Industrial sewage treatment intelligent collaborative control system and method based on digital twinning

Abstract

The application discloses an industrial sewage treatment intelligent collaborative control system and method based on digital twinning, and belongs to the technical field of sewage treatment. The system comprises a physical treatment system and a digital twinning intelligent control system. The physical treatment system comprises an adjusting pool, a magnetic loading flocculation sedimentation pool, an ozone catalytic oxidation tower, an intermediate pool, a dynamic membrane biological reactor and a clear water pool. The digital twinning intelligent control system comprises a multi-source data acquisition module, a coupling mechanism modeling module, a multi-step prediction module, a multi-objective collaborative optimization module and a feedback control module. The method comprises physical treatment and data acquisition, coupling mechanism model construction and synchronous evolution, multi-step prediction and risk identification, multi-objective collaborative optimization and strategy generation, feedback control and closed-loop correction. The application realizes the collaborative optimization of the physical treatment system and the digital model by constructing a cross-system material coupling circulation network and a digital twinning intelligent control system, improves the impact load resistance and treatment stability, and reduces the operation cost.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment, and in particular to an intelligent collaborative control system and method for industrial wastewater treatment based on digital twins. Background Technology

[0002] With the rapid development of the industrial economy, the discharge of industrial wastewater has been increasing year by year, containing large amounts of recalcitrant organic matter, ammonia nitrogen, total phosphorus, and emerging pollutants. Traditional industrial wastewater treatment processes often employ a combination of "physicochemical pretreatment + biological treatment + advanced treatment." However, existing technologies have the following shortcomings in practical applications: 1) Low removal rate of recalcitrant organic matter: Conventional activated sludge processes have poor tolerance to macromolecular organic matter and toxic substances, resulting in poor stability of the biochemical system.

[0003] 2) Rapid membrane fouling: Colloidal substances and microbial metabolites in the sludge mixture of the membrane bioreactor (MBR) can easily cause membrane pore blockage, and frequent cleaning increases operating costs.

[0004] 3) Weak resistance to shock loads: Industrial wastewater quality fluctuates greatly, and existing systems are mostly "response-based" controls, which can only be passively adjusted after a shock occurs, which can easily lead to the collapse of the biochemical system.

[0005] 4) Poor inter-unit coordination: Existing technologies mostly focus on the parameter optimization of a single unit (such as intelligent dosing and aeration control), lacking global coordinated control of multi-unit coupled processes, making it difficult to achieve system-level energy consumption optimization and stability improvement.

[0006] In recent years, artificial intelligence technology has been initially applied in the field of wastewater treatment. For example, some studies have used digital twin technology to construct high-precision digital models to simulate the flow and compositional changes of wastewater; others have used multi-objective optimization algorithms to optimize energy consumption and effluent quality. However, existing technologies still mostly remain at the optimization level of a single unit or a single objective. In particular, for multi-stage coupled processes of "physicochemical-advanced oxidation-biochemical" processes, the coupling relationships between units (such as the promotion of magnetic flocculation by sludge recirculation and the delay of membrane fouling by magnetic-biological composite flocs) have not yet been included in the modeling scope of digital twin models. This results in existing intelligent control schemes being unable to fully utilize the synergistic effects brought about by physical structure innovation.

[0007] Based on this, a digital twin-based intelligent collaborative control system and method for industrial wastewater treatment is proposed. Summary of the Invention

[0008] The purpose of this invention is to provide an intelligent collaborative control system and method for industrial wastewater treatment based on digital twins. By constructing a cross-system material coupling circulation network and a digital twin intelligent control system, the physical treatment system and the digital model are synergistically optimized, thereby improving the ability to withstand shock loads and the stability of treatment, and reducing operating costs.

[0009] To achieve the above objectives, the present invention provides an intelligent collaborative control system for industrial wastewater treatment based on digital twins, including a physical treatment system and a digital twin intelligent control system. The physical treatment system includes an equalization tank, a magnetically loaded flocculation sedimentation tank, an ozone catalytic oxidation tower, an intermediate water tank, a dynamic membrane bioreactor, and a clear water tank connected sequentially along the water flow direction. Digital twin intelligent control system, including: A multi-source data acquisition module is used to collect real-time operational data from the physical processing system; The coupling mechanism modeling module is used to construct a coupling mechanism model between the magnetically loaded flocculation sedimentation tank, the ozone catalytic oxidation tower, and the dynamic membrane bioreactor. The coupling mechanism model includes independent mass balance and reaction kinetic models for each unit, as well as interaction relationship models between units. The multi-step prediction module is used to predict the fluctuation of influent water quality and the evolution trend of membrane fouling within a preset time window based on the coupled mechanism model and the time series prediction model. The multi-objective collaborative optimization module is used to dynamically generate cross-unit collaborative control strategies with the objectives of achieving effluent quality standards, minimizing operating energy consumption, and minimizing membrane fouling rate when the prediction module identifies the risk of future shock loads or membrane fouling. The feedback control module is used to send the optimized control strategy to the actuator of the physical processing system, and collect actual effect data after execution to perform closed-loop correction on the coupling mechanism model.

[0010] Preferably, in the physical treatment system, the sludge outlet of the dynamic membrane bioreactor is connected to the magnetically loaded flocculation sedimentation tank through a first return pipeline, which is used to return the microbial-rich activated sludge to the magnetic loading unit. Automatic valves are installed on the pipeline between the intermediate water tank and the dynamic membrane bioreactor. An online water quality monitor is installed in the intermediate water tank to monitor the characteristic pollutant load of the ozone catalytic oxidation effluent in real time and adjust the amount of water entering the dynamic membrane bioreactor according to the load.

[0011] Preferably, the magnetic loading flocculation sedimentation tank includes a first flocculation zone, a second flocculation zone, and a sedimentation zone connected in sequence. The bottom of the sedimentation zone is equipped with a magnetic sludge collection hopper, which is connected to the first flocculation zone through a sludge return pipe equipped with a magnetic separation and recovery device to realize the internal circulation of magnetic powder.

[0012] Preferably, the interaction model between units includes: 1) A model for the promoting relationship between sludge recirculation and magnetic flocculation, which is based on the correlation function between the concentration of extracellular polymers and magnetic powder in the recirculated sludge and the floc particle size and settling velocity. 2) Model for the delay of membrane fouling by magnetic-biological composite flocs. This model is based on the correlation function between the particle size distribution of composite flocs, sludge specific resistance and the rate of increase of transmembrane pressure difference.

[0013] Preferably, the control strategy generated by the multi-objective collaborative optimization module includes a joint adjustment scheme for magnetic powder dosage, coagulant dosage, ozone dosage, water distribution volume, and sludge return flow.

[0014] Preferably, the operational data collected by the multi-source data acquisition module includes water quality data, microbial activity data, equipment operation data, and environmental data; microbial activity data is acquired through an online ATP detector and a real-time SOUR detection device, with a sampling frequency of 5 minutes / time.

[0015] Preferably, the coupling mechanism modeling module adopts a dual-engine collaborative architecture of "mechanism model + machine learning" to build independent models for each unit. The coupling relationship model between units is built based on empirical formulas or neural network models fitted from experimental data. The machine learning engine is used to learn and correct the overall prediction bias of the coupling mechanism model.

[0016] Preferably, the digital twin intelligent control system adopts a cloud-edge collaborative architecture, including edge computing nodes deployed on-site and model training centers deployed in the cloud; the edge computing nodes are responsible for real-time data acquisition, predictive inference, and rapid control response; the cloud is responsible for large-scale historical data training, model version updates, and cross-plant knowledge transfer.

[0017] This invention also provides an intelligent collaborative control method for industrial wastewater treatment based on digital twins, which employs the aforementioned control system and includes the following steps: Step 1, Physical Treatment and Data Acquisition: Industrial wastewater is sequentially treated in an equalization tank, a magnetically loaded flocculation sedimentation tank, and an ozone catalytic oxidation tower before entering an intermediate water tank; the amount of water entering the dynamic membrane bioreactor is adjusted according to the real-time monitoring of characteristic pollutant loads, and some of the excess sludge from the dynamic membrane bioreactor is returned to the magnetically loaded flocculation sedimentation tank to form "magnetic-biological composite flocs"; at the same time, the operation data of the physical treatment system is collected in real time. Step 2, Construction and Synchronous Evolution of Coupled Mechanism Model: Construct independent mechanism models for each unit of magnetic loading coagulation precipitation, ozone catalytic oxidation, and dynamic membrane bioreactor, as well as coupling relationship models between units, to form a complete coupled mechanism model. Then, use neural networks to learn and correct the overall prediction deviation of the coupled mechanism model, forming a digital twin that evolves synchronously with the physical treatment system. Step 3, Multi-step prediction and risk identification: Based on digital twins and time-series prediction models, predict the influent water quality fluctuations and membrane fouling evolution trends within future time windows; when a shock load or membrane fouling risk is predicted, trigger collaborative optimization; Step 4: Multi-objective collaborative optimization and strategy generation: With the objectives of achieving effluent quality standards, minimizing operating energy consumption, and minimizing membrane fouling rate, a multi-objective optimization algorithm is used to dynamically generate cross-unit collaborative control strategies. Step 5, Feedback Control and Closed-Loop Correction: Send the control strategy to the actuator and dynamically adjust the operating parameters; collect actual effect data after execution and perform closed-loop correction on the digital twin.

[0018] Preferably, in step 1, the amount of sludge returned to the magnetically loaded flocculation sedimentation tank is 50% to 100% of the total sludge discharged from the dynamic membrane bioreactor.

[0019] Preferably, in step 4, the multi-objective optimization algorithm adopts the MOEA / D algorithm based on knowledge transfer constraints, with the population size set to 100, the number of iterations set to 200, the crossover probability to 0.8, and the mutation probability to 0.1. A knowledge base is constructed using the Pareto optimal solutions of historical optimization tasks, and the initial population of the current optimization task is generated through knowledge transfer.

[0020] Therefore, the intelligent collaborative control system and method for industrial wastewater treatment based on digital twins of the present invention have the following beneficial effects: (1) The present invention returns the excess sludge from the dynamic membrane bioreactor to the magnetic loading flocculation sedimentation tank to form a “magnetic-biological composite floc”. The flocculation effect is enhanced by the flocculation bridging effect of extracellular polymers (EPS) in the sludge. At the same time, the large particle size of the composite floc effectively delays membrane fouling, thus achieving synergistic effect of physical and biological treatment.

[0021] (2) This invention not only constructs independent mass balance and reaction kinetic models for each unit, but also fits the model of the promoting relationship between sludge recirculation and magnetic flocculation and the model of the delaying relationship between magnetic-biological composite flocs and membrane fouling through experimental data, and uses a machine learning engine to correct the model prediction bias, forming a high-precision digital twin that evolves synchronously with the physical system.

[0022] (3) Based on the time series prediction model, predict the future fluctuation of influent water quality and membrane fouling trend. With the goal of "achieving effluent water quality standards + minimizing operating energy consumption + minimizing membrane fouling rate", use multi-objective optimization algorithm to dynamically generate cross-unit collaborative control strategy to realize feedforward-feedback joint control.

[0023] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the physical processing system structure in an embodiment of the present invention; Figure 2 This is a diagram illustrating the architecture of the digital twin intelligent control system in an embodiment of the present invention. Figure label: 1. Equalization tank; 2. Magnetic loading flocculation sedimentation tank; 21. First flocculation zone; 22. Second flocculation zone; 23. Sedimentation zone; 24. Magnetic mud collection hopper; 25. Magnetic separation and recovery device; 3. Ozone catalytic oxidation tower; 4. Intermediate water tank; 41. Online water quality monitor; 5. Dynamic membrane bioreactor; 6. Clear water tank. Detailed Implementation

[0025] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0027] Example like Figure 1 , Figure 2 As shown in the figure, this embodiment provides an intelligent collaborative control system for industrial wastewater treatment based on digital twins, applied to the treatment of dyeing and printing wastewater in an industrial park, with a designed flow rate of 1000 m³. 3 / d.

[0028] Step 1, Physical Treatment and Data Acquisition: The physical treatment system includes an equalization tank 1, a magnetically loaded flocculation sedimentation tank 2, an ozone catalytic oxidation tower 3, an intermediate water tank 4, a dynamic membrane bioreactor 5, and a clear water tank 6, which are connected sequentially along the water flow direction.

[0029] The magnetic loading flocculation sedimentation tank 2 is internally divided into a first flocculation zone 21, a second flocculation zone 22, and a sedimentation zone 23 by partitions. The first flocculation zone 21 is equipped with a stirrer connected to a magnetic powder dosing pipe and a coagulant dosing pipe. The second flocculation zone 22 is equipped with a slow-speed stirrer connected to a coagulant aid dosing pipe. The bottom of the sedimentation zone 23 is equipped with a magnetic sludge collection hopper 24, which is connected to the first flocculation zone 21 via a sludge return pipe. A magnetic separation and recovery device 25 is installed on the sludge return pipe. The magnetic separation and recovery device 25 employs a two-stage magnetic separation process to recover magnetic powder. The first stage removes magnetic powder from large particles of magnetic sludge, and the second stage separates fine magnetic powder particles. A backwashing device is also included to periodically clean the magnetic separation medium, ensuring a stable magnetic powder recovery rate greater than 99%.

[0030] The sludge outlet of the dynamic membrane bioreactor 5 is connected to the first flocculation zone 21 of the magnetically loaded flocculation sedimentation tank 2 via a first return pipeline. The "magnetic-biological composite flocs" mentioned here refer to a composite structure formed by the mutual adsorption and aggregation of activated sludge, magnetic powder, and floc particles. This structure exhibits excellent settling performance due to the high density of the magnetic powder, and its dense structure is further enhanced by the bridging effect of extracellular polymeric substances (EPS) in the activated sludge, effectively slowing down the membrane fouling process. An automatic valve is installed on the pipeline between the intermediate water tank 4 and the dynamic membrane bioreactor 5. An online water quality monitoring instrument 41 (COD monitor, ammonia nitrogen monitor) is installed in the intermediate water tank 4. The online water quality monitoring instrument 41 establishes real-time data interaction with the edge node of the digital twin intelligent control system. The data transmission frequency is consistent with the system sampling frequency, ensuring that the monitoring data is uploaded to the multi-source data acquisition module in real time.

[0031] The operation process of this physical processing system is as follows: After homogenization in equalization tank 1, wastewater enters magnetically loaded flocculation sedimentation tank 2. PAC (120 mg / L) and magnetic powder (300 mg / L) are added to the first flocculation zone 21, and the mixture is rapidly stirred. After entering the second flocculation zone 22, PAM (3 mg / L) is added, and the mixture is slowly stirred to form large, dense flocs. The flocs settle rapidly in sedimentation zone 23, and the supernatant enters the ozone catalytic oxidation tower 3. The settled magnetic sludge is recycled back to the first flocculation zone 21 after the magnetic powder is recovered by the magnetic separation and recovery device 25.

[0032] In ozone catalytic oxidation tower 3, ozone (basic dosage 150 mg / L, contact time 60 min) is introduced, and under the catalytic action of manganese-supported activated carbon, hydroxyl radicals are generated to decompose recalcitrant organic matter.

[0033] Ozone-treated water enters intermediate water tank 4. When the online water quality monitor 41 detects a normal load of characteristic pollutants, the automatic valve remains fully open; when a shock load is predicted, the system automatically reduces the valve opening.

[0034] The MLSS in the dynamic membrane bioreactor 5 is controlled at 10000 mg / L, the HRT is 8 h, and water is intermittently discharged by a suction pump (8 min pumping, 2 min stopping). The excess sludge produced by the dynamic membrane bioreactor 5 is recycled to the first flocculation zone 21 of the magnetically loaded flocculation sedimentation tank 2 at a ratio of 80%.

[0035] Step 2: Construction and synchronous evolution of the coupling mechanism model: The digital twin intelligent control system includes a multi-source data acquisition module, a coupling mechanism modeling module, a multi-step prediction module, a multi-objective collaborative optimization module, and a feedback control module. The system adopts a cloud-edge collaborative architecture, with edge computing nodes deployed in the field control cabinet and the cloud deployed on Alibaba Cloud ECS. Data interaction between the edge nodes and the cloud uses the MQTT communication protocol to ensure data security and transmission stability.

[0036] The multi-source data acquisition module collects in real time the influent and effluent water quality (COD, ammonia nitrogen, TP, BOD), microbial activity (ATP, SOUR), equipment operation data (transmembrane pressure difference, aeration rate, dosage of each agent, magnetic powder dosage) and environmental data (temperature, DO), with a sampling frequency of 5 minutes / time.

[0037] The coupling mechanism modeling module builds a model based on historical operational data from the previous three months (approximately 100,000 samples), as detailed below: 1) Independent unit mechanism model: The magnetically loaded coagulation unit is constructed based on colloidal stability theory and flocculation kinetics; the ozone catalytic oxidation unit is based on ozone decomposition kinetics and... OH reaction kinetics were constructed; the five-unit dynamic membrane bioreactor was constructed based on the activated sludge model (ASM1) and the membrane filtration model.

[0038] 2) Coupling relationship model: a. Construction of a model for the promoting relationship between sludge recirculation and magnetic flocculation: To establish this relationship model, batch flocculation experiments were conducted under laboratory conditions. The experimental water was taken from the effluent of a dyeing and printing wastewater equalization tank in an industrial park (COD 750-850 mg / L, pH 7.0-7.5, temperature 25±2℃). EPS concentration gradients (0, 20, 40, 60, 80, 100 mg / L) and magnetic powder dosage gradients (0, 100, 200, 300, 400, 500 mg / L) were set, and the floc particle size d50 and settling velocity were measured under different conditions. .

[0039] The results showed that both EPS concentration and magnetic powder concentration had a positive correlation with floc particle size, with the effect of magnetic powder concentration being more significant; floc particle size was also positively correlated with settling velocity. Based on the above experimental data, a nonlinear least squares regression analysis was performed to establish the relationship between EPS concentration, magnetic powder concentration, and d50. The quantitative correlation is represented by the following regression equation: ; ; in, d50 represents the extracellular polymeric concentration (mg / L), [magnetic powder] represents the magnetic powder concentration (mg / L), and d50 represents the flocculent particle size (μm). Settlement velocity (mm / s); The regression coefficients are obtained by fitting experimental data. In this embodiment, the goodness-of-fit R0 is... 2 ≥0.95. The range of regression coefficient values ​​is: Those skilled in the art can use the above regression method to obtain the corresponding model parameters based on specific water quality conditions.

[0040] This correlation is embedded in the model of the magnetically loaded coagulation unit to correct the calculation of floc settling performance.

[0041] b. Construction of a model for the delaying relationship between magneto-biological composite flocs and membrane fouling: Based on continuous operation monitoring data, transmembrane pressure difference (TMP), sludge specific resistance (SRF), and composite floc particle size (d50) data were collected under different operating conditions (sampling period of 6 months, obtaining approximately 2000 sets of valid data). Data analysis shows that the larger the composite floc particle size, the slower the rate of increase in transmembrane pressure difference, exhibiting an exponential decay trend; sludge specific resistance is positively correlated with the rate of increase in transmembrane pressure difference.

[0042] Based on the above data, a nonlinear regression method was used to establish the rate of increase of transmembrane pressure difference. The quantitative correlation between sludge specific resistance (SRF) and composite floc particle size (d50) is shown in the following form: ; in, The transmembrane pressure rise rate (kPa / d) is given by SRF, which is the sludge specific resistance (×10). 12 m / kg); The regression coefficients are obtained by fitting the monitoring data. In this embodiment, the goodness of fit R0 is... 2 ≥0.93, the range of regression coefficient values ​​is: The exponential decay equation reflects the delaying effect of composite floc particle size on membrane fouling: as d50 increases, The term decreases rapidly, thereby reducing the rate of increase in transmembrane pressure differential.

[0043] This correlation was embedded into the model of the 5-unit dynamic membrane bioreactor to correct the calculation of membrane fouling rate.

[0044] Model fusion and correction: BPNN (Backpropagation Neural Network) is used as the meta-learner. The deviation between the predicted and actual values ​​of the coupling mechanism model is used as the training objective to correct the model, forming a digital twin that evolves synchronously with the physical processing system. The model is retrained in the cloud every 24 hours, and the updated model parameters are distributed to the edge nodes.

[0045] The construction of the aforementioned coupling model enables the digital twin to accurately reflect the promoting effect of sludge recirculation on magnetic flocculation and the delaying effect of magnetic-biological composite flocs on membrane fouling. Verification showed that this coupled mechanism model improves the prediction accuracy of membrane fouling rate by approximately 20% compared to the traditional independent model, providing a reliable model foundation for subsequent multi-step prediction and multi-objective optimization.

[0046] Step 3: Multi-step prediction and risk identification: The multi-step prediction module uses the Informer time series prediction model (input sequence length of 48 time points, output prediction step length of 24 time points, corresponding to the next 2 hours) to predict the influent COD change trend and membrane fouling evolution trend in the next 2 hours.

[0047] During one operation, the multi-step prediction module predicted that the influent COD would rise from 800 mg / L to 1200 mg / L (exceeding the safety threshold of 1000 mg / L) in 1.5 hours, and that the rate of increase in transmembrane pressure difference would accelerate by 20%. The system automatically triggered collaborative optimization and proceeded to step 4.

[0048] Step 4: Multi-objective collaborative optimization and strategy generation: The multi-objective collaborative optimization module employs the MOEA / D algorithm based on knowledge transfer constraints, with the following parameters: population size 100, number of iterations 200, crossover probability 0.8, and mutation probability 0.1. The knowledge transfer constraints specifically include embedding the process parameter ranges corresponding to the historical optimal control strategy, water quality compliance constraints, and equipment operation safety constraints as prior knowledge into the algorithm's constraints. The specific mathematical expression is as follows: 200mg / L≤Magnetic powder dosage≤500mg / L, 80mg / L≤PAC dosage≤200mg / L, 100mg / L≤Ozone dosage≤250mg / L, 50%≤Intelligent water distribution valve opening ≤100%, 50%≤Sludge return flow ≤100%, and simultaneously meet the requirements of effluent COD≤50mg / L and ammonia nitrogen≤5mg / L.

[0049] The optimization variables include: magnetic powder dosage (200-500 mg / L), PAC dosage (80-200 mg / L), ozone dosage (100-250 mg / L), intelligent water distribution valve opening (50%-100%), and sludge return flow rate (50%-100%).

[0050] The multi-objective collaborative optimization module generates a Pareto front solution set within 30 seconds. Through fuzzy membership function (triangular membership function) decision-making, the optimal control strategy combination is selected: the magnetic powder dosage is increased from 300mg / L to 350mg / L, the PAC dosage is increased from 120mg / L to 150mg / L, the ozone dosage is increased from 150mg / L to 180mg / L, the opening of the intelligent water distribution valve is reduced from 100% to 70%, and the sludge return flow rate is increased from 80% to 100%.

[0051] Step 5: Feedback Control and Closed-Loop Correction The control strategy generated in step 4 is sent to the actuators of the physical treatment system, and the parameters of each unit are adjusted synchronously. Two hours after execution, the actual influent COD peak value was 1150 mg / L, the effluent COD was 48 mg / L, and the ammonia nitrogen was 3.2 mg / L, all of which stably met the standards. The system collects actual effect data, calculates the deviation from the predicted value (predicted effluent COD was 45 mg / L, deviation was 6.7%), and feeds this deviation back to the digital twin, triggering parameter fine-tuning of the BPNN meta-learner.

[0052] After six months of continuous operation and verification, this system has reduced PAC dosage by 18%, ozone dosage by 15%, aeration energy consumption by 25%, extended membrane cleaning cycle from 30 days to 75 days, reduced overall operating costs by 28%, and ensured stable and compliant effluent quality.

[0053] Comparative Example The same wastewater was treated using a conventional "coagulation sedimentation + A / O + MBR" process combined with traditional PID control. Results showed that the conventional process achieved a total COD removal rate of approximately 90%, with an average effluent COD of 80 mg / L, occasionally exceeding the standard. The MBR membrane module required monthly cleaning. The cost of PAC and carbon source reagents was approximately 22% higher than in the previous example. When faced with fluctuations in influent water quality (such as a sudden increase in COD to 1100 mg / L), the system required 24-48 hours to stabilize.

[0054] Therefore, the present invention provides an intelligent collaborative control system and method for industrial wastewater treatment based on digital twins. By constructing a cross-system material coupling circulation network and a digital twin intelligent control system, the physical treatment system and the digital model are synergistically optimized, thereby improving the ability to withstand shock loads and the stability of treatment, and reducing operating costs.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A digital twin-based intelligent collaborative control system for industrial wastewater treatment, characterized in that, It includes a physical treatment system and a digital twin intelligent control system. The physical treatment system includes an equalization tank, a magnetically loaded flocculation sedimentation tank, an ozone catalytic oxidation tower, an intermediate water tank, a dynamic membrane bioreactor, and a clear water tank, which are connected in sequence along the water flow direction. The digital twin intelligent control system includes: A multi-source data acquisition module is used to acquire real-time operational data from the physical processing system; The coupling mechanism modeling module is used to construct a coupling mechanism model between the magnetically loaded flocculation sedimentation tank, the ozone catalytic oxidation tower, and the dynamic membrane bioreactor. The coupling mechanism model includes independent mass balance and reaction kinetic models for each unit, as well as interaction relationship models between units. The multi-step prediction module is used to predict the fluctuation of influent water quality and the evolution trend of membrane fouling within a preset time window based on the coupled mechanism model and the time series prediction model. The multi-objective collaborative optimization module is used to dynamically generate cross-unit collaborative control strategies with the objectives of achieving effluent quality standards, minimizing operating energy consumption, and minimizing membrane fouling rate when the prediction module identifies the risk of future shock loads or membrane fouling. The feedback control module is used to send the optimized control strategy to the actuator of the physical processing system, and collect actual effect data after execution to perform closed-loop correction on the coupling mechanism model.

2. The intelligent collaborative control system for industrial wastewater treatment based on digital twins according to claim 1, characterized in that: In the physical treatment system, the sludge outlet of the dynamic membrane bioreactor is connected to the magnetically loaded flocculation sedimentation tank through the first return pipeline, which is used to return the microbial-rich activated sludge to the magnetic loading unit. Automatic valves are installed on the pipeline between the intermediate water tank and the dynamic membrane bioreactor. An online water quality monitor is installed in the intermediate water tank to monitor the characteristic pollutant load of the ozone catalytic oxidation effluent in real time and adjust the amount of water entering the dynamic membrane bioreactor according to the load.

3. The intelligent collaborative control system for industrial wastewater treatment based on digital twins according to claim 2, characterized in that: The magnetic loading flocculation sedimentation tank includes a first flocculation zone, a second flocculation zone, and a sedimentation zone connected in sequence. The bottom of the sedimentation zone is equipped with a magnetic sludge collection hopper, which is connected to the first flocculation zone through a sludge return pipe equipped with a magnetic separation and recovery device to realize the internal circulation of magnetic powder.

4. The intelligent collaborative control system for industrial wastewater treatment based on digital twins according to claim 3, characterized in that: The interaction model between units includes: 1) A model for the promoting relationship between sludge recirculation and magnetic flocculation effect, which is based on the correlation function between the concentration of extracellular polymers and magnetic powder in the recirculated sludge and the floc particle size and settling velocity. 2) Model for the delay of membrane fouling by magnetic-biological composite flocs. This model is based on the correlation function between the particle size distribution of composite flocs, sludge specific resistance and the rate of increase of transmembrane pressure difference.

5. The intelligent collaborative control system for industrial wastewater treatment based on digital twins according to claim 4, characterized in that: The control strategies generated by the multi-objective collaborative optimization module include joint adjustment schemes for magnetic powder dosage, coagulant dosage, ozone dosage, water distribution volume, and sludge return flow.

6. The intelligent collaborative control system for industrial wastewater treatment based on digital twins according to claim 5, characterized in that: The multi-source data acquisition module collects operational data including water quality data, microbial activity data, equipment operation data, and environmental data; microbial activity data is acquired through an online ATP detector and a real-time SOUR detection device.

7. The intelligent collaborative control system for industrial wastewater treatment based on digital twins according to claim 6, characterized in that: The coupling mechanism modeling module adopts a dual-engine collaborative architecture of "mechanism model + machine learning" to build independent models for each unit. The coupling relationship model between units is built based on empirical formulas or neural network models fitted from experimental data. The machine learning engine is used to learn and correct the overall prediction bias of the coupling mechanism model.

8. A digital twin-based intelligent collaborative control method for industrial wastewater treatment, employing the control system described in any one of claims 1-7, characterized in that, Includes the following steps: Step 1, Physical Treatment and Data Acquisition: Industrial wastewater is sequentially treated in an equalization tank, a magnetically loaded flocculation sedimentation tank, and an ozone catalytic oxidation tower before entering an intermediate water tank; the amount of water entering the dynamic membrane bioreactor is adjusted according to the real-time monitoring of characteristic pollutant loads, and some of the excess sludge from the dynamic membrane bioreactor is returned to the magnetically loaded flocculation sedimentation tank to form "magnetic-biological composite flocs"; at the same time, the operation data of the physical treatment system is collected in real time. Step 2, Construction and Synchronous Evolution of Coupling Mechanism Model: Construct independent mechanism models for each unit and coupling relationship models between units to form a complete coupling mechanism model. Then, use neural networks to learn and correct the overall prediction bias of the coupling mechanism model to form a digital twin that evolves synchronously with the physical processing system. Step 3, Multi-step prediction and risk identification: Based on digital twins and time-series prediction models, predict the influent water quality fluctuations and membrane fouling evolution trends within future time windows; when a shock load or membrane fouling risk is predicted, trigger collaborative optimization; Step 4: Multi-objective collaborative optimization and strategy generation: With the objectives of achieving effluent quality standards, minimizing operating energy consumption, and minimizing membrane fouling rate, a multi-objective optimization algorithm is used to dynamically generate cross-unit collaborative control strategies. Step 5, Feedback Control and Closed-Loop Correction: Send the control strategy to the actuator and dynamically adjust the operating parameters; collect actual effect data after execution and perform closed-loop correction on the digital twin.

9. The intelligent collaborative control method for industrial wastewater treatment based on digital twins according to claim 8, characterized in that: In step 1, the amount of sludge returned to the magnetically loaded flocculation sedimentation tank is 50% to 100% of the total sludge discharged from the dynamic membrane bioreactor.

10. The intelligent collaborative control method for industrial wastewater treatment based on digital twins according to claim 9, characterized in that: In step 4, the multi-objective optimization algorithm adopts the MOEA / D algorithm based on knowledge transfer constraints. It uses the Pareto optimal solutions of historical optimization tasks to build a knowledge base and generates the initial population for the current optimization task through knowledge transfer.

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