Integrated ultra-magnetic separation water purification system for sewage treatment

Abstract

The application discloses an integrated ultra-magnetic separation water purification system for sewage treatment, and relates to the technical field of sewage treatment equipment and automatic control, which comprises a magnetic powder adding unit, a flocculation reaction unit, an ultra-magnetic separation unit and a magnetic powder recycling unit which are connected in sequence, and is characterized in that the system further comprises a self-adaptive control unit. The integrated ultra-magnetic separation water purification system for sewage treatment effectively realizes real-time and accurate matching of the magnetic powder adding amount and the water pollution load by constructing a self-adaptive control mechanism based on multi-source information fusion and dynamic feedback verification. The system can actively respond to the fluctuation of water quality, maintain the magnetic powder recycling rate at a high level, reduce the additional consumption of magnetic powder, and ensure the stable and up-to-standard water quality, thereby directly reducing the operation and maintenance cost of the system.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment equipment and automated control technology, specifically to an integrated super magnetic separation water purification system for wastewater treatment. Background Technology

[0002] While existing magnetic separation water purification technology in the wastewater treatment field is widely used due to its high separation efficiency, its integrated equipment reveals a key drawback in actual operation. Current systems typically integrate magnetic powder dosing, flocculation reaction, and magnetic separation recovery into a single compact unit to achieve miniaturization and ease of operation. However, this fixed-mode integrated design is insufficiently adaptable to actual operating conditions with frequent fluctuations in influent water quality. The core problem lies in the system's lack of real-time sensing and response capabilities to influent water quality parameters, still generally employing a preset fixed magnetic powder dosing strategy. When the concentration of suspended solids in the influent suddenly increases, the original magnetic powder dosage cannot ensure sufficient flocculation and binding with pollutants, causing some magnetic powder to enter the separation stage without effectively loading pollutants, resulting in magnetic powder loss with the effluent and a significant decrease in recovery rate. Conversely, when the influent load is low, excessive magnetic powder dosing leads to unnecessary waste and burdens subsequent recovery units. Although existing technologies attempt to improve recovery efficiency by optimizing the magnetic drum structure or enhancing the magnetism of the magnetic powder itself, these improvements do not address the root cause of the problem: failing to achieve a dynamic and precise match between the magnetic powder dosage and the instantaneous influent pollution load. Therefore, existing integrated supermagnetic separation water purification systems are consistently plagued by problems such as unstable magnetic powder recycling efficiency due to water quality fluctuations, high operating costs, and consequently fluctuating effluent quality. This invention provides an integrated supermagnetic separation water purification system for wastewater treatment, aiming to solve the practical problem that existing equipment cannot adaptively adjust the magnetic powder dosage according to changes in influent water quality to stabilize magnetic powder recovery efficiency. Summary of the Invention

[0003] The purpose of this invention is to provide an integrated super magnetic separation water purification system for wastewater treatment, so as to solve the problems mentioned in the background art.

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an integrated super magnetic separation water purification system for wastewater treatment, comprising a magnetic powder dosing unit, a flocculation reaction unit, a super magnetic separation unit, and a magnetic powder recovery unit connected in sequence; the system further includes an adaptive control unit; the adaptive control unit includes a water quality monitoring module, a data processing module, and an instruction execution module; the water quality monitoring module is located at the inlet pipe upstream of the magnetic powder dosing unit, and is used to acquire raw data of the inlet water quality in real time; the data processing module is configured to receive the raw data and perform the following operations:

[0005] The raw data is processed based on a built-in flocculation kinetic model to generate an initial magnetic powder dosing command. This initial command is then input into an intelligent prediction model trained on historical operational data for effect prediction and verification. The intelligent prediction model outputs a correction factor to adjust the initial magnetic powder dosing command, generating an optimized magnetic powder dosing command. The command execution module is signal-connected to the magnetic powder dosing unit and is used to send the optimized command to the unit to control its actual dosing amount.

[0006] The magnetic powder recovery unit outlet is equipped with a magnetic powder recovery monitoring module, which is used to acquire actual magnetic powder recovery data and feed it back to the data processing module;

[0007] The data processing module is further configured to compare the actual magnetic powder recovery data with the prediction results of the intelligent prediction model, and to adaptively adjust the internal parameters of the flocculation kinetic model and the intelligent prediction model based on the comparison results.

[0008] Furthermore, the water quality monitoring module includes at least a suspended solids concentration sensor for detecting suspended solids concentration and a flow meter for detecting water flow rate; the input variables of the flocculation kinetic model include real-time data measured by the suspended solids concentration sensor and the flow meter.

[0009] Furthermore, the input variables of the flocculation kinetic model also include the water flow shear force parameters within the flocculation reaction unit; the water flow shear force parameters are obtained by conversion through the rotational speed of the agitator set within the flocculation reaction unit, or by conversion through the differential pressure data measured by the fluid pressure sensor group set within the flocculation reaction unit.

[0010] Furthermore, the intelligent prediction model is trained using machine learning algorithms, and its training data comes from the amount of magnetic powder added under different influent water quality conditions during the historical operation of the system, as well as the corresponding actual magnetic powder recovery rate and effluent suspended solids concentration.

[0011] Furthermore, the period for the data processing module to adaptively adjust the parameters of the flocculation kinetic model and the intelligent prediction model is shorter than the calculation period for generating a single optimized magnetic powder dosing command.

[0012] Furthermore, the system also includes an image recognition module; the image recognition module includes an image acquisition device disposed inside the flocculation reaction unit for acquiring images of the floc morphology; the image recognition module is communicatively connected to the data processing module, and the data processing module is configured with an image analysis algorithm for extracting floc feature parameters from the floc morphology images; the data processing module also uses the floc feature parameters as auxiliary decision variables when generating the optimized magnetic powder dosing command.

[0013] Furthermore, when the floc feature parameters extracted by the image analysis algorithm indicate an abnormal flocculation state, the data processing module triggers a diagnostic program; the diagnostic program is used to analyze whether the operating parameters of the magnetic powder dosing unit and the flocculation reaction unit deviate from the preset working range.

[0014] Furthermore, the magnetic powder recovery monitoring module includes a magnetic powder metering device installed on the powder outlet pipeline of the magnetic powder recovery unit.

[0015] Furthermore, the system's final outlet is equipped with an effluent water quality monitoring sensor; the effluent water quality monitoring sensor is connected to the data processing module, and the effluent water quality data measured by the effluent water quality monitoring sensor is fed back to the data processing module for comparison with the prediction results of the intelligent prediction model and for adjusting the model parameters.

[0016] Furthermore, the adaptive control unit is integrated into a separate industrial controller, which exchanges data and transmits control commands with other units of the system via an industrial bus.

[0017] This invention provides an integrated super magnetic separation water purification system for wastewater treatment. It has the following beneficial effects:

[0018] This integrated super magnetic separation water purification system for wastewater treatment effectively achieves real-time and precise matching between magnetic powder dosage and influent pollution load by constructing an adaptive control mechanism based on multi-source information fusion and dynamic feedback verification. The system proactively responds to fluctuations in water quality, maintaining a high magnetic powder recovery rate, reducing additional magnetic powder replenishment, and ensuring stable effluent quality that meets standards, directly lowering the system's operation and maintenance costs.

[0019] This integrated super magnetic separation water purification system for wastewater treatment possesses self-learning and continuous optimization capabilities. By feeding real-time operational results back to the core decision-making model and dynamically adjusting its parameters, the overall system performance continuously improves over time. Its integrated multi-dimensional monitoring and diagnostic functions further enhance operational reliability, reduce reliance on external human intervention, and thus ensure the long-term high efficiency and stability of the wastewater treatment process. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the module interaction of an integrated super magnetic separation water purification system for wastewater treatment according to the present invention;

[0021] Figure 2 This is a flowchart of the control algorithm for an integrated super magnetic separation water purification system for wastewater treatment according to the present invention.

[0022] Figure 3 This is a magnetic powder circulation material balance diagram for an integrated super magnetic separation and water purification system for wastewater treatment according to the present invention. Detailed Implementation

[0023] 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 only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please see Figures 1 to 3 This invention provides a technical solution: an integrated super magnetic separation water purification system for wastewater treatment, comprising a magnetic powder dosing unit, a flocculation reaction unit, a super magnetic separation unit, and a magnetic powder recovery unit connected in sequence. The system also includes an adaptive control unit; the adaptive control unit includes a water quality monitoring module, a data processing module, and an instruction execution module; the water quality monitoring module is located at the inlet pipe upstream of the magnetic powder dosing unit, used to acquire raw data of the inlet water quality in real time; the data processing module is configured to receive the raw data and perform the following operations:

[0025] The raw data is processed based on the built-in flocculation kinetics model to generate an initial magnetic powder dosing command. The initial magnetic powder dosing command is then input into an intelligent prediction model trained on historical operating data for effect prediction and verification. The intelligent prediction model outputs a correction factor to adjust the initial magnetic powder dosing command and generate an optimized magnetic powder dosing command. The command execution module is signal-connected to the magnetic powder dosing unit to send the optimized magnetic powder dosing command to the magnetic powder dosing unit to control its actual dosing amount.

[0026] The magnetic powder recovery unit outlet is equipped with a magnetic powder recovery monitoring module, which is used to acquire actual magnetic powder recovery data and feed it back to the data processing module;

[0027] The data processing module is further configured to compare the actual magnetic powder recovery data with the prediction results of the intelligent prediction model, and to adaptively adjust the internal parameters of the flocculation kinetic model and the intelligent prediction model based on the comparison results.

[0028] It should be further explained that the dynamic and precise adjustment of the magnetic powder dosage is achieved through an adaptive control unit, which includes a water quality monitoring module, a data processing module, and an instruction execution module. The water quality monitoring module is installed at the inlet pipe to collect raw data such as the concentration of suspended solids and flow rate of the inlet water in real time. After receiving this data, the data processing module first calculates the theoretical magnetic powder requirement through a built-in flocculation kinetic model and generates an initial dosing instruction. This model comprehensively considers variables such as pollutant load, water flow shear force, and reaction time to ensure that the theoretical value matches the actual operating conditions.

[0029] Subsequently, the initial command is input into an intelligent prediction model trained based on historical operating data for secondary verification. This model uses machine learning algorithms to analyze the correlation between past dosage and recovery rate, as well as effluent water quality, and outputs correction factors to fine-tune the initial command, generating an optimized dosage command. This process avoids the limitations of a single decision through multi-round model interaction. The command execution module sends the optimized command to the magnetic powder dosing unit to control the actual dosage of magnetic powder.

[0030] Meanwhile, the system is equipped with a magnetic powder recovery monitoring module at the outlet of the magnetic powder recovery unit. This module measures the actual recovery data in real time and feeds it back to the data processing module. The module compares the feedback data with the prediction results and dynamically adjusts the internal parameters of the flocculation kinetic model and the intelligent prediction model to form a closed-loop optimization, ensuring that the system can continuously adapt to water quality fluctuations.

[0031] Furthermore, to enhance reliability, the system can also integrate an image recognition module. This module analyzes the morphological characteristics of flocs within the flocculation reaction zone as auxiliary verification, triggering a diagnostic program to check the equipment's operating status when an anomaly is detected. This multi-level, adaptive control mechanism not only achieves real-time matching between magnetic powder dosing and influent contamination but also improves the system's long-term stability through iterative model learning.

[0032] The water quality monitoring module includes at least a suspended solids concentration sensor for detecting suspended solids concentration and a flow meter for detecting water flow rate; the input variables of the flocculation kinetic model include real-time data measured by the suspended solids concentration sensor and the flow meter.

[0033] It should be further explained that the water quality monitoring module specifically integrates a suspended solids concentration sensor using the laser scattering principle and a pipeline flow meter using the electromagnetic induction principle. The suspended solids concentration sensor is vertically inserted into the central flow region of the inlet pipe, monitoring and outputting a suspended solids concentration signal in milligrams per liter in real time; the flow meter is fixedly installed on a straight pipe section upstream of the sensor, continuously measuring and outputting an inlet flow rate signal in cubic meters per hour. The real-time monitoring data of these two physical quantities together constitute the core input variables of the flocculation kinetic model.

[0034] This model uses a built-in algorithm to couple the received concentration and flow signals to calculate the total amount of pollutants entering the system per unit time, and then generates an initial magnetic powder dosing command directly related to the pollutant load. This technical arrangement, which synchronously, in real-time, and quantitatively senses key influent parameters and uses them as the basis for model driving, ensures that the system can accurately capture water quality fluctuations at the source, providing a reliable data foundation for subsequent intelligent decision-making and dynamic adjustment. This fundamentally distinguishes it from technologies that use fixed or single-parameter control.

[0035] The input variables of the flocculation kinetic model also include the water flow shear force parameters within the flocculation reaction unit; the water flow shear force parameters are obtained by converting the rotational speed of the agitator set within the flocculation reaction unit, or by converting the differential pressure data measured by the fluid pressure sensor group set within the flocculation reaction unit.

[0036] It should be further explained that the hydrodynamic condition directly affecting the flocculation effect, namely the water flow shear force, is quantified and incorporated into the decision-making system of the flocculation kinetic model. This water flow shear force parameter is obtained through two feasible engineering methods: First, when the flocculation reaction unit is equipped with a paddle agitator with adjustable speed, the control system directly reads the real-time speed value of the agitator and, based on the speed-shear force relationship curve pre-calibrated by the agitator in a specific medium, converts the speed value into the corresponding water flow shear force value through a built-in conversion program; Second, when no agitator is set up or as a redundant verification, a pair of pressure sensors are installed at the upper and lower positions of the side wall of a specific section of the flocculation reaction unit to form a sensor group, which monitors the fluid pressure distribution in that section in real time. The data processing module, based on the measured pressure difference data and combined with the known structural dimensions and fluid properties of the reaction unit, performs calculations based on the principles of computational fluid dynamics to indirectly solve for the average water flow shear force characterizing the mixing intensity. The water flow shear force parameters obtained in this way, along with the influent suspended solids concentration and flow rate, serve as key input variables for the flocculation kinetics model. This enables the model to more accurately simulate the collision, adsorption, and floc growth process of magnetic powder and pollutants under dynamic water flow conditions, thereby outputting initial magnetic powder addition commands that better meet actual reaction requirements and improving the accuracy and adaptability of the system's decision-making in complex flow field environments.

[0037] The intelligent prediction model is trained using machine learning algorithms. Its training data comes from the amount of magnetic powder added under different influent water quality conditions during the system's historical operation, as well as the corresponding actual magnetic powder recovery rate and effluent suspended solids concentration.

[0038] It should be further explained that the intelligent prediction model is built and trained through supervised machine learning algorithms. Its training dataset comes from a large number of operating condition records accumulated during the system's historical operation. Each record contains the influent water quality parameters at a specific time point, the magnetic powder dosage calculated and executed accordingly, and the actual magnetic powder recovery rate and effluent suspended solids concentration measured after the dosage.

[0039] In practice, historical data is first cleaned and normalized to eliminate outliers and the influence of dimensions. Then, the influent suspended solids concentration, flow rate, and calculated magnetic powder dosage are used as model input features, and the corresponding actual magnetic powder recovery rate and effluent suspended solids concentration are used as prediction targets. Algorithms such as random forest or gradient boosting decision tree, which can handle nonlinear relationships, are used to train the model, thereby establishing a complex mapping relationship from "dosing decision" to "operational effect".

[0040] The trained model can proactively evaluate the initial magnetic powder dosing command generated by the flocculation kinetics model under new operating conditions. If the predicted effect deviates from the optimal range, it outputs a correction factor calculated based on the model's internal logic, which finely adjusts the initial command. By digitizing and modeling historical experience data, and making it an active verification and optimization process, a leap from open-loop decision-making based on instantaneous theory to closed-loop prediction integrating historical experience has been achieved. This makes the determination of magnetic powder dosing no longer a simple formula calculation, but an intelligent process that continuously learns and optimizes from its own operating history, improving adaptability and decision reliability in the face of complex and variable influent conditions.

[0041] The data processing module's cycle for adaptively adjusting parameters of the flocculation kinetics model and intelligent prediction model is shorter than the computation cycle for generating a single optimized magnetic powder dosing command.

[0042] It should be further explained that the process of adaptively adjusting the parameters of the flocculation kinetics model and the intelligent prediction model by the data processing module is designed as a background task independent of the generation of real-time control commands. Its execution cycle is set to be longer than the calculation cycle of a single optimized magnetic powder dosing command. Specifically, after completing a control loop from data acquisition, model calculation to command issuance, the system does not immediately start updating the model parameters. Instead, it temporarily stores the actual operating effect data collected in this loop, including the recovery data fed back by the magnetic powder recovery monitoring module and the water quality data fed back by the effluent water quality monitoring sensor, in a first-in-first-out data buffer.

[0043] Only when the buffer accumulates a certain number of new samples, or after a preset, relatively long time interval, does the data processing module initiate a batch learning and adjustment process for model parameters. This timing arrangement ensures the rapid response of real-time control, while providing the model with stable and sufficient new data for effective parameter learning. This allows the model to gradually adapt to long-term, slow changes in system characteristics, such as equipment wear or seasonal changes in water quality characteristics. Thus, while ensuring immediate control effectiveness, it achieves the effect of continuous self-optimization of the overall system performance.

[0044] The system also includes an image recognition module; the image recognition module includes an image acquisition device installed inside the flocculation reaction unit, used to acquire images of the floc morphology; the image recognition module is communicatively connected to the data processing module, which is equipped with an image analysis algorithm to extract floc feature parameters from the floc morphology images; the data processing module also uses the floc feature parameters as auxiliary decision variables when generating optimized magnetic powder dosing instructions.

[0045] It should be further explained that the system further integrates an image recognition module as a supplement and verification to the physical sensor-based control. This module includes an industrial-grade digital camera installed in the observation window or transparent bulkhead of the flocculation reaction unit, equipped with a dedicated lighting source to ensure image clarity. The camera continuously acquires dynamic images of the flocs in the reaction zone at set time intervals. The acquired image data is transmitted in real time to the data processing module, which has a pre-trained image analysis algorithm. This algorithm first preprocesses the image to enhance features, then identifies and segments individual flocs through edge detection and morphological operations, and finally extracts the average equivalent diameter, contour irregularity, and spatial distribution density of the flocs as quantified floc feature parameters.

[0046] These visual characteristic parameters, representing the quality of the flocculation process, are input in real time into intelligent prediction models or independent decision-making logic, serving as auxiliary decision variables for generating optimized magnetic powder dosing instructions. When the algorithm identifies abnormal morphological features such as persistently small average floc size or extremely uneven distribution, even if the water quality sensor data is within the normal range, the system will assign a higher decision weight to this visual feedback, thereby triggering preventative fine-tuning of the magnetic powder dosing amount. This mechanism, which integrates intuitive floc morphology monitoring with traditional physical parameter monitoring, constructs a multi-dimensional process monitoring system, enhancing the system's perception depth and decision robustness in complex reaction states.

[0047] When the floc feature parameters extracted by the image analysis algorithm indicate an abnormal flocculation state, the data processing module triggers a diagnostic program; the diagnostic program is used to analyze whether the operating parameters of the magnetic powder dosing unit and the flocculation reaction unit deviate from the preset working range.

[0048] It should be further explained that when the image recognition module's analysis algorithm continuously identifies abnormal floc morphology parameters, such as the average equivalent diameter of the flocs being significantly smaller than the historical normal range or the irregularity of the contour exceeding a set threshold, the data processing module will activate a multi-step diagnostic procedure. This diagnostic procedure does not simply adjust the magnetic powder dosage, but first systematically checks whether the key operating parameters of the magnetic powder dosing unit and the flocculation reaction unit deviate from their preset operating range.

[0049] Specifically, the diagnostic program will instruct the query of the material level in the storage bin of the magnetic powder dosing unit, the motor speed of the dry powder dosing machine, or the pipeline pressure of the wet dosing system to confirm whether the magnetic powder supply is smooth and accurate. At the same time, it will check whether the real-time speed of the agitator in the flocculation reaction unit is consistent with the set value, and retrieve historical data to determine the stability of the agitator speed, thereby assessing whether the shear force field provided by the mixing and stirring is uniform and stable.

[0050] This diagnostic mechanism intelligently correlates visually observed "abnormal results" with the "process parameters" of equipment operation. It can distinguish between fluctuations in dosage requirements caused by changes in water quality and a decline in process performance caused by poor equipment condition. This guides the system to carry out more targeted maintenance interventions or parameter compensations, avoiding misjudgments and ineffective adjustments by a single control loop under hidden equipment faults, and improving the overall intelligence level and operational reliability of the system.

[0051] The magnetic powder recovery monitoring module includes a magnetic powder metering device installed on the powder outlet pipeline of the magnetic powder recovery unit. It should be further explained that the magnetic powder recovery monitoring module achieves accurate measurement through a magnetic powder metering device installed on the powder outlet pipeline of the magnetic powder recovery unit. This device specifically uses a loss-in-weight balance or a Coriolis mass flow meter, which is directly mechanically connected to the conveying pipeline of the circulating magnetic powder, enabling continuous online measurement of the mass of magnetic powder recovered and ready for reuse per unit time. This metering device converts the collected instantaneous flow rate and cumulative mass data into standard electrical signals and transmits them to the data processing module in real time. These actual magnetic powder recovery data constitute the most critical quantitative basis in the system's feedback loop, enabling the data processing module to accurately compare the "theoretically predicted recovery situation" with the "actually measured recovery results."

[0052] Through this direct and quantitative feedback, the system can not only evaluate the effectiveness of a single control cycle, but also provide a real and reliable basis for the adaptive adjustment of parameters of the intelligent prediction model and the flocculation kinetic model, thereby ensuring that the entire adaptive control logic is based on objective material balance, and also improving the controllability and accuracy of the magnetic powder recycling process.

[0053] The system's final outlet is equipped with an effluent water quality monitoring sensor; the effluent water quality monitoring sensor is connected to the data processing module, and the effluent water quality data measured by the effluent water quality monitoring sensor is fed back to the data processing module for comparison with the prediction results of the intelligent prediction model and for adjusting the model parameters.

[0054] It should be further explained that the system is equipped with effluent water quality monitoring sensors at the final outlet pipe. This sensor group includes at least a turbidity meter and a suspended solids concentration sensor, which directly contact the treated effluent and monitor its key water quality indicators in real time. The effluent turbidity and suspended solids concentration data collected by these monitoring devices are continuously transmitted to the data processing module via signal lines. This module compares the received real-time effluent water quality data with the predicted values ​​of the intelligent prediction model for the current control cycle. When there is a continuous deviation between the measured values ​​and the predicted values, this deviation information is identified as an important basis for the model to be corrected.

[0055] These feedback data from the final effluent quality, together with the recovery data from the magnetic particle recovery monitoring module, constitute a more complete system performance evaluation system. These data are simultaneously used to drive the adaptive adjustment of parameters in the flocculation kinetics model and the intelligent prediction model. This design, which incorporates the final treatment effect into a real-time feedback closed loop, ensures that the system not only focuses on the accuracy of intermediate process parameters but also guarantees that the entire control strategy always aims to achieve optimal effluent quality. This achieves a leap from local optimization to global optimization, enhancing the system's adaptability and the stability of its treatment effect when facing complex influent conditions.

[0056] The adaptive control unit is integrated into a separate industrial controller, which exchanges data and transmits control commands with other units in the system via an industrial bus. It should be further noted that the adaptive control unit is specifically implemented as an independent industrial programmable logic controller (PLC). This controller serves as the core computational and command center of the entire system, internally containing a complete control algorithm that includes a flocculation kinetic model and an intelligent predictive model. This industrial controller establishes stable and reliable data connections with each physical unit of the system through a standard industrial Ethernet or PROFIBUS-DP fieldbus network.

[0057] Specifically, it establishes communication with the water quality monitoring module at the inlet pipe, the agitator and image acquisition device in the flocculation reaction unit, the feeder in the magnetic powder dosing unit, the metering device in the magnetic powder recovery unit, and the water quality monitoring sensor at the final outlet, acquiring all necessary monitoring data in real time and issuing control commands. This centralized controller integration and distributed bus connection method integrates complex multivariate sensing, intelligent model calculation, and decentralized execution control into an organic whole. This not only simplifies system wiring and maintenance but, more importantly, ensures the real-time, synchronous, and reliable nature of data exchange. It provides a solid hardware architecture foundation for the stable operation of multi-level, adaptive control strategies, thereby achieving an upgrade from discrete control to system-level intelligent integration.

[0058] This system effectively achieves real-time and precise matching between magnetic powder dosage and influent pollution load by constructing an adaptive control mechanism based on multi-source information fusion and dynamic feedback verification. The system proactively responds to fluctuations in water quality, maintaining a high magnetic powder recovery rate, reducing additional magnetic powder replenishment consumption, and ensuring stable effluent quality compliance, directly lowering system operation and maintenance costs.

[0059] Furthermore, the system possesses self-learning and continuous optimization capabilities. By feeding back real-time operational results to the core decision-making model and dynamically adjusting its parameters, the overall system performance continuously improves over time. Its integrated multi-dimensional monitoring and diagnostic functions further enhance operational reliability, reduce reliance on external human intervention, and thus ensure the long-term efficiency and stability of the wastewater treatment process.

[0060] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0061] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An integrated supermagnetic separation water purification system for wastewater treatment, comprising a magnetic powder dosing unit, a flocculation reaction unit, a supermagnetic separation unit, and a magnetic powder recovery unit connected in sequence, characterized in that, The system also includes an adaptive control unit; the adaptive control unit includes a water quality monitoring module, a data processing module, and an instruction execution module; the water quality monitoring module is located at the inlet pipe upstream of the magnetic powder dosing unit, and is used to acquire raw data of the inlet water quality in real time; the data processing module is configured to receive the raw data and perform the following operations: The raw data is processed based on a built-in flocculation kinetic model to generate an initial magnetic powder dosing command. This initial command is then input into an intelligent prediction model trained on historical operational data for effect prediction and verification. The intelligent prediction model outputs a correction factor to adjust the initial magnetic powder dosing command, generating an optimized magnetic powder dosing command. The command execution module is signal-connected to the magnetic powder dosing unit and is used to send the optimized command to the unit to control its actual dosing amount. The magnetic powder recovery unit outlet is equipped with a magnetic powder recovery monitoring module, which is used to acquire actual magnetic powder recovery data and feed it back to the data processing module; The data processing module is further configured to compare the actual magnetic powder recovery data with the prediction results of the intelligent prediction model, and to adaptively adjust the internal parameters of the flocculation kinetic model and the intelligent prediction model based on the comparison results. The input variables of the flocculation kinetic model also include the water flow shear force parameters within the flocculation reaction unit; the water flow shear force parameters are obtained by conversion of the agitator speed set within the flocculation reaction unit, or by conversion of the differential pressure data measured by the fluid pressure sensor group set within the flocculation reaction unit.

2. The integrated super magnetic separation water purification system for wastewater treatment according to claim 1, characterized in that: The water quality monitoring module includes at least a suspended solids concentration sensor for detecting suspended solids concentration and a flow meter for detecting water flow rate; the input variables of the flocculation kinetic model include real-time data measured by the suspended solids concentration sensor and the flow meter.

3. The integrated super magnetic separation water purification system for wastewater treatment according to claim 1, characterized in that: The intelligent prediction model is trained using machine learning algorithms. Its training data comes from the amount of magnetic powder added under different influent water quality conditions during the historical operation of the system, as well as the corresponding actual magnetic powder recovery rate and effluent suspended solids concentration.

4. The integrated super magnetic separation water purification system for wastewater treatment according to claim 3, characterized in that: The data processing module's cycle for adaptively adjusting the parameters of the flocculation kinetics model and the intelligent prediction model is shorter than the computation cycle for generating a single optimized magnetic powder dosing command.

5. The integrated super magnetic separation water purification system for wastewater treatment according to claim 1, characterized in that: The system also includes an image recognition module; the image recognition module includes an image acquisition device installed inside the flocculation reaction unit for acquiring images of the floc morphology; the image recognition module is communicatively connected to the data processing module, and the data processing module is configured with an image analysis algorithm for extracting floc feature parameters from the floc morphology images; the data processing module also uses the floc feature parameters as auxiliary decision variables when generating the optimized magnetic powder dosing command.

6. The integrated super magnetic separation water purification system for wastewater treatment according to claim 5, characterized in that: When the floc feature parameters extracted by the image analysis algorithm indicate an abnormal flocculation state, the data processing module triggers a diagnostic program; the diagnostic program is used to analyze whether the operating parameters of the magnetic powder dosing unit and the flocculation reaction unit deviate from the preset working range.

7. The integrated super magnetic separation water purification system for wastewater treatment according to claim 1, characterized in that: The magnetic powder recovery monitoring module includes a magnetic powder metering device installed on the powder outlet pipeline of the magnetic powder recovery unit.

8. The integrated super magnetic separation water purification system for wastewater treatment according to claim 1, characterized in that: The system's final outlet is equipped with an effluent water quality monitoring sensor; the effluent water quality monitoring sensor is connected to the data processing module, and the effluent water quality data measured by the effluent water quality monitoring sensor is fed back to the data processing module for comparison with the prediction results of the intelligent prediction model and for adjusting the model parameters.

9. An integrated super magnetic separation water purification system for wastewater treatment according to any one of claims 1 to 8, characterized in that: The adaptive control unit is integrated into a separate industrial controller, which exchanges data and transmits control commands with other units of the system via an industrial bus.

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