A membrane module water quality abnormal isolation control method, device, system and medium

Abstract

This application discloses a method, device, system, and medium for isolating and controlling abnormal water quality in membrane modules. The method includes: collecting real-time permeate data from each membrane shell in the same operating condition segment of the membrane module; determining a clogging threshold based on the real-time permeate data of each membrane shell; comparing the real-time permeate data of each membrane shell with the clogging threshold; identifying membrane shells whose real-time permeate data exceeds the clogging threshold as abnormal membrane shells; and isolating and controlling the abnormal membrane shells. This application, through independent data acquisition for each membrane shell and dynamic threshold comparison, can accurately distinguish between normal and abnormal membrane shells, with positioning accuracy covering individual membrane shells, providing core support for refined operation and maintenance. Upon identifying an abnormal membrane shell, it immediately isolates and controls the abnormal membrane shell, preventing the deteriorated permeate from the shell from continuously flowing into the permeate main pipe and causing contamination of the entire pipeline. This eliminates the need for shutdown and maintenance of the entire pipeline due to a single abnormal membrane shell, significantly reducing operation and maintenance costs and ensuring continuous and stable system operation.

Description

Technical Field

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

[0002] In modern water treatment processes such as municipal water supply treatment, industrial wastewater reuse, and seawater desalination, pressure-driven membrane separation technology has been widely used. The core of this type of technology system is the membrane module: membrane elements are installed inside a pressure-resistant vessel (referred to in the industry as a "membrane shell"), and a certain number of membrane shells are combined into a complex membrane module according to a certain water flow process logic.

[0003] In the daily operation of water treatment equipment, monitoring the quality of the produced water is a crucial measure to ensure stable system performance and that the produced water meets standards. The industry standard is to install monitoring instruments on the main produced water pipe to monitor key indicators such as turbidity, specific ion concentration, or total organic carbon in the final produced water. By analyzing the above monitoring data through the control system, operators can make an overall assessment of whether the membrane module's filtration performance is normal.

[0004] However, in practical applications, the above-mentioned technical solutions have obvious limitations. A large membrane module typically contains dozens or even hundreds of membrane housings, each of which is a relatively independent filtration unit. When the monitoring instruments on the main permeate pipe show abnormal water quality (such as increased permeate turbidity), this abnormal signal only indicates that the overall filtration quality of the entire system has deteriorated.

[0005] Because of the large number and compact structure of the membrane housings, a single permeate pipeline often collects permeate from multiple membrane housings. If the permeate from a particular membrane housing becomes abnormal, the final permeate from that pipeline will be continuously contaminated. As a result, the entire permeate pipeline needs to be shut down for maintenance, which results in high operating and maintenance costs. Summary of the Invention

[0006] The purpose of this application is to overcome the deficiencies of the prior art and provide a method, device, system and medium for isolating and controlling abnormal water quality in membrane modules, so as to solve the problems in the prior art.

[0007] To address the aforementioned problems, the first aspect of this application provides a method for isolating and controlling abnormal water quality in membrane modules, the method comprising:

[0008] Collect real-time water production data from each membrane shell in the same operating condition section of the membrane module; The clogging threshold is determined based on the real-time water production data of each membrane shell; Compare the real-time water production data of each membrane shell with the clogging threshold; Membrane shells whose real-time water production data exceeds the clogging threshold are identified as abnormal membrane shells. The abnormal membrane shell is isolated and controlled.

[0009] In one possible implementation, determining the clogging threshold based on real-time permeate data from each membrane shell includes: The average real-time water production data is calculated based on the real-time water production data of each membrane shell. Obtain the average historical water production data that has the smallest deviation from the average real-time water production data; The baseline blockage threshold corresponding to the smallest average historical water production data is used as the first-level early warning threshold. The cumulative runtime of each membrane shell is retrieved, and the aging loss coefficient is determined based on the cumulative runtime. The secondary fault threshold is obtained based on the aging loss coefficient and the primary warning threshold. The first-level warning threshold is lower than the second-level fault threshold.

[0010] In one possible implementation, comparing the real-time permeate data of each membrane shell with the clogging threshold includes: The real-time water production data of each membrane shell is compared with the first-level early warning threshold and the second-level fault threshold in a graded manner. If the real-time water production data is between the first-level warning threshold and the second-level fault threshold, the corresponding membrane shell is determined to be a slightly blocked abnormal membrane shell. If the real-time water production data exceeds the level 2 fault threshold, the corresponding membrane shell is determined to be a severely clogged abnormal membrane shell.

[0011] In one possible implementation, the isolation control of the abnormal membrane shell includes: For membrane shells with minor blockage, local flow restriction and isolation should be implemented, and in-situ flushing pretreatment should be initiated simultaneously; For severely clogged membrane shells, complete shut-off isolation is performed, and a backup membrane shell is automatically switched to maintain the system's permeate flux.

[0012] In one possible implementation, the local flow-limiting isolation of the slightly blocked membrane shell includes: Dynamically reduce the opening of the regulating valves on the product water side / inlet water side according to the degree of membrane blockage; After the flow restriction stabilizes, the low-pressure in-situ flushing process is started simultaneously.

[0013] In one possible implementation, the complete cutoff isolation of the severely blocked abnormal membrane shell includes: The inlet valve and product water valve of the abnormal membrane shell are closed synchronously, and the valve status is electrically locked.

[0014] In one possible implementation, the method further includes: Record the abnormal data, isolation duration, and operating parameters of the abnormal membrane shell to form a single shell abnormality ledger; Cluster analysis was performed on the abnormal log to identify common causes of blockage in membrane modules under the same operating conditions; Optimize the pretreatment feed water parameters, operating pressure, and flushing cycle of the membrane module based on common causes of clogging.

[0015] A second aspect of this application provides a membrane module water quality anomaly isolation and control device, the device comprising: The data acquisition module is used to collect real-time water production data from each membrane shell in the same operating condition section of the membrane module. The determination module is used to determine the clogging threshold based on the real-time permeate data of each membrane shell; The comparison module is used to compare the real-time water production data of each membrane shell with the clogging threshold. The determination module is used to determine that membrane shells whose real-time water production data exceeds the clogging threshold are abnormal membrane shells; An isolation module is used to isolate and control the abnormal membrane shell.

[0016] A third aspect of this application provides a water treatment system, including a membrane module and a membrane module water quality abnormality isolation and control device as described above; wherein the membrane module includes a plurality of membrane housings arranged in parallel.

[0017] A fourth 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 water quality anomaly isolation control method as described above.

[0018] The beneficial effects of this application include at least the following: This application, through independent data acquisition and dynamic threshold comparison for each membrane shell, can accurately distinguish between normal and abnormal membrane shells, with positioning accuracy covering individual membrane shells, providing core support for refined operation and maintenance. Upon identifying an abnormal membrane shell, it immediately isolates and controls it, preventing the deteriorated permeate from continuously flowing into the main permeate pipe and causing contamination of the entire pipeline. This eliminates the need for shutdown and maintenance of the entire pipeline due to a single abnormal membrane shell, significantly reducing operation and maintenance costs and ensuring continuous and stable system operation. Attached Figure Description

[0019] 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.

[0020] Figure 1 A schematic diagram of a membrane module is shown; Figure 2 A schematic diagram of the connection of membrane shells under the same operating conditions is shown; Figure 3 A flowchart of a method for isolating and controlling abnormal water quality in a membrane module is shown. Figure 4 A flowchart is shown to determine the clogging threshold based on real-time permeate data from each membrane shell; Figure 5 A schematic diagram of a membrane module water quality anomaly isolation control system is shown.

[0021] Explanation of key component symbols: 101-Membrane housing, 102-Inlet valve, 103-Product water valve, 201-Acquisition module, 202-Determination module, 203-Comparison module, 204-Judgment module, 205-Isolation module. Detailed Implementation

[0022] 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.

[0023] 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.

[0024] In traditional membrane filtration systems, multiple membrane housings operate in parallel, with their permeate flowing into a main permeate pipe. When a membrane element in one housing ruptures, fails to seal properly, or becomes severely contaminated, the quality of the permeate from that housing deteriorates rapidly; for example, the permeate conductivity increases rapidly. However, the existing system's online monitoring instruments on the main permeate pipe only respond after this substandard permeate mixes with the normal permeate, and the response signal reflects the overall deterioration of the permeate quality across the entire system. This leads to several problems: the malfunctioning membrane housing cannot be located in a timely manner, requiring a complete system shutdown and offline testing and troubleshooting of each housing individually. This results in significant unnecessary downtime and manpower investment, increases maintenance costs, and may even lead to the complete waste of the permeate due to delayed handling of the anomaly.

[0025] This application proposes a method, device, system, and medium for isolating and controlling abnormal water quality in membrane modules, which can effectively solve the above-mentioned problems.

[0026] See also Figures 1-2 As shown, the membrane module water quality anomaly isolation control system of this application adopts a multi-operating-condition segment modular distributed structure, which is divided into multiple independent and sequentially arranged operating condition segments. These segments are named K1, K2, ..., Kn in sequence, where n is the total number of operating condition segments. Each operating condition segment is an independent water treatment operation unit, capable of operating independently without interference, while also cooperating to complete the overall water treatment operation. Each operating condition segment contains multiple membrane housings 101 arranged in parallel. All membrane housings 101 within the same operating condition segment share a unified inlet and outlet water header. Core operating condition parameters such as inlet pressure, inlet water quality, operating temperature, and system load remain consistent, ensuring that all membrane housings within the same operating condition segment operate in an equal environment, providing a basis for subsequent membrane housing anomaly determination.

[0027] To address the independent management and on / off control requirements of each membrane housing, each membrane housing 101 is equipped with an inlet valve 102 at its inlet end and a product water valve 103 at its product water end. The inlet valve 102 controls the on / off state and flow rate of the corresponding membrane housing, while the product water valve 103 controls the output of purified water from the membrane housing. Through the dual-valve linkage control of the inlet valve 102 and product water valve 103, independent operation, individual shutdown, and precise isolation of a single membrane housing can be achieved without affecting the overall operating status of other parallel membrane housings within the same operating condition or other operating conditions. This provides hardware structural support for the core functions of single membrane housing anomaly isolation and non-stop operation and maintenance.

[0028] To achieve refined, differentiated, and traceable management of all membrane housings, this application assigns a unique serial number to each membrane housing 101, with a one-to-one correspondence between each housing. This serial number is pre-linked with comprehensive equipment information, including the membrane housing's installation location, operating condition, rated operating parameters, installation batch, and equipment model. Based on this unique serial number, the system can accurately pinpoint the specific physical installation location, K-series operating condition, and pipeline connection points of any membrane housing 101.

[0029] During data acquisition, when the controller executes the membrane shell permeate data acquisition program, it simultaneously retrieves the unique number of each membrane shell 101. It then associates and binds the real-time permeate flow rate, turbidity, operating pressure, permeate efficiency, and other real-time permeate data with the corresponding membrane shell number, while overlaying a data acquisition timestamp. This forms a standardized data record containing time information, a unique number, and real-time operating data, which is then stored in the system database. This data association method ensures that each set of permeate data can be traced back to the corresponding individual membrane shell.

[0030] When an abnormal membrane shell is identified by comparing real-time water production data with a preset clogging threshold, the system's abnormal alarm mechanism can be triggered immediately, and a dedicated alarm signal can be generated simultaneously.

[0031] In this application, the unique number of each membrane housing adopts a programmable and fixed encoding method, which can be in various forms such as numerical serial number, physical address encoding, and equipment unique identification code. For example, it can be in the form of numerical numbering M1, M2, M3..., Mn, or it can be in the form of bus address encoding corresponding to the installation point. The unique number of all membrane housings is pre-entered into the system and fixedly stored in the controller's dedicated register, which has the characteristics of data immutability, unique traceability, and fast retrieval response speed. During equipment operation, the controller can call and match the identification data of each membrane housing in real time, ensuring a one-to-one correspondence between the number and the membrane housing entity throughout the process. This ensures the accuracy and stability of membrane housing status monitoring, data association, anomaly location, and alarm prompts from the underlying data level, providing underlying data guarantee for the stable operation of the entire membrane housing anomaly monitoring and isolation control system.

[0032] See Figure 3 In this embodiment, a method for isolating and controlling abnormal water quality in membrane modules is proposed, the method comprising: S11, collect real-time water production data of each membrane shell in the same operating condition section of the membrane module; S12, determine the clogging threshold based on the real-time water production data of each membrane shell; S13, compare the real-time water production data of each membrane shell with the clogging threshold; S14, membrane shells whose real-time water production data exceeds the clogging threshold are identified as abnormal membrane shells; S15 is used to isolate and control abnormal membrane shells.

[0033] The automated operation system of the water treatment equipment includes multiple membrane modules, each comprising multiple membrane housings connected in parallel. All membrane housings within the same module operate under the same conditions, meaning they share the same inlet header and maintain consistent operating pressure and inlet water quality. Each membrane housing is independently equipped with high-precision permeate monitoring sensors, flow acquisition sensors, and data transmission units, all of which are connected to the system's backend control terminal in real time. Based on the uniformity of operating conditions within the same module, the system synchronously collects real-time permeate flow rate, permeate turbidity, permeate pressure, and permeate volume per unit time from all parallel membrane housings within that module, achieving independent, real-time data acquisition for each membrane housing. The data acquisition frequency can be set according to equipment operating requirements, ensuring that the data reflects the instantaneous operating status of each membrane housing in real time. The system performs preliminary noise reduction and interference removal on the collected raw data, eliminating the impact of equipment fluctuations, signal interference, and other irrelevant factors on data accuracy, providing data support for subsequent anomaly detection.

[0034] The clogging threshold is the core criterion for determining whether a membrane housing is clogged or abnormal. Unlike traditional fixed threshold methods, this application uses a dynamic threshold, which solves the problems of fixed thresholds failing to adapt to fluctuations in operating conditions and having low accuracy. Because water treatment equipment is affected by factors such as influent water temperature, raw water pollutant concentration, system load, and equipment operating time at different operating stages, the overall permeate baseline value will fluctuate slightly. Using a fixed threshold would easily lead to misjudgments or missed judgments. Therefore, the system dynamically generates a clogging threshold adapted to the current operating condition based on real-time permeate data from all normal membrane housings within the same operating period.

[0035] The system compares the real-time permeate data of each membrane shell with the dynamic clogging threshold corresponding to the current operating condition, generating an independent comparison result for each shell. During the comparison, the system not only compares instantaneous permeate data but also considers the continuous data change trend over a short period to determine whether the data deviation is a momentary fluctuation or a persistent anomaly. If the permeate data of a membrane shell shows only a single small fluctuation and quickly returns to the normal range, the system classifies it as a normal operating fluctuation and does not include it in the abnormal category. If the permeate data of a membrane shell continuously deviates from the standard range and exceeds the clogging threshold, it is identified as an abnormal membrane shell. Based on threshold comparison, single or multiple membrane shells with clogging or deterioration problems can be identified, solving the pain point of traditional operation and maintenance models that cannot accurately locate individual faulty units.

[0036] Upon identifying an abnormal membrane housing, a pre-set isolation control program is immediately triggered, enabling rapid fault handling without manual intervention. Each membrane housing is equipped with an independent inlet solenoid valve and product water control valve in the equipment piping. Based on the abnormal membrane housing's number, the system automatically closes the corresponding inlet and product water valves, isolating the abnormal membrane housing from the parallel-operating membrane housing group and cutting off its inlet and product water pathways, thus removing it from the current operating condition. No downtime or manual operation is required, minimizing the duration of fault impact. Simultaneously, the system records the abnormal membrane housing number, the time of the anomaly, and the abnormal data parameters, generating a fault warning log and pushing it to the maintenance terminal, providing data support for subsequent targeted repairs, cleaning, and replacement.

[0037] The above embodiments, based on the characteristic of unified operating parameters for membrane housings under the same operating conditions, achieve intelligent control of the entire process of membrane housing anomaly detection, judgment, and handling. This application, through independent data acquisition and dynamic threshold comparison for each housing, can accurately distinguish between normal and abnormal membrane housings, with positioning accuracy covering individual membrane housings, providing core support for refined operation and maintenance. Upon identifying an abnormal membrane housing, it is immediately isolated and controlled, preventing the deteriorated permeate from the housing from continuously flowing into the permeate main pipe and causing contamination of the entire pipeline. This eliminates the need for shutdown and maintenance of the entire pipeline due to a single abnormal membrane housing, significantly reducing operation and maintenance costs and ensuring continuous and stable system operation.

[0038] This application can be applied to conventional municipal water supply treatment and industrial pure water preparation scenarios, as well as high-salinity seawater desalination and high-pollution wastewater deep treatment scenarios. By adjusting the data collection frequency and clogging threshold, precise adaptation can be achieved.

[0039] In one alternative implementation, such as Figure 4 As shown, the clogging threshold is determined based on the real-time permeate data of each membrane shell, including: S21, the average real-time water production data is calculated based on the real-time water production data of each membrane shell; S22, obtain the average historical water production data with the smallest deviation from the average real-time water production data; S23, the baseline blockage threshold corresponding to the smallest average historical water production data is used as the first-level early warning threshold; S24, retrieve the cumulative running time of each membrane shell, and determine the aging loss coefficient based on the cumulative running time; S25, the secondary fault threshold is obtained based on the aging loss coefficient and the primary warning threshold.

[0040] Among them, the first-level warning threshold is lower than the second-level fault threshold.

[0041] For all membrane housings operating in parallel within the same operating condition segment, real-time permeate data for each membrane housing is collected synchronously. The average real-time permeate data for all membrane housings within the segment is calculated to obtain the average real-time permeate data under the current operating condition. The average real-time permeate data can objectively characterize the overall operating condition of the equipment, comprehensively reflecting the overall status of environmental parameters such as current influent water quality, water temperature, and operating pressure, providing benchmark parameters for subsequent historical operating condition matching.

[0042] The system has a pre-built historical operation database, which stores the average historical water production data and corresponding baseline clogging thresholds for different operating periods and conditions throughout the equipment's lifecycle. After obtaining the average real-time water production data for the current operating condition, the system traverses all average historical water production data in the database and filters out the average historical water production data with the smallest deviation from the current average real-time water production data by comparing the differences. This allows for the selection of historical operating data that is highly consistent with the current influent water quality, temperature, operating load, and other operating conditions, achieving dynamic adaptive matching of operating conditions and avoiding the problems of judgment failure, misjudgment, and missed judgment caused by the inability of traditional fixed thresholds to adapt to fluctuations in operating conditions.

[0043] Each set of average historical permeate data stored in the database is pre-bound with a corresponding baseline clogging threshold. This baseline clogging threshold is the critical judgment parameter for the membrane shell to show slight clogging and begin to deteriorate under the corresponding standard operating conditions. The system sets the baseline clogging threshold corresponding to the best historical data currently matched as the first-level warning threshold for the current operating conditions. The first-level warning threshold is the warning threshold for slight membrane shell abnormalities and early clogging.

[0044] During long-term continuous operation, membrane housings undergo irreversible natural aging phenomena such as material fatigue, natural micropore decay, and normal degradation of permeability. This type of performance degradation falls within the normal aging range of the equipment and is not a malfunction caused by impurities clogging the system; therefore, it cannot be effectively distinguished using a fixed threshold. Thus, this application implements dynamic threshold correction for membrane housings with different service lives and operating durations. Using the primary warning threshold obtained through operating condition matching as a benchmark, and combining it with the aging loss coefficient corresponding to the membrane housing, a secondary fault threshold is calculated. The primary warning threshold is lower than the secondary fault threshold, and the two thresholds form a stratified judgment interval, corresponding to the early warning state and the abnormal fault state of the membrane housing, respectively, achieving a refined stratified judgment of the membrane housing's operating status.

[0045] In an optional implementation, this application may employ an empirical linear model as the aging function to accurately calculate the aging loss coefficient based on the cumulative operating time of the membrane shell. The specific function expression is as follows: Y(t) = 1 + t / L θ.

[0046] Where Y(t) is the aging loss coefficient; t is the cumulative operating time of the membrane housing in hours; L is the rated design life of the membrane housing, a fixed constant set by the membrane housing manufacturer at the factory, in hours; θ is the allowable degradation coefficient increment at the end of the membrane housing's life, a dimensionless empirical constant, set by default to 0.2-0.3, which can be adjusted or modified according to different membrane materials and different application scenarios.

[0047] When the membrane housing is newly put into operation and the cumulative operating time t=0, the aging loss coefficient Y(t)=1, which means there is no aging loss.

[0048] The following detailed description is based on specific embodiments.

[0049] For example, a specific operating condition section is selected, in which ten sets of membrane shells with identical specifications and uniform operating conditions are arranged in parallel, designated as membrane shell No. 1 to No. 10. The system synchronously collects real-time permeate conductivity data of these ten sets of membrane shells, in μS / cm. The specific real-time permeate conductivity values ​​for each membrane shell are: 4.8, 5.1, 4.9, 5.3, 5.0, 5.2, 4.7, 8.6, 4.8, and 5.0.

[0050] The system calculates the average real-time permeate conductivity under the current operating conditions as 5.34 μS / cm based on the aforementioned real-time collected data. The system retrieves the built-in historical operating database, iterates through all stored average historical permeate conductivity data, and filters out the historical data with the smallest deviation from the current average real-time permeate conductivity of 5.34 μS / cm, obtaining the optimal matching historical data as 5.28 μS / cm. The system retrieves the baseline clogging threshold bound to this historical data, which is 6.5 μS / cm, and sets this baseline clogging threshold as the first-level warning threshold under the current operating conditions; that is, the first-level warning threshold = 6.5 μS / cm. Further, the system retrieves the cumulative operating time of a single membrane housing. Taking a membrane housing within this operating condition as an example, the cumulative operating time of this membrane housing is t = 8500 h. In this embodiment, the design life L = 15000 h set by the membrane housing manufacturer is selected, and the degradation coefficient increment θ is 0.21, resulting in an aging loss coefficient of 1.12. Based on this aging loss coefficient, the first-level warning threshold is corrected, resulting in a second-level fault threshold of 7.28 μS / cm.

[0051] The above-mentioned optional implementation methods obtain the baseline clogging threshold by matching historical operating data with the smallest deviation. This allows for dynamic adaptation to fluctuations in real-time influent water quality, temperature, pressure, and other operating conditions, completely avoiding the misjudgment and missed judgment problems that are prone to occur under complex operating conditions when using fixed thresholds, thus improving the adaptability of operating conditions. By introducing an aging loss coefficient based on the membrane shell's operating time for threshold correction, the natural performance degradation characteristics of the membrane shell during long-term operation are fully considered. The threshold is adaptively relaxed, effectively distinguishing between normal aging degradation of the membrane shell and real faults such as impurity clogging and membrane shell damage. This avoids judging normal aging as abnormal faults, significantly improving the accuracy of membrane shell anomaly judgment and the robustness of the system.

[0052] In an optional implementation, comparing the real-time permeate data of each membrane shell with a clogging threshold includes: The real-time water production data of each membrane shell is compared with the first-level early warning threshold and the second-level fault threshold in a graded manner. If the real-time water production data is between the first-level warning threshold and the second-level fault threshold, the corresponding membrane shell is determined to be a slightly blocked abnormal membrane shell. If the real-time water production data exceeds the level 2 fault threshold, the corresponding membrane shell is determined to be a severely clogged abnormal membrane shell.

[0053] After obtaining the first-level warning threshold and the second-level fault threshold, for each membrane shell in the same operating condition segment, retrieve its real-time water production data, such as real-time water production conductivity data, and independently compare it with the first-level warning threshold and the second-level fault threshold obtained by matching the current operating condition. When the real-time permeate data of the membrane housing falls between the Level 1 warning threshold and the Level 2 fault threshold, the membrane housing is determined to be slightly clogged. Slight clogging means that only a small amount of colloids and suspended solids are attached to the surface of the membrane housing, and the membrane pores are shallowly fouled, resulting in a slight decrease in filtration performance, but no irreversible serious faults such as damage or large-area pollutant accumulation have yet occurred.

[0054] When the real-time permeate data of the membrane housing exceeds the level 2 fault threshold, the membrane housing is determined to be severely clogged. Severe clogging corresponds to a large area of ​​pollutant accumulation inside the membrane housing, biofilm growth, or membrane housing cracking and damage, resulting in a significant failure of the filtration function. The substandard permeate will continuously contaminate the main pipe permeate, posing a significant water quality safety hazard.

[0055] When the real-time water production data of the membrane housing is lower than the first-level warning threshold, it is determined that the membrane housing is operating normally and there is no problem of blockage or deterioration.

[0056] For example, assuming the first-level warning threshold is 6.5 μS / cm and the second-level fault threshold is 7.28 μS / cm, the real-time permeate conductivity of membrane shell #8 in this operating condition is 8.6 μS / cm. Since 8.6 μS / cm > 7.28 μS / cm, membrane shell #8 is marked as a severely clogged abnormal membrane shell. Assuming the real-time permeate conductivity of membrane shells #1-7, #9, and #10 are 4.8, 5.1, 4.9, 5.3, 5.0, 5.2, 4.7, 4.8, and 5.0 μS / cm respectively, all values ​​are below the first-level warning threshold of 6.5 μS / cm, then all are determined to be normally operating membrane shells, requiring no alarm triggering or isolation operation.

[0057] In the above optional embodiments, by adopting a two-level hierarchical comparison of a first-level early warning threshold and a second-level fault threshold, compared with a single threshold judgment scheme, the fault degree of abnormal membrane shells can be finely divided, and the two different deterioration levels of slight blockage and severe blockage can be distinguished, providing accurate data basis for the control system to output differentiated isolation and operation and maintenance strategies.

[0058] In one optional implementation, isolating and controlling the abnormal membrane shell includes: For membrane shells with minor blockage, local flow restriction and isolation should be implemented, and in-situ flushing pretreatment should be initiated simultaneously; For severely clogged membrane shells, complete shut-off isolation is performed, and a backup membrane shell is automatically switched to maintain the system's permeate flux.

[0059] For membrane housings with minor blockage, it is not necessary to immediately and completely shut off the inlet and product water valves for isolation. Instead, local flow restriction isolation combined with in-situ flushing is used. This can trigger a low-level warning to prompt maintenance personnel to conduct online chemical cleaning during the planned downtime window without affecting the overall system flux. This avoids significant fluctuations in the total product water flux caused by direct isolation of slightly fouled membrane housings, ensuring stable system product water output. In specific implementation, the inlet valve 102 is an adjustable electric regulating valve. When a slightly blocked membrane housing is detected, the controller controls the inlet valve 102 to reduce its opening to a preset flow restriction degree (e.g., 30%-50% of the rated opening), while the product water valve 103 remains open at a small degree, without being completely shut off. Simultaneously with flow restriction isolation, the system automatically opens the in-situ flushing pipeline to perform low-pressure flushing and chemical pre-flushing on the membrane housing. Online flushing gradually removes shallow deposits on the membrane housing surface, striving to restore filtration performance without shutting down the system or removing the membrane housing.

[0060] For severely clogged membrane housings, full-path isolation control can be immediately implemented, simultaneously outputting a high-priority audible and visual alarm to quickly cut off the channel for degraded permeate water from the faulty membrane housing to flow into the main pipe, preventing the spread of large-scale water pollution. Specifically, when a membrane housing is determined to be severely clogged, the controller outputs a control signal to simultaneously and completely close the corresponding inlet valve 102 and permeate valve 103. This completely cuts off the inlet water flow from the main pipe to the membrane housing, preventing any raw water from continuously entering the membrane housing, and simultaneously completely blocks the connection between the membrane housing and the common permeate main pipe, ensuring that the degraded and contaminated permeate water inside the membrane housing cannot flow into the main pipe.

[0061] In the above optional embodiments, different isolation control strategies are adopted for slightly clogged and severely clogged membrane shells, realizing refined automatic operation management of the water treatment membrane module throughout the entire cycle, taking into account both the continuity of system water production and the safety of effluent water quality, and further reducing the production capacity loss and maintenance workload caused by unnecessary shutdowns.

[0062] In an optional implementation, performing local flow-limiting isolation on a slightly blocked membrane shell includes: Dynamically reduce the opening of the regulating valves on the product water side / inlet water side according to the degree of membrane blockage; After the flow restriction stabilizes, the low-pressure in-situ flushing process is started simultaneously.

[0063] For membrane housings with minor blockage, the opening of the inlet or product water valve can be dynamically reduced according to the degree of blockage. This allows for flushing of the membrane housing while maintaining its operation and utilizing hydraulic shear. After the flow rate stabilizes, a low-pressure in-situ flushing process is initiated. This gentle operation effectively flushes away deposits on the membrane surface, reduces operating pressure, and improves the online recovery rate of minor fouling.

[0064] After identifying a slightly blocked membrane housing, the system retrieves the difference between the real-time permeate data and the first-level warning threshold to quantify the severity of the shallow blockage. Based on the blockage level, the controller outputs an opening adjustment command, dynamically reducing the valve opening of either the inlet or permeate side electric regulating valve of the membrane housing. The higher the blockage level, the smaller the flow opening after valve adjustment. Once the valve opening adjustment is complete and the inlet and permeate flow rates stabilize, the system determines that the flow-limiting condition is stable and outputs a control signal to individually open the solenoid valve of the in-situ flushing branch associated with the membrane housing, initiating a low-pressure in-situ flushing process to continuously perform online hydraulic flushing on the single slightly blocked membrane housing. Low-pressure in-situ flushing is then initiated again after the flow-limiting state stabilizes.

[0065] The above-mentioned optional implementation method dynamically adjusts the valve opening based on the actual degree of clogging, rather than using a fixed opening to limit the flow. This can match different shallow clogging conditions and take into account both the stability of the total system water production and the control of the membrane load.

[0066] In other optional embodiments, the Euclidean distance between the real-time output data of the slightly clogged membrane shell and the average historical permeate data with the smallest deviation can be calculated, and an environmental condition deviation correction coefficient corresponding to the Euclidean distance can be matched. A larger Euclidean distance indicates a greater deviation between the current operating conditions and the operating conditions corresponding to the baseline clog threshold, and thus a higher environmental condition deviation correction coefficient; a smaller Euclidean distance indicates a smaller deviation between the current operating conditions and the operating conditions corresponding to the baseline clog threshold, and thus a lower environmental condition deviation correction coefficient. The current shallow layer clogging severity is obtained by multiplying the difference between the membrane shell's real-time permeate data and the first-level warning threshold by the environmental condition deviation correction coefficient. The controller outputs an opening adjustment command based on the clogging severity, dynamically reducing the valve opening of the electric regulating valve on the inlet side or the electric regulating valve on the permeate side of the membrane shell.

[0067] In the above optional implementation, the deviation of operating conditions is quantified by the Euclidean distance between real-time permeate data and the optimally matched historical average permeate data, enabling adaptive dynamic selection of the environmental condition deviation correction coefficient. The Euclidean distance objectively quantifies the overall difference between the current comprehensive operating conditions (such as water temperature, raw water turbidity, influent salinity, and operating pressure) and the historical operating conditions corresponding to the benchmark clogging threshold. The larger the Euclidean distance, the stronger the current operating condition disturbance, and the higher the corresponding correction coefficient; the smaller the Euclidean distance, the more closely the current operating environment matches the calibrated benchmark operating conditions, and the closer the corresponding correction coefficient is to 1. This accurately quantifies the degree of interference of operating condition fluctuations on the apparent permeate conductivity of the membrane housing. The shallow clogging degree is calculated based on the difference between real-time permeate data and the first-level warning threshold multiplied by the environmental condition deviation correction coefficient. This incorporates the operating condition disturbance interference into the clogging quantification calculation, avoiding overestimation / underestimation of the actual clogging degree of the membrane housing solely based on the conductivity difference. The controller outputs continuously adjustable valve opening commands based on the corrected degree of clogging. The higher the clogging quantification value, the more dynamically the flow opening of the regulating valve on the inlet or product water side is reduced, achieving a one-to-one adaptive match between the degree of clogging and the flow restriction opening. The flow restriction control scale closely matches the actual fouling state of the membrane housing.

[0068] In an optional implementation, performing complete shut-off isolation on a severely clogged membrane shell includes: The inlet valve and product water valve of the abnormal membrane shell are closed synchronously, and the valve status is electrically locked.

[0069] After the system determines that the membrane housing is severely blocked, the controller synchronously sends a valve closing control signal and drives the front inlet valve and the rear product water valve of the abnormal membrane housing to perform a full closing action in parallel. After the controller collects the position feedback signal of the two valves being fully closed, it immediately applies an electrical interlock command to the inlet valve and the product water valve synchronously to lock the current fully closed state of the valves and complete the complete isolation operation of the severely blocked membrane housing.

[0070] The aforementioned optional implementation method, by simultaneously closing the inlet and product water valves, can create a double isolation at both the upstream and downstream ends of the waterline. This achieves complete physical separation of the faulty membrane housing from the entire system pipeline, thoroughly preventing the flow of deteriorated and substandard product water inside the membrane housing into the common product water header, and eliminating the risk of a single severely faulty membrane housing contaminating all qualified product water. An electrical interlock control is added after the valves are fully closed to protect against scenarios such as control system signal jitter, remote misoperation, and accidental manual valve switching. This locks the valves in the fully closed state, preventing accidental valve opening during isolation and thus preventing fault recurrence, thereby improving the reliability and safety of severe fault isolation.

[0071] In an optional implementation, the membrane module water quality anomaly isolation control method further includes: Record the abnormal data, isolation duration, and operating parameters of the abnormal membrane shell to form a single shell abnormality ledger; Cluster analysis was performed on the abnormal records to identify common causes of blockage in membrane modules under the same operating conditions; Optimize the pretreatment feed water parameters, operating pressure, and flushing cycle of the membrane module based on common causes of clogging.

[0072] After identifying minor and severe clogging of the membrane housing and executing corresponding isolation control operations, the system automatically collects and stores comprehensive information about the fault, forming an independent single-housing-household anomaly log for each membrane housing. The log includes: the unique serial number of the anomalous membrane housing, the time of the anomaly, the anomaly type (minor clogging / severe clogging), the real-time permeate conductivity at the time of alarm triggering, the first-level warning threshold, the second-level fault threshold, the duration of the isolation, the valve opening adjustment parameters during the isolation phase, the current cumulative operating time of the membrane housing, the current influent turbidity, and the system operating pressure, among other key operating parameters.

[0073] The controller periodically retrieves the historical single-shell anomaly logs for all membrane housings within the same operating condition segment, grouping and summarizing them according to the frequency of anomalies, the periods when anomalies occur in concentrated areas, the corresponding influent water quality range, and the operating pressure range. The statistical results allow for a direct identification of common fault patterns. For example, if most membrane housings become clogged during periods of high influent turbidity, or if the frequency of anomalies increases significantly under prolonged high-pressure operation, or if the existing flushing cycle is too short, leading to continuous accumulation of contaminants, this allows for the rapid identification of common causes of frequent membrane module clogging within that operating condition segment.

[0074] The system automatically optimizes the operational control logic of the entire membrane module based on common clogging causes identified through cluster analysis. If the common cause is high suspended solids in the influent, the system increases the dosage of chemicals in the pretreatment equipment, enhances filtration efficiency, and optimizes the influent water quality parameters. If the common cause is persistently high transmembrane pressure, the system lowers the stable operating pressure range of the membrane module to reduce the compaction rate of contaminants on the membrane shell. If the common cause is an excessively long original flushing cycle interval, the system shortens the automatic in-situ flushing interval and increases the frequency of regular online flushing.

[0075] The above-mentioned optional implementation methods enable traceability of fault data for each membrane housing by automatically generating a single-shell anomaly log. By using clustering to organize the log data, common causes of batch blockages in the same operating condition can be located without complex calculations. Based on these common blockage causes, process conditions such as influent pretreatment, operating pressure, and flushing cycle can be optimized in advance to reduce the frequency of membrane housing blockage anomalies from the source, reduce the number of subsequent anomaly isolation and online flushing operations, reduce equipment wear caused by frequent valve adjustments, extend the overall service life of the membrane housing, and continuously reduce the long-term maintenance workload and consumable costs of the system.

[0076] like Figure 5 As shown in the figure, this embodiment proposes a membrane module water quality abnormality isolation and control device.

[0077] In some embodiments, the membrane module water quality anomaly isolation control device may include multiple functional modules composed of program code segments. The program code of each program segment in the membrane module water quality anomaly isolation control device may be stored in a memory and executed by at least one processor to perform (see details). Figure 3 (Description) Function of membrane module for isolating and controlling abnormal water quality.

[0078] In this embodiment, the membrane module water quality anomaly isolation control device can be divided into multiple functional modules according to its functions. These functional modules may include: a data acquisition module 201, a determination module 202, a comparison module 203, a judgment module 204, and an isolation module 205. As used in this application, a module refers to a series of computer-readable instruction segments that can be executed by at least one processor and perform a fixed function, stored in a memory. In this embodiment, the functions of each module will be detailed in subsequent embodiments.

[0079] The data acquisition module 201 is used to acquire real-time water production data of each membrane shell in the membrane module that is in the same operating condition. The determination module 202 is used to determine the clogging threshold based on the real-time water production data of each membrane shell; The comparison module 203 is used to compare the real-time water production data of each membrane shell with the clogging threshold. The determination module 204 is used to determine the membrane shells whose real-time water production data exceeds the clogging threshold as abnormal membrane shells; Isolation module 205 is used for isolation control of abnormal membrane shells.

[0080] It should be understood that the various variations and specific embodiments of the membrane module water quality anomaly isolation control method provided in the above embodiments are also applicable to the membrane module water quality anomaly isolation control device in this embodiment. Through the detailed description of the membrane module water quality anomaly isolation control method described above, those skilled in the art can clearly understand the implementation process of the membrane module water quality anomaly isolation control device in this embodiment. For the sake of brevity, it will not be described in detail here.

[0081] In this embodiment, a water treatment system is also proposed, including a membrane module and a water quality anomaly isolation and control device for the membrane module; wherein the membrane module includes multiple membrane shells arranged in parallel.

[0082] 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 water quality abnormality isolation control method mentioned above.

[0083] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally speaking, any function performed by a computer program can be easily implemented by the corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits.

[0084] 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.

[0085] 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 isolating and controlling abnormal water quality in a membrane module, characterized in that, The method includes: Collect real-time water production data from each membrane shell in the same operating condition section of the membrane module; The clogging threshold is determined based on the real-time water production data of each membrane shell; Compare the real-time water production data of each membrane shell with the clogging threshold; Membrane shells whose real-time water production data exceeds the clogging threshold are identified as abnormal membrane shells. The abnormal membrane shell is isolated and controlled.

2. The method for isolating and controlling abnormal water quality in membrane modules according to claim 1, characterized in that, The determination of the clogging threshold based on real-time water production data from each membrane shell includes: The average real-time water production data is calculated based on the real-time water production data of each membrane shell. Obtain the average historical water production data that has the smallest deviation from the average real-time water production data; The baseline blockage threshold corresponding to the smallest average historical water production data is used as the first-level early warning threshold. The cumulative runtime of each membrane shell is retrieved, and the aging loss coefficient is determined based on the cumulative runtime. The secondary fault threshold is obtained based on the aging loss coefficient and the primary warning threshold. The first-level warning threshold is lower than the second-level fault threshold.

3. The membrane module water quality abnormality isolation and control method according to claim 2, characterized in that, The step of comparing the real-time water production data of each membrane shell with the clogging threshold includes: The real-time water production data of each membrane shell is compared with the first-level early warning threshold and the second-level fault threshold in a graded manner. If the real-time water production data is between the first-level warning threshold and the second-level fault threshold, the corresponding membrane shell is determined to be a slightly blocked abnormal membrane shell. If the real-time water production data exceeds the level 2 fault threshold, the corresponding membrane shell is determined to be a severely clogged abnormal membrane shell.

4. The membrane module water quality abnormality isolation and control method according to claim 3, characterized in that, The isolation control of the abnormal membrane shell includes: For membrane shells with minor blockage, local flow restriction and isolation should be implemented, and in-situ flushing pretreatment should be initiated simultaneously; For severely clogged membrane shells, complete shut-off isolation is performed, and a backup membrane shell is automatically switched to maintain the system's permeate flux.

5. The membrane module water quality abnormality isolation and control method according to claim 4, characterized in that, The method of performing local flow-limiting isolation on slightly blocked or abnormal membrane shells includes: Dynamically reduce the opening of the regulating valves on the product water side / inlet water side according to the degree of membrane blockage; After the flow restriction stabilizes, the low-pressure in-situ flushing process is started simultaneously.

6. The membrane module water quality anomaly isolation and control method according to claim 4, characterized in that, The complete cut-off isolation of the severely blocked and abnormal membrane shell includes: The inlet valve and product water valve of the abnormal membrane shell are closed synchronously, and the valve status is electrically locked.

7. The method for isolating and controlling abnormal water quality in membrane modules according to claim 1, characterized in that, The method further includes: Record the abnormal data, isolation duration, and operating parameters of the abnormal membrane shell to form a single shell abnormality ledger; Cluster analysis was performed on the abnormal log to identify common causes of blockage in membrane modules under the same operating conditions; Optimize the pretreatment feed water parameters, operating pressure, and flushing cycle of the membrane module based on common causes of clogging.

8. A membrane module water quality abnormality isolation and control device, characterized in that, The device includes: The data acquisition module is used to collect real-time water production data from each membrane shell in the same operating condition section of the membrane module. The determination module is used to determine the clogging threshold based on the real-time permeate data of each membrane shell; The comparison module is used to compare the real-time water production data of each membrane shell with the clogging threshold. The determination module is used to determine that membrane shells whose real-time water production data exceeds the clogging threshold are abnormal membrane shells; An isolation module is used to isolate and control the abnormal membrane shell.

9. A water treatment system, characterized in that, It includes a membrane module and a membrane module water quality abnormality isolation and control device as described in claim 8; wherein the membrane module includes a plurality of membrane shells arranged in parallel.

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 water quality abnormality isolation control method as described in any one of claims 1 to 7.

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