Reverse osmosis membrane cleaning system

Abstract

The invention provides a reverse osmosis membrane cleaning system which is characterized in that a variable-frequency water pump control system is adopted to adjust water flow momentum according to cleaning parameters output by a matching algorithm, differential impact strength control of different areas on the surface of a membrane is realized through a flow adjusting valve group, and a stress distribution mode adaptive to pollution distribution is obtained; mechanical stress data of the membrane surface in the cleaning process are collected in real time through a membrane material stress monitoring sensor, if the stress value exceeds a membrane material safety threshold value, a stress protection mechanism is triggered, and the impact strength of a corresponding area is automatically reduced; establishing an adaptive parameter optimization algorithm according to pressure feedback data and a pollutant stripping effect evaluation result in the cleaning process, and calculating a parameter adjustment scheme of the next cleaning period through a tradeoff function of the cleaning effect and mechanical damage; a membrane performance degradation prediction model is established through accumulation of historical cleaning data, the remaining service life of a membrane material is predicted according to accumulated mechanical stress and cleaning frequency data, and a quantitative judgment basis of the membrane replacement opportunity is obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a reverse osmosis membrane cleaning system. BACKGROUND

[0002] As a core technology in the field of modern water treatment, reverse osmosis technology plays an irreplaceable role in key applications such as seawater desalination, industrial water preparation, and wastewater reuse. With the increasingly severe global water shortage problem, the stable operation and long-term performance maintenance of reverse osmosis systems have become an important foundation for water security. Current reverse osmosis membrane cleaning technology mainly relies on physical flushing and chemical cleaning. Physical flushing is simple to operate, but its ability to remove deep-seated membrane pore contaminants is limited. Chemical cleaning, while able to dissolve some contaminants, poses a risk of membrane material corrosion and environmental pollution. More critically, existing cleaning methods generally use a constant parameter cleaning mode, which is difficult to accurately control according to different pollution conditions. The technical challenges faced by reverse osmosis membranes in long-term operation are mainly due to the accuracy of water flow control during the cleaning process. The constant water flow impact force generated by traditional cleaning methods is unevenly distributed, causing excessive force on some areas of the membrane surface and insufficient cleaning in other areas. This uneven force field distribution directly affects the effective removal of contaminants. Inaccurate water flow control further leads to low matching of cleaning parameters and pollution conditions. When facing different types of pollution such as inorganic scale layer, organic contaminants, and biofilm, fixed cleaning intensity cannot achieve targeted treatment, resulting in the contradiction between over-cleaning and under-cleaning. Low matching of cleaning parameters also causes unnecessary mechanical stress on the membrane material during cleaning, which ultimately shortens the service life of the membrane and increases system maintenance costs. Therefore, how to accurately control the water flow during the reverse osmosis membrane cleaning process and dynamically match the cleaning parameters according to different pollution types and levels, while ensuring efficient cleaning and minimizing mechanical damage to the membrane material, has become a key issue for improving the overall performance and economy of reverse osmosis systems. SUMMARY

[0003] The present application provides a reverse osmosis membrane cleaning system, mainly comprising: A multi-point pressure sensor array is used to monitor the water flow impact intensity of each area on the surface of the reverse osmosis membrane in real time. The stress distribution values at different positions on the membrane surface are obtained through a pressure data acquisition module. The current water flow control state is determined according to the pressure variation amplitude and distribution uniformity coefficient. The composition of the membrane surface contaminants is identified and the pollution level is quantitatively evaluated by a spectrum analyzer. Key parameters such as inorganic scale layer thickness, organic contaminant concentration, and biofilm coverage rate are obtained. If the pollution type is an inorganic scale layer and the thickness exceeds the preset threshold, a high-intensity cleaning mode is started. A cleaning parameter dynamic matching algorithm is established according to the pollution type identification result and the pollution degree data, similarity calculation is performed on the pollution characteristic vector and a preset cleaning parameter library to determine the optimal water flow velocity, impact angle and cleaning time length combination parameters; A variable frequency water pump control system is adopted to adjust the water flow momentum according to the cleaning parameters output by the matching algorithm, and the flow regulating valve group is used to realize the differential impact intensity control of different regions on the membrane surface, so that the stress distribution mode suitable for the pollution distribution is obtained. The mechanical stress data of the membrane surface in the cleaning process are collected in real time through the membrane material stress monitoring sensor, and if the stress value exceeds the safety threshold of the membrane material, the stress protection mechanism is triggered to automatically reduce the impact intensity of the corresponding region. An adaptive parameter optimization algorithm is established according to the pressure feedback data and the pollution stripping effect evaluation result in the cleaning process, and the parameter adjustment scheme of the next cleaning cycle is calculated through the trade-off function of the cleaning effect and the mechanical damage. The cleaning effect evaluation module is used to detect and analyze the residual amount of pollutants on the membrane surface, the cleaning completion state is judged through the water permeability recovery degree and the surface cleanliness index, and if the cleaning effect does not reach the expected standard, the parameter matching and momentum control process are re-executed. A membrane performance degradation prediction model is established through the accumulation of historical cleaning data, the remaining service life of the membrane material is predicted according to the accumulated mechanical stress and cleaning frequency data, and the quantitative judgment basis of the membrane replacement time is obtained.

[0004] The technical scheme provided by the embodiment of the application can include the following beneficial effects: The application discloses an intelligent cleaning system and method for reverse osmosis membranes, aiming at the problems of low accuracy of water flow momentum control and low matching degree of cleaning parameters in the long-term operation of reverse osmosis membranes, and proposes a pollution monitoring scheme based on multi-point pressure sensing and spectral analysis, and realizes accurate adjustment of water flow momentum by combining a dynamic parameter matching algorithm and a variable frequency water pump control system. BRIEF DESCRIPTION OF DRAWINGS

[0005] Fig. 1 It is a flow chart of a reverse osmosis membrane cleaning system of the application.

[0006] Fig. 2 It is a schematic view of a reverse osmosis membrane cleaning system of the application.

[0007] Fig. 3 Another schematic view of a reverse osmosis membrane cleaning system of the present application. DETAILED DESCRIPTION

[0008] The technical solutions of the present application will be described clearly and completely below in conjunction with embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0009] As Figs. 1-3 The reverse osmosis membrane cleaning system of the present embodiment can specifically include: In step S101, a multi-point pressure sensor array is used to monitor the water flow impact intensity of each region on the surface of the reverse osmosis membrane in real time, force distribution values at different positions on the membrane surface are obtained through a pressure data acquisition module, and the current water flow momentum control state is judged according to the pressure variation amplitude and the distribution uniformity coefficient.

[0010] The surface of the reverse osmosis membrane is monitored in real time by a multi-point pressure sensor, pressure distribution data generated by water flow impact is obtained, and the pressure values of each monitoring point in the pressure distribution data are recorded. According to the pressure distribution data, the pressure variation amplitude of each region is calculated, a preset threshold range is compared, and it is judged whether there is an abnormal fluctuation region. If the pressure variation amplitude exceeds the preset threshold range, the pressure data of the abnormal fluctuation region is processed, time series features are extracted using a sliding window method, and fluctuation trend features are obtained. Through the fluctuation trend features, combined with a uniformity coefficient calculation method, the overall uniformity of the pressure distribution data is analyzed, and it is determined whether there is a local water flow impact abnormal region. If the uniformity coefficient is lower than a preset reference value, the pressure distribution data is processed by spatial interpolation, a high-resolution pressure distribution map is generated, and fine regional force difference data is obtained. According to the pressure distribution map, a support vector machine algorithm is used to classify the flow momentum control state, it is judged whether the flow momentum control state is stable, and a classification result is output. For the unstable state region in the classification result, flow momentum adjustment suggestion data is generated, the adjusted pressure distribution change is recorded, and the monitoring data is continuously updated.

[0011] Specifically, in the process of real-time monitoring of water flow impact intensity on the surface of the reverse osmosis membrane by using a multi-point pressure sensor array, first, a 4x4 array composed of 16 pressure sensors is arranged on the membrane surface, each sensor covers an area of 5 square centimeters, the monitoring range is 0 to 10 kilopascals, and the sampling frequency is 10 hertz, to capture the dynamic changes of water flow impact, and the data is sent to the central processing unit in real time through the wireless transmission module. Next, the pressure data acquisition module stores the readings of each sensor in time series form, assuming that the pressure values of the 16 sensors at a certain time are 3.2, 3.5, 2.8, 3.1, 3.4, 2.9, 3.0, 3.3, 3.6, 2.7, 3.2, 3.5, 2.9, 3.1, 3.4, and 3.0 kilopascals, the system automatically calculates the average pressure as 3.15 kilopascals, and calculates the dispersion of the pressure distribution as 0.26 through the standard deviation formula, indicating that the pressure distribution is relatively concentrated. Subsequently, the system judges the water flow momentum control state according to the pressure change amplitude and the uniformity coefficient of the distribution, sets the uniformity coefficient as the ratio of the standard deviation to the average pressure, i.e. 0.26 / 3.15=0.082, if the value is less than 0.1, it is determined that the distribution is uniform, and the water flow control state is good; at the same time, the system analyzes the change amplitude of the pressure values of each sensor in the past 5 minutes, if the maximum change amplitude is less than 0.5 kilopascals, it is considered that the water flow impact is stable. Combined with the two indicators, if the uniformity coefficient is less than 0.1 and the change amplitude is less than 0.5 kilopascals, the system automatically generates a feedback signal of "water flow control state normal"; if any indicator exceeds the standard, the system triggers an early warning and reduces the water inflow by 10% through the linkage flow regulation module to optimize the stress distribution on the membrane surface. Through the above algorithm and analysis, the system realizes closed-loop control from data acquisition to state judgment to feedback regulation, ensuring the efficiency and safety of the reverse osmosis membrane operation.

[0012] In step S102, the spectral analyzer is used to identify the composition of the membrane surface pollutants and quantitatively evaluate the pollution degree, to obtain key parameters such as inorganic scale thickness, organic pollutant concentration and biofilm coverage rate, and if the pollution type is inorganic scale and the thickness exceeds the preset threshold, the high-intensity cleaning mode is started.

[0013] The membrane surface is scanned by a spectral analysis device to obtain pollutant-related data, and initial scanning information is obtained. According to the initial scanning information, signal processing technology is used to denoise and feature extraction of data to determine the type and distribution characteristics of the pollutants. If the type of the pollutants is identified as an inorganic scale layer, the thickness parameter calculation module is used to compare the preset threshold value to determine whether it exceeds the limited range. If the thickness parameter exceeds the preset threshold value, the cleaning mode control unit is triggered to start the high-intensity cleaning process and obtain the cleaning execution state. For the cleaning execution state, the real-time monitoring module continuously collects membrane surface data to obtain post-cleaning pollutant residual information. By comparing the pollutant data before and after cleaning, it is determined whether the cleaning effect meets the expected standard, and the final evaluation result is obtained. If the cleaning effect does not meet the expected standard, the cleaning process is restarted by adjusting the cleaning mode parameters, and updated state data is obtained.

[0014] Specifically, the composition of the membrane surface pollutants is identified and the pollution degree is quantitatively evaluated by a spectral analyzer. First, the Fourier transform infrared spectrometer (FTIR) is used to scan the membrane surface to obtain infrared absorption spectrum data in the wavelength range of 400-4000 cm⁻¹. Combined with the principal component analysis (PCA) algorithm, the spectral data is dimensionally processed to extract the characteristic peak value. For example, the characteristic peak of calcium carbonate in the inorganic scale layer is at 875 cm⁻¹, the C-H bond characteristic peak of organic pollutants is at 2920 cm⁻¹, and the amide I band characteristic peak of protein in the biofilm is at 1650 cm⁻¹. By comparing with the standard spectrum library, the pollution type is identified and the matching degree is calculated. If the matching degree exceeds 90%, the pollution type is confirmed. Then, the pollution degree is quantified. For the thickness of the inorganic scale layer, the relationship between the absorption peak intensity and the thickness is calculated using the Lambert-Beer law. Assuming that the absorption coefficient is 0.5 cm⁻¹ and the measured absorption value is 0.8, the thickness is about 1.6 μm. For the concentration of organic pollutants, the peak area integration is compared with the standard curve. If the peak area is 1200 units, the corresponding concentration is 50 mg / L. For the coverage rate of the biofilm, the coverage area ratio is calculated by combining microscopic image analysis and spectral data. If the total area of the detection region is 100 mm² and the biofilm coverage area is 30 mm², the coverage rate is 30%. Subsequently, if the identified inorganic scale layer has a thickness exceeding the preset threshold value of 1.5 μm (the current value is 1.6 μm), the system automatically triggers the high-intensity cleaning mode. The control module sends instructions to the cleaning equipment to set the cleaning pressure to 5 MPa and the cleaning time to 30 minutes. The spectral data before and after cleaning is recorded to verify the cleaning effect. If the thickness decreases to less than 1.0 μm, the cleaning is stopped. Otherwise, the cleaning time is extended to 45 minutes, and the data is uploaded to the cloud database for pollution trend analysis. Compared with historical data, if the thickness growth rate exceeds 0.2 μm / week, the system automatically adjusts the monitoring frequency to once a day to form a closed-loop management to ensure the stability of the membrane performance.

[0015] Step S103, according to the pollution type identification result and the pollution degree data, a cleaning parameter dynamic matching algorithm is established, and similarity calculation is performed between the pollution feature vector and the preset cleaning parameter library to determine the optimal water flow speed, impact angle and cleaning time length combination parameters.

[0016] Data of pollution type and pollution degree are acquired to construct initial pollution feature information. A preprocessing method is used to standardize the initial pollution feature information to obtain a normalized pollution feature data set. According to the normalized pollution feature data set, a pollution feature vector is constructed, a preset cleaning parameter library is compared, a cosine similarity algorithm is used to calculate the matching degree of the pollution feature vector and each group of data in the cleaning parameter library, and a preliminary matching result set is determined. If the matching degree of the preliminary matching result set is higher than a preset threshold, the group of cleaning parameters with the highest matching degree is selected as a candidate combination; if it is lower than the preset threshold, the pollution feature vector is extracted again, additional data dimensions are added, and an updated matching result set is obtained. According to the updated matching result set, the water flow speed, impact angle and cleaning time length data in the candidate combination are extracted, historical cleaning records are compared and analyzed to determine whether there is a more optimal parameter combination, and an adjusted candidate parameter set is obtained. For the adjusted candidate parameter set, a simulation tool is used to virtually test the water flow speed and impact angle, if the test result meets the preset cleaning effect standard, the current parameters are retained; if not, the cleaning time length is fine-tuned to determine the final parameter combination. Through the final parameter combination, a control instruction of the cleaning equipment is generated, real-time feedback data of the equipment running state is acquired, it is judged whether it is consistent with the preset parameters, and a verification result of the running state is obtained. If the verification result of the running state shows that the feedback data deviates from the preset parameters, the equipment is calibrated in real time through an adjustment instruction to determine the stable execution state of the cleaning process.

[0017] Specifically, in the process of constructing the cleaning parameter dynamic matching algorithm, first, the pollution type identification system obtains pollution characteristic data, such as identifying that the pollution type is grease pollution, the pollution degree data is 5.2 millimeters in thickness, and the adhesion force is 3.8 newtons per square meter. The system converts these data into a pollution characteristic vector, such as the vector represented as [5.2, 3.8, 1], where 1 represents the category code of grease pollution. Next, the system calculates the similarity of the vector with the preset cleaning parameter library, which stores a variety of pollution characteristic corresponding cleaning parameter combinations, such as a record in the library is [5.0, 3.5, 1], and the corresponding cleaning parameters are water flow speed 2.5 meters per second, impact angle 45 degrees, and cleaning time 10 minutes. The similarity calculation uses the Euclidean distance algorithm, and the calculation result is sqrt((5.2-5.0)^2+(3.8-3.5)^2+(1-1)^2)=0.36, assuming that this is the closest record in the parameter library, the system determines it as the optimal match. Further analysis of the pollution degree data, the system finds that the thickness is slightly higher than the record in the library, so the water flow speed is adjusted by the linear interpolation algorithm, and the adjusted speed is calculated as 2.5+(5.2-5.0)*0.5=2.6 meters per second. At the same time, combined with the pollution adhesion force data, the system calls the preset rules, if the adhesion force is greater than 3.7 newtons per square meter, the impact angle is increased by 5 degrees, and the final adjustment is 50 degrees, while the cleaning time remains 10 minutes unchanged. The final optimal parameter combination is water flow speed 2.6 meters per second, impact angle 50 degrees, and cleaning time 10 minutes. The system automatically transmits the parameters to the cleaning equipment control module to ensure that the equipment performs the cleaning task according to the calculation results. To form a business closed loop, the system also records each matching result and actual cleaning effect data (such as residual thickness 0.1 millimeters after cleaning), and continuously optimizes the parameter library through machine learning algorithm to improve the subsequent matching accuracy.

[0018] Step S104, the variable frequency water pump control system adjusts the water flow momentum according to the cleaning parameters output by the matching algorithm, realizes the differential impact intensity control of different areas on the membrane surface through the flow regulating valve group, and obtains the stress distribution mode adapted to the pollution distribution.

[0019] The pollution distribution data of the membrane surface is collected in real time through the sensor network, and the pollution distribution model is processed to obtain the regional division result of the pollution distribution. According to the regional division result, the corresponding cleaning parameters are generated by using the matching algorithm, and the water flow size and impact intensity value required by each region are determined. According to the cleaning parameters, the operation frequency of the variable frequency water pump is adjusted, the flow size is dynamically adjusted through the control system, and the adaptive flow output mode is obtained. The flow output mode is accurately distributed in different regions through the flow regulating valve group. If the pollution distribution density of a certain region is higher than the preset threshold, the opening of the region valve is increased to obtain the differentiated impact intensity distribution. According to the impact intensity distribution, the stress distribution state of the membrane surface is monitored in real time. If the matching degree of the stress distribution and the pollution distribution is lower than the preset threshold, the control system is used to fine-tune the valve group control parameters to determine the optimized flow distribution scheme. The optimized flow distribution scheme is used to continuously update the operation state of the variable frequency water pump, the change trend of the stress distribution is analyzed through the information processing link, and the final cleaning effect data is obtained. According to the change trend, the cleaning parameters and the valve group control logic are dynamically adjusted to determine whether the cleaning effect reaches the preset standard, and a stable stress distribution mode is obtained.

[0020] Specifically, in the process of adjusting the water flow to adapt to the membrane surface pollution distribution in the variable frequency pump control system, first, the cleaning parameters are calculated by matching algorithm. Assuming that the system collects membrane surface pollution distribution data, the pollution degree from region A to region C is heavy (80% coverage), moderate (50% coverage) and light (20% coverage) in turn. The algorithm establishes a linear relationship model according to the pollution degree and impact strength demand, sets the impact strength proportional to the pollution coverage, and the calculation formula is: impact strength (Pa) = coverage (%) x 10. The impact strength of region A is 800 Pa, region B is 500 Pa, and region C is 200 Pa. The analysis result shows that different water flow pressures need to be applied to different regions to match the pollution removal demand. Then, the system transmits the calculated impact strength parameters to the variable frequency pump controller. The controller adjusts the pump frequency according to the pressure demand. Assuming that the reference frequency is 50 Hz, and every 100 Pa corresponds to a frequency increase of 5 Hz, then the frequency of region A is adjusted to 90 Hz, region B is 75 Hz, and region C is 60 Hz. By real-time monitoring of the deviation (error control within ± 5 Pa) between the pump output pressure and the target value, the accurate regulation of water flow is ensured. Subsequently, the flow regulating valve group receives the control signal according to the region division, and the valve opening degree is proportional to the frequency. The valve opening degree of region A is set to 90%, region B is 70%, and region C is 50%. Through sensor feedback of actual flow data (such as region A flow of 10 L / s, target of 10.2 L / s), the system automatically adjusts the valve opening degree to 91% to eliminate errors. The matching degree of flow distribution and pollution distribution is analyzed, and it is confirmed that the stress distribution mode is highly related to the pollution degree (correlation coefficient is above 0.95). In order to enhance the logic chain, the system can also combine real-time image analysis of the membrane surface to dynamically update the pollution distribution data. Assuming that it is updated every 5 minutes, if the pollution coverage of region A decreases to 60%, the impact strength is recalculated to 600 Pa, the frequency is adjusted to 80 Hz, and the valve opening degree is reduced to 80%, forming a closed loop control to ensure that the cleaning effect is synchronized with the pollution change. Through the above information technology means, the whole process realizes automatic regulation and control, and achieves the goal of differentiated impact strength control.

[0021] In step S105, the mechanical stress data of the membrane surface during the cleaning process is collected in real time by the membrane material stress monitoring sensor. If the stress value exceeds the safety threshold of the membrane material, the stress protection mechanism is triggered, and the impact strength of the corresponding region is automatically reduced.

[0022] The mechanical stress data of the membrane surface during the cleaning process is continuously collected by the membrane material stress monitoring sensor to obtain an initial stress data set. According to the initial stress data set, a pre-established analysis model is used for data processing to determine the stress value abnormal area and time period, and to obtain the high stress area distribution information. If the stress value of a certain area in the high stress area distribution exceeds the preset safety threshold value, the protection mechanism is triggered to generate an impact intensity adjustment instruction for the area to obtain an adjustment instruction set. Through the adjustment instruction set, the impact intensity reduction operation is performed on the cleaning equipment corresponding to the area, the adjusted stress data is recorded, and an updated stress data set is obtained. According to the updated stress data set, the mechanical stress change of each area is continuously monitored, and if the stress value of a certain area is still higher than the safety threshold value, a secondary adjustment instruction is generated to obtain a new adjustment instruction set. Through the new adjustment instruction set, the impact intensity of the corresponding area is further reduced, and the stress change data during the adjustment process is recorded to determine the final stress stable state. If the final stress stable state shows that the stress values of all areas are lower than the safety threshold value, the current cleaning parameters and stress data are saved to obtain a stable operating parameter set.

[0023] Specifically, during the membrane material cleaning process, the mechanical stress data on the membrane surface is collected in real time by the stress monitoring sensor. The specific implementation method is to uniformly arrange 10 high-precision stress sensors on the membrane surface, collect data once every second, and the data accuracy reaches 0.01 MPa. The collected stress value is sent in real time to the central processing system for storage and analysis through the wireless transmission module. The system has a built-in stress analysis algorithm. First, the average value of the collected 10-point data is calculated to obtain the average stress value of the current membrane surface. For example, if the 10-point data is 2.1, 2.3, 1.9, 2.0, 2.2, 2.4, 1.8, 2.5, 2.0, and 2.1 MPa, the average value is 2.13 MPa. Then, the system compares the average value with the preset safety threshold of 2.5 MPa. If the threshold is exceeded, the stress protection mechanism is triggered. To ensure accuracy, the system also performs variance analysis on the data to calculate the data volatility. The variance formula is the sum of the squared differences between each point data and the average value divided by the number of points. If the variance is greater than 0.05, it indicates that the local stress distribution is uneven, and the system will further locate the high-stress area. Then, the impact intensity of the corresponding area is automatically reduced. The specific method is that the system controls the servo motor of the cleaning equipment to adjust the nozzle angle and water pressure according to the position of the high-stress point, for example, reducing the water pressure from 5.0 MPa to 3.5 MPa, adjusting the nozzle angle from vertical 90 degrees to inclined 60 degrees, and reducing the cleaning frequency from 10 times per minute to 5 times per minute, to ensure that the stress value falls within the safe range. The entire process is automatically completed by the system. If the stress value continues to exceed the threshold for 10 seconds, the system will record abnormal data and generate an alarm log, which is uploaded to the cloud database for subsequent analysis and equipment maintenance, forming a complete data closed loop to ensure the safety of the cleaning process and the long-term stability of the membrane material.

[0024] Step S106, an adaptive parameter optimization algorithm is established according to the pressure feedback data and the pollutant stripping effect evaluation results in the cleaning process, and the parameter adjustment scheme of the next cleaning cycle is calculated through the trade-off function between cleaning effect and mechanical damage.

[0025] The pressure feedback data is obtained from the cleaning equipment, the pressure feedback data is collected in real time by using a sensor, and a pressure change trend in the cleaning process is obtained. According to the pressure change trend, in combination with evaluation data of contaminant stripping, a comparison is made with a preset threshold value. If the pressure change exceeds the preset threshold value range, a parameter optimization process is triggered, and a preliminary parameter adjustment direction is determined. For the preliminary parameter adjustment direction, evaluation data of the cleaning effect is obtained, a trade-off function value of the cleaning effect and mechanical damage is calculated through associated analysis with mechanical damage, and an optimized index after trade-off is obtained. According to the optimized index after trade-off, a self-adaptive algorithm is used to dynamically adjust the parameters of the cleaning cycle, and it is judged whether the adjusted parameters meet the preset cleaning effect standard. If the adjusted parameters meet the cleaning effect standard, the running data of the cleaning cycle is recorded by using a data analysis module, and prediction input data of the next cycle is obtained. Through the prediction input data, in combination with the feedback of the effect evaluation module, a specific parameter adjustment scheme of the next cleaning cycle is calculated, and the final running parameters are determined. According to the final running parameters, the control system of the cleaning equipment is updated, updated running state data is obtained, and it is judged whether the cleaning process is stably running.

[0026] Specifically, in the process of constructing the adaptive parameter optimization algorithm, first, the sensor collects real-time pressure feedback data during the cleaning process, for example, the current cleaning pressure is 2.5 MPa, the system records that the pressure fluctuation range is between 2.3 to 2.7 MPa per minute, and stores these data in the database for analysis, combined with the evaluation results of the pollutant stripping effect, assuming that the image recognition technology detects that the residual pollutant area ratio after cleaning is 5%, which is lower than the target value of 8%, indicating that the cleaning effect is good. Then, the system uses the trade-off function to quantitatively evaluate the cleaning effect and mechanical damage, sets the cleaning effect weight to 0.6 and the mechanical damage weight to 0.4, calculates the comprehensive score, among which the cleaning effect score is calculated based on the inverse ratio of residual area as 95 points, and the mechanical damage score is calculated by the number of pressure overruns (assuming 2 times) as 80 points, and the final comprehensive score is 0.6x95+0.4x80=89 points. Subsequently, the system adaptively adjusts the next cleaning cycle parameters according to the score results, if the comprehensive score is higher than 85 points, the pressure is appropriately reduced to reduce damage, the algorithm automatically adjusts the pressure to 2.4 MPa, at the same time shortens the cleaning time by 10%, from 60 seconds to 54 seconds, and stores the adjustment scheme in the control system. If the score is less than 85 points, increase the pressure to 2.6 MPa to improve the cleaning effect. The whole process is automatically completed through the data analysis module and the parameter optimization module, the system can also associate the equipment running state data, such as the cleaning nozzle wear rate (assuming 3%), if the wear rate exceeds 5%, the nozzle angle is adjusted instead of the pressure, forming the linkage logic of parameter adjustment and equipment maintenance, ensuring the balance between cleaning effect and equipment life, the adjusted parameters will be automatically issued to the execution unit through the industrial control system, realizing the whole process automation optimization.

[0027] Step S107, the cleaning effect evaluation module is used to detect and analyze the residual amount of membrane surface pollutants, the cleaning completion state is judged through the water permeability recovery degree and surface cleanliness index, if the cleaning effect does not reach the expected standard, the parameter matching and momentum control process is re-executed.

[0028] The membrane surface detection data is collected by the effect evaluation module, the distribution of the pollutant residues is analyzed, and a preliminary cleaning state judgment result is obtained. According to the preliminary cleaning state judgment result, related data of water permeability recovery is obtained, and in combination with the index value of surface cleanliness, a comprehensive score of cleaning effect evaluation is determined. If the comprehensive score of the cleaning effect evaluation is lower than a preset standard value, historical adjustment records are extracted from the parameter matching process, and the direction and amplitude of momentum control adjustment are determined. According to the direction and amplitude of the momentum control adjustment, a regression model established in advance is used to optimize the parameter matching process, and an adjusted parameter configuration scheme is obtained. Through the adjusted parameter configuration scheme, the input data of the cleaning state judgment is updated, new water permeability recovery and surface cleanliness indexes are obtained, and whether the cleaning effect is improved is judged. If the cleaning effect still does not reach the preset standard value, through a process retriggering mechanism, the detection frequency of the effect evaluation module is adjusted in combination with the distribution data of the pollutant residues, and an updated cleaning completion state is obtained. According to the updated cleaning completion state, the latest data of the membrane surface detection is analyzed, and in combination with the feedback of the cleaning effect evaluation, it is judged whether the parameters of the momentum control adjustment need to be further optimized.

[0029] Specifically, when the cleaning effect evaluation module is used to detect and analyze the residual amount of membrane surface pollutants, first, the membrane surface is comprehensively scanned by a high-precision optical scanning device to obtain the distribution data of the residual pollutants. Assuming that the scanning resolution is 0.1 microns, the detected coverage area of the pollutants is 5.2%, and the average size of the pollutant particles is calculated by an image processing algorithm to be 2.3 microns. Combined with the spectral characteristics of the pollutant types in the database, it is confirmed that the main pollutants are organic residues. The analysis result shows that the residual amount exceeds the threshold value of 3.0% of the standard value, and further cleaning is required. Subsequently, the recovery degree of the water permeability is evaluated, and the water flow sensor is used to monitor the water permeability of the membrane before and after cleaning. Assuming that the water permeability before cleaning is 60 L / m²·h, and the water permeability after cleaning is increased to 75 L / m²·h, the recovery rate is calculated as (75-60) / 60x100%=25%, and the target recovery rate is 30%, which is not up to standard. The system automatically records this data and generates an evaluation report. At the same time, the surface cleanliness index is detected by a laser reflectance tester, and the standard reflectivity is set to 90%. The measured value is 85.5%, which is lower than the standard. The system analyzes the cleanliness deviation of 4.5% according to the algorithm, and comprehensively judges the cleaning effect based on the residual amount of pollutants and the water permeability data. If the effect is not up to standard, the system automatically triggers the parameter matching process, recalculates the cleaning liquid concentration and flow rate based on historical cleaning data and machine learning models, and adjusts the concentration from 2.0% to 2.5% and the flow rate from 1.2 m / s to 1.5 m / s. The momentum control parameters are updated, and the momentum value is calculated as flow rate x flow, resulting in a new momentum of 1.5x75=112.5 kg·m / s, which is 25% higher than the original value of 90 kg·m / s, to enhance the cleaning impact. The system sends this parameter to the execution module for re-cleaning. Through the above process, a closed-loop logic from detection, evaluation to parameter optimization is formed to ensure that the cleaning effect gradually approaches the target value, while being associated with business needs, such as reducing membrane replacement frequency. Assuming that 100,000 yuan is saved annually, the system improves the economy.

[0030] In step S108, a membrane performance degradation prediction model is established based on historical cleaning data accumulation to predict the remaining service life of the membrane material based on accumulated mechanical stress and cleaning frequency data, and to obtain quantitative judgment basis for membrane replacement timing.

[0031] The historical data and cleaning records of the membrane material are obtained from the storage system, the initial feature set of performance degradation is obtained by preliminary arrangement according to the performance change, the pre-established prediction model is constructed according to the initial feature set of performance degradation, the related data of mechanical stress and cleaning frequency are input, the data is processed by regression analysis, and the residual life trend of the membrane material is determined. According to the residual life trend, the performance degradation law under different cleaning frequencies is analyzed by combining the service cycle information in the historical data, and the preliminary basis for replacement timing is obtained. Through the preliminary basis for replacement timing, the residual life trend is calibrated by fusing the quantitative evaluation standard, if the calibrated residual life is lower than the preset threshold, a replacement timing warning signal is triggered. According to the replacement timing warning signal, the membrane material characteristic information in the historical data is extracted, the influence degree of the cleaning record on the service cycle is analyzed by combining the initial feature set of performance degradation, and the priority ranking of the membrane material replacement is determined. According to the priority ranking, the data field related to the cleaning frequency in the storage system is obtained, the cleaning record is deeply mined by using a data analysis tool, and the influence amplitude of the cleaning frequency adjustment on the residual life is judged. According to the influence amplitude, the parameter configuration in the prediction model is updated, the performance degradation trend is recalculated by combining the cumulative information of mechanical stress, and the final replacement timing quantitative basis is determined.

[0032] Specifically, a membrane performance degradation prediction model is established by accumulating historical cleaning data. The system first extracts the accumulated cleaning records and membrane operation data in the past 5 years from the database, including the frequency, duration of each cleaning, and the mechanical stress test value of the membrane material. Assuming that the average cleaning frequency is 2.5 times per month and the cumulative mechanical stress peak value is 150 MPa. Then, the system uses time series analysis algorithm to construct a prediction model based on long short-term memory network, taking cleaning frequency and stress data as input variables. The training data set contains 1000 historical records, and the model prediction accuracy reaches 85.3%. Subsequently, the system predicts the remaining service life of the membrane material to be 1.8 years based on the model output and the current running time of the membrane of 3.2 years. The calculation process is: remaining life = initial life 5 years - current running time 3.2 years, and the result is corrected according to the stress accumulation loss coefficient 0.9. At the same time, the system analyzes the influence of cleaning frequency on life and finds that the life is shortened by about 0.1 year for every increase of 0.5 times / month in frequency, forming a data correlation. To further optimize management, the system automatically generates a membrane replacement timing recommendation report. If the predicted life is less than 1.5 years, a replacement reminder will be pushed to the management platform one month in advance to ensure business continuity. In addition, the system connects the prediction results with the inventory management module. Assuming that the current inventory of membranes is 10 pieces and 3 pieces need to be replaced in the next 12 months, the system automatically triggers the generation of a procurement plan to ensure smooth supply chain. Through the above logic, the system forms a complete closed loop from data extraction, model construction to life prediction and business linkage, providing a scientific basis for membrane management.

[0033] Although the embodiments of the present application have been shown and described above, it is understood that the above-described embodiments are exemplary and are not to be construed as limiting the present application, and that variations, modifications, substitutions and changes can be made by those skilled in the art without departing from the scope of the present application.

Claims

1. A reverse osmosis membrane cleaning system, characterized by, The method comprises the following steps: A water flow impact monitoring module is used to monitor the water flow impact intensity of each area on the membrane surface in real time by using a multi-point pressure sensor array, to obtain the stress distribution value of different positions on the membrane surface through a pressure data acquisition module, and to judge the water flow momentum control state based on the pressure variation amplitude and the distribution uniformity coefficient; A pollutant analysis module is used to identify the composition of the pollutants on the membrane surface and to quantitatively evaluate the pollution degree by using a spectrum analyzer, to obtain the inorganic scale thickness, organic pollutant concentration and biofilm coverage rate parameters, and to start a high-intensity cleaning mode when the pollution type is an inorganic scale layer and the thickness exceeds a preset threshold value; A cleaning parameter matching module is used to establish a cleaning parameter dynamic matching algorithm according to the pollution type identification result and the pollution degree data, to determine the optimal combination of water flow speed, impact angle and cleaning time length by calculating the similarity between the pollution feature vector and the preset cleaning parameter library, and to determine the optimal combination of water flow speed, impact angle and cleaning time length; A momentum control module is used to adjust the water flow momentum according to the cleaning parameters output by the matching algorithm by using a variable frequency water pump control system, and to realize the differential impact intensity control of different areas on the membrane surface by using a flow regulating valve group, so as to obtain a stress distribution mode suitable for the pollution distribution; A stress protection module is used to collect the mechanical stress data of the membrane surface during the cleaning process in real time by using a membrane material stress monitoring sensor, and to trigger the stress protection mechanism when the stress value exceeds the safety threshold of the membrane material, so as to automatically reduce the impact intensity of the corresponding area; An adaptive optimization module is used to establish an adaptive parameter optimization algorithm according to the pressure feedback data and the pollutant stripping effect evaluation result during the cleaning process, and to calculate the parameter adjustment scheme of the next cleaning cycle by using a cleaning effect and mechanical damage weighting function; A cleaning effect evaluation module is used to detect and analyze the residual amount of pollutants on the membrane surface, to judge the cleaning completion state by using the water permeability recovery degree and the surface cleanliness index, and to re-execute the parameter matching and momentum control process when the cleaning effect does not meet the expected standard; A life prediction module is used to establish a membrane performance degradation prediction model by accumulating historical cleaning data, to predict the remaining service life of the membrane material according to the cumulative mechanical stress and cleaning frequency data, so as to obtain a quantitative judgment basis for the membrane replacement timing.

2. The reverse osmosis membrane cleaning system of claim 1, wherein, The water flow impact monitoring module is specifically used for: obtaining the pressure distribution data of the water flow impact on each area on the membrane surface by using the multi-point pressure sensor array; calculating the pressure variation amplitude of each area and comparing it with a preset threshold value to identify abnormal fluctuation areas; extracting the time series fluctuation trend features of the pressure data of the abnormal fluctuation areas by using a sliding window method; analyzing the overall uniformity of the pressure distribution data by combining the fluctuation trend features and the uniformity coefficient analysis method to identify local water flow impact abnormal areas; when the uniformity coefficient is lower than a preset reference value, performing spatial interpolation processing on the pressure distribution data to generate a high-resolution pressure distribution map; classifying the flow momentum control state by using a support vector machine algorithm based on the pressure distribution map to identify unstable state areas; generating flow momentum adjustment suggestion data for the unstable state areas and recording the adjusted pressure distribution changes.

3. The reverse osmosis membrane cleaning system of claim 1, wherein, The pollution analysis module is specifically used for: Obtaining initial scanning information of the pollutants on the membrane surface by scanning the membrane surface with the spectrum analyzer; Processing the initial scanning information to remove noise and extract features to determine the type and distribution characteristics of the pollutants; When the type of the pollutants is identified as an inorganic scale layer, comparing the thickness parameter with a preset threshold value; When the thickness parameter exceeds the preset threshold value, triggering a cleaning mode control unit to start a high-intensity cleaning process; During the cleaning process, continuously collecting membrane surface data through a real-time monitoring module to obtain residual pollution information after cleaning; Evaluating the cleaning effect by comparing the pollution data before and after cleaning; When the cleaning effect does not meet the expected standard, adjusting the cleaning mode parameters and restarting the cleaning process.

4. The reverse osmosis membrane cleaning system of claim 1, wherein, The cleaning parameter matching module is specifically used for: Obtaining pollution type identification results and pollution degree data, constructing and standardizing to obtain a standardized pollution feature data set; Based on the standardized pollution feature data set, constructing a pollution feature vector, using a preset cleaning parameter library, and using a cosine similarity algorithm to calculate the matching degree of the pollution feature vector and each group of data in the cleaning parameter library to determine a preliminary matching result set; When the matching degree of the preliminary matching result set is higher than a preset threshold value, selecting the group with the highest matching degree as a candidate combination; When the matching degree is lower than the preset threshold value, performing secondary feature extraction on the pollution feature vector to increase the data dimension, and updating to obtain a matching result set; Extracting the water flow speed, impact angle, and cleaning duration data in the candidate combination, and determining whether there is a more optimal parameter combination by comparing and analyzing historical cleaning records to obtain an adjusted candidate parameter set; Using a simulation tool to virtually test the water flow speed and impact angle in the adjusted candidate parameter set, and if the test result meets the preset cleaning effect standard, the current parameters are retained, otherwise, the cleaning duration is fine-tuned to determine the final parameter combination; Based on the final parameter combination, generating a control instruction for the cleaning equipment, and obtaining real-time feedback data of the equipment operating state to verify its consistency with the preset parameters; When the verification result shows that the feedback data deviates from the preset parameters, adjusting the instruction to calibrate the equipment in real time.

5. The reverse osmosis membrane cleaning system of claim 1, wherein, The momentum control module is specifically used for: Real-time collection of pollution distribution data on the membrane surface through a sensor network, and processing using a pre-established pollution distribution model to obtain regional division results of the pollution distribution; According to the regional division results, using the matching algorithm to generate the water flow size and impact strength value required for each region; Adjusting the operating frequency of the variable frequency water pump according to the cleaning parameters to dynamically adjust the water flow size; Accurately distributing the water flow size in different regions through the flow regulating valve group, and when the pollution distribution density of a certain region is higher than a preset threshold value, increasing the opening of the corresponding valve in that region to form a differentiated impact strength distribution; Real-time monitoring of the stress distribution state of the membrane surface, and when the matching degree of the stress distribution state and the pollution distribution is lower than a preset threshold value, fine-tuning the control parameters of the flow regulating valve group to optimize the flow distribution scheme; The optimized flow distribution scheme is used to continuously update the running state of the variable frequency water pump, and cleaning effect data is obtained by analyzing the change trend of the stress distribution; According to the change trend, dynamically adjust the cleaning parameters and valve group control logic to determine whether a stable stress distribution mode is reached.

6. The reverse osmosis membrane cleaning system of claim 1, wherein, The stress protection module is specifically used for: Continuously collecting mechanical stress data of the membrane surface during the cleaning process through the membrane material stress monitoring sensor; Analyzing the mechanical stress data to identify areas and time periods where the stress value exceeds the preset safety threshold; When it is identified that the stress value of a certain area exceeds the safety threshold, triggering the stress protection mechanism and generating an impact intensity reduction instruction for the area; Executing the impact intensity reduction instruction to reduce the impact intensity of the corresponding area and recording the adjusted stress data; Continuously monitor the mechanical stress changes of each area, and if the stress value of a certain area is still higher than the safety threshold, generate and execute a secondary impact intensity reduction instruction; Record the stress change data during the adjustment process until the stress values of all areas are lower than the safety threshold, save the current cleaning parameters and stress data.

7. The reverse osmosis membrane cleaning system of claim 1, wherein, The adaptive optimization module is specifically used for: Real-time acquisition of pressure feedback data and its change trend from the cleaning equipment; When the pressure change trend exceeds the preset threshold range, trigger the parameter optimization process in combination with the pollutant stripping effect evaluation data; Obtain cleaning effect evaluation data and combine mechanical damage correlation analysis to calculate the trade-off function value of cleaning effect and mechanical damage; Based on the trade-off function value, use an adaptive algorithm to dynamically adjust the parameters of the cleaning cycle; When the adjusted parameters meet the preset cleaning effect standard, record the current cycle operation data as the prediction input data for the next cleaning cycle; Based on the prediction input data and the feedback of the effect evaluation module, calculate the specific parameter adjustment scheme for the next cleaning cycle; Update the control system of the cleaning equipment according to the parameter adjustment scheme.

8. The reverse osmosis membrane cleaning system of claim 1, wherein, The cleaning effect evaluation module is specifically used for: Collect and analyze membrane surface detection data to evaluate the distribution of pollutant residues and obtain preliminary cleaning state judgment results; Based on the water permeability recovery data and surface cleanliness indicators, calculate the comprehensive score of cleaning effect evaluation; When the comprehensive score is lower than the preset standard value, determine the adjustment direction and amplitude by referring to the historical parameter matching and momentum control adjustment records; Use the pre-established regression model to optimize the parameter matching process to obtain the adjusted parameter configuration scheme; Based on the adjusted parameter configuration scheme, update the input data to obtain new water permeability recovery degree and surface cleanliness indicators to evaluate whether the cleaning effect is improved; If the cleaning effect still does not meet the preset standard value, re-trigger the parameter matching and momentum control process and adjust the detection frequency; According to the updated detection data and evaluation feedback, determine whether further optimization of the momentum control parameters is needed.

9. The reverse osmosis membrane cleaning system of claim 1, wherein, The life prediction module is specifically used for: Obtain the historical data and cleaning records of the membrane material from the storage system, and organize the initial feature set of performance degradation; A membrane performance degradation prediction model is constructed, and cumulative mechanical stress and cleaning frequency data are input to determine the residual life trend of the membrane material through regression analysis; Combined with historical usage cycle information, the performance degradation law under different cleaning frequencies is analyzed to obtain preliminary basis for membrane replacement timing; The residual life trend is calibrated by fusing quantitative evaluation standards, and when the calibrated residual life is lower than the preset threshold, a replacement timing warning signal is triggered; According to the warning signal, the influence degree of cleaning records on the usage cycle is analyzed by combining the membrane material characteristic information and the initial feature set of performance degradation to determine the priority ranking of membrane material replacement; Historical data related to cleaning frequency is obtained, and data analysis tools are used to mine the influence amplitude of cleaning frequency adjustment on residual life; According to the influence amplitude, the parameter configuration of the prediction model is updated, and the performance degradation trend is recalculated by combining the cumulative mechanical stress information to determine the final quantitative basis for membrane replacement timing.

Images