A low turbidity water treatment method, system, medium and product

Abstract

A low-turbidity water treatment method, system, medium and product, wherein the method comprises: obtaining a first floc particle size of flocs; determining a particle size state of the first floc particle size; obtaining a morphological index of the flocs; when the particle size state is a first particle size state, determining a stirring frequency of a micro-flocculation reactor auxiliary stirring device and stirring; when the particle size state is a second particle size state, calculating an increase amount of a flocculating agent; obtaining a second floc particle size, re-determining the particle size state, and determining a flocculating agent formula of a micro-flocculation system, a dosing amount and a stirring frequency of the micro-flocculation reactor auxiliary stirring device according to the particle size state until the second floc particle size is within a preset particle size range, to obtain target flocs; obtaining a final floc particle size corresponding to the target flocs, determining a filtration parameter of a filter material layer of a deep filtration unit based on the final floc particle size, and controlling the filter material layer to filter according to the filtration parameter. The application can improve the low-turbidity water treatment efficiency.

Description

Technical Field

[0001] This application relates to the field of low-turbidity water treatment technology, specifically to a low-turbidity water treatment method, system, medium, and product. Background Technology

[0002] With the increasing demands for sustainable water resource utilization, the need for refined treatment of low-turbidity water is growing. Low-turbidity water is widely found in scenarios such as greywater reuse, rainwater harvesting, and river network regulation. Although its suspended solids concentration is low, the particle size is fine and it is not easy to settle. Traditional sedimentation treatment is inefficient and cannot meet increasingly stringent effluent quality standards. Therefore, filtration technology, especially filtration technology based on micro-flocculation processes, is gradually becoming an important means of treating low-turbidity water.

[0003] In existing technologies, a common method for treating low-turbidity water is a combined process of micro-flocculation and filtration. This process typically involves adding flocculants to the influent to form preliminary flocs from fine particles in the water, which are then physically intercepted by the filter media in the filter, thereby removing suspended solids.

[0004] However, the formation of flocs is often affected by many factors, such as fluctuations in raw water quality, chemical reaction rates, and stirring frequency, making it difficult to maintain a stable particle size. Therefore, in actual operation, floc particle size often deviates from the ideal range, affecting the efficiency of low-turbidity water treatment, and excessive chemical dosing can lead to water pollution.

[0005] Practice has shown that using traditional filters can easily lead to filter bed caking and blockage, causing filtration failure and making it impossible to achieve stable system operation. Summary of the Invention

[0006] This application provides a method, system, medium, and product for treating low-turbidity water, which solves the technical problems of floc particle size deviating from the ideal range affecting the efficiency of low-turbidity water treatment and composite process filters being prone to clogging and failure during the low-turbidity water treatment process, thereby improving the efficiency of low-turbidity water treatment.

[0007] The first aspect of this application provides a method for treating low-turbidity water, applied to a low-turbidity water treatment device. The low-turbidity water treatment device includes an intelligent micro-flocculation system of a floc control unit and a high-speed microfiltration filter of a deep filtration unit. The intelligent micro-flocculation system includes a micro-flocculation intelligent calculation and optimization system, a micro-flocculation dosing system, a micro-flocculation pipeline mixer, a micro-flocculation reactor, an online turbidity monitor, and an online floc particle size monitor. The method includes: obtaining a first floc particle size in the water to be filtered; determining the particle size state of the first floc particle size, the particle size state including a first particle size state and a second particle size state; obtaining morphological indicators of the floc, the morphological indicators being used to characterize the geometric structural features of the floc; when the particle size state is the first particle size state, determining the stirring frequency of the stirring device in the micro-flocculation reactor based on the morphological indicators and controlling the stirring device to stir according to the stirring frequency. The process continues until the target flocs meet the preset particle size range. When the particle size range is the second range, the amount of flocculant added is calculated based on the lower limit threshold of the particle size range between the first floc and the preset particle size range. The flocculant is added at the preset dosing point according to the added amount. The second floc particle size is obtained within a preset time period after the addition of the flocculant dosage. The particle size range is re-evaluated based on the second floc particle size. The stirring frequency of the stirring device in the micro-flocculation reactor is adjusted according to the particle size range. The stirring device is then controlled to stir according to the stirring frequency or to continue adding flocculant until the second floc particle size is within the preset particle size range, thus obtaining the target flocs. The final floc particle size corresponding to the target flocs is obtained. The filtration parameters of the high-speed microfiltration filter media layer are determined based on the final floc particle size. The operation of the high-speed microfiltration filter is controlled according to the filtration parameters.

[0008] Optionally, determining the particle size state of the first floc includes: acquiring the historical floc particle size of the water to be filtered within a preset historical time period; calculating the instantaneous particle size change rate based on the historical floc particle size and the first floc particle size; when the instantaneous particle size change rate is greater than a preset change rate threshold, stopping the particle size state determination; after a preset delay time, reacquiring the first floc particle size and calculating the instantaneous particle size change rate until the instantaneous particle size change rate is less than or equal to the preset change rate threshold; when the instantaneous particle size change rate is less than or equal to the preset change rate threshold, acquiring the ultrasonic attenuation coefficient of the floc; determining the target particle size level of the first floc particle size based on a preset particle size level, and... Obtain the reference attenuation range corresponding to the target particle size class. The reference attenuation range is a health status reference established by statistically analyzing the ultrasonic attenuation coefficients of flocs falling within the target particle size class under preset operating conditions. When the particle size of the first floc is greater than the upper limit threshold of the preset particle size range and the ultrasonic attenuation coefficient is within the reference attenuation range, the particle size state is determined to be the first particle size state. When the particle size of the first floc is less than the lower limit threshold of the preset particle size range, or when the particle size of the first floc is greater than the upper limit threshold of the preset particle size range and the ultrasonic attenuation coefficient is less than the lower limit threshold of the reference attenuation range, the particle size state is determined to be the second particle size state.

[0009] Optionally, the amount of flocculant added is calculated based on the lower limit threshold of the particle size between the first floc particle size and a preset particle size range. Specifically, this includes: calculating the target particle size difference between the first floc particle size and the lower limit threshold; correcting the first floc particle size based on the ultrasonic attenuation coefficient to obtain a floc particle size correction value; calculating the ratio of the target particle size difference to the floc particle size correction value to obtain a floc demand adjustment factor; multiplying the floc demand adjustment factor by a preset addition conversion coefficient to obtain an addition adjustment amount; and adding the addition adjustment amount to a preset baseline addition setting value to obtain the amount added.

[0010] Optionally, the final floc particle size corresponding to the target floc is obtained, and the filtration parameters of the high-speed microfiltration filter media layer are determined based on the final floc particle size. Specifically, this includes: calculating the volume-weighted average particle size of the final floc particle size; determining the target filtration rate and interception efficiency corresponding to the volume-weighted average particle size based on a preset particle size-filtration rate-interception efficiency mapping relationship, wherein the preset particle size-filtration rate-interception efficiency mapping relationship records the target filtration rate and interception efficiency of the flocs with different particle sizes under different filtration conditions; monitoring the real-time interception efficiency of the filter media layer and comparing the real-time interception efficiency with the upper limit of the allowable interception efficiency corresponding to the target filtration rate; if the real-time interception efficiency is less than the upper limit of the allowable interception efficiency, then the target filtration rate is determined as the filtration parameter; if the real-time interception efficiency is greater than or equal to the upper limit of the allowable interception efficiency, then the target filtration rate is reduced by a preset reduction step size until the real-time interception efficiency is less than the upper limit of the allowable interception efficiency, and then the adjusted filtration rate is determined as the filtration parameter.

[0011] Optionally, before obtaining the first floc particle size in the water to be filtered, the method includes: obtaining historical data of the water to be filtered, including historical turbidity values, historical total suspended solids, historical water flow rates, and historical floc particle size morphology and historical change rates during a pre-set small-scale treatment process; obtaining technical parameters of the deep filtration unit, including the range of suspended particle size interception, filtration accuracy, and interception efficiency; constructing a dosing model based on historical data and technical parameters, the dosing model being used to calculate the amount of flocculant to be added; obtaining the first online monitoring turbidity value of the water to be filtered and the second online monitoring turbidity value of the water to be filtered after initial filtration, and calculating the initial interception efficiency of the system based on the first and second online monitoring turbidity values; and then... Historical data is input into a preset threshold model to determine the preset turbidity threshold and preset interception threshold. When both the first and second online monitoring turbidity values ​​are less than the preset turbidity threshold, and the initial interception efficiency is greater than or equal to the preset interception threshold, the high-speed microfiltration filter of the filtration unit is controlled to operate independently. When both the first and second online monitoring turbidity values ​​are greater than or equal to the preset turbidity threshold, and the initial interception efficiency is less than the preset interception threshold, the flow rate of the water to be filtered and the total amount of suspended solids are obtained. The first online monitoring turbidity value, the second online monitoring turbidity value, the flow rate of the water to be filtered, and the total amount of suspended solids are input into the dosing model to obtain the initial amount of flocculant. The flocculant is then added to the preset dosing point according to the initial amount.

[0012] Optionally, after obtaining the final floc particle size corresponding to the target floc, determining the filtration parameters of the high-speed microfiltration filter media layer based on the final floc particle size, and controlling the operation of the high-speed microfiltration filter according to the filtration parameters, the method further includes: obtaining the third online monitoring turbidity value of the water to be filtered after adding flocculant and the fourth online monitoring turbidity value of the water to be filtered after filtration; calculating the real-time interception efficiency of the water to be filtered based on the third and fourth online monitoring turbidity values; adjusting the floc particle size state threshold range, flocculant addition amount, flocculant addition ratio, and reactor stirring parameters according to the third and fourth online monitoring turbidity values ​​and the real-time interception efficiency; and performing statistical analysis on the particle size state threshold range, flocculant addition amount, flocculant addition ratio, and reactor stirring parameters for continuous rolling optimization.

[0013] Optionally, the method further includes: acquiring the turbidity of the filtered water and determining whether the turbidity is within a preset turbidity target range; when the turbidity is higher than the upper turbidity threshold of the preset turbidity target range, calculating a first turbidity deviation value between the turbidity and the upper turbidity threshold, and calculating a positive particle size range adjustment amount for increasing the preset particle size range based on the first turbidity deviation value; adjusting the preset particle size range according to the positive particle size range adjustment amount; when the turbidity is lower than the lower turbidity threshold of the preset turbidity target range, calculating a second turbidity deviation value between the turbidity and the lower turbidity threshold, and calculating a negative particle size range adjustment amount for decreasing the preset particle size range based on the second turbidity deviation value; adjusting the preset particle size range according to the negative particle size range adjustment amount.

[0014] Secondly, this application provides a low-turbidity water treatment device, which includes: an intelligent micro-flocculation system of a floc control unit and a high-speed microfiltration filter of a deep filtration unit. The intelligent micro-flocculation system includes a micro-flocculation intelligent calculation optimization system and a micro-flocculation dosing system, a micro-flocculation pipeline mixer, a micro-flocculation reactor, an online turbidity monitor, and an online floc particle size monitor. The micro-flocculation reactor is provided with an anti-clogging wedge-shaped slotted mesh at its center. The anti-clogging wedge-shaped slotted mesh is provided with a baffle plate on its outer ring. An auxiliary stirrer is provided at the top of the micro-flocculation reactor, and stirring blades are installed on the impeller of the auxiliary stirrer. The micro-flocculation reactor is provided with an inlet, an outlet, a sewage outlet, and a PAM dosing port.

[0015] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a low-turbidity water treatment system, cause the low-turbidity water treatment system to perform the method described in the first aspect and any possible implementation thereof.

[0016] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a low-turbidity water treatment system, cause the low-turbidity water treatment system to perform the method described in the first aspect and any possible implementation thereof.

[0017] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages:

[0018] 1. By obtaining the first floc particle size in the water to be filtered and determining whether its particle size state belongs to the first or second particle size state, the structural complexity of the floc is further evaluated based on the morphological indicators of the floc. When the particle size is too large, the stirring frequency of the stirring device is determined according to the morphological indicators, and the stirring process is precisely controlled to optimize the floc structure and promote it to reach the preset conditions, avoiding floc breakage due to high-intensity stirring. When the particle size is too small, the amount of flocculant to be added is calculated based on the lower limit threshold of the first floc particle size and the preset particle size range, and the preset agent dosing point is controlled to achieve quantitative addition, thereby promoting floc particle size growth. Subsequently, by obtaining the second floc particle size, the particle size state is dynamically judged, forming a feedback-based iterative control mechanism to ensure that the final floc particle size stably falls within the preset particle size range, thus obtaining the target floc. After determining the target floc, its corresponding final floc particle size is obtained, and the filtration parameters of the filter media layer are determined based on this, thereby achieving precise control of the filter media layer filtration process. By combining dynamic judgment and closed-loop control, the efficiency of low turbidity water treatment is improved.

[0019] 2. After obtaining the first floc particle size, the instantaneous particle size change rate is calculated based on historical floc particle sizes. If the change rate exceeds a preset threshold, it indicates that the water body is in a floc fluctuation stage, and the particle size status judgment is temporarily suspended until the water body stabilizes. Data is then re-acquired to ensure that the particle size status assessment is based on the true characteristics under stable conditions. Provided the change rate meets stability requirements, the target particle size level is determined by combining the first floc particle size with the ultrasonic attenuation coefficient through a preset particle size level, and the corresponding benchmark attenuation range is queried. If the first floc particle size deviates from the preset particle size range and the attenuation coefficient is lower than the benchmark value, it indicates a loose floc structure, and is judged as the second particle size state. If the particle size is large but the attenuation coefficient is within the benchmark range, it indicates a compact floc structure with good filtration performance, and is judged as the first particle size state. This two-factor judgment method effectively avoids misjudgments due to particle size fluctuations or single indicators, ensuring that the particle size status determination is more scientific and engineering-adaptable. This provides a reliable basis for the dynamic adjustment of subsequent stirring or dosing strategies, enabling precise dosing and avoiding excessive dosing that could pollute the water body.

[0020] 3. By calculating the target particle size difference between the first floc particle size and the lower limit threshold, the quantitative target to be achieved by the reagent dosage was clarified. More importantly, an ultrasonic attenuation coefficient was introduced to correct the first floc particle size. Since the ultrasonic attenuation coefficient can indirectly reflect the deep morphological characteristics of the floc, such as density and structural strength, the obtained floc particle size correction value can more realistically and comprehensively characterize the current state of the floc and its response potential to the reagent compared to a single geometric size measurement. Based on this, by calculating the ratio of the target particle size difference to the floc particle size correction value, a reagent demand adjustment factor that dynamically reflects real-time needs was obtained, and this factor was converted into a specific dosage adjustment amount. Finally, by combining this dynamic dosage adjustment amount with the preset benchmark dosage setting value, the resulting addition amount not only considers the particle size difference that needs to be compensated, but also takes into account the inherent structural characteristics of the floc itself. While ensuring flocculation effect and steadily improving the processing efficiency of subsequent filtration units, it minimizes the over-dosing of reagents. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the structure of a low-turbidity water treatment device according to an embodiment of this application;

[0022] Figure 2 This is a schematic flowchart of a low-turbidity water treatment method according to an embodiment of this application;

[0023] Figure 3 This is another schematic diagram of a low-turbidity water treatment method in the embodiments of this application;

[0024] Figure 4 This is a schematic diagram of a low-turbidity water treatment system provided in an embodiment of this application.

[0025] Explanation of reference numerals in the attached figures: 401, Central Processing Unit; 402, Read-Only Memory; 403, Random Access Memory; 404, Bus; 405, Input / Output Interface; 406, Input Section; 407, Output Section; 408, Storage Section; 409, Communication Section; 410, Driver; 411, Removable Media. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0027] In this embodiment, the filter is a treatment unit integrating floc regulation and deep filtration functions, specifically designed to optimize the treatment effect of low-turbidity water. Specifically, the filter structurally includes at least three core components: a filter media stirring, cleaning, and regeneration device; an automatically suspended microfiltration membrane granular filter media layer; and a large-capacity settling zone. It is understood that in practical engineering applications, the filter may also include other conventional components, such as, but not limited to, inlet and outlet water pipes, valves, sensors for monitoring operating status (such as differential pressure gauges, turbidity meters, and particle size analyzers), and control units for automatic control, etc., which are not further limited here.

[0028] Figure 1 This is a schematic diagram of the low turbidity water treatment device in the embodiments of this application. The following is a description of the device in conjunction with... Figure 1 The low-turbidity water treatment device in this application will be described.

[0029] For equipment selection, a high-speed microfiltration filter with non-dead-end filtration using microfiltration membrane suspended particulate filter media was chosen. The filter layer is an automatically suspended filter layer formed by the buoyancy of water. Under high flow velocity and high water pressure conditions, the local gaps in the filter media will expand, and the volume of the filter layer will also expand. Only part of the water flow passes through the core of the current filter media particles for filtration. It is an atypical cross-flow upward-flowing deep filter. Its water flow direction is opposite to that of most traditional dead-end filters. The backwashing method is a powerful stirring and cleaning, and the filter media regeneration and cleaning intensity is much higher than that of traditional filters, ensuring that the filter layer is not easily clogged, caking, or fails. The filter has a large-volume settling zone, and the filtration principle combines reaction, flocculation, settling, and deep filtration functions, resulting in stronger interception capacity and better adaptability to the composite process of micro-flocculation + filtration.

[0030] The main functional equipment of the low-turbidity water treatment device, namely the micro-flocculation microfiltration filter, includes a flocculant preparation and dosing system, a micro-flocculation pipeline mixer, a micro-flocculation reactor, and a high-speed microfiltration filter. In this application, the high-speed microfiltration filter adopts a suspended particulate membrane filtration structure, which is composed of a movable suspended filter bed made of particulate membranes with specific particle size and density. Unlike the dead-end filtration of quartz stone and multi-media filter fixed beds, the high-speed microfiltration filter bed has both gap filtration and pore filtration. With the change of filtration rate and pressure, the filter media particles in the suspended filter layer form a dynamically adjustable filtration gap structure, which can reduce the pressure drop of the filter layer and reduce the probability of clogging. This suspended bed structure can maintain long-term operational stability without replacing the filter media through periodic strong stirring and cleaning, effectively solving the problems of fouling, clogging, and wormhole penetration of traditional fixed filter beds under low-turbidity water conditions.

[0031] To improve the mixing quality of the flocculant and raw water, a structural control device is installed at the front end of the filter layer: the micro-flocculation pipeline mixer adopts static pipeline mixing technology, based on hydraulic drive, and arranges spiral blades or turbulence-inducing components in the pipeline to allow the added flocculant (such as PAC (polyaluminum chloride) and PFC (polyferric aluminum composite salt)) to undergo preliminary uniform mixing with the water in a short time. This device requires no external power, has a simple structure, and can achieve rapid response. However, its control capability is limited in scenarios with large water quality fluctuations or strong requirements for the adaptability of the flocculant. Therefore, a micro-flocculation reactor is further added between the static mixing device and the filter. A PAM dosing port is provided on the pipeline at the front end of the reactor, and a PAM dosing port with dilution side filtration is provided at the top of the reactor, forming a multi-point dosing system. The internal structure includes a slotted mesh structure for re-uniform mixing of the flocculant and water, a controllable stirring impeller, and a floc buffer chamber. It has the function of adjusting the floc particle size distribution and enhancing the particle aggregation ability. It is the key unit for achieving precise control of floc structure in this application.

[0032] A straight pipe section with a diameter of 30 to 50 times is set between the microflocculation pipeline mixer and the microflocculation reactor, and a pipe section with a diameter of 5 to 10 times is set between the microflocculation reactor and the high-speed microfiltration filter. This design is not arbitrary, but a scientific arrangement based on repeated experimental verification and theoretical analysis, taking into account the kinetic characteristics of floc formation in low-turbidity water. Its purpose is to ensure that after the reagent and raw water achieve initial uniform dispersion in the static mixer, the initial flocs can complete micro-aggregation in a fully developed flow field, providing a good foundation for subsequent floc particle size control in the reactor. Before entering the filter bed, the system is equipped with an online particle size comprehensive evaluation module to comprehensively judge the floc particle size control effect. The water, after floc particle size control, enters the high-speed suspended particulate membrane filter bed. The filter media particles inside the filter are kept in a free suspension state by hydraulic buoyancy, ensuring filtration flux while forming a dynamically adaptive particulate membrane filter bed. During the filtration process, the system collects key parameters in real time, such as influent flow rate, influent turbidity, water temperature, head loss, floc particle size, and effluent turbidity, and compares the effluent water quality with the set target range.

[0033] If the effluent turbidity exceeds the target upper limit, the system immediately determines that the floc particle size is too small, automatically adjusts the target particle size control range upwards, and feeds back to the front-end micro-flocculation reactor to adjust the reaction intensity and dosage. If the effluent turbidity is below the target lower limit, it determines that the floc particle size control can be appropriately relaxed, and the system accordingly adjusts the target particle size range downwards and reduces the dosage, thereby reducing the filtration load and extending the filter bed's operating cycle. This feedback mechanism establishes a closed-loop control logic across the entire chain, from effluent water quality—floc particle size—influent water quality—filtration load—dosage—auxiliary stirring and mixing. Furthermore, to ensure operational stability and data closed-loop capability, the system integrates various online instruments, including pipeline flow meters, water temperature sensors, differential pressure head meters, online particle size analyzers, and online turbidity meters. These real-time monitoring data, after being statistically analyzed over a certain period, are fed back to the computer control and computing system for dynamic response. Based on a preset calculation model, the system calculates and adjusts the dosage, dosage, and auxiliary stirring parameters for the mixing reaction in real time, creating an AI-powered micro-flocculation high-speed microfiltration system supported by large amounts of data from a small system.

[0034] After effluent from the micro-flocculation reactor, the water passes through an inlet flow regulating valve before entering the main filter body. This valve, in conjunction with a flow meter, is used to fine-tune the inlet flow rate, ensuring a stable hydraulic field within the filter bed and preventing filter bed disturbance caused by water flow impact. The main filter body contains several key components, including a double-screen structure at the top of the filter chamber (providing an extra layer of protection against filter media escape) and a suspended particulate filter media layer, a middle agitator, and a bottom inlet filter screen and distributor.

[0035] The main filter body is equipped with an online pressure detection device to monitor the pressure difference before and after filtration in real time, i.e., the head loss. This parameter is an important basis for judging the operating status of the filter bed and whether backwashing is required. When the pressure difference exceeds the set threshold, the system can automatically or manually start the backwashing mode. During the backwashing process, the valve group switches to top inlet and bottom outlet, and in conjunction with the side agitator of the filter, the filter layer is loosened, the granular filter media is mixed and scrubbed, and the high-speed flow field washes the core pores of the filter media, causing the intercepted impurities to be stripped and discharged, thereby restoring the cleanliness of the filter bed and the permeability of the granular membrane pores, and extending the operating cycle. For easy maintenance and repair, the system is equipped with multiple manual valves and inspection ports to facilitate partial closure and process switching. Specifically, the micro-flocculation reactor is equipped with an anti-clogging wedge-shaped mesh at its center; the outer ring of the anti-clogging wedge-shaped mesh is equipped with a baffle; the top of the micro-flocculation reactor is equipped with an auxiliary top agitator, and the impeller of the auxiliary agitator is equipped with agitating blades; the micro-flocculation reactor is equipped with an inlet, an outlet, a sludge discharge outlet, and a PAM dosing port.

[0036] The anti-clogging wedge-shaped slotted mesh is located at the center of the micro-flocculation reactor. Its wedge-shaped slotted structure, with the narrow end facing the water flow direction, effectively prevents flocs from accumulating on the mesh surface and causing clogging. This slotted mesh divides the reactor into inner and outer zones, allowing flocs to form and gradually grow in the inner zone. Only after reaching a specific particle size can they pass through the mesh into the outer zone, achieving floc particle size screening and control. An annular baffle is located around the outer ring of the anti-clogging wedge-shaped slotted mesh, forming two clearly defined reaction zones. The inner zone is mainly used for initial floc formation, with more vigorous hydraulic conditions; the outer zone is used for further floc growth and maturation, with more gentle hydraulic conditions to prevent floc breakage. The height of the annular baffle is lower than the height of the reactor, creating a unified space in the upper area, facilitating water circulation.

[0037] An auxiliary mixer is installed at the top centerline of the micro-flocculation reactor, driven by a variable frequency speed-regulating motor, allowing for real-time adjustment of the mixing intensity according to water quality conditions and treatment requirements. Multiple layers of mixing blades are installed at different heights along the mixing shaft, with alternating rotation directions to create an alternating water flow shear force field, improving flocculation efficiency and preventing floc sedimentation. The inlet is located on the reactor side wall, tangential to the inner wall, creating a rotating inlet to enhance initial mixing. The outlet is located on the upper outer wall of the reactor, allowing mature flocs to flow out with the water and enter subsequent filtration units. The drain outlet is located at the bottom of the reactor for periodically removing sediment to prevent system siltation. The upper PAM dosing port is located at the top of the reactor for adding polyacrylamide and other flocculants, ensuring thorough mixing between the agent and the water. This design, through its unique structural layout and fluid dynamics principles, achieves effective control and treatment of micro-flocculation in low-turbidity water, improving system treatment efficiency and effluent quality.

[0038] Figure 2 This is a schematic flowchart of a low-turbidity water treatment method in an embodiment of this application. Please refer to... Figure 2 The low-turbidity water treatment method in this application embodiment is applied to a low-turbidity water treatment device. The low-turbidity water treatment device includes an intelligent micro-flocculation system of a floc control unit and a high-speed microfiltration filter of a depth filtration unit. The intelligent micro-flocculation system includes a micro-flocculation intelligent calculation and optimization system and a micro-flocculation dosing system, a micro-flocculation pipeline mixer, a micro-flocculation reactor, an online turbidity monitor, and an online floc particle size monitor. The method includes:

[0039] S201. Obtain the first floc particle size in the water to be filtered;

[0040] Flocs refer to visible aggregates formed during flocculation by the combination of tiny particles, colloids, and inorganic / organic matter under the action of flocculants. Their particle size directly affects the formulation of reagent dosing and subsequent reactor stirring strategies. The first floc particle size refers to the average particle size or particle size distribution characteristics of flocs naturally present in the water to be filtered or formed after initial reagent dosing, before further reagent intervention and auxiliary mixing. This is typically obtained through particle image analysis, laser particle size analyzer detection, or ultrasonic online particle size measurement. Preferably, in this embodiment, a laser particle size analyzer is used for measurement. Utilizing scattering theory, it infers the particle size distribution based on the scattering angle and intensity of the laser beam by the particles, enabling high-precision detection of flocs in the 0.1–1000 micrometer range, suitable for detecting fine flocs in low-turbidity water.

[0041] Before step S201, the low-turbidity water treatment method first performs the following steps: acquiring historical data of the water body to be filtered, including historical turbidity values, historical total suspended solids, historical water flow rates, and historical floc particle size morphology and historical change rate during the pre-set small-scale treatment process; acquiring technical parameters of the deep filtration unit, including the range of suspended particle size interception, filtration accuracy, and interception efficiency; constructing a dosing model based on historical data and technical parameters, which is used to calculate the amount of flocculant to be added; acquiring the first online monitoring turbidity value of the water body to be filtered and the second online monitoring turbidity value of the water body after initial filtration, and calculating the initial interception efficiency of the system based on the first and second online monitoring turbidity values; and transferring the historical data... The preset turbidity threshold and preset interception threshold are determined based on the input preset threshold model. When both the first and second online monitoring turbidity values ​​are less than the preset turbidity threshold, and the initial interception efficiency is greater than or equal to the preset interception threshold, the high-speed microfiltration filter of the filtration unit is controlled to operate independently. When both the first and second online monitoring turbidity values ​​are greater than or equal to the preset turbidity threshold, and the initial interception efficiency is less than the preset interception threshold, the flow rate of the water to be filtered and the total amount of suspended solids are obtained. The first online monitoring turbidity value, the second online monitoring turbidity value, the flow rate of the water to be filtered, and the total amount of suspended solids are input into the dosing model to obtain the initial addition amount of flocculant, and the preset dosing point is controlled to add flocculant according to the initial addition amount.

[0042] In the specific implementation process, the system first obtains historical data of the water body to be filtered through a data interface. The historical data includes four key dimensions: historical turbidity value, historical total suspended solids, historical water flow rate, and historical floc particle size morphology and historical change rate during the preset pilot treatment process. The historical turbidity value is composed of daily sampling and detection data for 12 consecutive months, recording the characteristics of water turbidity changes under different seasons and climatic conditions, with the unit being NTU; the historical total suspended solids is determined by the standard gravimetric method, recording the content of solid suspended solids in the water body, with the unit being mg / L; the historical water flow rate is recorded by an electromagnetic flowmeter, recording the influent flow rate of the treatment system, with the unit being m³ / h; the historical floc particle size morphology and historical change rate are obtained by simulating actual treatment conditions using laboratory pilot equipment, recording the floc formation characteristics under different turbidities and different reagent ratios.

[0043] Simultaneously acquire the technical parameters of the deep filtration unit, including the range of suspended particle sizes intercepted, filtration accuracy, and interception efficiency. The range of suspended particle sizes intercepted refers to the particle size range that the high-speed microfiltration filter can effectively retain, typically 5-150 micrometers; filtration accuracy refers to the minimum particle size that the filter media can guarantee to retain, measured in micrometers; and interception efficiency refers to the removal rate of particles within a specific size range by the filter media under standard test conditions, expressed as a percentage. These technical parameters directly affect flocculation treatment requirements and determine floc growth targets.

[0044] Based on collected historical data and technical parameters, a dosing model was constructed. The dosing model employs a multivariate nonlinear regression algorithm to establish a mapping relationship between turbidity, total suspended solids, water flow rate, and the optimal dosage of chemical reagents. The mathematical expression of the model is as follows:

[0045] D = α1·T^β1 + α2·S^β2 + α3·Q^β3 + α4·T·S + α5·T·Q + α6·S·Q + C, where D represents the amount of flocculant added (mg / L), T represents the turbidity value of the water (NTU), S represents the total suspended solids (mg / L), Q represents the water flow rate (m³ / h), α1 to α6 are weighting coefficients, β1 to β3 are exponential coefficients, and C is a constant term. All parameters are determined through training with historical data and are periodically updated with newly processed data to ensure model adaptability.

[0046] The dosing model also includes a compensation mechanism for seasonal factors, automatically adjusting model parameters according to the water quality characteristics of different seasons. For example, during the rainy season, the model increases the weight of sudden changes in turbidity; in the cold winter, considering the reduced chemical reaction rate, the model appropriately increases the dosage coefficient of the chemical agent.

[0047] The system uses an online turbidity monitor to acquire the first online turbidity value (influent turbidity) and the second online turbidity value (effluent turbidity) of the water to be filtered after initial filtration. The turbidity monitoring adopts the 90° scattered light measurement principle, with a measurement accuracy of ±0.01 NTU and a measurement interval of 5 minutes.

[0048] Based on real-time monitoring data, the system calculates the initial interception efficiency: Initial interception efficiency = (1 - Second online monitoring turbidity value / First online monitoring turbidity value) × 100%. The initial interception efficiency reflects the current filtration system's ability to remove suspended solids from the water, with a value range of 0-100%. This parameter serves as a key indicator for determining whether flocculation treatment needs to be initiated.

[0049] The system inputs historical data into a preset threshold model to determine the preset turbidity threshold and preset interception threshold. The threshold model, based on Bayesian decision theory, comprehensively considers water quality fluctuation patterns, seasonal variation characteristics, and filtration unit performance curves to calculate the optimal decision boundary. The preset turbidity threshold is typically set within the range of 1.0-3.0 NTU, and the preset interception threshold is generally within the range of 75%-85%, with the specific values ​​dynamically adjusted by the system based on historical operating data.

[0050] When both the first and second online turbidity monitoring values ​​are less than the preset turbidity threshold, and the initial interception efficiency is greater than or equal to the preset interception threshold, it indicates that the current suspended solids concentration in the water is low, and the high-speed microfiltration filter can independently meet the treatment requirements. The system determines that no flocculant needs to be added and directly controls the high-speed microfiltration filter of the filtration unit to operate independently, reducing reagent consumption, minimizing chemical pollution, and improving economic efficiency.

[0051] When both the first and second online monitoring turbidity values ​​are greater than or equal to the preset turbidity threshold, and the initial interception efficiency is less than the preset interception threshold, it indicates that the current concentration of suspended solids in the water is high, and the particle size distribution is unfavorable for direct filtration. The system determines that a flocculation treatment process needs to be initiated to promote the aggregation of fine particles into flocs suitable for filtration.

[0052] The system obtains the flow rate of the water to be treated (unit: m³ / h) through an online flow meter and the total suspended solids (unit: mg / L) through a suspended solids analyzer. These real-time parameters, along with the first and second online turbidity values, are input into the aforementioned dosing model to calculate the initial dosage of the flocculant (unit: mg / L). The formula for converting the dosage into the actual dosing flow rate is: Dosing flow rate = Water flow rate × Initial dosage ÷ Dosing concentration × 10⁻³. The system uses a precision metering pump to control the pre-set dosing point to add the flocculant according to the calculated dosing flow rate. The dosing point is located at the front end of the micro-flocculation pipeline mixer to ensure thorough mixing of the agent and the water. The dosing system employs a closed-loop control mechanism, adjusting the pump output in real time through flow feedback.

[0053] To conduct the measurement, the water sample to be filtered is first introduced into the sample cell of the measuring device. A constant-speed circulation pump maintains uniform sample flow to prevent floc settling or localized accumulation from affecting measurement accuracy. During the measurement, a laser source emits a laser beam of a fixed wavelength to illuminate the sample. A detector array records the intensity of scattered light at different angles. The system uses a built-in inversion algorithm to calculate the particle size distribution curve of the current flocs and extracts the volume median particle size D50 as the current first floc particle size. To improve measurement accuracy, multi-angle composite detection or image recognition methods can be used to verify the morphology of typical particles, thereby obtaining stable and reliable particle size data.

[0054] Because the concentration of particles in low-turbidity water is low, the particle size distribution is easily affected by factors such as water quality fluctuations, dosing time differences, and flow field disturbances. Therefore, it is necessary to continuously obtain the first floc particle size through real-time, online, and non-destructive means to ensure that the frequency adjustment of the auxiliary agitator of the subsequent micro-flocculation reactor or the calculation of the flocculant addition has dynamic response capability and accuracy guarantee, thereby improving the stability of the entire water treatment system and the ability to control the effluent water quality.

[0055] S202. Determine the particle size state of the first flocculent particles, wherein the particle size state includes a first particle size state and a second particle size state;

[0056] Because the particle size of flocs in low-turbidity water is affected by factors such as raw water disturbance, instantaneous flow regime changes, and external operating condition fluctuations, static judgment based solely on the particle size value at a single moment can easily lead to distorted state identification. Therefore, this embodiment introduces an analysis mechanism of instantaneous particle size change rate before judging the particle size state. By comparing the current first floc particle size with the historical particle size change trend, the stability of the floc particle size in the water body is assessed, ensuring that the state judgment is performed under the premise that the particle size change tends to be stable.

[0057] Figure 3 This is another schematic diagram of the low turbidity water treatment method in the embodiments of this application.

[0058] Please see Figure 3 , combined Figure 3 One embodiment of step S202 will be described in detail below:

[0059] S301. Obtain the historical floc particle size of the water body to be filtered within a preset historical time period, and calculate the instantaneous particle size change rate based on the historical floc particle size and the first floc particle size.

[0060] Historical floc size refers to a series of floc size data continuously measured within a preset historical time period. It is typically acquired using a sliding time window, for example, setting the historical time period to the first 5 minutes, collecting data every 10 seconds, and obtaining a total of 30 historical particle size values. The historical floc size, together with the first floc size defined in S101, is used to calculate the instantaneous particle size change rate. The instantaneous particle size change rate reflects the dynamic fluctuation of floc size in the water body recently, and its calculation method is as follows: ,in Indicates the particle size of the first floc. This represents the weighted average of historical particle sizes. This method helps determine whether the current floc particle size is stable, thus influencing whether to proceed with the status assessment process. This calculation is typically integrated into an online monitoring system, automatically performed by a PLC or edge computing module, and uploaded to the main control system in real time.

[0061] S302. When the instantaneous particle size change rate is greater than the preset change rate threshold, the determination of the particle size state is stopped. After a preset delay time, the first floc particle size is reacquired and the instantaneous particle size change rate is calculated until the instantaneous particle size change rate is less than or equal to the preset change rate threshold.

[0062] The preset change rate threshold is a permissible particle size fluctuation range set by the system based on historical operating data, typically between 10% and 30%. If the instantaneous particle size change rate exceeds this threshold, it indicates that the floc particle size in the water body is still in a dynamic fluctuation stage, possibly affected by factors such as delayed dosing, flow field disturbance, or uneven mixing. Directly judging the particle size status at this time is prone to misjudgment, therefore the judgment must be postponed. During this stage, the system enters a waiting mode. Based on the preset delay time, which is statistically derived from the average response time required for floc particle size fluctuations to stabilize during historical operation, and determined through multiple rounds of dynamic debugging (e.g., 30 seconds), the system re-collects the first floc particle size and repeats the change rate calculation until the particle size change rate is detected to be lower than or equal to the threshold, at which point it proceeds to the subsequent status judgment process. This mechanism is essentially a data stability judgment method with dynamic fault tolerance, effectively avoiding interference from instantaneous data anomalies on the overall judgment logic and improving the system's adaptability to actual operating conditions.

[0063] S303. When the instantaneous particle size change rate is less than or equal to the preset change rate threshold, the ultrasonic attenuation coefficient of the floc is obtained.

[0064] After the particle size variation stabilizes, the system further obtains the ultrasonic attenuation coefficient. This coefficient is based on the signal attenuation amplitude caused by particle scattering, absorption, and reflection during ultrasonic wave propagation in water, and is typically measured in dB / m. This parameter is closely related to the structural compactness, internal porosity, and particle distribution of the floc, and can serve as a quantitative indicator of the floc's structural health. The ultrasonic attenuation coefficient is usually detected using a multi-band pulse transmitter-receiver device. It is derived by measuring the signal attenuation rate in water and combining it with a spectral analysis model. Preferably, a fixed frequency (e.g., 2MHz) is used while maintaining a constant sound path, and detection is performed under constant temperature and current conditions to ensure data stability. This coefficient serves as an auxiliary feature for determining particle size status and is a key parameter for distinguishing between loosely structured large-particle flocs and densely structured large-particle flocs, solving the problem of misjudgment that may occur if the status is simply divided based on particle size range.

[0065] S304. Determine the target particle size level of the first floc based on the preset particle size level, and obtain the reference attenuation range corresponding to the target particle size level. The reference attenuation range is a health status reference established by statistically analyzing the ultrasonic attenuation coefficient of the floc falling within the target particle size level under preset operating conditions.

[0066] In step S304, the system matches the first floc particle size with a preset particle size class. The preset particle size class is a particle size range defined by the system based on historical operating conditions, typically divided into multiple ranges, such as less than 5μm, 5~15μm, 15~30μm, and 30~60μm. Each class corresponds to an optimal filtration performance range. After matching, the system further extracts the baseline attenuation range for that class. This range is the range of ultrasonic attenuation coefficients obtained through statistical analysis based on a large amount of normal operating data, usually expressed as a 95% confidence interval. For example, in the 15~30μm class, the attenuation coefficient is 0.85~1.12dB / m. By comparing the current ultrasonic attenuation coefficient with this baseline range, it can be determined whether the floc is in a healthy structural state within that particle size class. This method integrates both particle size and structure dimensions, avoiding misjudgments of treatment performance caused by excessively large particle sizes but loose structures, and establishing a scientific and quantifiable reference for the final determination of particle size status.

[0067] The particle size state is determined based on the first floc particle size and the reference attenuation range.

[0068] After matching the first floc particle size with the baseline decay range, determining the particle size state based on these two key parameters is the final decision-making step for accurately identifying the floc structure and rationally diverting the treatment path. The judgment logic for determining the particle size state based on the first floc particle size and the baseline decay range is further refined into two scenarios: first, identifying the initial particle size state where the floc particle size is initially formed but the structure is unstable, used to initiate the auxiliary stirring control mechanism; second, identifying the second particle size state where the structure is loose or the particle size is insufficient, used to trigger the chemical dosing treatment mechanism. Examples of these two scenarios are described below:

[0069] S305. When the particle size of the first floc is greater than the upper limit threshold of the preset particle size range, and the ultrasonic attenuation coefficient is within the reference attenuation range, the particle size state is determined to be the first particle size state.

[0070] The preset particle size range refers to the particle size range used to determine whether the current state of flocs has reached the critical condition for structural regulation. It is no longer merely used to assess the particle size adaptability of flocs to meet filtration requirements, but rather serves as a core criterion for determining whether the current water body needs to enter the chemical treatment path or the agitation regulation path. This range divides the floc particle size state into three different regulation response regions by setting lower and upper particle size thresholds, guiding the system to take corresponding treatment measures. Specifically, the setting of this particle size range is based on statistical analysis of a large amount of water treatment experimental data, combined with the structural maturity, reaction rate, and regulation response characteristics of flocs in different particle size ranges, to determine a set of particle size threshold ranges that can reflect the stage characteristics of floc formation and serve as a control path boundary benchmark. The benchmark decay range is a statistical range formed by collecting decay coefficient data under a large number of operating conditions for flocs within different particle size grades, used to define the normal decay upper and lower limits of structurally healthy flocs.

[0071] In this method, when the particle size of the first floc is greater than the upper limit threshold of the preset particle size range and the ultrasonic attenuation coefficient is within the reference attenuation range, the particle size state is determined to be the first particle size state, and the stirring operation is triggered accordingly. The reason is that although this type of floc already has a large particle size and the attenuation coefficient reflects that its structure is in a healthy state, its structure still has room for further densification and stabilization. Especially in the low turbidity water treatment scenario, the goal is not simply to pursue particle size growth, but to form a stable filter floc with high strength, low pressure loss and strong shear resistance.

[0072] First, a particle size larger than the upper threshold indicates that the flocs have completed the initial aggregation process and possess a certain size foundation. Second, an ultrasonic attenuation coefficient falling within the baseline attenuation range indicates that the internal structure of the flocs has reached a certain degree of density, without structural damage or hollowing. In this state, continued chemical addition may lead to excessive floc growth, forming a porous and fragile structure, which is detrimental to filtration efficiency and stability. However, stirring at this point can promote microscopic reorganization of the floc's internal framework through fluid shearing, reducing interparticle gaps and strengthening interfacial bonding, thereby improving the overall compressibility and filtration support of the flocs.

[0073] Furthermore, initiating stirring rather than adding chemicals based on this state helps reduce the risk of chemical waste and avoids particle redispersion or adsorption saturation caused by over-dosing. By identifying flocs with "sufficient particle size and healthy structure" as the first particle size state and guiding them into the stirring structure control mechanism, the goal of structural optimization rather than particle size regrowth can be achieved, ensuring that the final target flocs meet the optimal requirements of the filtration system in terms of both size, strength, and stability.

[0074] S306. When the particle size of the first floc is less than the lower limit threshold of the preset particle size range, or when the particle size of the first floc is greater than the upper limit threshold of the preset particle size range and the ultrasonic attenuation coefficient is less than the lower limit threshold of the reference attenuation range, the particle size state is determined to be the second particle size state.

[0075] In step S306, the system identifies whether the current flocs are in a non-ideal state of loose structure or insufficient particle size by jointly judging the first floc particle size and the ultrasonic attenuation coefficient, and determines the particle size state as the second particle size state, which serves as the control basis for whether further chemical treatment is needed. The implementation of this step depends on two core judgment conditions: first, whether the first floc particle size is less than the lower limit threshold of the preset particle size range; second, whether the first floc particle size is greater than the upper limit threshold, but its corresponding ultrasonic attenuation coefficient is lower than the lower limit threshold of the reference attenuation range.

[0076] The first floc particle size is a parameter obtained in real time by the system through an online particle size detection device (such as an optical particle size analyzer or image recognition module), used to reflect the stage of floc aggregation and development. The preset particle size range is a representative particle size range, usually obtained through a large number of experimental statistics, representing the transition range from floc formation to the ability to regulate structure. Its lower limit threshold is used to identify whether the floc is still in the initial formation stage. If the current first floc particle size is lower than this lower limit threshold, for example, less than 5 μm, it indicates that the flocculation reaction has not been fully carried out, and the bonding between particles has not yet formed an effective structure. At this time, even if the structural density is high, it is not enough to meet the requirements for subsequent filtration to form target flocs. Therefore, the system judges the particle size state as the second particle size state.

[0077] When the particle size of the first floc is larger than the upper limit threshold (e.g., exceeding 25 μm), but its corresponding ultrasonic attenuation coefficient is less than the lower limit threshold of the reference attenuation range, it indicates that although the floc has a large surface size, its internal structure is loose, with many voids and loose particle bonding, making it unable to form a stable supporting framework. Such flocs are highly susceptible to compression, breakage, or penetration during filtration, exhibiting typical "hypertrophic structure" characteristics and failing to meet the structural strength requirements for target flocs. The reference attenuation range is a structural strength reference range established for healthy flocs of different particle size grades. It is typically measured by ultrasonic detection devices to determine the degree of energy attenuation of sound waves propagating in water, reflecting particle density and particle interaction strength. If the test result is lower than the lower limit of this range, it indicates that the structure is not yet complete.

[0078] In the specific implementation process, the system first obtains the current first floc particle size, for example, 9.7 μm, which is lower than the lower limit threshold of 10.0 μm of the preset particle size range. Based on this, the system directly judges it as the second particle size state. Or in another case, if the current particle size is 27.4 μm, which exceeds the upper limit threshold of 25.0 μm, but its attenuation coefficient is only 0.68 dB / m, which is significantly lower than the lower limit of 0.85 dB / m of the benchmark attenuation range for this level, the system also judges the structure as a loose state, thereby identifying the particle size state as the second particle size state.

[0079] Once the second particle size state is determined, the system will initiate the dosing mechanism based on this state. By calculating the required amount of flocculant to be added and controlling the dosing equipment to add it proportionally, the system will promote particle aggregation and structural reconstruction, and drive the flocs to evolve towards the target particle size and structure.

[0080] Corresponding to the two abnormal states requiring regulation mentioned above, when the particle size of the first floc is within the preset particle size range (i.e., greater than or equal to the lower particle size threshold and less than or equal to the upper particle size threshold), the particle size state is determined to be a normal particle size state. It should be noted that the preset particle size range is an optimized range determined based on extensive experimental data or theoretical models. Flocs falling within this range have been proven to have suitable particle size and structural stability for filtration. Therefore, under this condition, the ultrasonic attenuation coefficient is no longer a necessary basis for determining the normal state. In this normal particle size state, it indicates that the current flocculation conditions and filtration parameters are well matched, the system is in or near its optimal operating condition, and no additional adjustment control is required.

[0081] S203. Obtain the morphological indices of the flocs, wherein the morphological indices are used to characterize the geometric structural features of the flocs;

[0082] Morphological indices refer to a set of image analysis parameters used to quantitatively describe the geometric structural characteristics of flocs. They typically include indices such as floc compaction, fractal dimension, roundness, and length-to-width ratio. Among these, compaction is the core index, mainly used to characterize the degree of particle packing and surface contour complexity within the floc. A higher value indicates a denser floc structure and a smoother surface, while a lower value indicates a looser floc structure and more complex boundaries.

[0083] In the specific implementation process, an online floc image acquisition system is used to acquire real-time images of floc particles in the water to be filtered. This system consists of a microscope camera, a uniform light source, a sampling flow tank, and an image processing module. After the acquired floc images are entered into the image recognition module, edge extraction algorithms (such as Canny edge detection or adaptive threshold segmentation) are used to identify the boundary contour of each floc, and a binary mask image is constructed based on morphological processing algorithms. Subsequently, the system calculates the basic geometric parameters of each floc, such as the projected area, perimeter, principal axis length, and secondary axis length, to derive the calculated value of the compactness index, which is defined as: This formula originates from the principles of image geometry. The compactness value of an ideal circle is 1, and the more irregular the shape and the more complex the boundary, the closer the compactness value is to 0. Other indicators, such as fractal dimension, are fitted through the scale-area variation relationship to reflect the roughness and structural complexity of the floc surface.

[0084] S204. When the particle size state is the first particle size state, the stirring frequency of the stirring device in the micro-flocculation reactor is determined based on the morphological index, and the stirring device is controlled to stir according to the stirring frequency until the target flocs obtained meet the preset particle size state.

[0085] In this embodiment, the system monitors the floc particle size status in real time. When the floc is detected to be in the first particle size state, the micro-flocculation intelligent calculation optimization system will determine the optimal stirring frequency of the stirring device in the micro-flocculation reactor based on morphological indicators, and control the stirring device to perform precise stirring according to the calculated stirring frequency until the target floc that meets the preset particle size state is generated.

[0086] The first particle size state refers to the state where the floc particle size is smaller than the lower limit threshold of the preset particle size range (usually 15-25 micrometers), indicating that the flocs have not yet formed a large enough size for subsequent filtration and capture. In low turbidity water (turbidity <5 NTU), the initial formation stage of flocs generally exhibits the first particle size state, with particle sizes typically distributed in the range of 0.01-15 micrometers, characterized by large quantity, small particle size, and high dispersion.

[0087] Morphological indices are a set of quantitative parameters characterizing the geometric structure of flocs, including four key parameters: floc fractal dimension D, roundness coefficient C, compactness coefficient F, and surface roughness R. These parameters are calculated from image data acquired by an online floc particle size monitoring instrument.

[0088] Fractal dimension D: Calculated using the Box-counting algorithm, it characterizes the structural complexity of the flocs, typically ranging from 1.2 to 2.5; Roundness coefficient C: Calculated by the ratio of the floc perimeter to its area, C = 4πA / P² (where A is the area and P is the perimeter), characterizing the degree to which the floc shape approximates a circle; Compactness coefficient F: Calculated by the ratio of the actual area of ​​the floc to the area of ​​the convex hull, characterizing the density of the flocs; Surface roughness R: Obtained by analyzing the floc contour through Fourier transform, characterizing the irregularity of the floc surface.

[0089] The micro-flocculation intelligent computation and optimization system first acquires real-time floc images using an online floc particle size monitor, and then extracts particle size data using image processing algorithms. When the system determines that the flocs are in the first particle size state, it executes the following specific steps:

[0090] The system performs binarization processing on the acquired floc images, extracts the floc contours, and calculates the four morphological parameters mentioned above. Images of the flocs in the microflocculation reactor are acquired using a high-resolution CCD camera (resolution ≥ 1920 × 1080 pixels) at a frame rate of no less than 30 fps to ensure the capture of dynamic changes in the flocs.

[0091] Stirring frequency determination: The system substitutes the acquired morphological indices into the pre-established stirring frequency model equation: F_stirring = K1·D + K2·C⁻¹ + K3·F + K4·R + K0, where K1 to K4 are weighting coefficients and K0 is the basic frequency constant. These parameters are determined through regression analysis of a large amount of experimental data.

[0092] The principle of the relationship between morphological indicators and stirring frequency: An increase in fractal dimension D indicates that the floc structure is more complex, and the stirring frequency needs to be reduced to avoid damaging the floc; a decrease in roundness coefficient C indicates that the floc shape is irregular, and the stirring frequency needs to be appropriately increased to promote floc reconstruction; an increase in compactness coefficient F indicates that the floc structure is dense, and the stirring intensity can be appropriately reduced; an increase in surface roughness R indicates that the floc surface is not smooth, and the stirring frequency needs to be adjusted to promote floc surface reshaping.

[0093] Stirring control implementation: The micro-flocculation intelligent calculation and optimization system transmits the calculated stirring frequency parameters to the frequency converter of the auxiliary top stirrer via an industrial control bus, precisely controlling the stirring rate. The stirring frequency range is typically between 20-120 rpm, and the system supports adjustments with a precision of 5 rpm to ensure accurate control of the floc growth environment. The stirring control employs a PID closed-loop control algorithm to suppress frequency fluctuations during the stirring process and maintain a stable hydraulic shear environment.

[0094] Real-time monitoring of floc condition: The system collects floc image data every 5 seconds, calculates the real-time particle size distribution, and determines whether the floc has reached the preset particle size state. The preset particle size state refers to the state where the average particle size of the floc reaches the range of 25-40 micrometers, and the particle size distribution uniformity coefficient (coefficient of variation) is less than 0.3.

[0095] Dynamic adjustment: If the floc growth rate is lower than expected (particle size increase of less than 5 micrometers within 1 minute), the system will automatically fine-tune the stirring frequency based on feedback data, increase the stirring intensity in the low-speed zone, and enhance the probability of floc collision; if floc breakage is detected (particle size suddenly decreases by more than 20%), the system will immediately reduce the stirring frequency, reduce hydraulic shear force, and protect the integrity of the floc.

[0096] S205. When the particle size state is the second particle size state, the amount of flocculant to be added is calculated based on the lower limit threshold of the particle size between the first floc particle size and the preset particle size range, and the flocculant to be added at the preset agent dosing point according to the amount of flocculant to be added.

[0097] When the system determines that the current floc particle size is in the second particle size state, in order to achieve effective growth and structural optimization of the floc particle size, the system needs to dynamically adjust the amount of flocculant added to compensate for the insufficient structure formation capacity. This process no longer uses a fixed dosage mode, but instead calculates an adaptive dosage based on the difference between the current particle size characteristics of the first floc and the preset particle size range, combined with the actual perceived value of the floc particle size. This achieves targeted enhanced flocculation and may include the following steps: calculating the target particle size difference between the first floc particle size and the lower limit threshold; correcting the first floc particle size based on the ultrasonic attenuation coefficient to obtain the floc particle size correction value; calculating the ratio of the target particle size difference to the floc particle size correction value to obtain the dosage adjustment factor; multiplying the dosage adjustment factor by the preset addition conversion coefficient to obtain the addition adjustment amount; and adding the addition adjustment amount to the preset baseline addition setting value to obtain the addition amount.

[0098] During implementation, the system first calculates the target particle size difference between the first floc particle size and the lower limit threshold of the preset particle size range. The first floc particle size refers to the average particle size of representative flocs in the loosely structured floc subgroup identified in the previous steps, typically obtained through image recognition algorithms or ultrasonic measurement techniques. The lower limit threshold is the lower bound of the preset target particle size range, representing the minimum particle size requirement for structurally stable flocs. By calculating the difference between the two, the system can quantify the degree of deficiency in particle size of the current loosely structured flocs. The larger the difference, the more severe the floc undersize, and the more necessary it is to increase the amount of reagent added subsequently.

[0099] Considering that particle size identification in online ultrasonic monitoring may be affected by medium attenuation, the system introduces an ultrasonic attenuation coefficient to correct the particle size of the first floc, obtaining a corrected floc particle size value. The ultrasonic attenuation coefficient is an empirical model established based on parameters such as the concentration, density, and temperature of suspended particles in the water, reflecting the impact of energy loss during ultrasonic signal propagation on the particle size measurement results. The system calculates the current attenuation coefficient based on the actual collected temperature, turbidity, and sound velocity changes, and then corrects the original particle size value, making the particle size measurement result closer to the true size of the floc.

[0100] The revised basic calculation formula is as follows: ,in, The first floc particle size, Here, λ is the particle size correction value for the flocs, and λ is the ultrasonic attenuation coefficient, typically a decimal between 0.05 and 0.3. λ can be estimated using the following relationship: Where F represents the floc concentration, T is the water temperature, and a, b, and c are system parameters obtained through experimental fitting, reflecting the attenuation response of sound waves under different media conditions. The system acquires water concentration and temperature data in real time through online sensors, substitutes them into the above model to calculate the current attenuation coefficient, and then applies it to the original particle size value to achieve automatic correction of ultrasonic measurement errors.

[0101] The corrected particle size value serves as a crucial reference for subsequent dosing calculations, ensuring the accuracy of the dosing strategy. After obtaining the target particle size difference and the corrected floc particle size, the system calculates the ratio between the two to obtain the reagent demand adjustment factor. This factor reflects the proportion of the current floc particle size being too small relative to its true state, quantifying the reagent adjustment required for structural reinforcement. For example, when the corrected particle size is significantly too small, while the target difference is large, the demand adjustment factor is significantly greater than 1, indicating that a substantial increase in reagent dosage is needed to compensate for the structural defects. This factor is calculated using simple division, but its physical significance lies in establishing a dynamic response relationship between particle size status and reagent demand.

[0102] The system further multiplies the reagent demand adjustment factor by the preset dosing conversion coefficient to obtain the dosing adjustment amount. The dosing conversion coefficient is an empirical parameter set by the system based on factors such as different water quality conditions, reaction tank size, and flocculant type, serving a dual function of unit conversion and proportional adjustment. Its principle is to normalize and correct the reagent response effect under different particle size states using historical operating data, thereby converting the dimensionless adjustment factor into an executable addition amount with actual reagent mass units. The dosing adjustment amount characterizes the amount of reagent required to increase or decrease relative to the default dosing setting under the current smaller particle size state. Finally, the system sums the dosing adjustment amount with the preset baseline dosing setting value to obtain the final reagent addition amount. The baseline dosing setting value is the basic reagent addition amount set by the system under normal operating conditions, representing the minimum reagent requirement required to maintain structural stability when the particle size state is normal. By superimposing with the dosing adjustment amount, the system completes a closed-loop control process from particle size state monitoring, correction, difference analysis to reagent dosage calculation.

[0103] S206. Obtain the second floc particle size of the floc within a preset time period after adding the flocculant dosage, re-determine the particle size state based on the second floc particle size, and continue to adjust the stirring frequency of the stirring device in the micro-flocculation reactor for the water to be filtered after adding the flocculant dosage according to the particle size state, and control the stirring device to stir according to the stirring frequency or continue to add the flocculant until the second floc particle size is within the preset particle size range, thereby obtaining the target floc.

[0104] In step S206, after the initial addition of the flocculant, the system needs to conduct a responsive evaluation of the agent's effect and adjust the subsequent treatment strategy accordingly. The core of this step is to determine whether the structure has reached the preset target by obtaining the new particle size state of the flocs within a preset time period after the agent is added, and then decide whether to perform auxiliary stirring to enhance the effect or continue adding the agent to promote the growth of the floc particle size to the target range.

[0105] The second floc particle size refers to the particle size of the new floc formed after a certain reaction time following the addition of the flocculant. This particle size is obtained by the system through an online particle size monitoring module, using the same measurement method as the first floc particle size, such as ultrasonic testing or image recognition, to ensure the continuity and comparability of the evaluation scale. The preset time period refers to a fixed reaction window set by the system according to the reaction kinetics of the flocculant, typically between 30 and 90 seconds, to ensure that the flocculant reacts fully with the colloidal particles in the water, allowing sufficient time for the floc structure to complete its initial reconstruction. By measuring the particle size at the end of this time period, the structural state after the flocculation reaction can be accurately reflected.

[0106] After obtaining the particle size of the second floc, the system uses a particle size state judgment model consistent with the first floc particle size to determine the current structural state. This model uses a preset particle size range as a benchmark, classifying the particle size state into either the second particle size state or the first particle size state. If the second floc particle size is still below the lower limit threshold, it indicates insufficient reagent reaction or high colloid concentration in the raw water, and the structure has not yet reached the target state; if it is above the upper limit, there may be a risk of over-flocculation. The judgment result will directly determine the next treatment path.

[0107] When the particle size remains at the second particle size state, the system will continue to use the aforementioned reagent addition strategy based on particle size difference to dynamically update the reagent addition amount. The specific calculation method is the same as in step S105 and will not be repeated here. The system continuously approaches the target particle size range by correcting the particle size value and adjusting the dosage in real time. During this process, after each round of reagent addition, the system waits for a preset time period, repeatedly obtains the second floc particle size, and makes a judgment, forming a closed-loop control logic.

[0108] If the current particle size state changes from the second particle size state to the first particle size state, it indicates that the newly added floc structure may have excessive aggregation or loose structure that has not been dispersed and reconstructed. At this time, the system will switch to the stirring control logic, call the aforementioned stirring strategy based on morphological indicators (consistent with the structure enhancement route in the first particle size state), and control the stirring device to run at a set frequency to promote the flocculant to react fully, while protecting the dense floc structure from being accidentally destroyed. If the second floc particle size is already within the preset particle size range, it indicates that the floc structure has reached the target morphology. The system confirms that there are structurally stable flocs in the current water sample that meet the requirements for sedimentation and filtration, completes the construction of the target flocs, and enters the next stage of filtration parameter setting and execution.

[0109] S207. Obtain the final floc particle size corresponding to the target floc, determine the filtration parameters of the high-speed microfiltration filter media layer based on the final floc particle size, and control the operation of the high-speed microfiltration filter according to the filtration parameters.

[0110] After obtaining the target flocs, the system acquires the corresponding final floc particle size. Based on the particle size characteristics of the target flocs, it determines the filtration parameters of the filter media layer and controls the filtration process accordingly. The core purpose of this step is to achieve a linkage and matching between the floc structure characteristics and the filtration operation status, ensuring that the filtration stage maintains high-efficiency retention capacity without prematurely causing filter media layer blockage or operational instability. Specifically, this may include the following steps: calculating the volume-weighted average particle size of the final floc; determining the target filtration rate and interception efficiency corresponding to the volume-weighted average particle size based on a preset particle size-filtration rate-interception efficiency mapping relationship, which records the target filtration rate and interception efficiency of flocs of different particle sizes under different filtration conditions; monitoring the real-time interception efficiency of the filter media layer and comparing the real-time interception efficiency with the upper limit of the allowable interception efficiency corresponding to the target filtration rate; if the real-time interception efficiency is less than the upper limit of the allowable interception efficiency, then the target filtration rate is determined as the filtration parameter; if the real-time interception efficiency is greater than or equal to the upper limit of the allowable interception efficiency, then the target filtration rate is reduced by a preset decreasing step size until the real-time interception efficiency is less than the upper limit of the allowable interception efficiency, and then the adjusted filtration rate is determined as the filtration parameter.

[0111] In practical implementation, to avoid the influence of individual outliers on the selection of filter parameters, the system does not directly use a single particle size value. Instead, it calculates a volume-weighted average particle size, that is, it weights all flocs according to their volume percentage to obtain a particle size index that better represents the overall structural characteristics of the flocs. This calculation method can be achieved through the following formula: ,in, Indicates particle size as The number of flocs, The particle size is calculated using a volume-weighted average. This method improves the stability of particle size representativeness and eliminates the interference of uneven structural distribution on filtration control decisions.

[0112] After obtaining the volume-weighted average particle size, the system calls a preset particle size-filtration rate mapping model to determine the target filtration rate corresponding to the current particle size. This mapping relationship is a structure-performance mapping table established through a large amount of experimental data, recording the optimal filtration rate of flocs with different particle size ranges under specific filtration conditions. Filtration conditions can include interception efficiency (such as turbidity removal rate) and filtration cycle (such as filter bed running time). The mapping table is established based on the principle that the larger the floc particle size, the more stable the structure, and the higher the speed allowed to pass through the filter layer. However, if the particle size is too small, the filtration rate needs to be reduced to prevent penetration of the filter media layer. During system operation, the volume-weighted average particle size is substituted into this mapping model as an input, and the system automatically retrieves the corresponding target filtration rate value, which is used for initial filtration parameter settings.

[0113] Real-time interception efficiency refers to the energy loss caused by resistance when water passes through the filter media layer, reflecting the degree of pore blockage or operating load level of the filter layer, and is obtained through a differential pressure sensor. The system compares the real-time interception efficiency with the upper limit of the allowable interception efficiency corresponding to the target filtration rate. This upper limit value is derived from the filter media layer design parameters or empirical models, representing the maximum operating load that the filter media layer can withstand at the current filtration rate.

[0114] When the real-time interception efficiency is lower than the upper limit of the allowable interception efficiency, it indicates that the filter media layer is operating normally. The system directly determines the target filtration rate as the final filtration parameter and controls the filtration module to perform steady-state filtration at that rate. At this time, the filtration process achieves a dynamic balance between structural protection, hydraulic stability, and treatment efficiency, and can operate for a long time without causing clogging or efficiency decline.

[0115] If the real-time interception efficiency reaches or exceeds the allowable upper limit, the system determines that the current filter media layer is under excessive load and needs to reduce the filtration rate to control operational risks. To achieve smooth adjustment, the system employs a preset rate reduction step strategy, which involves decreasing the target filtration rate at a set rate (e.g., 1 m / h each time) and re-monitoring the real-time interception efficiency after each adjustment. When the real-time interception efficiency corresponding to the adjusted filtration rate falls below the allowable upper limit again, the system uses that rate as the final filtration parameter and controls the filter media layer to operate at the new rate. This method effectively avoids hydraulic shocks caused by sudden adjustments while ensuring stable operation of the filtration system within safe thresholds.

[0116] During the filtration process, the preset particle size range directly affects the floc construction target in the structure control stage, and the floc structure significantly determines the final effluent quality. Therefore, in actual operation, the system needs to establish a feedback mechanism between structure control and effluent quality, so that the preset particle size range can dynamically self-correct according to the water quality of the filtered water. Optionally, a low-turbidity water treatment method may further include the following steps: obtaining the turbidity of the filtered water and determining whether the turbidity is within a preset turbidity target range; when the turbidity is higher than the upper turbidity threshold of the preset turbidity target range, calculating a first turbidity deviation value between the turbidity and the upper turbidity threshold, and calculating a positive particle size range adjustment amount for increasing the preset particle size range based on the first turbidity deviation value; adjusting the preset particle size range according to the positive particle size range adjustment amount; when the turbidity is lower than the lower turbidity threshold of the preset turbidity target range, calculating a second turbidity deviation value between the turbidity and the lower turbidity threshold, and calculating a negative particle size range adjustment amount for decreasing the preset particle size range based on the second turbidity deviation value; adjusting the preset particle size range according to the negative particle size range adjustment amount.

[0117] In the specific implementation process, the turbidity of the filtered water is obtained. Turbidity refers to the ability of the remaining suspended particles in the filtered water to scatter light, and it is an important indicator for measuring the filtration effect. Turbidity is usually obtained through an online turbidity meter, which uses the principle of light scattering to monitor the particle concentration in the water sample in real time. This measurement is performed at the outlet of the filtration unit to ensure that it reflects the final treatment result. The real-time value of the turbidity will serve as the basic input parameter for subsequent judgment and adjustment.

[0118] The system compares the currently acquired water turbidity with a pre-set turbidity target range. This range defines the permissible effluent turbidity under ideal filtration conditions and is typically determined by a combination of operational experience and discharge standards. For example, a system might set a target range of 1–3 NTU. If the current water turbidity is within this range, it indicates that the structural control and filtration operation are well matched, and no adjustment to the particle size control strategy is needed. However, if the water turbidity deviates from this range, it indicates that the current structural control target is either too lenient or too strict, requiring correction by adjusting the target particle size range for the flocs.

[0119] When the water turbidity exceeds the upper limit of the target turbidity range, it indicates that the current floc structure is unstable or the particle size is too small, causing fine particles to penetrate the filter layer and resulting in increased effluent turbidity. At this point, the system calculates the first turbidity deviation value between the water turbidity and the upper limit to quantify the degree of deviation. This deviation value is the current turbidity minus the upper limit value, expressed in NTU. Based on this deviation value, the system calls the mapping model to calculate a positive particle size range adjustment amount, which is the upward adjustment of the particle size range from the original preset particle size range. For example, the system can set a strategy of increasing the adjustment by 5 micrometers for every 1 NTU deviation. Therefore, when the current turbidity is 4.5 NTU, the upper limit is 3 NTU, and the deviation is 1.5 NTU, the corresponding positive particle size range adjustment amount is 7.5 micrometers. This adjustment amount will be used to expand the overall range of the preset particle size range, causing subsequent structural control targets to favor the construction of larger flocs, thereby reducing the risk of fine particle penetration.

[0120] The system then invokes the control module to adjust the preset particle size range based on the positive particle size range adjustment amount, that is, to increase the adjustment amount on the upper and lower limits of the original particle size range. The new range will be used for target setting in the next round of structure regulation process, enabling the system to automatically correct the floc construction strategy and move towards higher structural stability and filtration efficiency.

[0121] When the water turbidity is below the lower limit of the target turbidity range, the second turbidity deviation value between the water turbidity and the lower limit is calculated, and the negative particle size range adjustment amount is derived accordingly, that is, the value of the current particle size target range is lowered. For example, if the current effluent turbidity is 0.5 NTU, the lower limit is 1 NTU, and the deviation is 0.5 NTU, according to the mapping relationship of adjusting by 3 micrometers for every 1 NTU deviation, the negative adjustment amount is 1.5 micrometers. Based on this, the system shifts the original particle size range downward as a whole, making the structure control target more biased towards building medium-structure flocs, thereby relieving filtration pressure and extending the filter's operating cycle. Subsequently, the system adjusts the preset particle size range according to the negative particle size range adjustment amount, that is, reducing the adjustment amount on the original upper and lower limits respectively to form a new target range. This strategy can reduce the filtration load and improve system operating efficiency while maintaining effluent quality.

[0122] Through the coordinated execution of the above steps, the system realizes an adaptive feedback mechanism from effluent quality to structural control objectives, so that the particle size control strategy is no longer a static setting, but dynamically coupled with the final water quality result, thereby constructing an intelligent linkage closed loop between structure, filtration and water quality.

[0123] Furthermore, this embodiment establishes an intelligent filter media cleaning cycle determination system by real-time monitoring of floc particle size characteristics. The system monitors the head loss growth rate, effluent turbidity changes, and flow rate decreases during filter operation. Combined with floc characteristic data (such as particle size, distribution uniformity, and structural strength), it predicts the filter media fouling trend and automatically determines the optimal cleaning time. When the system detects that the head loss is approaching a preset threshold or the effluent quality begins to decline, it triggers a cleaning operation; simultaneously, it dynamically adjusts cleaning parameters based on seasonal changes and water quality fluctuations. Compared to traditional fixed-cycle cleaning, this method effectively reduces unnecessary cleaning frequency, saves cleaning water and energy consumption, extends filter media lifespan, and maintains stable treatment performance, making it particularly suitable for low-turbidity water treatment processes with significant water quality variations.

[0124] The system uses a mathematical model to predict the filter media fouling process. The core algorithm is:

[0125] Data acquisition phase: Floc characteristics were monitored every 10 minutes using an online laser particle size analyzer, and the average particle size (d) was recorded. 50 The uniformity coefficient (Cu) and structural strength index (Fi) are used. Simultaneously, head loss (ΔH) and effluent turbidity changes are monitored. Predictive calculation stage: The fouling prediction equation ΔH(t) = H0[1 + k1(t / t0)^α × f(d) is applied. 50 ,Cu,Fi)], where f(d 50(Cu,Fi) is the floc characteristic influence function, calculated from experimentally determined parameters β1, β2, and β3. Critical point determination: The system calculates the head loss growth curve in real time, predicts the time point tc when the set threshold (usually 1.5-2.5 times the initial head loss) is reached, and considers the safety margin coefficient η (usually 0.1-0.2) to determine the cleaning time T = tc(1-η). Self-correction mechanism: The system records the cleaning effect each time, calculates the cleaning recovery rate R = (H1-H2) / H1, and adjusts the prediction parameters when R is below 0.85. Simultaneously, the system continuously optimizes the prediction model parameters k1 and α using historical operating data to improve prediction accuracy. This prediction method can dynamically adjust the cleaning cycle according to the actual floc characteristics, avoiding resource waste or decreased filtration efficiency caused by cleaning too early or too late.

[0126] After obtaining the final floc particle size corresponding to the target floc, determining the filtration parameters of the high-speed microfiltration filter media layer based on the final floc particle size, and controlling the operation of the high-speed microfiltration filter according to the filtration parameters, the method further includes the following steps:

[0127] Obtain the third online monitoring turbidity value of the water to be filtered after adding the flocculant and the fourth online monitoring turbidity value of the water after filtration; calculate the real-time interception efficiency of the water to be filtered based on the third and fourth online monitoring turbidity values; adjust the particle size state threshold range of flocs, the amount of flocculant added, the flocculant addition ratio, and the reactor stirring parameters according to the third and fourth online monitoring turbidity values ​​and the real-time interception efficiency; perform statistical analysis on the particle size state threshold range, the amount of flocculant added, the flocculant addition ratio, and the reactor stirring parameters, and continuously optimize them.

[0128] The system acquires the third online turbidity value of the water to be filtered after the addition of flocculant and the fourth online turbidity value of the water after filtration through a distributed online turbidity monitoring network. The monitoring equipment uses a dual-beam scattering turbidity meter with a measurement range of 0.01-100 NTU and a measurement accuracy of ±0.01 NTU (in the low turbidity range). The third online turbidity sampling point is located at the outlet of the micro-flocculation reactor, reflecting the turbidity state of the water before filtration and after flocculation treatment; the fourth online turbidity sampling point is located at the outlet of the high-speed microfiltration filter, reflecting the final treatment effect of the entire system.

[0129] The monitoring system employs automatic compensation technology to eliminate the influence of factors such as water temperature fluctuations, air bubble interference, and pipe wall deposits, ensuring the accuracy of measurement data. The sampling frequency is once per minute, and the data acquisition interval can be automatically adjusted from 30 seconds to 5 minutes according to water quality fluctuations. The sampling frequency is increased when water quality fluctuations are large and decreased when water quality is stable, optimizing data storage space.

[0130] Based on real-time monitoring data, the system calculates the real-time interception efficiency using the following formula: Real-time interception efficiency = (1 - Fourth online monitoring turbidity value / Third online monitoring turbidity value) × 100%. The real-time interception efficiency reflects the actual removal capacity of the filtration system under the current floc morphology, providing a direct basis for subsequent parameter adjustments. The system also calculates the rate of change in interception efficiency to assess treatment stability: Rate of change in interception efficiency = (Current real-time interception efficiency - Average interception efficiency of the previous period) / Average interception efficiency of the previous period × 100%. Based on the third and fourth online monitoring turbidity values ​​and the real-time interception efficiency, the system adjusts four key treatment parameters through an adaptive control algorithm: floc particle size state threshold range, flocculant dosage, flocculant addition ratio, and reactor stirring parameters. The floc particle size state threshold range defines the standard limits for the system to judge the floc development stage. The system dynamically adjusts the particle size threshold range by analyzing the correlation between real-time interception efficiency and floc particle size: when the real-time interception efficiency is lower than the target value and the floc particle size distribution is concentrated, the system infers that the current floc structure may not be conducive to filtration and interception, and adjusts the particle size threshold range to shift to a higher range, prompting the system to generate larger flocs; when the real-time interception efficiency meets the target value but the energy consumption index is high, the system fine-tunes the particle size threshold range to shift to a lower range, seeking a balance point between interception efficiency and energy consumption.

[0131] Preferably, the particle size state threshold adjustment can adopt fuzzy control rules: when (real-time interception efficiency < 80%) and (fourth online monitoring turbidity value > 3 NTU), increase the lower limit of the particle size threshold by 30% and the upper limit by 15%; when (real-time interception efficiency > 95%) and (fourth online monitoring turbidity value < 0.1 NTU) and (energy consumption index > target value), decrease the lower limit of the particle size threshold by 20% and decrease the upper limit by 10%; when (real-time interception efficiency is between 80-95%) and (fourth online monitoring turbidity value is between 0.1-3 NTU), maintain the current threshold range.

[0132] The system achieves precise control of flocculant dosage by establishing dynamic response curves between the third and fourth online monitoring turbidity values ​​and the flocculant addition amount. Adjustments follow the principle of minimum dosage, seeking the most economical dosage while meeting effluent quality requirements.

[0133] The flocculant addition ratio refers to the mixing proportion of different types of flocculants (such as polyaluminum chloride, polyacrylamide, etc.). The system dynamically adjusts the flocculant ratio based on the relationship between real-time interception efficiency and environmental factors such as water temperature and pH: PAC to PAM ratio = f(water temperature, pH, third online monitoring turbidity value, real-time interception efficiency). The function f is constructed using a multivariate nonlinear regression method to consider the synergistic effect of different flocculants under different conditions. For example, under low-temperature conditions (<10℃), the system increases the PAM ratio to compensate for the reduced floc formation rate under low-temperature conditions; under high pH conditions (>9.0), the system increases the PAC ratio to enhance the charge neutralization effect.

[0134] The adjustment step size is constrained by water quality stability. When water quality fluctuates greatly, a small adjustment step size is used to avoid oscillations in treatment effect; when water quality is stable, a larger adjustment step size can be used to accelerate the optimization process. Based on the relationship between real-time interception efficiency and floc morphology characteristics, the system finely adjusts the stirring parameters in the micro-flocculation reactor, including stirring frequency, impeller speed, and stirring time distribution. The stirring parameter adjustment is based on the coupling analysis of hydraulic model and floc dynamics: when the floc density is low and the real-time interception efficiency is insufficient, the system increases the stirring intensity to improve floc density; when the floc size is small but the structure is compact, the system reduces the stirring intensity and extends the stirring time to promote floc growth; when floc breakage is detected, the system automatically adjusts the stirring area distribution to form an intensity gradient stirring field, with strong stirring in the front section promoting initial floc formation and weak stirring in the back section protecting floc growth.

[0135] The stirring frequency adjustment algorithm is as follows: Corrected stirring frequency = base stirring frequency × [1 + α·ln(target particle size / actual particle size) + β·ln(target density / actual density)], where α and β are weighting coefficients obtained through regression analysis of historical operating data. The system supports multi-segment stirring control, enabling differentiated stirring strategies for different regions of the reactor to maximize floc formation efficiency.

[0136] The system performs multidimensional statistical analysis on operational data regarding particle size threshold range, flocculant dosage, flocculant ratio, and reactor stirring parameters to achieve continuous rolling optimization. The analysis and optimization process includes: Data clustering and pattern recognition: The system uses the K-means++ clustering algorithm to classify historical operational data into different water quality conditions, such as low temperature and low turbidity, high temperature and high turbidity, and heavy rain impact, identifying the optimal parameter combinations and control modes for each scenario. Clustering features include water quality parameters such as water temperature, turbidity, pH, redox potential, and organic matter content, as well as external factors such as season and meteorological conditions. Principal component analysis: Through principal component analysis (PCA) algorithm, key influencing factors are extracted from dozens of monitoring parameters, reducing model complexity and improving computational efficiency. The system retains principal components with a cumulative contribution rate exceeding 85%, typically containing 3-5 key features, such as the turbidity-temperature composite index and the organic-inorganic ratio index. Response surface modeling: Based on key features and treatment effect data, the system establishes a multivariate response surface model to describe the impact of different control parameter combinations on the treatment effect. The model adopts a second-order polynomial form: Y = β0 + ∑βᵢXᵢ + ∑βᵢⱼXᵢXⱼ + ∑βᵢᵢXᵢ² + ε, where Y is the treatment effect index (such as interception efficiency, effluent turbidity, etc.), Xᵢ is the control parameter (such as flocculant dosage, stirring frequency, etc.), β is the regression coefficient, and ε is the error term. Parameter optimization search: The system uses a response surface model combined with a genetic algorithm to perform a multi-objective optimization search to find the optimal parameter combination that balances treatment effect, energy cost, and reagent consumption. The optimization objective function is: minF(X) = w1·f1(X) + w2·f2(X) + w3·f3(X), where f1(X) represents effluent turbidity, f2(X) represents energy cost, f3(X) represents reagent consumption, w1, w2, and w3 are weighting coefficients, and X is the parameter vector.

[0137] Parameter library update: The system evaluates the optimization results periodically (usually every 7 days), incorporating new parameter combinations with performance improvements exceeding 5% into the control parameter library and eliminating outdated parameter combinations, thus achieving dynamic updates and iterative optimization of the parameter library. The parameter library is stored in partitions according to water quality categories for easy real-time retrieval.

[0138] Seasonal Analysis and Pre-adjustment: The system performs seasonal analysis on long-term operating data to identify cyclical change patterns and establish seasonal forecasting models. Parameters are pre-adjusted in advance during seasonal transitions (such as the transition from spring to summer or the start of the rainy season) to reduce adaptation time and improve system response speed.

[0139] The system establishes a complete closed-loop feedback mechanism, feeding the optimization results back to the front-end control strategy:

[0140] Short-term optimization (real-time to hourly): Adjusting current operating parameters based on real-time monitoring data to cope with instantaneous water quality fluctuations. Medium-term optimization (daily to weekly): Optimizing control model parameters based on statistical analysis of treatment effects to adapt to periodic changes in water quality. Long-term optimization (monthly to quarterly): Updating the algorithm structure based on big data mining results to improve the overall adaptability and intelligence of the system.

[0141] The rolling optimization process employs an incremental learning mechanism, prioritizing the use of new data to validate and fine-tune the existing model. Full reconstruction is only triggered when the validation error exceeds a threshold, balancing computational resource consumption with optimization effectiveness.

[0142] The following describes a low-turbidity water treatment system according to an embodiment of the present invention from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 4 This is a schematic diagram of a low-turbidity water treatment system in an embodiment of this application.

[0143] It should be noted that, Figure 4 The structure of the low turbidity water treatment system shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0144] like Figure 4 As shown, a low-turbidity water treatment system includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) 402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403, such as performing the methods described in the above embodiments. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0145] The following components are connected to I / O interface 405: input section 406 including audio input devices, push-button switches, etc.; output section 407 including liquid crystal displays, etc.; storage section 408 including hard disks, etc.; and communication section 409 including network interface cards such as modems. Communication section 409 performs communication processing via a network such as the Internet. Drive 410 is also connected to I / O interface 405 as needed. Removable media 411, such as disks, optical disks, etc., are installed on drive 410 as needed so that computer programs read from them can be installed into storage section 408 as needed.

[0146] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in a low-turbidity water treatment system described in the above embodiments; or it may exist independently and not assembled into the low-turbidity water treatment system. The storage medium carries one or more computer programs, which, when executed by a processor of the low-turbidity water treatment system, cause the low-turbidity water treatment system to implement a low-turbidity water treatment method provided in the above embodiments.

Claims

1. A method for treating low-turbidity water, characterized in that, An application is made in a low-turbidity water treatment device, the low-turbidity water treatment device comprising an intelligent micro-flocculation system of a floc control unit and a high-speed microfiltration filter of a depth filtration unit, the intelligent micro-flocculation system comprising a micro-flocculation intelligent calculation and optimization system and a micro-flocculation dosing system, a micro-flocculation pipeline mixer, a micro-flocculation reactor, an online turbidity monitor and an online floc particle size monitor, the method comprising: Obtain the first floc particle size in the water to be filtered; The particle size state of the first floc is determined, including a first particle size state and a second particle size state. Specifically, determining the particle size state of the first floc includes: acquiring the historical floc particle size of the water to be filtered within a preset historical time period; calculating the instantaneous particle size change rate based on the historical floc particle size and the first floc particle size; when the instantaneous particle size change rate is greater than a preset change rate threshold, the determination of the particle size state is stopped; after a preset delay time, the first floc particle size is acquired again and the instantaneous particle size change rate is calculated until the instantaneous particle size change rate is less than or equal to the preset change rate threshold; when the instantaneous particle size change rate is less than or equal to the preset change rate threshold, the ultrasonic attenuation coefficient of the floc is acquired; and the particle size is determined based on a preset particle size level. The first floc particle size is assigned a target particle size level, and a reference attenuation range corresponding to the target particle size level is obtained. The reference attenuation range is a health status reference established by statistically analyzing the ultrasonic attenuation coefficients of the flocs falling within the target particle size level under preset operating conditions. When the first floc particle size is greater than the upper limit threshold of the preset particle size range and the ultrasonic attenuation coefficient is within the reference attenuation range, the particle size state is determined to be the first particle size state. When the first floc particle size is less than the lower limit threshold of the preset particle size range, or when the first floc particle size is greater than the upper limit threshold of the preset particle size range and the ultrasonic attenuation coefficient is less than the lower limit threshold of the reference attenuation range, the particle size state is determined to be the second particle size state. The morphological indices of the flocs are obtained, and the morphological indices are used to characterize the geometric structural features of the flocs; When the particle size is the first particle size, the stirring frequency of the stirring device in the micro-flocculation reactor is determined based on the morphological index, and the stirring device is controlled to stir according to the stirring frequency until the target flocs meet the preset particle size. When the particle size state is the second particle size state, the amount of flocculant added is calculated based on the lower limit threshold of the particle size between the first floc particle size and the preset particle size range, and the flocculant is added at the preset agent dosing point according to the amount added. The second floc particle size of the flocs within a preset time period after the addition of the flocculant dosage is obtained. Based on the second floc particle size, the particle size state is re-determined. According to the particle size state, the stirring frequency of the stirring device in the micro-flocculation reactor is further adjusted for the water to be filtered after the addition of the flocculant dosage. The stirring device is controlled to stir at the stirring frequency or the flocculant is continued to be added until the second floc particle size is within the preset particle size range, thus obtaining the target flocs. Obtain the final floc particle size corresponding to the target floc, determine the filtration parameters of the high-speed microfiltration filter media layer based on the final floc particle size, and control the operation of the high-speed microfiltration filter according to the filtration parameters.

2. The method according to claim 1, characterized in that, The calculation of the flocculant addition amount based on the lower limit threshold of the particle size range between the first floc particle size and the preset particle size range specifically includes: Calculate the target particle size difference between the first floc particle size and the lower limit threshold particle size; The particle size of the first floc is corrected based on the ultrasonic attenuation coefficient to obtain the floc particle size correction value. The ratio of the target particle size difference to the floc particle size correction value is calculated to obtain the reagent demand adjustment factor; Multiply the drug demand adjustment factor by the preset dosage conversion coefficient to obtain the dosage adjustment amount; The amount added is obtained by adding the adjustment amount to the preset baseline addition setting value.

3. The method according to claim 1, characterized in that, The step of obtaining the final floc particle size corresponding to the target floc, and determining the filtration parameters of the high-speed microfiltration filter media layer based on the final floc particle size, specifically includes: Calculate the volume-weighted average particle size of the final flocculent particles; Based on a preset particle size-filtration rate-interception efficiency mapping relationship, the target filtration rate and interception efficiency corresponding to the volume-weighted average particle size are determined. The preset particle size-filtration rate-interception efficiency mapping relationship records the target filtration rate and interception efficiency of the flocs with different particle sizes under different filtration conditions. Monitor the real-time interception efficiency of the filter layer and compare the real-time interception efficiency with the upper limit of the allowable interception efficiency corresponding to the target filtration rate; If the real-time interception efficiency is less than the upper limit of the allowed interception efficiency, then the target filtering rate is determined as the filtering parameter; If the real-time interception efficiency is greater than or equal to the upper limit of the allowed interception efficiency, the target filtering rate is reduced by a preset reduction step size until the real-time interception efficiency is less than the upper limit of the allowed interception efficiency, and the adjusted filtering rate is determined as the filtering parameter.

4. The method according to claim 1, characterized in that, Before obtaining the first floc particle size of the flocs in the water to be filtered, the method further includes: Acquire historical data of the water body to be filtered, including historical turbidity value, historical total suspended solids, historical water flow rate, and historical floc particle size morphology and historical change rate during the preset pilot treatment process; Obtain the technical parameters of the deep filtration unit, including the range of suspended particle size intercepted, filtration accuracy, and interception efficiency; A dosing model is constructed based on the historical data and the technical parameters. The dosing model is used to calculate the amount of flocculant to be added. The system obtains a first online monitoring turbidity value of the water to be filtered and a second online monitoring turbidity value of the water after initial filtration, and calculates the initial interception efficiency of the system based on the first online monitoring turbidity value and the second online monitoring turbidity value. The historical data is input into a preset threshold model to determine a preset turbidity threshold and a preset interception threshold; When both the first online monitoring turbidity value and the second online monitoring turbidity value are less than the preset turbidity threshold, and the initial interception efficiency is greater than or equal to the preset interception threshold, the high-speed microfiltration filter of the filtration unit is controlled to operate independently. When both the first online monitoring turbidity value and the second online monitoring turbidity value are greater than or equal to the preset turbidity threshold, and the initial interception efficiency is less than the preset interception threshold, the flow rate of the water to be filtered and the total amount of suspended solids are obtained. The first online monitoring turbidity value, the second online monitoring turbidity value, the flow rate of the water to be filtered, and the total amount of suspended solids are input into the dosing model to obtain the initial addition amount of flocculant. The preset dosing point is then controlled to add the flocculant according to the initial addition amount.

5. The method according to claim 1, characterized in that, After obtaining the final floc particle size corresponding to the target floc, determining the filtration parameters of the high-speed microfiltration filter media layer based on the final floc particle size, and controlling the operation of the high-speed microfiltration filter according to the filtration parameters, the method further includes: Obtain the third online monitoring turbidity value of the water to be filtered after adding the flocculant and the fourth online monitoring turbidity value of the water to be filtered after filtration; The real-time interception efficiency of the water body to be filtered is calculated based on the third online monitoring turbidity value and the fourth online monitoring turbidity value. The particle size state threshold range of the flocs, the amount of flocculant added, the flocculant addition ratio, and the reactor stirring parameters are adjusted based on the third online monitoring turbidity value, the fourth online monitoring turbidity value, and the real-time interception efficiency. The particle size threshold range, the amount of flocculant added, the flocculant addition ratio, and the reactor stirring parameters are statistically analyzed and continuously optimized.

6. The method according to claim 1, characterized in that, The method further includes: The turbidity of the filtered water is obtained, and it is determined whether the turbidity is within a preset turbidity target range. When the water turbidity is higher than the upper limit threshold of the preset turbidity target range, a first turbidity deviation value between the water turbidity and the upper limit threshold is calculated, and a positive particle size range adjustment amount for increasing the preset particle size range is calculated based on the first turbidity deviation value. The preset particle size range is adjusted according to the positive particle size range adjustment amount; When the water turbidity is lower than the lower limit threshold of the preset turbidity target range, a second turbidity deviation value between the water turbidity and the lower limit threshold is calculated, and a negative particle size range adjustment amount for lowering the preset particle size range is calculated based on the second turbidity deviation value. The preset particle size range is adjusted according to the negative particle size range adjustment amount.

7. A low-turbidity water treatment device, characterized in that, The device includes: The system comprises an intelligent micro-flocculation system for the floc control unit and a high-speed microfiltration filter for the deep filtration unit. The intelligent micro-flocculation system includes a micro-flocculation intelligent calculation and optimization system, a micro-flocculation dosing system, a micro-flocculation pipeline mixer, a micro-flocculation reactor, an online turbidity monitor, and an online floc particle size monitor. The micro-flocculation reactor has an anti-clogging wedge-shaped slotted mesh at its center and a baffle plate around its outer ring. An auxiliary mixer is located at the top of the reactor, with stirring blades mounted on its impeller. The reactor also includes an inlet, an outlet, a drain outlet, and a PAM (particulate matter) dosing port. The floc control unit is used to: obtain the first floc particle size of the flocs in the water to be filtered; The particle size state of the first floc is determined, including a first particle size state and a second particle size state. Specifically, determining the particle size state of the first floc includes: acquiring the historical floc particle size of the water to be filtered within a preset historical time period; calculating the instantaneous particle size change rate based on the historical floc particle size and the first floc particle size; when the instantaneous particle size change rate is greater than a preset change rate threshold, the determination of the particle size state is stopped; after a preset delay time, the first floc particle size is acquired again and the instantaneous particle size change rate is calculated until the instantaneous particle size change rate is less than or equal to the preset change rate threshold; when the instantaneous particle size change rate is less than or equal to the preset change rate threshold, the ultrasonic attenuation coefficient of the floc is acquired; and the particle size is determined based on a preset particle size level. The first floc particle size is assigned a target particle size level, and a reference attenuation range corresponding to the target particle size level is obtained. The reference attenuation range is a health status reference established by statistically analyzing the ultrasonic attenuation coefficients of the flocs falling within the target particle size level under preset operating conditions. When the first floc particle size is greater than the upper limit threshold of the preset particle size range and the ultrasonic attenuation coefficient is within the reference attenuation range, the particle size state is determined to be the first particle size state. When the first floc particle size is less than the lower limit threshold of the preset particle size range, or when the first floc particle size is greater than the upper limit threshold of the preset particle size range and the ultrasonic attenuation coefficient is less than the lower limit threshold of the reference attenuation range, the particle size state is determined to be the second particle size state. The morphological indices of the flocs are obtained, and the morphological indices are used to characterize the geometric structural features of the flocs; When the particle size is the first particle size, the stirring frequency of the stirring device in the micro-flocculation reactor is determined based on the morphological index, and the stirring device is controlled to stir according to the stirring frequency until the target flocs meet the preset particle size. When the particle size state is the second particle size state, the amount of flocculant added is calculated based on the lower limit threshold of the particle size between the first floc particle size and the preset particle size range, and the flocculant is added at the preset agent dosing point according to the amount added. The second floc particle size of the flocs within a preset time period after the addition of the flocculant dosage is obtained. Based on the second floc particle size, the particle size state is re-determined. According to the particle size state, the stirring frequency of the stirring device in the micro-flocculation reactor is further adjusted for the water to be filtered after the addition of the flocculant dosage. The stirring device is controlled to stir at the stirring frequency or the flocculant is continued to be added until the second floc particle size is within the preset particle size range, thus obtaining the target flocs. The deep filtration unit is used to: obtain the final floc particle size corresponding to the target floc, determine the filtration parameters of the high-speed microfiltration filter media layer based on the final floc particle size, and control the operation of the high-speed microfiltration filter according to the filtration parameters.

8. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on a low-turbidity water treatment system, the low-turbidity water treatment system performs the method as described in any one of claims 1-6.

9. A computer program product, characterized in that, When the computer program product is run on a low-turbidity water treatment system, it causes the low-turbidity water treatment system to perform the method as described in any one of claims 1-6.

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