Ultrafiltration membrane pollution prediction and self-adaptive backwashing control method

Abstract

This invention discloses a method for predicting and adaptively controlling ultrafiltration membrane fouling: Real-time acquisition of transmembrane pressure difference, water temperature, and operational data; calculation of the fouling index FI after viscosity compensation normalization and sliding filtering; and fitting of the fouling rate k using a sliding window least squares method. Based on FI and k, a graded prediction is made, and the remaining time to reach the backwash threshold is pre-scheduled according to k extrapolation, with the backwash cycle adaptively adjusted between 15 and 30 minutes. Upon triggering, a closed-loop sequence of low-pressure flushing, 0.25 MPa high-pressure flushing, and concentrate discharge is executed, with the effect evaluated by the pressure difference recovery rate and a self-calibrated benchmark. Continuous ineffectiveness prompts for chemical cleaning. A four-level safety interlock and a network disconnection degradation mode are implemented, and data is uploaded to the cloud for continuous optimization of thresholds and cycles. This invention provides accurate fouling detection, on-demand backwashing, verifiable effects, and full-process interlocking, significantly reducing backwash water and electricity consumption and extending membrane life.

Description

Technical Field

[0001] This invention relates to the field of membrane separation technology in wastewater treatment, specifically to a method for predicting and adaptively backwashing ultrafiltration membrane fouling. Background Technology

[0002] Ultrafiltration membrane modules are key components in industrial parks, municipal and other wastewater treatment plants to ensure effluent quality. The increase in transmembrane pressure due to membrane fouling directly determines operating energy consumption and membrane lifespan. Existing ultrafiltration systems generally use fixed-cycle backwashing: when fouling is light, backwashing is too frequent, wasting clean water and electricity, and shortening the lifespan of valves and membrane fibers; when fouling is heavy, backwashing is not timely, and excessive transmembrane pressure leads to membrane fiber breakage and reduced permeate flow.

[0003] While some systems have introduced differential pressure-triggered backwashing, they rely solely on the instantaneous value of the differential pressure, which has three main drawbacks: First, the transmembrane differential pressure varies significantly with water temperature—as the water temperature decreases, the viscosity of the water increases, and even if the degree of membrane fouling remains unchanged, the transmembrane differential pressure will increase significantly under the same flux. In winter, when the operating conditions are low, using a fixed differential pressure threshold to determine fouling will inevitably lead to false alarms and overwashing. Existing differential pressure triggering schemes generally do not include temperature compensation normalization. Second, the instantaneous value of the differential pressure is easily affected by pump start-up and shutdown, valve switching, and signal noise. The lack of a rate-dimensional criterion makes it impossible to distinguish between "slow fouling" and "rapid fouling." Third, there is a lack of closed-loop evaluation of the backwashing effect, and when backwashing is ineffective, it cannot be detected in time and escalated to chemical cleaning.

[0004] In addition, backwashing operations are strongly correlated with the liquid levels of the raw water tank, clear water tank, and concentrate tank. Without a systematic safety interlock device, there is a risk of idling, overflow, and dry pumping in unattended scenarios. Furthermore, a system operating independently at a single station cannot continuously optimize the fouling threshold by utilizing historical data from multiple stations and membrane modules.

[0005] Therefore, there is a need for an ultrafiltration membrane fouling prediction and adaptive backwashing control method that is based on temperature-compensated normalized transmembrane pressure difference and operating data, uses fouling index and fouling rate as dual criteria to predict and schedule membrane fouling in advance, adaptively adjusts the backwashing cycle, performs closed-loop control and benchmark self-calibration of backwashing sequence and effect, and has complete safety interlocking and cloud-based collaborative optimization capabilities. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide an ultrafiltration membrane fouling prediction and adaptive backwashing control method. This method constructs a fouling index using temperature-compensated normalized pressure difference, fits the fouling rate using least squares, predicts membrane fouling in stages based on dual criteria and extrapolates to schedule backwashing in advance, executes backwashing in a closed-loop manner according to time sequence, evaluates the backwashing effect using pressure difference recovery rate, and self-calibrates the cleanliness benchmark. This enables on-demand backwashing, verifiable backwashing effectiveness, full-process safety interlocking, and continuous cloud-based optimization.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for predicting and adaptively controlling ultrafiltration membrane fouling, comprising the following steps: S1, collecting transmembrane pressure difference ΔP, water temperature T, cumulative running time t, and cumulative permeate volume Q; S2, calculating the fouling index FI after viscosity compensation normalization and sliding filtering, and fitting the fouling rate k using least squares; S3, predicting and adjusting the backwashing cycle according to FI and k, and extrapolating the remaining time to schedule in advance; S4, performing backwashing in the sequence of low-pressure flushing, high-pressure flushing, and concentrate discharge, with the high-pressure set at 0.25 MPa; S5, evaluating the backwashing effect with a pressure difference recovery rate η≥80% and self-calibrating the cleanliness benchmark, with continuous ineffectiveness prompting chemical cleaning; S6, uploading the operating data to the cloud platform, with the cloud continuously optimizing the threshold and cycle and issuing updates.

[0008] Furthermore, the standard backwash cycle is 30 minutes, which can be adjusted between 15 minutes and 30 minutes by linear interpolation using FI. The number of backwashes within 24 hours is set to an upper limit. When the extrapolated remaining time tpre is less than the current cycle, backwashing is performed in advance according to the safety factor λ. Multi-membrane groups are queued in ascending order of tpre.

[0009] Furthermore, the backwash sequence is as follows: close the inlet and outlet water valves → low-pressure flushing → switch the three-way valve to high-pressure flushing for 60s to 80s → discharge concentrated water and sludge → restore filtration. Each duration can be remotely adjusted.

[0010] Furthermore, a four-level safety interlock is set up: pump stop when the raw water tank is low, backwashing is prohibited when the clean water tank is low, forced discharge when the concentrate tank is high, and shutdown when the differential pressure exceeds the limit; when the differential pressure signal is abnormal or the network is disconnected, it is downgraded to fixed-cycle backwashing and alarm is triggered.

[0011] Furthermore, viscosity compensation is calculated using the empirical formula μT / μT0=1 / (1+0.0337×(T-20)+0.000221×(T-20)²); the contamination rate is fitted using a sliding window least squares method.

[0012] Furthermore, the cloud platform establishes a membrane fouling profile, generates maintenance work orders, updates thresholds using an exponentially weighted moving average, and incorporates backwash water and electricity consumption into the ledger; the electrical control terminal adopts a power-type controller intelligent agent with STM32F407 as its core, and backwash triggering, timing execution, and safety interlocking are completed locally and autonomously.

[0013] Compared with the prior art, the present invention has the following beneficial effects: (1) Accurate pollution judgment: Temperature compensation normalization eliminates the influence of water temperature on pressure difference criteria, no false alarms in low temperature conditions in winter, sliding filter and least squares fitting suppress noise, and dual criteria can distinguish between slow pollution accumulation and rapid clogging; (2) Backwashing on demand and can be scheduled in advance: Backwashing is arranged in advance by extrapolating the remaining time, and backwashing of multiple membrane groups is staggered, and the water and electricity consumption of backwashing is significantly reduced; (3) Verifiable effect: Pressure difference recovery rate is evaluated in a closed loop and self-calibrated benchmark, and ineffective backwashing is detected in time to prevent membrane groups from operating with defects; (4) Safe and reliable: Four-level interlocking covers abnormal working conditions without on-duty personnel, and network failure and degradation do not affect backwashing; (5) Continuous optimization: The threshold and cycle are continuously optimized in the cloud based on the historical data of multiple membrane groups, which extends membrane life and reduces operation and maintenance costs.

[0014] Engineering applications have verified that, compared with fixed-cycle backwashing, this invention can reduce backwash water consumption by about 37%, backwash power consumption by about 31%, chemical cleaning frequency by about 50%, average transmembrane pressure difference by about 29%, and extend the expected life of the membrane module by more than double. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention. Figure 2 A schematic diagram of the ultrafiltration membrane module piping and backwashing circuit structure and control wiring for implementing the method of the present invention; Figure 3 This is a timing diagram of the backwashing execution of the present invention; Figure 4 This is a schematic diagram illustrating the principle of dual-criteria prediction and remaining time extrapolation in this invention. Figure 5 This is a comparison curve of the transmembrane pressure difference operation before and after the modification in an application case of the present invention; Figure 6 Figure 7 is a schematic diagram of the overall SCADA monitoring interface of the ultrafiltration membrane fouling prediction and adaptive backwashing control system of the present invention; Figure 8 is a partial schematic diagram of the monitoring interface of the ultrafiltration membrane unit system of the present invention.

[0016] In the diagram: 1—raw water tank, 2—ultrafiltration inlet pump, 3—inlet pressure transmitter, 4—ultrafiltration membrane module, 5—product water pressure transmitter, 6—clean water / recycled water tank, 7—low-pressure flushing circuit, 8—high-pressure flushing pump, 9—three-way valve, 10—concentrate discharge valve, 11—concentrate tank, 12—sludge discharge valve, 13—sludge bucket, 14—electrical control terminal, 15—cloud platform, 16, 17, 18—liquid level switch. Detailed Implementation

[0017] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0018] Example 1: As Figure 2As shown, the ultrafiltration membrane module system implementing this method includes a raw water tank 1, an ultrafiltration feed pump 2, an ultrafiltration membrane module 4, a clean water / recycled water tank 6, a low-pressure flushing circuit 7, a high-pressure flushing pump 8, a three-way valve 9, a concentrate discharge valve 10, a concentrate tank 11, a sludge discharge valve 12, a sludge bucket 13, an electrical control terminal 14, and a cloud platform 15. A feed water pressure transmitter 3 and a product water pressure transmitter 5 are respectively installed on the feed water side and product water side of the membrane module. The water temperature is collected by a temperature sensor on the feed water side. Level switches 16, 17, and 18 are installed in the raw water tank 1, clean water / recycled water tank 6, and concentrate tank 11, respectively. The electrical control terminal 14 collects pressure, temperature, and level signals via an RS485 bus, drives the feed pump, flushing pump, and each solenoid valve via a relay output circuit, and interacts with the cloud platform 15 via 4G / Ethernet.

[0019] like Figure 1 As shown, in step S1, the sampling period is 10s; in step S2, the transmembrane pressure difference is first converted to the normalized pressure difference ΔPn at a reference temperature of 20℃ using the empirical formula μT / μT0=1 / (1+0.0337×(T-20)+0.000221×(T-20)²), and then the pump valve disturbance is suppressed by 12-point moving average filtering. The contamination index FI=ΔPn / ΔP0; the contamination rate k is obtained by least squares linear fitting of no less than 90 filtered pressure difference points within a 15-minute sliding window, with units of h⁻¹. Temperature compensation decouples the criterion from the water temperature: when the water temperature drops from 25℃ to 8℃, the viscosity of the water increases by about 38%. The uncompensated pressure difference criterion will produce a false "contamination increment" of about the same magnitude. After compensation, this false increment is eliminated.

[0020] In step S3, the first threshold FI1 = 1.15, the second threshold FI2 = 1.35, and the rate threshold k0 = 0.15h⁻¹; when FI < FI1, maintain the standard backwash cycle T0 = 30min; when FI1 ≤ FI < FI2 or k > k0, adjust the cycle between 15min and 30min according to Tbw = 30 - 15 × (FI - FI1) / (FI2 - FI1), and the number of backwashes within any 24 hours shall not exceed 40; simultaneously, extrapolate the remaining time according to tpre = t0 + (FI2 - FI) / k, and when tpre is less than the current cycle, advance the next backwash time to t0 + 0.8 × tpre, such as... Figure 4 As shown; when multiple membrane units share a backwashing circuit, they are queued in ascending order of tpre for staggered backwashing; backwashing is triggered immediately when FI≥FI2.

[0021] In step S4, such as Figure 3As shown, the backwash sequence is as follows: close the inlet valve and product water valve, stop the inlet pump 2 → open the low-pressure flushing circuit 7 and flush at 0.10 MPa for 30 seconds → switch the three-way valve 9 and start the high-pressure flushing pump 8 to flush at 0.25 MPa for 60-80 seconds, with the high-pressure side trigger pressure set by the electrical contact pressure gauge → open the concentrate discharge valve 10 and sludge discharge valve 12 to discharge concentrate and sludge water for 20 seconds → close the backwash circuit and restore filtration. The set values ​​for each flushing pressure and duration can be remotely modified via the cloud platform.

[0022] In step S5, after backwashing ends and filtration is restored for 5 minutes, the differential pressure recovery rate η = (ΔPn before - ΔPn after) / ΔPn before is calculated; if η ≥ 80%, the backwashing is deemed effective; if η ≥ 95%, the cleanliness benchmark ΔP0 is updated with the current ΔPn, so that the contamination benchmark adapts and drifts with the natural aging of the membrane module, avoiding overwashing in the later stage caused by a fixed benchmark; if η is less than 80% for two consecutive times, a chemical cleaning prompt is output and a maintenance work order is generated.

[0023] In step S6, the electronic control terminal uploads the triggering conditions, timing parameters, differential pressure recovery rate, water temperature, and permeate volume for each backwash to the cloud platform; the cloud platform establishes a pollution profile for each membrane module and marks membrane modules with larger k values ​​under the same influent water quality conditions as key maintenance targets; each threshold is updated according to the exponential weighted moving average, i.e., θ←(1-α)θ+αθ*, α=0.2, and θ* is the optimal estimate obtained from the statistical data of the past 30 days; the platform also includes the backwash water consumption and sludge discharge volume in the energy consumption and material consumption ledger.

[0024] Safety interlocks and degradation: The feed pump stops when the raw water tank level is below the low-level set value; backwashing is prohibited when the clear water tank level is below the low-level set value; concentrate discharge is forcibly started when the concentrate tank level is above the high-level set value; the system immediately stops and alarms when the transmembrane pressure difference exceeds the safety limit of 0.25MPa. When the pressure transmitter signal is abnormal or communication with the cloud platform is interrupted, the system automatically degrades to a fixed-cycle 30-minute backwash and alarms. Backwash triggering, timing execution, and safety interlocks are all completed locally by the electrical control terminal.

[0025] The power control terminal adopts an STM32F407-based power controller intelligent agent with a main frequency of 168MHz. The above-mentioned filtering, fitting, prediction, scheduling and timing control programs run on the chip. The hardware configuration includes an RS485 interface, relay output and 4G / Ethernet communication module. The local autonomous design ensures that the backwashing operation is not interrupted in the event of a network outage.

[0026] Example 2 (Application Case): A wastewater treatment plant in an industrial park has a treatment capacity of 2000 m³ / d. The reclaimed water system is equipped with four PVDF hollow fiber ultrafiltration membrane modules, each with a membrane area of ​​50 m² and a design flux of 55 L / (m²·h). Before the upgrade, a fixed backwashing cycle of 30 minutes was used. In June 2025, the system was upgraded to the method of this invention, with the addition of influent / product water pressure transmitters and water temperature sensors, and the deployment of an electrical control terminal connected to a cloud platform. The system operated continuously for six months before and after the upgrade, and the operational data comparison is shown in Table 1. Figure 5 and Figure 6 As shown.

[0027] Before the modification, the fixed-cycle backwashing averaged 48 backwashes per day, and the transmembrane pressure differential baseline continued to climb, reaching the 0.25 MPa limit around day 26. Within six months, there were 4 chemical cleanings and 2 membrane fiber breakages. After the modification, the system backwashes on demand, averaging about 31 backwashes per day. The transmembrane pressure differential stabilized at around 0.17 MPa. Within six months, there were 2 chemical cleanings and no membrane fiber breakages occurred. Under winter water temperatures of 8℃~12℃, temperature compensation normalization eliminated the false pollution increase caused by viscosity increase and no low-temperature false triggering occurred.

[0028] Table 1 Comparison of operational indicators before and after the renovation (6-month statistics) Operating indicators Fixed-cycle backwashing (before modification) This invention (after modification) change Backwash water consumption (m³ / month) 186 117 Down 37% Backwash power consumption (kWh / month) 245 169 down 31% Average number of backwashes per day (times / set) 48 31 35% decrease Chemical cleaning (per six months) 4 2 50% decrease Average transmembrane pressure difference (MPa) 0.24 0.17 Down 29% Membrane filament breakage (times / year) 2 0 eliminate Membrane module expected lifespan (years) Approximately 2.5 ≥5 Extend by more than 100% like Figure 5 As shown, the transmembrane pressure differential curve changed from a continuous rise to a stable fluctuation after the modification, indicating that backwashing on demand promptly removed reversible fouling; Figure 6 As shown, the four indicators of backwash water consumption, power consumption, chemical cleaning frequency and average transmembrane pressure difference all decreased, verifying the technical effectiveness of dual-criteria prediction, advance scheduling and closed-loop evaluation.

[0029] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting and adaptively backwashing ultrafiltration membrane fouling, characterized in that, Includes the following steps: S1. The pressure transmitters installed on the inlet and outlet water sides of the ultrafiltration membrane module collect the transmembrane pressure difference ΔP in real time, collect the water temperature T simultaneously, and record the cumulative operating time t and cumulative water production Q of the membrane module. S2. Normalize the transmembrane pressure difference according to water temperature and viscosity compensation to obtain the normalized pressure difference ΔP. n =ΔP×μ T / μ T0 , where μ T Let μ be the dynamic viscosity of water at water temperature T. T0 The dynamic viscosity of water at the reference temperature T0 is given; after applying a moving average filter to the normalized pressure difference, the pollution index FI = ΔP is calculated. n / ΔP0, and perform least squares linear fitting on the normalized pressure difference sequence within the sliding time window. The slope of the fitting is the fouling rate k, where ΔP0 is the reference pressure difference of the membrane module in clean state. S3. Based on FI and k, classify and predict membrane fouling status and schedule backwashing in advance: When FI is less than the first threshold FI1, maintain the standard backwashing cycle; when FI reaches the first threshold FI1 and is less than the second threshold FI2, or when k is greater than the rate threshold k0, shorten the backwashing cycle proportionally and adjust according to t. pre =t0 + (FI2 - FI) / k Extrapolated to the remaining time t for FI to reach the second threshold FI2 pre Based on this, the backwashing time is scheduled in advance; when FI reaches the second threshold FI2, backwashing is triggered immediately; S4. After triggering backwashing, perform backwashing in the following sequence: first low-pressure flushing, then high-pressure flushing, and finally concentrated water discharge. The trigger pressure on the high-pressure side of backwashing is set to 0.25MPa by the electric contact pressure gauge. S5. After backwashing, calculate the differential pressure recovery rate η = (ΔP) n前 -ΔP n后 ) / ΔP n前 When η≥80%, backwashing is deemed effective and filtration operation is resumed; when η≥95%, the clean state baseline pressure difference ΔP0 is updated with the current normalized pressure difference. A chemical cleaning prompt will be issued when η is less than 80% twice consecutively. S6. Upload the triggering conditions, timing parameters and differential pressure recovery rate of each backwash to the cloud platform. The cloud platform will then continuously optimize the first threshold FI1, the second threshold FI2, the rate threshold k0 and the standard backwash cycle based on historical data from multiple membrane modules, and send the updated data to the power control terminal.

2. The ultrafiltration membrane fouling prediction and adaptive backwashing control method according to claim 1, characterized in that: In step S3, the standard backwash cycle T0 is 30 minutes; when shortening the backwash cycle, the backwash cycle is adjusted between 15 minutes and 30 minutes by linear interpolation according to Tbw = T0 - 15 × (FI - FI1) / (FI2 - FI1), and the number of backwashes within any 24 hours does not exceed the preset upper limit.

3. The ultrafiltration membrane fouling prediction and adaptive backwashing control method according to claim 1, characterized in that: In step S3, when the remaining time tpre is less than the current backwash cycle, the next backwash time is advanced to t0 + λ × tpre, where λ is a safety factor with a value of 0.7 to 0.

9. When multiple membrane units share the backwash circuit, they are backwashed in ascending order of tpre to avoid simultaneous backwashing.

4. The ultrafiltration membrane fouling prediction and adaptive backwashing control method according to claim 1, characterized in that: The backwashing sequence in step S4 is as follows: close the inlet valve and the product water valve → open the low-pressure flushing circuit and flush with the first pressure for the first duration → switch the three-way valve to open the high-pressure flushing circuit and flush with 0.25MPa for 60s to 80s → open the concentrate discharge valve and the sludge discharge valve to discharge the concentrate and sludge → close the backwashing circuit and restore filtration; the first pressure, the first duration, and the high-pressure flushing duration can all be remotely adjusted by the cloud platform.

5. The ultrafiltration membrane fouling prediction and adaptive backwashing control method according to claim 1, characterized in that: The method also includes safety interlock steps: when the raw water tank level is lower than the low setting value, the ultrafiltration feed pump is stopped; when the clear water tank level is lower than the low setting value, backwashing is prohibited; when the concentrate tank level is higher than the high setting value, concentrate discharge is forcibly started; when the transmembrane pressure difference exceeds the safety limit, the machine is immediately stopped and an alarm is triggered.

6. The ultrafiltration membrane fouling prediction and adaptive backwashing control method according to claim 1, characterized in that: In step S2, viscosity compensation is performed according to the empirical formula μT / μT0=1 / (1+a×(T-T0)+b×(T-T0)). 2 The calculation is performed, where a = 0.0337, b = 0.000221, and T0 = 20℃. The window length, sliding time window length, and sampling period of the moving average filter can all be configured. When the pressure transmitter signal is abnormal or communication is interrupted, it switches to the degradation mode of backwashing at a fixed period and outputs an alarm.

7. The ultrafiltration membrane fouling prediction and adaptive backwashing control method according to claim 1, characterized in that: In step S2, the least squares fitting is performed according to k = Σ(t) i -t̄)(ΔP ni -ΔP n̄ ) / Σ(t i -t̄) 2 Calculate, where t i ΔP ni Let t̄ and ΔP be the i-th sampling time within the sliding time window and their normalized pressure difference. n̄ Let i be the average of the sampling time and the normalized pressure difference within the window, i = 1, 2, ..., N, N ≥ 10.

8. The ultrafiltration membrane fouling prediction and adaptive backwashing control method according to claim 1, characterized in that: In step S4, the high-pressure flushing circuit is connected to the membrane module inlet side by an independent backwash pump via a three-way valve. The backwash water source is taken from the clean water tank, and the backwash sludge discharge water is discharged into the sludge bucket via the sludge discharge valve. The switching time of the three-way valve and the opening and closing time of the sludge discharge valve are automatically controlled by the programmable controller according to the specified timing.

9. The ultrafiltration membrane fouling prediction and adaptive backwashing control method according to claim 1, characterized in that: In step S6, the cloud platform establishes a pollution profile for each membrane module, marks membrane modules with higher pollution rates under the same influent water quality conditions as key maintenance targets and generates maintenance work orders; the platform updates the thresholds using an exponentially weighted moving average method, and includes backwash water consumption and backwash sludge discharge in the energy and material consumption ledger.

10. The ultrafiltration membrane fouling prediction and adaptive backwashing control method according to any one of claims 1 to 9, characterized in that: The electrical control terminal executing the method adopts a power-type controller intelligent agent with STM32F407 as its core. The electrical control terminal collects pressure transmitter and level switch signals via RS485 bus, drives backwash pump and solenoid valve via relay output circuit, and interacts with the cloud platform via 4G / Ethernet. Backwash triggering, timing execution and safety interlocking are completed autonomously locally on the electrical control terminal. The backwash operation is not affected by the interruption of cloud platform communication.

Images