Circulation coupling treatment system for industrial park water treatment station

Abstract

The invention relates to the technical field of industrial wastewater treatment, in particular to a circulation coupling treatment system for a water treatment station in an industrial park, which comprises a reaction monitoring module, a dominant analysis module, a feedback analysis module and a feedback regulation module. Determining a sliding window of the corresponding monitoring data; constructing a ternary array as a processing path array, and calculating a dominant coefficient at any moment by using the change trend and amplitude level of data points in a sliding window corresponding to the monitoring data at any moment, so as to serve as a dominant coefficient of each processing path in the processing path array; and performing weighted fusion on all the dominant coefficients in the processing path array so as to determine a feedback coefficient at any moment, and adjusting the current density by using the feedback coefficient at the current moment. The treatment efficiency of the organic wastewater is improved.

Description

Technical Field

[0001] This invention relates to the field of industrial wastewater treatment technology, specifically to a circulating coupled treatment system for a water treatment station in an industrial park. Background Technology

[0002] Industrial park wastewater is complex in composition and highly toxic, rich in recalcitrant organic matter (such as phenol and dyes), ammonia nitrogen, and heavy metal ions. While traditional biological treatment technologies (such as activated sludge processes) can reduce some BOD5 and COD, they suffer from low mineralization rates for stubborn pollutants and are easily affected by water quality fluctuations, making it difficult for effluent to consistently meet standards. Especially under the "dual carbon" target (carbon reduction and emission reduction), enterprises urgently need efficient and low-carbon treatment solutions to achieve resource recovery and zero emissions. Currently, electrochemical methods have become a research hotspot due to their multi-path synergistic mechanism. This technology integrates three core processes—electrochemical oxidation, electrocoagulation, and electroflotation—to improve wastewater treatment efficiency.

[0003] However, electrochemical processes are highly sensitive to the pH value of wastewater, which fluctuates dynamically. Existing systems rely on external acid or alkali additions for pH adjustment, leading to delayed human intervention. When pH is out of control, oxidation may be incomplete or flocculation may fail. Furthermore, the dosing process generates secondary pollution such as sulfates, increasing sludge disposal costs. Therefore, they fail to effectively control directional transformation (e.g., selective partial oxidation or complete mineralization) and are difficult to match with different wastewater characteristics. These problems highlight the rigid parameter adjustment of traditional electrochemical systems, their inability to dynamically respond to water quality changes, and their limitations on the synergistic effect of multi-path systems. Summary of the Invention

[0004] This invention provides a circulating coupling treatment system for water treatment plants in industrial parks to solve existing problems.

[0005] The circulating coupling treatment system for an industrial park water treatment station of the present invention adopts the following technical solution: One embodiment of the present invention provides a circulating coupled treatment system for a water treatment station in an industrial park, the system comprising the following modules: The reaction monitoring module is used to monitor the wastewater treatment tank through sensors and collect monitoring data; The dominant analysis module is used to determine the sliding window of the corresponding monitoring data based on the changing pattern characteristics of each monitoring data; construct a ternary array as the processing path array, and calculate the dominant coefficient at the time of the data point change trend and amplitude level in the corresponding sliding window of the monitoring data at any time, so as to serve as the dominant coefficient of each processing path in the processing path array. The feedback analysis module is used to perform weighted fusion of all dominant coefficients in the processing path array to determine the feedback coefficient at any given time. The feedback adjustment module is used to adjust the current density using the feedback coefficient at the current moment.

[0006] Optionally, the specific method for determining the sliding window of the corresponding monitoring data based on the changing patterns of each monitoring data point includes: The monitoring data is decomposed into time series, and the basic window length is determined based on the periodic characteristics of all monitoring data in the time series decomposition results. The length coefficient of the monitoring data is calculated by using the data changes within a basic window length based on the time-series decomposition results of arbitrary monitoring data. The final window length of the monitoring data is obtained by adjusting the base window length using a length coefficient.

[0007] Optionally, the specific method for performing time-series decomposition on the monitoring data and determining the basic window length based on the periodic characteristics of all monitoring data in the time-series decomposition results includes: The STL time series decomposition algorithm is used to decompose each monitoring data point to obtain the trend term, seasonal term, and residual term for each monitoring data point. The least squares method combined with trigonometric functions is used to perform curve fitting on the seasonal term of any monitoring data point. The curve fitting result is used as the seasonal function of the corresponding monitoring data point. The period value in the seasonal function of the monitoring data point is obtained as the period parameter of the monitoring data point. The reciprocal of the average period parameter of all monitoring data points is obtained as the basic window length.

[0008] Optionally, the method for calculating the length coefficient of the monitoring data by utilizing the data changes within the basic window length based on the time-series decomposition results of arbitrary monitoring data includes: Any monitoring data is designated as the target monitoring data. A sliding window with a window length equal to the base window length is constructed as the base window. Any time point in the target monitoring data is designated as the rightmost data point in the base window, thus making the base window the base window for that time. The data segments of the trend, seasonal, and residual terms of the target monitoring data within the base window are designated as the trend segment, seasonal segment, and residual segment at that time, respectively. The sequence of residuals formed between each data point in the seasonal segment of the target monitoring data and the corresponding seasonal function is obtained as the seasonal residual segment. Based on the changing trend of the trend segment of the target monitoring data at that time and the data dispersion of the seasonal and residual segments, the length coefficient of the target monitoring data at that time is calculated.

[0009] Optionally, the specific method for calculating the length coefficient of the target monitoring data at the specified time based on the changing trend of the trend segment of the target monitoring data at the specified time and the data dispersion of the seasonal residual segment and the residual segment is as follows: The seasonal residual segment and the coefficient of variation of all data points in the residual segment of the target monitoring data at the stated time are obtained respectively, and are used as the seasonal variation value and decomposed variation value of the target monitoring data at the stated time. The slope of the fitted straight line corresponding to all data points in the trend segment of the target monitoring data at the stated time is obtained by least squares method, and the absolute value of the slope is used as the trend value of the target monitoring data at the stated time. The length coefficient of the target monitoring data at the stated time is calculated based on the seasonal variation value, decomposed variation value and trend value of the target monitoring data.

[0010] Optionally, the construction of the ternary array as a processing path array, utilizing the changing trend and magnitude of data points within the corresponding sliding window at any given time, calculates the dominant coefficient at that time, which serves as the dominant coefficient for each processing path in the processing path array. The specific method includes: Electrochemical oxidation path, electrocoagulation path and electroflotation path are defined and collectively referred to as treatment paths. A ternary array is constructed, where each element is the dominant coefficient of the corresponding treatment path, thus obtaining the treatment path array. By utilizing the changing trends and magnitudes of data points within a sliding window corresponding to the final window length at any given time, the dominant factors of the electrochemical oxidation path, electrocoagulation path, and electroflotation path at that time are calculated respectively. The dominant factors of all processing paths at the given time are subjected to maximum-min normalization to obtain the dominant coefficients of each processing path at the given time.

[0011] Optionally, the monitoring data includes ORP time-series data, turbidity time-series data, and hydrogen concentration time-series data.

[0012] Optionally, the method for calculating the dominant factors of the electrochemical oxidation path, electrocoagulation path, and electroflotation path at any given time by utilizing the changing trends and amplitude levels of data points within a sliding window corresponding to the final window length of the monitoring data at any given time includes the following specific methods: The average slope of all data points within the final window for any monitoring data is taken as the local slope at the corresponding time in the final window; the average value of all data points within the final window for any monitoring data is taken as the local level value for the film and television course; the product of the local slope and the local level value of the monitoring data at the stated time is taken as the degree value at the corresponding time; the degree value of the ORP time series data at the stated time is taken as the dominant factor of the electrochemical oxidation path; the degree value of the turbidity time series data at the stated time is taken as the dominant factor of the electrocoagulation path; and the hydrogen concentration time series data is taken as the dominant factor of the electroflotation path.

[0013] Optionally, the specific method for weighted fusion of all dominant coefficients in the processing path array to determine the feedback coefficient at any given time includes: Set path weight coefficients and combine them with the dominant coefficients of all processing paths at any given time to calculate the collaborative demand index at that time. Preset adjustment parameters and calculate the feedback coefficient at the specified time based on the collaborative demand index at the specified time.

[0014] Optionally, the method for calculating the collaborative demand index at a given time by setting path weight coefficients and combining them with the dominant coefficients of all processing paths at any given time for weighted fusion includes: Set a path weight coefficient for each processing path. For any given time, multiply the path weight coefficient of any processing path by the dominant coefficient of the processing path as the coordination parameter of the processing path. The sum of the coordination parameters of all processing paths is used as the coordination requirement index at that time.

[0015] The beneficial effects of the technical solution of this invention are as follows: By collaboratively monitoring key parameters in the organic wastewater treatment process using multiple sensors, and combining time-series data analysis and path feature identification, dynamic tracking of the three main reaction pathways—electrochemical oxidation, electrocoagulation, and electroflotation—is achieved. Adaptive sliding windows and dominant coefficient calculations are used to accurately determine the dominant mechanism of each treatment stage, and current regulation requirements are quantified based on path weights and collaborative needs. Regulation commands are generated through feedback analysis to dynamically optimize the current density, ensuring that the electrode reactions always match the actual needs of the wastewater treatment process. This method improves the responsiveness and energy efficiency of the treatment system under complex water quality conditions, enhances the targeting and stability of pollutant removal, and effectively improves the treatment efficiency of organic wastewater. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a structural block diagram of a circulating coupling treatment system for an industrial park water treatment station according to the present invention. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a circulating coupled treatment system for an industrial park water treatment station proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] The following describes in detail, with reference to the accompanying drawings, a specific scheme for a circulating coupling treatment system for an industrial park water treatment station provided by the present invention.

[0021] Please see Figure 1 The diagram illustrates a structural block diagram of a circulating coupled treatment system for an industrial park water treatment station according to an embodiment of the present invention. The system includes the following modules: The reaction monitoring module 101 is used to monitor the wastewater treatment pond through sensors and collect monitoring data.

[0022] It should be noted that during the treatment of industrial wastewater using electrochemical technology, the electric field causes complex physicochemical reactions among different ions in the wastewater treatment tank. Specifically, oxidation-reduction reactions lead to the formation of flocculents or colloids in the wastewater, while microbubbles composed of hydrogen gas are generated at the cathode. These microbubbles are characterized by high dispersion and small size, causing them to adhere to the surface of hydrophobic substances such as flocs, oil droplets, and suspended particles formed during their ascent. Since the density of the bubble-particle aggregates formed by these microbubbles and hydrophobic substances is less than that of water, they quickly float to the surface to form a scum layer, thereby removing colloids, oils, and light suspended solids from the wastewater treatment tank. Therefore, in order to control the rate of different ion generation in the wastewater tank by adjusting the pH value during this process, thereby improving the treatment effect of organic wastewater, this embodiment of the invention chooses to solve the existing technical problems by analyzing the data of organic wastewater to obtain methods for regulating current density.

[0023] To implement the circulating coupled treatment system for an industrial park water treatment station proposed in this embodiment, it is first necessary to monitor the wastewater treatment pond using sensors and collect monitoring data. The specific process is as follows: The reaction monitoring module 101 includes a sensor array unit and a data preprocessing unit. The sensor array unit consists of multiple high-precision sensors, all integrated and installed at predetermined locations within the wastewater treatment tank (preferably near the electrode area to ensure the representativeness of the monitoring data), and is equipped with a programmable sampling frequency controller. The data preprocessing unit is integrated into the central processing system and configured to perform data noise reduction and standardization operations. Specific component configurations are as follows: Oxidation-reduction potential (ORP) sensor: used to collect ORP time-series data of organic wastewater in real time. Its measurement range is -1500 to +1500 mV, accuracy is ±1 mV, and sampling frequency is set to 1 to 5 Hz. Turbidity sensor: It uses the ratio method for real-time measurement to eliminate interference from light source fluctuations. It is used to collect time-series turbidity data of organic wastewater. Its measurement range is 0 to 1000 NTU, accuracy is ±2%, and sampling frequency is set to 1 to 5 Hz. Electrochemical hydrogen sensor: used to collect real-time time-series data of hydrogen concentration in organic wastewater. Its measurement range is 0 to 1000 ppm, accuracy is ±0.1 ppm, and sampling frequency is set to 1 to 5 Hz.

[0024] During the wastewater treatment process, when the electrodes are energized (the electrode operating voltage range is 2-30 V DC), the sensor array unit synchronously starts real-time data acquisition, generating multi-channel time-series data streams as raw monitoring data.

[0025] It should be noted that the pH value in the collected pH time series data directly reflects the acidity or alkalinity of the organic wastewater and is the core variable for feedback control; the ORP parameter in the ORP time series data is used to indicate the generation efficiency and oxidation state of -OH free radicals in the organic wastewater; the turbidity in the turbidity time series data is used to reflect the electrocoagulation effect in the electrochemical treatment process; the hydrogen concentration data reflects the electroflotation efficiency; and the hydrogen-floc mixing state directly affects the formation of scum.

[0026] The operation steps of the reaction monitoring module 101 are as follows to ensure the integrity of data acquisition and the scientific nature of preprocessing: Step S101: During the operation of the wastewater treatment tank, the sensor array unit continuously collects time-series data according to a preset sampling frequency.

[0027] As an example, the process of a sensor array unit acquiring monitoring data is as follows: The ORP time-series data is acquired through the ORP sensor, which quantitatively indicates the generation efficiency of hydroxyl radicals (·OH) and the redox state of the wastewater. Turbidity time-series data is obtained by combining the turbidity sensor with the ratio method. This data reflects the formation effect of flocs, colloids and suspended particles during the electrocoagulation process. The electrochemical hydrogen sensor acquires time-series data on hydrogen concentration, which monitors the concentration changes of tiny hydrogen bubbles generated in the cathode region and is used to quantify the dynamics of bubble-particle aggregation in the air flotation effect.

[0028] The monitoring data is time-series data and stored in a time-series format with timestamp accuracy at the millisecond level to ensure strict synchronization between the data and the processing process.

[0029] Step S102: The data preprocessing unit performs preprocessing on the collected raw monitoring data.

[0030] As an example, the process of preprocessing raw monitoring data is as follows: First, a Gaussian kernel function is applied to perform convolution filtering on all time-series data to suppress sensor noise and environmental interference, and output noise-reduced monitoring data.

[0031] Then, the Z-score normalization method is used to normalize the noise reduction data, eliminate the dimensional differences between different parameters, and generate a standardized monitoring dataset.

[0032] Thus, several types of monitoring data were obtained through the above methods.

[0033] The dominant analysis module 102 is used to determine the sliding window of the corresponding monitoring data according to the change pattern characteristics of each monitoring data; construct a ternary array as a processing path array; and calculate the dominant coefficient at the time by using the change trend and amplitude level of the data points in the corresponding sliding window of the monitoring data at any time, so as to serve as the dominant coefficient of each processing path in the processing path array.

[0034] It should be noted that during the treatment of organic wastewater by electrochemical methods, the degree of influence on different components in the organic wastewater exhibits path-dependent characteristics. Specifically, the redox reactions exhibited at both the anode and cathode, as well as the characteristics of electrocoagulation and electroflotation, result in varying contents and proportions of different ionic components in the electrochemical process. Therefore, under different pathways, the dominant pathways in wastewater treatment ponds differ. Thus, this embodiment of the invention selects to use the collected monitoring data for path analysis to obtain the dominant coefficients of different treatment pathways in the organic wastewater treatment process.

[0035] Specifically, in step S201, the sliding window for the corresponding monitoring data is determined based on the changing patterns and characteristics of each monitoring data.

[0036] It should be noted that the electrochemical treatment of organic wastewater integrates direct oxidation (electron transfer), indirect oxidation (active chlorine / free radicals), and electrocoagulation-flotation—a triple mechanism—through multi-path synergistic degradation, overcoming the limitations of traditional single oxidation technologies. For each treatment pathway, electrochemical oxidation enables organic pollutants and ammonia nitrogen to be oxidized and decomposed by strong oxidants directly or indirectly generated at the anode (reducing BOD5, COD, and ammonia nitrogen); electrocoagulation allows the generated metal hydroxide flocculant to adsorb and trap pollutants, forming flocs that settle to the bottom of the reactor by gravity. Electroflotation causes tiny hydrogen bubbles generated at the cathode to adhere to the pollutant flocs, causing them to float to the surface and form scum. It is evident that the physicochemical processes in different treatment pathways proceed at different rates. Therefore, when analyzing changes in monitoring data over a short period, directly analyzing all monitoring data through a window of the same size is clearly insufficient to effectively obtain the dominant coefficients of the treatment pathways in subsequent processes. Therefore, this embodiment of the invention selects to analyze the variation patterns of different monitoring data separately to determine the sliding window for subsequent calculation of the dominant coefficients, thereby improving the accuracy of the dominant coefficients in describing the dominance of the treatment pathways.

[0037] As a preferred embodiment, the method for determining the sliding window of the corresponding monitoring data based on the changing patterns of each monitoring data point includes: First, the monitoring data is decomposed into time series, and the basic window length is determined based on the periodic characteristics of all monitoring data in the time series decomposition results.

[0038] As an optional embodiment, the specific method for obtaining the basic window length is as follows: Each monitoring data point is decomposed using the STL time series decomposition algorithm to obtain the trend term, seasonal term, and residual term for each monitoring data point. The seasonal term of any monitoring data point is then curve-fitted using the least squares method combined with trigonometric functions. The curve-fitting result is used as the seasonal function of the corresponding monitoring data point. The period value in the seasonal function of the monitoring data point is obtained as the period parameter of the monitoring data point. The reciprocal of the average period parameter of all monitoring data points is obtained as the basic window length.

[0039] Then, the length coefficient of the monitoring data is calculated by using the data changes within the basic window length based on the time-series decomposition results of any monitoring data.

[0040] As an optional embodiment, the specific method for obtaining the length coefficient of the monitoring data is as follows: Any monitoring data is designated as the target monitoring data; a sliding window with a window length equal to the base window length is constructed as the base window; any moment in the target monitoring data is taken as the rightmost data point of the base window, thus making the base window the base window for that moment; the data segments of the trend term, seasonal term, and residual term of the target monitoring data within the base window are respectively designated as the trend segment, seasonal segment, and residual segment at that moment; the sequence formed by the residuals between each data point in the seasonal segment of the target monitoring data and the corresponding seasonal function is obtained as the seasonal residual segment; based on the changing trend of the trend segment of the target monitoring data at that moment and the data dispersion of the seasonal residual segment and the residual segment, the length coefficient of the target monitoring data at that moment is calculated.

[0041] As an optional embodiment, the specific method for calculating the length coefficient of the target monitoring data at the specified time, based on the trend of the trend segment of the target monitoring data at the specified time and the data dispersion of the seasonal residual segment and the residual segment, includes: obtaining the coefficient of variation of the target monitoring data at the specified time for the seasonal residual segment and all data points in the residual segment, respectively, as the seasonal variation value and decomposed variation value of the target monitoring data at the specified time; obtaining the slope corresponding to the fitted straight line of all data points in the trend segment of the target monitoring data at the specified time using the least squares method, and taking the absolute value of the slope as the trend value of the target monitoring data at the specified time; and calculating the length coefficient of the target monitoring data at the specified time based on the seasonal variation value, decomposed variation value, and trend value of the target monitoring data.

[0042] It should be noted that electrochemical wastewater treatment is a highly nonlinear dynamic process with varying time scales for each physicochemical pathway. For example, electrochemical oxidation is rapid, while flocculation and flotation are relatively slow. Using a uniform, fixed-length analysis window for feature extraction of all monitoring signals would result in smoothing distortion of rapidly changing signals and incomplete characterization of slowly changing signals, thus affecting the accuracy of dominant pathway identification. Therefore, the analysis window must be dynamically adjusted based on the dynamic characteristics of each monitoring variable to accurately reflect its inherent evolutionary patterns. To this end, this invention introduces a length coefficient to dynamically adjust the sliding window length for each monitoring data point. This coefficient is based on the STL time-series decomposition results, integrating the rate of change of the trend term, the volatility of the fitting residuals of the seasonal term, and the dispersion of the residuals, and is calculated through normalized weighting. The window length dynamically expands and contracts with the length coefficient, automatically shortening during periods of severe signal fluctuation to improve response speed and extending during periods of stability to enhance stability. This overcomes the limitations of traditional fixed-window analysis, enabling personalized and refined processing of multi-source heterogeneous monitoring data. Especially when facing shock loads or sudden changes in water quality, the system can still maintain a high level of state identification capability. The dominant factors obtained in this way are more representative, effectively supporting the reliability of subsequent feedback control decisions and improving the robustness and intelligence level of the entire cyclic coupling system.

[0043] As an optional embodiment, the specific calculation method for the length coefficient of the monitoring data for any monitoring data is as follows: in, Indicates time The length coefficient of the monitoring data described below; Indicates time The following are trend values ​​of the monitoring data; Indicates time The seasonal variation values ​​of the monitoring data described below; Indicates time The decomposition variation values ​​of the monitoring data described below; Represents a linear normalization function; This indicates the preset magnification parameter.

[0044] It should be noted that the multiplier parameter preset to 2 in this embodiment of the invention is based on experience and can be adjusted according to actual conditions. This embodiment of the invention does not impose any specific limitations.

[0045] Finally, the base window length is adjusted using a length coefficient to obtain the final window length of the monitoring data.

[0046] As an optional embodiment, the method of adjusting the basic window length using a length coefficient to obtain the final window length of the monitoring data includes: multiplying the length coefficient by the basic window length, and using the product as the final window length of the monitoring data at the corresponding time.

[0047] Step S202: Construct a ternary array as a processing path array. Utilize the changing trend and magnitude of data points within the corresponding sliding window at any given time to calculate the dominant coefficient at that time, which will serve as the dominant coefficient for each processing path in the processing path array.

[0048] As a preferred embodiment, the specific method for obtaining the dominant coefficient is as follows: First, electrochemical oxidation path, electrocoagulation path, and electroflotation path are defined and collectively referred to as processing paths. A ternary array is constructed, where each element is the dominant coefficient of the corresponding processing path, thus obtaining the processing path array.

[0049] Then, by utilizing the changing trends and magnitudes of data points within a sliding window corresponding to the final window length at any given time, the dominant factors of the electrochemical oxidation path, electrocoagulation path, and electroflotation path at that time are calculated respectively.

[0050] Finally, the dominant factors of all processing paths at the given time are subjected to max-min normalization to obtain the dominant coefficients of each processing path at the given time.

[0051] As an optional embodiment, the method for calculating the dominant factors of the electrochemical oxidation path, electrocoagulation path, and electro-floatation path at any given time by utilizing the changing trends and amplitude levels of data points within a sliding window corresponding to the final window length of the monitoring data at any given time includes the following specific acquisition methods: taking the average slope of all data points within the final window as the local slope at the corresponding time of the final window; taking the average value of all data points within the final window as the local level value; taking the product of the local slope and the local level value at the given time as the degree value at the corresponding time; taking the degree value of the ORP time series data at the given time as the dominant factor of the electrochemical oxidation path; taking the degree value of the turbidity time series data at the given time as the dominant factor of the electrocoagulation path; and taking the hydrogen concentration time series data as the dominant factor of the electro-floatation path.

[0052] It should be noted that the dominance coefficient describes the degree to which the corresponding treatment path is dominant in the organic wastewater treatment process at time t. A higher dominance coefficient indicates that the treatment path is dominant, meaning further adjustments to the electrode current parameters are needed to accelerate the process and ensure the dominant path quickly meets the current pollution source treatment requirements. For example, if the wastewater treatment tank already contains a large amount of flocs, it indicates that the degree of electrocoagulation is sufficiently high. To further treat the wastewater, electroflotation is needed to bring the floating flocs to the surface. To promote rapid and effective electroflotation, the generation rate of tiny hydrogen bubbles at the cathode needs to be increased to quickly attach the hydrogen bubbles to the floc surface and cause them to float. Regarding the calculation method of the dominance coefficient, the degree of ORP time-series data is used as the dominant factor of the electrochemical oxidation path, reflecting the free radical generation and oxidation capacity; turbidity changes correspond to the electrocoagulation path, reflecting the floc formation trend; and dynamic hydrogen concentration characterizes the generation potential of bubble-particle aggregates in the electroflotation path. Each dominant factor is calculated by multiplying the local slope and level value of the corresponding monitoring data within an adaptive sliding window, comprehensively reflecting the trend strength and amplitude level of the path at the current moment. This enables refined perception of the multi-path reaction process, giving the system the ability to "perceive, judge, and respond." Compared with traditional single-parameter control methods, this invention can more accurately capture the core driving force of a certain treatment stage, providing a scientific basis for subsequent differentiated current adjustment and significantly improving the response sensitivity and treatment efficiency of the electrochemical system for organic wastewater.

[0053] Thus, the dominant coefficient of any processing path at any time is obtained through the above method.

[0054] The feedback analysis module 103 is used to perform weighted fusion of all dominant coefficients in the processing path array to determine the feedback coefficient at any time.

[0055] It should be noted that although the treatment of organic wastewater is a dynamic system involving multiple pathways, the dominant role of different pathways varies at different times as physical and chemical processes evolve. Furthermore, the current requirements of organic wastewater for the electrochemical process differ depending on the pathway. Therefore, this embodiment of the invention obtains feedback coefficients based on the progress of the corresponding pathways under different degrees of dominance, thereby facilitating subsequent adjustment of the electrode current parameters to further improve the treatment efficiency of organic wastewater.

[0056] Specifically, firstly, a path weight coefficient is set, and then weighted and fused with the dominant coefficients of all processing paths at any given time to calculate the collaborative demand index at that time.

[0057] It should be noted that the electrochemical oxidation pathway has a weight of 0.4 to reflect its core role in COD / BOD5 degradation; the electrocoagulation pathway has a weight of 0.3 to reflect its contribution to suspended solids removal; and the electroflotation pathway has a weight of 0.3 to reflect its efficiency in preventing scum formation. Furthermore, the cumulative weight coefficients of all treatment pathways are 1. The specific weight values ​​can be adjusted within ±0.1 based on the wastewater characteristics, and this embodiment of the invention does not impose specific limitations.

[0058] As an optional embodiment, the method of setting path weight coefficients and combining them with the dominant coefficients of all processing paths at any given time to calculate the collaborative demand index at that time includes: setting a path weight coefficient for each processing path; for any given time, using the product between the path weight coefficient of any processing path and the dominant coefficient of the processing path as the collaborative parameter of the processing path; and using the sum of the collaborative parameters of all processing paths as the collaborative demand index at that time.

[0059] As an optional embodiment, the specific calculation method for the collaborative demand index is as follows: ,in Indicates time The following collaborative demand indicators; These are the weights of the electrochemical oxidation pathway, the electrocoagulation pathway, and the electroflotation pathway, respectively. Representing time respectively The dominant coefficients of the electrochemical oxidation pathway, electrocoagulation pathway, and electroflotation pathway.

[0060] It should be noted that when the collaborative demand index is higher than 0.6, it indicates that the system is in a high-activity processing stage, and the current needs to be increased to maintain the path collaborative efficiency; when it is lower than 0.3, it indicates that the processing process tends to be stable, and the current can be reduced to save energy. By obtaining the collaborative demand index, the overall demand for current regulation by multi-path coupling is specifically quantified.

[0061] Then, preset adjustment parameters are used, and the feedback coefficient at the specified time is calculated based on the collaborative demand index at that time.

[0062] As an optional embodiment, the preset adjustment parameters, and the calculation of the feedback coefficient at the specified time based on the collaborative demand index at the specified time, include the following specific calculation method: in, Indicates time The feedback coefficient below; To adjust the parameters; It is a natural constant; Indicates time The following collaborative demand indicators.

[0063] It should be noted that the preset adjustment parameters are based on experience. The value range is 1.5 to 2.5. In this embodiment of the invention, the value is 2 to balance response speed and stability. In other embodiments, it can be adjusted according to the actual situation. This embodiment of the invention does not impose specific limitations. hour, Then no current boost is needed; when When =1, Then it is close to the maximum adjustment demand; when hour, This indicates a moderate adjustment requirement. Additionally, the parameters... It can be debugged and optimized according to actual conditions. The larger, right The more sensitive it is to changes.

[0064] It should be noted that the feedback coefficient is used to represent the degree of intensity adjustment of the current density of the electrode at the current moment. The larger the value of the feedback coefficient, the higher the demand for intensity enhancement of the current density due to the synergistic effect of multiple processing paths at the current moment, and vice versa.

[0065] Thus, the feedback coefficient at the current moment is obtained through the above method.

[0066] The feedback adjustment module 104 is used to adjust the current density using the feedback coefficient at the current moment.

[0067] It should be noted that in industrial-grade water treatment scenarios, energy saving and high efficiency are always contradictory requirements. On the one hand, sufficiently high current density is needed to drive the coordinated operation of multiple reaction paths; on the other hand, continuous high-load operation can easily lead to problems such as electrode passivation and energy waste. Existing technologies mostly adopt constant current or timed current regulation strategies, lacking closed-loop perception of the actual treatment status, making it difficult to achieve the goal of on-demand energy supply. This invention designs an intelligent current regulation mechanism based on a feedback adjustment module. This module first integrates the dominant coefficients of each path with their preset weights to generate a coordinated demand index characterizing the overall coordinated demand, and then transforms it into a continuously adjustable feedback coefficient through an exponential nonlinear function. Finally, combining the base current density determined by the initial COD and the maximum adjustment range, the target current density is calculated and precisely tracked by a PID controller.

[0068] As an example, the method for adjusting the current density using the feedback coefficient at the current moment is as follows: First, the initial COD concentration of the wastewater in the wastewater treatment tank is obtained, thereby determining the baseline current density. And determine the maximum adjustment range as ,in This is the preset adjustment range parameter.

[0069] As an optional embodiment, the specific calculation method for the base current density is as follows: in, Indicates the base current density; This indicates the initial COD concentration of wastewater in the wastewater treatment tank (unit: ). ).

[0070] It should be noted that this is based on experience and presuppositions. The value range is 20% to 50%, preferably 30%, to avoid electrode polarization or excessive energy consumption. In other embodiments, it can be adjusted according to the actual situation. The embodiments of the present invention do not make specific limitations.

[0071] Then, the target current density is calculated by combining the base current density, the maximum adjustment range, and the feedback coefficient.

[0072] in, Indicates time The target current density is as follows; Indicates the base current density; Indicates the maximum adjustment range; Indicates time The feedback coefficient below.

[0073] Finally, at the moment The actual current density is adjusted to the target current density using a PID controller, wherein the proportional coefficient of the PID controller is set to... Integral coefficient Differential coefficients .

[0074] Using the above method, when the system detects that a certain path is dominant, such as when a large amount of flocs are formed and air flotation needs to be accelerated, the feedback coefficient increases and the current is automatically increased to enhance hydrogen production at the cathode; conversely, when the process is nearing completion, the feedback coefficient decreases and the system automatically reduces load to save energy.

[0075] This concludes the embodiment.

[0076] It should be noted that the embodiments used in this example The model is only used to represent negative correlations and the results of the constraint model output are in Within this range, in specific implementations, other models with the same purpose can be substituted; this embodiment is merely an example. The description will be based on a model, without making specific limitations on it. This refers to the input of the model.

[0077] 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 principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A circulating coupled treatment system for a water treatment station in an industrial park, characterized in that, The system includes the following modules: The reaction monitoring module is used to monitor the wastewater treatment tank through sensors and collect monitoring data; The dominant analysis module is used to determine the sliding window of the corresponding monitoring data based on the changing pattern characteristics of each monitoring data; construct a ternary array as the processing path array, and calculate the dominant coefficient at the time of the data point change trend and amplitude level in the corresponding sliding window of the monitoring data at any time, so as to serve as the dominant coefficient of each processing path in the processing path array. The feedback analysis module is used to perform weighted fusion of all dominant coefficients in the processing path array to determine the feedback coefficient at any given time. The feedback adjustment module is used to adjust the current density using the feedback coefficient at the current moment.

2. The circulating coupled treatment system for an industrial park water treatment station according to claim 1, characterized in that, The method for determining the sliding window for each monitoring data point based on its changing patterns includes the following specific methods: The monitoring data is decomposed into time series, and the basic window length is determined based on the periodic characteristics of all monitoring data in the time series decomposition results. The length coefficient of the monitoring data is calculated by using the data changes within a basic window length based on the time-series decomposition results of arbitrary monitoring data. The final window length of the monitoring data is obtained by adjusting the base window length using a length coefficient.

3. The circulating coupled treatment system for an industrial park water treatment station according to claim 2, characterized in that, The specific method for performing time-series decomposition on the monitoring data and determining the basic window length based on the periodic characteristics of all monitoring data in the time-series decomposition results includes: The STL time series decomposition algorithm is used to decompose each monitoring data point to obtain the trend term, seasonal term, and residual term for each monitoring data point. The least squares method combined with trigonometric functions is used to perform curve fitting on the seasonal term of any monitoring data point. The curve fitting result is used as the seasonal function of the corresponding monitoring data point. The period value in the seasonal function of the monitoring data point is obtained as the period parameter of the monitoring data point. The reciprocal of the average period parameter of all monitoring data points is obtained as the basic window length.

4. The circulating coupled treatment system for an industrial park water treatment station according to claim 2, characterized in that, The method for calculating the length coefficient of the monitoring data by utilizing the time-series decomposition results of arbitrary monitoring data within a basic window length to determine the data changes includes the following specific methods: Record any monitoring data as target monitoring data, construct a sliding window with a window length equal to the base window length, and use it as the base window; take any moment in the target monitoring data as the rightmost data point of the base window, so that the base window becomes the base window for the given moment. The data segments of the trend term, seasonal term, and residual term of the target monitoring data within the basic window are respectively denoted as the trend segment, seasonal segment, and residual segment at the stated time. The sequence of residuals formed between each data point in the seasonal segment of the target monitoring data and the corresponding seasonal function is obtained as the seasonal residual segment. Based on the changing trend of the trend segment of the target monitoring data at the stated time and the data dispersion of the seasonal residual segment and the residual segment, the length coefficient of the target monitoring data at the stated time is calculated.

5. The circulating coupled treatment system for an industrial park water treatment station according to claim 4, characterized in that, The method for calculating the length coefficient of the target monitoring data at the specified time, based on the changing trend of the trend segment of the target monitoring data at the specified time and the data dispersion of the seasonal residual segment and the residual segment, includes the following specific methods: The seasonal residual segment and the coefficient of variation of all data points in the residual segment of the target monitoring data at the specified time are obtained respectively, and are used as the seasonal variation value and decomposed variation value of the target monitoring data at the specified time. The slope of the fitted line corresponding to all data points in the trend segment of the target monitoring data at the specified time is obtained by the least squares method, and the absolute value of the slope is used as the trend value of the target monitoring data at the specified time. The length coefficient of the target monitoring data at the specified time is calculated based on the seasonal variation value, decomposition variation value, and trend value of the target monitoring data.

6. The circulating coupled treatment system for an industrial park water treatment station according to claim 1, characterized in that, The construction of a ternary array as a processing path array involves using the changing trends and magnitudes of data points within a sliding window at any given time to calculate the dominant coefficient at that time, which serves as the dominant coefficient for each processing path in the processing path array. The specific method includes: Electrochemical oxidation path, electrocoagulation path and electroflotation path are defined and collectively referred to as treatment paths. A ternary array is constructed, where each element is the dominant coefficient of the corresponding treatment path, thus obtaining the treatment path array. By utilizing the changing trends and magnitudes of data points within a sliding window corresponding to the final window length at any given time, the dominant factors of the electrochemical oxidation path, electrocoagulation path, and electroflotation path at that time are calculated respectively. The dominant factors of all processing paths at the given time are subjected to maximum-min normalization to obtain the dominant coefficients of each processing path at the given time.

7. The circulating coupled treatment system for an industrial park water treatment station according to claim 6, characterized in that, The monitoring data includes ORP time-series data, turbidity time-series data, and hydrogen concentration time-series data.

8. The circulating coupled treatment system for an industrial park water treatment station according to claim 7, characterized in that, The method for calculating the dominant factors of the electrochemical oxidation path, electrocoagulation path, and electroflotation path at any given time by utilizing the changing trends and amplitude levels of data points within a sliding window corresponding to the final window length of the monitoring data at any given time includes the following specific methods: The average slope of all data points within the final window for any monitoring data is taken as the local slope at the corresponding time in the final window; the average value of all data points within the final window for any monitoring data is taken as the local level value at the time; the product of the local slope and the local level value of the monitoring data at the time is taken as the degree value at the corresponding time; and the degree value of the ORP time series data at the time is taken as the dominant factor of the electrochemical oxidation pathway. The degree of turbidity time series data at the stated time is used as the dominant factor in the electrocoagulation path; Hydrogen concentration time series data is used as the dominant factor for the electric levitation path.

9. The circulating coupled treatment system for an industrial park water treatment station according to claim 1, characterized in that, The specific method for weighted fusion of all dominant coefficients in the processing path array to determine the feedback coefficient at any given time includes: Set path weight coefficients and combine them with the dominant coefficients of all processing paths at any given time to calculate the collaborative demand index at that time. Preset adjustment parameters and calculate the feedback coefficient at the specified time based on the collaborative demand index at the specified time.

10. The circulating coupled treatment system for an industrial park water treatment station according to claim 9, characterized in that, The method for calculating the collaborative demand index at a given time by setting path weight coefficients and combining them with the dominant coefficients of all processing paths at any given time through weighted fusion includes: Set a path weight coefficient for each processing path. For any given time, multiply the path weight coefficient of any processing path by the dominant coefficient of the processing path as the coordination parameter of the processing path. The sum of the coordination parameters of all processing paths is used as the coordination requirement index at that time.

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