A method for real-time monitoring and process control of a wastewater plant

Abstract

The application provides a wastewater station data real-time monitoring and process control method. The method comprises the following steps: collecting real-time operation data of each process unit and calibrating the data into unified time sequence data; generating a reliable data set through data quality processing and alarm fusion; constructing a process correlation mapping table, accurately identifying the current operation condition based on a multi-dimensional feature vector; inputting the feature vector into a prediction model to predict the operation state and effluent water quality, and calculating a risk evaluation value; generating a candidate control strategy according to the risk evaluation value, and selecting a target control strategy through multi-objective comprehensive evaluation under the condition of meeting the constraint condition; generating a control instruction to dynamically adjust the process parameters; and dynamically updating the prediction model, correlation weight and control period through deviation analysis. The application realizes closed-loop adaptive control of the wastewater station, and significantly improves the operation condition identification accuracy, response speed and effluent stability and standard reaching capacity.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, specifically a method for real-time data monitoring and process control of a wastewater treatment plant. Background Technology

[0002] Wastewater treatment plants are essential facilities in industrial production processes for treating industrial wastewater. They typically include multiple process units such as equalization tanks, biological treatment units, sedimentation units, disinfection units, and supporting conveying equipment. These units are connected sequentially according to a predetermined process flow to jointly complete the wastewater treatment process. Because the influent water quality, quantity, and equipment operating status constantly change with production conditions, wastewater treatment plants require continuous data collection from each process unit and timely adjustment of process parameters based on the operating status to ensure stable and compliant effluent.

[0003] Existing wastewater treatment plants typically employ automated monitoring systems to collect data in real time on flow rate, liquid level, water quality indicators, and equipment operating status. These systems then control relevant equipment according to preset rules, thereby achieving automated operation of the wastewater treatment process. However, existing monitoring methods mostly focus on individual process units or single monitoring parameters, failing to adequately consider the operational interrelationships between different process units. When wastewater load fluctuates, equipment operating status changes, or multiple process parameters interact, it becomes difficult to comprehensively reflect the overall operating status of the wastewater treatment plant. This results in low accuracy in identifying operating conditions, with process control relying primarily on adjustments to local parameters, and control strategies struggling to adapt promptly to changes in overall operating conditions.

[0004] Furthermore, existing process control methods typically treat operation monitoring, status analysis, and control regulation as independent functional modules, lacking data linkage and feedback update mechanisms between these components. When the operating conditions of a wastewater treatment plant continuously change, the control strategy struggles to adjust in a timely manner, easily leading to control response lag and impacting wastewater treatment efficiency, operational stability, and effluent quality.

[0005] Therefore, it is necessary to provide a real-time data monitoring and process control method for wastewater treatment plants. This method can fully utilize the real-time operating data of each process unit in the wastewater treatment plant, integrate the correlation between the process units, identify the overall operating condition of the wastewater treatment plant, and combine operating status prediction and control strategy optimization to achieve closed-loop control of the wastewater treatment plant's operation process, thereby improving the stability and control efficiency of the wastewater treatment plant's operation.

[0006] In summary, the present invention provides a method for real-time data monitoring and process control of wastewater treatment plants to solve the above-mentioned problems. Summary of the Invention

[0007] This invention provides a real-time data monitoring and process control method for wastewater treatment plants. By constructing a process correlation mapping table, the operational correlation between various process units of the wastewater treatment plant is introduced into the operational status analysis process. Combined with operating condition feature vectors, operational status prediction models, and multi-objective control strategy evaluation, a closed-loop control is achieved for wastewater treatment plant operating condition identification, risk prediction, control optimization, and feedback updates. This solves the problems in the prior art, such as insufficient utilization of the operational correlation between various process units, independent operation monitoring and process control, and difficulty in timely adapting control strategies to changes in operating conditions.

[0008] The specific technical solution of this invention is as follows:

[0009] A method for real-time data monitoring and process control of a wastewater treatment plant includes the following steps:

[0010] S1. Collect real-time operating data of each process unit of the wastewater treatment plant, and calibrate the time according to a unified time reference to form a unified time series data;

[0011] In this invention, real-time operating data includes process operating parameters, equipment operating parameters, water quality monitoring parameters, and alarm data. The process operating parameters include at least one of flow rate, liquid level, residence time, sludge return ratio, and aeration rate. The equipment operating parameters include at least one of equipment operating frequency, operating current, operating power, and start / stop status. The water quality monitoring parameters include at least one of pH value, COD, ammonia nitrogen, suspended solids, dissolved oxygen, and oxidation-reduction potential.

[0012] S2. Perform data quality processing on the unified time series data to generate a reliable dataset;

[0013] In this invention, when the number of consecutive missing sampling points is less than a preset value, linear interpolation or adjacent valid sampling values ​​are used for compensation; when the number of consecutive missing sampling points is greater than the preset value, the corresponding data is marked as failed data and is not included in the current control cycle condition identification.

[0014] S3. Construct a process association mapping table based on the material flow direction and processing sequence between each process unit. Extract pollution load characteristics, process stability characteristics, equipment health characteristics and processing efficiency characteristics based on the trusted dataset, construct an operating condition feature vector, and identify the current operating condition in conjunction with the process association mapping table.

[0015] In this invention, the operating condition distance and cosine similarity between the current operating condition feature vector and each historical operating condition feature vector are calculated respectively. When the operating condition distance is the smallest and the cosine similarity is greater than a preset threshold, it is determined to be the corresponding operating condition; otherwise, it is determined to be a new operating condition.

[0016] In this invention, assuming the wastewater treatment plant contains n process units, a process association mapping table is established: ,in: , This represents the influence weight of the i-th process unit on the j-th process unit. The weight is determined by the material flow direction, processing order, and parameter coupling degree. L stands for logistics connection; P stands for processing sequence; and C stands for parameter coupling.

[0017] In this invention, the working condition feature vector can be represented as: ,in, , Indicates the rate of change. Indicates the degree of fluctuation. This represents the cross-correlation coefficient, specifically: , , .

[0018] In this invention, the operating conditions, including the operating distance, can be expressed as: Cosine similarity can be expressed as: .

[0019] S4. Input the operating condition feature vector into the operating status prediction model to predict the operating status, effluent quality and process operation trend of each process unit, and calculate the risk assessment value of each process unit based on the prediction results and the process association mapping table.

[0020] In this invention, the prediction function can be expressed as: θ represents the prediction model parameters. Parameter updates can be expressed as: η is the learning rate, where L is the prediction error.

[0021] In this invention, historical operating data is divided into training set and validation set. The parameters of the prediction model are iteratively updated by minimizing the prediction error function until the prediction error meets the preset conditions to obtain the operating state prediction model.

[0022] In this invention, risk assessment can be expressed as: ,in: This indicates prediction bias; further: , respectively representing the impact caused by water quality, equipment, and alarms.

[0023] In this invention, risk levels are classified according to risk assessment values. When the risk assessment value is lower than a first threshold, it is determined to be low risk; when it is between the first threshold and a second threshold, it is determined to be medium risk; and when it is higher than the second threshold, it is determined to be high risk.

[0024] S5. Generate candidate process control strategies based on the risk assessment values, conduct a comprehensive evaluation under the conditions of meeting process operation constraints and effluent discharge requirements, and select the target process control strategy.

[0025] In this invention, the process operation constraints include the upper and lower limits of the fan frequency, the maximum flow rate of the dosing pump, the sludge return ratio range, and the emission standards.

[0026] In this invention, conservative control strategies, balanced control strategies, and enhanced control strategies are generated based on risk assessment values. Each control strategy corresponds to different aeration volume, dosage, and sludge return ratio adjustment schemes.

[0027] In this invention, the comprehensive evaluation can be expressed as: G is the evaluation index, which includes compliance rate, energy consumption, drug consumption, and equipment load.

[0028] In this invention, a larger comprehensive evaluation value indicates a better corresponding control strategy, and the control strategy with the largest comprehensive evaluation value is selected as the target control strategy.

[0029] S6. Generate control instructions based on the target process control strategy to dynamically adjust the process operating parameters;

[0030] In this invention, control commands are sent via PLC to the corresponding execution device to complete parameter adjustment, and the control results are recorded by the SCADA system.

[0031] S7. Collect real-time operating data after control, perform deviation analysis between predicted and actual results, update prediction model parameters, influence weights in the process correlation mapping table, and control strategy evaluation parameters based on the deviation, and adjust the control cycle.

[0032] In this invention, the control cycle refers to the time period corresponding to the system completing one data acquisition, working condition identification, operation prediction, strategy generation, control execution and feedback update, which can be dynamically adjusted according to the risk assessment value.

[0033] In this invention, when the prediction deviation is less than a preset threshold for multiple consecutive control cycles, the current prediction model parameters and process association mapping table parameters are maintained; otherwise, the update continues.

[0034] In this invention, the prediction model update can be expressed as: Process-related updates can be represented as: The evaluation update can be represented as: .

[0035] As an improvement of the present invention, the data quality processing in step S2 includes time synchronization, abnormal data processing, missing data compensation and alarm data fusion;

[0036] The abnormal data processing involves identifying abnormal equipment data, abnormal communication data, and normal process change data based on the data change rate of adjacent sampling points, equipment operating status, and communication status; correcting the abnormal equipment data and abnormal communication data; and removing data that exceeds the number of consecutive abnormalities and cannot be corrected; while retaining normal process change data that reflects wastewater quality fluctuations, shock loads, and process adjustments.

[0037] The alarm data fusion involves associating processed data with alarm data according to alarm occurrence time, alarm object, and alarm type to form the trusted dataset.

[0038] As an improvement of the present invention, the process association mapping table in step S3 is established based on the coupling relationship between the material flow direction, processing sequence and operating parameters of each process unit of the wastewater station, and represents the degree of influence of the change in the operating status of the upstream process unit on the change in the operating status of the downstream process unit.

[0039] The operating condition feature vector is calculated based on the rate of change, fluctuation characteristics, and cross-correlation coefficients of pollution load characteristics, process stability characteristics, equipment health characteristics, and treatment efficiency characteristics.

[0040] As an improvement of the present invention, the operation status prediction model in step S4 is constructed based on historical operation data, and the real-time operating condition feature vector corresponding to the current control cycle is input for prediction, and the operation status, effluent water quality and process operation trend of each process unit are output.

[0041] The risk assessment value is calculated by weighting the predicted results and the target operating values ​​together with the influence weights in the process association mapping table, based on the degree of deviation between the predicted results and the target operating values.

[0042] As an improvement of the present invention, the comprehensive evaluation in step S5 adopts a multi-index comprehensive evaluation, and calculates a comprehensive evaluation value for each candidate control strategy. The comprehensive evaluation value is calculated based on the corresponding weight of each evaluation index.

[0043] As an improvement of the present invention, the deviation analysis in step S7 includes:

[0044] Prediction deviation analysis calculates the difference between the predicted effluent quality and the actual effluent quality.

[0045] Control deviation analysis calculates the difference between the target setpoint and the actual executed value;

[0046] The parameters of the operation status prediction model are updated based on the prediction deviation, and the influence weights and control strategy evaluation parameters of the process association mapping table are updated based on the control deviation.

[0047] As an improvement of the present invention, it also includes establishing a historical operation knowledge base for the wastewater treatment plant. The knowledge base is used to match the operating condition feature vector and control strategy corresponding to the current operating condition, and when the matching is successful, the corresponding control strategy is invoked and the control strategy is used as a candidate control strategy to participate in the comprehensive evaluation.

[0048] In this invention, the knowledge base can be represented as: Where F is the operating condition; C is the control; and E is the effect, the matching can be represented as: .

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. This invention establishes a process association mapping table, which introduces the coupling relationship between material flow direction, processing sequence and operating parameters between process units into the operating condition identification process, thereby expanding the operating status analysis from single process unit analysis to overall wastewater station operation analysis, and improving the accuracy and stability of operating condition identification.

[0051] 2. This invention generates candidate process control strategies by using operating condition feature vectors, operating status prediction, and risk assessment, and determines the target control strategy by combining multi-index comprehensive evaluation, thereby realizing closed-loop linkage of operation monitoring, predictive analysis, and process control, and improving control response speed and the ability to stably meet effluent standards.

[0052] 3. This invention continuously updates the operating status prediction model, the influence weights in the process correlation mapping table, and the control strategy evaluation parameters through deviation analysis, enabling the control strategy to adaptively adjust with changes in the operating conditions of the wastewater treatment plant, thereby improving the long-term stability and robustness of the system. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0054] Figure 2 This is a schematic diagram of the wastewater monitoring equipment of the present invention.

[0055] Figure 3 This is a schematic diagram of the wastewater data acquisition device of the present invention.

[0056] Figure 4 This is a schematic diagram of the mapping relationship of the present invention. Detailed Implementation

[0057] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0058] like Figures 1-4As shown, the present invention provides a method for real-time data monitoring and process control of a wastewater treatment plant, comprising the following steps:

[0059] S1. Collect real-time operating data of each process unit of the wastewater treatment plant, and calibrate the time according to a unified time reference to form a unified time series data;

[0060] S2. Perform data quality processing on the unified time series data to generate a reliable dataset;

[0061] S3. Construct a process association mapping table based on the material flow direction and processing sequence between each process unit. Extract pollution load characteristics, process stability characteristics, equipment health characteristics and processing efficiency characteristics based on a reliable dataset, construct an operating condition feature vector, and identify the current operating condition in conjunction with the process association mapping table.

[0062] S4. Input the operating condition feature vector into the operating status prediction model to predict the operating status, effluent quality and process operation trend of each process unit, and calculate the risk assessment value of each process unit based on the prediction results and the process correlation mapping table.

[0063] S5. Generate candidate process control strategies based on risk assessment values, conduct a comprehensive evaluation under the conditions of meeting process operation constraints and effluent discharge requirements, and select the target process control strategy.

[0064] S6. Generate control commands based on the target process control strategy and dynamically adjust the process operating parameters;

[0065] S7. Collect real-time operating data after control, perform deviation analysis between predicted and actual results, update prediction model parameters, influence weights in the process correlation mapping table, and control strategy evaluation parameters based on the deviation, and adjust the control cycle.

[0066] The data quality processing in step S2 includes time synchronization, abnormal data processing, missing data compensation, and alarm data fusion.

[0067] Anomaly data processing involves identifying abnormal equipment data, abnormal communication data, and normal process change data based on the data change rate of adjacent sampling points, equipment operating status, and communication status; correcting abnormal equipment data and abnormal communication data; and removing data that exceeds the number of consecutive anomalies and cannot be corrected; while retaining normal process change data that reflects wastewater quality fluctuations, shock loads, and process adjustments.

[0068] Alarm data fusion involves associating processed data with alarm data according to alarm occurrence time, alarm object, and alarm type to form a reliable dataset.

[0069] In step S3, the process association mapping table is established based on the material flow direction, treatment sequence and operating parameter coupling relationship between each process unit of the wastewater station, indicating the degree of influence of changes in the operating status of upstream process units on changes in the operating status of downstream process units;

[0070] The operating condition feature vector is calculated based on the rate of change, fluctuation characteristics, and cross-correlation coefficients of pollution load characteristics, process stability characteristics, equipment health characteristics, and treatment efficiency characteristics.

[0071] In step S4, the operating status prediction model is built based on historical operating data. It is input to the real-time operating condition feature vector corresponding to the current control cycle for prediction, and outputs the operating status, effluent water quality and process operating trend of each process unit.

[0072] The risk assessment value is calculated by weighting the impact weights in the process correlation mapping table based on the degree of deviation between the predicted results and the target operating values.

[0073] In step S5, the comprehensive evaluation adopts a multi-index comprehensive evaluation, and the comprehensive evaluation value is calculated for each candidate control strategy. The comprehensive evaluation value is calculated based on the corresponding weight of each evaluation index.

[0074] The deviation analysis in step S7 includes:

[0075] Prediction deviation analysis calculates the difference between the predicted effluent quality and the actual effluent quality.

[0076] Control deviation analysis calculates the difference between the target setpoint and the actual executed value;

[0077] The parameters of the operating status prediction model are updated based on the prediction deviation, and the influence weights and control strategy evaluation parameters of the process association mapping table are updated based on the control deviation.

[0078] It also includes establishing a historical operation knowledge base for wastewater treatment plants. The knowledge base is used to match the operating condition feature vector and control strategy corresponding to the current operating condition, and to call the corresponding control strategy when the match is successful, and to use the control strategy as a candidate control strategy in the comprehensive evaluation.

[0079] Example 1: This example describes the treatment process for acidic, high-concentration copper-containing wastewater generated during printed circuit board (PCB) production. The wastewater treatment process includes, in sequence: an equalization tank, a primary chemical precipitation tank, a secondary coagulation and flocculation tank, and a final sedimentation tank. The effluent copper ion concentration is strictly controlled below 0.2 mg / L.

[0080] The wastewater treatment plant's distributed control system and the supervisory control and data acquisition (SCADA) system collect data on the following parameters at a fixed sampling period of 1 minute: influent flow rate (consistently around 120 m³ / h), influent pH value, influent copper ion concentration, sodium sulfide dosing pump frequency, ferrous sulfate dosing pump frequency, sodium hydroxide dosing pump frequency, and sludge level interface height in the sedimentation tank. Data from various sensors and actuators in the field are uniformly time-calibrated using a network time protocol to generate a continuous, unified time-series dataset. During a concentrated flushing operation of the production line, the influent copper ion concentration surged dramatically from 15 mg / L to 45 mg / L within 5 minutes, and the influent pH value decreased from 7.0 to 4.5.

[0081] The system detected a sharp increase in the copper ion concentration sensor reading in the influent. The system first retrieved the opening and communication status of the drain valve from the upstream etching production line, confirming normal communication and that the draining action was genuine, ruling out sensor electrode malfunction or communication interference. The system determined this data to be a normal process fluctuation due to a genuine shock load and retained it. Simultaneously, it correlated and fused the high copper ion concentration alarm and low pH alarm in the equalization tank according to their occurrence timestamps and equipment objects, marking the current data as a high-concentration acid shock data pair, thus generating a reliable dataset.

[0082] The system retrieves a pre-constructed process correlation mapping table. This table, in matrix form, expresses the influence weights of upstream equalization tank water quality on downstream chemical dosing reactions and sludge generation in sedimentation tanks. Based on a reliable dataset, the system calculates the pollution load change rate (copper ion concentration increase rate greater than 20 mg / L / min), pH fluctuation amplitude, and cross-correlation coefficients to construct an operating condition feature vector. The system compares the current feature vector with the historical database, calculates the operating condition distance and cosine similarity, and accurately identifies the current operating condition as an acidic high-copper shock condition.

[0083] The current operating condition feature vector is input into a pre-trained operational status prediction model (a three-layer neural network ANN trained using the Levenberg-Marquardt algorithm). The model predicts that if the original basic dosage is maintained, the effluent copper ion concentration will reach 1.2 mg / L after one hydraulic retention time, severely exceeding the control standard of 0.2 mg / L; simultaneously, due to insufficient sedimentation, the sludge level in the sedimentation tank will deteriorate to level 4 (turbid and yellowish water). The system, combining the risk weights of effluent quality exceeding standards and sludge floating in the process correlation mapping table, calculates a risk assessment value of 0.85 for the current operating condition (above the high-risk threshold of 0.7), and the system automatically triggers an early warning.

[0084] The system first retrieves historical operational knowledge and successfully matches effective control strategies under similar high copper shock conditions. Simultaneously, the system initiates a non-dominated sorting genetic algorithm for real-time multi-objective optimization calculations. The optimization process considers the total treatment cost of reagent procurement and sludge disposal, the effluent copper ion concentration, and the sludge level as three conflicting optimization objectives. Under the constraints of an effluent copper ion concentration less than or equal to 0.2 mg / L, a sludge level less than or equal to level 3, and a lower limit of chemical reaction equivalent, a Pareto optimal solution set is generated. The system employs a multi-index comprehensive evaluation function, assigning the highest weight to the compliance rate and the second highest to reagent consumption. The optimal objective strategy is selected from the solution set: increasing the sodium sulfide dosage from 20 mg / L to 58.5 mg / L, adjusting the ferrous sulfate dosage to 210 mg / L, and simultaneously increasing the sodium hydroxide dosage to maintain the pH of the reaction tank at 7.5.

[0085] The system translates the target process control strategy into specific control commands and sends them to the field PLC controller via industrial Ethernet. The PLC automatically increases the inverter output frequency of the sodium sulfide dosing pump and the ferrous sulfate dosing pump to achieve dynamic and precise adjustment of the dosage of the reagents, while automatically activating the sludge scraper in the sedimentation tank to accelerate sludge removal.

[0086] One hour after the control command was executed (i.e., the cycle of wastewater passing through the sedimentation tank), the system collected the actual effluent quality after control: the actual effluent copper ion concentration was 0.18 mg / L, and the sludge level stabilized at level 2, successfully mitigating the risk of exceeding standards. The system then performed deviation analysis.

[0087] Prediction bias: The difference between the predicted copper ion concentration in the effluent (0.20 mg / L) and the actual effluent concentration (0.18 mg / L) is 0.02 mg / L;

[0088] Control deviation: The difference between the target set flow rate of the dosing pump and the actual flow rate fed back by the flow meter is less than 1%.

[0089] Because the prediction deviation was extremely small and within acceptable limits, the system fine-tuned the local weights of the neural network prediction model and stored the successful control vector and results under this acidic high-copper shock into the historical knowledge base. Simultaneously, the system automatically shortened the control cycle from 15 minutes to 5 minutes during the shock, and then adjusted the control cycle back to 15 minutes after the influent water quality stabilized, completing closed-loop control.

[0090] Example 2: This example describes the ultraviolet disinfection process of semiconductor plant wastewater after secondary biological and sedimentation treatment, before it enters the effluent regeneration and reuse unit. The reuse standard requires that the coliform count be strictly controlled below 1000 CFU / 100mL. This treatment unit includes a pipeline ultraviolet / titanium dioxide photocatalytic reactor, equipped with an online spectrometer, a redox potential sensor, a suspended solids sensor, and an influent booster pump.

[0091] The system uses a 10-second sampling period to collect real-time data on suspended solids concentration, turbidity, water temperature (25℃), pH value, internal redox potential of the reactor, 254nm wavelength ultraviolet light intensity measured by an online spectrometer, and the flow rate of the influent booster pump (initial flow rate 1800mL / min). All data are then time-calibrated to construct a unified time series.

[0092] During operation, the turbidity sensor experienced a momentary, extremely high value due to occasional microbubbles generated within the pipeline. Based on the data change rate of adjacent sampling points and the equipment communication status, the system determined that this single-point jump was caused by abnormal communication / equipment data due to bubble interference, and corrected it using the average of adjacent valid values. However, subsequently, the suspended solids sensor showed a steady increase for five consecutive sampling points (from 10 mg / L to 26.4 mg / L, with turbidity rising to 13.7 NTU). The system determined this to be genuine water quality deterioration caused by sludge runoff from the upstream sedimentation tank and retained it intact. Simultaneously, the system timestamped the UV transmittance decrease alarm and the low ORP alarm to form a reliable dataset reflecting the light shielding effect.

[0093] The system retrieves the process correlation mapping table. The mapping matrix shows that the increase in upstream suspended solids and turbidity will have a strong physical shielding and scattering effect on ultraviolet light irradiation, directly leading to a significant decrease in the effective ultraviolet light dose actually absorbed by the water body. The system extracts the cross-correlation coefficient between the current suspended solids change rate, turbidity and ultraviolet light intensity attenuation, constructs a feature vector, and accurately identifies the current operating condition as a high suspended solids shading ultraviolet light condition caused by upstream sludge runoff.

[0094] The operating condition feature vector (SS=26.4mg / L, turbidity=13.7NTU, initial flow rate of 1800mL / min corresponding to a hydraulic retention time of 5 seconds) was input into the operating state prediction model based on a backpropagation neural network. The model predicted that the current actual effective ultraviolet light dose was only 552μW∙s / cm^2, and the residual E. coli count in the effluent would rise to 2118CFU / 100mL, far exceeding the reuse standard of 1000CFU / 100mL. Combined with the process correlation mapping table, the impact of sludge runoff on disinfection effectiveness was extremely high, and the system calculated a microbial exceedance risk assessment value of 0.92 (high risk state).

[0095] Since directly turning on the backup UV lamp would significantly increase power consumption and there is a preheating delay when turning on the lamp, the system generated three candidate process control strategies:

[0096] Conservative strategy: Maintain the current flow rate, issue a warning to the upstream sedimentation tank to increase sludge discharge, and wait for the water quality to recover naturally;

[0097] Enhancement strategy: Force activation of the entire backup UV lamp array;

[0098] Balanced control strategy: Reduce the frequency of the influent booster pump, reduce the water flow rate from 1800 mL / min to 750 mL / min, and extend the sterilization hydraulic residence time of wastewater in the UV / titanium dioxide reactor from 5 seconds to 12 seconds.

[0099] The system performs a multi-objective comprehensive evaluation of candidate strategies (evaluation indicators include: E. coli compliance rate, booster pump energy consumption, UV lamp energy consumption, and equipment load). The evaluation results show that the balanced control strategy has the highest comprehensive score: by extending the residence time, the actual absorbed UV dose can be increased to 1883 μW∙s / cm^2, ensuring efficient E. coli inactivation without requiring additional lamps, resulting in the lowest overall energy consumption. Therefore, the balanced control strategy is selected as the target process control strategy.

[0100] The system generates control commands and sends them to the frequency converter of the inlet booster pump, smoothly reducing the pump's operating frequency and precisely adjusting the water flow rate to 750 mL / min, thus extending the hydraulic residence time to 12 seconds.

[0101] After the control was activated, the redox potential sensor reading inside the reactor gradually increased from 45 mV and stabilized at 102 mV (indicating that titanium dioxide generated highly oxidizing hydroxyl radicals upon UV excitation, and the photocatalytic reaction was restored). Subsequent laboratory culture verification showed that the actual residual E. coli count in the effluent decreased to 493 CFU / 100 mL, meeting the reuse standard.

[0102] The system performed a deviation analysis: the predicted residual bacterial count of the model at a residence time of 12 seconds was 500 CFU / 100mL, with a deviation of only 1.4% from the actual measured value (493 CFU / 100mL). The system confirmed that the prediction model had good accuracy. The parameters of the neural network model were updated with a small gradient, and the nonlinear mapping weights of SS-turbidity-residence time-ORP were permanently stored in the historical operation knowledge base, providing second-level response decision support for future high turbidity occlusion conditions.

[0103] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for real-time data monitoring and process control of a wastewater treatment plant, characterized in that, Includes the following steps: S1. Collect real-time operating data of each process unit of the wastewater treatment plant, and calibrate the time according to a unified time reference to form a unified time series data; S2. Perform data quality processing on the unified time series data to generate a reliable dataset; S3. Construct a process association mapping table based on the material flow direction and processing sequence between each process unit. Extract pollution load characteristics, process stability characteristics, equipment health characteristics and processing efficiency characteristics based on the trusted dataset, construct an operating condition feature vector, and identify the current operating condition in conjunction with the process association mapping table. S4. Input the operating condition feature vector into the operating status prediction model to predict the operating status, effluent quality and process operation trend of each process unit, and calculate the risk assessment value of each process unit based on the prediction results and the process association mapping table. S5. Generate candidate process control strategies based on the risk assessment values, conduct a comprehensive evaluation under the conditions of meeting process operation constraints and effluent discharge requirements, and select the target process control strategy. S6. Generate control instructions based on the target process control strategy to dynamically adjust the process operating parameters; S7. Collect real-time operating data after control, perform deviation analysis between predicted and actual results, update prediction model parameters, influence weights in the process correlation mapping table, and control strategy evaluation parameters based on the deviation, and adjust the control cycle.

2. The method for real-time data monitoring and process control of a wastewater treatment plant according to claim 1, characterized in that, The data quality processing in step S2 includes time synchronization, abnormal data processing, missing data compensation, and alarm data fusion. The abnormal data processing involves identifying abnormal equipment data, abnormal communication data, and normal process change data based on the data change rate of adjacent sampling points, equipment operating status, and communication status. The abnormal data of the equipment and communication is corrected, and data that exceeds the number of consecutive abnormalities and cannot be corrected is removed; normal process change data that reflects wastewater quality fluctuations, shock loads, and process adjustments are retained. The alarm data fusion involves associating processed data with alarm data according to alarm occurrence time, alarm object, and alarm type to form the trusted dataset.

3. The method for real-time data monitoring and process control of a wastewater treatment plant according to claim 1, characterized in that, The process association mapping table in step S3 is established based on the coupling relationship between the material flow direction, treatment sequence and operating parameters of each process unit in the wastewater station, and represents the degree of influence of the change in the operating status of the upstream process unit on the change in the operating status of the downstream process unit. The operating condition feature vector is calculated based on the rate of change, fluctuation characteristics, and cross-correlation coefficients of pollution load characteristics, process stability characteristics, equipment health characteristics, and treatment efficiency characteristics.

4. The method for real-time data monitoring and process control of a wastewater treatment plant according to claim 1, characterized in that, The operation status prediction model in step S4 is built based on historical operation data. It is input to the real-time operating condition feature vector corresponding to the current control cycle for prediction, and outputs the operation status, effluent water quality and process operation trend of each process unit. The risk assessment value is calculated by weighting the predicted results and the target operating values ​​together with the influence weights in the process association mapping table, based on the degree of deviation between the predicted results and the target operating values.

5. The method for real-time data monitoring and process control of a wastewater treatment plant according to claim 1, characterized in that, The comprehensive evaluation in step S5 adopts a multi-index comprehensive evaluation, and calculates a comprehensive evaluation value for each candidate control strategy. The comprehensive evaluation value is calculated based on the corresponding weight of each evaluation index.

6. The method for real-time data monitoring and process control of a wastewater treatment plant according to claim 1, characterized in that, The deviation analysis in step S7 includes: Prediction deviation analysis calculates the difference between the predicted effluent quality and the actual effluent quality. Control deviation analysis calculates the difference between the target setpoint and the actual executed value; The parameters of the operation status prediction model are updated based on the prediction deviation, and the influence weights and control strategy evaluation parameters of the process association mapping table are updated based on the control deviation.

7. The method for real-time data monitoring and process control of a wastewater treatment plant according to claim 1, characterized in that, It also includes establishing a historical operation knowledge base for wastewater treatment plants. This knowledge base is used to match the operating condition feature vector and control strategy corresponding to the current operating condition, and to call the corresponding control strategy when the match is successful, and to use the control strategy as a candidate control strategy in the comprehensive evaluation.

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