Intelligent dosing control system and method for micro-flocculation-ultrafiltration process

Abstract

The present application relates to a kind of intelligent dosing control system and method of micro-flocculation-ultrafiltration process, belong to water treatment technical field, to solve the problem of membrane pollution intensification, unstable operation and cost increase caused by inaccurate coagulant dosage in prior art.The technical scheme includes: real-time acquisition of alum flower image data in micro-flocculation or ultrafiltration unit by image acquisition module, particle size acquisition module monitors the micro-particle size data of micro-flocculation unit effluent, sensor module collects water quality and quantity parameters, intelligent control center generates dosing instruction based on comprehensive analysis of these data by calculation prediction model, and realizes closed-loop control through dosing module.The method uses real-time feedback to adjust the precise optimization of dosing amount, effectively improves the alum flower structure and micro-particle distribution, significantly reduces the risk of membrane hole blockage, improves process stability, reliability and economy, and ensures the long-term efficient operation of micro-flocculation-ultrafiltration short process.

Description

Technical Field

[0001] This invention belongs to the field of water treatment technology and relates to an intelligent dosing control system and method for a micro-flocculation-ultrafiltration process. Background Technology

[0002] Ultrafiltration (UF) technology has been widely used in wastewater treatment and reuse due to its high efficiency in removing pollutants such as suspended solids, colloids, and bacteria from water. However, in practical engineering, suspended solids, colloids, and organic matter in raw water can easily deposit on the membrane surface or clog the membrane pores, causing membrane fouling. This leads to problems such as a rapid decline in membrane permeate flux, increased cleaning frequency, and increased operating energy consumption. To mitigate membrane fouling and extend membrane lifespan, conventional processes typically include multiple pretreatment units such as coagulation, flocculation, sedimentation, and filtration before the ultrafiltration unit. However, this makes the entire process lengthy, significantly increasing the project's construction investment and operating costs.

[0003] Studies have shown that adding microflocculation pretreatment before ultrafiltration is an effective short-process technology. By adding coagulants, their hydrolysis products form a loose, dynamic microflocculated filter cake layer on the membrane surface. This filter cake layer can adsorb and intercept pollutants in the subsequent incoming water, thereby effectively mitigating membrane fouling. However, this process has extremely stringent requirements on the dosage of coagulants. Too little dosage will not form an effective filter cake layer; too much dosage may lead to an overly dense filter cake layer, which will also increase filtration resistance. More importantly, inappropriate dosage can also result in a large number of microparticles in the water that are invisible to the naked eye and have a particle size similar to that of the membrane pores. These particles are easily embedded inside the membrane pores during filtration, causing irreversible membrane fouling that is difficult to reverse with conventional chemical cleaning. This has become a key technical bottleneck limiting the long-term stable operation and widespread application of the microflocculation-ultrafiltration process.

[0004] Therefore, there is an urgent need to develop a system and method that can accurately and intelligently control the dosage of coagulants to ensure that the formed flocs help to build the optimal filter cake layer, while promoting the transfer of the particle size of the microparticles to a larger size, thereby minimizing membrane pore blockage and ensuring the long-term stable operation of the microflocculation-ultrafiltration short process. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide an intelligent dosing control system and method for a micro-flocculation-ultrafiltration process, which aims to achieve precise dosing of coagulants, thereby ensuring the long-term stable operation of the process.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] This invention provides an intelligent dosing control system for a micro-flocculation-ultrafiltration process, comprising:

[0008] The image acquisition module is used to acquire image data of floc in the micro-flocculation unit or ultrafiltration unit;

[0009] The particle size acquisition module is used to collect microparticle size data of the effluent from the microflocculation unit.

[0010] The sensor module is used to collect data on the quality and quantity of influent water.

[0011] The dosing module is used to add coagulant to the micro-flocculation unit according to the dosing command;

[0012] The system also includes an intelligent control center, which is connected to each module. This center receives and comprehensively analyzes the floc image data, microparticle size data, and water quality and quantity parameter data. Based on a preset calculation and prediction model, it generates dosing instructions and sends them to the dosing module to achieve closed-loop control.

[0013] This invention also provides a corresponding intelligent dosing control method. This method utilizes the aforementioned system, the core of which lies in: real-time acquisition of multi-dimensional data, including floc images, microparticle size, and water quality and quantity, and inputting this data into a trained computational prediction model. This model can predict whether the floc morphology and microparticle size distribution in the effluent are ideal under the current operating conditions and dosing dosage. If not, the model will automatically calculate the minimum dosing dosage that simultaneously achieves the optimal target state for both, and issue instructions for automatic adjustment, thereby realizing intelligent, precise, and economical dosing control throughout the entire process.

[0014] The beneficial effects of this invention are as follows: By establishing a computational prediction model based on the dual objectives of floc characteristics and microparticle size, and combining it with real-time feedback for closed-loop control, this invention achieves precise and intelligent dosing of coagulants in the microflocculation-ultrafiltration process. This not only improves the structure of the filter cake layer on the membrane surface, making it both effective in intercepting pollutants and easy to clean, but more importantly, it can actively control the particle size distribution of microparticles, promoting their transformation into larger particles that are less likely to clog membrane pores. This fundamentally solves the problem of irreversible membrane fouling, thereby ensuring the long-term stable operation of this efficient, short-process microflocculation-ultrafiltration technology and laying the foundation for its application in wastewater treatment scenarios with higher pollution loads.

[0015] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0016] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0017] Figure 1 This is a structural block diagram of an intelligent dosing control system for a micro-flocculation-ultrafiltration process in one embodiment of the present invention;

[0018] Figure 2 This is a schematic flowchart of an intelligent dosing control method for micro-flocculation-ultrafiltration process in one embodiment of the present invention. Detailed Implementation

[0019] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0020] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0021] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0022] Reference Figure 1This embodiment discloses an intelligent dosing control system applied to a micro-flocculation-ultrafiltration process. The process flow first allows for the selection of pretreatment units such as hardness removal, silica removal, and fluoride removal based on the raw water quality. Then, the wastewater enters the micro-flocculation unit (in this embodiment, a stirred tank type). There, coagulants (such as polyaluminum chloride, ferric chloride, etc.) are added through a dosing module, causing colloids and suspended solids in the water to aggregate into visible flocs. Simultaneously, a large number of invisible microparticles are also present in the water. The mixture carrying the flocs and microparticles enters a submerged ultrafiltration unit for solid-liquid separation, and the permeate is then used in subsequent processes.

[0023] To achieve precise dosing of chemicals in the micro-flocculation unit, the specific steps include:

[0024] 1. Sensor Module: Used to collect water quality and quantity parameters of the incoming water. This module consists of various online instruments installed on the inlet pipeline, such as electromagnetic flow meters, pH meters, thermometers, conductivity meters, and turbidity meters. They continuously monitor fluctuations in the quality and quantity of the incoming water and transmit the data to the intelligent control center in real time.

[0025] 2. Image Acquisition Module: Used to acquire floc image data. In this embodiment, this module preferably includes an underwater industrial camera, a matching power supply module, and a photoelectric signal transmission module. The underwater industrial camera is installed at the end of the micro-flocculation tank, i.e., before the inlet of the ultrafiltration unit, to ensure that the floc morphology of the flocs, after sufficient reaction, is captured before entering the membrane tank. The high-definition image signal acquired by the camera is transmitted to the intelligent control center through the photoelectric signal transmission module.

[0026] 3. Particle Size Acquisition Module: This module is used to collect microparticle size data. It includes an online laser particle size analyzer and a sampling tube. One end of the sampling tube is connected to the end of the microflocculation tank, and the other end is connected to the inlet of the online laser particle size analyzer. Automatic and continuous sampling and analysis of the effluent from the microflocculation unit is achieved through a peristaltic pump or similar method. The online laser particle size analyzer can accurately measure the particle size distribution of micron- and submicron-sized particles in the water sample and transmit the data to the intelligent control center.

[0027] 4. Dosing Module: Used to execute dosing commands. This module mainly includes a storage tank, a variable frequency dosing pump, and corresponding pipe valves. The variable frequency dosing pump receives control signals (usually frequency or analog signals) from the intelligent control center and precisely adds coagulant to the micro-flocculation unit. Simultaneously, the operating status of the dosing pump (such as real-time dosing rate) is also transmitted back to the intelligent control center as feedback signals.

[0028] 5. Control Center: As the "brain" of the system, it is the core of the entire closed-loop control. It typically consists of an industrial computer or a PLC (Programmable Logic Controller), integrating data storage devices, signal transmission modules, image analysis modules, and a core computational prediction model. Its operation is as follows:

[0029] Data reception and storage: Receive and store real-time data from the three acquisition modules mentioned above, as well as feedback data from the dosing module.

[0030] Image Analysis: The built-in image analysis module processes the received alum flower image data and automatically extracts key alum flower feature parameters.

[0031] Computation and Decision: The core computational prediction model performs complex analysis and calculations based on all received real-time data to ultimately generate the optimal dosing instructions.

[0032] Command issuance: The calculated dosing command is issued to the dosing module for execution via the signal transmission module.

[0033] Reference Figure 2 In conjunction with the above system, the specific steps are as follows:

[0034] Step 1: Construction of the prediction model

[0035] Before the system is officially put into operation or during the debugging phase, it is necessary to build and train a computational prediction model.

[0036] Setting Control Objectives: Clearly define dual control objectives. Use microparticle size parameters as the primary control objective, as they directly relate to irreversible membrane fouling. Use floc characteristic parameters as the secondary objective.

[0037] The auxiliary control target is related to the quality of the dynamic filter cake layer.

[0038] Determine the parameters and target values:

[0039] For the particle size parameter of microparticles, this embodiment preferably adopts...

[0040] The minimum boundary particle size (D10), which is the particle size value corresponding to a cumulative particle size distribution reaching 10%, is used as a key control indicator. Its target value is preferably set between 0.5 μm and 5 μm to ensure that the size of most microparticles is much larger than the average pore size of the ultrafiltration membrane (e.g., 0.05 μm), thereby avoiding embedded fouling.

[0041] For the characteristic parameters of alum floc, this embodiment preferably adopts

[0042] Two-dimensional fractal dimension is used as an indicator. The fractal dimension can characterize the density and complex structure of floc. Its target value is preferably set between 0.8 and 1.5 to form a filter cake layer with a loose structure but effective dirt interception.

[0043] Model Training: Under different influent water quality and quantity conditions, the coagulant dosage was systematically varied through small-scale or pilot-scale tests, while simultaneously collecting corresponding influent data, floc 2D fractal dimension, microparticle D10 value, and dosage data. Using this massive amount of data as input, machine learning algorithms (such as neural networks, support vector machines, etc.) or multiple regression analysis methods were employed to construct a computational prediction model that accurately reflects the nonlinear relationship between "input (water quality and quantity, dosage)" and "output (fractal dimension, D10)".

[0044] Step 2: Real-time Control and Optimization

[0045] Once the model is built, the system enters normal automatic operation mode.

[0046] S1: Real-time acquisition of multi-dimensional data: The system continuously acquires data such as influent flow rate, pH, and turbidity through the sensor module; acquires images of flocs and extracts the two-dimensional fractal dimension through the image acquisition module; acquires the particle size distribution of microparticles and obtains the D10 value through the particle size acquisition module; and records the current dosage of the dosing module.

[0047] S2: Real-time data input model: All real-time data collected in S1 is used as input and fed into the pre-built computational prediction model.

[0048] S3: Prediction and Judgment: Based on real-time input, the model quickly predicts whether the two-dimensional fractal dimension and D10 value of the effluent from the micro-flocculation unit can simultaneously meet the preset target value range under the current dosage.

[0049] S4: Dosage Decision and Implementation

[0050] If the S3 judgment result is "yes", it indicates that the current dosage is appropriate, and the intelligent control center will instruct the dosing module.

[0051] Maintain the current dosage until the next control cycle.

[0052] If the S3 judgment result is "no", it indicates that the current dosage is inappropriate. At this point, the intelligent control center will use the model to perform reverse optimization calculations, ensuring that both the fractal dimension and D10 objectives are met.

[0053] The group with the lowest predicted dosage is designated as the optimal dosage group. Subsequently, the intelligent control center converts this optimal value into a specific control signal and sends it to the variable frequency pump of the dosing module to achieve automatic and precise adjustment of the dosage.

[0054] Step 3: Feedback Adjustment

[0055] To ensure the robustness and accuracy of the control, this method includes a feedback regulation closed loop.

[0056] After adjusting the dosage in S4, the system will not terminate the judgment. After a brief stabilization period, the system will...

[0057] Re-collect floc image data and microparticle size data at the end of the microflocculation unit to determine whether the actual operating results under the new dosage meet the preset target requirements.

[0058] If so, it proves that the model's prediction and adjustment were successful, and the system will maintain the dosage and continue to the next round of real-time monitoring.

[0059] If not, it indicates that the predicted dosage is still not entirely appropriate due to interference from some unmodeled factors or model bias. In this case, the system uses this "actual result" as new feedback information and returns to the optimization calculation step S4 to fine-tune the dosage again until the fractal dimension and D10 value of the effluent are both stable within the target range. This feedback adjustment mechanism ensures the final effectiveness of the system control.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. 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 be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An intelligent dosing control system for a micro-flocculation-ultrafiltration process, characterized in that: include: The image acquisition module is used to acquire image data of floc in the micro-flocculation unit or ultrafiltration unit; The particle size acquisition module is used to acquire the particle size data of the microparticles in the effluent from the microflocculation unit. The sensor module is used to collect data on the quality and quantity of influent water. The dosing module is used to add coagulant to the micro-flocculation unit according to the dosing command; The intelligent control center is connected to the image acquisition module, the particle size acquisition module, the sensor module, and the dosing module, respectively. It receives the floc image data, the microparticle size data, and the water quality and quantity parameter data. Based on a preset calculation and prediction model, it analyzes and calculates to generate the dosing instruction and sends it to the dosing module. The intelligent control center has the calculation and prediction model built-in. This model predicts the floc characteristic parameters and microparticle size parameters corresponding to different dosing amounts based on the input floc image data, microparticle size data, water quality and quantity parameter data, and the dosing amount data fed back by the dosing module. It then generates the dosing instruction that enables the parameters to reach preset target values.

2. The intelligent dosing control system for the micro-flocculation-ultrafiltration process according to claim 1, characterized in that: The image acquisition module includes an underwater industrial camera located at the end of the micro-flocculation unit or the beginning of the ultrafiltration unit.

3. The intelligent dosing control system for the micro-flocculation-ultrafiltration process according to claim 1, characterized in that: The particle size acquisition module includes an online laser particle size analyzer and a sampling tube for introducing the effluent from the micro-flocculation unit into the online laser particle size analyzer.

4. A smart dosing control method for a micro-flocculation-ultrafiltration process, characterized in that: Includes the following steps: S1: Collect influent water quality and quantity data, floc image data in the micro-flocculation unit or ultrafiltration unit, and microparticle size data of the effluent from the micro-flocculation unit. S2: Input the data collected in real time into the calculation prediction model; S3: The calculation and prediction model predicts whether the characteristic parameters of floc and the particle size parameters of microparticles in the effluent reach the preset target values ​​under the current dosage. S4: If the prediction result of S3 is yes, then maintain the current dosage; if the prediction result is no, then the optimal dosage required to make the characteristic parameters of the alum flower and the particle size parameters of the microparticles reach the target value is calculated by the calculation prediction model, and the dosage is controlled. The method further includes the step of constructing the computational prediction model: taking the microparticle size parameter as the main control target and the floc characteristic parameter as the auxiliary control target, and setting target values ​​for them respectively; using water quality and quantity data, floc image data, microparticle size data, and dosage data as model inputs, constructing the computational prediction model that can predict the changes in floc characteristic parameters and microparticle size parameters in the effluent under different dosages.

5. The intelligent dosing control method for the micro-flocculation-ultrafiltration process according to claim 4, characterized in that: The alum floc characteristic parameters include two-dimensional fractal dimension and equivalent particle size; the microparticle particle size parameter is the minimum boundary particle size D10.

6. The intelligent dosing control method for the micro-flocculation-ultrafiltration process according to claim 5, characterized in that: The target value for the minimum boundary particle size D10 is set between 0.5 μm and 5 μm.

7. The intelligent dosing control method for the micro-flocculation-ultrafiltration process according to claim 5, characterized in that: The target value of the two-dimensional fractal dimension in the alum flower characteristic parameters is set between 0.8 and 1.

5.

8. The intelligent dosing control method for the micro-flocculation-ultrafiltration process according to claim 4, characterized in that: The method further includes a feedback adjustment step: after the dosing is performed, the floc image data and microparticle size data are collected again to determine whether the actual effluent reaches the target value; if so, the current dosing amount is maintained; if not, the process returns to S4 until the effluent meets the standard.

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