A membrane module flow anomaly identification control method, water treatment system and medium

Abstract

The application discloses a membrane module flow anomaly identification control method, a water treatment system and a medium, and relates to the technical field of water treatment. The method comprises the following steps: after a preset time period of initial operation of a membrane module, the water production flow rates of all membrane shells in the membrane module are monitored in real time; based on the water production flow rates of all membrane shells, an upper limit threshold of the current water production flow rate of the membrane shell and a lower limit threshold of the current water production flow rate of the membrane shell are obtained; a flow deviation membrane shell in the membrane module is determined based on the upper limit threshold of the current water production flow rate of the membrane shell; a blocked membrane shell in the membrane module is determined based on the lower limit threshold of the current water production flow rate of the membrane shell; and flow rate control is performed on the flow deviation membrane shell and the blocked membrane shell respectively. According to the water production flow rate, the upper and lower double thresholds are drawn, the flow deviation membrane shell is identified based on the upper limit threshold, the blocked membrane shell is determined based on the lower limit threshold, and flow rate control is completed in different types. The double threshold strategy can accurately distinguish the abnormal types of the membrane shells, abandon the disadvantages of unified adjustment, and realize fine and differentiated regulation.

Description

Technical Field

[0001] This application relates to the field of water treatment technology, specifically to a method for identifying and controlling abnormal flow in a membrane module, a water treatment system, and a medium. Background Technology

[0002] During the operation of a water treatment membrane system, it is necessary to monitor the total permeate flow rate and total influent pressure in real time. If any abnormalities occur, such as a decrease in permeate flow or an increase in inter-stage pressure differential, the operating conditions must be adjusted promptly. Currently, the industry commonly uses manual adjustment of the permeate valve openings of all membrane housings. This method of uniformly adjusting all membrane housings cannot differentiate the actual operating status of each membrane segment or individual membrane housing, nor can it dynamically adapt to fluctuations in influent water quality or changes in membrane fouling levels. Long-term uniform adjustment can also easily lead to load imbalances in some membrane elements, accelerate localized fouling and aging, and make it difficult to ensure the stable and efficient operation of the entire membrane system. Summary of the Invention

[0003] The purpose of this application is to overcome the deficiencies of the prior art and provide a method for identifying and controlling abnormal flow in membrane modules, a water treatment system, and a medium.

[0004] The first aspect of this application provides a method for identifying and controlling abnormal flow in a membrane module, the method comprising:

[0005] After the membrane module has been in operation for a preset period of time, the permeate flow rate of each membrane shell in the membrane module is monitored in real time. Based on the permeate flow rate of each membrane shell, the upper limit threshold and the lower limit threshold of the current permeate flow rate of the membrane shell are obtained. The deflection membrane housing in the membrane module is determined based on the upper limit threshold of the current permeate flow rate of the membrane housing. The blockage of the membrane housing in the membrane module is determined based on the lower limit threshold of the current water production flow rate of the membrane housing; Flow rate control was applied to the deflection membrane and the blockage membrane, respectively.

[0006] Optionally, obtaining the upper limit threshold and the lower limit threshold of the current permeate flow rate of the membrane shell based on the permeate flow rate of each membrane shell includes: Calculate the average and standard deviation of the current permeate flow rate for all the membrane housings; The first and second candidate values ​​are determined based on the mean and standard deviation; Set the minimum value between the first candidate value and the second candidate value as the upper limit threshold; The third and fourth candidate values ​​were determined based on the mean and standard deviation. The maximum value among the third and fourth candidate values ​​is set as the lower limit threshold.

[0007] Optionally, determining the deflection membrane housing in the membrane module based on the upper limit threshold of the current permeate flow rate of the membrane housing includes: The current water production flow rate of each membrane shell is compared with the current upper limit threshold to determine whether there is a membrane shell whose water production flow rate exceeds the current upper limit threshold. If it exists, then the membrane shell is marked as a deflection membrane shell.

[0008] Optionally, determining the clogged membrane housing in the membrane module based on the lower limit threshold of the current permeate flow rate of the membrane housing includes: The current permeate flow rate of each membrane shell is compared with the current lower limit threshold to determine whether there is a membrane shell with a permeate flow rate lower than the current lower limit threshold. If present, the membrane shell is marked as a blocked membrane shell.

[0009] Optionally, the flow rate control for the deflection membrane and the blockage membrane respectively includes: Reduce the opening of the valve on the inlet side of the deflection membrane housing; after the permeate flow rate of all the membrane housings stabilizes, monitor whether the current permeate flow rate of all the membrane housings is lower than the upper limit threshold. If not, reduce the opening of the valve on the inlet side of the deflection membrane housings again until the current permeate flow rate of all the membrane housings is lower than the upper limit threshold, and end this deflection adjustment. Increase the opening of the valve on the water production side of the blocked membrane housing; after the water production flow of all the membrane housings stabilizes, monitor whether the current water production flow of the blocked membrane housing increases. If the current water production flow of the blocked membrane housing increases and is greater than the current lower threshold, then remove the mark on the blocked membrane housing.

[0010] Optionally, the method further includes: If the current water production flow rate of the blocked membrane shell is still lower than the current lower threshold after the valve opening on the water production side is increased, a prompt will be issued and the blocked membrane shell will be cleaned.

[0011] Optionally, during the deflection adjustment process, the valve on the inlet side of the deflection diaphragm is adjusted to reduce its opening by 5%-30% of its total opening each time. During the blockage adjustment process, the valve on the water production side of the blockage membrane housing is adjusted to increase its opening by 5%-10% of its total opening each time.

[0012] Optionally, the method further includes: After the first predetermined time period ends, the bias current adjustment is performed on the membrane module again. When it is detected that the number of bias current membrane shells exceeds the first preset ratio of the total number of membrane shells in the membrane module, the first predetermined time period is shortened. The first predetermined time period is dynamically adjusted according to the trigger frequency of the bias current adjustment. After the second predetermined time period ends, the membrane module is subjected to blockage adjustment again. When the number of blocked membrane shells is detected to exceed the second preset ratio of the total number of membrane shells in the membrane module, the second predetermined time period is shortened. The second predetermined time period is dynamically adjusted according to the trigger frequency of the blockage adjustment.

[0013] A second aspect of this application provides a water treatment system, including a membrane module and a membrane module flow anomaly identification and control device; wherein the membrane module includes a plurality of membrane housings arranged in parallel; The membrane module flow anomaly identification and control device is used to execute the membrane module flow anomaly identification and control method.

[0014] A third aspect of this application provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the membrane module flow anomaly identification and control method.

[0015] The beneficial effects of this application include at least the following: This application monitors the permeate flow rate of each membrane shell in the membrane module in real time after the initial preset operating period. Based on the permeate flow rate of each membrane shell, it obtains an upper limit threshold and a lower limit threshold for the current permeate flow rate of each membrane shell. The upper limit threshold identifies membrane shells with flow deviation, and the lower limit threshold identifies blocked membrane shells, thus achieving flow rate control based on these types of flow deviations. This dual-threshold strategy accurately distinguishes the types of membrane shell anomalies, eliminating the drawbacks of uniform regulation and achieving refined and differentiated control. It effectively balances the system's water flow distribution, improves blockage and flow deviation problems, adapts to dynamic changes in water quality and membrane fouling, and enhances system operational stability and the level of intelligent control. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic diagram of a membrane module is shown; Figure 2 A flowchart illustrating a method for identifying and controlling abnormal flow in a membrane module is shown. Figure 3 A flowchart of a bias flow regulation operation is shown, which illustrates the logical relationship between decision branch, loop return and stability conditions; Figure 4A flowchart of a blockage regulation operation is shown, which illustrates the logic branches of threshold comparison, opening adjustment, and cleaning triggering. Figure 5 An abnormal flow identification and control device for membrane modules is shown.

[0018] Explanation of key component symbols: 10-Membrane housing, 11-Inlet valve, 12-Outlet valve, 100-Monitoring module, 200-Threshold calculation module, 300-Comparison module, 400-Valve adjustment module, 500-Control module, 600-Cleaning module. Detailed Implementation

[0019] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the technical solutions of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the described embodiments are merely some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0020] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0021] In this application, the membrane housing, also called a membrane pressure vessel, is a pressure-resistant outer shell used in membrane-based water treatment equipment to house and protect membrane elements (such as reverse osmosis membranes and ultrafiltration membranes). After the water to be treated enters the membrane housing, it is treated by the membrane elements within the housing. The filtered water is then discharged from the membrane housing through its drain end. In this application, valve opening refers to the degree to which the valve is open; 0% represents fully closed, and 100% represents fully open. In this application, "current" refers to the moment or time period corresponding to the execution of the specified step.

[0022] This embodiment proposes a method for identifying and controlling abnormal flow in a membrane module, which includes entering a balanced adjustment mode after the membrane module has been running for a preset time. The preset time is greater than one hour; specifically, it can be set to one and a half hours or two hours, etc.

[0023] Example 1 like Figure 1As shown, each membrane module includes multiple membrane housings 10 arranged in parallel. All membrane housings 10 within the same membrane module are in the same operating condition. The number of membrane housings 10 in the membrane module can be set as needed; for example, the number of membrane housings 10 in the module can be two, three, four, five, eight, etc. In this embodiment, the number of membrane housings 10 in the membrane module is six.

[0024] Each membrane housing 10 is equipped with a valve on both the inlet and outlet sides, namely an inlet valve 11 and an outlet valve 12. The inlet valve 11 is used to control the flow rate of water flowing into the membrane housing (i.e., the inlet flow rate), and the outlet valve 12 is used to control the flow rate of water flowing out of the membrane housing (i.e., the product flow rate). Furthermore, each membrane housing is also equipped with a flow sensor on the product side to detect the product flow rate of the membrane housing.

[0025] See Figure 2 The membrane module flow anomaly identification and control method includes: Step S1: After the membrane module has been running for a preset period of time, monitor the permeate flow rate of each membrane shell in the membrane module in real time. Step S2: Based on the permeate flow rate of each membrane shell, obtain the upper limit threshold and the lower limit threshold of the current permeate flow rate of the membrane shell. Step S3: Determine the deflection membrane housing in the membrane module based on the upper limit threshold of the current permeate flow rate of the membrane housing; Step S4: Determine the blocked membrane shell in the membrane module based on the lower limit threshold of the current permeate flow rate of the membrane shell; Step S5: Flow rate control is performed on the deflection membrane and the blockage membrane, respectively.

[0026] As a preferred implementation, the process for determining the upper limit threshold includes: Calculate the average u and standard deviation s of the current permeate flow rate for all membrane housings, where the average is set as u and the standard deviation is set as s; The first candidate value and the second candidate value are determined based on the mean and standard deviation, wherein the first candidate value is set as h1 and the second candidate value is set as h2, h1=u+2s and h2=1.2u; Set the minimum value between the first and second candidate values ​​as the upper limit threshold.

[0027] Analysis of the first candidate value. The formula for calculating the first candidate value originates from the outlier detection theory in statistics. Within the same operating range, the permeate flow rate of parallel membrane housings, after excluding systematic factors such as manufacturing tolerances and installation differences, should approximately follow a normal distribution. According to the properties of the normal distribution: the probability of a data point falling within the interval [us, u+s] is approximately 68.3%; the probability of a data point falling within the interval [u-2s, u+2s] is approximately 95.4%; and the probability of a data point exceeding u+2s is only about 2.3%. This means that if the permeate flow rate of a certain membrane housing exceeds u+2s, statistically speaking, it can be determined that the high flow rate of this membrane housing is not due to random fluctuations, but rather to a systematic "flow deviation" problem.

[0028] Analysis of the second candidate value: 1.2u is an absolute constraint on the upper limit of permeate flow. If the permeate flow of a certain membrane shell exceeds the population mean by 20%, even if the population dispersion is small, the flow velocity at the membrane surface of that shell is already far higher than the design condition. When the system as a whole is operating well and the permeate flow of each membrane shell is highly consistent, the standard deviation s approaches 0. If only u+2s is relied upon, the upper limit threshold will be almost equal to the mean u, causing even small normal fluctuations to trigger flow deviation flags and valve adjustments, leading to system oscillations. Introducing 1.2u as a reference value and taking the smaller of u+2s can effectively reduce the system instability caused by the sensitivity of anomaly identification.

[0029] The upper threshold is set to min(h1, h2): Under conditions of good system consistency (small s): h1 = u + 2s ≈ u, h2 = 1.2u > h1. In this case, the minimum value is h1, dominated by statistical criteria. This means that even when the overall operating conditions are good, the system can still detect if an individual stands out excessively in a highly consistent group. Under conditions of high system dispersion (large s): h1 may surge due to the large dispersion, even exceeding the design flow limit. In this case, h2=1.2u becomes an effective constraint. Taking h2 as the minimum value, the absolute permeate protection is dominant to ensure that a single membrane shell will not become unrecognizable due to the overall deterioration of the group.

[0030] As a preferred implementation, the process for determining the lower threshold includes: Calculate the average u and standard deviation s of the current permeate flow rate for all membrane housings, where the average is set as u and the standard deviation is set as s; The third and fourth candidate values ​​are determined based on the mean and standard deviation, where the third candidate value is set as h3 and the fourth candidate value is set as h4, h3 = u - 2s and h4 = 0.8u; Set the maximum value between the third and fourth candidate values ​​as the lower threshold.

[0031] Analysis of the third candidate value: Referring to the analysis of the first candidate value, a permeate flow rate below u⁻²s indicates that the membrane shell deviates from the normal distribution range of the population with a confidence level exceeding 95%. This quantitative criterion effectively distinguishes between "slight fluctuations" and "abnormal fouling." In the non-uniform fouling scenario described in the background art, a flow rate decrease caused by slight deposition may only deviate from the mean by about one standard deviation, which can still be recovered through system self-regulation; however, a deviation of more than two standard deviations usually indicates that the flow channel has become significantly narrowed, requiring active intervention.

[0032] Analysis of the fourth candidate value: 0.8u is an absolute constraint on the lower limit of permeate flow. Taking reverse osmosis membranes as an example, reverse osmosis membranes need to maintain a minimum cross-flow velocity at the membrane surface to prevent uncontrolled concentration polarization and contaminant deposition. When the permeate flow is lower than 80% of the population average, the flow velocity within the membrane housing is severely insufficient. Even if the dispersion is still acceptable from a population statistical perspective, intervention should be initiated. In the later stages of system operation, membrane elements generally experience compaction and mild fouling, increasing the dispersion of flow distribution and thus increasing s. At this point, u-2s will decrease sharply. If this is still used as the sole criterion, membrane housings with permeate flow reduced to their limit may not be identified as clogged. In this case, 0.8u becomes a safety net, ensuring that severely degraded individual membranes are identified in a timely manner.

[0033] The lower threshold is set to max(h3, h4): When the system consistency is good (s is small): h3 = u - 2s closely follows the mean, h4 = 0.8u may be even lower, and the maximum value is h3. At this time, the statistical sensitivity is high and the blockage of the membrane shell can be detected in time. When the system dispersion is large (s is large): h3 may drop to an extremely low value (even close to zero) due to the increase of s. If this is used as the lower limit, even if a membrane shell produces almost no water, it may not be judged as blocked. The max value is h4=0.8u, which is dominated by the absolute water production, to ensure that blocked membrane shells can be identified.

[0034] In this embodiment, during the execution of the membrane module flow anomaly identification and control method, the permeate flow rate, upper threshold, and lower threshold of the membrane housing change as the total operating time of the membrane module increases. In this embodiment, the permeate flow rate of each membrane housing is collected in real time, and the upper and lower thresholds are calculated based on the current permeate flow rate, thereby achieving dynamic identification and adjustment of abnormal membrane housing states.

[0035] In this embodiment, the initial calculation parameters used (such as the coefficient 2 in u+2s, the coefficient 1.2 in 1.2u, and the lower limit 0.8) are all factory preset values. To improve the adaptability of long-term operation, the system also performs a self-learning process: the control device records the flow data of each membrane housing, the calculated threshold, and the final judgment result during each flow deviation adjustment and blockage adjustment. After accumulating a certain amount of data, the standard deviation coefficient and proportional coefficient are dynamically optimized using the recursive least squares method. For example, if historical data shows that most "flow deviation" markers do not ultimately lead to performance degradation, the system can automatically increase the coefficient in 2s to 2.5 to reduce sensitivity; conversely, if a small deviation is detected and causes rapid fouling, the coefficient can be decreased to improve the warning sensitivity. This self-learning mechanism makes the threshold calculation continuously closer to the normal fluctuation range of the specific system, significantly reducing the false alarm and false negative rates.

[0036] See Figure 3 The operation process of bias flow regulation includes: Step S21: Based on the current permeate flow rate of all membrane housings, obtain the upper limit threshold of the current permeate flow rate of the membrane housings. Step S31: Compare the current permeate flow rate of each membrane shell with the current upper limit threshold. Step S41: Determine whether there is a membrane shell whose permeate flow rate exceeds the current upper limit threshold; Step S51: If it exists, mark the membrane housing with the current permeate flow rate exceeding the current upper limit threshold as a flow-off membrane housing; if it does not exist, directly end this flow-off adjustment. Step S61: Reduce the opening of the valve on the inlet side of the deflector membrane housing; Step S71: After the permeate flow rate of all membrane shells stabilizes, monitor whether the current permeate flow rate of all membrane shells is lower than the upper limit threshold. If yes, then end the current flow deviation adjustment in step S81; otherwise, repeat steps S21-S71 until the current permeate flow rate of all membrane housings is lower than the current upper limit threshold, then end the current flow deviation adjustment.

[0037] During flow deviation regulation, by monitoring the permeate flow rate of each membrane housing within the same operating range in real time, and generating an upper limit threshold based on the current permeate flow rate of all membrane housings, abnormally high flow deviation membrane housings can be automatically and promptly identified before severe performance degradation occurs. For the identified flow deviation membrane housings, the opening of the inlet valve is automatically reduced to actively suppress its hydraulic load and prevent irreversible physical and chemical damage to the membrane elements inside the membrane housing.

[0038] In step S61, during the deflection adjustment process, the valve on the inlet side of the deflection membrane is adjusted to reduce its opening by 5%-30% of the total valve opening each time, preferably 10%.

[0039] In this embodiment, the method for determining whether the permeate flow rate of the membrane shell is stable is as follows: Within a preset time period (e.g., 2-3 hours), monitor the permeate flow rate of the membrane housing at each time interval (e.g., 10 minutes). Compare the obtained maximum and minimum permeate flow rates. If the maximum permeate flow rate does not exceed the preset range of the minimum permeate flow rate (e.g., 20-30%), it indicates that the permeate flow rate of the membrane housing is in a stable state. For example, if the preset range is 20%, the maximum permeate flow rate of the membrane housing is 220 liters per hour, and the minimum permeate flow rate is 200 liters per hour. (220 / 200-1)×100%=10%<20%, ​​therefore, the permeate flow rate of the membrane housing is in a stable state.

[0040] See Figure 3 Furthermore, the equilibrium adjustment mode also includes congestion adjustment, wherein the operation process of congestion adjustment includes: Step S22: Based on the current permeate flow rate of all membrane housings, obtain the lower limit threshold of the current permeate flow rate of the membrane housings. Step S32: Compare the current permeate flow rate of each membrane shell with the current lower limit threshold. Step S42: Determine whether there is a membrane shell with a permeate flow rate lower than the current lower threshold. In step S52, if a membrane shell with a permeate flow rate lower than the current lower threshold is marked as a blocked membrane shell, if no membrane shell is marked as a blocked membrane shell, return to step S22 and repeat step S22-step S52 after a certain time interval (e.g., 30 minutes or one hour). Step S62: Increase the opening of the valve on the permeate side of the blocked membrane housing; Step S72: After the permeate flow rate of all membrane housings stabilizes, monitor whether the current permeate flow rate of the clogged membrane housing has increased. Step S82: If the current permeate flow rate of the blocked membrane increases and exceeds the current lower threshold, then the marker for the blocked membrane is removed.

[0041] During clogging regulation, the permeate flow rate of all membrane housings within the same operating range is monitored in real time, and a lower limit threshold is generated based on the current permeate flow rate of all membrane housings. In this way, when a membrane housing is clogged, it can automatically and promptly identify clogged membrane housings with abnormally low permeate flow rates. Specifically, for the identified clogged membrane housings, the opening of the valve on the permeate side is automatically increased to improve its permeate flow rate.

[0042] Based on one or more of the above embodiments, as a preferred implementation, the blockage regulation operation process further includes: step S83, after increasing the opening of the valve on the water production side of the blocked membrane housing, if the current water production flow of the blocked membrane housing is still lower than the current lower limit threshold, a prompt is issued and the blocked membrane housing is cleaned.

[0043] During the clogging adjustment process, the valve on the permeate side of the clogging membrane housing is adjusted to increase its opening by 5%-10% of the total valve opening each time.

[0044] Specifically, in step S62, the opening of the outlet valve of the clogged membrane housing can be gradually increased, for example, by 5%-10% of the total valve opening each time. After each adjustment, it is monitored whether the current product water flow of the clogged membrane housing increases and exceeds the current lower threshold. If not, the opening of the outlet valve of the clogged membrane housing is increased again until the valve opening reaches 100%. When the valve opening on the product water side of the clogged membrane housing reaches 100%, if the current product water flow of the clogged membrane housing is still lower than the current lower threshold, a prompt is issued and the clogged membrane housing is cleaned.

[0045] The equalization regulation mode includes flow offset regulation and blockage regulation. In some embodiments, the two processes of flow offset regulation and blockage regulation can be executed alternately, for example, flow offset regulation is executed first, and then blockage regulation is executed, and so on. In other embodiments, the two processes of flow offset regulation and blockage regulation can be executed in parallel.

[0046] The membrane module flow anomaly identification and control method can identify both flow deviation and blockage based on the permeate flow rate. For membrane housings with flow deviation, the valve opening on the feed side is reduced; for membrane housings with blockage, the valve opening on the permeate side is increased. Furthermore, if the current permeate flow rate of the blocked membrane housing remains below the current lower threshold after increasing the valve opening on the permeate side, targeted cleaning is performed.

[0047] Cleaning methods for clogged membrane housings include chemical cleaning and physical backwashing. When the system determines that the permeate flow rate of a membrane housing remains below the lower threshold and the permeate-side valve is fully open, it can issue an alarm and automatically trigger an online chemical cleaning program for that membrane housing. For example, depending on the type of membrane element inside the housing, an acidic or alkaline cleaning agent can be selected to clean the clog. Physical backwashing refers to water entering through the permeate side of the membrane housing and exiting through the inlet side.

[0048] The upper and lower thresholds are dynamically set based on statistical data. Both the upper and lower thresholds are based on real-time collected permeate flow rates of each membrane shell and are dynamically adjusted.

[0049] During the flow deviation adjustment process, if the current permeate flow rate of each membrane housing is lower than the upper limit threshold, the flow deviation adjustment is terminated directly. After the current flow deviation adjustment ends, the membrane module performs flow deviation adjustment again after a first predetermined time period. This achieves closed-loop control: flow deviation adjustment → stabilization period → flow deviation adjustment, thereby enabling periodic monitoring and adjustment of whether flow deviation exists in the membrane housings, ensuring the normal operation of each membrane housing in the membrane module.

[0050] During the clogging adjustment process, if the current permeate flow rate of each membrane housing is higher than the lower threshold, the clogging adjustment is terminated directly. After the initial clogging adjustment, the membrane module undergoes another clogging adjustment after a second predetermined time period. This achieves closed-loop control: clogging adjustment → stabilization period → clogging adjustment. This allows for periodic monitoring and adjustment of membrane housing clogging status, ensuring the normal operation of each membrane housing within the membrane module.

[0051] Based on one or more of the above embodiments, as a preferred implementation, the first predetermined time period is dynamically adjusted according to the trigger frequency of the bias flow adjustment: when the number of bias flow membrane shells is detected to exceed a first preset proportion (e.g., 30%) of the total number of membrane shells in the membrane module, the first predetermined time period is shortened.

[0052] Based on the above rules, when frequent flow deviations are detected, the first predetermined time is adjusted, thereby increasing the adjustment frequency and enabling the water treatment system to operate more stably.

[0053] The second predetermined time period is dynamically adjusted according to the trigger frequency of the blockage regulation: when the number of blocked membrane shells is detected to exceed the second preset proportion (e.g., 30%) of the total number of membrane shells in the membrane module, the second predetermined time period is shortened.

[0054] Based on the above rules, when frequent blockages are detected, the second scheduled time is adjusted, thereby increasing the frequency of adjustment and enabling the water treatment system to operate more stably.

[0055] The membrane module flow anomaly identification and control method proposed in this application can monitor and identify the permeate flow rate of each membrane shell in the membrane module. When an abnormality in the permeate flow rate of a membrane shell is detected, the method enters the equalization adjustment mode and adjusts the membrane shell accordingly. The equalization adjustment mode includes flow deviation adjustment and clogging adjustment.

[0056] During flow deviation regulation, by monitoring the permeate flow rate of each membrane housing within the same operating range in real time, and generating an upper limit threshold based on the current permeate flow rate of all membrane housings, abnormally high flow deviation membrane housings can be automatically and promptly identified before severe performance degradation occurs. For the identified flow deviation membrane housings, the opening of the inlet valve is automatically reduced to actively suppress its hydraulic load and prevent irreversible physical and chemical damage to the membrane elements inside the membrane housing.

[0057] During clogging regulation, by monitoring the permeate flow rate of each membrane housing within the same operating range in real time, and generating a lower limit threshold based on the current permeate flow rate of all membrane housings, the system can automatically and promptly identify clogged membrane housings with abnormally low permeate flow rates. For the identified clogged membrane housings, the system automatically increases the opening of the valve on the permeate side to improve its permeate flow rate.

[0058] The adjustment process is based on real-time flow feedback and cyclic verification, realizing dynamic adaptive equilibrium adjustment. It completely overcomes the shortcomings of traditional manual adjustment, such as poor accuracy, slow response, and inability to adapt to changes in water quality and membrane fouling. It ensures that the membrane module maintains hydraulic balance throughout its entire life cycle, significantly delays the performance degradation of the membrane element, and extends the overall service life of the membrane module.

[0059] See Figure 4 In this embodiment, a membrane module flow anomaly identification and control device is also proposed, comprising: The monitoring module 100 is used to monitor the permeate flow rate of each membrane shell in the membrane module in real time after the membrane module has been running for a preset period of time. The threshold calculation module 200 is used to obtain the upper limit threshold and the lower limit threshold of the current permeate flow rate of the membrane shell based on the permeate flow rate of each membrane shell. The comparison module 300 is used to determine the deflection membrane shell in the membrane module based on the upper limit threshold of the current permeate flow rate of the membrane shell; and to determine the blockage membrane shell in the membrane module based on the lower limit threshold of the current permeate flow rate of the membrane shell. The valve regulating module 400 is used to control the flow rate of the deflection membrane and the blockage membrane respectively.

[0060] In a preferred embodiment, the device further includes a control module 500, configured to perform flow bias adjustment on the membrane module again after the first predetermined time period ends of the flow bias adjustment; when the number of flow bias membrane shells is detected to exceed a first preset proportion of the total number of membrane shells in the membrane module, the first predetermined time period is shortened, the first predetermined time period being dynamically adjusted according to the trigger frequency of the flow bias adjustment; and configured to perform blockage adjustment on the membrane module again after the second predetermined time period ends of the blockage adjustment; when the number of blockage membrane shells is detected to exceed a second preset proportion of the total number of membrane shells in the membrane module, the second predetermined time period is shortened, the second predetermined time period being dynamically adjusted according to the trigger frequency of the blockage adjustment.

[0061] In a preferred embodiment, the device also includes a cleaning module 600, which, after increasing the opening of the valve on the water production side of the clogged membrane housing, if the current water production flow of the clogged membrane housing is still lower than the current lower threshold, issues a prompt and cleans the clogged membrane housing.

[0062] In this embodiment, a water treatment system is also proposed, including a membrane module and the membrane module flow anomaly identification and control device mentioned above. The membrane module includes multiple membrane housings arranged in parallel, all of which are in the same operating condition.

[0063] The membrane module flow anomaly identification and control device is used to balance and adjust the membrane module so that the permeate flow rate of all membrane shells in the membrane module is within a preset range. The upper limit of the preset range corresponds to an upper threshold, and the lower limit corresponds to a lower threshold.

[0064] In this embodiment, a readable storage medium is also proposed, on which a computer program is stored. When the computer program is executed by a processor, it implements the membrane module flow anomaly identification and control method mentioned above.

[0065] Example 2 In this embodiment, the water treatment system includes multiple membrane modules, and different membrane modules can be set under different operating conditions. In each membrane module, all membrane shells are in the same operating condition segment.

[0066] The inlet water pressure and other parameters will vary depending on the operating conditions. Each membrane module independently executes the membrane module flow anomaly identification and control method mentioned in Example 1.

[0067] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0068] The terms “first,” “second,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

Claims

1. A method for identifying and controlling abnormal flow in a membrane module, characterized in that, The method includes: After the membrane module has been in operation for a preset period of time, the permeate flow rate of each membrane shell in the membrane module is monitored in real time. Based on the permeate flow rate of each membrane shell, the upper limit threshold and the lower limit threshold of the current permeate flow rate of the membrane shell are obtained. The deflection membrane housing in the membrane module is determined based on the upper limit threshold of the current permeate flow rate of the membrane housing. The blockage of the membrane housing in the membrane module is determined based on the lower limit threshold of the current water production flow rate of the membrane housing; Flow rate control was applied to the deflection membrane and the blockage membrane, respectively.

2. The membrane module flow anomaly identification and control method according to claim 1, characterized in that, The process of obtaining the upper and lower threshold values ​​of the current permeate flow rate of each membrane shell based on its individual permeate flow rate includes: Calculate the average and standard deviation of the current permeate flow rate for all the membrane housings; The first and second candidate values ​​are determined based on the mean and standard deviation; Set the minimum value between the first candidate value and the second candidate value as the upper limit threshold; The third and fourth candidate values ​​were determined based on the mean and standard deviation. The maximum value among the third and fourth candidate values ​​is set as the lower limit threshold.

3. The membrane module flow anomaly identification and control method according to claim 2, characterized in that, The determination of the deflection membrane housing in the membrane module based on the upper limit threshold of the current permeate flow rate of the membrane housing includes: The current water production flow rate of each membrane shell is compared with the current upper limit threshold to determine whether there is a membrane shell whose water production flow rate exceeds the current upper limit threshold. If it exists, then the membrane shell is marked as a deflection membrane shell.

4. The membrane module flow anomaly identification and control method according to claim 2, characterized in that, The determination of the clogged membrane shell in the membrane module based on the lower limit threshold of the current permeate flow rate of the membrane shell includes: The current permeate flow rate of each membrane shell is compared with the current lower limit threshold to determine whether there is a membrane shell with a permeate flow rate lower than the current lower limit threshold. If present, the membrane shell is marked as a blocked membrane shell.

5. The membrane module flow anomaly identification and control method according to claim 1, characterized in that, The flow rate control for the deflection membrane and the blockage membrane includes: Reduce the opening of the valve on the inlet side of the deflection membrane housing; after the permeate flow rate of all the membrane housings stabilizes, monitor whether the current permeate flow rate of all the membrane housings is lower than the upper limit threshold. If not, reduce the opening of the valve on the inlet side of the deflection membrane housings again until the current permeate flow rate of all the membrane housings is lower than the upper limit threshold, and end this deflection adjustment. Increase the opening of the valve on the water production side of the blocked membrane housing; after the water production flow of all the membrane housings stabilizes, monitor whether the current water production flow of the blocked membrane housing increases. If the current water production flow of the blocked membrane housing increases and is greater than the current lower threshold, then remove the mark on the blocked membrane housing.

6. The membrane module flow anomaly identification and control method according to claim 5, characterized in that, The method further includes: If the current water production flow rate of the blocked membrane shell is still lower than the current lower threshold after the valve opening on the water production side is increased, a prompt will be issued and the blocked membrane shell will be cleaned.

7. The membrane module flow anomaly identification and control method according to claim 5, characterized in that, During the deflection adjustment process, the valve on the inlet side of the deflection diaphragm is adjusted to reduce its opening by 5%-30% of the total valve opening each time. During the blockage adjustment process, the valve on the water production side of the blockage membrane housing is adjusted to increase its opening by 5%-10% of its total opening each time.

8. The membrane module flow anomaly identification and control method according to claim 1, characterized in that, The method further includes: After the first predetermined time period ends, the bias current adjustment is performed on the membrane module again. When it is detected that the number of bias current membrane shells exceeds the first preset ratio of the total number of membrane shells in the membrane module, the first predetermined time period is shortened. The first predetermined time period is dynamically adjusted according to the trigger frequency of the bias current adjustment. After the second predetermined time period ends, the membrane module is subjected to blockage adjustment again. When the number of blocked membrane shells is detected to exceed the second preset ratio of the total number of membrane shells in the membrane module, the second predetermined time period is shortened. The second predetermined time period is dynamically adjusted according to the trigger frequency of the blockage adjustment.

9. A water treatment system, characterized in that, The system includes a membrane module and a membrane module flow anomaly identification and control device; wherein the membrane module includes multiple membrane housings arranged in parallel; The membrane module flow anomaly identification and control device is used to execute the membrane module flow anomaly identification and control method as described in any one of claims 1 to 8.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the membrane module flow anomaly identification and control method as described in any one of claims 1 to 8.

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