Industrial high-salinity wastewater quality-grading and stage-grading treatment control system and method

Abstract

The invention relates to the technical field of high-salinity wastewater treatment, in particular to an industrial high-salinity wastewater quality-grading and stage-grading treatment control system and method. The method comprises the following steps: collecting an original wastewater parameter flow; performing feature extraction on the original wastewater parameter flow to obtain smooth wastewater feature data; according to the smooth wastewater characteristic data, carrying out diversion decision on wastewater to obtain a wastewater diversion instruction; separating the wastewater according to the wastewater diversion instruction, and performing salt content grading collection to obtain pretreated wastewater data; calculating a salt load distribution imbalance index according to the pretreated wastewater data; and carrying out wastewater re-separation according to the salt load distribution imbalance index to obtain three-stage quality-divided wastewater data. According to the method, the purity and the recycling rate of salt products can be improved, and efficient recycling of resources is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-salt wastewater treatment, and in particular to a control system and method for quality-based and graded treatment of industrial high-salt wastewater. Background Art

[0002] In fine chemical production, saline wastewater generated by specific processes exhibits significant multi-source heterogeneity. Early wastewater treatment methods primarily focused on removing single pollutants. With increasing environmental protection requirements and a deeper understanding of complex wastewater, treatment processes have gradually shifted toward integrated treatment and resource recovery. However, current treatment technologies for high-salinity wastewater still face numerous challenges.

[0003] The wastewater treatment system of an aniline intermediate synthesis plant highlighted these issues. It simultaneously receives wastewater from three sources: crystallization mother liquor, equipment rinse water, and exhaust gas scrubber drainage. The sodium chloride concentration fluctuates randomly between 8% and 15%. The existing treatment process uses a unified concentration and crystallization route, resulting in frequent carbonization and coking of organic matter in the evaporator. This is especially true when treating intermittently discharged rinse water. The system struggles to respond within 30 minutes to sudden changes in conductivity, leading to evaporation efficiency degradation. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide a control system and method for the quality classification and treatment of industrial high-salt wastewater to solve at least one of the above technical problems.

[0005] To achieve the above object, a method for controlling the quality and graded treatment of industrial high-salt wastewater comprises the following steps:

[0006] Step S1: collecting raw wastewater parameter flow; extracting features from the raw wastewater parameter flow to obtain smoothed wastewater characteristic data; making wastewater diversion decisions based on the smoothed wastewater characteristic data to obtain wastewater diversion instructions;

[0007] Step S2: Separate the wastewater according to the wastewater diversion instruction and collect salt content by grade to obtain pre-treated wastewater data; calculate the salt load distribution imbalance index based on the pre-treated wastewater data; re-separate the wastewater based on the salt load distribution imbalance index to obtain three-level quality-classified wastewater data;

[0008] Step S3: performing gradient membrane separation treatment on the separated wastewater based on the three-level quality-separated wastewater data to obtain graded salt treatment liquid data; performing membrane flux attenuation pattern recognition based on the graded salt treatment liquid data to obtain membrane fouling trend data; controlling membrane surface pollutants based on the membrane fouling trend data to obtain membrane separation efficiency monitoring data;

[0009] Step S4: performing phase equilibrium prediction modeling on the concentrated liquid based on the graded salt treatment liquid data to obtain co-crystallization behavior prediction data; performing evaporator operation control based on the co-crystallization behavior prediction data to obtain process closed-loop monitoring data, wherein the concentrated liquid is the graded salt treatment liquid;

[0010] Step S5: constructing a wastewater treatment virtual system environment based on the membrane separation efficiency monitoring data and the process closed-loop monitoring data; and establishing a final system operation plan based on the wastewater treatment virtual system environment.

[0011] The present invention precisely diverts and pre-treats wastewater from different sources at the initial stage of wastewater treatment, ensuring stable wastewater characteristics before subsequent treatment, thereby enhancing the flexibility and adaptability of the treatment system. By integrating salt content into a graded collection system and dynamically adjusting the pump assembly and micro-nano bubble generator, the burden on the evaporator can be effectively reduced, avoiding evaporator failures and high maintenance costs caused by excessive salt concentration, while also improving wastewater treatment efficiency and quality. By monitoring membrane flux attenuation in real time and identifying membrane fouling trends, membrane surface contaminants can be controlled, extending the membrane's service life and reducing membrane replacement costs while ensuring the stability and sustainability of the membrane separation process. By utilizing phase equilibrium predictive modeling, cocrystallization behavior can be accurately predicted and evaporator operation can be optimized and controlled, effectively preventing coking on the evaporator wall, improving evaporator operating efficiency and service life, while also increasing the purity and recovery rate of the salt product and achieving efficient resource recycling. By integrating various data to construct a virtual wastewater treatment system environment, a comprehensive and scientific operation plan and decision-making basis are provided for the wastewater treatment system, enabling it to flexibly respond to different operating conditions and needs, thereby achieving an intelligent and efficient wastewater treatment system.

[0012] Preferably, the present invention further provides an industrial high-salt wastewater quality classification and treatment control system, which is used to execute the industrial high-salt wastewater quality classification and treatment control method as described above. The industrial high-salt wastewater quality classification and treatment control system comprises:

[0013] The wastewater collection and diversion module is used to collect the original wastewater parameter flow; extract the characteristics of the original wastewater parameter flow to obtain smoothed wastewater characteristic data; make diversion decisions on the wastewater based on the smoothed wastewater characteristic data to obtain wastewater diversion instructions;

[0014] The salt-grade pump control module is used to separate wastewater according to the wastewater diversion instruction and collect salt content in a graded manner to obtain pre-treated wastewater data; calculate the salt load distribution imbalance index based on the pre-treated wastewater data; and re-separate the wastewater based on the salt load distribution imbalance index to obtain three-grade quality-classified wastewater data;

[0015] The separation monitoring module is used to perform gradient membrane separation treatment on the separated wastewater based on the three-level quality-separated wastewater data to obtain graded salt treatment liquid data; perform membrane flux attenuation pattern recognition based on the graded salt treatment liquid data to obtain membrane fouling trend data; and perform membrane surface pollutant control based on the membrane fouling trend data to obtain membrane separation efficiency monitoring data;

[0016] The physical model evaporation module is used to perform phase equilibrium prediction modeling on the concentrated liquid based on the graded salt treatment liquid data to obtain co-crystallization behavior prediction data; the evaporator operation is controlled based on the co-crystallization behavior prediction data to obtain process closed-loop monitoring data, wherein the concentrated liquid is the graded salt treatment liquid;

[0017] The architecture solution module is used to build a wastewater treatment virtual system environment based on membrane separation efficiency monitoring data and process closed-loop monitoring data; and to build a final system operation plan based on the wastewater treatment virtual system environment.

[0018] The present invention uses a wastewater collection and diversion module to accurately divert wastewater from different sources, ensuring the stability of wastewater characteristics in subsequent treatment links and improving the flexibility and adaptability of wastewater treatment. The salt grade pump control module can dynamically adjust the pump group and micro-nano bubble generator according to the salt load distribution imbalance index, effectively reducing the burden on the evaporator, reducing maintenance costs, improving wastewater treatment efficiency and quality, and making the evaporator operation more stable and efficient. The separation monitoring module monitors membrane flux attenuation in real time and identifies membrane contamination trends, controls membrane surface pollutants, extends the service life of the membrane, reduces membrane replacement costs, and ensures the stability and sustainability of the membrane separation process. The physical model evaporation module performs phase equilibrium prediction modeling based on graded salt treatment liquid data, accurately controls evaporator operation, avoids coking on the evaporator wall, improves evaporator operation efficiency and service life, and simultaneously improves the purity and recovery rate of salt products, realizes efficient resource recycling, and brings significant economic and environmental benefits. The architecture solution module integrates various types of data to construct a wastewater treatment virtual system environment and determine the final system operation plan, so that the wastewater treatment system can better respond to different working conditions and needs, and realize intelligent and efficient wastewater treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent from reading the detailed description made with reference to the following drawings:

[0020] Figure 1 A schematic flow chart of the steps of a method for controlling the quality-based and graded treatment of industrial high-salt wastewater according to an embodiment is shown.

[0021] Figure 2 A detailed flowchart of step S2 of an embodiment is shown.

[0022] Figure 3A detailed flowchart of step S4 of an embodiment is shown. DETAILED DESCRIPTION

[0023] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0025] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0026] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for controlling the quality and graded treatment of industrial high-salt wastewater, comprising the following steps:

[0027] Step S1: collecting raw wastewater parameter flow; extracting features from the raw wastewater parameter flow to obtain smoothed wastewater characteristic data; making wastewater diversion decisions based on the smoothed wastewater characteristic data to obtain wastewater diversion instructions;

[0028] Step S2: Separate the wastewater according to the wastewater diversion instruction and collect salt content by grade to obtain pre-treated wastewater data; calculate the salt load distribution imbalance index based on the pre-treated wastewater data; re-separate the wastewater based on the salt load distribution imbalance index to obtain three-level quality-classified wastewater data;

[0029] Step S3: performing gradient membrane separation treatment on the separated wastewater based on the three-level quality-separated wastewater data to obtain graded salt treatment liquid data; performing membrane flux attenuation pattern recognition based on the graded salt treatment liquid data to obtain membrane fouling trend data; controlling membrane surface pollutants based on the membrane fouling trend data to obtain membrane separation efficiency monitoring data;

[0030] Step S4: performing phase equilibrium prediction modeling on the concentrated liquid based on the graded salt treatment liquid data to obtain co-crystallization behavior prediction data; performing evaporator operation control based on the co-crystallization behavior prediction data to obtain process closed-loop monitoring data, wherein the concentrated liquid is the graded salt treatment liquid;

[0031] Step S5: constructing a wastewater treatment virtual system environment based on the membrane separation efficiency monitoring data and the process closed-loop monitoring data; and establishing a final system operation plan based on the wastewater treatment virtual system environment.

[0032] In this example, a sensor network was deployed at the wastewater treatment project's crystallization mother liquor production points, equipment flushing points, and exhaust gas scrubber discharge points. Each node was equipped with a conductivity sensor, a Mettler-Toledo SevenLine™ pH sensor, a Pt100 platinum resistance temperature sensor, a Hach 2100N turbidity sensor, and a Sievers 900 series TOC analyzer. This sensor network topology, implemented via an industrial fieldbus, collected raw wastewater parameter streams in real time, with a data acquisition frequency set at 5 seconds. Advantech's PCI-1710 series data acquisition cards were used to transmit the collected data to a supervisory control and data acquisition (SCADA) system. Outliers were identified and corrected in the raw wastewater parameter streams to generate a pretreated wastewater parameter set. The wastewater indices were then standardized to form a wastewater index parameter table. This parameter table was processed using multi-layer wavelet transform decomposition to obtain a set of wastewater parameter wavelet coefficients. After noise was filtered out, an inverse wavelet transform was performed to reconstruct the data, resulting in smoothed wastewater characteristic data. Based on smoothed wastewater feature data, a pre-set stacked autoencoder performs dimensionality reduction on the wastewater feature vector set to obtain low-dimensional wastewater feature data. A wastewater source feature fingerprint library is then constructed to match wastewater source features and generate a wastewater source probability distribution table. Based on this table, wastewater salinity load predictions are calculated for wastewater generation sources to obtain wastewater salinity concentration predictions. Combined with the low-dimensional wastewater feature data, multi-layer perceptron classification is performed to predict wastewater grade. After time series smoothing, stable wastewater diversion instructions are generated. Based on the wastewater diversion instructions, valve execution status data is parsed and controlled to divert wastewater through a three-way diversion valve system consisting of cascaded 485 bus intelligent electric valves (response time less than 200ms, positioning accuracy ±1%). The wastewater is then divided into high-salt load, medium-salt load, and low-salt load water. Salt content is then graded and collected to obtain pretreated wastewater data. A salt load imbalance index is calculated based on this data, and wastewater is further separated based on this index to obtain three-level quality-based wastewater data. Based on the three-level quality-separated wastewater data, the separated wastewater is subjected to gradient membrane separation treatment to obtain graded salt treatment liquid data. This data is used to identify membrane flux attenuation patterns, generating membrane fouling trend data. This data is then used to control membrane surface contaminants and obtain membrane separation efficiency monitoring data. Simultaneously, phase equilibrium predictive modeling is performed on the concentrate based on the graded salt treatment liquid data to obtain cocrystallization behavior prediction data. This data is then used to control evaporator operation and obtain process closed-loop monitoring data. By integrating the graded salt treatment liquid data, membrane separation efficiency monitoring data, and process closed-loop monitoring data, a virtual wastewater treatment system environment is constructed, and the final system operation plan is determined based on this architecture.

[0033] Preferably, step S1 includes the following steps:

[0034] Step S11: Deploy multiple types of sensors at the wastewater source to obtain sensor network topology data; the sensor network topology includes crystallization mother liquor points, equipment flushing water points, and exhaust gas scrubber drainage points. Each node is equipped with a conductivity sensor, a pH sensor, a temperature sensor, a turbidity sensor, and a total organic carbon content sensor.

[0035] Step S12: Real-time collection of wastewater parameters is performed based on the sensor network topology data to obtain a raw wastewater parameter stream, wherein the data collection frequency is 5 seconds per time;

[0036] Step S13: Identify and repair abnormal values ​​of the original wastewater parameter stream to obtain a pre-treated wastewater parameter set; standardize wastewater indicators based on the pre-treated wastewater parameter set to obtain a wastewater indicator parameter table;

[0037] Step S14: performing multi-layer wavelet transform decomposition on the wastewater index parameter table to obtain a wastewater parameter wavelet coefficient set; performing noise filtering based on the wastewater parameter wavelet coefficient set to obtain a denoised wastewater parameter wavelet coefficient set;

[0038] Step S15: reconstructing the denoised wastewater parameter wavelet coefficient set by inverse wavelet transform to obtain smoothed wastewater characteristic data;

[0039] Step S16: making wastewater diversion decisions based on the smoothed wastewater characteristic data to obtain wastewater diversion instructions.

[0040] In this embodiment, sensor nodes are deployed at the crystallization mother liquor point, equipment flushing water point, and exhaust gas scrubber drain point in the wastewater treatment system. Each node is equipped with a conductivity sensor, a pH sensor, a temperature sensor, a turbidity sensor, and a total organic carbon content sensor (such as a TOC analyzer). These sensors are connected to form a sensor network topology via an industrial fieldbus, enabling real-time monitoring of wastewater sources and acquiring sensor network topology data. Based on this established sensor network topology, a data acquisition card collects data from each sensor at a rate of 5 seconds per time, including parameters such as conductivity, pH, temperature, turbidity, and total organic carbon content, to generate a raw wastewater parameter stream. For example, at the crystallization mother liquor point, data such as conductivity and pH are collected every 5 seconds and transmitted to a data acquisition and monitoring system via industrial Ethernet, forming a continuous raw wastewater parameter stream. The raw wastewater parameter stream is then imported into data cleaning software (such as the Truedata data cleaning tool). The software's outlier detection function was used to identify outliers in the raw data using a boxplot method. For example, for conductivity data, the interquartile range (IQR) was calculated, and values ​​outside 1.5 times the IQR range were labeled as outliers. Outliers were repaired using linear interpolation, which interpolates and replaces outliers based on the values ​​of adjacent normal data points. The repaired data was standardized according to preset wastewater indicator standards (such as the corresponding limits in the "Surface Water Environmental Quality Standard" GB3838-2002). Data of different dimensions and dimensions were converted to dimensionless standardized values, ultimately generating a wastewater indicator parameter table. The wastewater indicator parameter table was decomposed using a multi-layer wavelet transform using MATLAB software. In MATLAB, the Daubechies wavelet (db4) was selected as the mother wavelet, and a three-layer wavelet decomposition was performed on the wastewater parameter data. The specific steps are as follows: Using the wavedec function in MATLAB's Wavelet Toolbox, input the wastewater parameter data (e.g., conductivity series) into the function and set the decomposition layer number to 3 to obtain the wastewater parameter wavelet coefficient set. Visualization tools (such as MATLAB's plot function) are used to view the coefficients of each layer after wavelet decomposition and analyze the signal characteristics and noise distribution at different scales. Using the waverec function in MATLAB's Wavelet Toolbox, input the denoised wastewater parameter wavelet coefficient set into the function and specify the reconstructed wavelet function as db4 to perform an inverse wavelet transform. This process converts the wavelet domain coefficients back into the time domain signal to obtain smoothed wastewater characteristic data. Using MATLAB's plotting function, compare the original wastewater parameter data with the reconstructed smoothed wastewater characteristic data. For the detailed implementation process of step S16, please refer to the sub-steps of step S16.

[0041] Preferably, step S16 includes the following steps:

[0042] Step S161: extracting time-frequency features from the smoothed wastewater feature data to obtain a wastewater feature vector set;

[0043] Step S162: using a preset stacked autoencoder to reduce the dimension of the wastewater feature vector set to obtain low-dimensional wastewater feature data;

[0044] Step S163: constructing a wastewater source feature fingerprint library based on the wastewater feature low-dimensional data;

[0045] Step S164: performing wastewater source feature matching on the wastewater feature low-dimensional data based on the wastewater source feature fingerprint library to obtain a wastewater source probability distribution table;

[0046] Step S165: Calculating the wastewater salt load prediction value for the wastewater generation source based on the wastewater source probability distribution table to obtain wastewater salt concentration prediction data;

[0047] Step S166: Perform multi-layer perceptron classification based on the wastewater salinity prediction data and the wastewater characteristic low-dimensional data to obtain a wastewater grade prediction result;

[0048] Step S167: performing time-series smoothing on the wastewater level prediction results to obtain stable wastewater classification decision data; and generating a wastewater diversion instruction according to the stable wastewater classification decision data.

[0049] In this example, smoothed wastewater parameter data (such as conductivity and pH) was decomposed using wavelet packet transform in MATLAB. The db4 wavelet basis function was selected, and the decomposition level was three. Wavelet packet transform was used to obtain characteristic coefficients for different frequency bands, forming a set of wastewater feature vectors. For example, for conductivity data, wavelet packet transform yielded multiple feature vectors containing different frequency components, each representing the conductivity variation characteristics at different time scales. Dimensionality reduction was performed on the wastewater feature vector set using a pre-configured stacked autoencoder model in the TensorFlow library in Python. A stacked autoencoder was constructed in TensorFlow, with the number of input layer nodes set to the dimension of the feature vector (assuming 100), the number of hidden layer 1 nodes to 50, the number of hidden layer 2 nodes to 25, and the number of output layer nodes to 25. The Adam optimization algorithm was used, with a learning rate of 0.001, 100 training rounds, and a batch size of 32. The wastewater feature vector set was input into the model for training, ultimately generating low-dimensional wastewater feature data with a dimensionality reduced to 25. Based on this low-dimensional wastewater feature data, a wastewater source feature fingerprint library was constructed using the Scikit-learn library in Python. The low-dimensional data was further analyzed using Scikit-learn's PCA (Principal Component Analysis) tool to extract the primary characteristic components. For example, after normalizing the low-dimensional data, the covariance matrix was calculated, and its eigenvalues ​​and eigenvectors were obtained to identify the top principal components. These principal components and their corresponding eigenvectors were used as feature fingerprints to construct a wastewater source feature fingerprint library, with one feature fingerprint corresponding to each wastewater source (e.g., crystallization mother liquor point, equipment rinse water point). Based on the wastewater source feature fingerprint library, the K-nearest neighbor (KNN) algorithm in Scikit-learn was used in Python to match wastewater source features to the low-dimensional wastewater feature data. The K value was set to 5, and the Euclidean distance between the low-dimensional data and each feature fingerprint in the fingerprint library was calculated to identify the five nearest neighbors. Based on the source labels of the neighbors, the frequency of each source was counted to generate a probability distribution table for the wastewater sources. For example, if three of the matching results are neighbors belonging to a crystallization mother liquor point, one to an equipment flushing water point, and one to an exhaust gas scrubber drain point, the probability distribution table indicates a 60% probability for the crystallization mother liquor point, a 20% probability for the equipment flushing water point, and a 20% probability for the exhaust gas scrubber drain point. Using the statsmodels library in Python, we calculate the wastewater salinity load prediction for each wastewater source based on the wastewater source probability distribution table. Assuming the average salinity concentration for each source is known (e.g., 12% for the crystallization mother liquor point, 5% for the equipment flushing water point, and 3% for the exhaust gas scrubber drain point), we perform a weighted average based on the probabilities in the probability distribution table.For example, if the probability distribution table shows a 60% probability for the crystallization mother liquor point, a 20% probability for the equipment flushing water point, and a 20% probability for the exhaust gas scrubber discharge point, the predicted wastewater salinity concentration is: 12% × 60% + 5% × 20% + 3% × 20% = 9.2%. This method yields predicted wastewater salinity concentration data. Using the Python TensorFlow library, a pre-configured multi-layer perceptron (MLP) model is used to perform MLP classification on the predicted wastewater salinity concentration data and low-dimensional wastewater feature data. The MLP model is constructed in TensorFlow. The number of input layer nodes is set to equal the dimension of the low-dimensional wastewater feature data (assuming 25) plus the dimension of the predicted salinity concentration data (assuming 1), or 26. The number of hidden layer 1 nodes is 64, with the activation function being ReLU. The number of hidden layer 2 nodes is 32, with the activation function being ReLU. The number of output layer nodes is equal to the number of wastewater grade categories (assuming high salt, medium salt, and low salt), with the activation function being Softmax. The Adam optimization algorithm was used with a learning rate of 0.001, 200 training rounds, and a batch size of 32. Data was fed into the model for training, ultimately generating wastewater classification predictions. For example, the probability of a wastewater sample being classified as high-salinity was 85%, medium-salinity was 10%, and low-salinity was 5%. Time series smoothing of the wastewater classification predictions was performed using the pandas library in Python. The predictions were arranged chronologically to form time series data. Moving average smoothing was performed using a window size of 5 time steps and a step size of 1. The mean probability of each class within each window was calculated to generate stable wastewater classification decision data. For example, for predictions over five consecutive time steps (with high-salinity probabilities of 85%, 88%, 82%, 86%, and 87%, respectively), the moving average probability of high-salinity was (85+88+82+86+87) / 5 = 85.6%. According to the stabilized decision data, when the probability of a certain level exceeds the set threshold for five consecutive time steps (such as the high salt threshold is 80%), the corresponding wastewater diversion instruction is generated, and the control valve directs the wastewater to the corresponding treatment unit.

[0050] Preferably, step S2 includes the following steps:

[0051] Step S21: parsing the wastewater diversion instruction to obtain valve execution status data;

[0052] Step S22: Controlling the three-way diversion valve group to divert the wastewater according to the valve execution status data, and obtaining the wastewater physical diversion execution result, wherein the wastewater after diversion includes high-salt load wastewater, medium-salt load wastewater, and low-salt load wastewater. The three-way diversion valve group adopts a 485 bus cascaded intelligent electric valve, with a response time of <200ms and a positioning accuracy of ±1%;

[0053] Step S23: Feedback monitoring of the wastewater physical diversion execution results to obtain diversion execution confirmation data;

[0054] Step S24: based on the diversion execution confirmation data, the high-salt load wastewater, the medium-salt load wastewater and the low-salt load wastewater are collected in a targeted manner to obtain pre-treated wastewater data;

[0055] Step S25: performing multi-parameter pseudo-color thermal imaging on the pre-treated wastewater data to obtain a salt load spatial distribution map;

[0056] Step S26: Calculating the unit load distribution balance according to the salt load spatial distribution diagram to obtain the salt load distribution imbalance index;

[0057] Step S27: Re-separate the wastewater according to the salt load distribution imbalance index to obtain three-level quality-classified wastewater data.

[0058] In this embodiment, a process control system (PCS) is used in the central control room of a wastewater treatment plant to interpret wastewater diversion instructions. This system can break down complex diversion instructions into specific valve execution status data, including valve opening, open / close time, and operating sequence information. Based on this valve execution status data, the wastewater treatment plant uses Rotork IQ series intelligent electric valves to construct a three-way diversion valve group. These valves are cascaded via a 485 bus. The measured response time is less than 150ms, and the positioning accuracy reaches ±0.5%, exceeding system requirements. When treating high-salinity wastewater, the valves rapidly adjust their opening according to the instructions, ensuring precise diversion of wastewater to the appropriate treatment units. At the wastewater treatment plant, Rosemount 3051 pressure transmitters and FMU14 series flow sensors with HART communication protocol monitor the physical diversion execution results of the three-way diversion valve group in real time. These sensors transmit wastewater pressure and flow data to the Siemens SIMATIC PCS7 system. The system compares this real-time data with the expected diversion results to generate accurate diversion execution confirmation data. Based on confirmed data from diversion execution, the wastewater treatment plant uses a Siemens SIMATIC PCS7 system to perform targeted collection of wastewater with different salt loads. The system automatically controls wastewater delivery to the appropriate regulating tank or pretreatment unit based on wastewater type. For example, high-salinity wastewater is directed to the high-salinity wastewater regulating tank, while medium-salinity wastewater and low-salinity wastewater are sent to their corresponding medium-salinity and low-salinity wastewater regulating tanks, respectively. After targeted collection, level sensors and water quality monitors in each regulating tank feed data back to the control system, generating comprehensive pretreated wastewater data. In the wastewater treatment plant's water quality analysis laboratory, FLIR thermal imaging cameras are used to perform multi-parameter pseudo-color thermal imaging of pretreated wastewater. The thermal imaging cameras are installed above the wastewater regulating tanks and regularly capture thermal images of the wastewater within them. FLIR ResearchIR software analyzes the thermal images and, combined with wastewater salinity, temperature, and flow rate data, colors are used to identify areas within different parameter ranges, generating intuitive spatial distribution maps of salt load. For example, high salt load areas are represented in red, medium salt load areas in yellow, and low salt load areas in blue, clearly showing the spatial distribution of the wastewater salt load. In the central control room of the wastewater treatment plant, Aspen PIMS petroleum refining and chemical process simulation software is used to calculate the load distribution balance of the wastewater treatment unit based on the salt load spatial distribution map. The distribution map is imported into the software, which automatically analyzes the salt load conditions in each area of ​​the wastewater treatment unit. Based on the preset balance evaluation model, the salt load distribution imbalance index is calculated. For the detailed implementation process of step S27, please refer to the sub-steps of step S27.

[0059] Preferably, step S27 includes the following steps:

[0060] Step S271: performing fuzzy logic regulation demand level mapping according to the salt load distribution imbalance index to obtain the flow regulation demand level;

[0061] Step S272: performing optimal flow allocation on the pump groups based on the flow regulation demand level to obtain an optimal flow allocation decision plan;

[0062] Step S273: generating a variable frequency pump group coordinated control parameter set according to the optimal flow distribution decision plan; performing closed-loop feedback control on the pump group according to the variable frequency pump group coordinated control parameter set to obtain flow rebalancing execution data;

[0063] Step S274: Real-time monitoring and feedback of the flow rebalancing execution data are performed to obtain salt load balance closed-loop control parameters; online UV-visible spectroscopy detection of organic matter content in the wastewater is performed to obtain wastewater organic load distribution data;

[0064] Step S275: Calculating the oxidation demand based on the wastewater organic load distribution data to obtain microbubble oxidation treatment parameters;

[0065] Step S276: controlling the micro-nano bubble generator to perform shear resonance cavitation according to the microbubble oxidation treatment parameters to obtain bubble characteristic control data;

[0066] Step S277: Perform gas-liquid interface enhanced oxidation treatment on the wastewater according to the bubble characteristic control data to obtain three-level quality-classified wastewater data.

[0067] In this embodiment, in the central control room of a wastewater treatment plant, operators use Honeywell's Experion PKS system to map fuzzy logic regulation demand levels based on the salt load imbalance index. The salt load imbalance index is input into the Honeywell Experion PKS system's fuzzy logic controller. The controller is preconfigured with a fuzzy rule base that maps index ranges to flow regulation demand levels. For example, a salt load imbalance index between 0 and 0.2 is mapped to a "low" demand level; between 0.2 and 0.5 to a "medium" level; and above 0.5 to a "high" level. Based on the real-time monitored index value, the system automatically matches the corresponding fuzzy rule to determine the current flow regulation demand level, providing a basis for subsequent pump group flow regulation. Based on the flow regulation demand level obtained in step S271, the wastewater treatment plant's pump group control system uses Emerson's AMS Suite predictive maintenance software to optimize flow allocation for the pump groups. Based on the demand level, the pre-set flow allocation model in AMS Suite is invoked. For example, when the demand level is "high," the model increases the flow allocation for the high-salinity wastewater pump group from 50% to 70% of the baseline value, while correspondingly reducing the flow rates of the medium-salinity and low-salinity wastewater pump groups. The system generates an optimal flow allocation decision plan based on the current wastewater flow rate and pump group operating status through simulation. This plan is then sent to the Siemens Simatic PCS7 process control system to guide the actual operation of the pump groups. In the wastewater treatment plant's pump group control room, operators use a Schneider Electric Modicon Quantum PLC based on the optimal flow allocation decision plan generated in step S272 to generate a coordinated control parameter set for the variable-frequency pump groups. Based on the flow requirements in the plan, the PLC programming software sets parameters such as the pump group's operating frequency, start / stop sequence, and speed to generate a specific control parameter set. For example, the operating frequency of the high-salinity wastewater pump group can be adjusted from 50Hz to 55Hz to increase flow rate. The parameter set is downloaded to the PLC using Schneider Electric's Unity Pro XL software, implementing closed-loop feedback control of the variable-frequency pump groups. The PLC monitors the pump's operating status, such as current, voltage, and flow rate, in real time. It adjusts control parameters based on feedback to ensure optimal operation, ultimately generating flow rebalancing execution data. In the wastewater treatment plant's central control room, the flow rebalancing execution data obtained in step S273 is monitored and fed back in real time using the Siemens Simatic PCS7 process control system. This system collects data in real time through a distributed sensor network (such as pressure transmitters and flow meters) and displays it intuitively on the PCS7 monitoring interface.At the same time, a Hach DR3900 spectrophotometer is used to perform online UV-visible spectroscopy testing of organic matter content in the wastewater, with a testing frequency set to once every minute. This data is transmitted to the PCS7 system and combined with flow rate data to generate wastewater organic load distribution data. For example, if the organic matter content in a certain area exceeds a preset threshold for five consecutive minutes, the system automatically triggers an alarm and adjusts the corresponding treatment parameters to ensure the stability and efficiency of the wastewater treatment process. In the wastewater treatment plant's water quality analysis laboratory, technicians use the EFDC (Environmental Fluid Dynamics Code) hydrodynamic water quality model based on the wastewater organic load distribution data obtained in step S274 to calculate the oxidation demand. The organic load distribution data is imported into the EFDC model, and simulation parameters such as wastewater flow rate, microbial concentration in the reactor, and reaction time are set. The simulation is then started, and the model calculates the theoretical amount of oxidant required for wastewater treatment based on the built-in biochemical reaction kinetic equations. For example, if the wastewater organic load is high, the model calculates that 20 grams of oxidant is required per cubic meter of wastewater. The calculation results are recorded as microbubble oxidation treatment parameters to provide precise dosage guidance for subsequent oxidation treatment. In the micro-nano bubble generator control room of the wastewater treatment plant, the operator uses Guangzhou Tekote's micro-nano bubble generator to perform shear resonance cavitation operation based on the microbubble oxidation treatment parameters obtained in step S275. The treatment parameters are input into the control panel of the bubble generator, and the shear frequency is set to 20kHz, the cavitation pressure is set to 0.5MPa, and the running time is 30 minutes. After starting the equipment, the bubble generator generates a large number of micro-nano bubbles with a diameter of 20-50 microns through high-speed shear and resonance cavitation. The bubble particle size analyzer provided by the equipment is used to monitor bubble characteristics in real time, such as particle size distribution and number concentration, to generate bubble characteristic control data. For example, the monitoring results show that the average bubble particle size is 30 microns and the concentration is 1×. / mL, meeting the process requirements for wastewater treatment. In the oxidation reaction tank area of ​​the wastewater treatment plant, the operator uses the oxidant dosing system and the micro-nano bubble generator to coordinately carry out gas-liquid interface enhanced oxidation treatment based on the bubble characteristic control data obtained in step S276. According to the bubble characteristic data, the dosage and frequency of the oxidant dosing system are set, for example, 10 liters of oxidant are added per hour. At the same time, the micro-nano bubble generator is started to evenly inject the bubbles into the reaction tank. At the gas-liquid interface, the micro-nano bubbles fully contact and react with the oxidant to achieve deep oxidation treatment of the wastewater. The treatment effect is monitored in real time by an online water quality monitor (such as a COD analyzer), and the water quality parameters of the treated wastewater are recorded to obtain three-level quality-separated wastewater data.

[0068] Preferably, step S3 includes the following steps:

[0069] Step S31: Parameter analysis and separation treatment path planning are performed on the three-level quality-separated wastewater data to obtain a membrane treatment process configuration plan;

[0070] Step S32: pre-treating and filtering the low-salt-load wastewater according to the membrane treatment process configuration plan to obtain low-salt wastewater clarified liquid data; performing soluble pollutant detection on the low-salt wastewater clarified liquid data to obtain a low-salt wastewater component spectrum;

[0071] Step S33: selecting a nanofiltration membrane and configuring system parameters based on the low-salt wastewater component spectrum to obtain a nanofiltration process parameter set; performing nanofiltration on the clarified low-salt wastewater according to the nanofiltration process parameter set to obtain a wastewater nanofiltration treatment data stream;

[0072] Step S34: performing interception efficiency evaluation based on the wastewater nanofiltration treatment data stream to obtain nanofiltration interception efficiency evaluation data;

[0073] Step S35: Optimizing reverse osmosis parameters based on the nanofiltration energy efficiency evaluation data to obtain a reverse osmosis process parameter set; performing reverse osmosis treatment on the nanofiltration filtrate according to the reverse osmosis process parameter set to obtain low-salt treated liquid data;

[0074] Step S36: Pre-filtering the medium-salt-loaded wastewater to obtain medium-salt wastewater clarified liquid data; configuring a membrane distillation process based on the medium-salt wastewater clarified liquid data to obtain a membrane distillation parameter set; performing direct contact membrane distillation on the medium-salt wastewater clarified liquid based on the membrane distillation parameter set to obtain medium-salt treated liquid data;

[0075] Step S37: Pre-treating and buffering the high-salt-load wastewater to obtain high-salt wastewater conditioning liquid data; configuring a pervaporation-forward osmosis collaborative process based on the high-salt wastewater conditioning liquid data to obtain a pervaporation-forward osmosis collaborative process parameter set; and performing pervaporation-forward osmosis directional separation treatment on the high-salt wastewater conditioning liquid based on the pervaporation-forward osmosis collaborative process parameter set to obtain high-salt treated liquid data.

[0076] Step S38: Recording the low-salt treatment liquid data, the medium-salt treatment liquid data, and the high-salt treatment liquid data as graded salt treatment liquid data; performing membrane flux attenuation pattern recognition based on the graded salt treatment liquid data to obtain membrane fouling trend data;

[0077] Step S39: construct an alternating electric field according to the membrane fouling trend data to obtain an alternating electric field parameter configuration scheme; perform alternating electric field assisted control on each membrane unit based on the alternating electric field parameter configuration scheme to obtain membrane separation efficiency monitoring data, wherein each membrane unit is a nanofiltration membrane unit, a reverse osmosis membrane unit, a membrane distillation unit, a pervaporation membrane unit and a forward osmosis membrane unit.

[0078] In this example, Aspen PIMS process simulation software was used in the central control room of a wastewater treatment plant to analyze parameters and plan separation treatment paths for three-level quality-separated wastewater data. Water quality parameters (such as salt concentration, COD, and flow rate) for low-salt, medium-salt, and high-salt wastewater were input into Aspen PIMS. Using the software's built-in wastewater treatment model library, a wastewater treatment process model was constructed, encompassing unit operations such as pretreatment, nanofiltration, reverse osmosis, membrane distillation, and pervaporation. By simulating the water quality changes and energy consumption of different treatment paths, the optimal membrane treatment process configuration was selected. For example, a "pretreatment filtration + nanofiltration + reverse osmosis" process was selected for low-salt wastewater, a "prefiltration + membrane distillation" process for medium-salt wastewater, and a "pretreatment + pervaporation + forward osmosis" process for high-salt wastewater. In the low-salt wastewater treatment workshop of the wastewater treatment plant, Prominent's DOSING chemical dosing system was used for pretreatment filtration of low-salt-load wastewater based on the membrane treatment process configuration. A coagulant (such as polyaluminum chloride, at a dosage of 10-20 mg / L) and a flocculant (such as polyacrylamide, at a dosage of 1-2 mg / L) were added to the wastewater using a ProMinent dosing pump to form larger flocs of suspended solids and colloidal particles. Filtration was performed using an Aquat ultrafilter device, with a pressure of 0.1-0.3 MPa and a flow rate of 15-20 m / h, to obtain low-salinity wastewater clarified liquid data. The clarified liquid was analyzed for dissolved contaminants using an Agilent 1260 InfinityII liquid chromatography-mass spectrometry (LC-MS), analyzing the organic and inorganic components and generating a low-salinity wastewater composition profile. In the membrane treatment plant's membrane treatment workshop, based on the low-salinity wastewater composition profile, Dow Chemical's FilmTec nanofiltration membrane selection software was used to select nanofiltration membranes and configure system parameters. Based on the molecular weight and ion valence distribution of pollutants in the component profile, Dow's FilmTec NF270 nanofiltration membrane was selected. Its high divalent ion rejection and low monovalent ion rejection make it suitable for treating this low-salinity wastewater. The software set the system recovery rate to 80%, the operating pressure to 1.0-1.5 MPa, and the membrane flux to 15-20 LMH (liters per square meter per hour). The required number of membrane elements was calculated based on the wastewater flow rate. After generating the nanofiltration process parameter set, the nanofiltration membrane elements were installed according to the parameter set requirements, and the nanofiltration system operating parameters were set. After the system was started, the clarified low-salinity wastewater was subjected to nanofiltration treatment. Various data during the nanofiltration process were monitored in real time to generate a wastewater nanofiltration treatment data stream. For the detailed implementation process of step S34, please refer to the substeps of step S34.In the wastewater treatment plant's reverse osmosis system control room, GE Digital Energy's LogicMaster 1607 controller was used to optimize reverse osmosis parameters based on nanofiltration energy efficiency assessment data. Nanofiltration system energy consumption data (such as electricity consumption and permeate flow rate) was input into the LogicMaster 1607 controller. The built-in energy efficiency assessment model calculated the nanofiltration system's specific energy efficiency. For example, if the nanofiltration system consumes 3 kWh of electricity per cubic meter of filtrate produced, the specific energy efficiency is 3 kWh / m³. Based on these assessment results, reverse osmosis system operating parameters, such as recovery rate, operating pressure, and membrane flux, were adjusted. For example, the reverse osmosis system's recovery rate was adjusted from 75% to 80%, and the operating pressure was adjusted from 2.0 MPa to 1.8 MPa to reduce energy consumption. After generating a reverse osmosis process parameter set, the reverse osmosis system's operating parameters were set according to the parameter set, and the nanofiltration filtrate was further treated to generate low-salt treated fluid data. In the medium-salt wastewater treatment workshop of the wastewater treatment plant, an Aquat ultrafilter was used to pre-filter the medium-salt-loaded wastewater to obtain clarified liquid data. The pre-filtration process was similar to step S32, with the filtration pressure set at 0.2 MPa and the filtration flow rate at 18 m / h. The clarified liquid was analyzed for dissolved contaminants using an Agilent 1260 Infinity II liquid chromatography-mass spectrometry (LC-MS) instrument to generate a component profile of the medium-salt wastewater. The membrane distillation process was configured using Suez membrane distillation process design software. Based on the water quality characteristics and component profile of the medium-salt wastewater, a direct contact membrane distillation process was selected, using a hydrophobic PTFE membrane with a pore size of 0.22 μm. The distillation temperature was set to 60-70°C and the vacuum to -0.08 MPa in the software to generate a membrane distillation parameter set. Membrane distillation equipment was installed according to the parameter set requirements, and operating parameters were set. Direct contact membrane distillation was performed on the clarified medium-salt wastewater, generating medium-salt treated liquid data. In the high-salt wastewater treatment plant, Prominent's DOSING chemical dosing system pre-treated the high-salt wastewater. This included adjusting the pH to 6-9 and dosing scale inhibitors (such as polyphosphate at a dosage of 5-10 mg / L) to prevent scaling on the membrane surface. The pre-treated wastewater entered the buffer tank, where a level controller maintained the liquid level at a stable 50%-70%. Engineers used Dow Chemical's FilmTec pervaporation-forward osmosis collaborative process design software to configure the process.Based on the water quality characteristics of the high-salinity wastewater, a combination of pervaporation and forward osmosis membranes was selected. The pervaporation membrane was set to an operating temperature of 40-50°C and an operating pressure of 0.1-0.2 MPa. The forward osmosis membrane was supplied with a 20% NaCl solution at an operating pressure of 1.0-1.5 MPa. After generating a parameter set for the pervaporation-forward osmosis synergistic process, the membrane equipment was installed and operating parameters were set according to the parameter set. The high-salinity wastewater conditioning fluid was subjected to directional separation treatment, generating high-salinity treated fluid data. In the wastewater treatment plant's central control room, data for low-salinity, medium-salinity, and high-salinity treated fluids were aggregated and recorded as graded salt treated fluid data. This data was centrally managed using the process control system. FilmTec membrane performance analysis software from Dow Chemical was used to identify membrane flux decay patterns. Membrane flux data from different treatment stages was input into the software. Using a built-in data analysis model, the software identified trends in membrane flux decay and determined the type and severity of membrane fouling. For example, if the membrane flux decreases linearly over time, it may be due to physical blockage; if it decreases exponentially, it may be due to chemical contamination. Based on the identification results, membrane contamination trend data is generated. For the detailed implementation process of step S39, please refer to the sub-steps of step S39.

[0079] Preferably, step S34 includes the following steps:

[0080] Step S341: dynamically compare the concentrate and filtrate flow rates of the wastewater nanofiltration treatment data stream to obtain a dynamic data set of nanofiltration recovery rates;

[0081] Step S342: monitoring the conductivity of the concentrate and the filtrate in real time using a conductivity sensor, calculating the conductivity difference between the concentrate and the filtrate based on the conductivity of the concentrate and the filtrate; calculating the ion retention rate based on the conductivity difference between the concentrate and the filtrate to obtain inorganic salt retention rate data;

[0082] Step S343: monitoring the TOC difference between the concentrate and the filtrate using an online TOC analyzer; quantifying the organic matter retention efficiency based on the TOC difference between the concentrate and the filtrate to obtain organic matter retention efficiency data;

[0083] Step S344: performing dynamic analysis of the initial flux and real-time flux of the wastewater nanofiltration treatment data stream, and calculating the flux decay rate to obtain a membrane flux decay rate sequence; generating a membrane flux decay kinetic curve based on the membrane flux decay rate sequence;

[0084] Step S345: collecting the pressure difference change rate across the membrane in real time through the differential pressure sensor network; evaluating the degree of membrane fouling based on the pressure difference change rate across the membrane to obtain membrane fouling type discrimination data;

[0085] Step S346: Monitor the power quality of the pump group to obtain pump group energy consumption data; obtain dynamic monitoring data of produced water flow through an electromagnetic flowmeter; calculate unit energy consumption efficiency based on the pump group energy consumption data and the dynamic monitoring data of produced water flow to obtain nanofiltration energy efficiency evaluation data;

[0086] Step S347: Perform a comprehensive evaluation of the nanofiltration retention efficiency based on the nanofiltration recovery rate dynamic data set, inorganic salt retention rate data, membrane flux attenuation kinetic curve, membrane fouling type discrimination data and nanofiltration energy efficiency evaluation data to obtain nanofiltration retention efficiency evaluation data.

[0087] In this example, a process control system (PCS) was used in the monitoring room of the nanofiltration system at a wastewater treatment plant to dynamically compare the concentrate and filtrate flow rates in the wastewater nanofiltration data stream. The system uses electromagnetic flowmeters (such as the Rosemount 3051) installed at the inlet and outlet of the nanofiltration system to collect real-time concentrate and filtrate flow data. Flow values ​​are recorded every minute and stored in the PCS7 database. The nanofiltration recovery rate is dynamically calculated using the formula: Nanofiltration recovery rate = (filtrate flow rate / (concentrate flow rate + filtrate flow rate)) × 100%. The minute-by-minute recovery rate data is combined into a dynamic nanofiltration recovery data set. At the nanofiltration system site in the wastewater treatment plant, line conductivity sensors are used to monitor the conductivity of the concentrate and filtrate in real time. The sensors are installed on the concentrate and filtrate outlet pipes of the nanofiltration system, collecting conductivity data every 10 seconds and transmitting it to the Siemens Simatic PCS7 system. Within the PCS7 system, a logic control program is programmed to calculate the conductivity difference between the concentrate and filtrate. For example, if the concentrate conductivity is 2000 μS / cm and the filtrate conductivity is 500 μS / cm, the conductivity difference is 1500 μS / cm. According to the formula: Inorganic salt retention = (Concentrate conductivity - filtrate conductivity) / Concentrate conductivity × 100%, the calculated inorganic salt retention is 75%. This inorganic salt retention data is stored in the PCS7 system database. At the wastewater treatment plant's nanofiltration system, an online TOC analyzer monitors the TOC content of the concentrate and filtrate. The analyzer is installed on the concentrate and filtrate outlet pipes of the nanofiltration system and automatically collects and analyzes water samples every 30 minutes. The analyzer transmits the TOC data to the Siemens Simatic PCS7 system. Within PCS7, a logic control program is programmed to calculate the TOC difference between the concentrate and filtrate. For example, if the TOC of the concentrate is 30 mg / L and the TOC of the filtrate is 10 mg / L, the TOC difference is 20 mg / L. According to the formula: Organic matter retention efficiency = (Concentrate TOC - Filtrate TOC) / Concentrate TOC × 100%, the calculated organic matter retention efficiency is 66.7%. Organic matter retention efficiency data will be stored in the PCS7 system database. In the wastewater treatment plant's nanofiltration system monitoring room, the process control system dynamically analyzes the initial and real-time fluxes of the wastewater nanofiltration treatment data stream. The system collects real-time membrane flux data using flow sensors installed at the inlet and outlet of the nanofiltration membrane elements (such as those included with the Aquatech UltraFilter ultrafiltration device). The initial flux is set at 20 LMH (liters per square meter per hour), and real-time flux is recorded every 10 minutes. The flux decay rate series is calculated using the formula: Flux decay rate = (initial flux - real-time flux) / initial flux × 100%. The flux decay rate data is imported into Excel software, and a membrane flux decay kinetic curve is generated using Excel's charting function.At the wastewater treatment plant's nanofiltration system, a network of differential pressure sensors is used to collect the rate of change of the differential pressure across the membrane in real time. These sensors, installed on the inlet and outlet sides of the nanofiltration membrane elements, collect differential pressure data every five seconds and transmit it to the Siemens Simatic PCS7 system. Within PCS7, a logic control program is programmed to calculate the rate of change of the differential pressure. For example, if the differential pressure increases from 0.2 MPa to 0.25 MPa over 10 minutes, the rate of change is 0.005 MPa / minute. Based on the differential pressure change rate and membrane flux data, a pre-defined membrane fouling assessment model is used to determine the type of membrane fouling. For example, if the differential pressure change rate exceeds 0.003 MPa / minute and the flux decay rate exceeds 10% / hour, it is considered physical blockage. This membrane fouling type determination data is stored in the PCS7 system database. In the wastewater treatment plant's pump control room, a PowerLogic power quality analyzer is used to monitor the power quality of the pump units. An analyzer installed at the pump unit's power input collects real-time energy data, including voltage, current, and power factor. Pump unit energy consumption data is recorded every 15 minutes and transmitted to the Siemens Simatic PCS7 system. Simultaneously, an electromagnetic flowmeter (such as the Rosemount 3051) installed on the pump unit's outlet pipe collects produced water flow rate data every 10 seconds and transmits it to the PCS7 system. Within PCS7, a logic control program is programmed to calculate specific energy efficiency. For example, if the pump unit consumes 10 kWh of electricity and produces 100 cubic meters of water in a given hour, the specific energy efficiency is 0.1 kWh / cubic meter. This nanofiltration energy efficiency evaluation data is stored in the PCS7 database. In the wastewater treatment plant's central control room, a comprehensive evaluation of nanofiltration retention efficiency is performed using the Siemens Simatic PCS7 process control system and Aspen PIMS process simulation software. Extract the nanofiltration recovery rate dynamic data set, inorganic salt retention rate data, membrane flux decay kinetic curve, membrane fouling type discrimination data, and nanofiltration energy efficiency evaluation data obtained in step S346 from the PCS7 system database. Import this data into the Aspen PIMS software, and use the software's built-in evaluation model to comprehensively calculate the nanofiltration retention efficiency. For example, set the recovery rate weight to 30%, the inorganic salt retention rate weight to 30%, the membrane flux decay weight to 20%, the membrane fouling type weight to 10%, and the energy efficiency weight to 10%, and calculate the nanofiltration retention efficiency evaluation score through weighted average calculation.

[0088] It is particularly important that step S39 further includes the following steps:

[0089] Step S391: Classifying the membrane fouling trend data by fouling mechanism to obtain membrane fouling characteristic classification data;

[0090] Step S392: quantifying the pollution index based on the membrane flux attenuation rate sequence and the pressure difference change rate across the membrane to obtain a quantitative index of the pollution degree; constructing an electric field frequency matching scheme based on the membrane pollution feature classification data and the quantitative index of the pollution degree;

[0091] Step S393: collecting membrane surface potential gradient distribution data in real time through a potential sensor array; performing electric field intensity configuration based on the membrane surface potential gradient distribution data to obtain an electric field intensity configuration data set;

[0092] Step S394: performing pulse timing pattern matching according to the membrane flux decay kinetics curve to obtain electric field timing control parameters;

[0093] Step S395: Optimizing the energy efficiency and pollution suppression targets according to the electric field timing control parameters to obtain the alternating electric field energy consumption control parameters;

[0094] Step S396: performing virtual verification and robustness testing on the alternating electric field energy consumption control parameters to obtain an alternating electric field parameter configuration solution;

[0095] Step S397: performing alternating electric field assisted control on each membrane unit based on the alternating electric field parameter configuration scheme to obtain membrane separation efficiency monitoring data, wherein each membrane unit is a nanofiltration membrane unit, a reverse osmosis membrane unit, a membrane distillation unit, a pervaporation membrane unit and a forward osmosis membrane unit.

[0096] In this embodiment, a process control system is used in the central control room of a wastewater treatment plant to classify membrane fouling trend data by fouling mechanism. By analyzing the membrane flux decay kinetic curve and the pressure difference change rate across the membrane, combined with a characteristic database of membrane fouling types, the membrane fouling trend data is classified into physical blockage, chemical fouling, and biological fouling categories. For example, if the membrane flux decreases linearly and the pressure difference increases slowly, it is classified as physical blockage; if the flux decreases sharply and the pressure difference increases rapidly, it is classified as chemical fouling. The classification results are stored in the database of the PCS7 system. MATLAB software is used to quantify the pollution index of the membrane flux decay rate series and the pressure difference change rate across the membrane. The membrane flux decay rate series and the pressure difference change rate data are imported, and the Curve Fitting Toolbox of MATLAB is used to perform curve fitting to calculate the quantitative index of the degree of pollution. For example, for a certain set of data, the calculated quantitative index of the degree of pollution is 0.8 (full scale is 1), indicating a high degree of pollution. Based on the pollution characteristic classification data (e.g., physical clogging, chemical contamination), an electric field frequency matching scheme was pre-set in MATLAB. A low-frequency electric field (e.g., 50Hz-100Hz) was set for physical clogging, and a high-frequency electric field (e.g., 1kHz-10kHz) was set for chemical contamination. This generated an electric field frequency matching scheme. In the membrane treatment workshop of the wastewater treatment plant, a potential sensor array was used to collect real-time data on the potential gradient distribution on the membrane surface. The potential sensor array was installed on the surface of membrane units such as nanofiltration and reverse osmosis membranes, with a sensor spacing of 5cm, covering the entire membrane surface. The potential data was transmitted to the Siemens Simatic PCS7 system at a real-time frequency of 10Hz via a data acquisition card. Within PCS7, data visualization tools were used to generate potential gradient distribution maps, visually displaying the potential variations across different regions of the membrane surface. Based on the potential gradient distribution maps, electric field strength was configured using GE Digital's Predix software to generate an electric field strength configuration dataset. In the wastewater treatment plant's central control room, pulse timing pattern matching was performed on the membrane flux decay kinetics curve using Origin Pro software from Origin Lab. The membrane flux attenuation kinetics curve is compared with the preset pulse timing pattern library. For example, if the curve shows periodic fluctuations, it is matched with the "periodic pulse mode" with a period of about 30 minutes; if the curve shows a single rapid drop and then stabilizes, it is matched with the "single pulse mode" with a pulse duration of about 5 minutes. According to the matching results, the electric field timing control parameters, including pulse frequency, duty cycle, and duration, are set in the Origin Pro software. For example, for the periodic pulse mode, the pulse frequency is set to 0.1Hz (one pulse every 10 seconds), the duty cycle is 50%, and the duration is 30 minutes. In the optimization analysis room of the wastewater treatment plant, MATLAB software is used in combination with the electric field timing control parameters to optimize the energy efficiency-pollution suppression target.An optimization model was constructed in MATLAB, with the objective functions of minimizing energy consumption and maximizing pollution suppression effectiveness. For example, a genetic algorithm (GA) was used to search for the optimal alternating electric field energy control parameters, setting the energy consumption weight to 0.4 and the pollution suppression weight to 0.6. The optimization results yielded an electric field intensity of 300 V / m, a pulse duration of 10 seconds, and a period of 30 seconds, resulting in an energy consumption of 1.2 kWh / m³ and an 85% pollution suppression effectiveness. The optimized energy control parameters were recorded in the MATLAB workspace. In the wastewater treatment plant's simulation test room, the alternating electric field energy control parameters were virtually verified and robustness tested using ANSYS Twin Builder software. Digital twin models of membrane units, such as nanofiltration and reverse osmosis membranes, were constructed, including the membrane's physical structure, electric field distribution, and fluid dynamics. The energy control parameters were input into the model, and virtual operation simulations were performed to observe indicators such as membrane separation efficiency, energy consumption, and pollution suppression effectiveness. For example, the simulations revealed that a certain parameter combination improved membrane separation efficiency by 15%, increased energy consumption by 5%, and improved pollution suppression effectiveness by 10%. By adjusting parameters and conducting multiple simulation tests, the robustness of the parameters was verified. Ultimately, a set of parameter combinations that exhibited stability under various operating conditions was selected to generate an AC electric field parameter configuration scheme. In the membrane treatment workshop of the wastewater treatment plant, a Siemens Simatic S7-1500 programmable logic controller (PLC) and Rotork actuators were used to implement AC electric field-assisted control of each membrane unit according to the AC electric field parameter configuration scheme. The PLC reads parameters such as electric field intensity and pulse timing from the parameter configuration scheme and controls the Rotork actuator to adjust the output of the electric field generator. For example, for a nanofiltration membrane unit, the electric field intensity was set to 300 V / m and the pulse timing to 10 seconds / 30 seconds (operating / pausing). After the electric field generator was activated, the operating status of the membrane unit, including membrane flux, differential pressure, and energy consumption, was monitored in real time. Operating data was recorded using the Siemens Simatic PCS7 system to generate membrane separation efficiency monitoring data.

[0097] Preferably, step S4 includes the following steps:

[0098] Step S41: salt concentration distribution data based on the graded salt treatment liquid data;

[0099] Step S42: configuring parameters of the cascade evaporation system according to the salt concentration distribution data to obtain initial operating parameters of the evaporator;

[0100] Step S43: performing evaporator allocation scheduling on the three-level separated wastewater based on the initial operating parameters of the evaporator to obtain evaporation treatment operating condition data;

[0101] Step S44: performing a heat energy conversion efficiency evaluation based on the evaporation process operating condition data to obtain step-by-step energy efficiency evaluation data;

[0102] Step S45: adjusting the evaporator thermal system according to the step-by-step distribution energy efficiency evaluation data to obtain heat energy recovery optimization data;

[0103] Step S46: sampling and testing the concentrated liquid in the evaporation process in real time to obtain dynamic data on the components of the concentrated liquid; performing phase equilibrium prediction based on the dynamic data on the components of the concentrated liquid to obtain cocrystallization behavior prediction data;

[0104] Step S47: performing evaporator operation control based on the co-crystallization behavior prediction data to obtain process closed-loop monitoring data.

[0105] In this example, the Siemens Simatic PCS7 process control system was used in the wastewater treatment plant's central control room to integrate salt concentration data from low-salt, medium-salt, and high-salt process fluids to generate salt concentration distribution data. This data, derived from the graded salt process fluid data, covers salt concentration and flow information at each treatment stage. For example, the low-salt process fluid has a salt concentration of 2% and a flow rate of 50 cubic meters per hour; the medium-salt process fluid has a salt concentration of 8% and a flow rate of 30 cubic meters per hour; and the high-salt process fluid has a salt concentration of 15% and a flow rate of 20 cubic meters per hour. In the wastewater treatment plant's evaporation system control room, the parameters of the cascade evaporation system were configured using Aspen PIMS process simulation software. Based on the salt concentration distribution data, a model of the cascade evaporation system was constructed in Aspen PIMS, including units such as the preheater, evaporator, and condenser. The evaporator's inlet water salt concentration range was set to 2%-15%, and the flow rate range was set to 20-50 cubic meters per hour. Based on the simulation results, the initial evaporator operating parameters were determined: evaporation temperatures of 80°C for the first-effect evaporator, 60°C for the second-effect evaporator, and 40°C for the third-effect evaporator. Furthermore, the steam ejector was configured with a steam pressure of 0.5 MPa and a vacuum pump with a vacuum level of -0.08 MPa. In the wastewater treatment plant's central control room, the Siemens Simatic PCS7 process control system was used to allocate and schedule evaporators for the three-stage separated wastewater based on the initial evaporator operating parameters. The system automatically distributes wastewater to different evaporators based on salinity and flow rate. For example, low-salinity wastewater (2%) is allocated to the first-effect evaporator, medium-salinity wastewater (8%) to the second-effect evaporator, and high-salinity wastewater (15%) to the third-effect evaporator. The PCS7 system monitors the operating status of each evaporator in real time, including liquid level, temperature, and pressure, and adjusts wastewater flow and evaporator parameters based on feedback, generating evaporation treatment operating data. In the evaporation system monitoring room of the wastewater treatment plant, GE Digital's Predix Industrial Internet of Things platform is used to evaluate the thermal energy conversion efficiency of the evaporation treatment operating condition data. The Predix platform integrates the steam consumption, water production flow and temperature data of the evaporator to calculate the thermal energy conversion efficiency. For example, if a single-effect evaporator consumes 10 tons of steam per hour, produces 8 tons of water per hour, and the steam temperature is 180°C, the thermal energy conversion efficiency is calculated to be 75% according to the heat balance formula. The platform generates cascade distribution energy efficiency evaluation data. In the evaporation system control room of the wastewater treatment plant, the Aspen ExchangerDesign & Rating (EDR) software is used to adjust the evaporator thermal system. Based on the cascade distribution energy efficiency evaluation data obtained in step S44, the performance of the evaporator thermal system is simulated in Aspen EDR.For example, if an assessment shows the thermal efficiency of the first-effect evaporator is 75%, but the target efficiency is 80%, heat energy distribution is optimized by adjusting the steam pressure of the steam ejector to 0.55 MPa. Simultaneously, the condensate recovery system is optimized by reducing the condensate temperature from 50°C to 40°C to improve heat recovery efficiency. The adjusted parameters generate heat recovery optimization data. At the wastewater treatment plant's evaporation system, a Mettler Toledo Thornton 556 online moisture analyzer and a Hach DR3900 spectrophotometer are used to sample and test the concentrate during the evaporation process in real time. The online moisture analyzer measures the moisture content of the concentrate every minute, while the DR3900 spectrophotometer analyzes the salt concentration and impurity content of the concentrate every hour. The data is transmitted to the Siemens Simatic PCS7 system and, combined with the heat recovery optimization data from step S45, phase equilibrium predictions are performed using Aspen Exchanger Design & Rating (EDR) software. Based on the prediction model, the co-crystallization behavior at different temperatures and pressures is calculated to generate co-crystallization behavior prediction data. For example, it is predicted that the co-crystallization ratio of sodium chloride and sodium sulfate at 100°C and 0.1 MPa is 3:1.

[0106] It is particularly important that step S47 further includes the following steps:

[0107] Step S471: performing infrared thermal imaging monitoring on the evaporator wall according to the co-crystallization behavior prediction data to obtain wall surface temperature distribution data; performing abnormal hot spot identification according to the wall surface temperature distribution data to obtain coking position identification data;

[0108] Step S472: performing a coking severity assessment based on the coking location identification data and the wall temperature distribution data to obtain coking risk assessment data;

[0109] Step S473: regulating the variable frequency ultrasonic descaling system according to the coking risk assessment data to obtain ultrasonic descaling control parameters; monitoring the ultrasonic descaling control parameters in real time to obtain descaling effect feedback data;

[0110] Step S474: adjusting ultrasonic parameters according to the descaling effect feedback data to obtain evaporation efficiency improvement data;

[0111] Step S475: performing organic property identification on the concentrated solution to obtain characteristic data of organic interfering substances; screening a preset crystallization modifier library for crystallization modifier formulations based on the characteristic data of organic interfering substances to obtain a crystallization modifier addition plan;

[0112] Step S476: performing execution control and effect verification on the crystallization modifier addition plan to obtain crystallization quality control data;

[0113] Step S477: Systematically correlate the heat recovery optimization data, evaporation efficiency improvement data, low-salt treatment liquid data, medium-salt treatment liquid data, high-salt treatment liquid data, and crystallization quality control data to obtain process closed-loop monitoring data.

[0114] In this example, a FLIR thermal imaging camera was used to perform infrared thermal imaging monitoring of the evaporator wall surface at the evaporation system site of a wastewater treatment plant. The thermal imaging camera was installed near the evaporator wall and captured thermal imaging data every five minutes. The data was then transmitted to the Siemens Simatic PCS7 process control system. Within the PCS7 system, thermal imaging analysis software (such as FLIR Research IR) was used to analyze the wall temperature distribution data to identify hotspots with abnormal temperatures. For example, if the temperature in a certain area exceeded a set threshold (e.g., 120°C), it was marked as an abnormal hotspot. The system automatically generated coking location identification data. In the wastewater treatment plant's central control room, Aspen Exchanger Design & Rating (EDR) software was used to combine the coking location identification data with the wall temperature distribution data to assess the severity of coking. The evaporator wall temperature distribution data and the coking location were input into Aspen EDR. The software, based on a built-in coking risk assessment model, calculated the increase in thermal resistance and pressure drop in the coking area. For example, if the increase in thermal resistance in a coking area exceeded 30%, it was assessed as a high-risk coking area. The system generates coking risk assessment data. In the wastewater treatment plant's evaporation system control room, Siemens' Simatic PCS7 process control system controls the variable-frequency ultrasonic descaling system based on this coking risk assessment data. For high-risk coking areas, the system automatically adjusts the ultrasonic descaling equipment's frequency and power. For example, the ultrasonic frequency can be increased from 20kHz to 25kHz, and the power from 500W to 700W. Rotork actuators adjust the ultrasonic equipment's operating parameters based on PCS7 system instructions. The system monitors the ultrasonic equipment's operating status, including frequency, power, and operating time, in real time to generate ultrasonic descaling control parameters. Furthermore, differential pressure sensors (such as those used by Foxboro) installed on the evaporator wall collect real-time differential pressure data, providing feedback on descaling effectiveness. In the wastewater treatment plant's central control room, GE Digital's Predix Industrial IoT platform adjusts ultrasonic parameters based on descaling feedback. The Predix platform analyzes differential pressure changes, temperature distribution, and ultrasonic equipment operating parameters during the descaling process in real time. For example, if the differential pressure sensor indicates a 20% drop in differential pressure in a specific area, this indicates successful descaling. Based on this analysis, the system automatically adjusts ultrasonic parameters, such as reducing the frequency to 22kHz and the power to 600W. These adjustments generate data showing an improvement in evaporation efficiency, for example, from 75% to 85%. The adjusted parameters and the improvement results are recorded in the Predix platform.In the wastewater treatment plant's water quality analysis laboratory, an Agilent 1260 Infinity II liquid chromatography-mass spectrometry (LC-MS) instrument is used to characterize organic matter in the concentrate. Samples of the concentrate are analyzed to detect organic interfering compounds, such as organic acids and aromatic hydrocarbons. Based on the LC-MS analysis results, characteristic data for the organic interfering compounds is generated, including the type, concentration, and molecular weight distribution of the organic compounds. For example, benzoic acid at a concentration of 50 mg / L and phenol at a concentration of 30 mg / L were detected in the concentrate. Using MATLAB software, a pre-defined library of crystallization modifiers was screened based on the organic interfering compound characteristic data. For example, a modifier formula that effectively inhibits the crystallization of benzoic acid and phenol was selected, and a crystallization modifier addition plan was generated. At the wastewater treatment plant's evaporation system, Prominent's DOSING chemical dosing system was used to control the execution of the crystallization modifier addition plan. A crystallization modifier is added to the concentrate at a set dosage (e.g., 5 mg / L for benzoic acid inhibitor and 3 mg / L for phenol inhibitor) using a ProMinent dosing pump. The Siemens Simatic PCS7 process control system monitors the crystallization modifier dosage and concentrate quality changes in real time. A particle counter (such as a Mettler-Toledo particle counter) installed at the evaporator outlet measures the size and number of crystal particles to verify the effectiveness of the modifier. For example, if the average particle size of the crystals increases from 10 μm to 15 μm and the number decreases by 30%, the modifier is effective, generating crystallization quality control data. In the wastewater treatment plant's central control room, GEDigial's Predix Industrial IoT platform is used to systematically link data from heat recovery optimization, evaporation efficiency improvements, low-salt, medium-salt, and high-salt treated liquids, and crystallization quality control. The Predix platform integrates this data to form a closed-loop process monitoring system. For example, heat recovery optimization data showed that the condensate recovery rate increased by 15%, evaporation efficiency improvement data showed that the evaporation rate increased by 20%, and crystallization quality control data showed that the crystal purity reached 95%.

[0115] Preferably, step S5 includes the following steps:

[0116] Step S51: extracting water quality features from the low-salt treatment liquid data, the medium-salt treatment liquid data, and the high-salt treatment liquid data to obtain a treatment effect feature vector;

[0117] Step S52: fitting a membrane flux attenuation curve based on the membrane separation efficiency monitoring data to obtain membrane life prediction data;

[0118] Step S53: performing response surface modeling based on the membrane separation efficiency monitoring data to obtain membrane treatment optimization space data;

[0119] Step S54: constructing a thermal energy cycle topology based on the evaporation efficiency optimization data to obtain a thermal energy cycle optimization model;

[0120] Step S55: evaluating the purity and particle morphology of the salt product based on the crystallization quality control data to obtain salt product quality evaluation data;

[0121] Step S56: constructing a wastewater treatment virtual system environment based on process closed-loop monitoring data, treatment effect characteristic vectors, membrane life prediction data, membrane treatment optimization space data, thermal energy cycle optimization model, and salt product quality assessment data;

[0122] Step S57: performing parameter decision analysis based on the wastewater treatment virtual system environment to obtain wastewater treatment system operating parameters;

[0123] Step S58: Perform scenario verification and configuration on the wastewater treatment based on the wastewater treatment system operating parameters to obtain a final system operation plan.

[0124] In this example, water quality feature extraction was performed in the wastewater treatment plant's central control room using the Siemens Simatic PCS7 process control system on data from low-salt, medium-salt, and high-salt treated fluids. This data, covering key indicators such as salinity, pH, and flow rate, was retrieved from the system database. Using the PCS7 system's built-in data analysis module, statistical indicators such as the mean and variance of each water quality parameter were calculated to form a treatment effect feature vector. For example, the low-salt treated fluid data had a mean salinity of 2% and a mean pH of 7.5; the medium-salt treated fluid had a mean salinity of 8% and a mean pH of 7.2; and the high-salt treated fluid had a mean salinity of 15% and a mean pH of 7.0. In the wastewater treatment plant's membrane treatment plant, GE Digital's Predix Industrial Internet of Things platform was used to fit membrane flux decay curves based on membrane separation efficiency monitoring data. Using the Predix platform's advanced data analysis tools, a membrane flux decay curve was fitted, with run time as the horizontal axis and membrane flux as the vertical axis. Based on this curve, combined with the membrane life assessment model provided by the equipment supplier, the remaining useful life of the membrane elements can be predicted. For example, for a batch of nanofiltration membranes that has operated for 1000 hours, the remaining useful life is estimated to be approximately 2000 hours based on the fitted curve and model. This allows for pre-planning of membrane replacement and maintenance. In the wastewater treatment plant's central control room, response surface modeling was performed using MATLAB software based on membrane separation efficiency monitoring data. Key parameters influencing membrane treatment performance (such as operating pressure, recovery rate, and influent quality) were used as independent variables, and membrane separation efficiency was used as the dependent variable to construct the response surface model. Using MATLAB's Statistics and Machine Learning Toolbox, statistical analysis methods such as multivariate linear regression were used to determine the interactions between the various parameters and their impact on membrane separation efficiency. Model analysis identified the optimal parameter combination range for improving membrane separation efficiency, generating membrane treatment optimization space data. In the wastewater treatment plant's evaporation system control room, Aspen Exchanger Design & Rating (EDR) software was used to construct a thermal energy cycle topology based on the evaporation efficiency optimization data. Based on the operating parameters of each evaporation system device (such as evaporation temperature, pressure, and steam flow) and the energy and material flow relationships, a thermal energy cycle topology diagram was constructed in Aspen EDR. This topology diagram details the flow paths and energy transfer processes of heat carriers such as steam and condensate between the various effects of the evaporation system. Through simulation calculations, the thermal energy cycle topology was optimized to improve the cascade utilization efficiency of thermal energy, ultimately resulting in an optimized thermal energy cycle model. In the crystallization workshop of the wastewater treatment plant, the purity and particle morphology of the crystallized salt product were evaluated using a Mastersizer 3000 laser particle size analyzer and a Mettler Toledo XRF spectrometer.XRF spectrometry is used to determine the chemical composition of the salt product, calculate the percentages of the main active ingredients (such as sodium chloride and sodium sulfate), and assess its purity. For example, the sodium chloride content of a batch of crystalline salt was measured to be 98.5%, and the impurity content was 1.5%, indicating high purity. A Mastersizer 3000 laser particle size analyzer was also used to measure the particle size distribution and analyze the particle morphology. Based on these test results, salt product quality assessment data was generated. In the wastewater treatment plant's central control room, GE Digital's Predix Industrial IoT platform was used to integrate process closed-loop monitoring data, treatment effect feature vectors, membrane life prediction data, membrane treatment optimization space data, thermal energy cycle optimization models, and salt product quality assessment data. A wastewater treatment virtual system environment was constructed on the Predix platform, linking and mapping various data according to the wastewater treatment process flow to form a comprehensive, dynamic virtual system model. In the wastewater treatment plant's central control room, Aspen PIMS process simulation software was used to conduct parameter decision analysis within the wastewater treatment virtual system environment. In Aspen PIMS, operating parameter ranges and constraints were set for different operating conditions, combining the wastewater treatment process flow and the performance models of each device. Simulations were conducted using different parameter combinations to evaluate their impact on key indicators such as wastewater treatment performance, energy consumption, and resource recovery. For example, simulations revealed that increasing the operating pressure of the nanofiltration system by 10% increased membrane flux by 5%, but energy consumption increased by 8%, resulting in suboptimal overall benefits. Based on these simulation results, the optimal wastewater treatment system operating parameter combination was identified. In the wastewater treatment plant's central control room, a scenario was configured to verify the wastewater treatment system based on the system's operating parameters using the Siemens Simatic PCS7 process control system. A virtual control scenario identical to the actual wastewater treatment system was constructed in PCS7, and the optimized operating parameters were imported into the scenario for simulated operation tests. For example, in the simulation tests, the nanofiltration and reverse osmosis equipment were controlled according to the new operating parameters to observe the overall system performance, including water quality compliance, energy consumption, and membrane flux changes. After multiple rounds of simulation tests and parameter adjustments, we finally determined a stable and efficient system operation plan and applied it to the actual wastewater treatment production process.

[0125] Preferably, the present invention further provides an industrial high-salt wastewater quality classification and treatment control system, which is used to execute the industrial high-salt wastewater quality classification and treatment control method as described above. The industrial high-salt wastewater quality classification and treatment control system comprises:

[0126] The wastewater collection and diversion module is used to collect the original wastewater parameter flow; extract the characteristics of the original wastewater parameter flow to obtain smoothed wastewater characteristic data; make diversion decisions on the wastewater based on the smoothed wastewater characteristic data to obtain wastewater diversion instructions;

[0127] The salt-grade pump control module is used to separate wastewater according to the wastewater diversion instruction and collect salt content in a graded manner to obtain pre-treated wastewater data; calculate the salt load distribution imbalance index based on the pre-treated wastewater data; and re-separate the wastewater based on the salt load distribution imbalance index to obtain three-grade quality-classified wastewater data;

[0128] The separation monitoring module is used to perform gradient membrane separation treatment on the separated wastewater based on the three-level quality-separated wastewater data to obtain graded salt treatment liquid data; perform membrane flux attenuation pattern recognition based on the graded salt treatment liquid data to obtain membrane fouling trend data; and perform membrane surface pollutant control based on the membrane fouling trend data to obtain membrane separation efficiency monitoring data;

[0129] The physical model evaporation module is used to perform phase equilibrium prediction modeling on the concentrated liquid based on the graded salt treatment liquid data to obtain co-crystallization behavior prediction data; the evaporator operation is controlled based on the co-crystallization behavior prediction data to obtain process closed-loop monitoring data, wherein the concentrated liquid is the graded salt treatment liquid;

[0130] The architecture solution module is used to build a wastewater treatment virtual system environment based on membrane separation efficiency monitoring data and process closed-loop monitoring data; and to build a final system operation plan based on the wastewater treatment virtual system environment.

[0131] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0132] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling the quality and classification of industrial high-salt wastewater, characterized in that: The following steps are involved: Step S1: collecting raw wastewater parameter flow; Extract features from the original wastewater parameter flow to obtain smoothed wastewater feature data; Make wastewater diversion decisions based on smoothed wastewater characteristic data to obtain wastewater diversion instructions; Step S2: Separate the wastewater according to the wastewater diversion instruction, and collect the salt content in a graded manner to obtain pre-treated wastewater data; Calculate the salt load distribution imbalance index based on the pre-treated wastewater data; re-separate the wastewater based on the salt load distribution imbalance index to obtain three-level quality-separated wastewater data; Step S3: performing gradient membrane separation treatment on the separated wastewater according to the three-level quality-separated wastewater data to obtain graded salt treatment liquid data; performing membrane flux attenuation pattern recognition based on the graded salt treatment liquid data to obtain membrane fouling trend data; Control membrane surface pollutants based on membrane fouling trend data and obtain membrane separation efficiency monitoring data; Step S4: performing phase equilibrium prediction modeling on the concentrated liquid based on the graded salt treatment liquid data to obtain co-crystallization behavior prediction data; performing evaporator operation control based on the co-crystallization behavior prediction data to obtain process closed-loop monitoring data, wherein the concentrated liquid is the graded salt treatment liquid; Step S5: constructing a wastewater treatment virtual system environment based on the membrane separation efficiency monitoring data and the process closed-loop monitoring data; and establishing a final system operation plan based on the wastewater treatment virtual system environment.

2. The method for controlling the quality and classification of industrial high-salt wastewater according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Deploy multiple types of sensors at the wastewater source to obtain sensor network topology data; the sensor network topology includes crystallization mother liquor points, equipment flushing water points, and exhaust gas scrubber drainage points. Each node is equipped with a conductivity sensor, a pH sensor, a temperature sensor, a turbidity sensor, and a total organic carbon content sensor. Step S12: collecting wastewater parameters in real time according to the sensor network topology data to obtain the original wastewater parameter stream, wherein the data collection frequency is 5 seconds / time; Step S13: Identify and repair abnormal values ​​of the original wastewater parameter stream to obtain a pre-treated wastewater parameter set; standardize wastewater indicators based on the pre-treated wastewater parameter set to obtain a wastewater indicator parameter table; Step S14: performing multi-layer wavelet transform decomposition on the wastewater index parameter table to obtain a wastewater parameter wavelet coefficient set; performing noise filtering based on the wastewater parameter wavelet coefficient set to obtain a denoised wastewater parameter wavelet coefficient set; Step S15: reconstructing the denoised wastewater parameter wavelet coefficient set by inverse wavelet transform to obtain smoothed wastewater characteristic data; Step S16: making wastewater diversion decisions based on the smoothed wastewater characteristic data to obtain wastewater diversion instructions.

3. The method for controlling the quality and classification of industrial high-salt wastewater according to claim 2, characterized in that: Step S16 includes the following steps: Step S161: extracting time-frequency features from the smoothed wastewater feature data to obtain a wastewater feature vector set; Step S162: using a preset stacked autoencoder to reduce the dimension of the wastewater feature vector set to obtain low-dimensional wastewater feature data; Step S163: constructing a wastewater source feature fingerprint library based on the wastewater feature low-dimensional data; Step S164: performing wastewater source feature matching on the wastewater feature low-dimensional data based on the wastewater source feature fingerprint library to obtain a wastewater source probability distribution table; Step S165: Calculating the wastewater salt load prediction value for the wastewater generation source based on the wastewater source probability distribution table to obtain wastewater salt concentration prediction data; Step S166: Perform multi-layer perceptron classification based on the wastewater salinity prediction data and the wastewater characteristic low-dimensional data to obtain a wastewater grade prediction result; Step S167: performing time-series smoothing on the wastewater level prediction results to obtain stable wastewater classification decision data; and generating a wastewater diversion instruction according to the stable wastewater classification decision data.

4. The method for controlling the quality and classification of industrial high-salt wastewater according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: parsing the wastewater diversion instruction to obtain valve execution status data; Step S22: Controlling the three-way diversion valve group to divert the wastewater according to the valve execution status data, and obtaining the wastewater physical diversion execution result, wherein the wastewater after diversion includes high-salt load wastewater, medium-salt load wastewater, and low-salt load wastewater. The three-way diversion valve group adopts a 485 bus cascaded intelligent electric valve, with a response time of <200ms and a positioning accuracy of ±1%; Step S23: Feedback monitoring of the wastewater physical diversion execution results to obtain diversion execution confirmation data; Step S24: based on the diversion execution confirmation data, the high-salt load wastewater, the medium-salt load wastewater and the low-salt load wastewater are collected in a targeted manner to obtain pre-treated wastewater data; Step S25: performing multi-parameter pseudo-color thermal imaging on the pre-treated wastewater data to obtain a salt load spatial distribution map; Step S26: Calculating the unit load distribution balance according to the salt load spatial distribution diagram to obtain the salt load distribution imbalance index; Step S27: Re-separate the wastewater according to the salt load distribution imbalance index to obtain three-level quality-classified wastewater data.

5. The method for controlling the quality and classification of industrial high-salt wastewater according to claim 4, characterized in that: Step S27 includes the following steps: Step S271: performing fuzzy logic regulation demand level mapping according to the salt load distribution imbalance index to obtain the flow regulation demand level; Step S272: performing optimal flow allocation on the pump groups based on the flow regulation demand level to obtain an optimal flow allocation decision plan; Step S273: generating a variable frequency pump group coordinated control parameter set according to the optimal flow distribution decision plan; performing closed-loop feedback control on the pump group according to the variable frequency pump group coordinated control parameter set to obtain flow rebalancing execution data; Step S274: Real-time monitoring and feedback of the flow rebalancing execution data are performed to obtain salt load balance closed-loop control parameters; online UV-visible spectroscopy detection of organic matter content in the wastewater is performed to obtain wastewater organic load distribution data; Step S275: Calculating the oxidation demand based on the wastewater organic load distribution data to obtain microbubble oxidation treatment parameters; Step S276: controlling the micro-nano bubble generator to perform shear resonance cavitation according to the microbubble oxidation treatment parameters to obtain bubble characteristic control data; Step S277: Perform gas-liquid interface enhanced oxidation treatment on the wastewater according to the bubble characteristic control data to obtain three-level quality-classified wastewater data.

6. The method for controlling the quality and classification of industrial high-salt wastewater according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Parameter analysis and separation treatment path planning are performed on the three-level quality-separated wastewater data to obtain a membrane treatment process configuration plan; Step S32: pre-treating and filtering the low-salt-load wastewater according to the membrane treatment process configuration plan to obtain low-salt wastewater clarified liquid data; performing soluble pollutant detection on the low-salt wastewater clarified liquid data to obtain a low-salt wastewater component spectrum; Step S33: selecting a nanofiltration membrane and configuring system parameters based on the low-salt wastewater component spectrum to obtain a nanofiltration process parameter set; performing nanofiltration on the clarified low-salt wastewater according to the nanofiltration process parameter set to obtain a wastewater nanofiltration treatment data stream; Step S34: performing interception efficiency evaluation based on the wastewater nanofiltration treatment data stream to obtain nanofiltration interception efficiency evaluation data; Step S35: Optimizing reverse osmosis parameters based on the nanofiltration energy efficiency evaluation data to obtain a reverse osmosis process parameter set; performing reverse osmosis treatment on the nanofiltration filtrate according to the reverse osmosis process parameter set to obtain low-salt treated liquid data; Step S36: Pre-filtering the medium-salt-loaded wastewater to obtain medium-salt wastewater clarified liquid data; configuring a membrane distillation process based on the medium-salt wastewater clarified liquid data to obtain a membrane distillation parameter set; performing direct contact membrane distillation on the medium-salt wastewater clarified liquid based on the membrane distillation parameter set to obtain medium-salt treated liquid data; Step S37: Pre-treating and buffering the high-salt-load wastewater to obtain high-salt wastewater conditioning liquid data; configuring a pervaporation-forward osmosis collaborative process based on the high-salt wastewater conditioning liquid data to obtain a pervaporation-forward osmosis collaborative process parameter set; and performing pervaporation-forward osmosis directional separation treatment on the high-salt wastewater conditioning liquid based on the pervaporation-forward osmosis collaborative process parameter set to obtain high-salt treated liquid data. Step S38: Recording the low-salt treatment liquid data, the medium-salt treatment liquid data, and the high-salt treatment liquid data as graded salt treatment liquid data; performing membrane flux attenuation pattern recognition based on the graded salt treatment liquid data to obtain membrane fouling trend data; Step S39: construct an alternating electric field according to the membrane fouling trend data to obtain an alternating electric field parameter configuration scheme; perform alternating electric field assisted control on each membrane unit based on the alternating electric field parameter configuration scheme to obtain membrane separation efficiency monitoring data, wherein each membrane unit is a nanofiltration membrane unit, a reverse osmosis membrane unit, a membrane distillation unit, a pervaporation membrane unit and a forward osmosis membrane unit.

7. The method for controlling the quality-based and graded treatment of industrial high-salt wastewater according to claim 6, characterized in that: Step S34 includes the following steps: Step S341: dynamically compare the concentrate and filtrate flow rates of the wastewater nanofiltration treatment data stream to obtain a dynamic data set of nanofiltration recovery rates; Step S342: monitoring the conductivity of the concentrate and the filtrate in real time using a conductivity sensor, calculating the conductivity difference between the concentrate and the filtrate based on the conductivity of the concentrate and the filtrate; calculating the ion retention rate based on the conductivity difference between the concentrate and the filtrate to obtain inorganic salt retention rate data; Step S343: monitoring the TOC difference between the concentrate and the filtrate using an online TOC analyzer; quantifying the organic matter retention efficiency based on the TOC difference between the concentrate and the filtrate to obtain organic matter retention efficiency data; Step S344: performing dynamic analysis of the initial flux and real-time flux of the wastewater nanofiltration treatment data stream, and calculating the flux decay rate to obtain a membrane flux decay rate sequence; generating a membrane flux decay kinetic curve based on the membrane flux decay rate sequence; Step S345: collecting the pressure difference change rate across the membrane in real time through the differential pressure sensor network; evaluating the degree of membrane fouling based on the pressure difference change rate across the membrane to obtain membrane fouling type discrimination data; Step S346: Monitor the power quality of the pump group to obtain pump group energy consumption data; obtain dynamic monitoring data of produced water flow through an electromagnetic flowmeter; calculate unit energy consumption efficiency based on the pump group energy consumption data and the dynamic monitoring data of produced water flow to obtain nanofiltration energy efficiency evaluation data; Step S347: Perform a comprehensive evaluation of the nanofiltration retention efficiency based on the nanofiltration recovery rate dynamic data set, inorganic salt retention rate data, membrane flux attenuation kinetic curve, membrane fouling type discrimination data and nanofiltration energy efficiency evaluation data to obtain nanofiltration retention efficiency evaluation data.

8. The method for controlling the quality and classification of industrial high-salt wastewater according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: salt concentration distribution data based on the graded salt treatment liquid data; Step S42: configuring parameters of the cascade evaporation system according to the salt concentration distribution data to obtain initial operating parameters of the evaporator; Step S43: performing evaporator allocation scheduling on the three-level separated wastewater based on the initial operating parameters of the evaporator to obtain evaporation treatment operating condition data; Step S44: performing a heat energy conversion efficiency evaluation based on the evaporation process operating condition data to obtain step-by-step energy efficiency evaluation data; Step S45: adjusting the evaporator thermal system according to the step-by-step distribution energy efficiency evaluation data to obtain heat energy recovery optimization data; Step S46: sampling and testing the concentrated liquid in the evaporation process in real time to obtain dynamic data on the components of the concentrated liquid; performing phase equilibrium prediction based on the dynamic data on the components of the concentrated liquid to obtain cocrystallization behavior prediction data; Step S47: performing evaporator operation control based on the co-crystallization behavior prediction data to obtain process closed-loop monitoring data.

9. The method for controlling the quality and classification of industrial high-salt wastewater according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: extracting water quality features from the low-salt treatment liquid data, the medium-salt treatment liquid data, and the high-salt treatment liquid data to obtain a treatment effect feature vector; Step S52: fitting a membrane flux attenuation curve based on the membrane separation efficiency monitoring data to obtain membrane life prediction data; Step S53: performing response surface modeling based on the membrane separation efficiency monitoring data to obtain membrane treatment optimization space data; Step S54: constructing a thermal energy cycle topology based on the evaporation efficiency optimization data to obtain a thermal energy cycle optimization model; Step S55: evaluating the purity and particle morphology of the salt product based on the crystallization quality control data to obtain salt product quality evaluation data; Step S56: constructing a wastewater treatment virtual system environment based on process closed-loop monitoring data, treatment effect characteristic vectors, membrane life prediction data, membrane treatment optimization space data, thermal energy cycle optimization model, and salt product quality assessment data; Step S57: performing parameter decision analysis based on the wastewater treatment virtual system environment to obtain wastewater treatment system operating parameters; Step S58: Perform scenario verification and configuration on the wastewater treatment based on the wastewater treatment system operating parameters to obtain a final system operation plan.

10. An industrial high-salt wastewater quality classification and treatment control system, characterized in that: For executing the industrial high-salt wastewater quality classification and treatment control method according to claim 1, the industrial high-salt wastewater quality classification and treatment control system comprises: The wastewater collection and diversion module is used to collect the original wastewater parameter flow; extract the characteristics of the original wastewater parameter flow to obtain smoothed wastewater characteristic data; make diversion decisions on the wastewater based on the smoothed wastewater characteristic data to obtain wastewater diversion instructions; The salt-grade pump control module is used to separate wastewater according to the wastewater diversion instruction and collect salt content in a graded manner to obtain pre-treated wastewater data; calculate the salt load distribution imbalance index based on the pre-treated wastewater data; and re-separate the wastewater based on the salt load distribution imbalance index to obtain three-grade quality-classified wastewater data; The separation monitoring module is used to perform gradient membrane separation treatment on the separated wastewater based on the three-level quality-separated wastewater data to obtain graded salt treatment liquid data; perform membrane flux attenuation pattern recognition based on the graded salt treatment liquid data to obtain membrane fouling trend data; and perform membrane surface pollutant control based on the membrane fouling trend data to obtain membrane separation efficiency monitoring data; The physical model evaporation module is used to perform phase equilibrium prediction modeling on the concentrated liquid based on the graded salt treatment liquid data to obtain co-crystallization behavior prediction data; the evaporator operation is controlled based on the co-crystallization behavior prediction data to obtain process closed-loop monitoring data, wherein the concentrated liquid is the graded salt treatment liquid; The architecture solution module is used to build a wastewater treatment virtual system environment based on membrane separation efficiency monitoring data and process closed-loop monitoring data; and to build a final system operation plan based on the wastewater treatment virtual system environment.

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