A small-sized nanofiltration membrane testing device and testing and online monitoring method

Abstract

This invention relates to the field of membrane treatment technology, and discloses a small nanofiltration membrane testing device and a testing and online monitoring method. The device includes a material tank, the output end of which is connected to the input end of a feed pump. The output end of the feed pump is connected to the input ends of three sets of cross-flow filtration membrane modules. The first output ends of the three sets of cross-flow filtration membrane modules are interconnected and connected to the input end of a pressure regulating valve. The output end of the pressure regulating valve is connected to the input end of a flow meter, and the output end of the flow meter is connected to the input end of the material tank. This invention also discloses a testing and online monitoring method for the small nanofiltration membrane testing device. This invention can simultaneously test different nanofiltration membranes, ultrafiltration membranes, or reverse osmosis membranes. The monitoring method allows for real-time observation of membrane material performance indicators, improving testing efficiency. Furthermore, the device is small, portable, and requires little floor space, making it suitable for long-term use in various laboratories.

Description

Technical Field

[0001] This invention relates to the field of membrane treatment technology, and more specifically, to a small nanofiltration membrane testing device and a testing and online monitoring method. Background Technology

[0002] Nanofiltration technology, characterized by low operating pressure and high flux, is widely recognized as one of the most efficient and energy-saving methods for wastewater treatment or reuse. The flux of conventional nanofiltration membranes is higher than that of conventional reverse osmosis membranes, and the operating pressure required for nanofiltration is significantly lower than that for reverse osmosis. However, the salt rejection rate of nanofiltration membranes is significantly lower than that of reverse osmosis membranes, mainly due to their loose active layer and inherently large pore size. Many studies have reportedly attempted to improve the salt rejection capacity of nanofiltration membranes by adding nanomaterials or surface coatings. In terms of testing separation performance, how to simultaneously test nanofiltration membranes before and after modification is currently a major research focus.

[0003] In the testing of nanofiltration membranes, it is crucial to test the separation performance of different nanofiltration membranes under identical feed temperature, pressure, and flow rate conditions, enabling comparative analysis of the separation performance of different types of nanofiltration membranes. However, the utility model patent with application number 2020020116396.7 discloses a nanofiltration membrane performance testing device, which only has one nanofiltration cell. This device cannot simultaneously test different nanofiltration membranes under the same conditions, and when comparing the membrane performance indicators of different nanofiltration membranes, it cannot guarantee the consistency of the feed liquid and the testing environment, resulting in a lack of unified testing standards for the measured membrane performance indicators.

[0004] Most nanofiltration membrane testing instruments on the market currently cannot accurately control test temperature, pressure, and flow rate, and lack real-time monitoring and alarms for experimental conditions throughout the testing process. Furthermore, existing laboratory nanofiltration membrane testing instruments cannot achieve integrated testing of water flux and salt rejection rate, requiring the use of other equipment. Patent application number 201910910465.3 discloses an integrated membrane performance testing device and method, which can simultaneously test the performance of ultrafiltration and nanofiltration membrane materials; however, this device lacks online monitoring of test parameters such as temperature and pressure, and cannot achieve real-time observation and early warning of water flux and salt rejection rate.

[0005] In addition, cross-flow filtration membrane modules with smaller test areas experience more uniform stress, resulting in more accurate test results. For laboratory nanofiltration membrane testing conditions, nanofiltration membrane testing instruments should be small and portable, while increasing the pressure range, and simultaneously enabling the application of materials such as nanofiltration, ultrafiltration, and reverse osmosis membranes, while saving energy and reducing floor space. Therefore, how to design a small-scale nanofiltration membrane testing device that simultaneously achieves real-time monitoring and alarm of test conditions and separation performance is a key technical problem that needs to be solved by those skilled in the art.

[0006] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0007] To address the problems in related technologies, this invention proposes a small nanofiltration membrane testing device and a testing and online monitoring method to overcome the aforementioned technical problems existing in the prior art.

[0008] Therefore, the specific technical solution adopted by the present invention is as follows:

[0009] According to one aspect of the present invention, a small nanofiltration membrane testing device is provided, comprising a material tank, a liquid level controller disposed at the bottom of the material tank, an output end of the material tank connected to the input end of a feed pump, and an external feed valve connected between the feed pump and the material tank, one end of the external feed valve having an external feed port, the output end of the feed pump being connected to the input ends of three sets of cross-flow filtration membrane assemblies respectively, the first output ends of the three sets of cross-flow filtration membrane assemblies being interconnected and connected to the input end of a pressure regulating valve, the output end of the pressure regulating valve being connected to the input end of a flow meter, and a discharge valve disposed between the pressure regulating valve and the flow meter, the output end of the flow meter being connected to the input end of the material tank, and a connection between the flow meter and the material tank. An external reflux valve is provided, with one end of which is connected to an external reflux port. The input ends of the three cross-flow filtration membrane modules are each connected to a membrane module feed valve. The first output ends of the three cross-flow filtration membrane modules are each connected to a membrane module discharge valve. The second output ends of the three cross-flow filtration membrane modules are each connected to a corresponding permeate outlet. The first output ends of the permeate outlets are each connected to a corresponding beaker. Each beaker has a corresponding electronic balance at its bottom. The second output ends of the permeate outlets are interconnected and connected to a material tank. Pressure sensors are installed between the membrane module feed valve and the feed pump, and between the cross-flow filtration membrane modules and the membrane module discharge valve. A temperature sensor is also installed between the pressure regulating valve and the flow meter.

[0010] Furthermore, to better facilitate testing of the nanofiltration membranes, the three cross-flow filtration membrane modules are arranged in parallel, and each module contains either the same nanofiltration membrane or three different types of nanofiltration membranes. Each cross-flow filtration membrane module includes a membrane base plate, on top of which a matching membrane sheet is mounted. The membrane sheet and the base plate are connected by a sealing ring to ensure feed water stability. A membrane pressure plate, which also matches the base plate, is mounted on top of the membrane sheet and connected to the base plate by several fastening bolts. The pressure plate has a permeate outlet on its top, and each of the three permeate outlets is connected to a corresponding permeate outlet.

[0011] Furthermore, to better facilitate the testing of nanofiltration membranes, the small nanofiltration membrane testing device also includes a conductivity meter mounted on one side of the electronic balance. The output of the conductivity meter is electrically connected to the monitoring terminal, and the output of the monitoring terminal is electrically connected to a mobile terminal. The monitoring terminal is equipped with an inlet / outlet water pressure monitoring module, a water temperature detection module, a water storage monitoring module, a permeate monitoring module, and a processor. The inlet / outlet water pressure monitoring module acquires the pressure values ​​from the pressure sensor to determine the inlet and outlet water pressures for each process. The water temperature detection module acquires the temperature values ​​from the temperature sensor to determine the water temperature during the filtration process. The water storage monitoring module uses a level controller to monitor the water storage volume inside the material tank; when the monitored water storage volume falls below a preset value, it notifies the operator to open the external feed valve to supply water. The permeate monitoring module acquires the instantaneous water volume and conductivity data of the permeate at the electronic balance, monitoring the performance test results of the total water volume and salt rejection rate. The processor analyzes and processes the data from each module.

[0012] According to another aspect of the present invention, a method for testing and online monitoring of a small nanofiltration membrane testing device is provided, comprising the following steps:

[0013] S1. The operator selects the appropriate nanofiltration membrane according to the test conditions, fixes it at the cross-flow filtration membrane assembly, and turns on the equipment;

[0014] S2. Introduce the feed liquid into the material tank, and after the liquid level controller senses the preset liquid level, use the processor to control the small nanofiltration membrane test device to operate normally.

[0015] S3. Utilize the pre-built valve opening prediction model to output the optimal valve opening corresponding to the real-time monitoring data, and adjust the pressure regulating valve according to the optimal valve opening.

[0016] S4. Control the feed liquid to pass through the pressure sensor at the input end of the cross-flow filtration membrane assembly, the cross-flow filtration membrane assembly, and the pressure sensor at the first output end of the cross-flow filtration membrane assembly in sequence before re-entering the material tank. The permeate enters the material tank through the permeate outlet. The test process is carried out when the operating pressure and flow rate are stable and the water flow distribution and flow rate are uniform.

[0017] S5. Connect the three permeate outlets to the corresponding beakers and turn on the monitoring terminal to record the running time and water quality data.

[0018] S6. The concentrated water after being treated by the cross-flow filtration membrane module is returned to the material tank. The purified water is tested by the conductivity meter at the electronic balance to determine whether the cross-flow filtration membrane module is working properly.

[0019] S7. Collect water quality data for a preset time using an electronic balance and conductivity meter, and monitor the data based on the readings of the pressure sensor and temperature sensor on the instrument panel.

[0020] S8. Analyze the separation performance of the nanofiltration membrane based on the collected permeate data;

[0021] S9. After the test process is completed, open the discharge valve to empty all types of water, close the discharge valve, add pure water to the material tank at the preset water level to clean the instrument, reopen the discharge valve and empty all the cleaning water, turn off the feed pump, and close the test device when there is no brackish water or prepared pure water and cleaning water in the instrument.

[0022] Furthermore, in order to obtain the optimal valve opening, a pre-built valve opening prediction model is used to output the optimal valve opening corresponding to the real-time monitoring data, and the pressure regulating valve is adjusted according to the optimal valve opening, including the following steps:

[0023] S31. Collect and preprocess historical operating data of the pressure regulating valve. The operating data includes the pressure, flow rate, and temperature of the feed liquid, as well as the valve opening of the pressure regulating valve.

[0024] S32. A BP neural network valve opening prediction model is constructed using the pressure, flow rate, and temperature of the feed liquid as the number of input layer nodes and the valve opening of the pressure regulating valve as the number of output layer nodes.

[0025] S33. The connection weights and threshold parameters of the BP neural network valve opening prediction model are optimized using the improved Seagull optimization algorithm, and then trained to obtain the optimized BP neural network valve opening prediction model.

[0026] S34. Obtain real-time data on the pressure, flow rate, and temperature of the feed liquid, and use the optimized BP neural network valve opening prediction model to output the optimal valve opening corresponding to the real-time data.

[0027] S35. Generate a control signal based on the optimal valve opening and send the control signal to the pressure regulating valve actuator to adjust the opening of the pressure regulating valve.

[0028] Furthermore, to accelerate the network training process, the connection weights and threshold parameters of the BP neural network valve opening prediction model are optimized using an improved Seagull optimization algorithm, and the optimized BP neural network valve opening prediction model is obtained through training, including the following steps:

[0029] S331. Encode the initial connection weights and thresholds of the BP neural network into an initial seagull population, initialize the seagull population size and the preset number of iterations, and assign an initial position to each seagull.

[0030] S332, and use the BP neural network valve opening prediction model and the current position of the seagull to calculate the fitness of each seagull, and select the seagull with the best fitness as the best seagull for the current iteration.

[0031] S333. Update the position of each seagull according to its fitness, and use the Logistic mapping to perform chaotic iteration on the position of the seagull. After the iteration is completed, reverse map the result back to the original solution space, calculate the new fitness value, and output the new solution if the fitness value of the new solution is better than the old solution; otherwise, retain the old solution.

[0032] S334. Determine whether the preset number of iterations or the required precision has been reached. If yes, output the final position as the optimal seagull position. If no, return to S332.

[0033] S335. Decode the optimal seagull position into the connection weights and threshold parameters of the BP neural network valve opening prediction model, and use these parameters to train the model until the preset training requirements are met, thus obtaining the optimized BP neural network valve opening prediction model.

[0034] Furthermore, in order to enable testing of nanofiltration membranes made of different materials or the same material, the separation performance of the nanofiltration membrane is analyzed based on the collected permeate data, including:

[0035] When the nanofiltration membranes used are made of the same material, analyze and compare the consistency and error of three sets of data within 10 minutes. If the error is less than the preset error threshold, continue to measure the data within the preset time. At the same time, calculate the instantaneous water production, total water volume and salt rejection performance of the cross-flow filtration membrane module. If the error is greater than the preset error threshold, select to close the corresponding permeate outlet of the cross-flow filtration membrane module with the error greater than the preset error threshold, and then continue to measure the data within the preset time.

[0036] When different nanofiltration membranes are used, the stability of three sets of data within 10 minutes is analyzed and compared. After the data stabilizes, the data within a preset time is measured again. At the same time, the instantaneous water production, total water volume and salt rejection performance of the cross-flow filtration membrane module are calculated, and the separation performance of different nanofiltration membranes is analyzed and compared.

[0037] The formula for calculating the total water volume of a cross-flow filtration membrane module is:

[0038]

[0039] The formula for calculating the salt rejection rate of the cross-flow filtration membrane module is as follows:

[0040]

[0041] In the formula, m 组件 R represents the total water volume of the cross-flow filtration membrane module within time t; m0 represents the instantaneous water volume of the cross-flow filtration membrane module at time t; 组件 Indicates the salt rejection rate of the cross-flow filtration membrane module; C 进料液 Indicates the concentration of the feed liquid; C透过液 Indicates the concentration of the permeate; σ 进料液 σ represents the conductivity of the feed liquid. 透过液 This indicates the conductivity of the permeate.

[0042] The beneficial effects of this invention are as follows:

[0043] 1) This invention ensures consistency in performance testing and performance index testing by passing the feed liquid in the material tank into three cross-flow filtration membrane modules and collecting the instantaneous weight and conductivity of the permeate in the sample cell over a certain period of time. In addition, this invention can simultaneously test different nanofiltration membrane materials by changing the pressure, feed liquid concentration and type, and test time during the testing process. That is, using nanofiltration membranes, ultrafiltration membranes, or reverse osmosis membranes can reduce errors or achieve comparative performance analysis. The performance index of the membrane material can be observed in real time using monitoring methods, thereby improving testing efficiency.

[0044] 2) This invention can monitor water temperature, water pressure and test results online, and use a monitoring terminal to calculate the total weight of the permeate and the salt rejection rate in real time, which facilitates constant monitoring and subsequent maintenance.

[0045] 3) The design of this invention can be used for performance testing of nanofiltration membranes, that is, using three sets of parallel cross-flow filtration membrane modules to collect the weight and conductivity of the permeate obtained from filtration.

[0046] 4) This invention designs an automated monitoring system for real-time monitoring of the desalination separation performance and system operation of the device. It can record various data from pressure sensors, temperature sensors, conductivity meters, electronic balances, etc., thereby providing early warning of equipment failures.

[0047] 5) The device of the present invention is small and portable, the equipment is simple, the cross-flow filter membrane assembly has a small area and uniform pressure distribution, it is suitable for long-term use in different laboratories, and energy can be saved by closing the valve during the process, which is simple and efficient. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a schematic diagram of a small nanofiltration membrane testing device according to an embodiment of the present invention;

[0050] Figure 2This is a schematic diagram of the cross-flow filtration membrane assembly in a small nanofiltration membrane testing device according to an embodiment of the present invention.

[0051] In the picture:

[0052] 1. Material tank; 101. External feed valve; 102. External reflux valve; 103. Discharge valve; 104. ; 2. Feed pump; 3. Cross-flow filtration membrane module; 301. Membrane module feed valve; 302. Membrane module discharge valve; 303. Permeate outlet; 304. Beaker; 4. Pressure regulating valve; 5. Flow meter; 6. External feed port; 7. External reflux port; 8. Pressure sensor; 9. Temperature sensor; 10. Liquid level controller; 11. Electronic balance; 12. Monitoring terminal; 13. Mobile terminal; 001. Membrane module base plate; 002. Sealing ring; 003. Membrane sheet; 004. Membrane module pressure plate; 005. Permeate outlet; 006. Fastening bolts. Detailed Implementation

[0053] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention. The components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0054] According to embodiments of the present invention, a small nanofiltration membrane testing device and a testing and online monitoring method are provided.

[0055] Example 1

[0056] like Figures 1-2 As shown, a small nanofiltration membrane testing device includes a material tank 1. The output end of the material tank 1 is connected to the input end of a feed pump 2, and an external feed valve 101 is connected between the feed pump 2 and the material tank 1. One end of the external feed valve 101 is provided with an external feed port 6. The output end of the feed pump 2 is connected to the input ends of three sets of cross-flow filtration membrane assemblies 3. The first output ends of the three sets of cross-flow filtration membrane assemblies 3 are interconnected and connected to the input end of a pressure regulating valve 4. The output end of the pressure regulating valve 4 is connected to the input end of a flow meter 5, and a discharge valve 103 is provided between the pressure regulating valve 4 and the flow meter 5. The output end of the flow meter 5 is connected to the input end of the material tank 1, and an external return valve 102 is also connected between the flow meter 5 and the material tank 1. One end of the external return valve 102 is also connected to an external return port 7. Through a reasonable, compact, and orderly arrangement, the pipeline connection distance between the systems is reduced, and the structural volume of the device is reduced.

[0057] The input ends of the three cross-flow filtration membrane modules 3 are respectively connected to the membrane module feed valve 301, the first output ends of the three cross-flow filtration membrane modules 3 are respectively connected to the membrane module discharge valve 302, the second output ends of the three cross-flow filtration membrane modules 3 are respectively connected to the corresponding permeate outlet 303, and the first output ends of the permeate outlet 303 are respectively connected to the corresponding beaker 304. The second output ends of the permeate outlet 303 are interconnected and connected to the material tank 1.

[0058] In practical applications, the feed liquid can be placed in the material tank 1. Before testing, the permeate is re-entered into the material tank 1 through the permeate outlet 303 via the feed pump 2 and the cross-flow filter membrane assembly 3. During testing, it enters the corresponding beaker 304. The concentrate is re-entered into the material tank 1 through the pressure regulating valve 4 and the flow meter 5 to achieve filtration circulation. Finally, the liquid is drained by opening the discharge valve 103. When the external feed valve 101 and the external return valve 102 are opened, the feed liquid can enter the external return port 7 through the external feed port 6 via the feed pump 2, the cross-flow filter membrane assembly 3, the pressure regulating valve 4, and the flow meter 5 to achieve filtration circulation. Finally, the liquid is drained by opening the discharge valve 103.

[0059] Specifically, the three sets of cross-flow filtration membrane components 3 are arranged in parallel, and the cross-flow filtration membrane components 3 can hold three identical nanofiltration membranes for testing, reducing experimental errors; or they can hold three different nanofiltration membranes prepared under the same conditions for comparative testing, and observe the separation performance of different nanofiltration membranes in real time.

[0060] Specifically, the cross-flow filtration membrane module 3 includes a membrane base plate 001, a membrane sheet 003 that cooperates with the top of the membrane base plate 001, and the membrane sheet 003 and the membrane base plate 001 are connected by a sealing ring 002; a membrane pressure plate 004 that cooperates with the membrane base plate 001 is provided on the top of the membrane sheet 003, and the membrane pressure plate 004 and the membrane base plate 001 are connected by several fastening bolts 006; a permeate outlet 005 is provided on the top of the membrane pressure plate 004, and the three permeate outlets 005 are respectively connected to the three corresponding permeate outlets 303.

[0061] In practical applications, the membrane 003 is laid flat between the membrane module base plate 001 and the membrane module pressure plate 004, and the sealing ring 002 is placed on the membrane module base plate 001. The membrane module base plate 001 and the membrane module pressure plate 004 are identical in shape and size, and each of the four corners of the membrane module base plate 001 and the membrane module pressure plate 004 has several threaded holes. The membrane module base plate 001 and the membrane module pressure plate 004 are connected to the membrane 003 by fastening bolts 006. The permeate outlet 005 is connected to the corresponding permeate outlet 303. Both the membrane module base plate 001 and the membrane module pressure plate 004 are made of SUS316L stainless steel.

[0062] The effective test area of ​​diaphragm 003 is 1.8. 2 πcm2 Suitable for laboratory filtration instruments with an area of ​​2. 2 πcm 2 The nanofiltration membrane, ultrafiltration membrane, or reverse osmosis membrane prepared.

[0063] In addition, the aforementioned small nanofiltration membrane testing device measures 650*400*600cm and is equipped with casters for easy movement and fixation.

[0064] Example 2

[0065] The difference between Example 2 and Example 1 is that Example 2 adds a monitoring device that is compatible with Example 1. This monitoring device also includes an automated monitoring system for real-time monitoring of the device's dilution separation performance and system operation, which facilitates the stable use of the device.

[0066] Specifically, a pressure sensor 8 is installed between the membrane module feed valve 301 and the feed pump 2, and between the cross-flow filter membrane module 3 and the membrane module discharge valve 302. A temperature sensor 9 is also installed between the pressure regulating valve 4 and the flow meter 5. A liquid level controller 10 is installed at the bottom of the material tank 1.

[0067] Electronic balance 11 is placed under beaker 304 to measure the total water volume of the permeate. A conductivity meter is installed on one side of electronic balance 11. The data from electronic balance 11 and conductivity meter are fed back to monitoring terminal 12 (preferably a computer in this embodiment) to monitor the instantaneous water volume of cross-flow filter membrane assembly 3 and issue an early warning. At the same time, the total water volume and salt rejection rate data of the device are sent to the mobile APP to provide a basis for subsequent maintenance and replacement.

[0068] The monitoring terminal 12 is equipped with an inlet and outlet water pressure monitoring module, a water temperature detection module, a water storage monitoring module, a permeate monitoring module, and a processor;

[0069] Among them, the inlet and outlet water pressure monitoring module is used to obtain the pressure value of pressure sensor 8 and determine the inlet and outlet water pressure of each process.

[0070] The water temperature detection module is used to obtain the temperature value of the temperature sensor 9 and determine the water temperature during the filtration process;

[0071] The water storage monitoring module is used to obtain the water storage volume of material tank 1. By monitoring the water level, the liquid level controller 10 detects that the water storage volume is lower than a certain preset value. The operator can open the external feed valve 101 to supply water to it.

[0072] The permeate monitoring module is used to acquire instantaneous water volume and conductivity data of the permeate at 11 points on the electronic balance, and to feed back the data to the monitoring system at the monitoring terminal 12 to monitor performance test results such as total water volume and salt rejection rate.

[0073] The processor is used to analyze and process the data from each module.

[0074] When the corresponding pressure sensor displays data, the corresponding pressure monitoring module group is activated; when the corresponding pressure sensor does not display data, the corresponding pressure monitoring module group is deactivated. When the corresponding temperature sensor displays data, the corresponding pressure monitoring module group is activated; when the corresponding temperature sensor does not display data, the corresponding pressure monitoring module group is deactivated. When the electronic balance 11 displays instantaneous water volume and conductivity data, the corresponding permeable liquid monitoring module group is activated. When the electronic balance 11 does not display instantaneous water volume and conductivity data, the corresponding permeable liquid monitoring module group is deactivated. When the electronic balance 11 displays instantaneous water volume and conductivity data, the data collected by the online monitoring system is marked throughout the process, and feedback and alerts are sent to the mobile terminal 13. Operators can observe and record the results through the mobile terminal 13.

[0075] The functions of the automated monitoring system are as follows:

[0076] 1) Inlet and outlet water pressure monitoring module:

[0077] One pressure sensor 8 is installed between the feed pump 2 and the cross-flow filter membrane module 3; the other three pressure sensors 8 are installed between the cross-flow filter membrane module 3 and the pressure regulating valve 4, respectively. High and low pressure protection measures for the feed pump 2 ensure stable inlet flow and pressure, preventing damage to the cross-flow filter membrane module 3 due to abnormally high pressure. If the pressure sensor reading is too high, exceeding the set value, the device will alarm and stop the pump, prompting operators to check for malfunctions in the feed pump 2 or pipe rust and blockage. If the reading is too low, an alarm will sound to check for leaks in the membrane module inlet valve 301 and membrane module outlet valve 302, or leaks in the pipeline system. The system has an automatic overpressure protection function; it will automatically shut down when the dangerous value is exceeded, ensuring the safety of personnel and equipment during use.

[0078] 2) Water temperature detection module:

[0079] Temperature sensor 9 is located between pressure regulating valve 4 and flow meter 5 to monitor the temperature throughout the process. If the temperature sensor reading exceeds the set value, the device will alarm, and the operator can connect refrigerant to the material tank to lower the temperature. If the temperature sensor reading remains above the set value for more than 30 minutes, the device will alarm and stop the pump. The operator should then stop the equipment and restart it once the temperature has dropped to room temperature. If the temperature sensor reading is too low, the operator can connect heat transfer fluid to the material tank to raise the temperature. The system has an automatic over-temperature protection function; it will automatically shut down when the dangerous value is exceeded, ensuring the safety of materials and equipment during use.

[0080] 3) Water storage monitoring module:

[0081] The level controller 10 is located at the bottom of the material tank 1 and is equipped with a waterless feed liquid protection alarm. According to the fluctuation of the feed liquid level, the operator operates the external feed valve 101 and the external return valve 102. If the water level in the material tank 1 is lower than the warning level, the operator opens the external feed valve 101, and the feed liquid enters the system from the external feed port 6 to replenish the raw water in the material tank 1. When the water level reaches the highest level, the external feed valve 101 is closed. If the water level in the material tank 1 is too high, the operator opens the external return valve 102, and the feed liquid is discharged from the system from the external return port 7 to lower the water level in the material tank 1 to the intermediate level, and then closes the external return valve 102.

[0082] When the water level in material tank 1 drops continuously over a period of time, deviates significantly from the center line, or even exceeds the lower warning line, it can be judged as an abnormal control status. The unit will shut down and alarm. The operator should check whether each piece of equipment and membrane material is damaged and needs maintenance or replacement, as well as whether the pipeline system is leaking or seeping.

[0083] 4) Water quality monitoring module:

[0084] Electronic balance 11 is placed under beaker 304 to measure the total permeate volume. Simultaneously, a conductivity meter is installed at electronic balance 11 to measure the purified water conductivity σ (unit: uS / cm), and the data is fed back in real-time to monitoring terminal 12 to plot the water volume and conductivity change curves during operation for quality management. This allows for monitoring of the total water volume and salt rejection rate of the device and cross-flow filtration membrane module 3. Simultaneously, the total water volume and salt rejection rate data are sent to mobile terminal 13 (i.e., mobile APP) to provide a basis for subsequent maintenance and replacement. According to the data, if the purified water conductivity σ exceeds the set value, the device will shut down and alarm. The operator will check whether each part is damaged, whether the sealing ring of the cross-flow filter membrane module is damaged and needs to be repaired or replaced, and whether the membrane material is damaged. If both the purified water conductivity σ and the purified water meet the standard, three data points will be collected. If both the purified water conductivity σ and the purified water meet the standard, the feed valve 301 of the membrane module corresponding to the cross-flow filter membrane module 3 that does not meet the standard will be closed. If only one of the purified water conductivity σ and the purified water meets the standard, the feed valve 301 of the membrane module corresponding to the two cross-flow filter membrane modules 3 that do not meet the standard will be closed.

[0085] In summary, automated monitoring systems can record data from pressure sensors, temperature sensors, conductivity meters, electronic balances, and other sources, establish online archives of the device's operating status, provide early warnings of equipment malfunctions, and offer maintenance guidelines for subsequent repairs.

[0086] Early warning of equipment failures allows for timely maintenance and repair, improving the system's operating status and stabilizing water production, inlet / outlet pressure difference, and effluent quality, thus reducing economic losses.

[0087] Example 3

[0088] Based on Example 2, the present invention also provides a testing and online monitoring method for a small nanofiltration membrane testing device, the method comprising the following steps:

[0089] S1. The operator selects the appropriate nanofiltration membrane according to the test conditions and fixes it at point 3 of the cross-flow filtration membrane assembly, and checks whether the power supply and instrument panel of the equipment are normal.

[0090] S2. Introduce the feed liquid into the material tank 1, and after the liquid level controller 10 senses the preset liquid level, use the processor to control the small nanofiltration membrane test device to operate normally.

[0091] S3. Utilize the pre-built valve opening prediction model to output the optimal valve opening corresponding to the real-time monitoring data, and adjust the pressure regulating valve 4 according to the optimal valve opening to keep the inlet water pressure of the cross-flow filter membrane assembly 3 within the normal operating range.

[0092] The process of using a pre-built valve opening prediction model to output the optimal valve opening corresponding to real-time monitoring data, and then adjusting the pressure regulating valve 4 according to the optimal valve opening, includes the following steps:

[0093] S31. Collect historical operating data of pressure regulating valve 4 and perform preprocessing (including removing outliers, normalization, etc.). The operating data includes data such as the pressure, flow rate, and temperature of the feed liquid, as well as the valve opening of the pressure regulating valve.

[0094] S32. A BP neural network valve opening prediction model is constructed using the pressure, flow rate, and temperature of the feed liquid as the number of input layer nodes and the valve opening of the pressure regulating valve as the number of output layer nodes.

[0095] S33. The connection weights and threshold parameters of the BP neural network valve opening prediction model are optimized using the improved Seagull optimization algorithm, and then trained to obtain the optimized BP neural network valve opening prediction model.

[0096] Specifically, the connection weights and threshold parameters of the BP neural network valve opening prediction model are optimized using the improved Seagull optimization algorithm, and the optimized BP neural network valve opening prediction model is obtained through training. The steps include:

[0097] S331. Encode the initial connection weights and thresholds of the BP neural network into an initial seagull population, initialize the seagull population size and preset number of iterations, and assign an initial position to each seagull. Use a formula to process these positions to prevent position overlap.

[0098] S332, and use the BP neural network valve opening prediction model and the current position of the seagull (i.e. the current model parameters) to calculate the fitness of each seagull, and select the seagull with the highest fitness as the best seagull for the current iteration;

[0099] S333. Update the position of each seagull according to the fitness, and use the Logistic mapping to perform chaotic iteration on the position of the seagull (i.e. model parameters). After the iteration is completed, the result is inversely mapped back to the original solution space, and the new fitness value is calculated. If the fitness value of the new solution is better than the old solution, the new solution is output; otherwise, the old solution is retained.

[0100] Specifically, "new solution" and "old solution" represent two different parameter states in the optimization process.

[0101] New solution: This is the new parameter state obtained at the current iteration step by applying chaotic optimization (here, the Logistic map). It reflects a new possible point in the search space, which corresponds to a new model parameter setting (in this case, the connection weights and threshold parameters of the valve opening prediction model).

[0102] Previous solution: This is the state of the model parameters that existed before the current iteration step. It is the optimal parameter setting obtained after the previous iteration step.

[0103] In the chaotic optimization step, a new solution is generated iteratively based on the old solution using a chaotic mapping function. Once a new solution is generated, its fitness value (i.e., the performance of the prediction model corresponding to this parameter setting) is calculated. If the fitness value of the new solution is better than that of the old solution, the new solution is accepted and replaces the old solution, becoming the basis for the next iteration; otherwise, the old solution is retained for the next iteration.

[0104] S334. Determine whether the preset number of iterations or the required accuracy has been reached. If yes, output the final position as the optimal seagull position (i.e., the optimal model parameters). If no, return to S332.

[0105] S335. Decode the optimal seagull position into the connection weights and threshold parameters of the BP neural network valve opening prediction model, and use these parameters to train the model until the preset training requirements are met, thus obtaining the optimized BP neural network valve opening prediction model.

[0106] S34. Obtain real-time data on the pressure, flow rate, and temperature of the feed liquid, and use the optimized BP neural network valve opening prediction model to output the optimal valve opening corresponding to the real-time data.

[0107] S35. Generate a control signal based on the optimal valve opening and send the control signal to the pressure regulating valve actuator to adjust the opening of the pressure regulating valve 4.

[0108] S4. Control the feed liquid to pass through the pressure sensor 8 at the input end of the cross-flow filter membrane assembly 3, the cross-flow filter membrane assembly 3, and the pressure sensor 8 at the first output end of the cross-flow filter membrane assembly 3 in sequence before re-entering the material tank 1. The permeate enters the material tank 1 through the permeate outlet 303. When the operating pressure and flow rate are stable, the water flow is evenly distributed and the flow rate is uniform, and the test process can be carried out.

[0109] S5. After the device is running smoothly, connect the three permeate outlets 303 to the corresponding beakers 304, and turn on the monitoring terminal 12 to record the running time and water quality data.

[0110] S6. The concentrated water after being treated by the cross-flow filtration membrane module 3 is returned to the material tank 1. The purified water is judged to be working properly based on the reading of the conductivity meter at the electronic balance 11.

[0111] S7. Collect water quality data for 5-10 minutes using an electronic balance 11 and a conductivity meter, and monitor the data based on the readings of the pressure sensor and temperature sensor on the instrument panel.

[0112] Specifically, the instrument panel in the above steps is a display device connected to the pressure sensor, or it can be directly represented as the pressure sensor. The pressure sensor reading is the real-time pressure reading, and the temperature reading is the real-time temperature reading. These readings can be obtained directly from the pressure sensor and the temperature sensor.

[0113] In addition, the water quality data monitored within the preset time after S7 is mainly for simple observation of whether the test membrane meets the basic requirements. For example, the amount of purified water collected in half an hour should generally be more than 100g. If this requirement cannot be met, the test can be stopped directly.

[0114] S8. Analyze the separation performance of the nanofiltration membrane based on the collected permeate data;

[0115] The analysis of nanofiltration membrane separation performance based on collected permeate data includes:

[0116] When the nanofiltration membranes used are made of the same material, analyze and compare the consistency and error of the three sets of data within 10 minutes. If the error is less than the preset error threshold (small error), continue to measure the data for a certain period of time. At the same time, calculate the instantaneous water production, total water volume and salt rejection performance of the cross-flow filtration membrane module 3. If the error is greater than the preset error threshold (large error), the corresponding permeate outlet 303 of the cross-flow filtration membrane module 3 with the error greater than the preset error threshold can be closed. Then continue to measure the data for a certain period of time.

[0117] When different nanofiltration membranes are used, the stability of three sets of data within 10 minutes is analyzed and compared. After the data stabilizes, the data is measured for a certain period of time. At the same time, the instantaneous water production, total water volume and salt rejection performance of the cross-flow filtration membrane module 3 are calculated, and the separation performance of different nanofiltration membranes is analyzed and compared.

[0118] Specifically, the formula for calculating the total water volume of the cross-flow filtration membrane module within time t is:

[0119]

[0120] The formula for calculating the salt rejection rate of the cross-flow filtration membrane module within time t is:

[0121]

[0122] In the formula, m 组件 R represents the total water volume of the cross-flow filtration membrane module within time t; m0 represents the instantaneous water volume of the cross-flow filtration membrane module at time t; 组件 Indicates the salt rejection rate of the cross-flow filtration membrane module; C 进料液 Indicates the concentration of the feed liquid; C 透过液 Indicates the concentration of the permeate (mg / L); σ 进料液 σ represents the conductivity of the feed liquid. 透过液 It indicates the conductivity of the permeate. The conductivity of the feed liquid and the permeate is monitored in real time. If the conductivity of any part is too high, the corresponding process is faulty and can be intervened in time.

[0123] S9. After the test process is completed, open the discharge valve 103 to empty all types of usable water, close the discharge valve 103, add pure water at the preset water level to the material tank 1 to clean the instrument, reopen the discharge valve 103 and empty all the cleaning water, close the feed pump 2, and close the test device when there is no brackish water or prepared pure water and cleaning water in the instrument.

[0124] In summary, by means of the above-mentioned technical solution of the present invention, the present invention ensures the consistency of different performance tests and the consistency of performance index tests by passing the feed liquid in the material tank into three cross-flow filter membrane modules respectively, and collecting the instantaneous weight and conductivity of the permeate in the sample cell within a certain period of time.

[0125] Furthermore, this invention can simultaneously test different nanofiltration membrane materials by changing the pressure, feed concentration and type, and test time during the device testing process. That is, using nanofiltration membranes, ultrafiltration membranes, or reverse osmosis membranes can reduce errors or achieve comparative performance analysis. By using monitoring methods, the performance indicators of membrane materials can be observed in real time, thereby improving testing efficiency.

[0126] Furthermore, this invention allows for online monitoring of water temperature, water pressure, and test results, enabling real-time calculation of the total weight and salt rejection rate of the permeate using a monitoring terminal, facilitating continuous monitoring and subsequent maintenance. The design of this invention can be used for performance testing of nanofiltration membranes, specifically by utilizing three sets of parallel cross-flow filtration membrane modules to collect the weight and conductivity of the filtered permeate.

[0127] In addition, this invention designs an automated monitoring system for real-time monitoring of the desalination separation performance and system operation of the device. It can record various data from pressure sensors, temperature sensors, conductivity meters, electronic balances, etc., thereby providing early warning of equipment failures and providing a basis for subsequent maintenance.

[0128] Furthermore, the device of this invention is small and portable, with simple equipment. The cross-flow filter membrane assembly has a small area and uniform pressure distribution, making it suitable for long-term use in different laboratories. Energy can be saved by closing the valve during the process, making it simple and efficient.

[0129] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "setting," "connection," "fixing," "screw connection," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal connection of two components or the interaction between two components. Unless otherwise explicitly limited, those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0130] 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, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A testing and online monitoring method for a small nanofiltration membrane testing device, implemented based on a small nanofiltration membrane testing device, characterized in that, The method includes the following steps: S1. The operator selects the appropriate nanofiltration membrane according to the test conditions and fixes it at the cross-flow filtration membrane assembly (3), and checks whether the power supply and instrument panel of the equipment are normal. S2. Introduce the feed liquid into the material tank (1), and after the liquid level controller (10) senses the preset liquid level, use the processor to control the small nanofiltration membrane test device to operate normally. S3. Using a pre-built valve opening prediction model, output the optimal valve opening corresponding to the real-time monitoring data, and adjust the pressure regulating valve (4) according to the optimal valve opening; specifically including: S31. Collect historical operating data of the pressure regulating valve (4) and preprocess it, wherein the operating data includes the pressure, flow rate, and temperature data of the feed liquid and the valve opening of the pressure regulating valve; S32. A BP neural network valve opening prediction model is constructed using the pressure, flow rate, and temperature of the feed liquid as the number of input layer nodes and the valve opening of the pressure regulating valve as the number of output layer nodes. S33. The connection weights and threshold parameters of the BP neural network valve opening prediction model are optimized using the improved Seagull optimization algorithm, and then trained to obtain the optimized BP neural network valve opening prediction model. S34. Obtain real-time data on the pressure, flow rate, and temperature of the feed liquid, and use the optimized BP neural network valve opening prediction model to output the optimal valve opening corresponding to the real-time data. S35. Generate a control signal based on the optimal valve opening and send the control signal to the pressure regulating valve actuator to adjust the opening of the pressure regulating valve (4); S4. Control the feed liquid to pass through the pressure sensor (8) at the input end of the cross-flow filter membrane assembly (3), the cross-flow filter membrane assembly (3), and the pressure sensor (8) at the first output end of the cross-flow filter membrane assembly (3) in sequence before re-entering the material tank (1). The permeate enters the material tank (1) through the permeate outlet (303). When the operating pressure and flow rate are stable, make the water flow uniformly distributed and the flow rate uniform, and then the test process can be carried out. S5. Connect the three permeate outlets (303) to the corresponding beakers (304) respectively, and turn on the monitoring terminal (12) to record the running time and water quality data. S6. The concentrated water after being treated by the cross-flow filter membrane module (3) is returned to the material tank (1). Based on the reading of the conductivity meter at the electronic balance (11), it is determined whether the cross-flow filter membrane module (3) is working properly. S7. Collect water quality data for a preset time using an electronic balance (11) and a conductivity meter, and monitor the data based on the readings of the pressure sensor and temperature sensor on the instrument panel. S8. Analyze the separation performance of the nanofiltration membrane based on the collected permeate data; specifically including: When the nanofiltration membranes used are made of the same material, analyze and compare the data within 10 minutes of the three groups to see if they are consistent and the magnitude of the error. If the error is less than the preset error threshold, continue to measure the data within the preset time. At the same time, calculate the instantaneous water production, total water volume and salt rejection performance of the cross-flow filtration membrane module (3). If the error is greater than the preset error threshold, select to close the corresponding permeate outlet (303) of the cross-flow filtration membrane module (3) with the error greater than the preset error threshold, and then continue to measure the data within the preset time. When different materials are used for nanofiltration membranes, analyze and compare whether the data within 10 minutes of the three groups are stable. After the data is stable, continue to measure the data within the preset time. At the same time, calculate the instantaneous water production, total water volume and salt interception and separation performance of the cross-flow filtration membrane module (3) and analyze and compare the separation performance of different nanofiltration membranes. The formula for calculating the total water volume of a cross-flow filtration membrane module is: ； The formula for calculating the salt rejection rate of the cross-flow filtration membrane module is as follows: ； In the formula, m 组件 R represents the total water volume of the cross-flow filtration membrane module within time t, m0 represents the instantaneous water volume of the cross-flow filtration membrane module at time t, and R 组件 C represents the salt rejection rate of the cross-flow filtration membrane module. 进料液 C represents the concentration of the feed solution. 透过液 σ represents the concentration of the permeate. 进料液 σ represents the conductivity of the feed liquid. 透过液 Indicates the conductivity of the permeate; S9. After the test process is completed, open the discharge valve (103) to empty all kinds of water, close the discharge valve (103), add pure water at the preset water level to the material tank (1) to clean the instrument, reopen the discharge valve (103) and empty all the cleaning water, close the feed pump (2), and close the test device when there is no brackish water or prepared pure water and cleaning water in the instrument.

2. The testing and online monitoring method for a small nanofiltration membrane testing device according to claim 1, characterized in that, The small nanofiltration membrane testing device includes a material tank (1), the output end of which is connected to the input end of a feed pump (2), and an external feed valve (101) is connected between the feed pump (2) and the material tank (1). One end of the external feed valve (101) is provided with an external feed port (6). The output end of the feed pump (2) is connected to the input ends of three sets of cross-flow filtration membrane assemblies (3), and the first output ends of the three sets of cross-flow filtration membrane assemblies (3) are interconnected. The pressure regulating valve (4) is connected to the input end of the pressure regulating valve (4), the output end of the pressure regulating valve (4) is connected to the input end of the flow meter (5), and a discharge valve (103) is provided between the pressure regulating valve (4) and the flow meter (5). The output end of the flow meter (5) is connected to the input end of the material tank (1), and an external return valve (102) is also connected between the flow meter (5) and the material tank (1). One end of the external return valve (102) is also connected to an external return port (7). The input ends of the three sets of cross-flow filtration membrane modules (3) are respectively connected to membrane module feed valves (301), the first output ends of the three sets of cross-flow filtration membrane modules (3) are respectively connected to membrane module discharge valves (302), the second output ends of the three sets of cross-flow filtration membrane modules (3) are respectively connected to the corresponding permeate outlets (303), and the first output ends of the permeate outlets (303) are respectively connected to the corresponding beakers (304). The bottom of each beaker (304) is equipped with a corresponding electronic balance (11). The second output ends of the permeate outlets (303) are interconnected and connected to the material tank (1). Pressure sensors (8) are provided between the membrane module feed valve (301) and the feed pump (2), and between the cross-flow filtration membrane module (3) and the membrane module discharge valve (302). A temperature sensor (9) is also provided between the pressure regulating valve (4) and the flow meter (5).

3. The testing and online monitoring method for a small nanofiltration membrane testing device according to claim 2, characterized in that, The three sets of cross-flow filtration membrane assemblies (3) are arranged in parallel, and the three sets of cross-flow filtration membrane assemblies (3) are provided with the same nanofiltration membrane or three different nanofiltration membranes.

4. The testing and online monitoring method of a small nanofiltration membrane testing device according to claim 2, characterized in that, The cross-flow filtration membrane module (3) includes a membrane base plate (001), and a membrane sheet (003) is provided on the top of the membrane base plate (001) to cooperate with it, and the membrane sheet (003) and the membrane base plate (001) are connected by a sealing ring (002). The top of the membrane (003) is provided with a membrane pressure plate (004) that cooperates with the membrane base plate (001), and the membrane pressure plate (004) and the membrane base plate (001) are connected by several fastening bolts (006). The top of the membrane pressure plate (004) is provided with a permeate outlet (005), and the three sets of permeate outlets (005) are respectively connected to the three sets of permeate outlets (303).

5. The testing and online monitoring method for a small nanofiltration membrane testing device according to claim 2, characterized in that, The material tank (1) is equipped with a liquid level controller (10) at the bottom of its interior.

6. The testing and online monitoring method for a small nanofiltration membrane testing device according to claim 5, characterized in that, The small nanofiltration membrane testing device also includes a conductivity meter set on one side of the electronic balance (11). The output of the conductivity meter is electrically connected to the monitoring terminal (12). The output of the monitoring terminal (12) is electrically connected to the mobile terminal (13). The monitoring terminal (12) is equipped with an inlet and outlet water pressure monitoring module, a water temperature detection module, a water storage monitoring module, a permeate monitoring module, and a processor. The inlet and outlet water pressure monitoring module is used to obtain the pressure value of the pressure sensor (8) and determine the inlet and outlet water pressure of each process. The water temperature detection module is used to obtain the temperature value of the temperature sensor (9) and determine the water temperature during the filtration process; The water storage monitoring module is used to monitor the water storage inside the material tank (1) using the liquid level controller (10). When the monitored water storage is lower than the preset value, the operator is notified to open the external feed valve (101) to supply water. The permeate monitoring module is used to acquire the instantaneous water volume and conductivity data of the permeate at the electronic balance (11), and to monitor the performance test status of the total water volume and salt rejection rate. The processor is used to analyze and process the data from each module.

7. The testing and online monitoring method for a small nanofiltration membrane testing device according to claim 1, characterized in that, The process of optimizing the connection weights and threshold parameters of the BP neural network valve opening prediction model using the improved Seagull optimization algorithm and training it to obtain the optimized BP neural network valve opening prediction model includes the following steps: S331. Encode the initial connection weights and thresholds of the BP neural network into an initial seagull population, initialize the seagull population size and the preset number of iterations, and assign an initial position to each seagull. S332. Calculate the fitness of each seagull using the BP neural network valve opening prediction model and the current position of the seagull, and select the seagull with the best fitness as the best seagull for the current iteration. S333. Update the position of each seagull according to its fitness, and use the Logistic mapping to perform chaotic iteration on the position of the seagull. After the iteration is completed, reverse map the result back to the original solution space, calculate the new fitness value, and output the new solution if the fitness value of the new solution is better than the old solution; otherwise, retain the old solution. S334. Determine whether the preset number of iterations or the required precision has been reached. If yes, output the final position as the optimal seagull position. If no, return to S332. S335. Decode the optimal seagull position into the connection weights and threshold parameters of the BP neural network valve opening prediction model, and use these parameters to train the model until the preset training requirements are met, thus obtaining the optimized BP neural network valve opening prediction model.

Images