Efficient purification process for removing COD (Chemical Oxygen Demand) of desulfurization wastewater by electrocatalytic oxidation method

Abstract

The invention discloses a high-efficiency purification process for removing COD (Chemical Oxygen Demand) of desulfurization wastewater by an electrocatalytic oxidation method, and relates to the technical field of environmental protection, and the high-efficiency purification process comprises the following steps: reducing initial COD of wastewater through sulfite oxidation pretreatment, removing heavy metal ions through heavy metal chemical precipitation, and degrading organic matters through electrocatalytic oxidation advanced treatment. Ozone is used for synergistically strengthening and realizing cyclic utilization of oxygen resources; according to the method, the current density of electrocatalytic oxidation is dynamically regulated and controlled, a matching section group is determined by constructing a current density-COD change curve, dividing sections and constructing a section matching model, then a current density group is constructed, a benefit stable value and an efficient judgment value are obtained through quantitative analysis, and the optimal current density is determined; and an electrode prediction model is constructed to predict the expected service life of the electrode by using a long-short-term memory network algorithm. The desulfurization wastewater OD can be efficiently purified, the current density is optimized, and the service life of the electrode is predicted.

Description

Technical Field

[0001] The invention relates to the technical field of environmental protection, and in particular to a high-efficiency purification process for removing COD from desulfurization wastewater by an electrocatalytic oxidation method. Background Art

[0002] In the field of desulfurization wastewater treatment, with increasingly stringent environmental protection requirements, efficient removal of COD (Chemical Oxygen Demand) in desulfurization wastewater has become an important issue.

[0003] A Chinese patent application with publication number CN111533319B discloses a device and method for treating COD in desulfurization wastewater by ozone oxidation, including: oxidizing sulfite in the desulfurization wastewater into sulfate to initially reduce the COD in the desulfurization wastewater; removing heavy metals in the desulfurization wastewater; removing suspended matter and part of the COD in the desulfurization wastewater by flocculation and clarification; further removing the COD in the desulfurization wastewater under the joint action of a catalyst and ozone and discharging it after meeting the standards.

[0004] The current density in electrocatalytic oxidation has not been analyzed and dynamically regulated in the prior art. When treating desulfurization wastewater, different current densities have a significant impact on the COD removal effect during the electrocatalytic oxidation process. If the current density is dynamically regulated during the deep treatment of electrocatalytic oxidation, that is, the current density and COD of electrocatalytic oxidation are obtained, the curve is analyzed and divided into sections, and a section matching model is constructed to determine the matching section group, it is helpful to grasp the characteristics of different reaction stages, explore the rules of intermediate product generation, and provide a basis for determining the optimal current density, thereby optimizing the reaction process and improving the COD removal efficiency.

[0005] The existing technology does not conduct a quantitative evaluation of the benefits of the treatment process, which is not conducive to achieving a balance between treatment effect and power consumption efficiency. If a current density group is constructed and the COD removal efficiency ratio under different current densities is calculated to obtain a high-efficiency judgment value, the degree of benefit fluctuation can be measured, the optimal current density can be screened out, and a balance between treatment effect and power consumption efficiency can be achieved.

[0006] The existing technology does not conduct predictive analysis on the service life of electrodes, which may affect the continuity and stability of wastewater treatment when the equipment fails suddenly, and increase maintenance costs and downtime. If the current density is optimized and an electrode prediction model is constructed, the model training and verification are carried out by obtaining data on electrode service life, maintenance times, potential drift rate, electrode mass loss acceleration, etc., the expected service life of the electrode can be predicted, so that the staff can plan electrode maintenance and replacement work in advance, ensure the stable operation of the wastewater treatment system, reduce equipment maintenance costs, and solve the shortcomings of the existing technology in equipment service life management.

[0007] To this end, the present invention provides a high-efficiency purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation. Summary of the invention

[0008] The object of the present invention is to provide a highly efficient purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation, so as to solve at least one of the above-mentioned problems of the prior art.

[0009] In a first aspect, the present invention provides an efficient purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation, comprising the following steps:

[0010] S1, sulfite oxidation pretreatment to reduce the initial COD of desulfurization wastewater;

[0011] S2, heavy metal chemical precipitation, remove heavy metal ions in wastewater by chemical precipitation;

[0012] S3, electrocatalytic oxidation deep treatment, further degrading COD in wastewater;

[0013] S4. Ozone synergistic enhancement and resource recycling: the oxygen produced by ozone decomposition is pumped back to the sulfite oxidation pretreatment step through an air pump.

[0014] In a second aspect, the present invention provides a method for dynamically controlling current density, which is used to determine the optimal current density for removing COD from desulfurization wastewater by electrocatalytic oxidation, and realize a highly efficient purification process for COD in desulfurization wastewater, comprising the following steps:

[0015] Step 1: obtaining the current density and COD change rate of electrocatalytic oxidation, and performing curve analysis to divide the curve into multiple sections;

[0016] Step 2: Based on the multiple segments divided by the curve, a segment matching model is constructed, and matching analysis is performed between the stages divided by the curve and the reaction control stages to determine the matching segment group;

[0017] Step 3: extract the current density-COD change curve corresponding to the matching section group, construct the current density group, quantitatively analyze the benefits of the current density group, obtain the benefit stability value, sort the current density groups based on the benefit stability value, and construct the current density benefit group;

[0018] Step 4: numerically analyze the COD purification amount of different current densities in the current density group to obtain the high efficiency judgment value, sort the current density based on the high efficiency judgment value, and construct a current density high efficiency group;

[0019] Step 5: Based on the current density high-efficiency group and the current density benefit group, determine the optimal current density, build an electrode prediction model, and determine the expected service life of the optimal current density.

[0020] In a third aspect, the present invention provides a dynamic control system for current density, which is used to implement a dynamic control method for current density, comprising the following modules:

[0021] Segment division module: used to obtain the current density and COD change rate of electrocatalytic oxidation, and perform curve analysis to divide the curve into multiple segments;

[0022] Matching analysis module: Based on multiple segments divided by the curve, it is used to build a segment matching model, perform matching analysis on the phase divided by the curve and the reaction control phase, and determine the matching segment group;

[0023] Benefit analysis module: extract the current density-COD change curve corresponding to the matching section group, use it to construct the current density group, conduct quantitative analysis on the benefits of the current density group, obtain the benefit stability value, sort the current density groups based on the benefit stability value, and construct the current density benefit group;

[0024] High efficiency analysis module: used to perform numerical analysis on the COD purification amount of different current densities in the current density group, obtain the high efficiency judgment value, sort the current density based on the high efficiency judgment value, and construct the current density high efficiency group;

[0025] Life prediction module: Based on the current density high-efficiency group and the current density benefit group, it is used to determine the optimal current density, build an electrode prediction model, and determine the expected service life of the optimal current density.

[0026] Beneficial effects of the present invention:

[0027] 1. Through sulfite oxidation pretreatment, heavy metal chemical precipitation, electrocatalytic oxidation deep treatment, ozone synergistic enhancement and resource recycling, the COD of desulfurization wastewater can be effectively reduced. Sulfite oxidation pretreatment converts reducing sulfite into sulfate to reduce initial COD; electrocatalytic oxidation deep treatment, strong oxidizing free radicals generated at the anode degrade organic matter; ozone synergistic enhancement further mineralizes organic matter, achieving efficient purification of wastewater COD and improving wastewater treatment quality.

[0028] 2. Dynamically control the current density in the electrocatalytic oxidation deep treatment. Obtain the current density and COD and perform curve analysis, divide the curve segments, and build a segment matching model. This can clarify the reaction characteristics of different stages, explore the rules of intermediate product generation, and provide a basis for determining the optimal current density. Construct a current density group, conduct a quantitative analysis of its benefits, and obtain a stable benefit value and a high-efficiency judgment value. Sort the current density, construct a current density benefit group and a current density high-efficiency group, and then determine the preferred current density. It is helpful to measure the degree of benefit fluctuation, and can also screen out the current density value that makes the system achieve the best stability and benefit balance, optimize the operating parameters of the electrocatalytic oxidation process, and achieve efficient purification of desulfurization wastewater COD by electrocatalytic oxidation.

[0029] 3. Based on the optimal current density, the electrode prediction model is constructed using the long short-term memory network algorithm. The model is trained and verified by taking the electrode service life and maintenance times as input features. This is helpful to predict the expected service life of the electrode under the optimal current density, reduce the possibility of decreased treatment efficiency due to electrode failure, ensure the operation of the wastewater treatment system, and reduce equipment maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 This is a flow chart of an efficient purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation provided in Example 1 of the present invention;

[0032] Figure 2 It is a flow chart of a method for dynamic control of current density provided by the present invention;

[0033] Figure 3 This is a module diagram of a dynamic control system for current density provided by Embodiment 4 of the present invention;

[0034] Figure 4 It is a structural diagram of a computer device provided in Embodiment 5 of the present invention.

[0035] In the figure: 3, computer device; 301, processor; 302, memory; 303, computer program. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0037] Embodiment 1

[0038] Figure 1 A flow chart of an efficient purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation provided in Example 1 of the present invention specifically includes the following steps:

[0039] S1. Sulfite oxidation pretreatment: After the desulfurization wastewater enters the aeration tank, pure oxygen or compressed air is introduced through the bottom aeration plate through the microporous aeration system of the aeration tank to convert the reduced sulfite in the desulfurization wastewater into sulfate, thereby reducing the initial COD of the desulfurization wastewater;

[0040] S2. Chemical precipitation of heavy metals: remove heavy metal ions from wastewater by chemical precipitation. The desulfurization wastewater pretreated by sulfite oxidation is reacted with lime milk and sulfide reagent. A mechanical stirring device is used to stir the wastewater. After standing, the heavy metal sludge is discharged to obtain a supernatant.

[0041] S3. Electrocatalytic oxidation deep treatment: Electrocatalytic oxidation technology is used to degrade organic matter in the supernatant through the strong oxidizing free radicals generated at the anode, further degrading the COD in the wastewater;

[0042] S4, ozone synergistic enhancement and resource recycling, further mineralize organic matter through ozone catalytic oxidation, and the oxygen produced by ozone decomposition is returned to the S1 aeration tank through an air pump to achieve the recycling of oxygen resources and efficient purification of COD in wastewater.

[0043] Embodiment 2

[0044] like Figure 2 As shown, an efficient purification process for removing COD from desulfurized wastewater by electrocatalytic oxidation method, further comprising a dynamic control method for current density, determining the preferred current density for removing COD from desulfurized wastewater by electrocatalytic oxidation method, and realizing an efficient purification process for COD from desulfurized wastewater, specifically, comprising the following steps;

[0045] Step 1: obtaining the current density and COD value of electrocatalytic oxidation, and performing curve analysis to divide the curve into multiple sections;

[0046] In some embodiments, current and voltage sensors are connected to the power supply circuit of the electrochemical reactor to obtain the current value and voltage value of the electrocatalytic oxidation, and the dynamic effective area of ​​the electrode reaction is obtained by cyclic voltammetry;

[0047] Obtaining the current value and dynamic effective area of ​​the electrocatalytic oxidation process, performing ratio processing on the current value and the dynamic effective area to obtain the current density;

[0048] In some embodiments, the COD of the wastewater during the electrocatalytic oxidation process is obtained by a COD online monitor;

[0049] Obtain the COD of wastewater at the time of detection in real time and calculate the COD change rate;

[0050] By changing the current value of electrocatalytic oxidation, the current density is changed, and the current density-COD change curve is drawn with the current density as the X-axis and the COD change rate as the Y-axis;

[0051] Calculating the curvature of the current density-COD change curve, and dividing the current density-COD change curve into a plurality of sections according to different characteristics of the curvature change of the current density-COD change curve;

[0052] For example, the current density-COD change curve is divided into three current density sections: low, medium, and high. The current density range of the low current density section is [0,30], and the curvature is close to 0.01. In the low current density section, the curvature of the current density-COD change curve is low and approximates a straight line.

[0053] The current density range of the medium current density section is (30,30], and the maximum curvature is 0.15. In the medium current density section, the curvature of the current density-COD change curve reaches the maximum value, and the curvature of the curve is the largest;

[0054] The current density range of the high current density section is (60,100], the curvature is -0.08, and the curvature of the current density-COD change curve in the medium current density section becomes negative, and the curve is concave downward. The unit of current density is A / cm 2 ;

[0055] It should be noted that the purpose of dividing the current density-COD change curve into multiple sections is: Function 1: clarify the reaction characteristics of different stages. The reaction mechanism of the electrocatalytic oxidation process may be different under different current densities. By dividing the curve sections, each reaction stage can be clearly defined;

[0056] Function 2: Explore the generation of intermediate products. Different sections may correspond to the generation and transformation of different intermediate products. By analyzing the curve characteristics of different sections, the generation rules of intermediate products can be inferred;

[0057] Function 3: It provides a basis for determining the optimal current density. Different sections reflect the different degrees of influence of current density on COD removal effect. By dividing the current density-COD change curve into multiple sections, it is possible to find the section with the largest and relatively stable COD change rate.

[0058] Step 2: Based on the multiple segments divided by the curve, a segment matching model is constructed, and matching analysis is performed between the stages divided by the curve and the reaction control stages to determine the matching segment group;

[0059] In some embodiments, the control phase of the reaction is divided into a charge transfer control phase, an electrochemical mass transfer control phase, and a side reaction dominant phase;

[0060] It should be noted that the charge transfer control stage reflects the rate of change of COD during the electrocatalytic oxidation reaction, which is mainly determined by the charge transfer step on the electrode surface. With the increase of current density, COD will decrease according to a certain linear law;

[0061] The electrochemical mass transfer control stage, that is, the mass transfer process between the reactants or products on the electrode surface and the main body of the solution becomes the control step of the entire reaction rate. With the increase of current density, the COD removal rate no longer presents a simple linear relationship;

[0062] The side reaction-dominated stage refers to the situation in which, in the electrochemical reaction system, some side reactions compete with the main reaction of COD removal, causing the COD removal rate to no longer change according to the normal trend, resulting in a decrease in the COD removal rate or a situation in which the COD removal rate basically stops changing.

[0063] According to the order of current density, the multiple segments divided by the curve are sorted, a segment sorting table is established, and a segment matching model is constructed to verify whether the stages divided by the curve match the reaction control stages;

[0064] SS1. Preliminary matching verification of correlation coefficient: Linear dynamics verification of current density-COD change curve is performed, and each segment in the segment sorting table is split to obtain preliminary matching segments and fusion transformation segments;

[0065] Specifically, according to the order of the stage ranking table, the current density-COD change curve in each section is extracted;

[0066] The current density-COD change curve in the section is set to the current density gradient range, the mean value of the current density within the gradient range is recorded, and the current density sequence is constructed;

[0067] Obtain the mean value of COD change rate within the gradient range and construct a concentration change sequence;

[0068] The Pearson correlation coefficient calculation formula is used to calculate the correlation coefficient between the current density series and the concentration change series;

[0069] Based on the correlation coefficient, each segment in the segment sorting table is split. If the correlation coefficient is within the preset range, the split segment is preliminarily matched with the reaction control stage to obtain a preliminary matching segment and a fusion transformation segment;

[0070] For example, if the preset correlation coefficient range is [-1, 0.70), [0.70, 0.90), [0.95, 1], when the correlation coefficient between the current density sequence and the concentration change sequence is 0.95, the correlation coefficient between the current density sequence and the concentration change sequence shows a strong linear correlation, and it is preliminarily considered that the current density-COD change curve is in the charge transfer control stage, that is, the preliminary matching section;

[0071] It should be noted that the core feature of the charge transfer control zone is the linear or quasi-linear relationship between current density and COD removal rate. The preset range of the correlation coefficient also includes multiple segments, which are used to verify the control stage. In the range of [0.90, 0.95), it is considered to be the conversion stage from the charge transfer control stage to the electrochemical mass transfer control stage, that is, the fusion conversion stage.

[0072] Due to the one-to-one correspondence between the preliminary transformation section and the reaction control stage, there are multiple preliminary transformation sections and multiple fusion transformation sections;

[0073] SS2, slope change rematch verification: Analyze the slope of the current density-COD change curve in the fusion conversion stage, split the fusion conversion stage, and if the split fusion conversion stage meets the preset conditions, divide the split stage into the corresponding preliminary matching stage, and the remaining mark is the secondary conversion section;

[0074] Obtain the slope of the current density-COD change curve in the fusion conversion section, analyze the fusion conversion stage, and return the current density of the fusion conversion section that meets the expectations to the preliminary matching stage;

[0075] Specifically, the current density-COD change curve in the fusion conversion section is obtained, the slope of any two points is calculated, and the slope of any two points is matched with the preset slope range;

[0076] If the slopes of any two points are within the preset slope range, the fusion transformation section is split, and those that meet the slope range are assigned to the corresponding preliminary transformation stage;

[0077] If the split fusion transformation segment does not meet the slope range, a secondary transformation segment is obtained;

[0078] SS3, collect the overpotential of the electrode, establish the Butler-Volmer equation, and merge the split secondary conversion section into the corresponding primary conversion section:

[0079] In some embodiments, the Butler-Volmer equation is established by collecting the reference electrode and measuring the overpotential of the working electrode, combining the current density, the number of reaction electrons, the Faraday constant, the gas constant and the absolute temperature T, to obtain the charge transfer coefficient;

[0080] By setting different charge transfer coefficient ranges through the charge transfer coefficient of the secondary conversion section, the secondary conversion section is split;

[0081] If the charge transfer coefficient of the split secondary conversion section meets the charge transfer coefficient range of the reaction control stage, the split secondary conversion section is merged into the corresponding primary conversion section;

[0082] Based on the three steps S1, S2, and S3, the preliminary transformation sections corresponding to different reaction control stages are obtained, and matching section groups are constructed;

[0083] The technical solution of this embodiment is: obtaining the current density and COD change rate of electrocatalytic oxidation, and performing curve analysis, dividing the curve into multiple segments, constructing a segment matching model based on the multiple segments divided by the curve, matching and analyzing the stages divided by the curve with the reaction control stages, and determining the matching segment group; this is conducive to grasping the characteristics of different reaction stages, exploring the laws of intermediate product generation, and providing a basis for determining the optimal current density.

[0084] Embodiment 3

[0085] like Figure 2 As shown, a method for dynamic control of current density also includes the following steps:

[0086] Step 3: extract the current density-COD change curve corresponding to the matching section group, construct the current density group, quantitatively analyze the benefits of the current density group, obtain the benefit stability value, sort the current density groups based on the benefit stability value, and construct the current density benefit group;

[0087] The current density-COD change curve is split into multiple sub-curve segments, and the current density corresponding to the maximum value point of the COD change rate in each sub-curve segment in the charge transfer control stage is obtained and put into the current density group;

[0088] Obtain the separation point between the charge transfer control stage and the electrochemical mass transfer control stage in the matching section group, and obtain the current density corresponding to the maximum value point of the COD change rate in the sub-curve segment closest to the separation point in the charge transfer control stage, and put it into the current density group;

[0089] Obtain the separation point between the side reaction dominant stage and the electrochemical mass transfer control stage in the matching section group, and obtain the current density corresponding to the maximum value point of the COD change rate in the sub-curve segment closest to the side reaction dominant stage and the separation point, and put them into the current density group;

[0090] Based on the current density group, the COD removal efficiency ratio under different current densities was obtained;

[0091] Specifically, during the monitoring period, the total amount of COD removed at a single current density and the power consumption of electrocatalytic oxidation during the monitoring period are calculated;

[0092] The total amount of COD removed during the monitoring period is processed by ratio with the power consumption of electrocatalytic oxidation to obtain the COD removal benefit ratio in each period;

[0093] The COD removal benefit ratios in all monitoring periods are summed and averaged to obtain the benefit mean ratio;

[0094] Obtain the standard deviation of the COD removal benefit ratio in the entire monitoring period, perform ratio processing on the benefit mean ratio and the standard deviation of the COD removal benefit ratio in the entire monitoring period, and obtain the benefit stability value;

[0095] According to the order of the benefit stability value from small to large, the current density in the current density group is sorted to construct the current density benefit group;

[0096] It should be noted that the role of the benefit stability value is: role 1, to measure the degree of benefit fluctuation. By processing the ratio of the benefit mean ratio to the standard deviation, it can intuitively reflect the relative fluctuation of the COD removal benefit ratio in different monitoring periods;

[0097] Function 2, screening the optimal current density, helps to screen out the current density value that can make the system achieve the best stability and benefit balance from the current density group, and provides a basis for optimizing the operating parameters of the electrocatalytic oxidation process.

[0098] Step 4: numerically analyze the COD purification amount of different current densities in the current density group to obtain the high efficiency judgment value, sort the current density based on the high efficiency judgment value, and construct a current density high efficiency group;

[0099] Select the current density from the current density group in order, obtain the COD change rate of the current density at different monitoring times, take the monitoring time as the X-axis and the COD change rate as the Y-axis, and construct a time-COD change curve in a two-dimensional rectangular coordinate system;

[0100] It should be noted that there are multiple monitoring moments within the monitoring cycle;

[0101] Obtain multiple maximum points on the time-COD change curve and the COD change rate corresponding to the maximum points;

[0102] Compare the COD change rate corresponding to the maximum value point with the preset change threshold;

[0103] If the COD change rate corresponding to the maximum value point is higher than the preset change threshold, the monitoring time corresponding to the maximum value is obtained, and a maximum time group is constructed;

[0104] According to the positive replacement order of the monitoring time, the maximum time group is sorted to obtain the maximum time range;

[0105] At the maximum and minimum monitoring moments of the maximum time range, two vertical lines are drawn to intersect with the X-axis, and the area of ​​the closed figure formed by the time-COD variation curve, the X-axis, and the two vertical lines is obtained to obtain the COD efficient purification amount;

[0106] Obtain the monitoring time from the start to the end of electrocatalytic oxidation, the area of ​​the time-COD change curve, and obtain the total COD purification amount;

[0107] The COD high-efficiency purification amount and the COD total purification amount are processed to obtain the high-efficiency purification ratio;

[0108] Obtain the length of the monitoring time from the start to the end of the electrocatalytic oxidation to obtain the purification time;

[0109] Ratio processing is performed between the time length of the maximum time range and the purification time length to obtain the efficient time length ratio;

[0110] Perform weighted sum processing on the high-efficiency time ratio and the high-efficiency purification ratio to obtain a high-efficiency judgment value;

[0111] Obtaining high efficiency judgment values ​​of different current densities in the current density group, sorting the current densities in descending order of the high efficiency judgment values, and constructing a current density high efficiency group;

[0112] It should be noted that the role of the high efficiency judgment value is: Role 1, evaluate the purification effect of current density. A high high efficiency judgment value means that at this current density, not only does the COD removal amount account for a large proportion of the total purification amount, but the time of the reaction high efficiency stage accounts for a relatively ideal proportion of the entire purification time, indicating that this current density can remove COD more effectively;

[0113] Function 2: Screening the optimal current density, by quantifying the high efficiency of different current densities, so as to provide data basis for screening the current density that can achieve the best effect of the electrocatalytic oxidation process.

[0114] Step 5: Based on the current density high-efficiency group and the current density benefit group, determine the optimal current density, build an electrode prediction model, and determine the expected service life of the optimal current density;

[0115] Standardize the current density high efficiency group and current density benefit group to construct the standard high efficiency group and standard benefit group;

[0116] It should be noted that the number of current densities in the current density high efficiency group and the current density benefit group remained consistent;

[0117] By formula: Obtain the current density with the largest preferred value F among different current densities j as the preferred current density, where j is the number of the current density, Xw j , Gx j They represent the benefit stability value of different current density j in the standard high-efficiency group and the high-efficiency judgment value of different current density j in the standard high-efficiency group, and α and β are 0.43 and 0.72 respectively;

[0118] It should be noted that the role of the optimal current density is: Role 1, improve the treatment efficiency and effect. In the electrocatalytic oxidation process, different current densities have different effects on the COD removal effect. The optimal current density is determined by comprehensively considering the benefit stability value and the high efficiency judgment value, which can enable the system to achieve the best stability and benefit balance;

[0119] Function 2: Reduce treatment costs. Optimizing current density can reduce power consumption per unit of wastewater treatment while ensuring treatment effect;

[0120] Function 3: Providing key parameters for process optimization: Determining the optimal current density provides a direction for optimizing the entire electrocatalytic oxidation process. Based on this, researchers can further study the synergistic effect of other process parameters and current density to achieve overall process optimization and improve the overall performance of the treatment system.

[0121] Based on the preferred current density, the electrode prediction model is constructed using the long short-term memory network (LTSM) algorithm to predict the expected service life of the electrode under the preferred current density;

[0122] Specifically, the construction method of the electrode prediction model is:

[0123] S1, build a time series dataset;

[0124] Based on the optimal current density, the electrode service life and the number of electrode maintenance times in the time dimension under the optimal current density are obtained from the electrocatalytic oxidation engineering log to construct a time series data set.

[0125] It should be noted that the service life of the electrode is quantified by recording the usage time of the electrode every hour and the number of times the electrode is maintained, where the number of times the electrode is maintained includes: the number of times the electrode is cleaned and replaced;

[0126] S2, feature input;

[0127] By obtaining the slope of the potential change with time from the electrocatalytic oxidation engineering log, the potential drift rate and the electrode mass loss acceleration are obtained, and the potential drift rate and the electrode mass loss acceleration are used as input features;

[0128] The benefit stability value and high efficiency judgment value calculated by the optimal current density are used as input features to predict the life of the associated electrode;

[0129] It should be noted that the potential shift rate is obtained by measuring the potential value at the center point of the electrode surface to obtain the slope of the potential change over time;

[0130] S3. Model selection and training

[0131] Use the XGBoost algorithm to process input features and the LTSM algorithm to process time series datasets;

[0132] It should be noted that XGBoost (eXtreme Gradient Boosting) is a gradient boosting tree algorithm, which is often used to deal with classification and regression problems of structured data, and is used to predict some related indicators based on input features, such as electrode performance indicators;

[0133] LSTM is a special type of recurrent neural network (RNN) that can effectively handle long-term dependencies in time series data. In this scenario, it can be used to predict the expected service life of the electrode over time;

[0134] Construct the loss function: Where γ is the weight coefficient, which is 1.263, MSE is the mean square error between the predicted life and the actual life of the electrode, and t is the expected service life of the electrode;

[0135] S4. Validation and model updating

[0136] Verify according to the current density partition, so that the prediction error of the electrode prediction model at the optimal current density is less than the preset error threshold;

[0137] Preferably, the error threshold is 10%;

[0138] Continuously obtain input features from the electrocatalytic oxidation engineering log. When the deviation between the actual electrode life and the predicted life is higher than the preset error threshold, perform incremental model training, and professional technicians in this field update the weight parameters α and β;

[0139] Output the expected service life of the electrode at the optimal current density based on the electrode prediction model;

[0140] It should be noted that the role of determining the expected service life of the electrode is as follows: Role 1: Ensure the stability of the treatment process: When treating desulfurization wastewater by electrocatalytic oxidation, the electrode is the core component. Knowing the expected life of the electrode, the staff can plan maintenance and replacement plans in advance;

[0141] Function 2: Reduce equipment maintenance costs: Knowing the expected life of the electrode in advance can avoid emergency repair or replacement costs caused by sudden electrode damage.

[0142] The technical solution of this embodiment is: extract the current density-COD change curve corresponding to the matching section group, construct a current density group, quantitatively analyze the benefits of the current density group to obtain a benefit stability value, sort the current density groups based on the benefit stability value, construct a current density benefit group, numerically analyze the COD purification amount of different current densities in the current density group to obtain a high efficiency judgment value, sort the current density based on the high efficiency judgment value, construct a current density high efficiency group, determine the preferred current density based on the current density high efficiency group and the current density benefit group, construct an electrode prediction model, determine the expected service life of the preferred current density, screen out the optimal current density, achieve a balance between treatment effect and power consumption efficiency, and be able to predict the expected service life of the electrode, so that the staff can plan electrode maintenance and replacement work in advance to ensure the stable operation of the wastewater treatment system.

[0143] Embodiment 4

[0144] like Figure 3 As shown, an efficient purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation method also includes a dynamic control system for current density, which is used to implement a dynamic control method for current density, including the following modules:

[0145] Segment division module: used to obtain the current density and COD change rate of electrocatalytic oxidation, and perform curve analysis to divide the curve into multiple segments;

[0146] Matching analysis module: Based on multiple segments divided by the curve, it is used to build a segment matching model, perform matching analysis on the phase divided by the curve and the reaction control phase, and determine the matching segment group;

[0147] Benefit analysis module: extract the current density-COD change curve corresponding to the matching section group, construct the current density group, conduct quantitative analysis on the benefits of the current density group, obtain the benefit stability value, sort the current density groups based on the benefit stability value, and construct the current density benefit group;

[0148] High efficiency analysis module: perform numerical analysis on the COD purification amount of different current densities in the current density group to obtain the high efficiency judgment value, sort the current density based on the high efficiency judgment value, and construct the current density high efficiency group;

[0149] Life prediction module: Based on the current density high-efficiency group and the current density benefit group, determine the optimal current density, build an electrode prediction model, and determine the expected service life of the optimal current density.

[0150] Embodiment 5

[0151] Reference Figure 4 The embodiment of the present invention further provides a computer device 3, comprising: a memory 302 and a processor 301 and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, a dynamic control method for current density as described in any one of the above methods is implemented.

[0152] The computer device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that

[0153] Figure 4 It is only an example of computer device 3 and does not constitute a limitation on computer device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0154] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0155] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 3. Further, the memory 302 may also include both an internal storage unit of the computer device 3 and an external storage device. The memory 302 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0156] Embodiment 6

[0157] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, a method for dynamic control of current density as described in any one of the above methods is implemented.

[0158] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0159] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0160] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0161] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0162] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0163] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0164] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An efficient purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation, characterized in that: The following steps are involved: Extract the current density-COD change curve corresponding to the matching section group, construct the current density group, conduct quantitative analysis on the benefits of the current density group, obtain the benefit stability value, sort the current density groups based on the benefit stability value, and construct the current density benefit group; The COD purification amount of different current densities in the current density group is numerically analyzed to obtain the high efficiency judgment value, and the current density is sorted based on the high efficiency judgment value to construct a current density high efficiency group; Based on the current density high-efficiency group and the current density benefit group, the optimal current density is determined, an electrode prediction model is constructed, and the expected service life of the optimal current density is determined.

2. The highly efficient purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation according to claim 1 is characterized in that: The matching segment group is obtained in the following manner: Conduct curve analysis on current density and COD change rate, and divide the curve into multiple sections; Based on the multiple segments divided by the curve, a segment matching model is constructed, and matching analysis is performed on the stages divided by the curve and the reaction control stages to determine the matching segment group.

3. The high-efficiency purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation according to claim 2 is characterized in that: The segment matching model is constructed as follows: The linear kinetics of the current density-COD curve was verified to obtain the preliminary matching section and fusion conversion section; The slope of the current density-COD change curve in the fusion conversion stage is analyzed, and those that meet the preset conditions are divided into the preliminary matching stage, and the remaining sections are marked as secondary conversion sections; The overpotential of the electrode was collected, the Butler-Volmer equation was established, and the split secondary conversion sections were merged into the corresponding primary conversion sections.

4. The highly efficient purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation according to claim 1 is characterized in that: The current density benefit group is constructed as follows: The current density in the matching section group is divided to construct the current density group, and the COD change rate of the current density group is numerically analyzed to obtain the benefit stability value; The current density is sorted according to the benefit stability value to construct the current density benefit group.

5. The high-efficiency purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation according to claim 1 is characterized in that: The method for obtaining the benefit stability value is as follows: The total amount of COD removed in the monitoring period is processed by ratio with the power consumption to obtain the COD removal benefit ratio in each period; Calculate the mean of the COD removal benefit ratio in all monitoring periods to obtain the mean benefit ratio; The benefit mean ratio was processed with the standard deviation of the COD removal benefit ratio during the entire monitoring period to obtain the benefit stability value.

6. The highly efficient purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation according to claim 1 is characterized in that: The method for obtaining the current density high efficiency group is: Based on the current density group, a time-COD variation curve was constructed; Perform area analysis on the time-COD variation curve to obtain the COD efficient purification amount and the total COD purification amount; Numerical analysis is performed on the COD high-efficiency purification amount and the COD total purification amount to obtain the high-efficiency judgment value; According to the high efficiency judgment value, the current density is sorted to construct a current density high efficiency group.

7. The high-efficiency purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation according to claim 6 is characterized in that: The COD efficient purification amount and the COD purification total amount are obtained as follows: Obtain multiple maximum points on the time-COD change curve, conduct comparative analysis, and construct a maximum moment group; Sort the maximum moment groups to obtain the maximum moment range; At the maximum and minimum monitoring moments in the maximum time range, the area of ​​the corresponding time-COD change curve is obtained to obtain the COD efficient purification amount; The monitoring time from the start to the end of the electrocatalytic oxidation and the area of ​​the time-COD change curve are obtained to obtain the total COD purification amount.

8. The high-efficiency purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation according to claim 6 is characterized in that: The method for obtaining the efficient determination value is: The COD high-efficiency purification amount and the COD total purification amount are processed to obtain the high-efficiency purification ratio; Obtain the purification time of electrocatalytic oxidation, perform ratio processing on the time length of the maximum time range and the purification time, and obtain the high-efficiency time ratio; The high-efficiency time ratio and the high-efficiency purification ratio are weighted and summed to obtain a high-efficiency judgment value.

9. The highly efficient purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation according to claim 1 is characterized in that: The preferred current density is obtained as follows: Standardize the current density high efficiency group and current density benefit group to construct the standard high efficiency group and standard benefit group; Based on the efficiency stability value and the efficiency judgment value of the standard high-efficiency group and the standard benefit group, the maximum value of the optimal value of different current densities is obtained through a weighted formula; The current density corresponding to the maximum value of the preferred value is obtained as the preferred current density.

10. The high-efficiency purification process for removing COD from desulfurization wastewater by electrocatalytic oxidation according to claim 1, characterized in that: The expected service life is obtained as follows: Based on the preferred current density, an electrode prediction model was constructed using the long short-term memory network algorithm to predict the expected service life of the electrode at the preferred current density.

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