deconvolution peak decomposition on the net absorption spectrum of organic matter, and output the type of each organic component and its relative content.

[0022] The oxidation parameter generation module is used to query the component and halogenation reaction activity database based on the identified organic component types, calculate the comprehensive halogenation risk index, input the comprehensive halogenation risk index and chlorine-carbon molar ratio into the risk-aware oxidation mode decision model, and generate a multi-level oxidation parameter sequence.

[0023] The ultrasonic parameter calculation module is used to calculate the standing wave mode spectrum at different ultrasonic frequencies based on the electrode spacing value, cavity geometric parameters and nanofiltration concentrate sound velocity value, through the sound field finite element simulation model, to screen the ultrasonic frequency with the highest overlap between the antinode position and the surface of the titanium-based nano-conductive ceramic anode, and to generate the focused ultrasonic control parameters of the electrode surface.

[0024] The electrocatalytic oxidation execution module is used to drive the ultrasonic transducer to establish a high-intensity cavitation zone on the surface of the titanium-based nano-conductive ceramic anode according to the focused ultrasonic control parameters of the electrode surface, and at the same time control the titanium-based nano-conductive ceramic anode to perform graded electrocatalytic oxidation treatment according to the multi-level oxidation parameter sequence.

[0025] This invention achieves accurate identification of organic components through inorganic salt interference spectral subtraction and Gaussian deconvolution peak decomposition. It reduces the risk of chlorinated byproduct formation by generating a multi-level oxidation parameter sequence with component differentiation through comprehensive halogenation risk index assessment and risk-aware oxidation mode decision model. It optimizes ultrasonic frequency through acoustic field finite element simulation to focus cavitation energy on the electrode surface to enhance the mass transfer process. It achieves targeted cleaning of local contamination and reduces energy consumption through zoned current density monitoring. It ensures the safety of effluent by monitoring trihalomethane concentration online and dynamically adjusting anode potential. This invention solves the technical problems of organic matter identification deviation, difficulty in controlling chlorinated byproducts, inaccurate identification of local electrode contamination, and mismatch of ultrasonic cavitation energy distribution in high-salt and high-chlorine nanofiltration concentrate environments. It achieves the technical effects of improving COD removal efficiency and reducing chlorinated byproduct concentration. Attached Figure Description

[0026] Figure 1 This is a flowchart of the COD removal method for nanofiltration concentrate provided in this embodiment of the invention;

[0027] Figure 2 This is a distribution diagram of the main parameters of the influent water quality provided in an embodiment of the present invention;

[0028] Figure 3 This is the organic component identification result provided in the embodiments of the present invention;

[0029] Figure 4 This invention provides the effect of ultrasonic frequency on the overlap of antinodes.

[0030] Figure 5 This is a comparison of water quality before and after treatment provided in the embodiments of the present invention. Detailed Implementation

[0031] In nanofiltration concentrate COD removal processes, titanium-based nano-conductive ceramic anode electrocatalytic oxidation technology is widely used for the oxidative degradation of organic pollutants. Nanofiltration concentrate typically contains high concentrations of inorganic salts and chloride ions. Existing technologies use water quality parameters to set oxidation process parameters, apply a constant potential to the entire electrode for treatment, and regularly clean and maintain the electrode.

[0032] However, high-salt, high-chlorine nanofiltration concentrate treatment faces several related technical problems: First, high concentrations of inorganic anions generate strong absorption interference in the ultraviolet to visible wavelength range, leading to deviations in the identification of organic components based on spectral analysis and making it impossible to accurately distinguish the chlorination reaction tendencies of organic compounds with different structures; Second, the uniform oxidation parameter setting that does not distinguish easily chlorinated components leads to the generation of a large number of chlorination byproducts, threatening the safety of the effluent; Third, large-area electrode surfaces are locally contaminated due to uneven distribution of influent, making it difficult for overall impedance monitoring to identify locally contaminated areas, and uniform cleaning results in energy waste and insufficient cleaning efficiency; Fourth, ultrasonic waves easily form standing waves in narrow electrode gaps, and the cavitation energy distribution is mismatched with the mass transfer requirements of the electrode surface, affecting oxidation efficiency.

[0033] At least one embodiment of the present invention discloses a method for COD removal from nanofiltration concentrate, such as... Figure 1 As shown, it includes the following steps:

[0034] Step 1: Collect nanofiltration concentrate water quality data and reactor geometric parameters;

[0035] The system collects full-band UV-Vis absorption spectrum data, main inorganic anion concentration data, and chloride ion concentration data of nanofiltration concentrate, and obtains the electrode spacing value and cavity geometric parameters of the electrocatalytic reactor to generate a raw water quality characteristic dataset and a reactor structural parameter set.

[0036] It should be noted that the full-band ultraviolet to visible absorption spectrum data is a continuous absorption spectrum with a wavelength range of 200 nm to 800 nm. The main inorganic anion concentration data includes the concentration values ​​of anions such as nitrate, sulfate, and carbonate that have absorption characteristics in the ultraviolet to visible band.

[0037] It should be noted that the cavity geometry parameters include the length, width, and height of the reaction cavity, as well as the coordinates of the inlet and outlet positions. These cavity geometry parameters are used for subsequent acoustic field simulation calculations.

[0038] Step 2: Subtract interference from inorganic salts and identify organic components;

[0039] Based on the concentration data of each inorganic anion and the pre-stored standard molar absorption coefficient spectrum, the inorganic salt interference spectrum is calculated. The inorganic salt interference spectrum is then subtracted from the original spectrum to obtain the net absorption spectrum of organic matter. Gaussian deconvolution peak decomposition is performed on the net absorption spectrum of organic matter, and the organic matter type is matched with the absorption peak characteristic database to output the type and relative content of each organic component.

[0040] It should be noted that the calculation of the inorganic salt interference spectrum is based on the Beer-Lambert law, the specific expression of which is:

[0041]

[0042] in, wavelength Inorganic salts at the location interfere with absorbance. For the first Inorganic anions at wavelength Standard molar absorption coefficient at that location For the first The concentration of inorganic anions, Optical path length This represents the number of types of inorganic anions. The number represents the inorganic anion.

[0043] Furthermore, the standard molar absorption coefficient spectra are stored in a pre-established inorganic anion spectral database, which contains standard molar absorption coefficient values ​​of common inorganic anions such as nitrate, sulfate, and carbonate in the wavelength range of 200 nm to 800 nm. These standard molar absorption coefficient values ​​are obtained through spectral determination experiments of standard solutions.

[0044] It should be noted that Gaussian deconvolution peak decomposition decomposes the net absorption spectrum of organic matter into a superposition of multiple Gaussian peaks. The center wavelength, peak width, and peak height of each Gaussian peak are matched with the characteristic parameters in the absorption peak characteristic database of organic matter type to identify the type of organic matter component. The integral area ratio of each Gaussian peak characterizes the relative content of the corresponding component.

[0045] Furthermore, the database of organic pollutant types and absorption peak characteristics stores the characteristic absorption peak parameters of common organic pollutants. These parameters include characteristic absorption peaks of aromatic compounds around 254 nm, characteristic absorption peaks of compounds containing conjugated double bonds in the range of 280 nm to 350 nm, and characteristic absorption peaks of compounds containing carbonyl groups in the range of 200 nm to 240 nm. These characteristic absorption peak parameters were obtained and stored through spectroscopic determination experiments of standard organic solutions.

[0046] Step 3: Assess halogenation risk and generate a multi-level oxidation parameter sequence;

[0047] Based on the identified organic component types, the component and halogenation reactivity database is queried to extract the halogenation reaction rate constants for each organic component type, and the comprehensive halogenation risk index of the nanofiltration concentrate is calculated. The comprehensive halogenation risk index and the chlorine-carbon molar ratio are input into a risk-aware oxidation mode decision model to generate a multi-level oxidation parameter sequence differentiated by component.

[0048] Furthermore, the component and halogenation reaction activity database stores halogenation reaction rate constants and structure-sensitive factors for different organic component types. These halogenation reaction rate constants and structure-sensitive factors were obtained through batch experiments. Specifically, various standard organic compounds were subjected to electrocatalytic oxidation experiments in chloride-containing electrolyte solutions. The halogenation reaction rate constants for each organic component type were obtained by measuring the formation rate of chlorinated byproducts at different anodic potentials. The values ​​of the structure-sensitive factors were determined by analyzing the number and position of easily chlorinated groups such as active aromatic rings and unsaturated double bonds in the organic molecule structure.

[0049] It should be noted that the formula for calculating the comprehensive halogenation risk index is as follows:

[0050]

[0051] in, To comprehensively assess the halogenation risk index, For the first The relative content weights of various organic component types For the first Rate constants for halogenation reactions of various organic component types For the first Structure-sensitive factors of various organic component types The number of types of organic components identified. This refers to the serial number of the organic component type. (Structure Sensitive Factor) Structure-sensitive factors characterize the sensitivity of organic molecular structures to halogenation reactions. The values ​​were obtained from the component and halogenation reaction activity database. Organic components containing active aromatic rings or unsaturated double bonds have higher structure sensitivity factor values.

[0052] It should be noted that the chloride-carbon molar ratio is the molar ratio of chloride ion concentration to organic carbon concentration, and it characterizes the degree of excess of chloride ions relative to organic matter in nanofiltration concentrate.

[0053] It should be noted that, since the comprehensive halogenation risk index and the chlorine-carbon molar ratio have different dimensions and numerical ranges, before inputting them into the risk-aware oxidation mode decision model, the comprehensive halogenation risk index and the chlorine-carbon molar ratio are respectively subjected to mean normalization based on the range, which scales the comprehensive halogenation risk index and the chlorine-carbon molar ratio to the range of 0 to 1, thereby eliminating the influence of the difference in dimensions on the calculation of the risk-aware oxidation mode decision model.

[0054] It should be noted that the risk-aware oxidation mode decision model is a pre-trained mapping model. The input to the risk-aware oxidation mode decision model is the normalized comprehensive halogenation risk index and the chlorine-carbon molar ratio. The output of the risk-aware oxidation mode decision model is a multi-level oxidation parameter sequence. The multi-level oxidation parameter sequence includes the anode potential value, the target current density value, and the treatment time corresponding to each treatment level. Among them, the treatment level for high halogenation risk components adopts a lower anode potential to suppress the indirect chlorination pathway.

[0055] Furthermore, the risk-aware oxidation mode decision model is constructed using a multilayer perceptron neural network. The input layer of the multilayer perceptron receives two features: the normalized comprehensive halogenation risk index and the chloride-carbon molar ratio. After processing through two hidden layers, the output layer of the multilayer perceptron outputs the anode potential, target current density, and treatment time for each treatment level in the multi-level oxidation parameter sequence via a fully connected layer. Supervised learning is employed during training, using historical water quality data and corresponding optimal oxidation parameters as training samples. The mean squared error loss function is used, and the Adam optimization algorithm is employed for optimization.

[0056] Furthermore, the optimal oxidation parameters in the training samples were obtained through actual operating data of historical treatment batches. Specifically, records of electrocatalytic oxidation treatment under different water quality conditions were collected, and treatment batches that simultaneously met the COD removal rate target and had the lowest chlorination byproduct concentration were selected. The anode potential value, current density target value, and treatment time corresponding to these treatment batches were extracted as optimal oxidation parameters. The optimal oxidation parameters were then paired with the comprehensive halogenation risk index and chlorine-carbon molar ratio of the corresponding treatment batches to form training samples.

[0057] Step 4: Calculate the focused ultrasound control parameters for the electrode surface;

[0058] Based on the electrode spacing, cavity geometry, and nanofiltration concentrate sound velocity, standing wave mode patterns within the electrocatalytic reactor at different ultrasonic frequencies were calculated using a finite element method (FEA) simulation model. The ultrasonic frequency with the highest overlap between the antinodes and the surface of the titanium-based conductive nanoceramic anode was selected to generate focused ultrasonic control parameters for the electrode surface.

[0059] It should be noted that the finite element method for sound field simulation is a conventional method for sound field calculation. The inputs to the finite element method for sound field simulation are the geometric parameters of the electrocatalytic reactor, the sound velocity of the nanofiltration concentrate, and the ultrasonic frequency. The output of the finite element method for sound field simulation is the spatial distribution data of the sound pressure amplitude within the cavity.

[0060] Furthermore, the sound velocity value of nanofiltration concentrate is calculated using empirical formulas based on water temperature and dissolved salt concentration. The sound velocity value of conventional nanofiltration concentrate in the temperature range of 20℃ to 30℃ is 1480m / s to 1520m / s.

[0061] It should be noted that the standing wave pattern spectrum is the spatial distribution data of the sound pressure amplitude in the cavity of the electrocatalytic reactor. The antinode is the spatial region with the largest sound pressure amplitude, and the ultrasonic cavitation effect is strongest at the antinode.

[0062] It should be noted that the formula for calculating the overlap between the antinode position and the surface of the titanium-based nano-conductive ceramic anode is as follows:

[0063]

[0064] in, The degree of overlap between the antinode position and the surface of the titanium-based nano-conductive ceramic anode. This represents the overlap area between the antinode region and the surface of the titanium-based nano-conductive ceramic anode. This represents the total surface area of ​​the titanium-based nano-conductive ceramic anode. The antinode region is defined as the spatial region where the sound pressure amplitude exceeds 90% of the maximum sound pressure amplitude.

[0065] Furthermore, the electrode surface focused ultrasound control parameters include the selected ultrasound frequency value, the driving power of the ultrasound transducer, and the installation position coordinates of the ultrasound transducer in the cavity. These electrode surface focused ultrasound control parameters are used to guide the working configuration of the ultrasound transducer and ensure the establishment of a high-intensity cavitation zone on the surface of the titanium-based nano-conductive ceramic anode.

[0066] Furthermore, the driving power of the ultrasonic transducer is determined based on the volume of the electrocatalytic reactor cavity and the target cavitation intensity. In this embodiment, the driving power of the ultrasonic transducer is set to a range of 0.5W to 2W per liter of cavity volume. The installation position coordinates of the ultrasonic transducer in the cavity are determined by the simulation results of the acoustic field finite element simulation model, and the installation position coordinates of the ultrasonic transducer that can form antinodes on the surface of the titanium-based nano-conductive ceramic anode are selected.

[0067] Step 5: Perform electrocatalytic oxidation treatment;

[0068] The ultrasonic transducer is driven by the focused ultrasonic control parameters on the electrode surface to establish a high-intensity cavitation zone on the surface of the titanium-based nano-conductive ceramic anode. At the same time, the titanium-based nano-conductive ceramic anode is controlled according to the multi-level oxidation parameter sequence to perform staged electrocatalytic oxidation treatment to oxidize and degrade organic matter in nanofiltration concentrate.

[0069] It should be noted that staged electrocatalytic oxidation refers to performing oxidation treatment sequentially according to the parameters of each treatment level in a multi-stage oxidation parameter sequence, with the treatment time of each treatment level determined by the corresponding parameters in the multi-stage oxidation parameter sequence. By focusing cavitation energy on the boundary layer region of the titanium-based nano-conductive ceramic anode surface, the mass transfer process of organic matter to the titanium-based nano-conductive ceramic anode surface is enhanced.

[0070] In this embodiment of the application, in order to promptly identify and remove local contamination on the surface of the titanium-based nano-conductive ceramic anode, the electrocatalytic oxidation process further includes the following steps:

[0071] The local current density values ​​of each electrode zone are continuously collected to generate a current density distribution matrix. The deviation rate between the local current density value of each electrode zone and the initial reference current density value is calculated. Local contaminated zones with deviation rates exceeding a preset threshold are identified. Polarity reversal pulse cleaning is performed only on the locally contaminated zones, while other electrode zones maintain the anodic working state.

[0072] It should be noted that the surface of the partitioned titanium-based nano-conductive ceramic anode is divided into multiple independent controllable electrode partitions, each of which has an independent current acquisition circuit and polarity control circuit.

[0073] Furthermore, the partitioned titanium-based nano-conductive ceramic anode divides the surface of the anode into multiple independent controllable electrode zones by setting an insulating isolation strip on the anode substrate. Each independent controllable electrode zone is connected to an independent current sensor to collect local current density values, and each independent controllable electrode zone is connected to an independent power control unit to achieve independent polarity control of the independent controllable electrode zone.

[0074] It should be noted that the formula for calculating the deviation rate is:

[0075]

[0076] in, For the first Deviation rate of each independent controllable electrode zone For the first Current current density of each independent controllable electrode zone For the first The initial reference current density for each independently controllable electrode zone This refers to the serial number of the independently controllable electrode zone. Initial reference current density. The first step at the start of electrocatalytic oxidation treatment The current density values ​​measured in a stable operating state for each independent controllable electrode zone.

[0077] Furthermore, the preset threshold is determined based on the characteristics of the titanium-based nano-conductive ceramic anode material and the type of contaminants. When a contamination layer forms on the surface of the titanium-based nano-conductive ceramic anode, causing the current density to drop beyond the preset threshold, it indicates that the independent controllable electrode zone needs cleaning. In this embodiment, the preset threshold is set to a range of 15% to 25%.

[0078] It should be noted that polarity reversal pulse cleaning involves briefly applying a reverse potential pulse to a localized contaminated area, using electrochemical stripping to remove the contaminant deposits on the surface of the titanium-based nano-conductive ceramic anode.

[0079] Furthermore, the specific operation of polarity reversal pulse cleaning is as follows: the working polarity of the local contaminated zone is reversed from the anode to the cathode, and a negative potential pulse is applied. The potential amplitude of the negative potential pulse is -1.5V to -3V, and the duration of the negative potential pulse is 10 seconds to 30 seconds. The organic deposit layer and oxide passivation layer on the surface of the titanium-based nano-conductive ceramic anode are removed through the cathodic reduction reaction and the mechanical stripping effect generated by hydrogen evolution. After the polarity reversal pulse cleaning is completed, the anode working state of the local contaminated zone is restored.

[0080] In this embodiment of the application, in order to ensure the safety of the concentration of chlorinated byproducts in the effluent, the electrocatalytic oxidation process further includes the following steps:

[0081] Trihalomethane concentration data in water are continuously collected using online ultraviolet absorption spectrometry. The measured trihalomethane concentrations are compared with trihalomethane emission limits to calculate a safety margin. When the safety margin is lower than the warning threshold, an anode potential correction value is calculated based on the halogenation characteristics of the components at the current treatment level, and adjustments are implemented.

[0082] It should be noted that the formula for calculating the safety margin value is as follows:

[0083]

[0084] in, As a safety margin value, For trihalomethane emissions limits, This is the actual measured concentration of trihalomethanes.

[0085] Furthermore, the warning threshold is set based on the response time and safety margin of the electrocatalytic oxidation process. When the safety margin value is lower than the warning threshold, anode potential adjustment is initiated to ensure that process adjustment is completed before the measured trihalomethane concentration reaches the trihalomethane emission limit. In this embodiment, the warning threshold is set in the range of 20% to 30%.

[0086] It should be noted that the constrained optimization algorithm takes maximizing the COD removal rate as the objective function and the trihalomethane generation rate being lower than the safety constraint value as the constraint condition. The constrained optimization algorithm outputs the anode potential correction value that satisfies the constraint condition.

[0087] Furthermore, the constrained optimization algorithm adopts a sequential quadratic programming algorithm. The objective function of the sequential quadratic programming algorithm is to maximize the reduction of COD concentration per unit time. The constraint condition of the sequential quadratic programming algorithm is that the increase of trihalomethane concentration per unit time does not exceed the safety constraint value. The decision variable of the sequential quadratic programming algorithm is the anode potential correction value. The anode potential correction value that maximizes the objective function under the constraint condition is obtained through iterative solution.

[0088] Furthermore, the safety constraint value is calculated based on the current safety margin and the remaining treatment time. The safety constraint value represents the maximum permissible rate of increase in trihalomethane concentration within the remaining treatment time, ensuring that the measured trihalomethane concentration does not exceed the trihalomethane emission limit at the end of the electrocatalytic oxidation treatment. The specific calculation method for the safety constraint value is as follows: divide the difference between the current measured trihalomethane concentration and the trihalomethane emission limit by the remaining treatment time to obtain the maximum permissible rate of increase in trihalomethane concentration. This maximum permissible rate of increase in trihalomethane concentration is the safety constraint value.

[0089] Step 6: Collect effluent water quality data and determine whether it meets the standards;

[0090] Collect COD concentration data of the effluent and calculate the COD removal rate. When the COD removal rate reaches the preset target value and the concentration of all chlorinated byproducts is lower than the emission limit of chlorinated byproducts, output a treatment completion signal and generate compliant treated effluent.

[0091] It should be noted that the formula for calculating COD removal rate is:

[0092]

[0093] in, COD removal rate The influent COD concentration, This refers to the COD concentration in the effluent.

[0094] Furthermore, the preset target value is determined based on the subsequent treatment requirements or discharge standards of nanofiltration concentrate. In this embodiment, the preset target value is set in the range of 80% to 95%.

[0095] Furthermore, the concentration of chlorinated byproducts includes the concentration of typical chlorinated disinfection byproducts such as trihalomethanes and haloacetic acids, and the emission limits for each chlorinated byproduct are determined in accordance with national or local water pollutant emission standards.

[0096] This embodiment overcomes the interference of high concentrations of inorganic anions on the spectral identification of organic matter by employing a spectral decoupling method of inorganic salt interference spectral subtraction and Gaussian deconvolution peak decomposition, thereby accurately identifying the types and relative contents of each organic component in nanofiltration concentrate.

[0097] This implementation method assesses the risk of halogenation based on the comprehensive halogenation reactivity index of the components and generates a multi-level oxidation parameter sequence with component differentiation using a risk-aware oxidation mode decision model. This allows for the use of a lower anodic potential for the treatment level of components with high halogenation risk, suppressing the indirect chlorination pathway. Therefore, it reduces the risk of chlorination byproduct formation from the parameter setting stage.

[0098] This embodiment calculates the standing wave mode spectrum using a finite element simulation model of the acoustic field and selects the ultrasonic frequency with the highest overlap between the antinode position and the surface of the titanium-based nano-conductive ceramic anode. This focuses the cavitation energy on the boundary layer region of the titanium-based nano-conductive ceramic anode surface, thereby enhancing the mass transfer process of organic matter to the surface of the titanium-based nano-conductive ceramic anode and improving the electrocatalytic oxidation efficiency.

[0099] This implementation identifies locally contaminated zones by monitoring zone current density and calculating deviation rate, and performs polarity reversal pulse cleaning only on these zones. This avoids the energy waste caused by overall cleaning and improves the targeting and efficiency of cleaning.

[0100] This embodiment provides real-time safety assurance for the concentration of chlorinated byproducts by monitoring the concentration of trihalomethanes online and calculating the safety margin value. When the safety margin value is lower than the warning threshold, the anode potential is adjusted using a constrained optimization algorithm.

[0101] In summary, this implementation method resolves the contradiction between COD removal efficiency and effluent safety in high-salt, high-chlorine nanofiltration concentrate environments through synergistic effects of spectral decoupling identification, halogenation risk perception parameter generation, ultrasonic cavitation focusing to enhance mass transfer, targeted cleaning of local contaminants, and byproduct constraint optimization and control.

[0102] A wastewater treatment plant in an industrial park (hereinafter referred to as "Plant A") generates approximately 120 tons of nanofiltration concentrate daily from its nanofiltration deep treatment unit. This concentrate originates from the concentrated liquid obtained after multi-stage treatment of mixed wastewater from dyeing, electroplating, and chemical industries within the park. Plant A has constructed a new titanium-based nano-conductive ceramic anode electrocatalytic oxidation treatment system to treat the nanofiltration concentrate for COD removal before discharge. This system is equipped with partitioned titanium-based nano-conductive ceramic anodes (electrode area 0.8 m², divided into 16 independent controllable electrode partitions, numbered blocks 1 to 16), an ultrasonic transducer array, and an online ultraviolet absorption detector. The influent COD concentration is approximately 480 mg / L, and the preset COD removal rate target is 85%, meaning the effluent COD concentration must not exceed 72 mg / L. The following is a complete implementation example of a batch treatment process.

[0103] After the influent pump is started, the operator uses an online spectrometer to collect the full-band ultraviolet to visible absorption spectrum of this batch of nanofiltration concentrate in the range of 200nm to 800nm. Simultaneously, the concentrations of major inorganic anions and chloride ions are determined using an ion chromatograph. The reactor's geometric parameters are directly retrieved from the equipment installation file.

[0104] Table 1. Raw water quality data and reactor parameters for this batch of influent:

[0105]

[0106] The original absorption spectrum output by the spectrometer shows a strong absorption peak at 254 nm (original absorbance 0.923), and also has obvious broad peaks in the range of 280 nm to 320 nm (original absorbance 0.61 to 0.78). However, due to the superposition absorption of high concentration of inorganic anions, the characteristic peaks of organic matter are severely masked, and further interference subtraction processing is required.

[0107] Figure 2 This displays the concentration distribution of the main water quality parameters in the nanofiltration concentrate influent.

[0108] The system retrieves the standard molar absorption coefficient spectra of nitrate, sulfate, and carbonate in the range of 200 nm to 800 nm from the inorganic anion spectral database, along with the optical path length. The equivalent optical path length is 10 mm (equivalent to a 1 cm cuvette). Taking a wavelength of 254 nm as an example, calculate the absorbance due to inorganic salt interference:

[0109]

[0110]

[0111] Substitute the values ​​(the standard molar absorption coefficient of nitrate at 254 nm is...). L / (mg·cm), sulfate is approximately L / (mg·cm), carbonate ions are approximately L / (mg·cm):

[0112]

[0113] The net absorbance of organic matter at 254 nm after subtraction is After interference subtraction across the entire wavelength range, Gaussian deconvolution peak decomposition was performed on the net absorption spectrum of organic compounds, and the organic compound type was matched with the absorption peak characteristic database. The identification results are as follows.

[0114] Table 2. Results of organic component identification:

[0115]

[0116] Figure 3 The display shows four types of organic components identified through spectral analysis and their relative contents.

[0117] The system retrieves the halogenation reaction rate constants and structure sensitivity factors of each component from the component and halogenation reactivity database, and calculates the comprehensive halogenation risk index. :

[0118]

[0119]

[0120]

[0121]

[0122] Simultaneously calculate the chloride-carbon molar ratio. Given a chloride ion concentration of 3850 mg / L and a molar mass of 35.5 g / mol, the chloride ion molar concentration is: mol / L; Based on a COD concentration of 480 mg / L (using a coefficient of 0.375), the organic carbon concentration is approximately 180 mg / L, with a molar concentration of... mol / L; Chlorine-to-carbon molar ratio .

[0123] right molar ratio of chlorine to carbon After performing mean normalization based on the range, the data are input into the risk-aware oxidation mode decision model, and the model outputs the following three-level oxidation parameter sequence.

[0124] Table 3: Multi-level oxidation parameter sequence

[0125]

[0126] Components A and B have high structure sensitivity factors (2.1 and 1.8, respectively) due to their active aromatic rings and unsaturated double bonds. The corresponding treatment levels use lower anodic potentials to suppress the indirect chlorination pathway and effectively reduce the risk of chlorination byproduct formation.

[0127] Based on a water temperature of 26℃ and dissolved salt concentration, the sound velocity of this batch of nanofiltration concentrate was calculated to be 1503 m / s. The cavity geometry (length 600 mm, width 400 mm, height 350 mm), electrode spacing of 12 mm, and sound velocity of 1503 m / s were input into the acoustic field finite element simulation model. Standing wave mode spectra were calculated sequentially for the frequency band from 18 kHz to 80 kHz with a step size of 1 kHz, and the overlap between the antinode positions and the anode surface at each frequency was extracted. .

[0128] Taking a frequency of 35kHz as an example, the simulation shows that the overlap area between the antinode region (the region where the sound pressure amplitude exceeds 90% of the maximum value) and the anode surface is 0.61m², and the total area of ​​the anode surface is 0.80m². Therefore:

[0129]

[0130] Table 4 Simulation results of antinode overlap at key frequency points:

[0131]

[0132] Simulation results show the overlap at 47kHz The optimal ultrasonic frequency is achieved at 92.5%. The effective cavity volume is 84L, and the drive power is configured at 1.2W per liter. The ultrasonic transducer drive power is set to... The value is rounded to 101W. The transducer installation position coordinates were determined by simulation results to be 45mm off the water inlet side of the bottom center of the cavity. The final generated electrode surface focused ultrasonic control parameters are: frequency 47kHz, drive power 101W, and installation coordinates (300mm, 200mm, 0mm).

[0133] Figure 4 The degree of overlap between the antinode region and the anode surface at different ultrasonic frequencies is shown to help select the optimal ultrasonic frequency.

[0134] The system drives the ultrasonic transducer at 47kHz and 101W to establish a high-intensity cavitation zone on the anode surface, and then initiates the staged electrocatalytic oxidation treatment according to the three-level oxidation parameter sequence.

[0135] Localized pollution monitoring and targeted cleaning:

[0136] Approximately 18 minutes into the second stage of processing, the system detected a significant decrease in local current density in blocks 7 and 12. Taking block 7 as an example, the initial reference current density was 19.8 mA / cm², and the current current density was 15.6 mA / cm², with a deviation rate of:

[0137]

[0138] The value exceeded the preset threshold by 20%, and the system determined that block 7 was a locally contaminated area. The deviation rate of block 12 was also calculated to be 23.7%, which also exceeded the threshold. The system only performed polarity reversal pulse cleaning (potential -2.2V, lasting 20 seconds) on blocks 7 and 12, while the remaining 14 areas maintained anodic operation. After cleaning, the two areas restored anodic polarity, and the current density returned to the normal range.

[0139] Online monitoring and potential regulation of trihalomethanes:

[0140] Nine minutes into the third-stage treatment process, the online monitoring instrument measured a trihalomethane concentration of 58 μg / L in the water. The trihalomethane emission limit is 80 μg / L, and the safety margin is:

[0141]

[0142] This value is close to the lower limit of the warning threshold (set to 25% for this batch), and the system initiates the constraint optimization algorithm. With 13 minutes remaining in processing time, the trihalomethane concentration is 22 μg / L below the limit. The maximum allowable rate of increase is calculated as follows: The sequential quadratic programming algorithm aims to maximize the COD removal rate while ensuring that the rate of increase in trihalomethanes does not exceed [a certain threshold]. For constraint purposes, the output anode potential correction value is... The third-stage anode potential was adjusted from 2.15V to 2.03V to ensure that the trihalomethane concentration did not exceed the emission limit at the end of the treatment.

[0143] After all three stages of treatment are completed (total 85 minutes), the system collects effluent water quality data and calculates the COD removal rate:

[0144]

[0145] Table 5 Results of Effluent Water Quality Compliance Assessment:

[0146]

[0147] The COD removal rate of the effluent was 85.8%, exceeding the preset target value of 85%. The concentrations of trihalomethanes and haloacetic acids were both below the emission limits. The system output a processing completion signal, indicating that this batch of nanofiltration concentrate has met the standards.

[0148] Figure 5The treatment effect was verified by comparing the COD concentration, trihalomethane concentration, and haloacetic acid concentration in the influent and effluent.

[0149] During this batch processing, the data flows completely along the following logical chain: The raw spectral absorbance data (e.g., 0.923 at 254nm) and inorganic anion concentration data collected in step 1 enter step 2. After interference subtraction (interference absorbance calculated to be 0.846) and Gaussian deconvolution, four types of organic components and their relative contents (38% / 31% / 19% / 12%) are identified; this component information is then combined with the halogenation reaction activity database in step 3 to calculate... The decision model is then driven by the chlorine-carbon molar ratio of 7.23, outputting a three-level differentiated oxidation parameter sequence. Step 4 uses the cavity geometry parameters and sound velocity of 1503 m / s from Step 1 to simulate and screen the optimal ultrasonic frequency of 47 kHz (overlap rate 92.5%), providing a basis for mass transfer enhancement in Step 5. In Step 5, while performing graded oxidation, targeted cleaning is triggered by deviation rate calculation (21.2% deviation rate in block 7), and constraint optimization potential correction (-0.12V) is triggered by safety margin value calculation (27.5%), achieving dynamic safety control throughout the process. Finally, Step 6 calculates the COD removal rate to 85.8%, and combined with byproduct concentration verification, confirms that the batch treatment fully meets the standards. The inputs of each step are directly derived from the outputs of the preceding steps, and the data flow has a strict causal logical relationship.

[0150] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method for COD removal from nanofiltration concentrate, characterized in that, Includes the following steps: Collect full-band UV-Vis absorption spectrum data, inorganic anion concentration data, and chloride ion concentration data of nanofiltration concentrate, and obtain the electrode spacing value and cavity geometry parameters of the electrocatalytic reactor; Based on the concentration data of each inorganic anion and the pre-stored standard molar absorption coefficient spectrum, the inorganic salt interference spectrum is calculated. The inorganic salt interference spectrum is subtracted from the original spectrum to obtain the net absorption spectrum of organic matter. The Gaussian deconvolution peak decomposition of the net absorption spectrum of organic matter is performed to output the type of each organic component and its relative content. Based on the identified organic component types, the component and halogenation reactivity database is queried to calculate the comprehensive halogenation risk index. The comprehensive halogenation risk index and the chlorine-carbon molar ratio are then input into the risk-aware oxidation mode decision model to generate a multi-level oxidation parameter sequence. The comprehensive halogenation risk index is calculated by summing the products of the relative content weights of each organic component type, the halogenation reaction rate constant, and the structure sensitivity factor; wherein the halogenation reaction rate constant and the structure sensitivity factor are obtained from a component and halogenation reactivity database. The risk-aware oxidation mode decision model is constructed using a multilayer perceptron neural network. The input layer receives the normalized comprehensive halogenation risk index and the chlorine-carbon molar ratio, and the output layer outputs the anode potential value, current density target value, and processing time for each treatment level in the multi-level oxidation parameter sequence. In the multi-level oxidation parameter sequence, the treatment level for high halogenation risk components adopts a lower anode potential to suppress the indirect chlorination path. The method for calculating the overlap between the antinode position and the surface of the titanium-based nano-conductive ceramic anode is as follows: divide the overlapping area between the antinode region and the surface of the titanium-based nano-conductive ceramic anode by the total area of ​​the surface of the titanium-based nano-conductive ceramic anode; wherein, the antinode region is defined as the spatial region where the sound pressure amplitude exceeds 90% of the maximum sound pressure amplitude; Based on the electrode spacing, cavity geometric parameters and nanofiltration concentrate sound velocity, the standing wave mode spectrum at different ultrasonic frequencies is calculated by the acoustic field finite element simulation model. The ultrasonic frequency with the highest overlap between the antinode position and the surface of the titanium-based nano-conductive ceramic anode is selected to generate the focused ultrasonic control parameters of the electrode surface. The ultrasonic transducer is driven by the focused ultrasonic control parameters on the electrode surface to establish a high-intensity cavitation zone on the surface of the titanium-based nano-conductive ceramic anode. At the same time, the titanium-based nano-conductive ceramic anode is controlled to perform graded electrocatalytic oxidation treatment according to the multi-level oxidation parameter sequence.

2. The COD removal method for nanofiltration concentrate according to claim 1, characterized in that, The calculation of the inorganic salt interference spectrum is based on Beer-Lambert's law. The standard molar absorption coefficient of each inorganic anion at each wavelength is accumulated by multiplying the product of the corresponding concentration and optical path length to obtain the inorganic salt interference absorbance at each wavelength.

3. The COD removal method for nanofiltration concentrate according to claim 1, characterized in that, The Gaussian deconvolution peak decomposition decomposes the net absorption spectrum of organic matter into a superposition of multiple Gaussian peaks. The center wavelength, peak width, and peak height of each Gaussian peak are matched with the organic matter type and the characteristic parameters in the absorption peak feature database to identify the organic component type. The integral area ratio of each Gaussian peak represents the relative content of the corresponding component.

4. The COD removal method for nanofiltration concentrate according to claim 1, characterized in that, The electrocatalytic oxidation process also includes: continuously collecting the local current density values ​​of each electrode zone to generate a current density distribution matrix, calculating the deviation rate between the local current density value of each electrode zone and the initial reference current density value, identifying locally contaminated zones with deviation rates exceeding a preset threshold, performing polarity reversal pulse cleaning only on the locally contaminated zones, and maintaining the anodic working state for other electrode zones; wherein, the deviation rate is the absolute value of the difference between the current current density of each electrode zone and the initial reference current density divided by the initial reference current density.

5. The COD removal method for nanofiltration concentrate according to claim 4, characterized in that, The specific operation of the polarity reversal pulse cleaning is as follows: the working polarity of the local contaminated zone is reversed from the anode to the cathode, a negative potential pulse is applied, and the organic deposit layer and oxide passivation layer on the surface of the titanium-based nano-conductive ceramic anode are removed through the mechanical stripping effect generated by the cathode reduction reaction and hydrogen evolution. After the polarity reversal pulse cleaning is completed, the anode working state of the local contaminated zone is restored.

6. The COD removal method for nanofiltration concentrate according to claim 1, characterized in that, The electrocatalytic oxidation process also includes: continuously collecting trihalomethane concentration data in water using online ultraviolet absorption, comparing the measured trihalomethane concentration with the trihalomethane emission limit to calculate a safety margin value, and when the safety margin value is lower than the warning threshold, calculating the anode potential correction value using a constrained optimization algorithm and performing adjustment; wherein, the safety margin value is the difference between the trihalomethane emission limit and the measured trihalomethane concentration divided by the trihalomethane emission limit; the constrained optimization algorithm takes maximizing the COD removal rate as the objective function and uses the trihalomethane generation rate being lower than the safety constraint value as the constraint condition.

7. A COD removal system for nanofiltration concentrate, used to perform the COD removal method for nanofiltration concentrate according to any one of claims 1 to 6, characterized in that, include: The water quality and parameter acquisition module is used to acquire full-band ultraviolet to visible absorption spectrum data, inorganic anion concentration data, and chloride ion concentration data of nanofiltration concentrate, and to obtain the electrode spacing value and cavity geometric parameters of the electrocatalytic reactor. The organic component identification module is used to calculate the inorganic salt interference spectrum based on the concentration data of each inorganic anion and the pre-stored standard molar absorption coefficient spectrum, subtract the inorganic salt interference spectrum from the original spectrum to obtain the net absorption spectrum of organic matter, perform Gaussian deconvolution peak decomposition on the net absorption spectrum of organic matter, and output the type of each organic component and its relative content. The oxidation parameter generation module is used to query the component and halogenation reaction activity database based on the identified organic component types, calculate the comprehensive halogenation risk index, input the comprehensive halogenation risk index and chlorine-carbon molar ratio into the risk-aware oxidation mode decision model, and generate a multi-level oxidation parameter sequence. The ultrasonic parameter calculation module is used to calculate the standing wave mode spectrum at different ultrasonic frequencies based on the electrode spacing value, cavity geometric parameters and nanofiltration concentrate sound velocity value, through the sound field finite element simulation model, to screen the ultrasonic frequency with the highest overlap between the antinode position and the surface of the titanium-based nano-conductive ceramic anode, and to generate the focused ultrasonic control parameters of the electrode surface. The electrocatalytic oxidation execution module is used to drive the ultrasonic transducer to establish a high-intensity cavitation zone on the surface of the titanium-based nano-conductive ceramic anode according to the focused ultrasonic control parameters of the electrode surface, and at the same time control the titanium-based nano-conductive ceramic anode to perform graded electrocatalytic oxidation treatment according to the multi-level oxidation parameter sequence.

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