Flocculation dynamic control method and device, electronic equipment and storage medium

Abstract

The invention discloses a flocculation dynamic control method and device, electronic equipment and a storage medium, and relates to the technical field of water treatment.The method comprises the steps that flocculation process parameters corresponding to a sewage flocculation device are collected in real time; the flocculation process parameters comprise at least one of the particle size of flocs in the sewage at the current moment, the Zeta potential of a shearing surface of a sewage stirring module in the sewage flocculation device and the shearing stress of the shearing surface; determining real-time control parameters of the sewage flocculation device based on at least one of the floc particle size, the Zeta potential and the shear stress; and controlling the sewage flocculation device to perform sewage flocculation treatment based on the real-time control parameters. According to the method, accurate control over the flocculation process is achieved, the problems of blind flocculant adding, high energy consumption and poor floc stability caused by parameter lag, high energy consumption and insufficient floc stability in a traditional process are effectively solved, and the floc forming effect is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of water treatment, and in particular, to a method, device, electronic device and storage medium for dynamic control of flocculation. Background Art

[0002] As the core link of water treatment, flocculation directly affects the effluent water quality and treatment cost. In related technologies, the flocculation treatment of sewage usually relies on manual experience or fixed program control to achieve.

[0003] However, due to parameter lag, high energy consumption and insufficient floc stability in the above methods, problems such as low flocculation treatment efficiency and waste of chemicals are caused, which in turn affects the floc formation effect. Therefore, how to improve the floc formation effect is an urgent problem to be solved at present. Summary of the Invention

[0004] The present application provides a method, device, electronic device and storage medium for dynamic control of flocculation, which can solve the problems of low treatment efficiency and waste of chemicals caused by parameter lag, high energy consumption and insufficient floc stability in traditional flocculation processes, realize precise control of the flocculation process, and improve the floc formation effect.

[0005] In a first aspect, an embodiment of the present application provides a method for dynamic control of flocculation, the method comprising:

[0006] Collecting in real time the flocculation process parameters corresponding to the sewage flocculation device; the flocculation process parameters include at least one of the floc particle size in the sewage at the current moment, the Zeta potential of the shear plane of the sewage stirring module in the sewage flocculation device, and the shear stress of the shear plane;

[0007] Determining the real-time control parameters of the sewage flocculation device based on at least one of the floc particle size, Zeta potential and shear stress;

[0008] Controlling the sewage flocculation device to perform sewage flocculation treatment based on the real-time control parameters.

[0009] In a second aspect, an embodiment of the present application further provides a computer program product, including a computer program, which implements the method for dynamic control of flocculation according to any embodiment of the present application when executed by a processor.

[0010] In a third aspect, an embodiment of the present application further provides a device for dynamic control of flocculation, the device comprising:

[0011] A real-time acquisition module, configured to collect in real time the flocculation process parameters corresponding to the sewage flocculation device; the flocculation process parameters include at least one of the floc particle size in the sewage at the current moment, the Zeta potential of the shear plane of the sewage stirring module in the sewage flocculation device, and the shear stress of the shear plane;

[0012] A determination module, configured to determine real-time control parameters of the sewage flocculation device based on at least one of the floc particle size, Zeta potential, and shear stress;

[0013] A control module, configured to control the sewage flocculation device to perform sewage flocculation treatment based on the real-time control parameters.

[0014] In a fourth aspect, an embodiment of the present application provides an electronic device, including:

[0015] One or more processors;

[0016] A memory, configured to store one or more programs,

[0017] When the one or more programs are executed by the one or more processors, the one or more processors implement the flocculation dynamic control method according to any embodiment of the present application.

[0018] In a fifth aspect, an embodiment of the present application provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, the flocculation dynamic control method according to any embodiment of the present application is implemented.

[0019] An embodiment of the present application proposes a flocculation dynamic control method, device, electronic device, and storage medium, including: real-time collecting flocculation process parameters corresponding to a sewage flocculation device; the flocculation process parameters include at least one of the floc particle size in the sewage at the current moment, the Zeta potential of the shear surface of the sewage stirring module in the sewage flocculation device, and the shear stress of the shear surface; determining real-time control parameters of the sewage flocculation device based on at least one of the floc particle size, Zeta potential, and shear stress; controlling the sewage flocculation device to perform sewage flocculation treatment based on the real-time control parameters. That is to say, in the technical solution of the present application, based on at least one of the floc particle size, Zeta potential, and shear stress, the real-time control parameters of the sewage flocculation device can be dynamically determined, so as to control the sewage flocculation device to perform sewage flocculation treatment based on the real-time control parameters, realizing precise control of the flocculation process, effectively solving the problems of blind flocculant dosing, high energy consumption, and poor floc stability caused by parameter lag, high energy consumption, and insufficient floc stability in the traditional process, and improving the floc formation effect. Description of the Drawings

[0020] Figure 1 It is a schematic flow chart of the flocculation dynamic control method provided by an embodiment of the present application;

[0021] Figure 2 It is a schematic structural diagram of the sewage flocculation device provided by an embodiment of the present application;

[0022] Figure 3 It is a schematic structural diagram of the flocculation dynamic control device provided by an embodiment of the present application;

[0023] Figure 4 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the present application and are not intended to limit the present application. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions of the present application, not all of the structures.

[0025] In order to facilitate a clearer understanding of the various embodiments of the present application, some relevant knowledge is first introduced as follows.

[0026] The flocculation dynamic control method involved in the embodiments of the present application is particularly suitable for the fields of municipal sewage deep treatment, industrial wastewater reuse and micro-polluted water purification.

[0027] Flocculation, as a core step in water treatment, directly impacts effluent quality and treatment costs. In traditional processes, flocculant overdosage rates can reach as high as 35%-50%, increasing treatment costs by 12%-18% per ton of water. Mechanical agitation energy consumption can reach 30%-40%, and low sedimentation efficiency due to a particle size standard deviation greater than 20% can extend treatment cycles by 40%-60%. Furthermore, traditional flocculation processes rely primarily on manual experience or fixed program control, resulting in the following technical bottlenecks:

[0028] 1. Parameter hysteresis problem: The flocculant dosage is usually adjusted based on historical data of influent water quality or empirical formulas, which makes it difficult to respond to water quality fluctuations (such as a sudden increase in turbidity or changes in pollutant composition) in real time, resulting in excessive or insufficient dosage (over-dosage rate can reach 30%-50%), affecting the floc formation effect.

[0029] 2. Imbalance between energy consumption and efficiency: The mechanical stirring mode is limited by the nonlinear relationship between speed and shear force. Inefficient stirring (less than 60% energy utilization) can easily cause floc breakage, while high-intensity stirring increases energy consumption (the power consumption of traditional stirring systems accounts for 25%-40% of the total treatment cost).

[0030] 3. Lack of microstructure monitoring: The relevant technologies lack online monitoring of key microscopic parameters of flocs, making it difficult to understand the dynamic laws of floc formation.

[0031] 4. Insufficient floc stability: The inability to dynamically adjust the stirring intensity according to the floc growth stage (e.g., low shear force is required to avoid floc breakage during the mature stage), resulting in easy disintegration of flocs or poor sedimentation performance (supernatant turbidity fluctuation range of ±10NTU).

[0032] 5. Difficulty in capturing nano-pollutants: In related technologies, the interception efficiency of colloidal particles (particle size 1 - 100 nm) and emerging pollutants (such as microplastics) is insufficient, and it is necessary to additionally increase the dosage of coagulant or add complex pretreatment units.

[0033] In view of the above technical problems, the present application provides a flocculation dynamic control method, device, electronic device and storage medium. The core lies in integrating a high-precision sensor network (including a shear stress sensor, a floc particle size imaging system and an on-line Zeta potential measurement module) with a multi-objective optimization algorithm to achieve dynamic collaborative regulation of the dosage of flocculant, stirring intensity and reactor operation parameters, and solve the problems of low treatment efficiency and chemical agent waste caused by parameter lag, high energy consumption and insufficient floc stability in traditional flocculation processes.

[0034] Figure 1 As shown in the flowchart of the flocculation dynamic control method provided in an embodiment of the present application, this method can be executed by a flocculation dynamic control device or an electronic device. The device or the electronic device can be implemented in a software and / or hardware manner, and the device or the electronic device can be integrated in any intelligent device with network communication functions. As Figure 1 shown, the flocculation dynamic control method may include the following steps:

[0035] S101. Real-time collect the flocculation process parameters corresponding to the sewage flocculation device; the flocculation process parameters include at least one of the floc particle size in the sewage at the current moment, the Zeta potential of the shear plane of the sewage stirring module in the sewage flocculation device, and the shear stress of the shear plane.

[0036] In the embodiment of the present application, the sewage flocculation device is used to flocculate sewage. Figure 2 As shown in the structural schematic diagram of the sewage flocculation device provided in an embodiment of the present application. The device may include: a laser source, a stirring tank, a settling column, a CCD camera and / or a high-resolution CCD camera, a personal computer, a control panel; further, the device may also include: a laser control unit, a spherical lens, a cylindrical lens, a gear motor, a speed controller, a modified rubber tube, a fixture, an impeller, an adjustable bracket, a firewire interface.

[0037] In some embodiments, the flocculation process parameters are collected in real time through a parameter monitoring module.

[0038] Optionally, the parameter monitoring module at least includes a floc particle size imaging system, an on-line Zeta potential probe and a shear stress sensor, which are respectively used to collect the floc particle size, Zeta potential and shear stress.

[0039] For example: (1) Shear stress collection: The shear stress at the edge of the stirring paddle is detected by a piezoelectric sensor (range 0 - 500 Pa, accuracy ±1 Pa), and the sampling frequency is 100 Hz.

[0040] (2) Floc particle size imaging: Use a high-speed camera to capture the movement trajectory of the flocs, and combine with the PIV algorithm to calculate the particle size distribution (resolution 0.1 μm).

[0041] (3) Zeta potential measurement: Use an online probe (detection accuracy ±5 mV), and automatically calibrate every 30 seconds.

[0042] S102. Determine the real-time control parameters of the sewage flocculation device based on at least one of the floc particle size, Zeta potential, and shear stress.

[0043] In the embodiment of the present application, based on at least one of the floc particle size, Zeta potential, and shear stress, the change trend of the floc particle size can be predicted, and the optimal real-time control parameters can be obtained by combining with the MOPSA algorithm, so as to drive each module (such as an electromagnetic stirrer, an ultrasonic dispersion module, a flocculant metering pump, etc.) in the sewage flocculation device to work together.

[0044] S103. Control the sewage flocculation device to perform sewage flocculation treatment based on the real-time control parameters.

[0045] The embodiment of the present application proposes a flocculation dynamic control method. Based on at least one of the floc particle size, Zeta potential, and shear stress, the real-time control parameters of the sewage flocculation device can be dynamically determined, so as to control the sewage flocculation device to perform sewage flocculation treatment based on the real-time control parameters, realizing precise control of the flocculation process, effectively solving the problems of blind flocculant dosing, high energy consumption, and poor floc stability caused by parameter lag, high energy consumption, and insufficient floc stability in the traditional process, and improving the floc formation effect.

[0046] Optionally, determining the real-time control parameters of the sewage flocculation device based on at least one of the floc particle size, Zeta potential, and shear stress can be specifically implemented through the following steps:

[0047] Step 1). Based on the floc particle size, Zeta potential, and shear stress, determine the change trend of the floc particle size within a preset time period.

[0048] Step 2). Based on the preset constraint conditions, determine the operating parameters of the sewage flocculation device at the current moment.

[0049] Step 3). Determine the real-time control parameters based on at least one of the particle size change trend and the operating parameters.

[0050] In practical applications, after collecting the floc particle size, Zeta potential, and shear stress, data preprocessing is required.

[0051] Specifically, first, the signal noise is eliminated based on the wavelet denoising algorithm, and then the image is binarized by OpenCV to extract the proportion of the floc area.

[0052] After data preprocessing, the sewage flocculation device is dynamically adjusted through real-time control parameters to achieve precise control of the flocculation process.

[0053] Optionally, based on the floc particle size, Zeta potential, and shear stress, the particle size change trend of the floc within a preset time period is determined. Specifically, it can be achieved through the following steps:

[0054] The floc particle size, Zeta potential, and shear stress are input into the particle size change prediction model to obtain the particle size change trend output by the particle size change prediction model; the particle size change trend is calculated by the particle size change prediction model based on formula (1);

[0055] Among them, formula (1) is:

[0056]

[0057] Among them, represents the change trend of the floc particle size at time t; k represents the polymerization rate constant; Z ε represents the Zeta potential; ε represents the dielectric constant of the medium; τ represents the shear stress.

[0058] In the embodiments of the present application, the particle size change prediction model can be an LSTM model.

[0059] Specifically, the embodiments of the present application use an LSTM neural network to predict the particle size change trend of the floc within a future preset time period (for example, the next 30 seconds) (with an error < 8%).

[0060] In the embodiments of the present application, the above formula (1) can be understood as a floc particle size growth formula, and the change trend of the floc particle size over time is predicted through an LSTM neural network.

[0061] Optionally, based on preset constraint conditions, the operating parameters of the sewage flocculation device at the current moment are determined. Specifically, it can be achieved through the following steps:

[0062] Based on the preset constraint conditions, at least one of the flocculant consumption, energy consumption, and treatment time of the sewage flocculation device at the current moment is determined;

[0063] The preset constraint conditions are represented by the following formula (2):

[0064]

[0065] Among them, p represents the weight factor; C represents the flocculant consumption; E represents the energy consumption; T represents the treatment time; Dp Denote the floc diameter as D target Denote the preset floc diameter as D0, and the shear stress as τ

[0066] In the embodiments of the present application, after predicting the change trend of the floc diameter within a preset future time period by using an LSTM neural network, a multi-objective particle swarm optimization algorithm (MOPSA) is combined to solve a multi-objective optimization model, and the multi-objective optimization model can be understood as preset constraint conditions, which are represented by the above formula (2).

[0067] In the embodiments of the present application, a constraint optimization model is constructed with the flocculant consumption (C), energy consumption (E), and treatment time (T) as the objective functions. The MOPSA algorithm with an adaptive weight is adopted, and the weight factor is dynamically adjusted according to P = 0.7C + 0.2E + 0.1T, where C is the chemical consumption (mg / L·m 3 ), E is the energy consumption (kW·h / m 3 ), and T is the treatment cycle (min). The algorithm iteration is realized through the mixed programming of MATLAB and PyTorch, and the number of iterations ≤ 500 times, and the convergence accuracy ≥ 98%.

[0068] Optionally, based on at least one of the particle size change trend and operating parameters, determine real-time control parameters, which can be specifically realized through the following steps:

[0069] Based on at least one of the particle size change trend and operating parameters, determine at least one of the first real-time control parameter, the second real-time control parameter, and the third real-time control parameter;

[0070] Among them, the first real-time control parameter is used to change the stirring intensity of the stirring module at the current moment; the second real-time control parameter is used to change the flocculant dosage of the sewage flocculation device at the current moment; the third real-time control parameter is used to change the stirring mode of the sewage flocculation device.

[0071] In the embodiments of the present application, the stirring mode includes an electromagnetic stirring mode and an ultrasonic dispersion mode. In practical applications, the shear stress sensor adopts a low shear force mode (i.e., the electromagnetic stirring mode, with a stirring speed < 50 rpm) in the initial stage of floc formation (particle size < 10 μm) to prevent nanoparticle breakage; the ultrasonic dispersion module (cavitation intensity 5 W / cm 2 ) is started during the peak period of the pollution load during the stirring process to increase the colloidal particle capture efficiency to more than 95%.

[0072] In the embodiments of the present application, the specific process of determining the real-time control parameters can refer to the following logic:

[0073] def dynamic_control():

[0074] while True:

[0075] # Read parameters

[0076] shear_stress = get_sensor_data('shear')

[0077] floc_size = get_image_analysis()

[0078] zeta = get_zeta_value()

[0079] # Calculate target parameters

[0080] target_size = 30 #μm (set according to water quality)

[0081] if floc_size < target_size * 0.8 or shear_stress > 400:

[0082] activate_stir('increase', 15%) # Increase stirring intensity by 15%

[0083] adjust_pam(0.05) # Increase the dosage of flocculant by 5%

[0084] elif zeta < -10:

[0085] activate_ultrasound('on', 3W / cm 2 ) # Start ultrasonic dispersion

[0086] else:

[0087] optimize_energy() # Enter energy-saving mode (electromagnetic stirring mode)

[0088] Optionally, control the sewage flocculation device to perform sewage flocculation treatment based on real-time control parameters, which can be specifically implemented by at least one of the following methods:

[0089] Method 1: Generate a first real-time control instruction based on at least one of the first real-time control parameter and the third real-time control parameter; control the sewage flocculation device to perform sewage flocculation treatment based on the first real-time control instruction; the first real-time control instruction is used to control the stirring module to change the stirring intensity and / or stirring mode.

[0090] In the embodiments of the present application, a PID controller is used to adjust the rotation speed of the stirring module.

[0091] Based on the first real-time control parameter for changing the stirring intensity of the stirring module at the current moment and the third real-time control parameter for changing the stirring mode of the sewage flocculation device, a first real-time control instruction can be generated. Through the first real-time control instruction, the stirring intensity and / or the stirring mode of the stirring module in the sewage flocculation device can be controlled.

[0092] Method 2: Based on the second real-time control parameter, use the fuzzy logic algorithm to determine the second real-time control instruction; based on the second real-time control instruction, control the sewage flocculation device to perform sewage flocculation treatment; the second real-time control instruction is used to control the dosage of the flocculant at the current moment of the sewage flocculation device.

[0093] In the above Method 1 and Method 2, according to the output of the optimization algorithm, control instructions are generated to control the stirring intensity and the dosage of PAM of the sewage flocculation device.

[0094] In the embodiments of the present application, the dosage of the flocculant is dynamically adjusted based on the fuzzy logic algorithm.

[0095] The following further describes the flocculation dynamic control method provided in the embodiments of the present application in combination with specific embodiments.

[0096] 1. Experimental equipment, including:

[0097] (1) Reactor unit: Double-layer 16L stainless steel conical reactor (volume 50L, cone angle 60°).

[0098] (2) Sensor array:

[0099] Shear stress sensor;

[0100] High-speed camera;

[0101] Zeta potential probe.

[0102] (3) Actuator:

[0103] Electromagnetic stirrer (0 - 3kW, rotation speed 0 - 3000rpm);

[0104] Ultrasonic dispersion module (28kHz, titanium alloy transducer);

[0105] Flocculant metering pump (0 - 50Hz, frequency conversion control).

[0106] (4) Data processing unit: Embedded computer (running FreeRTOS + Python (PyTorch)).

[0107] 2. Experimental conditions, including:

[0108] Influent water quality: turbidity 250 ± 30 NTU, COD 180 ± 20 mg / L, pH 6.8 - 7.2, temperature 22 ± 1 °C.

[0109] Initial parameters: stirring speed 50 rpm, PAM dosage 0.2 mg / L, pH adjusted to 7.0.

[0110] 3. Operation process, including:

[0111] (1) Parameter monitoring and pre - treatment (executed every 30 seconds):

[0112] Shear stress, floc size, and Zeta potential data are transmitted to the computer through a data acquisition card (sampling rate 1 MS / s);

[0113] Wavelet denoising algorithm (signal - to - noise ratio ≥ 30 dB) eliminates sensor noise;

[0114] OpenCV image binary processing extracts the proportion of floc area (confidence level > 95%).

[0115] (2) Dynamic control decision:

[0116] def dynamic_control():

[0117] while True:

[0118] shear_stress = get_sensor_data('shear') # 0 - 500 Pa

[0119] floc_size = get_image_analysis() # 0.1 μm resolution

[0120] zeta = get_zeta_value() # ±5 mV accuracy

[0121] target_size = 30 # μm (dynamically adjusted according to water quality)

[0122] if floc_size < target_size * 0.8 or shear_stress > 400:

[0123] activate_stir('increase', 15%) # Increase the stirring intensity by 15%

[0124] adjust_pam(0.05) # Increase the PAM dosage by 5%

[0125] elif zeta < - 10:

[0126] activate_ultrasound('on',3) # Activate ultrasound (3W / cm 2 )

[0127] else:

[0128] optimize_energy() # Energy-saving mode (PID speed control to 50rpm)

[0129] # Feedback regulation

[0130] pid_controller.set_speed(target_speed) # PID response time < 5s

[0131] pam_pump.set_frequency(pam_freq) # Fuzzy logic dynamic regulation

[0132] (3) Termination conditions:

[0133] The sedimentation rate of flocs is detected continuously for 3 times ≥ 10cm / min;

[0134] The turbidity of the supernatant < 1NTU and the Zeta potential > -15mV.

[0135] Through experiments and comparison with traditional processes, the experimental results shown in Table 1 below are obtained:

[0136] Table 1

[0137] Parameter This embodiment Traditional process Improvement range Reagent utilization rate 92％ 60-70％ +33％ Stirring energy consumption <![CDATA[1.2kW·h / m 3 > <![CDATA[2.1kW·h / m 3 > -42％ Treatment cycle 15 min 40 min -62.5％ Nanoparticle retention rate 96％ 60％ +60％

[0138] Compared with the traditional process, this application realizes the comprehensive effects of a 33% increase in the utilization rate of chemicals, a 42% reduction in energy consumption, a 62.5% shortening of the treatment cycle, and a 60% increase in the interception rate of nanoparticles through multi-parameter dynamic regulation (shear stress, floc particle size, Zeta potential) and intelligent algorithms (LSTM prediction + MOPSA optimization). At the same time, the standard deviation of the floc particle size is reduced from 20% in the traditional process to < 10%; the fluctuation range of the Zeta potential is reduced from ±15mV to ±3mV, indicating a significant improvement in the surface charge stability of the flocs.

[0139] The flocculation dynamic control method provided by this application can specifically achieve the following beneficial effects:

[0140] (1) In terms of precise control: Under the traditional process method, the floc particle size distribution is wide, with a standard deviation greater than 20%, which easily causes the situation of "large particles wrapping small particles", resulting in very low sedimentation efficiency. Moreover, the Zeta potential is only measured by off-line experiments, such as electrophoresis, and it is impossible to feedback the surface charge state of the flocs in real time, making it difficult to dynamically adjust the coagulation conditions. This application is different. It integrates a high-precision shear stress sensor, a particle size imaging system, and an on-line Zeta potential probe to construct a full-scale observation network of the microscopic characteristics of the flocs. At the same time, based on the LSTM neural network, the growth trend of the flocs is predicted, and the prediction error is less than 8%. Then, combined with the PID controller, the stirring intensity and the PAM dosage are adjusted in real time, and finally the standard deviation of the floc particle size is less than 10%, and the Zeta potential fluctuation range is within ±3 mV.

[0141] (2) In terms of energy conservation and consumption reduction: In the traditional process, mechanical stirring relies on a fixed speed, usually greater than 1000 rpm, and its energy consumption accounts for 30% - 40% of the total treatment cost; the addition of flocculants relies on empirical formulas, which leads to a widespread over-addition rate of about 30% - 50%. This application has a stirring module with efficient energy utilization. The electromagnetic stirrer uses stepless speed regulation technology, and the speed regulation range is between 0 and 3000 rpm. By optimizing the matching relationship between the magnetic field strength (0.8 T) and the speed, the stirring energy efficiency is increased to 82%, while the energy efficiency of traditional mechanical stirring is less than 60%. And the ultrasonic dispersion module can replace part of the mechanical stirring function. In the low shear force stage (floc maturation period), it maintains the structural stability of the flocs with low energy consumption below 5 W / cm 2 . In addition, there is an intelligent chemical dosing control algorithm. This algorithm establishes a non-linear relationship model between the PAM dosage, the Zeta potential, and the shear stress based on the fuzzy logic algorithm. The dosing error is less than 2%, and it can also dynamically adjust the metering pump frequency (0 - 50 Hz) to avoid the situation of excessive or insufficient chemicals. In this way, the stirring energy consumption can be reduced by 42% (compared with traditional mechanical stirring), and the chemical utilization rate reaches 92%.

[0142] (3) In terms of wide adaptability: Traditional processes are relatively sensitive to water quality fluctuations and require frequent manual interventions, such as adjusting the pH or adding coagulants. Moreover, under conditions of high turbidity (greater than 200 NTU) or high COD (greater than 400 mg / L), problems such as slow floc sedimentation or secondary colloid disintegration are likely to occur. The embodiment of this application has a strong adaptive parameter adjustment ability. Based on the multi-objective optimization algorithm (P = 0.7C + 0.2E + 0.1T), it can dynamically balance the flocculation effect and energy consumption requirements. Through the shear stress-particle size correlation model, the stirring mode can be automatically adjusted. For example, during the high shear force stage, the aggregation of pollutants is accelerated, and during the low shear force stage, the solidification of flocs is promoted. At the same time, the coagulation mechanism can be strengthened. During the peak period of pollution load (such as a sudden increase in turbidity), the ultrasonic cavitation module assists in generating microbubbles with a cavitation intensity of 5 W / cm 2 , providing additional collision energy for the capture of colloidal particles, thereby improving the capture efficiency of colloidal particles. In this way, it is possible to handle complex water quality with an influent turbidity between 5 - 500 NTU and a COD concentration between 100 - 500 mg / L.

[0143] (4) In terms of nanoscale capture: Traditional flocculation processes rely on macroscopic flocs (particle size greater than 100 μm) to intercept pollutants, and the capture efficiency for nanoscale colloids (such as microplastics and drug residues) is relatively low, less than 60%, so additional sand filtration or activated carbon adsorption units are required.

[0144] The embodiment of this application can achieve optimized control of the microstructure. Specifically, by precisely adjusting the Zeta potential (maintained at -5 to 0 mV) and shear stress (less than 100 Pa), nanoscale particles are induced to coagulate by charge neutralization and van der Waals forces. At the initial stage of floc formation (particle size less than 10 μm), a low shear force mode (stirring speed below 50 rpm) is adopted to prevent nanoscale particles from being mechanically broken. At the same time, there is an efficient capture mechanism. The conical reactor (cone angle 60°) combined with the axial flow mode of electromagnetic stirring can extend the residence time of nano-flocs in the reactor (exceeding 8 minutes). The high-speed camera and PIV velocity measurement system monitor the movement trajectory of flocs in real time to ensure that nanoscale particles can be effectively captured into the floc network, and thus colloidal particles with a size of 10 - 50 nm can be effectively captured (interception efficiency greater than 95%).

[0145] Figure 3 This is a schematic structural diagram of a flocculation dynamic control device provided by an embodiment of this application. As Figure 3 shown, the flocculation dynamic control device includes:

[0146] A real-time acquisition module 301 is configured to acquire in real time the flocculation process parameters corresponding to a sewage flocculation device; the flocculation process parameters include at least one of the floc particle size in the sewage at the current moment, the Zeta potential of the shear plane of the sewage stirring module in the sewage flocculation device, and the shear stress of the shear plane.

[0147] A determination module 302 is configured to determine the real-time control parameters of the sewage flocculation device based on at least one of the floc particle size, the Zeta potential, and the shear stress.

[0148] A control module 303 is configured to control the sewage flocculation device to perform sewage flocculation treatment based on the real-time control parameters.

[0149] An embodiment of the present application provides a flocculation dynamic control device, which can dynamically determine the real-time control parameters of a sewage flocculation device based on at least one of the floc particle size, the Zeta potential, and the shear stress, so as to control the sewage flocculation device to perform sewage flocculation treatment based on the real-time control parameters, realizing precise control of the flocculation process, effectively solving the problems of blind flocculant dosing, high energy consumption, and poor floc stability caused by parameter lag, high energy consumption, and insufficient floc stability in the traditional process, and improving the floc formation effect.

[0150] Optionally, the determination module 302 is further configured to:

[0151] Determine the particle size change trend of the floc particle size within a preset time period based on the floc particle size, the Zeta potential, and the shear stress;

[0152] Determine the operating parameters of the sewage flocculation device at the current moment based on the preset constraint conditions;

[0153] Determine the real-time control parameters based on at least one of the particle size change trend and the operating parameters.

[0154] Optionally, the determination module 302 is further configured to:

[0155] Input the floc particle size, the Zeta potential, and the shear stress into a particle size change prediction model to obtain the particle size change trend output by the particle size change prediction model; the particle size change trend is calculated by the particle size change prediction model based on formula (1);

[0156] Wherein, formula (1) is:

[0157]

[0158] Wherein, represents the change trend of the floc particle size at time t; k represents the polymerization rate constant; Z ε represents the Zeta potential; ε represents the dielectric constant of the medium; τ represents the shear stress.

[0159] Optionally, the determining module 302 is further configured to:

[0160] Determine at least one of the flocculant consumption, energy consumption, and treatment time of the sewage flocculation device at the current moment based on preset constraint conditions;

[0161] The preset constraint conditions are represented by the following formula (2):

[0162]

[0163] where p represents a weight factor; C represents the flocculant consumption; E represents the energy consumption; T represents the treatment time; D p represents the floc particle size; D target represents the preset floc particle size; τ represents the shear stress.

[0164] Optionally, the determining module 302 is further configured to:

[0165] Determine at least one of the first real-time control parameter, the second real-time control parameter, and the third real-time control parameter based on at least one of the particle size change trend and the operating parameters;

[0166] where the first real-time control parameter is used to change the stirring intensity of the stirring module at the current moment; the second real-time control parameter is used to change the flocculant dosage of the sewage flocculation device at the current moment; the third real-time control parameter is used to change the stirring mode of the sewage flocculation device.

[0167] Optionally, the control module 303 is further configured to perform at least one of the following:

[0168] Generate a first real-time control instruction based on at least one of the first real-time control parameter and the third real-time control parameter; control the sewage flocculation device to perform sewage flocculation treatment based on the first real-time control instruction; the first real-time control instruction is used to control the stirring module to change the stirring intensity and / or the stirring mode;

[0169] Determine a second real-time control instruction based on the second real-time control parameter using a fuzzy logic algorithm; control the sewage flocculation device to perform sewage flocculation treatment based on the second real-time control instruction; the second real-time control instruction is used to control the flocculant dosage of the sewage flocculation device at the current moment.

[0170] As Figure 4 shown, the electronic device 12 is presented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0171] Bus 18 represents one or more of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, an Accelerated Graphics Port, a processor, or a local bus using any of the several bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0172] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including both volatile and nonvolatile media, removable and non-removable media.

[0173] System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / nonvolatile computer system storage media. By way of example only, storage system 34 can be used for reading and writing on non-removable, nonvolatile magnetic media ( Figure 4 not shown and typically called a "hard disk drive"). Although Figure 4 not shown in, a disk drive for reading and writing on a removable nonvolatile disk (such as a "floppy disk"), and an optical disk drive for reading and writing on a removable nonvolatile optical disk (such as a CD-ROM, a DVD-ROM, or other optical media) can be provided. In these cases, each drive can be connected to bus 18 by one or more data media interfaces. Memory 28 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of the embodiments of the present application.

[0174] A program / utility 40 having a set (at least one) of program modules 42 can be stored, for example, in memory 28, and such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may include an implementation of a network environment. Program modules 42 generally carry out the functions and / or methods in the embodiments described in the present application.

[0175] The electronic device 12 can also communicate with one or more external devices 14 (such as a keyboard, a pointing device, a display 24, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 12, and / or communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 22. Also, the electronic device 12 can communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 20. As shown in the figure, the network adapter 20 communicates with other modules of the electronic device 12 through the bus 18. It should be understood that although Figure 4 not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0176] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the flocculation dynamic control method provided by the embodiments of the present application.

[0177] The embodiments of the present application also provide a computer storage medium.

[0178] The computer-readable storage medium of the embodiments of the present application can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device.

[0179] A computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0180] The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, and the like, or any suitable combination of the foregoing.

[0181] The computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0182] The embodiments of this application also provide a computer program product.

[0183] The various embodiments of the systems and technologies described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), systems-on-a-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer program products, which can include one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor. The programmable processor can be a dedicated or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0184] Note that the above are only the preferred embodiments of the present application and the technical principles applied. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and various obvious changes, re-adjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments only. Without departing from the concept of the present application, more other equivalent embodiments can be included, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A flocculation dynamic control method, characterized in that The method includes: Collecting in real time the flocculation process parameters corresponding to the sewage flocculation device; the flocculation process parameters include at least one of the floc particle size in the sewage at the current moment, the Zeta potential of the shear plane of the sewage stirring module in the sewage flocculation device, and the shear stress of the shear plane; Determining the real-time control parameters of the sewage flocculation device based on at least one of the floc particle size, Zeta potential, and shear stress; Controlling the sewage flocculation device to perform sewage flocculation treatment based on the real-time control parameters.

2. The flocculation dynamic control method according to claim 1, wherein The determining the real-time control parameters of the sewage flocculation device based on at least one of the floc particle size, Zeta potential, and shear stress includes: Determining the particle size change trend of the floc particle size within a preset time period based on the floc particle size, the Zeta potential, and the shear stress; Determining the operating parameters of the sewage flocculation device at the current moment based on preset constraint conditions; Determining the real-time control parameters based on at least one of the particle size change trend and the operating parameters.

3. The flocculation dynamic control method according to claim 2, wherein The determining the particle size change trend of the floc particle size within a preset time period based on the floc particle size, the Zeta potential, and the shear stress includes: Inputting the floc particle size, the Zeta potential, and the shear stress into a particle size change prediction model to obtain the particle size change trend output by the particle size change prediction model; the particle size change trend is calculated by the particle size change prediction model based on formula (1); Wherein, the formula (1) is: Among them, represents the change trend of the floc particle size at time t; k represents the polymerization rate constant; Z ε represents the Zeta potential; ε represents the dielectric constant of the medium; τ represents the shear stress.

4. The flocculation dynamic control method according to claim 2, wherein The determining the operating parameters of the sewage flocculation device at the current moment based on preset constraint conditions includes: Determining at least one of the flocculant consumption, energy consumption, and treatment time of the sewage flocculation device at the current moment based on the preset constraint conditions; The preset constraint conditions are represented by the following formula (2): Among them, p represents the weighting factor; C represents the consumption of the flocculant; E represents the energy consumption; T represents the treatment time; D p represents the floc particle size; D target represents the preset floc particle size; τ represents the shear stress.

5. The flocculation dynamic control method according to any one of claims 2-4, characterized in that The determining the real-time control parameters based on at least one of the particle size change trend and the operating parameters includes: Determining at least one of the first real-time control parameter, the second real-time control parameter, and the third real-time control parameter based on at least one of the particle size change trend and the operating parameters; Wherein, the first real-time control parameter is used to change the stirring intensity of the stirring module at the current moment; the second real-time control parameter is used to change the flocculant dosage of the sewage flocculation device at the current moment; the third real-time control parameter is used to change the stirring mode of the sewage flocculation device.

6. The flocculation dynamic control method according to claim 5, characterized in that The controlling the sewage flocculation device to perform sewage flocculation treatment based on the real-time control parameters includes at least one of the following: Generating a first real-time control instruction based on at least one of the first real-time control parameter and the third real-time control parameter; controlling the sewage flocculation device to perform sewage flocculation treatment based on the first real-time control instruction; The first real-time control instruction is used to control the stirring module to change the stirring intensity and / or the stirring mode; Determining a second real-time control instruction based on the second real-time control parameter using a fuzzy logic algorithm; Based on the second real-time control instruction, control the sewage flocculation device to perform sewage flocculation treatment; the second real-time control instruction is used to control the dosage of flocculant at the current moment of the sewage flocculation device.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the flocculation dynamic control method according to any one of claims 1 to 6.

8. A flocculation dynamic control device, characterized in that, The device includes: A real-time acquisition module, configured to acquire in real time the flocculation process parameters corresponding to the sewage flocculation device; the flocculation process parameters include at least one of the floc particle size in the sewage at the current moment, the Zeta potential of the shear plane of the sewage stirring module in the sewage flocculation device, and the shear stress of the shear plane. A determination module, configured to determine the real-time control parameters of the sewage flocculation device based on at least one of the floc particle size, Zeta potential, and shear stress. A control module, configured to control the sewage flocculation device to perform sewage flocculation treatment based on the real-time control parameters.

9. An electronic device, characterized in that, Including: One or more processors; A memory, configured to store one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the flocculation dynamic control method according to any one of claims 1 to 6.

10. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the flocculation dynamic control method according to any one of claims 1 to 6.

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