Recycling treatment process of photovoltaic fluorine-containing wastewater

Abstract

The invention discloses a photovoltaic fluorine-containing wastewater recycling treatment process, and particularly relates to the field of wastewater treatment.The photovoltaic fluorine-containing wastewater recycling treatment process comprises the following steps that the pH value of fluorine-containing wastewater is adjusted in a multi-stage adjusting mode, activated carbon or kieselguhr serves as a filtering medium, and particle impurities and suspended solids are removed; the solid content in the wastewater is reduced to a set threshold value. Through multi-stage pH value adjustment, optimized filtration, electrolytic treatment and ion exchange resin defluorination processes, and in combination with real-time monitoring of a sensor and judgment of a decision tree model, high efficiency and automation of wastewater treatment are realized; according to the invention, fluorine ions, heavy metals and organic matters are fully removed in each stage, and dynamic adjustment of the proportion of the filtering medium is ensured, so that the whole process is stable and efficient, and the recycling standard is reached.

Description

Technical Field

[0001] The present invention relates to the technical field of wastewater treatment, and more specifically, to a recycling and reuse treatment process of photovoltaic fluorine-containing wastewater. Background Art

[0002] The recycling and reuse of photovoltaic fluorine-containing wastewater refers to the use of a series of technical means to treat the fluorine-containing wastewater generated in the production process of the photovoltaic industry to make it meet the environmental protection standards for reuse or discharge, thereby reducing wastewater discharge, lowering treatment costs and achieving effective recycling of water resources. This type of treatment usually involves a combination of multiple methods such as physical, chemical and biological methods.

[0003] In view of the difficulty in treating fluorine-containing wastewater and incomplete removal of pollutants in photovoltaic production, in the existing technology, the steps of filtering and removing suspended matter, heavy metals, organic matter, etc. may have unstable efficiency under different concentrations or load conditions, affecting the overall wastewater treatment effect. Fluoride ion removal is not complete: Since fluoride ion is a component that is difficult to remove, the existing technology may result in fluoride ion concentration not meeting the standard during the treatment process, thereby failing to achieve the recycling standard. There are deficiencies in real-time monitoring and regulation of wastewater treatment parameters, resulting in the inability to make dynamic adjustments in a timely manner, reducing the treatment effect and system stability. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a recycling and reuse treatment process for photovoltaic fluorine-containing wastewater, wherein the pH value of the fluorine-containing wastewater is adjusted by a multi-stage adjustment method, and particulate impurities and suspended matter are removed by using activated carbon or diatomaceous earth as a filter medium, so that the solid content in the wastewater is reduced to a set threshold value, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: comprising the following steps:

[0006] The pH value of fluoride-containing wastewater is adjusted by a multi-stage adjustment method;

[0007] Activated carbon or diatomaceous earth is used as a filter medium to remove particulate impurities and suspended solids, so that the solid content in the wastewater is reduced to a set threshold value; after the solid suspended solid content in the wastewater is controlled below the set suspended solid content threshold value through the filter medium, alumina and zeolite are used to remove residual organic matter and trace heavy metal ions in the wastewater to obtain primary filtered wastewater;

[0008] The primary filtered wastewater is subjected to oxidation or reduction decomposition of the residual organic matter and heavy metal ions in the wastewater through electrolysis reaction, and the fluoride ion concentration of the wastewater after electrochemical treatment is monitored. When the concentration drops to the set fluoride ion concentration threshold, the defluorination stage is entered;

[0009] In the defluorination stage, selective ion exchange resin is added to the wastewater after electrochemical treatment to remove fluoride ions in a targeted manner. After the wastewater is treated by ion exchange, when the fluoride ion content drops to the set fluoride ion content threshold, it enters the recycling mode;

[0010] The fluoride ion concentration, pH value and heavy metal content of the wastewater are monitored by fluoride ion selective electrodes, heavy metal sensors and pH sensors, and a monitoring data set is generated. The monitoring data set is used as the input value of the decision tree model, and the threshold corresponding to the monitoring data set forms a tree structure. Each node represents a feature for judgment, and each branch represents a decision made according to the threshold of the feature. The accuracy of the model is verified by cross-validation. The constructed decision tree is applied to the monitoring of fluoride ion concentration, pH value and heavy metal content data. The rules set by the decision tree are used to judge whether the recycling standards are met, and when the fluoride ion concentration drops to the concentration threshold set by the system, it is switched to the recycling mode.

[0011] In a preferred embodiment, the multi-stage regulation method for fluorine-containing wastewater includes:

[0012] The pH value of the wastewater is adjusted to a set range, wherein an alkaline or acidic regulator is first added to the fluorine-containing wastewater to adjust the pH value to a neutral to weakly alkaline range of pH7-8 parameters to reduce the initial acidity or alkalinity of the wastewater, and a secondary pH adjustment is performed on the adjusted wastewater by adding an alkaline or acidic regulator to control the pH value within a set range of 6.5 to 7.5 to ensure the removal of fluoride ions in the wastewater during the filtration process.

[0013] In a preferred embodiment, when wastewater enters the filter medium, if the suspended matter concentration exceeds the set filtration threshold, the filtration efficiency will decrease, affecting the normal operation of the electrolysis unit. The particle swarm optimization model is used to adjust the ratio of activated carbon to diatomaceous earth to ensure that the filtration efficiency is close to the set filtration threshold and reduce the impact of residual tiny impurities on subsequent treatment steps. The suspended matter filtration formula is:

[0014]

[0015] Wherein, E represents the filtration efficiency, ranging from 0 to 1, indicating the filtration effect. The closer the value is to 1, the closer the filtration effect is to the set filtration threshold. C8 represents the concentration of suspended solids in the wastewater, and 50 represents the set solid suspended solids concentration threshold, indicating the allowable suspended solids concentration. When C8 = 50, the formula Equal to 1; 5 represents the curve slope control coefficient in the formula, which affects the sensitivity of the filtration efficiency E to the suspended matter concentration C8. The larger the slope, the more drastic the response of the filtration efficiency to the concentration change; e represents the base of the natural logarithm, which is used to calculate exponential decay. The formula obtains the value of the filtration efficiency E by performing exponential decay processing on the change of the suspended matter concentration C8.

[0016] In a preferred embodiment, the proportion of the filter medium is dynamically adjusted based on the particle swarm optimization model to ensure that the filtration efficiency E remains within the set filtration threshold. The particle swarm optimization model can effectively find the medium proportion at different concentrations, thereby increasing the filtration efficiency. The filtration efficiency E judgment formula is as follows:

[0017] v i =w·v i +c1·r1·(p best -x i )+c2·r2·(g best -x i )

[0018] Among them, v i represents the speed of particle i, the speed of proportional change; w represents the inertia coefficient, which controls the degree to which the particle retains the current speed; c1 and c2 represent learning factors, which determine the movement weight of the particle towards the individual extreme value and the global extreme value; r1 and r2 represent random numbers, which increase the randomness of the search; x i =x i +v i represents the new position of particle i; both the new particle speed and the new position need to be evaluated to see if they meet the target value set by a process, that is, within the specified ratio range, the current and initial positions are combined in the speed evaluation, and the weight of the random distribution consideration is set according to the specified ratio. Each particle records the position corresponding to the specified speed in the decision-making process, that is, the individual local position p best At the same time, the position reached by all particles in the group that meets the process setting range is recorded as the global position g best ; When the particle's fitness P ≥ 0.95 and the global position reaches g that meets the process settings best When , it is considered that the optimal combination is found and the iteration stops.

[0019] In a preferred embodiment, during the electrolysis and defluorination stage of the primary filtered wastewater treatment, heavy metals and organic matter in the primary filtered wastewater are removed by a combination of current and voltage, and the electrolysis reaction rate formula is:

[0020]

[0021] Where R represents the decomposition rate, which indicates the speed of pollutant removal; I·V represents the current and voltage, where I represents the current intensity during the electrolysis process and V represents the voltage applied during the electrolysis process; C metal and C organic It represents the concentration of heavy metals and organic matter in wastewater through the combination of current and voltage; 0.8 is expressed as a constant coefficient, which is used to adjust the unit or proportion of the calculation result; 1.2 represents an exponent, which is used to express the nonlinear relationship in the formula, indicating the amplitude of the rate change with the parameter. Under the conditions of current and voltage, it is the rate of removing organic matter and heavy metals from wastewater; the exponent 1.2 means that the removal effect is not a simple linear relationship, but slightly amplifies the influence of current and voltage to ensure the removal of pollutants. After electrolysis, the wastewater enters the defluorination stage, and fluoride ions are removed by ion exchange resin. The formula for the change of fluoride ion concentration in the exchange resin treatment is:

[0022]

[0023] Among them, C F -(t) represents the fluoride ion concentration in the wastewater at time t; 10 represents the initial fluoride ion concentration, that is, the fluoride ion concentration in the wastewater is 10 mg / L at the beginning of the defluorination treatment; e represents the base of the natural logarithm, which is approximately equal to 2.718 and is used to calculate exponential decay; -0.05 represents the exponential decay coefficient, which represents the rate at which the fluoride ion concentration decreases naturally; the negative sign represents that the concentration decreases over time; the integral symbol, represents the cumulative change from time 0 to t, 0.02 represents the adsorption rate coefficient, which represents the efficiency of removing fluoride ions. The larger the value, the faster the removal rate; 10-C F -(t) represents the current concentration difference of the remaining fluoride ions, indicating the amount of fluoride ions that need to be removed; 1+e ∧ {-0.1(t-20)} represents the function used to smooth the adsorption process, where -0.1 controls the influence of time variation; t-20 means that the time center of the adsorption process is around 20 minutes; 10·e -0.05t Represents the natural decay of fluoride ion concentration; It indicates how fluoride ions are dynamically adsorbed during the treatment process. As time t passes, this item indicates the amount by which the fluoride ion concentration is gradually reduced at a certain adsorption rate. By considering the adsorption rate of fluoride ions removed by the ion exchange resin, the difference in the current fluoride ion concentration, and the adjustment of the defluorination rate by time, the changing trend of the fluoride ion concentration during this period of time is calculated. When the fluoride ion concentration threshold is reached and the fluoride ion content drops to the set threshold of 1 mg / L, the system automatically switches to the recycling mode.

[0024] In a preferred embodiment, a monitoring data set containing the following data is generated by real-time monitoring of key indicators of wastewater using a fluoride ion selective electrode, a heavy metal sensor, and a pH sensor:

[0025] Fluoride ion concentration C F -, pH value, indicating the acidity and alkalinity of wastewater; heavy metal concentration C metal , the monitoring data set is input into the decision tree model for judgment; each node of the decision tree model represents a feature, for example, fluoride ion concentration, and each branch makes a decision based on whether the feature value reaches the threshold until it reaches the leaf node to output the judgment result. In order to correct and monitor the data in real time, we can use its electrolysis reaction rate and fluoride ion concentration changes to judge the fluoride ion concentration, pH value and heavy metal content monitored by the sensor in real time. The monitoring data set is used as a decision tree model to determine whether the wastewater meets the recycling standards. Secondly, when the monitoring data fluctuates or differs greatly from historical data, it is corrected in combination with the above formula to ensure the accuracy of the judgment. The fluoride ion concentration formula is used to predict the expected changes of fluoride ions at different time points, and the sensor data is corrected to avoid error accumulation.

[0026] In a preferred embodiment, the fluoride ion concentration, pH value and heavy metal content are input into the decision tree model, and the fluoride ion concentration C F- , pH value, heavy metal concentration C metal , forming a training set of monitoring data; assuming that the data set contains 100 data, use 5-fold cross validation, that is, divide the data set into 5 subsets, each subset contains 20 data, and the subsets can be randomly divided to ensure that different types of data in each subset are evenly distributed. The first verification:

[0027] Use subset 1 as the validation set, and subsets 2, 3, 4, and 5 as the training sets. Train the decision tree model on these 80 data points to learn how to determine whether the fluoride ion concentration, pH value, and heavy metal concentration meet the recycling standards. Use the 20 data points in subset 1 to verify the accuracy of the model and obtain the first verification result.

[0028] Second verification:

[0029] Use subset 2 as the validation set, subset 1, subset 3, subset 4, and subset 5 as the training set, and perform validation on 20 data in subset 2 to obtain the second validation result, which is used as the training model; repeat the above steps, continue to use each subset as the validation set in turn, and use the remaining subsets as the training set, for a total of 5 iterations. Record the performance of the model on the validation set, the accuracy, and the error rate for each iteration, calculate the average performance, and average the results of the 5 validations to obtain the model index; for example, if the accuracy of the 5 validations is 85%, 88%, 86%, 87%, and 89%, respectively, the average accuracy of the model is: The monitoring data set is divided into multiple subsets for K-fold cross-validation. Different subsets are used to train and validate the model each time. In each iteration, the model is trained with the training set and evaluated with the validation set. The decision tree model's judgment accuracy on fluoride ion concentration, pH value, and heavy metal concentration is used to determine whether the model is stable based on the average result of cross-validation. When a certain value performs significantly lower than the average, it is necessary to adjust the model parameters or use a more complex model. Cross-validation can be used to improve the judgment accuracy of the model and reduce the misjudgment rate.

[0030] Technical effects and advantages of the present invention:

[0031] 1. Introduce fluoride ion selective electrodes, heavy metal sensors and pH sensors for real-time monitoring, generate data sets, and use decision tree models for judgment and correction to ensure judgment accuracy. Cross-validation improves the stability and generalization ability of the model, thereby reducing misjudgments and ensuring that the system can automatically switch to recycling mode when the fluoride ion concentration threshold is reached;

[0032] 2. Remove organic matter, heavy metals and fluoride ions from wastewater through electrolysis and ion exchange resin to ensure that wastewater treatment meets the set standards, thereby achieving efficient reuse of wastewater, reducing environmental pollution and improving resource sustainability;

[0033] 3. Through multi-stage pH adjustment of wastewater, fluoride ions and other pollutants are effectively removed under pH conditions, which improves the efficiency of subsequent filtration and electrolytic treatment. Activated carbon or diatomaceous earth is used as the filtration medium and combined with the particle swarm optimization model to make the filtration process more efficient and reduce the impact of suspended matter on the electrolytic unit and subsequent processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a process flow chart of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0036] Refer to the instruction manual Figure 1 A recycling and reuse process for photovoltaic fluorine-containing wastewater according to an embodiment of the present invention comprises the following steps:

[0037] The pH value of fluoride-containing wastewater is adjusted by a multi-stage adjustment method;

[0038] Activated carbon or diatomaceous earth is used as a filter medium to remove particulate impurities and suspended solids, so that the solid content in the wastewater is reduced to a set threshold value; after the solid suspended solid content in the wastewater is controlled below the set suspended solid content threshold value through the filter medium, alumina and zeolite are used to remove residual organic matter and trace heavy metal ions in the wastewater to obtain primary filtered wastewater;

[0039] The primary filtered wastewater is subjected to oxidation or reduction decomposition of the residual organic matter and heavy metal ions in the wastewater through electrolysis reaction, and the fluoride ion concentration of the wastewater after electrochemical treatment is monitored. When the concentration drops to the set fluoride ion concentration threshold, the defluorination stage is entered;

[0040] In the defluorination stage, selective ion exchange resin is added to the wastewater after electrochemical treatment to remove fluoride ions in a targeted manner. After the wastewater is treated by ion exchange, when the fluoride ion content drops to the set fluoride ion content threshold, it enters the recycling mode;

[0041] The fluoride ion concentration, pH value and heavy metal content of the wastewater are monitored by fluoride ion selective electrodes, heavy metal sensors and pH sensors, and a monitoring data set is generated. The monitoring data set is used as the input value of the decision tree model, and the threshold corresponding to the monitoring data set forms a tree structure. Each node represents a feature for judgment, and each branch represents a decision made according to the threshold of the feature. The accuracy of the model is verified by cross-validation. The constructed decision tree is applied to the monitoring of fluoride ion concentration, pH value and heavy metal content data. The rules set by the decision tree are used to judge whether the recycling standards are met, and when the fluoride ion concentration drops to the concentration threshold set by the system, it is switched to the recycling mode.

[0042] The multi-stage regulation of fluoride-containing wastewater includes:

[0043] The pH value of the wastewater is adjusted to a set range, wherein an alkaline or acidic regulator is first added to the fluoride-containing wastewater to adjust the pH value to a neutral to weakly alkaline range of pH7-8 parameters to reduce the initial acidity or alkalinity of the wastewater, and a secondary pH value adjustment is performed in the adjusted wastewater, and an alkaline or acidic regulator is added to control the pH value within a set range of 6.5 to 7.5 to ensure that fluoride ions are removed from the wastewater during the filtration process. The pH value of the wastewater is adjusted to a set range, first adjusted to pH7-8, and second adjusted to pH6.5-7.5 to ensure that the chemical environment of the wastewater in subsequent filtration and treatment steps is suitable for the removal of fluoride ions and other pollutants. An appropriate pH value helps to improve the removal efficiency of the filter medium and ion exchange resin, prevents fluoride ions from forming compounds that are difficult to remove under conditions of excessively high or low pH, and ensures that the wastewater treatment process is stable and efficient;

[0044] When wastewater enters the filter medium, if the suspended solids concentration exceeds the set filtration threshold, the filtration efficiency will decrease, affecting the normal operation of the electrolysis unit. The particle swarm optimization model is used to adjust the ratio of activated carbon to diatomaceous earth to ensure that the filtration efficiency is close to the set filtration threshold and reduce the impact of residual tiny impurities on subsequent treatment steps. The suspended solids filtration formula is:

[0045]

[0046] Wherein, E represents the filtration efficiency, ranging from 0 to 1, indicating the filtration effect. The closer the value is to 1, the closer the filtration effect is to the set filtration threshold. C8 represents the concentration of suspended solids in the wastewater, and 50 represents the set solid suspended solids concentration threshold, indicating the allowable suspended solids concentration. When C8 = 50, the formula =1; 5 represents the curve slope control coefficient in the formula, which affects the sensitivity of the filtration efficiency E to the suspended matter concentration C8. The larger the slope, the more drastic the response of the filtration efficiency to the concentration change. e represents the base of the natural logarithm, which is used to calculate the exponential decay. The formula performs exponential decay processing on the change of the suspended matter concentration C8 to obtain the value of the filtration efficiency E.

[0047] It should be noted that the condition of filtration efficiency E is to ensure that the concentration of suspended solids in the wastewater is C s It is within the set allowable threshold, such as C8=50 or below, so as to ensure that the filtration effect is close to the ideal state. When the filtration efficiency E is close to 1, it means that the filtration process of the wastewater meets the set standards and can effectively remove suspended matter to avoid adverse effects on subsequent treatment steps. Through the exponential decay treatment of C8, the system can monitor and optimize the filtration process to ensure that the wastewater can reach the expected purity and quality after treatment;

[0048] The proportion of the filter media is dynamically adjusted based on the particle swarm optimization model to ensure that the filtration efficiency E remains within the set filtration threshold. The particle swarm optimization model can effectively find the medium proportion at different concentrations, thereby increasing the filtration efficiency. The filtration efficiency E judgment formula is as follows:

[0049] v i =w·v i +c1·r1·(p best -x i )+c2·r2·(g best -x i )

[0050] Among them, v irepresents the speed of particle i, the speed of proportional change; w represents the inertia coefficient, which controls the degree to which the particle retains the current speed; c1 and c2 represent learning factors, which determine the movement weight of the particle towards the individual extreme value and the global extreme value; r1 and r2 represent random numbers, which increase the randomness of the search; x i =x i +v i represents the new position of particle i; both the new particle speed and the new position need to be evaluated to see if they meet the target value set by a process, that is, within the specified ratio range, the current and initial positions are combined in the speed evaluation, and the weight of the random distribution consideration is set according to the specified ratio. Each particle records the position corresponding to the specified speed in the decision-making process, that is, the individual local position p best At the same time, the position reached by all particles in the group that meets the process setting range is recorded as the global position g best ; When the particle's fitness P ≥ 0.95 and the global position reaches g that meets the process settings best When , it is considered that the optimal combination is found and the iteration stops;

[0051] The process efficiency condition based on the particle swarm optimization model is judged to dynamically adjust the proportion of the filter medium; the filtration efficiency is kept close to the set process threshold. By monitoring and adjusting the speed and position of the particles, the model can find a combination that meets the standard, thereby ensuring that the process speed is appropriate and avoiding abnormal substances from being affected by exceeding the standard and continuing to be processed. When the process efficiency of the global compliance position reaches the set value, such as ≥0.95, it indicates that a combination that meets the quality has been found, the process requirements are met and the iteration is stopped;

[0052] In the electrolysis and defluorination stage of primary filtered wastewater treatment, heavy metals and organic matter in the primary filtered wastewater are removed by combining current and voltage. The electrolysis reaction rate formula is:

[0053]

[0054] Where R represents the decomposition rate, which indicates the speed of pollutant removal; I·V represents the current and voltage, where I represents the current intensity during the electrolysis process and V represents the voltage applied during the electrolysis process; C metal and C organicIt represents the concentration of heavy metals and organic matter in wastewater through the combination of current and voltage; 0.8 is expressed as a constant coefficient, which is used to adjust the unit or proportion of the calculation result; 1.2 represents an exponent, which is used to express the nonlinear relationship in the formula, indicating the amplitude of the rate change with the parameter. Under the conditions of current and voltage, it is the rate of removing organic matter and heavy metals from wastewater; the exponent 1.2 means that the removal effect is not a simple linear relationship, but slightly amplifies the influence of current and voltage to ensure the removal of pollutants. After electrolysis, the wastewater enters the defluorination stage, and fluoride ions are removed by ion exchange resin. The formula for the change of fluoride ion concentration in the exchange resin treatment is:

[0055]

[0056] Among them, C F- (t) represents the fluoride ion concentration in the wastewater at time t; 10 represents the initial fluoride ion concentration, that is, the fluoride ion concentration in the wastewater at the beginning of the defluorination treatment is 10 mg / L; e represents the base of the natural logarithm, which is approximately equal to 2.718 and is used to calculate exponential decay; -0.05 represents the exponential decay coefficient, which represents the rate at which the fluoride ion concentration decreases naturally; the negative sign represents that the concentration decreases over time; the integral symbol, represents the cumulative change from time 0 to t, 0.02 represents the adsorption rate coefficient, which represents the efficiency of removing fluoride ions. The larger the value, the faster the removal rate; 10-C F- (t) represents the current concentration difference of the remaining fluoride ions, indicating the amount of fluoride ions that need to be removed; 1+e ∧ {-0.1(t-20)} represents the function used to smooth the adsorption process, where -0.1 controls the influence of time variation; t-20 means that the time center of the adsorption process is around 20 minutes; 10·e -0.05t Represents the natural decay of fluoride ion concentration; Indicates how fluoride ions are dynamically adsorbed during the treatment process. As time t passes, this item indicates the amount by which the fluoride ion concentration is gradually reduced at a certain adsorption rate. By considering the adsorption rate of fluoride ions removed by the ion exchange resin, the difference in the current fluoride ion concentration, and the adjustment of the defluorination rate by time, the change trend of the fluoride ion concentration during this period of time is calculated. When the fluoride ion concentration threshold is reached, when the fluoride ion content drops to the set threshold of 1 mg / L, the system automatically switches to the recycling mode;

[0057] Among them, the condition for judging the change of fluoride ion concentration is to monitor the removal process of fluoride ions in wastewater, ensure that the concentration gradually decreases during the treatment process and reaches the set threshold of 1mg / L. Through this formula, the natural attenuation of fluoride ions and the removal effect of resin adsorption can be analyzed to optimize the treatment process. When the fluoride ion concentration reaches the set threshold, the system will automatically switch to the recycling mode to ensure the high efficiency and stability of wastewater treatment.

[0058] By using fluoride ion selective electrodes, heavy metal sensors and pH sensors to monitor key indicators of wastewater in real time, a monitoring data set containing the following data is generated:

[0059] Fluoride ion concentration C F- , pH value, indicating the acidity and alkalinity of wastewater; heavy metal concentration C metal , input the monitoring data set into the decision tree model for judgment; each node of the decision tree model represents a feature, for example, fluoride ion concentration, and each branch makes a decision based on whether the feature value reaches the threshold until it reaches the leaf node to output the judgment result. In order to correct and monitor the data in real time, we can use its electrolysis reaction rate and fluoride ion concentration changes to judge the fluoride ion concentration, pH value and heavy metal content monitored by the sensor in real time. The monitoring data set is used as a decision tree model to determine whether the wastewater meets the recycling standards. Secondly, when the monitoring data fluctuates or differs greatly from the historical data, the above formula is combined for correction to ensure the accuracy of the judgment. The fluoride ion concentration formula is used to predict the expected changes of fluoride ions at different time points, and the sensor data is corrected to avoid error accumulation;

[0060] Furthermore, by monitoring whether the fluoride ion concentration, pH value and heavy metal content in real time meet the wastewater recycling standards, the wastewater treatment effect can be judged in real time by using the decision tree model and the electrolysis reaction rate formula, as well as the fluoride ion concentration change formula for correction, to ensure the accuracy of the data. When the monitoring data fluctuates or differs greatly from the historical data, the system can combine these formulas for correction to correct the sensor data, avoid error accumulation, and ensure the continuous stability and efficient operation of wastewater treatment.

[0061] Fluoride ion concentration, pH value and heavy metal content were input into the decision tree model. F- , pH value, heavy metal concentration C metal , forming a training set of monitoring data; assuming that the data set contains 100 data, use 5-fold cross validation, that is, divide the data set into 5 subsets, each subset contains 20 data, and the subsets can be randomly divided to ensure that different types of data in each subset are evenly distributed. The first verification:

[0062] Use subset 1 as the validation set, and subsets 2, 3, 4, and 5 as the training sets. Train the decision tree model on these 80 data points to learn how to determine whether the fluoride ion concentration, pH value, and heavy metal concentration meet the recycling standards. Use the 20 data points in subset 1 to verify the accuracy of the model and obtain the first verification result.

[0063] Second verification:

[0064] Use subset 2 as the validation set, subset 1, subset 3, subset 4, and subset 5 as the training set, and perform validation on 20 data in subset 2 to obtain the second validation result, which is used as the training model; repeat the above steps, continue to use each subset as the validation set in turn, and use the remaining subsets as the training set, for a total of 5 iterations. Record the performance of the model on the validation set, the accuracy, and the error rate for each iteration, calculate the average performance, and average the results of the 5 validations to obtain the model index; for example, if the accuracy of the 5 validations is 85%, 88%, 86%, 87%, and 89%, respectively, the average accuracy of the model is: The monitoring data set is divided into multiple subsets for K-fold cross-validation. Different subsets are used each time to train and validate the model. In each iteration, the model is trained with the training set and evaluated with the validation set. The decision tree model determines the accuracy of fluoride ion concentration, pH value, and heavy metal concentration. The stability of the model is determined based on the average result of cross-validation. When the performance of a certain value is significantly lower than the average, the model parameters need to be adjusted or a more complex model needs to be used. The judgment accuracy of the model can be improved through cross-validation to reduce the misjudgment rate.

[0065] Furthermore, the fluoride ion concentration, pH value and heavy metal concentration conditions are judged to ensure that these key indicators meet the standards for wastewater recycling and reuse. By inputting these data into the decision tree model and performing cross-validation, the accuracy and stability of the model can be verified, which ensures that the system can accurately judge whether the wastewater meets the reuse standards in real-time monitoring, thereby maintaining an efficient and stable wastewater treatment process;

[0066] Furthermore, the accuracy and stability of the decision tree model on fluoride ion concentration, pH value and heavy metal concentration data were verified. By using 5-fold cross-validation, the data set was divided into different subsets for multiple training and validation, and the average accuracy of the model was calculated. If the model performs consistently in each validation, it means that the model has good generalization ability and stability, and can thus reliably determine whether the wastewater treatment meets the recycling standards.

[0067] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A recycling and reuse process for photovoltaic fluorine-containing wastewater, characterized in that: The following steps are involved: The pH value of fluoride-containing wastewater is adjusted by a multi-stage adjustment method; Activated carbon or diatomaceous earth is used as a filter medium to remove particulate impurities and suspended solids, so that the solid content in the wastewater is reduced to a set threshold value; after the solid suspended solid content in the wastewater is controlled below the set suspended solid content threshold value through the filter medium, alumina and zeolite are used to remove residual organic matter and trace heavy metal ions in the wastewater to obtain primary filtered wastewater; The primary filtered wastewater is subjected to oxidation or reduction decomposition of the residual organic matter and heavy metal ions in the wastewater through electrolysis reaction, and the fluoride ion concentration of the wastewater after electrochemical treatment is monitored. When the concentration drops to the set fluoride ion concentration threshold, the defluorination stage is entered; In the defluorination stage, selective ion exchange resin is added to the wastewater after electrochemical treatment to remove fluoride ions in a targeted manner. After the wastewater is treated by ion exchange, when the fluoride ion content drops to the set fluoride ion content threshold, it enters the recycling mode; The fluoride ion concentration, pH value and heavy metal content of the wastewater are monitored by fluoride ion selective electrodes, heavy metal sensors and pH sensors, and a monitoring data set is generated. The monitoring data set is used as the input value of the decision tree model, and the threshold corresponding to the monitoring data set forms a tree structure. Each node represents a feature for judgment, and each branch represents a decision made according to the threshold of the feature. The accuracy of the model is verified by cross-validation. The constructed decision tree is applied to the monitoring of fluoride ion concentration, pH value and heavy metal content data. The rules set by the decision tree are used to judge whether the recycling standards are met, and when the fluoride ion concentration drops to the concentration threshold set by the system, it is switched to the recycling mode.

2. A recycling and reuse treatment process for photovoltaic fluorine-containing wastewater according to claim 1, characterized in that: The multi-stage regulation of fluoride-containing wastewater includes: The pH value of the wastewater is adjusted to a set range, wherein an alkaline or acidic regulator is first added to the fluoride-containing wastewater, and a secondary pH adjustment is performed in the adjusted wastewater, wherein an alkaline or acidic regulator is added to control the pH value within a set range of 6.5 to 7.5 to ensure that fluoride ions are removed from the wastewater during the filtration process.

3. A recycling and reuse treatment process for photovoltaic fluorine-containing wastewater according to claim 2, characterized in that: When wastewater enters the filter medium, if the suspended solids concentration exceeds the set filter threshold, the filtration efficiency will decrease, affecting the normal operation of the electrolysis unit. The particle swarm optimization model is used to adjust the ratio of activated carbon to diatomaceous earth to ensure that the filtration efficiency is close to the set filter threshold and reduce the impact of residual impurities on subsequent treatment steps. The suspended solids filtration formula is: Where E represents the filtration efficiency, ranging from 0 to 1; C8 represents the concentration of suspended solids in the wastewater; when C8 = 50, the formula Equal to 1; e represents the base of the natural logarithm, which is used to calculate exponential decay. This formula obtains the value of the filtration efficiency E by performing exponential decay processing on the change of the suspended matter concentration C8.

4. A recycling and reuse treatment process for photovoltaic fluorine-containing wastewater according to claim 3, characterized in that: The proportion of the filter media is dynamically adjusted based on the particle swarm optimization model to ensure that the filtration efficiency E remains within the set filtration threshold. The particle swarm optimization model can effectively find the medium proportion at different concentrations, thereby increasing the filtration efficiency. The filtration efficiency E judgment formula is as follows: v i =w·v i +c1·r1·(p best -x i )+c2·r2·(g best -x i ) Among them, v i represents the speed of particle i, the speed of proportional change; w represents the inertia coefficient, which controls the degree to which the particle retains the current speed; c1 and c2 represent learning factors, which determine the movement weight of the particle towards the individual extreme value and the global extreme value; r1 and r2 represent random numbers, which increase the randomness of the search; x i =x i +v i represents the new position of particle i; both the new particle speed and the new position need to be evaluated to see if they meet the target value set by a process, that is, within the specified ratio range, the current and initial positions are combined in the speed evaluation, and the weight of the random distribution consideration is set according to the specified ratio. Each particle records the position corresponding to the specified speed in the decision-making process, that is, the individual local position p best At the same time, the position reached by all particles in the group that meets the process setting range is recorded as the global position g best ; When the particle's fitness P ≥ 0.95 and the global position reaches g that meets the process settings best When , it is considered that the optimal combination is found and the iteration stops.

5. A recycling and reuse treatment process for photovoltaic fluorine-containing wastewater according to claim 4, characterized in that: In the electrolysis and defluorination stage of primary filtered wastewater treatment, heavy metals and organic matter in the primary filtered wastewater are removed by combining current and voltage. The electrolysis reaction rate formula is: Where R represents the decomposition rate, which indicates the speed of pollutant removal; I·V represents the current and voltage, where I represents the current intensity during the electrolysis process and V represents the voltage applied during the electrolysis process; C metal and C organic Indicates the concentration of heavy metals and organic matter in wastewater through a combination of current and voltage; after electrolysis, the wastewater enters the defluorination stage, and fluoride ions are removed through ion exchange resin. The formula for the change of fluoride ion concentration during the treatment of exchange resin is: in, represents the fluoride ion concentration in the wastewater at time t; e represents the base of the natural logarithm, represents the cumulative change from time 0 to t; Indicates the current concentration difference of the remaining fluoride ions, indicating the amount of fluoride ions that need to be removed; 1+e ∧ {−0.1(t−20)} represents the function used to smooth the adsorption process, where −0.1 controls the effect of time variation; It indicates how fluoride ions are dynamically adsorbed during the treatment process. As time t passes, this item represents the amount by which the fluoride ion concentration is gradually reduced at a certain adsorption rate. The defluorination rate is adjusted by considering the adsorption rate of ion exchange resin to remove fluoride ions, the difference in current fluoride ion concentrations, and time.

6. A recycling and reuse treatment process for photovoltaic fluorine-containing wastewater according to claim 5, characterized in that: By using fluoride ion selective electrodes, heavy metal sensors and pH sensors to monitor key indicators of wastewater in real time, a monitoring data set containing the following data is generated: Fluoride ion concentration pH value, indicating the acidity and alkalinity of wastewater; heavy metal concentration C metal , input the monitoring data set into the decision tree model for judgment; each node of the decision tree model represents a feature, and each branch makes a decision based on whether the feature value reaches the threshold, until it reaches the leaf node to output the judgment result; Among them, the electrolysis reaction rate and the change of fluoride ion concentration are used to determine the fluoride ion concentration, pH value and heavy metal content monitored by the sensor.

7. A recycling and reuse treatment process for photovoltaic fluorine-containing wastewater according to claim 6, characterized in that: Fluoride ion concentration, pH value and heavy metal content were input into the decision tree model. pH value, heavy metal concentration C metal , forming a training set of monitoring data; assuming that the data set contains 100 data, use 5-fold cross validation, that is, divide the data set into 5 subsets, each subset contains 20 data, and the subsets can be randomly divided to ensure that different types of data in each subset are evenly distributed. The first verification: Use subset 1 as the validation set, and subsets 2, 3, 4, and 5 as the training sets. Train the decision tree model on these 80 data points to learn how to determine whether the fluoride ion concentration, pH value, and heavy metal concentration meet the recycling standards. Use the 20 data points in subset 1 to verify the accuracy of the model and obtain the first verification result. Second verification: Use subset 2 as the validation set, subset 1, subset 3, subset 4, and subset 5 as the training set, and verify on the 20 data in subset 2 to obtain the second validation result, and use the second validation result as the training model; repeat the above steps, continue to use each subset as the validation set in turn, and use the remaining subsets as the training set, for a total of 5 iterations. Record the performance of the model on the validation set, the accuracy, and the error rate for each iteration, calculate the average performance, and average the results of the 5 validations to obtain the model indicators; the decision tree model's judgment accuracy on fluoride ion concentration, pH value, and heavy metal concentration, judge whether the model is stable based on the average result of cross-validation. When the performance of a certain value is significantly lower than the average, it is necessary to adjust the model parameters or use a more complex model, and improve the judgment accuracy of the model through cross-validation to reduce the misjudgment rate.

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