Electrochemical wastewater treatment method and system

Abstract

The application discloses an electrochemical wastewater treatment method and system, which comprises the following steps: collecting the concentration of pollutants in wastewater and the state parameters of an electrochemical reactor in real time through a sensor array; analyzing the change trend of the pollutants based on the data, and determining the optimized target value of the generation proportion of active substances in combination with historical data; classifying the state of the water body by using a trained classification model, and judging whether dynamic adjustment is needed; when needed, calculating the state parameter correction amount according to the deviation between the target value and the actual value, and adjusting the operation of the reactor; then, monitoring the proportion of active substances and the degradation efficiency after adjustment, and dynamically updating the optimization target according to the proportion of active substances and the degradation efficiency and the trend of the pollutants, so that continuous self-adaptive regulation and control are realized. The application realizes intelligent closed-loop control of reaction parameters, significantly improves the stability of the treatment efficiency, and effectively reduces the energy consumption and the risk of secondary pollution.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wastewater treatment, in particular to an electrochemical wastewater treatment method and system. BACKGROUND

[0002] In the field of water treatment technology, developing efficient and environmentally friendly treatment methods is of great significance to protect water resources safety and ecological balance. With the acceleration of industrialization and urbanization, the types of pollutants in water bodies are becoming increasingly complex. Traditional physical, chemical or biological treatment methods often appear to be inadequate in the face of such complex water bodies with multiple pollutants coexisting, and it is difficult to stably and efficiently achieve the purification goal. Therefore, electrochemical water treatment technology, as a new emerging advanced oxidation technology, has attracted widespread attention. It generates active free radicals with strong oxidation in situ through electrochemical reaction to degrade pollutants, providing a new path to solve the problem of refractory organic wastewater.

[0003] However, the existing electrochemical water treatment technology has exposed a core bottleneck in its popularization and application: the treatment process lacks intelligent adaptability. Most existing systems run with fixed parameters after setting the reaction conditions (such as voltage and current), and cannot respond to the dynamic fluctuations of the water quality and the composition of pollutants in the water body. When the type, concentration or proportion of pollutants in the water body changes, the fixed electrical parameters cannot ensure that the active free radicals are always maintained at the optimal generation ratio, resulting in unstable treatment efficiency. Sometimes, in order to ensure that the effluent meets the standard, a conservative strategy of excessive energy addition (such as increasing the current) must be adopted, which not only causes waste of energy consumption, but also may cause the risk of secondary pollution due to side reactions. SUMMARY

[0004] Therefore, the technical problem to be solved by the present application is to overcome the problem that the existing electrochemical wastewater treatment process cannot accurately control the generation ratio of active substances to adapt to the dynamically changing water quality, resulting in unstable treatment efficiency, high energy consumption and the risk of secondary pollution. The present application provides an electrochemical wastewater treatment method, which can realize intelligent closed-loop control of reaction parameters, thereby significantly improving the stability of treatment efficiency and reducing energy consumption and the risk of secondary pollution.

[0005] To solve the above technical problems, the present application provides an electrochemical wastewater treatment method, comprising the following steps:

[0006] Real-time collection of multiple pollutant concentration data in the wastewater to be treated by a sensor array, and acquisition of state parameters of the electrochemical reactor;

[0007] Based on the pollutant concentration data, the change trend of the pollutant concentration is analyzed, and based on the change trend and historical treatment data, the optimal target value of the generation ratio of active substances is determined;

[0008] The current pollutant concentration data and the state parameters of the electrochemical reactor are input into the trained classification model to classify the current water body state, and it is judged whether the state parameters need to be dynamically adjusted according to the classification result;

[0009] When it is judged that dynamic adjustment is needed, the correction amount of the state parameters of the electrochemical reactor is calculated according to the deviation between the optimization target value and the actual value of the active substance generation ratio;

[0010] The state parameters of the electrochemical reactor are adjusted according to the correction amount, and the generation ratio of the active substance and the pollutant degradation efficiency after adjustment are monitored; the optimization target value of the active substance generation ratio is dynamically updated according to the monitored generation ratio of the active substance and the pollutant degradation efficiency and the change trend of the pollutant concentration, so as to realize continuous self-adaptive regulation and control of the electrochemical reaction process.

[0011] In an embodiment of the present application, the sensor array is periodically sent a synchronous acquisition instruction by the system clock, triggering various sensors deployed at different positions to sample data at the same time base. The original output signals of all sensors obtained by the synchronous acquisition instruction are packaged together with their sensor identifiers and time stamps to form an original data packet.

[0012] In an embodiment of the present application, the optimization target value of the active substance generation ratio is determined based on the change trend and historical processing data, including the following steps:

[0013] The pollutant concentration data collected in each successful processing period in the history, arranged in time sequence, the corresponding electrochemical reactor state parameters, and the final pollutant degradation efficiency are packaged together to form a historical case unit;

[0014] The real-time pollutant concentration data collected currently is divided into continuous real-time trend segments according to a preset time window, and a standardized real-time trend feature vector is generated by feature extraction for each segment;

[0015] The real-time trend feature vector is matched with the initial trend segment feature vectors of all historical case units in the dynamic case library within the same length to calculate the similarity, and the top K historical cases with the highest similarity are selected as the preferred case set;

[0016] Each historical case in the preferred case set is analyzed to find the performance transition stage with the fastest pollutant degradation efficiency improvement from the process after the initial trend segment, and the active substance generation ratio value corresponding to the stage is recorded; based on the K active substance generation ratio values, the optimization target value of the current required active substance generation ratio is generated by weighted average calculation.

[0017] In an embodiment of the present application, the extracted features include: the mean of the pollutant concentration of the trend segment, the linear fitting slope in the preset time interval, and the fluctuation variance of the concentration sequence.

[0018] In an embodiment of the present application, the construction and training of the classification model includes the following steps:

[0019] Based on limited historical water quality data, a basic classification model is pre-trained as an initial model;

[0020] The real-time collected data packet is labeled with a category and stored in association with the pollutant degradation efficiency improvement rate corresponding to the moment, forming a dynamic evolution case library;

[0021] Periodically check the state category of the recent output of the basic classification model, and the matching degree of the actual pollutant degradation efficiency improvement rate that occurs thereafter, and when the matching degree is continuously low, trigger model updating;

[0022] From the dynamic evolution case library, filter out successful cases similar to the current water quality features and with high degradation efficiency improvement rate, and use these cases to incrementally train the current model to generate an optimized classification model.

[0023] In an embodiment of the present application, the current water body state is classified, and whether dynamic adjustment of the state parameters is needed is determined according to the classification result, including the following steps:

[0024] Input the real-time data into the classification model in dynamic evolution to obtain a preliminary state category;

[0025] According to the preliminary state category, retrieve the adjustment strategy adopted by the case that finally achieved a high degradation efficiency improvement rate under the same category from the dynamic evolution case library;

[0026] Integrate the preliminary state category and the retrieved historical successful strategy to generate a specific control instruction, and the control instruction includes maintaining observation, preventive fine-tuning, regular adjustment, or emergency intervention;

[0027] Execute the control instruction, and store the actual degradation efficiency improvement rate after this adjustment as an effect feedback in the dynamic evolution case library for subsequent model updating.

[0028] In an embodiment of the present application, the state parameters of the electrochemical reactor include working voltage, working current, anode electrode potential, cathode electrode potential, and electrolyte temperature.

[0029] In an embodiment of the present application, according to the deviation between the optimization target value and the actual value of the active substance generation ratio, the correction amount of the state parameters of the electrochemical reactor is calculated, specifically including the following steps:

[0030] Based on the pre-established electrochemical reaction knowledge base, a dynamic control priority is assigned to the state parameters; wherein the parameters that have the most direct and rapid influence ability on the active substance generation ratio are given the highest priority;

[0031] First, only for the highest priority parameters, a first correction amount is calculated according to the deviation, and the first correction amount is taken as the leading correction instruction;

[0032] After applying the leading correction instruction, the secondary parameter change value caused by the leading correction instruction is estimated; then, for the secondary parameters, a compensation correction amount opposite to the estimated change value direction is calculated to offset the indirect influence brought by the leading correction instruction adjustment, and the compensation correction amount is taken as the auxiliary correction instruction;

[0033] The leading correction instruction and the auxiliary correction instruction are synthesized to generate the final collaborative correction amount for adjusting multiple state parameters at the same time.

[0034] In an embodiment of the present application, the active substance includes hydroxyl radicals and sulfate radicals, and the active substance generation ratio is the molar concentration ratio of hydroxyl radicals to sulfate radicals.

[0035] To solve the above technical problems, the present application also provides an electrochemical wastewater treatment system for realizing the above method, comprising:

[0036] A sensor array is configured to collect real-time data of the concentration of multiple pollutants in the wastewater to be treated and obtain the state parameters of the electrochemical reactor;

[0037] A data processing and control unit is in communication connection with the sensor array, and comprises: a trend analysis and target setting module configured to analyze a change trend of the pollutant concentration based on the pollutant concentration data, and determine an optimized target value of the active substance generation ratio based on the change trend and stored historical treatment data; a state diagnosis and decision module configured to input the current pollutant concentration data and the state parameters of the electrochemical reactor into a trained classification model, classify the current water body state, and determine whether the state parameters of the electrochemical reactor need to be dynamically adjusted according to the classification result; a control quantity calculation module configured to calculate a correction quantity of the state parameters of the electrochemical reactor according to a deviation between the optimized target value and an actual value of the current active substance generation ratio when it is determined that the state parameters need to be dynamically adjusted; an instruction generation and output module configured to generate a control instruction according to the correction quantity to adjust the state parameters of the electrochemical reactor; and an adaptive learning and updating module configured to dynamically update the optimized target value of the active substance generation ratio according to the monitored active substance generation ratio, pollutant degradation efficiency and change trend of the pollutant concentration, so as to realize continuous adaptive regulation and control of the electrochemical reaction process.

[0038] An electrochemical reactor is in communication connection with the data processing and control unit, and is configured to receive the control instruction and adjust the operating state thereof to perform wastewater treatment.

[0039] The above technical solution of the present application has the following advantages over the prior art:

[0040] The electrochemical wastewater treatment method of the present application constructs an intelligent closed-loop control system capable of real-time sensing and autonomous decision making driven by data, upgrades the traditional electrochemical wastewater static treatment to a dynamic adaptive process, dynamically detects the change trend of the pollutants, dynamically matches the generation ratio of the active substance (oxidation-reduction free radicals), and regulates and controls the state parameters of the electrochemical reactor, so that the stability and reliability of the treatment process are fundamentally improved, the reaction efficiency is fundamentally improved, and the risk of secondary pollution caused by insufficient or over-reaction is reduced. Even in the case of fluctuation of the influent water quality, the effluent water quality can also meet the standard. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to make the content of the present application more easily understood, the present application will be further described in detail below according to specific embodiments of the present application and in conjunction with the accompanying drawings, in which:

[0042] Figure 1 is a step flow chart of the electrochemical wastewater treatment method of the present application;

[0043] Figure 2 is a step flow chart of determining the optimized target value of the active substance generation ratio of the present application;

[0044] Figure 3 is a step flow chart of the classification model construction and training of the present application;

[0045] Figure 4 is a step flow chart of the water body state classification and dynamic adjustment decision of the present application;

[0046] Figure 5 is a step flow chart of the state parameter correction amount calculation of the present application;

[0047] Figure 6 is a structural framework diagram of the electrochemical wastewater treatment system of the present application. DETAILED DESCRIPTION

[0048] The present application will be further described below in conjunction with the drawings and specific embodiments, so that those skilled in the art can better understand the present application and implement it, but the embodiments are not limiting to the present application.

[0049] Referring to Figure 1 The present application discloses an electrochemical wastewater treatment method. First, a sensor array is used to continuously monitor the water body and the reactor, real-time collection of the concentration data of various pollutants in the wastewater to be treated, and acquisition of the state parameters of the electrochemical reactor, so that the small fluctuations of the pollutant concentration and the subtle changes of the reaction state can be captured in real time, and a solid data foundation is provided for subsequent intelligent decision-making.

[0050] In the present embodiment, the sensor array is deployed at the water inlet or the reaction zone of the electrochemical reactor, and can include but is not limited to a pH sensor, an oxidation-reduction potential (ORP) sensor, a specific ion selective electrode (such as for detecting chloride ions and heavy metal ions), and an ultraviolet-visible (UV-Vis) spectrum online analyzer, etc., for real-time acquisition of the concentration data of various pollutants (such as organic dyes, phenol, cyanide, etc.). At the same time, through the control system of the electrochemical reactor itself or additional sensors, the state parameters thereof are acquired, including but not limited to working voltage, working current, electrode potential, electrolyte temperature, etc.

[0051] Subsequently, the change trend of the pollutants is further analyzed, and a more forward-looking and reasonable active substance generation ratio target value is predicted and set in combination with historical treatment experience (historical data); this step has a certain predictability, so that it can prepare for the upcoming water quality changes in advance.

[0052] Specifically, the collected real-time pollutant concentration data is compared with the previous time or even historical data, and the instantaneous change rate and medium-term change trend of the pollutant concentration are analyzed through methods such as moving average method, exponential smoothing method or simple difference calculation; combined with the historical processing data stored in the database (which records the ideal generation proportion of active substances such as ·OH and SO4·⁻ under different water quality conditions to achieve the best degradation efficiency), the pre-set algorithm model is used to determine the optimization target value of the generation proportion of active substances in the current and future short time, which aims to pre-match the change of water quality, so that the system has foresight.

[0053] Next, the trained classification model is used to accurately classify and diagnose the current complex water body state, intelligently determine whether parameter adjustment is really needed, and avoid unnecessary response in insignificant minor fluctuations, thereby improving the stability and energy efficiency of the system.

[0054] Specifically, the trained classification model, such as a model using support vector machine (SVM) or decision tree algorithm, is pre-trained using a large amount of labeled historical running data (data samples including pollutant concentration, state parameters and corresponding artificial labels of “need adjustment” or “no need adjustment”). The current data vector obtained is input into the model, and the model outputs a classification result, such as “water quality is stable, no need to adjust”, “pollutant type mutation, need to adjust” or “concentration fluctuation exceeds limit, need to adjust”.

[0055] Once it is confirmed that adjustment is needed, the deviation between the preset target and the actual state is calculated, and the correction amount of the state parameters of the electrochemical reactor is accurately calculated through the feedback control mechanism.

[0056] In this embodiment, the actual value of the current active substance generation proportion is obtained through an electrochemical workstation or indirect calculation (for example, by reverse calculation through current efficiency and reaction rate model), and the deviation between the actual value and the determined optimization target value is calculated. A classic feedback control algorithm (such as a PID controller algorithm) can be used to calculate the accurate correction amount of the electrochemical reactor working voltage and / or current, electrode potential, electrolyte temperature required to eliminate the deviation. For example, if the current generation proportion of ·OH is lower than the target value, the controller may calculate that the anode potential needs to be raised by a certain value (such as 0.5V).

[0057] Finally, after the adjustment is performed, the effect is immediately monitored, i.e., the actual change in the generation proportion of active substances and the degradation efficiency of pollutants, and the optimization target value of the next round of control is dynamically updated in combination with the continuous change trend of the pollutant concentration, which will serve as the benchmark for the next round of control. This means that the adjustment of the state parameter is not a fixed value, but a dynamic value that is constantly self-optimized according to the treatment effect and the inflow situation. Through the "monitoring-analysis-decision-execution-feedback-update" cycle, the entire electrochemical water treatment process forms an intelligent closed-loop control system that can actively track water quality changes and constantly self-optimize, achieving continuous self-adaptive regulation of the entire electrochemical reaction process and continuously ensuring efficient, stable and economic treatment effect.

[0058] Specifically, in the present embodiment, the data acquisition process of the sensor array is realized through a synchronous packaging mechanism to ensure the unity and integrity of the data basis. The process starts with the system clock periodically issuing a synchronous acquisition instruction, which can instantly trigger various sensors (such as pH sensors, ORP sensors, UV spectrometers, etc.) deployed at different positions such as the reactor inlet, the reaction zone and the outlet to perform data sampling at the same time base. This synchronization eliminates the problem of data time misalignment caused by the response time difference of the sensors from the source, laying a solid foundation for subsequent construction of data that accurately reflects the same instantaneous working condition.

[0059] After completing the synchronous sampling, the data packaging phase begins. The original output signals of all sensors obtained under this synchronous acquisition instruction are packaged together with their corresponding sensor identifiers and accurate timestamps to form a structured raw data packet. By attaching an identity identifier (sensor identifier) and time information (timestamp) to each data point, this step realizes the embedding of data traceability. This means that if any abnormal data is found in any subsequent processing step, it can quickly and accurately trace back to the specific sensor and the time of data generation.

[0060] The synchronous packaging mechanism used in the present embodiment integrates multiple asynchronous signals that may be disorganized and difficult to associate into standardized data packets that are time-aligned and have clear sources. This high-quality data basis makes the information relied on by subsequent intelligent decision-making processes such as trend analysis and state classification consistent and traceable, thereby fundamentally improving the accuracy of the system's perception of reality and the reliability of its decision-making, and avoiding control misjudgments caused by asynchronous and chaotic data.

[0061] Referring to Figure 2As shown, in the embodiment, the process of determining the active substance generation ratio optimization target value is realized by an intelligent decision mechanism based on case-based reasoning and performance transition analysis, including the following steps: construction of a knowledge base, the pollutant concentration data collected in time sequence in each successful treatment period in history, the corresponding electrochemical reactor state parameters and the final compliance pollutant degradation efficiency are packaged and stored together as structured historical case units, these case units constitute the knowledge base for the system to cope with complex water quality changes, the value lies in the abstract processing experience is converted into data template which can be directly called and compared.

[0062] When the target value decision is needed, first, the real-time collected pollutant concentration data is divided into continuous real-time trend segments according to the preset time window, then multi-dimensional feature extraction is performed on each segment, specifically, the features extracted include: synchronously calculating the mean value of pollutant concentration of the trend segment to reflect the overall pollution load, analyzing the linear fitting slope in the preset time interval to quantify the change rate and direction, and calculating the fluctuation variance of the concentration sequence to evaluate the water quality stability, these three features together constitute a standardized real-time trend feature vector which can fully describe the current water quality dynamics, among them, the mean value carries the load information, the slope reveals the evolution energy, and the variance transmits the key information of the system disturbance degree.

[0063] After obtaining the feature vector, start the case matching process, calculate the similarity between the real-time trend feature vector and the initial trend segment feature vector of each historical case unit in the dynamic case library within the same length, form an optimal case set by screening the top K historical cases with the highest similarity, in essence, the closest historical successful experience to the current working condition is quickly retrieved from the knowledge base, and this retrieval mechanism based on similarity principle ensures that the subsequent decision is based on the effective strategy verified by practice.

[0064] Next, the optimal case set is deeply mined, for each historical case in it, the performance transition stage with the fastest pollutant degradation efficiency is accurately located from the process after the initial trend segment, and the active substance generation ratio value corresponding to this stage is recorded. This step is not simply copying the final parameters of the historical cases, but focusing on the key stage with the highest performance in the treatment process, so as to extract the optimization parameters that can best stimulate the processing potential of the system. Finally, based on the K active substance generation ratio values from the performance transition stage, the weighted average calculation is performed to generate the optimization target value required at present. This method of integrating the core parameters of the optimal treatment stage of multiple successful cases not only ensures the diversity of the decision basis, but also ensures the advancement of the target value by focusing on the best practice, so as to formulate the optimization strategy most suitable for the current water quality conditions based on the most essential part of the historical experience.

[0065] In this embodiment, the discrete historical operation data is converted into decision-making knowledge with guiding significance by combining case-based reasoning with performance transition analysis. The reliability of the strategy is ensured through similar working condition matching, and the efficiency of the strategy is ensured through focusing on the performance transition stage. Finally, a target value setting mechanism with experience inheritance and performance optimization is formed, which provides a stable and advanced control benchmark for the entire adaptive regulation.

[0066] Referring to Figure 3 As shown in the drawings, the embodiment further discloses a method for constructing and training a classification model. A dynamic evolution mechanism is adopted to ensure that the model can continuously adapt to changes in water quality characteristics and maintain high-precision classification ability. The mechanism starts with a basic classification model pre-trained based on limited historical water quality data. Although the model has preliminary classification ability, its knowledge system is not perfect. To overcome this limitation, the data packets collected in real time are continuously labeled with categories based on water quality characteristics during operation, and these labels are associated with the actual pollutant degradation efficiency improvement rate that occurs after the corresponding time of the data packet. A dynamic evolution case library is formed, which not only records the instantaneous state of the water body, but more importantly, establishes a causal relationship between the state and the subsequent treatment effect, providing valuable feedback information for the evolution of the model.

[0067] Further, by checking the matching degree between the state category output by the classification model recently and the actual pollutant degradation efficiency improvement rate that occurs subsequently, the trend of decline in model decision effectiveness can be detected. When the matching degree is consistently low, it indicates that the current classification standard of the model cannot accurately predict the actual treatment effect. At this time, the model updating process is triggered. In the updating stage, successful cases similar to the current main water quality characteristics and finally achieving high degradation efficiency improvement rate are selected from the dynamic evolution case library. These excellent cases verified by practice constitute the most valuable training samples. By incrementally training the current model with these high-quality samples, the ability to accurately identify and classify the current main water quality conditions can be targetedly strengthened on the basis of preserving existing knowledge, thereby generating a new generation of classification model that is optimized and more suitable for the actual operating environment.

[0068] The dynamic evolution training mechanism disclosed in this embodiment successfully transforms the classification model from a static knowledge carrier into an organism with autonomous learning ability, realizes the continuous monitoring of the model performance through the establishment of a "decision-effect" feedback loop; through incremental learning based on successful cases, the correctness and efficiency of the model evolution direction are ensured. This not only effectively solves the problem of model performance degradation caused by the time-varying characteristics of water quality, but also enables the classification model to accumulate experience continuously and gradually grow into a model highly consistent with a specific processing scenario, thereby providing more and more reliable state judgment basis for the entire adaptive control process.

[0069] Referring to Figure 4 As shown, based on the above classification model, the classification of the current water body state is realized by a hierarchical control mechanism through case reasoning, including the following steps: inputting the real-time collected data into the classification model in dynamic evolution, obtaining a preliminary state category about the current water body state. This preliminary classification result is a rapid and preliminary diagnosis of the current working condition, which constitutes the starting point of the decision-making process. Taking this preliminary state category as an index, further intelligent retrieval is performed from the dynamic evolution case library to find the specific adjustment strategies adopted by those successful cases that have achieved high degradation efficiency improvement rate.

[0070] In this embodiment, the current preliminary classification result is deeply integrated with the effective operation experience verified by practice, and the most successful processing strategy under similar diagnostic conclusions in history is mined to provide rich experience reference for the current decision-making, so that the decision-making process is not only based on the current instantaneous state, but also integrates the essence of historical wisdom. On this basis, the decision synthesis stage is entered, and the severity of the working condition indicated by the preliminary state category and the best practice path revealed by the retrieved historical successful strategies are comprehensively considered to generate the current specific and hierarchical control instructions. These instructions are finely divided into different levels such as maintenance observation, preventive fine-tuning, regular adjustment or emergency intervention, thereby realizing the precise matching of control strength and working condition demand.

[0071] Finally, the hierarchical control instructions are executed, and the actual observed degradation efficiency improvement rate after this adjustment is stored back to the dynamic evolution case library as the key effect feedback, which is associated with the decision-making basis and operation instructions of this time. This closed-loop feedback step makes every adjustment action and its actual effect into new data, not only enriching the content of the case library, but more importantly, providing a direct basis for evaluating the effectiveness of the subsequent decision-making of the classification model.

[0072] The hierarchical control mechanism of the embodiment combines intelligent classification and case-based reasoning organically through the coherent process of preliminary diagnosis-experience retrieval-decision synthesis-effect feedback, and has the ability to learn autonomously from the success and failure of historical decisions through continuous experience accumulation and feedback, ultimately improving the decision quality, adaptability and overall processing efficiency.

[0073] Referring to Figure 5 In the embodiment, the process of regulating the state parameters of the electrochemical reactor is further disclosed, which is realized through a synergistic control mechanism of priority layering and coupling compensation. The state parameters of the electrochemical reactor include working voltage, working current, anode electrode potential, cathode electrode potential and electrolyte temperature, which together constitute a multi-dimensional control vector describing the working state of the reactor. Among them, the working voltage and current are directly related to the energy supply level of the reaction system as the external driving input of the system; the anode and cathode electrode potentials reflect the thermodynamic tendency at the electrode / solution interface, directly regulating the electrochemical reaction path and rate; and the electrolyte temperature, as an important environmental variable, has a significant impact on the reaction process through affecting ion mobility, reaction rate constant, etc. These parameters are interrelated and coupled, and together determine the generation efficiency and proportion of active substances.

[0074] When calculating the correction amount, all parameters are not adjusted indiscriminately, but a dynamic regulation priority is assigned to these state parameters based on a pre-established electrochemical reaction knowledge base. According to electrochemical principles and experimental data, the knowledge base determines the sensitivity and response speed of different parameters on the proportion of target active substances, and assigns the highest priority to the parameters that have the most direct and rapid influence ability (usually electrode potential or working voltage). This priority allocation strategy ensures that the main contradiction can be grasped and the most effective control variable can be adjusted first.

[0075] Subsequently, the hierarchical control stage is entered: first, only for the parameter with the highest priority, the first correction amount is calculated according to the deviation between the optimization target value and the actual value, forming the leading correction instruction. This step aims to quickly eliminate most of the deviations through the most effective control channel. However, due to the internal coupling relationship, the adjustment of the leading parameter often triggers a chain reaction of secondary parameters (such as adjusting the voltage leading to changes in current and temperature). To solve this problem, after applying the leading correction instruction, the changes in secondary parameters caused by the adjustment behavior are estimated, and a compensation correction amount opposite in direction to the estimated change value is calculated for these secondary parameters, forming an auxiliary correction instruction. This compensation mechanism can prospectively offset the coupling interference between parameters, avoiding control oscillation or overshoot caused by internal coupling.

[0076] Finally, the primary correction instruction and the auxiliary correction instruction are synthesized to generate the final collaborative correction quantity which can adjust multiple state parameters simultaneously. This collaborative control mechanism based on priority sorting and coupling compensation ensures the efficiency of the adjustment action through priority division, and can quickly respond to changes. Through the coupling compensation mechanism, the stability and precision of multivariable control are significantly improved, effectively avoiding the mutual interference between parameters. Finally, through instruction synthesis, the collaborative control of the electrochemical reactor is realized, which can realize precise and stable control of the active substance generation ratio in a complex multi-parameter coupling environment, providing a reliable technical guarantee for the entire adaptive control process.

[0077] It should be noted that the core of the electrochemical wastewater treatment process is to accurately regulate the generation ratio of specific active substances. The method specifically limits the active substances participating in the synergistic effect to hydroxyl radicals (·OH) and sulfate radicals (SO4·⁻), and takes the molar concentration ratio of hydroxyl radicals to sulfate radicals as the quantitative indicator of the active substance generation ratio. Among them: hydroxyl radicals are a non-selective oxidant with extremely high oxidation potential, which can quickly attack and degrade various organic pollutants; while sulfate radicals have a slightly lower oxidation potential, but have a longer half-life in water, can diffuse outside the reaction core area, and can oxidize and degrade pollutants more persistently, and the selective reaction with the background matrix of the water body also shows different degradation paths.

[0078] Taking the molar concentration ratio of the two radicals as the control target is essentially to realize the directional allocation of oxidation capacity by regulating the competitive reaction path in the electrochemical reaction process. On the anode surface, by controlling the potential, current density and electrolyte composition (especially the concentration of sulfate ions), the relative rates of the two parallel reactions of water molecule discharge to generate ·OH and sulfate ion oxidation to generate SO4·⁻ can be affected. When treating wastewater containing complex components or high-stability pollutants, adjusting this ratio means that the ratio between instantaneous strong oxidation capacity and persistent slow-release oxidation capacity can be flexibly adjusted, so as to construct the most effective oxidation attack combination according to the molecular structure and reaction characteristics of different pollutants.

[0079] The technical solution explicitly takes the molar concentration ratio of ·OH / SO4·⁻ as the core controlled variable, significantly improving the pertinence and adaptability of the treatment process. Compared with traditional methods that only control the total oxidant concentration or the concentration of a single free radical, the proportional control can intelligently and dynamically adjust the generation spectrum of free radicals according to the real-time monitored types and concentrations of pollutants. For example, in the face of easily degradable pollutants, the proportion of ·OH can be increased to achieve rapid purification; when dealing with refractory pollutants or needing to penetrate mass transfer limitations, the proportion of SO4·⁻ can be appropriately increased to utilize its longer lifetime for deep oxidation. This mechanism based on free radical synergy and proportional regulation fundamentally enhances the core ability of electrochemical water treatment technology to cope with complex and variable water quality, enabling it to maintain high degradation efficiency while avoiding ineffective consumption of oxidants through precise control, thereby achieving the overall optimization of treatment efficiency and operating economy.

[0080] Reference Figure 6 To implement the above method, the embodiment discloses an electrochemical wastewater treatment system for implementing the above method, comprising:

[0081] A sensor array configured to collect real-time data of concentrations of multiple pollutants in the wastewater to be treated and obtain state parameters of the electrochemical reactor;

[0082] A data processing and control unit in communication connection with the sensor array, the data processing and control unit comprising: a trend analysis and target setting module configured to analyze the change trend of the concentrations of the pollutants based on the data of the concentrations of the pollutants, and determine an optimized target value of the generation proportion of active substances based on the change trend and stored historical treatment data; a state diagnosis and decision module configured to input the current data of the concentrations of the pollutants and the state parameters of the electrochemical reactor into a trained classification model, classify the current state of the water body, and determine whether to dynamically adjust the state parameters of the electrochemical reactor according to the classification result; a control amount calculation module configured to calculate a correction amount of the state parameters of the electrochemical reactor according to the deviation between the optimized target value and the actual value of the current generation proportion of active substances when it is determined to dynamically adjust; an instruction generation and output module configured to generate a control instruction according to the correction amount to adjust the state parameters of the electrochemical reactor; and an adaptive learning and updating module configured to dynamically update the optimized target value of the generation proportion of active substances according to the monitored generation proportion of active substances, the degradation efficiency of pollutants and the change trend of the concentrations of pollutants, so as to realize continuous adaptive regulation of the electrochemical reaction process.

[0083] An electrochemical reactor in communication connection with the data processing and control unit, configured to receive the control instruction and adjust the operating state thereof to perform wastewater treatment.

[0084] Obviously, the above embodiments are merely example for clearly illustrating, and are not limitation to the embodiments. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, all the embodiments are not required to be enumerated, and the obvious changes or variations derived from the above are still within the protection scope of the present application.

Claims

1. An electrochemical wastewater treatment method, characterized in that, Includes the following steps: The sensor array collects real-time data on the concentration of various pollutants in the wastewater to be treated and obtains the state parameters of the electrochemical reactor. Based on pollutant concentration data, the changing trends of pollutant concentrations are analyzed, and based on these trends and historical treatment data, the optimized target value for the proportion of active substances generated is determined. This includes: packaging and storing pollutant concentration data collected in each successful historical treatment cycle, arranged in a time series, along with the corresponding electrochemical reactor state parameters and the final pollutant degradation efficiency, into a historical case unit; dividing the currently collected real-time pollutant concentration data into continuous real-time trend segments according to a preset time window, and extracting features from each segment to generate a standardized real-time trend feature vector; and comparing the real-time trend feature vector with all historical cases in the dynamic case library. Example units perform similarity matching calculations on the feature vectors of initial trend segments within the same time period, and select the top K historical cases with the highest similarity as the preferred case set; analyze each historical case in the preferred case set, and identify the efficiency transition stage with the fastest improvement in pollutant degradation efficiency after its initial trend segment, and record the corresponding active substance generation ratio value; based on the K active substance generation ratio values, calculate the optimized target value of the current required active substance generation ratio through weighted average; the active substances include hydroxyl radicals and sulfate radicals, and the active substance generation ratio is the ratio of the molar concentrations of hydroxyl radicals to sulfate radicals; The current pollutant concentration data and the state parameters of the electrochemical reactor are input into the trained classification model to classify the current water state, and the classification results determine whether the state parameters need to be dynamically adjusted. When it is determined that dynamic adjustment is required, the correction amount of the state parameters of the electrochemical reactor is calculated based on the deviation between the optimized target value and the actual value of the current active substance generation ratio. The state parameters of the electrochemical reactor are adjusted according to the correction amount, and the generation ratio of active substances and the pollutant degradation efficiency are monitored after the adjustment. Based on the monitored trends of the generation ratio of active substances, the pollutant degradation efficiency, and the pollutant concentration, the optimized target value of the generation ratio of active substances is dynamically updated to achieve continuous adaptive control of the electrochemical reaction process.

2. The electrochemical wastewater treatment method according to claim 1, characterized in that: The sensor array is periodically issued a synchronous acquisition command by the system clock, which triggers various sensors deployed in different locations to sample data at the same time base point. The original output signals of all sensors obtained under the synchronous acquisition command, together with their sensor identifiers and timestamps, are packaged together to form an original data packet.

3. The electrochemical wastewater treatment method according to claim 1, characterized in that: The extracted features include: the mean pollutant concentration of the trend segment, the slope of the linear fit within the preset time interval, and the variance of the concentration sequence.

4. The electrochemical wastewater treatment method according to claim 1, characterized in that: The construction and training of the classification model includes the following steps: Based on limited historical water quality data, a basic classification model is pre-trained as the initial model. The real-time collected data packets are tagged with categories and stored in association with the corresponding pollutant degradation efficiency improvement rate at that moment, forming a dynamic evolution case library; Regularly check the matching degree between the state category recently output by the basic classification model and the actual improvement rate of pollutant degradation efficiency. When the matching degree is consistently low, trigger a model update. Successful cases that are similar to the current water quality characteristics and ultimately achieved a high degradation efficiency improvement rate are selected from the dynamic evolution case library. These cases are used to incrementally train the current model and generate an optimized classification model.

5. The electrochemical wastewater treatment method according to claim 4, characterized in that: The current water body state is classified, and based on the classification results, it is determined whether dynamic adjustment of state parameters is necessary. This includes the following steps: Real-time data is input into a dynamically evolving classification model to obtain a preliminary state category; Based on the initial state category, retrieve the adjustment strategies adopted by historical cases of the same category that ultimately achieved a high degradation efficiency improvement rate from the dynamic evolution case library; Based on the preliminary status category and the retrieved historical successful strategies, a specific control instruction is generated for the current situation. The control instruction includes maintaining observation, preventive fine-tuning, routine adjustment, or emergency intervention. The control command is executed, and the actual degradation efficiency improvement rate after this adjustment is used as effect feedback. It is then stored back in the dynamic evolution case library for subsequent model updates.

6. The electrochemical wastewater treatment method according to claim 1, characterized in that: The state parameters of the electrochemical reactor include operating voltage, operating current, anode electrode potential, cathode electrode potential, and electrolyte temperature.

7. The electrochemical wastewater treatment method according to claim 6, characterized in that: Based on the deviation between the optimized target value and the actual value of the current active substance generation ratio, the correction amount of the state parameters of the electrochemical reactor is calculated, specifically including the following steps: Based on a pre-established electrochemical reaction knowledge base, a dynamic control priority is assigned to the state parameters; among them, the parameters that have the most direct and rapid impact on the proportion of active substances generated are given the highest priority. First, for the highest priority parameter only, calculate its first correction amount based on the deviation, and use this first correction amount as the dominant correction instruction; After applying the dominant correction command, the change in secondary parameters caused by the dominant correction command is estimated; then, for the secondary parameters, a compensation correction amount is calculated in the opposite direction to the estimated change value to offset the indirect effect of the adjustment brought about by the dominant correction command, and this compensation correction amount is used as an auxiliary correction command. The dominant correction instruction and the auxiliary correction instruction are combined to generate the final coordinated correction amount for adjusting multiple state parameters simultaneously.

8. An electrochemical wastewater treatment system for implementing the method described in any one of claims 1 to 7, characterized in that: include: A sensor array is configured to collect real-time data on the concentration of various pollutants in the wastewater to be treated and to acquire the state parameters of the electrochemical reactor. A data processing and control unit, communicatively connected to the sensor array, includes: a trend analysis and target setting module: based on the pollutant concentration data, analyzing the changing trend of pollutant concentration, and determining the optimized target value of the active substance generation ratio based on the changing trend and stored historical processing data; a state diagnosis and decision module: inputting the current pollutant concentration data and the state parameters of the electrochemical reactor into a trained classification model, classifying the current water state, and determining whether the state parameters of the electrochemical reactor need to be dynamically adjusted based on the classification results; a control quantity calculation module: when it is determined that dynamic adjustment is needed, calculating the correction amount of the state parameters of the electrochemical reactor based on the deviation between the optimized target value and the actual value of the current active substance generation ratio; an instruction generation and output module: generating control instructions based on the correction amount to adjust the state parameters of the electrochemical reactor; and an adaptive learning and updating module: dynamically updating the optimized target value of the active substance generation ratio based on the monitored changes in the active substance generation ratio, pollutant degradation efficiency, and pollutant concentration, to achieve continuous adaptive control of the electrochemical reaction process. An electrochemical reactor, communicatively connected to the data processing and control unit, is configured to receive the control commands and adjust its operating state to perform wastewater treatment.

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