An adaptive nanobubble water treatment device and method based on conductivity feedback

Abstract

This invention relates to the field of industrial circulating water system water treatment technology, specifically to an adaptive nanobubble water treatment device and method based on conductivity feedback. The device includes a data acquisition module, a control module, an adjustment execution module, and a nanobubble generating module. The data acquisition module collects the conductivity and temperature of the circulating water in real time; the control module receives the real-time data, calculates the expected conductivity value at the current moment using a conductivity prediction model, judges the deviation between the measured value and the expected value, and initiates an adaptive adjustment process when parameter adjustment is needed, sending an adjustment command to the adjustment execution module; the adjustment execution module translates the adjustment command into physical actions, changing the operating parameters of the nanobubble generating module; the nanobubble generating module introduces nanobubbles into the circulating water. This invention uses conductivity as the core feedback signal, achieving online optimization of operating parameters through adaptive adjustment, adapting to changes in operating conditions, and continuously maintaining optimized scale inhibition effects.

Description

Technical Field

[0001] This invention relates to the field of water treatment technology for industrial circulating water systems, specifically to an adaptive nanobubble water treatment device and method based on conductivity feedback. Background Technology

[0002] Circulating water systems are core supporting units in industrial production. The inner walls of system pipes and equipment are highly susceptible to the precipitation and deposition of scale, typically calcium carbonate. This scale buildup significantly reduces circulating water flow efficiency, increases system energy consumption, and in severe cases, can cause pipe blockage, accelerated wear and tear on circulating water system equipment, and a substantial increase in operation and maintenance costs. Existing water treatment technologies for scale prevention or removal, such as chemical scale inhibitors which can cause secondary water pollution, mechanical cleaning which requires system shutdown and disrupts production, and physical field scale inhibition methods with fixed parameters that cannot adapt to dynamic operating conditions, all have significant limitations.

[0003] Studies have shown that nanobubbles possess properties distinctly different from macrobubbles, such as large specific surface area, high interfacial potential, strong adsorption capacity, and long residence time. In water bodies, they can promote the formation of crystallizing fouling in circulating water by providing numerous bulk nucleation sites, adsorbing scale-causing ions, and regulating crystal morphology, rather than depositing on the inner walls of circulating water system pipes and equipment. In other words, by altering the formation sites of crystallizing fouling, they reduce fouling deposition in circulating water systems at its source. Furthermore, nanobubbles use readily available air sources as a medium, resulting in no secondary pollution and low operating costs. Therefore, nanobubbles, as a novel, green, and pollution-free water treatment technology, possess significant advantages and have already been applied in industrial scenarios such as circulating water systems, seawater desalination, and membrane filtration.

[0004] Existing nanobubble water treatment technologies all operate with fixed parameters. Once the core parameters of the nanobubble water treatment device, such as gas flow rate, liquid flow rate, and operating pressure, are set, they are not adjusted. However, the operating conditions of industrial circulating water systems, such as scale-causing ion concentration, temperature, pH value, and flow rate, are constantly changing, and fixed parameters cannot keep pace with these changes. This leads to a decrease in the bulk crystallization efficiency of the circulating water, increased scale deposition on the inner walls of pipes and equipment, and a significant reduction in scale inhibition effectiveness. Furthermore, existing technologies lack characteristic signals that can reflect the bulk crystallization effect in real time and conveniently, making it impossible to establish a dynamic correlation between the operating parameters of the nanobubble water treatment device and the scale inhibition effect, thus failing to guarantee continuous optimization of the scale inhibition effect. Summary of the Invention

[0005] To address the problems existing in the background technology, this invention provides an adaptive nanobubble water treatment device and method based on conductivity feedback. Conductivity is selected as the core feedback signal that reflects the scale inhibition effect of nanobubbles in real time. The operating parameters of the nanobubble water treatment device are dynamically adjusted through an adaptive adjustment strategy to ensure that, under any operating condition, the nanobubbles generated by the device always efficiently promote the bulk crystallization of circulating water and prevent the deposition of scale on the inner walls of the circulating water system pipelines and equipment, thus maintaining the optimal scale inhibition effect.

[0006] According to the present invention, an adaptive nanobubble water treatment device based on conductivity feedback includes a data acquisition module, a control module, an adjustment and execution module, and a nanobubble generation module.

[0007] The acquisition module is used to collect the conductivity and water temperature of the circulating water in real time at multiple points in the core detection area of ​​the circulating water system.

[0008] The control module is used to receive real-time data uploaded by the acquisition module, calculate the expected value of conductivity at the current moment through the built-in conductivity prediction model, determine the deviation between the real-time conductivity and the expected value, and start the adaptive adjustment process when the parameters need to be adjusted. The optimal operating parameters under the current working conditions are found online through adaptive adjustment, and corresponding control commands are generated.

[0009] The adjustment execution module is used to receive the control commands from the control module and convert them into specific physical adjustment actions to adjust the gas flow rate, liquid flow rate and operating pressure of the nanobubble generator module.

[0010] The nanobubble generating module is used to operate according to the parameters adjusted by the regulating execution module, and to introduce nanobubbles into the circulating water to realize the scale inhibition function of the nanobubble water treatment device.

[0011] Preferably, the main components of the acquisition module are an industrial-grade online conductivity sensor, a temperature sensor, and a data acquisition card; multiple conductivity and water temperature acquisition points are set in the circulating water system to avoid interference from local disturbances on the measurement results.

[0012] The online conductivity sensor has a built-in temperature compensation function and outputs a real-time conductivity value that has been temperature compensated. At the same time, the water temperature collected by the temperature sensor is used as the input parameter of the conductivity prediction model to calculate the expected conductivity value at the current moment. The data acquisition card filters, amplifies, and converts the signals from each sensor to analog-to-digital, and transmits the real-time data to the control module through the industrial bus.

[0013] Preferably, the control module adopts an industrial-grade micro industrial computer with an embedded computing chip; the industrial computer has a built-in conductivity prediction model and adaptive adjustment algorithm; the control module has a reserved standard industrial bus interface.

[0014] Preferably, the control module includes the following functions: receiving real-time conductivity data uploaded by the acquisition module. and water temperature The system performs data preprocessing and noise reduction; calculates the expected conductivity value at the current moment using a built-in conductivity prediction model; determines whether the operating parameters need to be adjusted based on the deviation between the measured and expected values; initiates an adaptive adjustment process when the operating parameters need to be adjusted to optimize the operating parameters of the nanobubble generator module under the current operating conditions online; and generates adjustment instructions for the adjustment execution module.

[0015] Preferably, the regulating execution module includes a gas flow controller, an electric water inlet regulating valve, and a variable frequency booster pump controller; the gas flow controller is installed in the air inlet pipe of the nanobubble generating module, and the electric water inlet regulating valve is installed in the water inlet pipe of the nanobubble generating module; the regulating execution module precisely adjusts the opening degree or speed of each actuator according to the instructions issued by the control module, so as to achieve precise control of the operating parameters of the nanobubble generating module.

[0016] Preferably, the nanobubble generating module is designed based on the principle of high-pressure dissolution and low-pressure release, and includes at least the following basic components:

[0017] A variable frequency booster pump is used to change the operating pressure of the nanobubble generating module. It is electrically connected to the variable frequency booster pump controller of the regulating execution module;

[0018] The intake pipe is connected to the gas source via a gas flow controller for precise control of the gas flow rate. ;

[0019] The inlet pipe introduces circulating water through an electric inlet regulating valve to control the liquid flow rate. ;

[0020] The high-pressure dissolving device is connected to the outlet of the booster pump and the gas inlet pipeline respectively. It is equipped with a gas-liquid mixing device inside, which is used to force the gas to dissolve in the high-pressure circulating water to form a supersaturated gas-liquid mixture.

[0021] The pressure relief device has its input end connected to the output end of the high-pressure dissolving device. It is equipped with a throttling orifice plate or pressure reducing valve inside, which is used to quickly release the high-pressure supersaturated gas-liquid mixture to normal pressure or low pressure, so that the dissolved gas in the circulating water can be released instantly to form a large number of nanobubbles.

[0022] The outlet pipe is connected to the output end of the pressure relief device and is used to introduce circulating water containing nanobubbles into the circulating water system.

[0023] The components work in tandem: the booster pump pressurizes the circulating water to a set value, and the gas is forcibly dissolved in the water in the high-pressure dissolving device, forming a supersaturated gas-liquid mixture; after the mixture is rapidly depressurized by the pressure-reducing release device, the dissolved gas is instantly released, forming nanobubbles with a particle size of 10~1000nm. The operating pressure is adjusted by the execution module. Gas flow rate Liquid flow rate Precise control.

[0024] This invention also provides an adaptive nanobubble water treatment method based on conductivity feedback, which is implemented based on the aforementioned adaptive nanobubble water treatment device based on conductivity feedback, and includes the following steps:

[0025] Step S1: In the control module, set the effective control range, adjustment step size, adjustment sequence, and algorithm variable parameters for the nanobubble generator module's operating parameters. With the circulating water system in a clean state, based on the initial conductivity of the circulating water measured by the acquisition module, set the minimum critical bulk phase crystallization efficiency (this needs to be reset after each cleaning of the circulating water system), and calculate the target conductivity threshold. The nanobubble generator module starts operating with the initial parameters.

[0026] Step S2: At the end of each data acquisition cycle, the control module acquires the real-time conductivity and water temperature of the circulating water system and records the time that the nanobubbles have been in action. Based on the initial conductivity, water temperature, action time, and conductivity target threshold, the conductivity prediction model is used to calculate the expected conductivity value that should be achieved at the current moment, and the deviation between the real-time conductivity and the current expected value is calculated.

[0027] Step S3: The control module determines whether the deviation between the real-time conductivity and the current expected value meets the convergence accuracy requirements. If it does, it continues to maintain the current parameters and returns to step S2; if it does not, it proceeds to step S4 to determine the trend.

[0028] Step S4: In the next acquisition cycle, the control module determines whether the conductivity deviation value of the current cycle has decreased compared to the previous cycle. If the deviation value has decreased, it indicates that the parameter adjustment direction is correct, and the current parameter operation is maintained, returning to step S2; if the deviation value has not decreased, it indicates that the current parameter can no longer meet the scale inhibition requirements or the adjustment effect is not good, and proceeds to step S5.

[0029] Step S5: When the control module determines that the operating parameters of the nanobubble generator module need to be adjusted, it initiates the adaptive parameter adjustment process, sends adjustment commands to the adjustment execution module according to the preset parameter adjustment sequence and adjustment step size, and explores online the operating parameters that meet the scale inhibition requirements under the current working conditions. By cyclically executing steps S2 to S5, real-time, closed-loop, and adaptive control of the operating parameters of the nanobubble water treatment device is achieved.

[0030] Preferably, the operating parameters of the nanobubble generating module that require setting an effective control range in step S1 include: operating pressure. Gas flow rate Liquid flow rate Operating pressure adjustment step size Gas flow rate adjustment step size Liquid flow rate adjustment step Algorithm variable parameters include: convergence accuracy. Data collection cycle Adjusting the settling time Initial conductivity Data was collected in standby mode of the nanobubble generator module; final target threshold for conductivity. Based on the initial conductivity and minimum bulk crystallization efficiency Calculation; Initial running parameters Set to the midpoint of the adjustment range for each parameter.

[0031] Preferably, the real-time conductivity in step S2 and water temperature Simultaneous data collection is achieved through multiple sampling points set up in the core detection area of ​​the circulating water system. The average conductivity and water temperature of each sampling point after calibration are used as the algorithm input values ​​to eliminate the influence of local disturbances; nanobubble action time. Recording data cumulatively from the moment the nanobubble generation module is activated. The expected conductivity value to be achieved at the current moment. Based on the initial conductivity Water temperature Final target threshold and duration of action calculate.

[0032] Preferably, the convergence accuracy in step S3 This is the preset allowable deviation range for conductivity.

[0033] Preferably, the conductivity deviation value of the previous cycle in step S4 Used to determine the trend of deviation value changes.

[0034] Preferably, the adaptive parameter adjustment process in step S5 specifically includes: recording the current operating parameters and conductivity deviation value, and using the conductivity deviation value as the performance evaluation benchmark; adjusting each parameter according to the preset operating parameter adjustment sequence; and waiting for the preset stabilization time. (Adjustment action takes effect) Evaluate the adjustment effect; based on the effect evaluation, decide whether to continue adjusting in the same direction or in the opposite direction; lock the optimal parameter and then change to the next parameter; finally generate the operating parameters that meet the convergence accuracy under the current working conditions. =[ , , ] T . Attached Figure Description

[0035] Figure 1 This is a schematic diagram of an adaptive nanobubble water treatment device based on conductivity feedback.

[0036] Figure 2 This is a schematic diagram of the data acquisition module structure;

[0037] Figure 3 A schematic diagram of the structure of the adjustment execution module and the nanobubble generation module;

[0038] Figure 4 This is a schematic diagram of the process of an adaptive nanobubble water treatment method based on conductivity feedback.

[0039] Figure 5 The following are comparison charts showing the operational effects of the present invention, where (a) is a comparison chart of conductivity and (b) is a comparison chart of the quantification of dirt deposition. Detailed Implementation

[0040] like Figure 1 As shown, the adaptive nanobubble water treatment device based on conductivity feedback includes a data acquisition module, a control module, an adjustment and execution module, and a nanobubble generating module. The data acquisition module collects the conductivity and temperature of the circulating water in real time; the control module receives the real-time data, calculates the expected conductivity value at the current moment using a conductivity prediction model, determines whether the deviation between the measured value and the expected value meets the convergence accuracy requirements, and initiates an adaptive adjustment process when parameter adjustment is needed, sending an adjustment command to the adjustment and execution module; the adjustment and execution module converts the adjustment command into an adjustment action; and the nanobubble generating module introduces nanobubbles into the circulating water.

[0041] like Figure 2 As shown, the data acquisition module includes an industrial-grade online conductivity sensor, a temperature sensor, and a data acquisition card. Multiple acquisition points are set up in the circulating water system. The online conductivity sensor has a built-in temperature compensation function and outputs calibrated real-time conductivity. Meanwhile, the temperature sensor collects the water temperature. The data acquisition card serves as the input parameter for the conductivity prediction model, used to calculate the expected conductivity value at the current moment. It filters, amplifies, and performs analog-to-digital conversion on the signals from various sensors, and transmits the real-time data to the control module via an industrial bus.

[0042] like Figure 3 As shown, the regulating execution module and the nanobubble generating module are connected in a combined manner. The regulating execution module includes a gas flow controller, an electric inlet water regulating valve, and a variable frequency booster pump controller, which are respectively connected to the air inlet pipe, water inlet pipe, and variable frequency booster pump of the nanobubble generating module. The air inlet pipe of the nanobubble generating module is connected to a gas source, and the water inlet pipe is connected to circulating water. The gas and liquid are mixed in a high-pressure dissolving device to form a supersaturated gas-liquid mixture. After being rapidly depressurized by a pressure reducing and releasing device, nanobubbles are formed and introduced into the circulating water system through the water outlet pipe. After receiving instructions from the control module, the regulating execution module precisely adjusts the gas inflow rate, liquid flow rate, and operating pressure, with a control accuracy of ≤±0.5%.

[0043] like Figure 4 As shown, the specific implementation steps of the adaptive nanobubble water treatment device are introduced, combining the adaptive nanobubble water treatment method based on conductivity feedback:

[0044] Step S1: First, set the operating parameters (gas flow rate) of the nanobubble generator module in the control module. Liquid flow rate Operating pressure ) range and corresponding parameter adjustment step size (gas flow rate adjustment step size) Liquid flow rate adjustment step Operating pressure adjustment step size Set the order of adjusting the running parameters. Set the algorithm parameters, including: convergence accuracy. Data collection cycle and adjusting settling time .

[0045] With the nanobubble generator module in standby mode, the initial conductivity of the circulating water is collected and recorded by the acquisition module. (The circulating water system needs to be recalibrated for the first run after cleaning or water change). Based on scale inhibition requirements and preliminary experimental data, the minimum critical bulk crystallization efficiency is set. According to Kohlrausch's law, the conductivity of a solution... With ion concentration Proportional:

[0046] (1)

[0047] In equation (1), The molar conductivity is expressed in S·cm. 2 ·mol -1 ;

[0048] Let the initial concentration of scale-causing ions in the circulating water be... After being acted upon by nanobubbles, the concentration was reduced to [a certain level]. Then the bulk crystallization efficiency The definition of is:

[0049] (2)

[0050] In equation (2), The ion concentration at which the crystallization reaction reaches equilibrium, in mol / L;

[0051] Will Substituting into the conductivity formula, we get:

[0052] (3)

[0053] In equation (3), The final stable value of conductivity (corresponding to) );

[0054] Taking calcium carbonate crystals as an example (the most common component of limescale), due to their extremely low equilibrium solubility in circulating water, therefore... much smaller (In this embodiment) / <0.01), the final target threshold for conductivity is calculated as shown in equation (4):

[0055] (4)

[0056] Start the nanobubble generator module and set initial operating parameters. Set the value to the middle value of each parameter adjustment range.

[0057] Step S2: At the end of each acquisition cycle, the control module acquires the real-time conductivity. Water temperature And record the time the nanobubbles have been in action. The control module uses a conductivity prediction model based on crystallization kinetics to calculate the expected conductivity value to be achieved at the current moment. According to JMAK crystallization kinetics theory, the solid phase precipitation volume fraction... With time The relationship is shown in equation (5).

[0058] (5)

[0059] In equation (5), The reaction rate constant reflects the speed of the crystallization process;

[0060] The Avrami index reflects the dimensional characteristics of nucleation and growth mechanisms;

[0061] According to the Arrhenius equation, the reaction rate constant... With water temperature The relationship is shown in equation (6):

[0062] (6)

[0063] In equation (6), Pre-exponential factors;

[0064] The activation energy of the reaction is expressed in J / mol.

[0065] is the molar gas constant, J / (mol·K);

[0066] Water temperature Corresponding thermodynamic temperature, K;

[0067] During the crystallization process of calcium carbonate, the relative decrease rate of electrical conductivity The conductivity is directly proportional to the volume fraction of the deposited material, and thus the power-law model expression for the expected conductivity value is derived as shown in equation (7).

[0068] (7)

[0069] In equation (7), h is the time scale factor that reflects the speed of the crystallization process;

[0070] To reflect the decay characteristics of the crystallization rate over time;

[0071] According to crystallization kinetics theory, when crystal growth is diffusion-controlled, The value is typically between 1.0 and 1.5; when controlled by interfacial reaction, The value is relatively small.

[0072] Equation (6) can be used to derive the expression for the rate of decrease in conductivity as shown in Equation (8), which is used to characterize the dynamic characteristics of the crystallization process.

[0073] (8)

[0074] Equation (8) shows that the rate of decrease in conductivity decreases monotonically with time, which is consistent with the physical law that the supersaturation gradually decreases during the crystallization of calcium carbonate.

[0075] Experiments were conducted under different initial conductivity levels (500–3500 μS / cm, increasing by 500 μS / cm each time) and different water temperatures (20–50 °C, increasing by 10 °C each time), and the conductivity changes over time were recorded. The experimental data were then calculated and fitted using the nonlinear least squares method to obtain the specific values ​​of the aforementioned parameters under the current operating conditions.

[0076] The calculated conductivity deviation is shown in equation (9):

[0077] (9)

[0078] Step S3: The control module determines the real-time conductivity. Compared with current expected value Does the absolute value of the deviation satisfy the following? If the conditions are met, it indicates that the conductivity decrease curve is in line with expectations and the scale inhibition effect is good. Maintain the current parameters and return to step S2. If the conditions are not met, it means that the current operating parameters may need to be adjusted. Proceed to step S4 to determine the trend.

[0079] The theoretical basis for the conclusion in step S3 that "the absolute value of conductivity deviation characterizes the scale inhibition effect of the nanobubble water treatment device" is as follows:

[0080] Electrical conductivity is a core physical quantity characterizing the ion concentration in a solution, and it exhibits a strict linear positive correlation with the concentration of scale-causing ions in circulating water. The process of nanobubbles promoting bulk crystallization is essentially a process in which scale-causing ions precipitate from circulating water to form solid crystals, leading to a continuous decrease in ion concentration, which is directly manifested as a characteristic decrease in the conductivity of circulating water. Based on this premise, the concentration of scale-causing ions in circulating water is negatively correlated with the amount of fouling: the higher the bulk crystallization efficiency, the more scale-causing ions precipitate in the circulating water, the less fouling can be deposited on the inner walls of the circulating water system pipes and equipment, and the better the scale inhibition effect. Therefore, the trend and measured value of conductivity can directly and in real time reflect the bulk crystallization efficiency of nanobubbles, i.e., the scale inhibition effect: a continuous and stable decrease in conductivity and convergence to the target threshold indicates high bulk crystallization efficiency and excellent scale inhibition effect; a deviation of conductivity from the expected decrease indicates a decrease in bulk crystallization efficiency and an increased risk of fouling deposition.

[0081] Based on the above theoretical basis, this invention uses conductivity as the target threshold. For the global optimization objective, the expected conductivity value to be achieved at the current moment is... To provide a real-time benchmark, the real-time conductivity is calculated. Compared with expected value By measuring the deviation value, the scale inhibition effect of the current operating parameters of the nanobubble water treatment device can be evaluated in real time, and it can be determined whether the parameters need to be adjusted.

[0082] Step S4: In the next acquisition cycle, the control module compares the absolute value of the current deviation with the absolute value of the deviation in the previous cycle.

[0083] like This indicates that the absolute value of the deviation is decreasing, the parameter adjustment direction is correct, maintain the current parameter operation, and return to step S2;

[0084] like This indicates that the current parameters can no longer meet the scale inhibition requirements or the adjustment effect is not good, so proceed to step S5.

[0085] Step S5: When parameter adjustment is required, the control module initiates the adaptive parameter adjustment process. The specific adjustment process is as follows: First, record the current operating parameters. =[ , , ] T and the absolute value of the current conductivity deviation and with This serves as the benchmark for performance evaluation. Then, following the sequence set in step S1, each parameter is adjusted sequentially. An adjustment command is sent to the adjustment execution module for each selected parameter, initially attempting to adjust it in an increasing direction by a fixed step size. After the adjustment execution module executes the command and reaches the preset adjustment stabilization time, the new conductivity is collected, and the absolute value of the new conductivity deviation is calculated. .Compare and ,like This indicates that adjusting the operating parameters in this direction effectively reduced the conductivity deviation, further confirming... If the convergence accuracy requirement is met, it means the adjusted operating parameters can meet the scale inhibition requirements, and the process returns to step S2; if the convergence accuracy requirement is not met, continue adjusting the phase length in the same direction until... If the convergence accuracy requirement is met; If the selected parameter fails to improve, adjust it in the opposite direction and re-evaluate. When the selected operating parameter achieves optimal performance (continuing to adjust in the same direction no longer reduces the conductivity deviation), lock that parameter, select the next parameter in a preset order, and repeat this process until the conductivity deviation meets the convergence accuracy requirements, thus obtaining the optimal parameter combination for the current operating condition. =[ , , ] T By cyclically executing steps S2 to S5, real-time, closed-loop, and adaptive control of the operating parameters of the nanobubble water treatment device is achieved.

[0086] The theoretical basis for the conclusion in step S5 that "adjusting the operating parameters of the nanobubble generator module can effectively reduce the conductivity deviation value" is as follows:

[0087] Operating parameters of the nanobubble generator module (gas flow rate) Liquid flow rate Operating pressure This directly affects the median particle size of the generated nanobubbles. and quantity concentration Studies have shown that the above parameters have an impact on... and The regulation exhibits a nonlinear coupling relationship: , Specific surface area of ​​nanobubbles With median particle size Inversely proportional: In the formula This is the equivalent density of the bubbles. Therefore, and These factors collectively determine the ability of nanobubbles to promote bulk crystallization. According to the charge stability model, the static voltage generated by the accumulation of surface charge on the nanobubbles can balance the Laplace pressure, thereby inhibiting the diffusion and dissolution of gas inside the bubbles. The surface charge density is closely related to the adsorption behavior of scale-causing ions in the solution, and this adsorption behavior is regulated by operating parameters.

[0088] Based on the above theoretical basis, when the real-time conductivity Significantly higher than expected (Right now When the current rate of decrease in conductivity is insufficient, it indicates that the operating parameters need to be adjusted to change the situation. , .when Significantly lower than expected (Right now If the rate of descent is too rapid, it indicates that the parameters need to be adjusted to avoid excessive energy consumption or deviation from the optimal operating condition. The effectiveness of parameter adjustment is measured by the absolute value of the deviation. The direction of change is used to evaluate: if after adjustment A decrease indicates that the adjustment direction is correct; if... An increase indicates an incorrect direction; it should be adjusted in the opposite direction.

[0089] Example:

[0090] This embodiment demonstrates the scale inhibition effect of the adaptive nanobubble water treatment device and method based on conductivity feedback described in this invention. A circulating water simulation system with a circulating water volume of 30L and an initial conductivity of... The initial water temperature was controlled at 25±0.3℃, with a flow rate of 2860±2 μS / cm. The total water hardness (calculated as calcium carbonate) was 1000 mg / L. Operating parameters were set in the control module of the adaptive nanobubble water treatment device as follows: gas flow rate 50~650 mL / min, liquid flow rate 1000~5000 mL / min, and operating pressure 0.4~0.8 MPa. The adjustment steps for each parameter were set to 100 mL / min, 500 mL / min, and 0.1 MPa, respectively. The adjustment sequence was: gas flow rate, liquid flow rate, operating pressure. Algorithm variable settings included: convergence accuracy of 25 μS / cm, data acquisition cycle of 10 min, and stabilization time of 5 min. Initial operating parameters were set to the median values ​​of each parameter's adjustment range. Based on scale inhibition requirements, the minimum critical bulk phase crystallization efficiency was set to 33%. The calculated final target threshold for conductivity was 1916 μS / cm. After 18 hours of operation, the real-time conductivity of the nanobubble water treatment device decreased to 1925±2 μS / cm, meeting the convergence accuracy requirements. During operation, the gas flow rate was optimized from the initial 350 mL / min to 150 mL / min, the liquid flow rate from 3000 mL / min to 4000 mL / min, and the operating pressure was optimized to 0.5 MPa. The real-time conductivity and fouling deposition quantification data of the circulating water simulation system are as follows: Figure 5 As shown, the nanobubble water treatment unit always maintains the initial operating parameters; the adaptive nanobubble water treatment unit continuously optimizes the operating parameters, ultimately reducing the amount of dirt deposited by 50%.

[0091] The above description is merely a preferred embodiment of the present invention and does not limit the scope of the patent. Any equivalent structural modifications made based on the inventive concept of the present invention and the description and drawings, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of the present invention.

Claims

1. An adaptive nanobubble water treatment device based on conductivity feedback, characterized in that, include: The data acquisition module is used to collect the conductivity and temperature of circulating water at multiple points in the circulating water system in real time. The control module is used to receive real-time data uploaded by the acquisition module, calculate the expected conductivity value that should be reached at the current moment through the built-in conductivity prediction model, calculate the deviation between the real-time conductivity and the expected value, start the adaptive adjustment process when the parameters need to be adjusted, and generate corresponding control commands. The adjustment execution module is used to receive the control commands from the control module and convert them into specific adjustment actions to adjust the gas flow rate, liquid flow rate and operating pressure of the nanobubble generator module. The nanobubble generating module is used to operate according to the parameters adjusted by the regulating execution module, and to introduce nanobubbles into the circulating water to realize the scale inhibition function of the water treatment device.

2. The adaptive nanobubble water treatment device based on conductivity feedback according to claim 1, characterized in that, The main components of the acquisition module are an industrial-grade online conductivity sensor, a temperature sensor, and a data acquisition card. Multiple conductivity and water temperature acquisition points are set in the circulating water system. The online conductivity sensor has a built-in temperature compensation function and outputs the real-time conductivity value after temperature compensation. The data acquisition card filters, amplifies, and converts the signals from each sensor into analog and digital signals, and transmits the real-time data to the control module through an industrial bus.

3. The adaptive nanobubble water treatment device based on conductivity feedback according to claim 1, characterized in that, The control module adopts an industrial-grade micro industrial computer with an embedded computing chip; the industrial computer has a built-in conductivity prediction model and adaptive adjustment algorithm; the control module has a reserved standard industrial bus interface.

4. The adaptive nanobubble water treatment device based on conductivity feedback according to claim 1, characterized in that, The regulating execution module includes a gas flow controller, an electric water inlet regulating valve, and a variable frequency booster pump controller; the gas flow controller is installed in the air inlet pipe of the nanobubble generating module, and the electric water inlet regulating valve is installed in the water inlet pipe of the nanobubble generating module; the regulating execution module realizes precise control of the operating parameters of the nanobubble generating module according to the instructions issued by the control module.

5. The adaptive nanobubble water treatment device based on conductivity feedback according to claim 1, characterized in that, The nanobubble generating module is designed based on the principle of high-pressure dissolution and low-pressure release, and includes at least a variable frequency booster pump, an air inlet pipe, a water inlet pipe, a high-pressure dissolution device, a pressure-reducing and releasing device, and a water outlet pipe. The air inlet pipe is connected to the air source through a gas flow controller, and the water inlet pipe is connected to the circulating water through an electric water inlet regulating valve. The booster pump is located on the water inlet pipe and is electrically connected to the variable frequency booster pump controller. The high-pressure dissolution device is connected to the booster pump outlet and the air inlet pipe, and has an internal gas-liquid mixing device. The input end of the pressure-reducing and releasing device is connected to the output end of the high-pressure dissolution device, and has an internal throttling orifice plate or pressure-reducing valve. The water outlet pipe is connected to the output end of the pressure-reducing and releasing device, and introduces the circulating water containing nanobubbles into the circulating water system.

6. An adaptive nanobubble water treatment method based on conductivity feedback, characterized in that, The adaptive nanobubble water treatment device based on conductivity feedback as described in claim 1 is implemented by comprising the following steps: Step S1: Set the effective control range, adjustment step size, adjustment sequence, and algorithm variable parameters of the nanobubble generator module's operating parameters; set the minimum critical bulk phase crystallization efficiency according to the scale inhibition requirements; calculate the target conductivity threshold based on the initial conductivity of the circulating water measured by the acquisition module; start the nanobubble generator module with the initial operating parameters. Step S2: At the end of each acquisition cycle, obtain the real-time conductivity and water temperature, and record the time that the nanobubbles have been in action; based on the initial conductivity, water temperature, action time, and conductivity target threshold, use the conductivity prediction model to calculate the expected conductivity value that should be achieved at the current moment, and calculate the deviation between the real-time conductivity and the current expected value. Step S3: The control module determines whether the deviation between the real-time conductivity and the current expected value meets the convergence accuracy requirements; if it does, it continues to maintain the current parameters and returns to step S2; if it does not, it proceeds to step S4 to determine the trend. Step S4: The control module determines whether the conductivity deviation value of the current cycle has decreased compared to the previous cycle. If the deviation value has decreased, it indicates that the parameter adjustment direction is correct. The current parameter operation is maintained, and the process returns to step S2. If the deviation value has not decreased, it indicates that the current parameter can no longer meet the scale inhibition requirements or the adjustment effect is not good. The process proceeds to step S5. Step S5: Start the adaptive parameter adjustment process, send adjustment instructions to the adjustment execution module according to the preset parameter adjustment sequence and adjustment step size, and explore the operating parameters that meet the scale inhibition requirements under the current working conditions online; by cyclically executing steps S2 to S5, realize the real-time, closed-loop, adaptive control of the operating parameters of the nano bubble water treatment device.

7. The adaptive nanobubble water treatment method based on conductivity feedback according to claim 6, characterized in that, In step S1, the operating parameters include gas flow rate. Liquid flow rate Operating pressure The adjustment step size includes the gas flow rate adjustment step size. Liquid flow rate adjustment step Operating pressure adjustment step size ; The algorithm variable parameter is the convergence accuracy. Data collection cycle The initial conductivity Data was collected while the nanobubble generator module was in standby mode; the conductivity target threshold was... Based on the initial conductivity and minimum bulk crystallization efficiency Calculation; the initial operating parameters are set to the midpoint of each parameter adjustment range.

8. The adaptive nanobubble water treatment method based on conductivity feedback according to claim 6, characterized in that, In step S2, the real-time conductivity Simultaneous data collection is performed at multiple sampling points set up in the circulating water system, and the average conductivity value after calibration at each sampling point is used as the algorithm input value; the water temperature... Data is collected synchronously at multiple sampling points set up in the circulating water system, and the average value is used as the algorithm input value; the nanobubble action time The data is accumulated starting from the moment the nanobubble generation module is activated.

9. The adaptive nanobubble water treatment method based on conductivity feedback according to claim 6, characterized in that, In step S3, the convergence accuracy This is the preset allowable deviation range for conductivity.

10. The adaptive nanobubble water treatment method based on conductivity feedback according to claim 6, characterized in that, In step S4, the conductivity deviation value of the previous cycle Used to determine the trend of deviation changes.

11. The adaptive nanobubble water treatment method based on conductivity feedback according to claim 6, characterized in that, In step S5, the adaptive parameter adjustment process specifically includes: recording the current operating parameters. =[ , , ] T and conductivity deviation value The conductivity deviation value is used as the performance evaluation benchmark; each parameter is adjusted according to the preset operating parameter adjustment sequence; and a preset stabilization time is waited for. Then, new conductivity values ​​are collected, and the new conductivity deviation value is calculated. By comparison and Evaluate the adjustment effect and decide whether to continue adjustment in the same or opposite direction based on the evaluation; after locking the optimal parameter, switch to the next parameter; finally, make the conductivity deviation value meet the convergence accuracy requirements to obtain the optimal parameter under the current operating conditions. =[ , , ] T Return to step S2 to continue monitoring.

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