Electrocatalytic treatment method for removing uranium-containing wastewater

Abstract

The invention discloses an electro-catalysis treatment method for removing uranium-containing wastewater, and relates to the technical field of electro-catalysis, according to the electro-catalysis treatment method, an embedded sensor network is used for collecting data such as concentration, conductivity and pH value of uranyl ions U (VI) and coexisting metal ions in wastewater in real time, time sequence data are corrected through DTW, and time dimension inconsistency deviation is eliminated; based on the preprocessed data, a deep learning model combining a convolutional neural network CNN and a recurrent neural network RNN is constructed, the CNN is used for extracting spatial characteristics of ion concentration distribution, the RNN captures time dependence characteristics of dynamic changes of ions, an attention mechanism is introduced to focus key interference ions, and a characteristic matrix for priority processing is generated; according to the method, the electrocatalysis operation parameters are further optimized through the interference ion characteristics output by model prediction and the dynamic influence of the interference ion characteristics on U (VI) selective adsorption, so that the adsorption efficiency and energy consumption are balanced, and meanwhile, global optimization is performed in combination with a comprehensive objective function, so that the adsorption efficiency of uranyl ions is optimal.

Description

Technical Field

[0001] The present invention relates to the technical field of electrocatalysis, and particularly to an electrocatalytic treatment method for removing uranium-containing wastewater. Background Art

[0002] During the processes of nuclear industry, uranium mining and smelting, a large amount of uranium-containing wastewater is discharged. The wastewater contains uranyl ions (UO 2 2+ , namely U(VI)), and at the same time, it is often accompanied by various coexisting metal ions, such as calcium ions, magnesium ions, iron ions, and other non-metal anions such as chloride ions and sulfate ions. These wastewater components make it particularly difficult to extract U(VI) therefrom. Due to the similarity of uranyl ions in chemical properties to some metal ions, in traditional electrocatalytic treatment methods, these ions will competitively adsorb or reduce, thus causing significant interference to the selective extraction of uranium.

[0003] In traditional electrocatalytic methods, most use specific electrode materials or optimize reaction conditions to improve the selectivity for U(VI). However, in a complex wastewater environment, there will be competitive adsorption of other metal ions, which easily leads to a decrease in the extraction efficiency of uranium. In addition, the traditional methods have poor adaptability to different water qualities, and in wastewater with high-concentration coexisting ions, the treatment performance is likely to decrease significantly.

[0004] To address the interference of complex water quality on the selective extraction of U(VI), some traditional solutions introduce functional groups with coordination selectivity (such as amidoxime or phosphonic acid groups) to enhance the specific adsorption of uranium; or improve the migration rate of uranyl ions to the electrode through dynamic electric field regulation technology while reducing the adsorption of other ions. However, these methods still face problems such as adsorption saturation, high energy consumption, and environmental unfriendliness in a complex system, and it is difficult to meet the actual needs. Therefore, how to further improve the selective extraction ability for U(VI) and reduce the interference of coexisting ions remains an important challenge that the electrocatalytic treatment technology needs to solve. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides an electrocatalytic treatment method for removing uranium-containing wastewater to solve the problems that traditional electrocatalytic methods are difficult to cope with the dynamic interference of metal ions in complex wastewater and the decline in selective extraction efficiency.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] An embodiment of the present invention provides an electrocatalytic treatment method for removing uranium-containing wastewater, which includes,

[0009] Step S1, deploying a monitoring sensor network to collect real-time wastewater composition data during wastewater treatment and preprocessing the collected data;

[0010] Step S2, based on the wastewater component data pretreated in step S1, a deep learning model is constructed to analyze the characteristics of the multi-ion system in the wastewater components and predict the interference effect in the U(VI) extraction process;

[0011] Step S3, adjusting the electrocatalytic operation parameters based on the interference prediction result of step S2;

[0012] Step S4, continuously monitor the concentration changes of U(VI) and other metal ions in the wastewater under the adjusted operating parameters, input the monitoring data into the deep learning model of step S2, update the interference effect prediction results in real time, and adjust the electrocatalytic operating parameters.

[0013] As a preferred embodiment of the electrocatalytic treatment method for removing uranium-containing wastewater of the present invention, the wastewater components include uranyl ion U(VI) concentration, coexisting metal ion concentration, conductivity and pH value;

[0014] The data preprocessing method is: normalizing the data, eliminating abnormal data, and using the dynamic time alignment algorithm DTW to correct the time series data.

[0015] As a preferred solution of the electrocatalytic treatment method for removing uranium-containing wastewater described in the present invention, the step of correcting the time series data using the dynamic time alignment algorithm DTW is as follows:

[0016] A dynamic programming matrix is ​​constructed to calculate the cumulative alignment distance between the target time series and the actual time series based on the point-to-point difference between the two series. The alignment distance calculation formula is:

[0017]

[0018] Where D(i, j) represents the cumulative alignment distance between time series X and Y at positions i and j, d(x i ,y j ) represents the Euclidean distance between X and Y at corresponding points i and j, calculated as |x i -y j |,x i Represents the value of the time series X at the i-th time point, where i ranges from 1, 2, ..., n, y jDenote the value of time series Y at the j-th time point, where the value range of j is 1, 2, ..., m. D(i - 1, j) represents the cumulative alignment distance extending from the (i - 1)-th point of sequence X to the j-th point of Y, D(i, j - 1) represents the cumulative alignment distance extending from the i-th point of sequence X to the (j - 1)-th point of Y, and D(i - 1, j - 1) represents the cumulative alignment distance extending from both sequence X and Y to i - 1 and j - 1;

[0019] By backtracking the dynamic programming matrix, obtain the optimal alignment path between the two time series. The optimal distance calculation formula is:

[0020] P = {(p 1 , q 1 ), (p 2 , q 2 ), …, (p k , q k )},

[0021] where P represents the index set of the optimal alignment path, p i represents the index of the i-th alignment point of time series X in the path, q i represents the index of the i-th alignment point of time series Y in the path, k represents the total length of the alignment path, satisfying k ≥ max(n, m), where n and m are the lengths of X and Y respectively;

[0022] According to the optimal alignment path, perform non-linear resampling on the collected time series data to generate calibration data consistent with the reference time series, expressed as:

[0023]

[0024] where X′ represents the time series X corrected by the alignment path, represents the value of time series X corresponding to the index p i in the alignment path P, and k represents the total length of the alignment path.

[0025] As a preferred solution of the electrocatalytic treatment method for removing uranium-containing wastewater described in the present invention, wherein: the characteristics of the multi-ion system include the spatial characteristics of ion concentration distribution and the time-dependent characteristics of ion dynamic changes;

[0026] The deep learning model includes:

[0027] Use a convolutional neural network CNN to extract the spatial characteristics of ion concentration distribution in the wastewater, specifically the concentration correlation between ions;

[0028] Use a recurrent neural network RNN to capture the time-dependent characteristics of ion dynamic changes;

[0029] The predicted output of the interference effect includes: the main characteristics of interfering ions and their dynamic effects on the selective adsorption of U(VI).

[0030] As a preferred embodiment of the electrocatalytic treatment method for removing uranium-containing wastewater according to the present invention, wherein: the step of using a convolutional neural network CNN to extract the spatial characteristics of ion concentration distribution in wastewater is as follows.

[0031] Use a convolutional neural network CNN to process the corrected time series data, extract the concentration correlation between different ions in a multi-ion system, and analyze the spatial characteristics of ion concentration distribution. The convolution extraction formula is:

[0032]

[0033] Wherein, H(i, j) represents the value at the i-th row and j-th column in the feature matrix generated after the convolution operation, σ(·) represents the ReLU activation function, σ(x) = max(0, x), F(i + p, q) represents the value of the sub-region starting from the position (i, q) in the input data matrix F, W(p, q) represents the weight value at the p-th row and q-th column in the convolution kernel weight matrix, the size of the convolution kernel is k × m, b represents the bias of the convolution operation, k represents the row dimension of the convolution kernel, m represents the column dimension of the convolution kernel, and is consistent with the number of ion species in the input matrix.

[0034] As a preferred embodiment of the electrocatalytic treatment method for removing uranium-containing wastewater according to the present invention, wherein: the step of using a recurrent neural network RNN to capture the time-dependent characteristics of ion dynamic changes is as follows.

[0035] Use a recurrent neural network RNN to perform time series analysis on the extracted spatial characteristics, capture the dynamic change characteristics of a multi-ion system, reflect the time dependence of ion concentration changes, and the analysis formula is:

[0036] h t = f(W h h t-1 + W x x t + b h ),

[0037] Wherein, h t represents the hidden state vector of the recurrent neural network at time step t, f(·) represents the Tanh function, W h represents the weight matrix from the hidden state of the previous time step to the current hidden state, h t-1 represents the hidden state vector at the previous time step t - 1, W x represents the weight matrix from the input vector of the current time step to the hidden state, x tThe input vector representing the current time step, which is derived from the features of the F matrix at time step t, b h Represents the bias vector of the hidden state;

[0038] In step S2, an attention mechanism is introduced. According to the dynamic characteristics of the model output, the interfering ions that have the greatest impact on the selective adsorption of uranyl ions are determined, and a characteristic matrix to be preferentially processed is generated. The attention weight calculation formula is:

[0039]

[0040]

[0041] where α i Represents the attention weight of ion i, e i Represents the characteristic score of ion i, v represents the projection vector for calculating the attention weight, W h Represents the weight matrix for the linear transformation of the hidden state, h t Represents the hidden state vector at the current time step t, W s Represents the weight matrix for the linear transformation of ion characteristics, s i Represents the feature vector of ion i,

[0042] Weighted interference characteristic output formula:

[0043] H f = ∑ i α i s i ,

[0044] where H f Represents the interference ion characteristic matrix weighted by the attention mechanism, α i Represents the attention weight of ion i, s i Represents the feature vector of ion i.

[0045] As a preferred embodiment of the electrocatalytic treatment method for removing uranium-containing wastewater described in the present invention, wherein: the operating parameter adjustment method includes:

[0046] Adjust the electric field strength and electrode voltage;

[0047] Adjust the reaction time to balance the adsorption efficiency and energy consumption.

[0048] As a preferred embodiment of the electrocatalytic treatment method for removing uranium-containing wastewater described in the present invention, wherein: the step of adjusting the electrocatalytic operating parameters based on the interference prediction result of step S2 is,

[0049] According to the influence of the interfering ions on the adsorption efficiency of uranyl ions, dynamically adjust the electric field strength and electrode voltage. Let the target optimized electric field strength be Eopt , the optimized target electrode voltage is V opt , and the optimization formula is:

[0050] E opt = E init + ΔE,

[0051] V opt = V init + ΔV,

[0052] where, E opt represents the optimized electric field strength, E init represents the initial electric field strength, ΔE represents the adjustment amount, which is dynamically calculated based on the prediction result of the interference effect, V opt represents the optimized electrode voltage, V init represents the initial electrode voltage, ΔV represents the adjustment amount, which is dynamically calculated based on the prediction result of the interference effect,

[0053] Let the target adsorption efficiency be η t , and the current adsorption efficiency be η c , and the reaction time adjustment formula is:

[0054]

[0055] where, t opt represents the optimized reaction time, t init represents the initial reaction time, η t represents the target adsorption efficiency, η c represents the current adsorption efficiency,

[0056] Define the comprehensive optimization objective function J, considering the comprehensive adsorption efficiency, residual concentration and energy consumption. The objective function formula is:

[0057] J = ω 1 ·η + ω 2 ·(1 - C e ) - ω 3 ·E c ,

[0058] where, J represents the comprehensive optimization target value, ω 1 , ω 2 , ω 3 are weight factors, which respectively control the importance of adsorption efficiency, residual concentration and energy consumption. η represents the adsorption efficiency, C e represents the residual concentration, which is equal to the concentration of unadsorbed uranyl ions, E c represents the energy consumption, which is dynamically calculated based on the electric field strength and reaction time,

[0059] Through the gradient descent method for E opt , V opt , topt Perform iterative updates to maximize J.

[0060] As a preferred embodiment of the electrocatalytic treatment method for removing uranium-containing wastewater described in the present invention, in step S4, an embedded online sensor is used for real-time monitoring, and the online sensor collects dynamic concentration data of uranyl ions U(VI) and coexisting metal ions in the wastewater.

[0061] As a preferred embodiment of the electrocatalytic treatment method for removing uranium-containing wastewater described in the present invention, the step of updating the interference effect prediction result in real time and adjusting the electrocatalytic operation parameters is as follows:

[0062] Using an embedded online sensor, real-time collect the concentration change data of uranyl ions and other metal ions in the wastewater, and set the real-time monitoring data as Z t ={Z t，1 , z t，2 , …, z t，m}, representing the ion concentration vector at time t, and input the monitoring data into a deep learning model for prediction.

[0063] The deep learning model outputs an updated interference prediction result H′(t), and the formula is:

[0064] H′(t)=f model (Z t ),

[0065] where H′(t) represents the updated interference prediction characteristic matrix at time t, and f model represents the deep learning model constructed in step S2, and Z t represents the monitoring data vector at time t;

[0066] According to the updated interference prediction result H′(t), adjust the operation parameters, and the formula is:

[0067] E opt =E init +ΔE t , V opt =V init +ΔV t ,

[0068] where ΔE t and ΔV t respectively represent the changes in the electric field strength and electrode voltage adjusted according to the prediction result at time t.

[0069] The beneficial effects of the present invention are as follows: In the present invention, an embedded sensor network is used to collect data such as the concentrations, conductivity, and pH values of uranyl ions U(VI) and coexisting metal ions in wastewater in real time. The time series data is corrected by DTW to eliminate the deviation of inconsistent time dimensions. Based on the preprocessed data, a deep learning model combining a convolutional neural network (CNN) and a recurrent neural network (RNN) is constructed. The CNN is used to extract the spatial characteristics of the ion concentration distribution, and the RNN captures the time-dependent characteristics of the dynamic changes of ions. An attention mechanism is introduced to focus on key interfering ions, generating a characteristic matrix for priority processing, so as to achieve a deep analysis of complex multi-ion systems. Through the interfering ion characteristics predicted and output by the model and their dynamic effects on the selective adsorption of U(VI), the electrocatalytic operation parameters are further optimized to balance the adsorption efficiency and energy consumption. At the same time, a comprehensive objective function is combined for global optimization to maximize the adsorption efficiency of uranyl ions.

[0070] In the present invention, a real-time monitoring and feedback mechanism is introduced, which can dynamically update the prediction results of the deep learning model and adjust the operation parameters according to real-time data, overcoming the problem of the decline in adsorption efficiency under the interference of high-concentration coexisting metal ions in traditional methods, significantly improving the selective extraction ability of U(VI), enhancing the adaptability to complex water quality environments, and at the same time reducing energy consumption through dynamic optimization, realizing the high efficiency, economy, and sustainability of wastewater treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0072] Figure 1 It is a schematic flow chart of the electrocatalytic treatment method for removing uranium-containing wastewater of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0073] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings in the specification.

[0074] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0075] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0076] Example 1, reference Figure 1 This embodiment provides an electrocatalytic treatment method for removing uranium-containing wastewater, comprising the following steps:

[0077] Step S1, deploying a monitoring sensor network to collect real-time wastewater composition data during wastewater treatment and preprocessing the collected data;

[0078] Wastewater components include uranyl ion U(VI) concentration, coexisting metal ion concentration, conductivity, and pH value;

[0079] The data preprocessing method is: normalize the data, remove abnormal data, and use the dynamic time alignment algorithm DTW to correct the time series data;

[0080] The steps of using the dynamic time alignment algorithm DTW to correct time series data are as follows:

[0081] A dynamic programming matrix is ​​constructed to calculate the cumulative alignment distance between the target time series and the actual time series based on the point-to-point difference between the two series. The alignment distance calculation formula is:

[0082]

[0083] Where D(i, j) represents the cumulative alignment distance between time series X and Y at positions i and j, d(x i ,y j ) represents the Euclidean distance between X and Y at corresponding points i and j, calculated as |x i -y j |,x i Represents the value of the time series X at the i-th time point, where i ranges from 1, 2, ..., n, y j represents the value of time series Y at the jth time point, where j ranges from 1, 2, ..., m. D(i-1, j) represents the cumulative alignment distance from the i-1th point of sequence X to the j-th point of Y. D(i, j-1) represents the cumulative alignment distance from the i-th point of sequence X to the j-1th point of Y. D(i-1, j-1) represents the cumulative alignment distance from both sequences X and Y to i-1 and j-1.

[0084] By backtracking the dynamic programming matrix, the optimal alignment path between the two time series is obtained. The optimal distance calculation formula is:

[0085] P = {(p 1 , q 1 ), (p 2 , q 2 ), …, (p k , q k )},

[0086] where P represents the index set of the optimal alignment path, p i represents the index of the i-th alignment point of the time series X in the path, q i represents the index of the i-th alignment point of the time series Y in the path, k represents the total length of the alignment path, satisfying k ≥ max(n, m), where n and m are the lengths of X and Y respectively;

[0087] According to the optimal alignment path, the collected time series data is non-linearly resampled to generate calibration data consistent with the reference time series, expressed as:

[0088]

[0089] where, X ′ represents the time series X after calibration through the alignment path, represents the value of the time series X corresponding to the index p i in the alignment path P, and k represents the total length of the alignment path;

[0090] Specifically, through the dynamic programming method, the DTW algorithm is used to calculate the optimal alignment path between time series, solve the data deviation problem caused by inconsistent time dimensions, and obtain the calibrated time series after path backtracking.

[0091] Step S2, based on the wastewater component data preprocessed in step S1, construct a deep learning model to analyze the characteristics of the multi-ion system in the wastewater component and predict the interference effect during the U(VI) extraction process;

[0092] The characteristics of the multi-ion system include the spatial characteristics of the ion concentration distribution and the time-dependent characteristics of the ion dynamic changes;

[0093] The deep learning model includes:

[0094] Use the convolutional neural network CNN to extract the spatial characteristics of the ion concentration distribution in the wastewater, specifically the concentration correlation between ions;

[0095] Use the recurrent neural network RNN to capture the time-dependent characteristics of the ion dynamic changes;

[0096] The interference effect prediction output includes: the main characteristics of the interfering ions and their dynamic effects on the selective adsorption of U(VI);

[0097] The steps of using a Convolutional Neural Network (CNN) to extract the spatial features of ion concentration distribution in wastewater are as follows:

[0098] Use a Convolutional Neural Network (CNN) to process the corrected time series data, extract the concentration correlation between different ions in a multi-ion system, and analyze the spatial characteristics of ion concentration distribution. The convolution extraction formula is:

[0099]

[0100] Among them, H(i, j) represents the value at the i-th row and j-th column in the feature matrix generated after the convolution operation, σ(·) represents the ReLU activation function, σ(x) = max(0, x), F(i + p, q) represents the value of the sub-region starting from the position (i, q) in the input data matrix F, W(p, q) represents the weight value at the p-th row and q-th column in the convolution kernel weight matrix. The size of the convolution kernel is k × m, b represents the bias of the convolution operation, k represents the row dimension of the convolution kernel, m represents the column dimension of the convolution kernel, which is consistent with the number of ion species in the input matrix;

[0101] The steps of using a Recurrent Neural Network (RNN) to capture the time-dependent characteristics of ion dynamic changes are as follows:

[0102] Use a Recurrent Neural Network (RNN) to perform time series analysis on the extracted spatial characteristics, capture the dynamic change characteristics of a multi-ion system, reflect the time dependence of ion concentration changes, and the analysis formula is:

[0103] h t = f(W h h t-1 + W x x t + b h ),

[0104] Among them, h t represents the hidden state vector of the recurrent neural network at time step t, f(·) represents the Tanh function, W h represents the weight matrix from the hidden state of the previous time step to the current hidden state, h t-1 represents the hidden state vector at the previous time step t - 1, W x represents the weight matrix from the input vector of the current time step to the hidden state, x t represents the input vector of the current time step, which comes from the feature of the F matrix at time step t, b h represents the bias vector of the hidden state;

[0105] In step S2, an attention mechanism is introduced. According to the dynamic characteristics of the model output, the interfering ions that have the greatest impact on the selective adsorption of uranyl ions are determined, and a characteristic matrix to be preferentially processed is generated. The attention weight calculation formula is as follows:

[0106]

[0107]

[0108] where α i represents the attention weight of ion i, e i represents the characteristic score of ion i, v represents the projection vector for calculating the attention weight, W h represents the weight matrix for the linear transformation of the hidden state, h t represents the hidden state vector at the current time step t, W s represents the weight matrix for the linear transformation of ion characteristics, s i represents the feature vector of ion i.

[0109] Weighted interfering characteristic output formula:

[0110] H f = ∑ i α i s i ,

[0111] where H f represents the interfering ion characteristic matrix weighted by the attention mechanism, α i represents the attention weight of ion i, s i represents the feature vector of ion i;

[0112] Specifically, the spatial distribution characteristics of the wastewater ion concentration are extracted by CNN, the time-dependent characteristics are captured by combining with RNN, and then the attention mechanism is introduced to focus on the ions with significant interference effects, realizing the in-depth analysis of the dynamic characteristics of the multi-ion system and preferentially analyzing the characteristics of key interfering ions.

[0113] Step S3: Based on the interference prediction results in step S2, adjust the electrocatalysis operation parameters;

[0114] The ways to adjust the operation parameters include:

[0115] Adjust the electric field strength and electrode voltage;

[0116] Adjust the reaction time to balance the adsorption efficiency and energy consumption;

[0117] Based on the interference prediction results in step S2, the steps to adjust the electrocatalysis operation parameters are as follows.

[0118] Dynamically adjust the electric field strength and electrode voltage according to the influence of interfering ions on the adsorption efficiency of uranyl ions. Let the optimized electric field strength be E opt , and the optimized electrode voltage be V opt . The optimization formula is:

[0119] E opt = E init + ΔE,

[0120] V opt = V init + ΔV,

[0121] where E opt represents the optimized electric field strength, E init represents the initial electric field strength, ΔE represents the adjustment amount, which is dynamically calculated based on the prediction result of the interference effect, and V opt represents the optimized electrode voltage, V init represents the initial electrode voltage, and ΔV represents the adjustment amount, which is dynamically calculated based on the prediction result of the interference effect.

[0122] Let the target adsorption efficiency be η t , and the current adsorption efficiency be η c . The reaction time adjustment formula is:

[0123]

[0124] where t opt represents the optimized reaction time, t init represents the initial reaction time, η t represents the target adsorption efficiency, and η c represents the current adsorption efficiency.

[0125] Define the comprehensive optimization objective function J, which comprehensively considers the adsorption efficiency, residual concentration, and energy consumption. The formula of the objective function is:

[0126] J = ω 1 ·η + ω 2 ·(1 - C e ) - ω 3 ·E c ,

[0127] where J represents the comprehensive optimization objective value, ω 1 , ω 2 , ω 3 are weight factors, which respectively control the importance of the adsorption efficiency, residual concentration, and energy consumption. η represents the adsorption efficiency, C e represents the residual concentration, which is equal to the concentration of unadsorbed uranyl ions, and E c represents the energy consumption, which is dynamically calculated based on the electric field strength and reaction time.

[0128] Iteratively update E opt , V opt , t opt to maximize J;

[0129] Specifically, predict the result through the interference effect, dynamically adjust the electric field strength, electrode voltage and reaction time to ensure the optimal adsorption efficiency of uranyl ions.

[0130] Step S4: Continuously monitor the concentration changes of U(VI) and other metal ions in the wastewater under the adjusted operating parameters, input the monitoring data into the deep learning model in Step S2, update the interference effect prediction result in real time, and adjust the electrocatalysis operating parameters;

[0131] In Step S4, an embedded online sensor is used for real-time monitoring, and the online sensor collects the dynamic concentration data of uranyl ions U(VI) and coexisting metal ions in the wastewater;

[0132] The steps of updating the interference effect prediction result in real time and adjusting the electrocatalysis operating parameters are as follows:

[0133] Use the embedded online sensor to collect the concentration change data of uranyl ions and other metal ions in the wastewater in real time. Let the real-time monitoring data be Z t ={z t，1 , z t，2 , …, z t，m}, representing the ion concentration vector at time t. Input the monitoring data into the deep learning model for prediction.

[0134] The deep learning model outputs the updated interference prediction result H′(t), and the formula is:

[0135] H′(t) = f model (Z t ),

[0136] where H′(t) represents the updated interference prediction characteristic matrix at time t, and f model represents the deep learning model constructed in Step S2, and Z t represents the monitoring data vector at time t;

[0137] According to the updated interference prediction result H′(t), adjust the operating parameters, and the formula is:

[0138] E opt = E init + ΔE t , V opt = V init + ΔV t ,

[0139] Among them, ΔE t and ΔV t respectively represent the electric field strength adjusted according to the prediction result and the change amount of the electrode voltage at time t;

[0140] Specifically, the concentration changes of uranyl ions and coexisting metal ions in the wastewater are monitored in real time through an embedded online sensor, and the monitoring data are dynamically input into the deep learning model to continuously update the prediction result of the interference effect. According to the real-time updated prediction result, the operating parameters are dynamically adjusted to improve the adsorption efficiency and system stability in the wastewater treatment process.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An electrocatalytic treatment method for removing uranium-containing wastewater, characterized in that: include, Step S1, deploying a monitoring sensor network to collect real-time wastewater composition data during wastewater treatment and preprocessing the collected data; Step S2, based on the wastewater component data pretreated in step S1, a deep learning model is constructed to analyze the characteristics of the multi-ion system in the wastewater components and predict the interference effect in the U(VI) extraction process; Step S3, adjusting the electrocatalytic operation parameters based on the interference prediction result of step S2; Step S4, continuously monitor the concentration changes of U(VI) and other metal ions in the wastewater under the adjusted operating parameters, input the monitoring data into the deep learning model of step S2, update the interference effect prediction results in real time, and adjust the electrocatalytic operating parameters.

2. The electrocatalytic treatment method for removing uranium-containing wastewater according to claim 1, characterized in that: The wastewater components include uranyl ion U(VI) concentration, coexisting metal ion concentration, conductivity and pH value; The data preprocessing method is: normalizing the data, eliminating abnormal data, and using the dynamic time alignment algorithm DTW to correct the time series data.

3. The electrocatalytic treatment method for removing uranium-containing wastewater according to claim 2, characterized in that: The steps of correcting the time series data using the dynamic time alignment algorithm DTW are: A dynamic programming matrix is ​​constructed to calculate the cumulative alignment distance between the target time series and the actual time series based on the point-to-point difference between the two series. The alignment distance calculation formula is: Where D(i,j) represents the cumulative alignment distance between time series X and Y at positions i and j, d(x i ,y j ) represents the Euclidean distance between X and Y at corresponding points i and j, calculated as |x i -y j |, x i represents the value of the time series X at the i-th time point, where i ranges from 1, 2, …, n, and y j represents the value of time series Y at the jth time point, where j ranges from 1, 2, …, m. D(i-1, j) represents the cumulative alignment distance from the i-1th point of sequence X to the jth point of Y. D(i, j-1) represents the cumulative alignment distance from the i-th point of sequence X to the j-1th point of Y. D(i-1, j-1) represents the cumulative alignment distance from both sequences X and Y to i-1 and j-1. By backtracking the dynamic programming matrix, the optimal alignment path between the two time series is obtained. The optimal distance calculation formula is: P}{(p1,q1),(p2,q2),…,(p k ,q k )}, Among them, P represents the index set of the optimal alignment path, p i represents the index of the i-th alignment point of the time series X in the path, q i represents the index of the i-th alignment point of the time series Y in the path, k represents the total length of the alignment path, and satisfies k ≥ max(n,m), where n and m are the lengths of X and Y respectively; According to the optimal alignment path, the collected time series data is nonlinearly resampled to generate correction data consistent with the reference time series, which is expressed as: Among them, X ′ represents the time series X after correction by the alignment path, Indicates the time series X corresponding to index p in the alignment path P i The value of , k represents the total length of the alignment path.

4. The electrocatalytic treatment method for removing uranium-containing wastewater according to claim 3, characterized in that: The characteristics of the multi-ion system include the spatial characteristics of ion concentration distribution and the time-dependent characteristics of ion dynamic changes; The deep learning model includes: Convolutional neural network (CNN) was used to extract the spatial characteristics of ion concentration distribution in wastewater, specifically the concentration correlation between ions. The recurrent neural network (RNN) is used to capture the time-dependent characteristics of ion dynamic changes; The interference effect prediction output includes: the main characteristics of the interfering ions and their dynamic effects on the selective adsorption of U(VI).

5. The electrocatalytic treatment method for removing uranium-containing wastewater according to claim 4, characterized in that: The steps of using convolutional neural network (CNN) to extract the spatial characteristics of ion concentration distribution in wastewater are: The convolutional neural network (CNN) was used to process the corrected time series data, extract the concentration correlation between different ions in the multi-ion system, and analyze the spatial characteristics of the ion concentration distribution. The convolution extraction formula is: Among them, H(i,j) represents the value of the i-th row and j-th column in the feature matrix generated after the convolution operation, σ(·) represents the ReLU activation function, σ(x)=max(0,x), F(i+p,q) represents the value of the sub-region starting from the position (i,q) in the input data matrix F, W(p,q) represents the weight value of the p-th row and q-th column in the convolution kernel weight matrix, the size of the convolution kernel is k×m, b represents the bias of the convolution operation, k represents the row dimension of the convolution kernel, and m represents the column dimension of the convolution kernel, which is consistent with the number of ion species in the input matrix.

6. The electrocatalytic treatment method for removing uranium-containing wastewater according to claim 5, characterized in that: The step of using a recurrent neural network (RNN) to capture the time-dependent characteristics of the dynamic changes of ions is as follows: The recurrent neural network (RNN) is used to perform time series analysis on the extracted spatial characteristics, capture the dynamic change characteristics of the multi-ion system, and reflect the time dependence of the change in ion concentration. The analysis formula is: h t =f(W h h t-1 +W x x t +b h ), Among them, h t represents the hidden state vector of the recurrent neural network at time step t, f(·) represents the Tanh function, and W h Represents the weight matrix from the previous time step hidden state to the current hidden state, h t-1 represents the hidden state vector at the previous time step t-1, W x Represents the weight matrix from the input vector to the hidden state at the current time step, x t represents the input vector of the current time step, which comes from the features of the F matrix at time step t, b h Bias vector representing the hidden state; In step S2, the attention mechanism is introduced to determine the interfering ions that have the greatest impact on the selective adsorption of uranyl ions based on the dynamic characteristics of the model output, and generate a priority feature matrix. The attention weight calculation formula is: Among them, α i represents the attention weight of ion i, e i represents the feature score of ion i, v represents the projection vector calculated by attention weight, W h The weight matrix representing the linear transformation of the hidden state, h t represents the hidden state vector at the current time step t, W s The linear transformation weight matrix representing the ion characteristics, s i represents the eigenvector of ion i, Weighted interference characteristic output formula: H f =∑ i a i s i , Among them, H f represents the interference ion feature matrix after weighting by the attention mechanism, α i represents the attention weight of ion i, s i represents the eigenvector of ion i.

7. The electrocatalytic treatment method for removing uranium-containing wastewater according to claim 6, characterized in that: The operating parameter adjustment method includes: Adjust the electric field strength and electrode voltage; The reaction time was adjusted to balance the adsorption efficiency and energy consumption.

8. The electrocatalytic treatment method for removing uranium-containing wastewater according to claim 7, characterized in that: The step of adjusting the electrocatalytic operation parameters based on the interference prediction result of step S2 is: According to the influence of interfering ions on the adsorption efficiency of uranyl ions, the electric field strength and electrode voltage are dynamically adjusted. The target optimized electric field strength is set as E opt , the target optimized electrode voltage is V opt , the optimization formula is: E opt =E init +ΔE, V opt =V init +ΔV, Among them, E opt represents the optimized electric field strength, E init represents the initial electric field strength, ΔE represents the adjustment amount, which is dynamically calculated based on the interference effect prediction results, V opt represents the optimized electrode voltage, V init represents the initial electrode voltage, ΔV represents the adjustment amount, which is dynamically calculated based on the interference effect prediction results. Assume the target adsorption efficiency is η t , the current adsorption efficiency is η c , the reaction time adjustment formula is: Among them, t opt represents the optimized reaction time, t init represents the initial reaction time, η t represents the target adsorption efficiency, η c represents the current adsorption efficiency, Define the comprehensive optimization objective function J, which integrates adsorption efficiency, residual concentration and energy consumption. The objective function formula is: J=ω1·η+ω2·(1-C e )-ω3·E c , Among them, J represents the comprehensive optimization target value, ω1, ω2, ω3 are weight factors, which control the importance of adsorption efficiency, residual concentration and energy consumption respectively, η represents the adsorption efficiency, C e represents the residual concentration, which is equal to the concentration of unadsorbed uranyl ions, E c Represents energy consumption, which is dynamically calculated based on electric field strength and reaction time. By gradient descent method, E opt ,V opt ,t opt Perform iterative updates to maximize J.

9. The electrocatalytic treatment method for removing uranium-containing wastewater according to claim 8, characterized in that: In step S4, an embedded online sensor is used for real-time monitoring, and the online sensor collects dynamic concentration data of uranyl ions U(VI) and coexisting metal ions in the wastewater.

10. The electrocatalytic treatment method for removing uranium-containing wastewater according to claim 9, characterized in that: The step of updating the interference effect prediction result in real time and adjusting the electrocatalytic operation parameters is as follows: Using embedded online sensors, the concentration change data of uranyl ions and other metal ions in wastewater are collected in real time. The real-time monitoring data is Z t ={z t,1 ,z t,2 ,…,z t,m }, represents the ion concentration vector at time t, and the monitoring data is input into the deep learning model for prediction. The deep learning model outputs the updated interference prediction result H ′ (t), the formula is: H ′ (t)=f model (Z t ), Among them, H ′ (t) represents the interference prediction characteristic matrix after updating at time t, f model represents the deep learning model constructed in step S2, Z t Represents the monitoring data vector at time t; According to the updated interference prediction result H ′ (t), adjust the operating parameters, the formula is: E opt =E init +ΔE t ,V opt =V init +ΔV t , Where, ΔE t and ΔV t They respectively represent the electric field intensity and electrode voltage change adjusted according to the prediction results at time t.

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