An ultrafiltration membrane health monitoring method and device, electronic equipment and storage medium

Abstract

The application provides an ultrafiltration membrane health monitoring method and device, electronic equipment and storage medium. The method comprises the following steps: obtaining historical working condition data of an ultrafiltration membrane to be tested; the historical working condition data comprises historical working condition data of the ultrafiltration membrane to be tested in a membrane wire fracture condition and historical working condition data of the ultrafiltration membrane to be tested in a normal condition; based on the historical working condition data, a membrane wire fracture early warning model of the ultrafiltration membrane to be tested is constructed; current working condition data of the ultrafiltration membrane to be tested is obtained; and the current working condition data is input into the membrane wire fracture early warning model to obtain a health monitoring result of the ultrafiltration membrane to be tested. The method provided by the above scheme utilizes the membrane wire fracture early warning model, carries out health monitoring on the ultrafiltration membrane to be tested according to the current working condition data of the ultrafiltration membrane to be tested, improves the monitoring efficiency, and can discover the abnormality of the ultrafiltration membrane to be tested in the early stage of membrane wire fracture.

Description

Technical Field

[0001] This application relates to the field of big data early warning technology, and in particular to an ultrafiltration membrane health monitoring method, device, electronic device and storage medium. Background Technology

[0002] Ultrafiltration membranes contain tens of thousands of membrane fibers. Colloidal particles, suspended solids, and large organic molecules in the raw water that are larger than the pore size of the membrane surface are retained and removed by the membrane, thus purifying the water. Over time, ultrafiltration membranes may experience pore deformation or fiber breakage. The integrity of the membrane fibers is a crucial parameter for ensuring the membrane filtration process. If membrane fiber breakage is not detected in time, it will significantly affect the quality of the produced water and cause frequent fouling of subsequent reverse osmosis treatment equipment.

[0003] In existing technologies, technicians typically conduct periodic integrity checks on the ultrafiltration membrane fibers to determine if any fibers have broken. However, this manual monitoring process is cumbersome and reduces monitoring efficiency. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and storage medium for ultrafiltration membrane health monitoring, in order to overcome the shortcomings of existing technologies, such as low efficiency in ultrafiltration membrane health monitoring.

[0005] The first aspect of this application provides a method for monitoring the health of an ultrafiltration membrane, comprising:

[0006] Acquire historical operating condition data of the ultrafiltration membrane under test; the historical operating condition data includes historical operating condition data of the ultrafiltration membrane under test under the condition of membrane fiber breakage and historical operating condition data under normal conditions;

[0007] Based on the historical operating data, a membrane fiber breakage early warning model for the ultrafiltration membrane under test is constructed.

[0008] Obtain the current operating condition data of the ultrafiltration membrane under test;

[0009] The current operating condition data is input into the membrane fiber breakage early warning model to obtain the health monitoring results of the ultrafiltration membrane under test.

[0010] Optionally, the step of constructing a membrane fiber breakage early warning model for the ultrafiltration membrane under test based on the historical operating data includes:

[0011] Based on the historical operating data, a training sample set and a test sample set are constructed;

[0012] Based on the initial membrane filament rupture early warning model, the training samples are classified according to the differences in operating conditions among the training samples in the training sample set to distinguish between membrane filament rupture training samples and normal training samples, thus obtaining the trained membrane filament rupture early warning model.

[0013] The test samples in the test sample set are input into the trained membrane filament rupture early warning model. Based on the trained membrane filament rupture early warning model, the model test result corresponding to the test sample is determined according to the difference in operating conditions between the test sample and the membrane filament rupture training sample, and / or, according to the difference in operating conditions between the test sample and the normal training sample.

[0014] When the model test results indicate that the accuracy of the trained membrane fiber breakage early warning model reaches a preset standard, the trained membrane fiber breakage early warning model is determined as the membrane fiber breakage early warning model of the ultrafiltration membrane to be tested.

[0015] Optionally, inputting the current operating condition data into the membrane fiber breakage early warning model to obtain the health monitoring results of the ultrafiltration membrane under test includes:

[0016] The current operating condition data is input into the membrane fiber breakage early warning model, so that the health monitoring result of the ultrafiltration membrane under test is determined based on the difference in operating condition indicators between the current operating condition data and the membrane fiber breakage training samples, and / or, based on the difference in operating condition indicators between the current operating condition data and the normal training samples.

[0017] Optional, also includes:

[0018] When the health monitoring results of the ultrafiltration membrane under test indicate that the ultrafiltration membrane under test is currently abnormal, the turbidity information of the feed water of the ultrafiltration membrane under test is obtained.

[0019] Based on a pre-defined membrane fiber breakage analysis model, the cause of the abnormality of the ultrafiltration membrane under test is determined according to the turbidity information of the feed water to the ultrafiltration membrane under test.

[0020] Optionally, based on a preset membrane fiber breakage analysis model, the cause of the abnormality of the ultrafiltration membrane under test is determined according to the influent turbidity information of the ultrafiltration membrane under test, including:

[0021] Based on a preset membrane fiber breakage analysis model, the current influent turbidity is determined according to the influent turbidity information of the ultrafiltration membrane under test, to determine whether the abnormality of the ultrafiltration membrane under test is caused by membrane fiber breakage.

[0022] Optionally, determining whether the abnormality of the ultrafiltration membrane is caused by membrane fiber breakage, based on the current influent turbidity information characterized by the influent turbidity information of the ultrafiltration membrane under test, includes:

[0023] When the current influent turbidity, as indicated by the influent turbidity information of the ultrafiltration membrane under test, is less than the preset influent turbidity threshold, the cause of the abnormality of the ultrafiltration membrane under test is determined to be membrane fiber breakage.

[0024] When the current influent turbidity, as indicated by the influent turbidity information of the ultrafiltration membrane under test, is not less than the preset influent turbidity threshold, the cause of the abnormality of the ultrafiltration membrane under test is determined to be an influent filter malfunction.

[0025] Optionally, the current operating condition data includes the inlet flow rate of the ultrafiltration self-cleaning filter, the inlet and outlet differential pressure of the reverse osmosis security filter, the turbidity of the feed water to the ultrafiltration membrane under test, the turbidity of the product water from the ultrafiltration membrane under test, the frequency feedback of the backwash water pump, and the inlet and outlet differential pressure of the ultrafiltration membrane under test.

[0026] A second aspect of this application provides an ultrafiltration membrane health monitoring device, comprising:

[0027] The first acquisition module is used to acquire historical operating condition data of the ultrafiltration membrane under test; the historical operating condition data includes historical operating condition data of the ultrafiltration membrane under test under the condition of membrane fiber breakage and historical operating condition data under normal conditions;

[0028] The model building module is used to build an early warning model for membrane fiber breakage of the ultrafiltration membrane under test based on the historical operating data.

[0029] The second acquisition module is used to acquire the current operating condition data of the ultrafiltration membrane under test;

[0030] The monitoring module is used to input the current operating condition data into the membrane fiber breakage early warning model to obtain the health monitoring results of the ultrafiltration membrane under test.

[0031] Optionally, the model building module is specifically used for:

[0032] Based on the historical operating data, a training sample set and a test sample set are constructed;

[0033] Based on the initial membrane filament rupture early warning model, the training samples are classified according to the differences in operating conditions among the training samples in the training sample set to distinguish between membrane filament rupture training samples and normal training samples, thus obtaining the trained membrane filament rupture early warning model.

[0034] The test samples in the test sample set are input into the trained membrane filament rupture early warning model. Based on the trained membrane filament rupture early warning model, the model test result corresponding to the test sample is determined according to the difference in operating conditions between the test sample and the membrane filament rupture training sample, and / or, according to the difference in operating conditions between the test sample and the normal training sample.

[0035] When the model test results indicate that the accuracy of the trained membrane fiber breakage early warning model reaches a preset standard, the trained membrane fiber breakage early warning model is determined as the membrane fiber breakage early warning model of the ultrafiltration membrane to be tested.

[0036] Optionally, the monitoring module is specifically used for:

[0037] The current operating condition data is input into the membrane fiber breakage early warning model, so that the health monitoring result of the ultrafiltration membrane under test is determined based on the difference in operating condition indicators between the current operating condition data and the membrane fiber breakage training samples, and / or, based on the difference in operating condition indicators between the current operating condition data and the normal training samples.

[0038] Optionally, the device further includes:

[0039] The cause analysis module is used to obtain the influent turbidity information of the ultrafiltration membrane under test when the health monitoring results indicate that the ultrafiltration membrane under test is currently abnormal; based on a preset membrane fiber breakage analysis model, the module determines the cause of the abnormality of the ultrafiltration membrane under test according to the influent turbidity information of the ultrafiltration membrane under test.

[0040] Optionally, the cause analysis module is specifically used for:

[0041] Based on a preset membrane fiber breakage analysis model, the current influent turbidity is determined according to the influent turbidity information of the ultrafiltration membrane under test, to determine whether the abnormality of the ultrafiltration membrane under test is caused by membrane fiber breakage.

[0042] Optionally, the cause analysis module is specifically used for:

[0043] When the current influent turbidity, as indicated by the influent turbidity information of the ultrafiltration membrane under test, is less than the preset influent turbidity threshold, the cause of the abnormality of the ultrafiltration membrane under test is determined to be membrane fiber breakage.

[0044] When the current influent turbidity, as indicated by the influent turbidity information of the ultrafiltration membrane under test, is not less than the preset influent turbidity threshold, the cause of the abnormality of the ultrafiltration membrane under test is determined to be an influent filter malfunction.

[0045] Optionally, the current operating condition data includes the inlet flow rate of the ultrafiltration self-cleaning filter, the inlet and outlet differential pressure of the reverse osmosis security filter, the turbidity of the feed water to the ultrafiltration membrane under test, the turbidity of the product water from the ultrafiltration membrane under test, the frequency feedback of the backwash water pump, and the inlet and outlet differential pressure of the ultrafiltration membrane under test.

[0046] A third aspect of this application provides an electronic device, comprising: at least one processor and a memory;

[0047] The memory stores computer-executed instructions;

[0048] The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method described in the first aspect above and various possible designs of the first aspect.

[0049] The fourth aspect of this application provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the method described in the first aspect above and various possible designs of the first aspect.

[0050] The technical solution of this application has the following advantages:

[0051] This application provides a method, apparatus, electronic device, and storage medium for ultrafiltration membrane health monitoring. The method includes: acquiring historical operating condition data of the ultrafiltration membrane under test; the historical operating condition data includes historical operating condition data of the ultrafiltration membrane under test in the event of membrane fiber rupture and historical operating condition data under normal conditions; constructing a membrane fiber rupture early warning model for the ultrafiltration membrane under test based on the historical operating condition data; acquiring current operating condition data of the ultrafiltration membrane under test; and inputting the current operating condition data into the membrane fiber rupture early warning model to obtain the health monitoring results of the ultrafiltration membrane under test. The method provided above improves monitoring efficiency by utilizing the membrane fiber rupture early warning model and performing health monitoring of the ultrafiltration membrane under test based on the current operating condition data of the ultrafiltration membrane under test, and can detect abnormalities of the ultrafiltration membrane under test in the early stage of membrane fiber rupture. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings.

[0053] Figure 1 This is a schematic diagram of the structure of the ultrafiltration membrane health monitoring system on which the embodiments of this application are based;

[0054] Figure 2 A schematic flowchart illustrating the ultrafiltration membrane health monitoring method provided in this application embodiment;

[0055] Figure 3 A schematic diagram illustrating the construction process of the membrane filament breakage early warning model provided in this application embodiment;

[0056] Figure 4 A schematic flowchart of an exemplary ultrafiltration membrane health monitoring method provided in the embodiments of this application;

[0057] Figure 5 This is a schematic diagram of the structure of the ultrafiltration membrane health monitoring device provided in the embodiments of this application;

[0058] Figure 6 The present application provides a schematic diagram of the structure of an electronic device.

[0059] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the present disclosure in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0061] Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. In the following descriptions of embodiments, "a plurality of" means two or more, unless otherwise explicitly defined.

[0062] Ultrafiltration is a physical separation process involving tangential flow of fluid across a membrane surface, driven by relatively low pressure and separating and filtering solutes according to their molecular weight. Raw water is pressurized and filtered through the membrane fibers, which have a specific pore size. Each ultrafiltration membrane contains tens of thousands of fibers. Colloidal substances, suspended solids, and large organic molecules larger than the membrane's pore size are retained and removed, thus purifying the water. Over time, ultrafiltration membranes may experience pore deformation or fiber breakage. Fiber integrity is a crucial parameter for ensuring the filtration process. Failure to detect fiber breakage promptly will significantly impact the quality of the permeate, leading to frequent fouling of subsequent reverse osmosis equipment. Improper operation and backwashing parameter control, or fiber aging, can all cause fiber breakage within the membrane module, affecting the quality of the ultrafiltration permeate. Current technology typically involves technicians periodically inspecting the membrane fibers to check for breakage. However, manual monitoring is cumbersome and inefficient.

[0063] To address the aforementioned issues, the ultrafiltration membrane health monitoring method, apparatus, electronic device, and storage medium provided in this application acquire historical operating condition data of the ultrafiltration membrane under test. This historical operating condition data includes data from when membrane fibers breakage occurs and data from when the membrane is under normal operating conditions. Based on this historical operating condition data, a membrane fiber breakage early warning model for the ultrafiltration membrane under test is constructed. The current operating condition data of the ultrafiltration membrane under test is then acquired. This current operating condition data is input into the membrane fiber breakage early warning model to obtain the health monitoring results of the ultrafiltration membrane under test. The method provided above, by utilizing the membrane fiber breakage early warning model and based on the current operating condition data of the ultrafiltration membrane under test, improves monitoring efficiency and enables timely detection of abnormalities in the ultrafiltration membrane at the initial stage of membrane fiber breakage.

[0064] The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0065] First, the structure of the ultrafiltration membrane health monitoring system on which this application is based will be described:

[0066] The ultrafiltration membrane health monitoring method, apparatus, electronic device, and storage medium provided in this application are suitable for monitoring the integrity of ultrafiltration membranes. Figure 1 The diagram shown is a structural schematic of the ultrafiltration membrane health monitoring system based on an embodiment of this application. It mainly includes an ultrafiltration membrane under test, a data acquisition device, and an ultrafiltration membrane health monitoring device for monitoring the health of the ultrafiltration membrane under test. Specifically, the data acquisition device collects historical and current operating condition data of the ultrafiltration membrane under test and sends the collected data to the ultrafiltration membrane health monitoring device, which then performs health monitoring of the ultrafiltration membrane under test based on the obtained data.

[0067] This application provides a method for monitoring the health of an ultrafiltration membrane, used to monitor the integrity of the ultrafiltration membrane. The execution subject of this application is an electronic device, such as a server, desktop computer, laptop computer, tablet computer, or other electronic device capable of performing big data processing on the operating data of the ultrafiltration membrane.

[0068] like Figure 2 The diagram shown is a flowchart illustrating the ultrafiltration membrane health monitoring method provided in this application embodiment. The method includes:

[0069] Step 201: Obtain historical operating data of the ultrafiltration membrane to be tested.

[0070] The historical operating data includes historical operating data of the ultrafiltration membrane under test when the membrane fibers break and historical operating data under normal conditions.

[0071] Specifically, the actual operating data of the ultrafiltration membrane under test can be obtained over the past year. In order to ensure the data quality of historical operating data, it can be preprocessed after obtaining the historical operating data. The preprocessing methods mainly include changing the data format and taking the average of the nearest neighbor data of the median of the operating data.

[0072] Step 202: Based on historical operating data, construct an early warning model for membrane fiber breakage of the ultrafiltration membrane under test.

[0073] Specifically, a preset machine learning algorithm can be used to construct an initial membrane fiber rupture early warning model. Then, based on the obtained historical operating data, the initial membrane fiber rupture early warning model can be trained so that the final membrane fiber rupture early warning model can classify the operating data of the ultrafiltration membrane under test under membrane fiber rupture conditions and the operating data under normal conditions.

[0074] Step 203: Obtain the current operating condition data of the ultrafiltration membrane to be tested.

[0075] It should be noted that the specific operating parameters included in the current operating data can be determined based on the relevant faults and causes that have occurred in the history of the ultrafiltration system.

[0076] If the ultrafiltration membrane under test is used in a thermal power plant for ultrafiltration boiler feedwater, the current operating data of the membrane includes multiple operating indicators such as the inlet flow rate of the ultrafiltration self-cleaning filter, the inlet and outlet differential pressure of the reverse osmosis security filter, the turbidity of the feed water to the ultrafiltration membrane, the turbidity of the permeate water, the backwash pump frequency feedback, and the inlet and outlet differential pressure of the ultrafiltration membrane. The backwash pump frequency feedback includes the frequency feedback of all backwash pumps involved in the backwashing of the ultrafiltration membrane. Correspondingly, the historical operating data used to train the membrane fiber breakage early warning model also includes the same operating indicators.

[0077] Specifically, to ensure the reliability of the current operating condition data, the current operating condition data can be collected within five minutes before the first filtration operation cycle after the chemical enhanced backwashing of the ultrafiltration membrane under test. The specific values ​​of each operating condition index can be taken as the average of the median nearest neighbor data within these five minutes.

[0078] Step 204: Input the current operating condition data into the membrane fiber breakage early warning model to obtain the health monitoring results of the ultrafiltration membrane under test.

[0079] Specifically, after the current operating condition data is input into the membrane fiber breakage early warning model, the model can distinguish whether the current operating condition data is the operating condition data of the ultrafiltration membrane under test under the condition of membrane fiber breakage based on the various operating condition indicators included in the current operating condition data, and then output the corresponding health monitoring results.

[0080] Based on the above embodiments, as an implementable approach, in one embodiment, a membrane fiber breakage early warning model for the ultrafiltration membrane under test is constructed based on historical operating condition data, including:

[0081] Step 2021: Based on historical operating condition data, construct training sample sets and test sample sets;

[0082] Step 2022: Based on the initial membrane filament rupture early warning model, the training samples are classified according to the differences in operating conditions among the training samples in the training sample set to distinguish between membrane filament rupture training samples and normal training samples, thus obtaining the trained membrane filament rupture early warning model.

[0083] Step 2023: Input the test samples from the test sample set into the trained membrane filament breakage early warning model, and determine the model test result corresponding to the test sample based on the difference in operating conditions between the test sample and the membrane filament breakage training sample, and / or, based on the difference in operating conditions between the test sample and the normal training sample.

[0084] Step 2024: When the model test results indicate that the accuracy of the trained membrane fiber breakage early warning model reaches the preset standard, the trained membrane fiber breakage early warning model is determined as the membrane fiber breakage early warning model of the ultrafiltration membrane to be tested.

[0085] It should be noted that the membrane filament breakage early warning model provided in this application embodiment can be constructed based on one or more of the following algorithms: weighted k-nearest neighbor algorithm, support vector machine algorithm, autoencoder algorithm, fully connected neural network, etc.

[0086] Specifically, such as Figure 3The diagram illustrates the construction process of the membrane fiber breakage early warning model provided in this embodiment. First, historical operating condition data is cleaned and tagged to distinguish between historical operating condition data of the ultrafiltration membrane under test in the event of membrane fiber breakage and historical operating condition data under normal conditions. This distinguishes between membrane fiber breakage training samples and normal training samples. A certain number of membrane fiber breakage training samples and normal training samples are then used to construct a training sample set, with the remaining portion serving as a test sample set. The training sample set is then used for model training. Based on the initial membrane fiber breakage early warning model and referring to the tags of each sample, the training samples are classified according to the differences in operating condition indicators between the training samples in the training sample set. This determines the feature information of membrane fiber breakage training samples and normal training samples. Model parameters are then set based on the training results, where the differences in operating condition indicators between training samples can be quantified as distance. Further, after completing a certain degree of model training, the trained membrane fiber breakage early warning model is tested based on the test samples in the test sample set. The model test results are determined based on the classification results of the trained membrane fiber breakage early warning model for each test sample in the test set and the tags of each test sample. If the model test results indicate that the accuracy of the trained membrane fiber breakage early warning model meets the preset standard, the trained membrane fiber breakage early warning model will be determined as the membrane fiber breakage early warning model for the ultrafiltration membrane to be tested; otherwise, model training will continue.

[0087] Specifically, the accuracy of the membrane filament breakage early warning model can be calculated using the following formula:

[0088]

[0089] Where accuracy represents the accuracy of the membrane filament breakage early warning model, N accuracy N represents the number of test samples that were correctly classified. test This indicates the total number of test samples contained in the test sample set.

[0090] Specifically, when the accuracy of the trained membrane fiber breakage early warning model reaches 90%, the trained membrane fiber breakage early warning model can be determined as the membrane fiber breakage early warning model for the ultrafiltration membrane under test.

[0091] When using the weighted k-nearest neighbor algorithm to construct a membrane filament breakage early warning model, the model parameter k can be set to 6. That is, the 6 training samples closest to the object to be classified are selected in the training sample set, and then the label that appears most frequently in these 6 training samples is determined as the classification result. The distance can be Euclidean distance, the weight calculation function is a Gaussian function, and the variance of the Gaussian function is set to 5.

[0092] Accordingly, in one embodiment, the current operating condition data can be input into the membrane fiber breakage early warning model, so as to determine the health monitoring result of the ultrafiltration membrane under test based on the membrane fiber breakage early warning model, according to the difference in operating condition indicators between the current operating condition data and the membrane fiber breakage training samples, and / or, according to the difference in operating condition indicators between the current operating condition data and the normal training samples.

[0093] Specifically, when using the weighted k-nearest neighbor algorithm to construct a membrane fiber breakage early warning model, the distance between the current operating condition data and each membrane fiber breakage training sample can be determined based on the difference in operating condition indicators between the current operating condition data and the membrane fiber breakage training samples. Similarly, the distance between the current operating condition data and each normal training sample can be determined based on the difference in operating condition indicators between the current operating condition data and the normal training samples. The k training samples that are closest to the current operating condition data are then selected, and the label that appears most frequently in these k training samples is determined as the health monitoring result of the ultrafiltration membrane to be tested.

[0094] Specifically, if the quantified distance between the operating condition indicators of the current operating condition data and the membrane fiber breakage training sample is less than a preset distance threshold, the determined health monitoring result of the ultrafiltration membrane under test can indicate that the ultrafiltration membrane under test is currently abnormal; correspondingly, if the quantified distance between the operating condition indicators of the current operating condition data and the normal training sample is less than a preset distance threshold, the determined health monitoring result of the ultrafiltration membrane under test can indicate that the ultrafiltration membrane under test is normal.

[0095] Based on the above embodiments, since the application environment of ultrafiltration membranes is difficult to control uniformly, the health monitoring results of the ultrafiltration membrane under test may indicate that the membrane fibers have broken, but in reality, other factors have affected the ultrafiltration effect of the ultrafiltration membrane, achieving an ultrafiltration effect similar to that under the condition of membrane fiber breakage. Therefore, in order to further help ultrafiltration membrane maintenance personnel to specifically eliminate abnormal faults of ultrafiltration membranes, as an implementable method, in one embodiment, the method further includes:

[0096] Step 301: When the health monitoring results of the ultrafiltration membrane under test indicate that the ultrafiltration membrane under test is currently abnormal, obtain the turbidity information of the feed water of the ultrafiltration membrane under test.

[0097] Step 302: Based on the preset membrane fiber breakage analysis model, determine the cause of the abnormality of the ultrafiltration membrane under test according to the turbidity information of the feed water of the ultrafiltration membrane under test.

[0098] It should be noted that the membrane fiber breakage analysis model belongs to the mechanism model of ultrafiltration system.

[0099] Specifically, in one embodiment, based on a preset membrane fiber breakage analysis model, the current influent turbidity, as represented by the influent turbidity information of the ultrafiltration membrane under test, can be used to determine whether the abnormality of the ultrafiltration membrane under test is caused by membrane fiber breakage.

[0100] Specifically, the turbidity information of the influent to the ultrafiltration membrane under test can be the influent turbidity of the ultrafiltration membrane under test in the current operating condition data of the ultrafiltration membrane under test.

[0101] Specifically, based on the membrane fiber breakage analysis model, the influent turbidity threshold can be defined according to the operating parameters and operating status of the ultrafiltration membrane-related equipment. Then, based on the relationship between the current influent turbidity and the influent turbidity threshold represented by the influent turbidity information of the ultrafiltration membrane under test, it can be determined whether the abnormality of the ultrafiltration membrane under test is caused by membrane fiber breakage.

[0102] Specifically, in one embodiment, when the current influent turbidity represented by the influent turbidity information of the ultrafiltration membrane under test is less than a preset influent turbidity threshold, the cause of the abnormality of the ultrafiltration membrane under test can be determined to be membrane fiber breakage; when the current influent turbidity represented by the influent turbidity information of the ultrafiltration membrane under test is not less than the preset influent turbidity threshold, the cause of the abnormality of the ultrafiltration membrane under test can be determined to be influent filter malfunction.

[0103] The preset influent turbidity threshold can be 5 NTU.

[0104] Specifically, such as Figure 4 The diagram shown is a schematic flowchart of an exemplary ultrafiltration membrane health monitoring method provided in this application embodiment. When the health monitoring result output by the membrane fiber breakage early warning model indicates that the ultrafiltration membrane under test is currently abnormal, the output state is 1; otherwise, the output state is 0. When the cause of the abnormality output by the membrane fiber breakage analysis model is that the influent filter is abnormal, that is, the current influent turbidity is less than 5 NTU, the output state is 1; otherwise, the output state is 0. The output states of the membrane fiber breakage early warning model and the membrane fiber breakage analysis model can be integrated into a switch sequence, such as 10 or 11.

[0105] Furthermore, the health monitoring results of the ultrafiltration membrane under test determined by the membrane fiber breakage early warning model and the causes of abnormality determined by the membrane fiber breakage analysis model can be combined to generate early warning information to be pushed.

[0106] For example, when the output status of the membrane fiber rupture early warning model is 1 and the output status of the membrane fiber rupture analysis model is 1, the early warning information to be pushed is "The ultrafiltration system is malfunctioning. Please check the influent water quality and backwash the multi-media filter (influent filter)". When the output status of the membrane fiber rupture early warning model is 1 and the output status of the membrane fiber rupture analysis model is 0, the early warning information to be pushed is "The ultrafiltration system is malfunctioning. Please shut down the equipment and check the membrane fiber integrity".

[0107] The ultrafiltration membrane health monitoring method provided in this application acquires historical operating condition data of the ultrafiltration membrane under test. This historical operating condition data includes data from previous years when membrane fibers broke and data from previous years under normal conditions. Based on this historical operating condition data, a membrane fiber breakage early warning model for the ultrafiltration membrane under test is constructed. The current operating condition data of the ultrafiltration membrane under test is then acquired. This current operating condition data is input into the membrane fiber breakage early warning model to obtain the health monitoring results of the ultrafiltration membrane under test. The method provided above, by utilizing the membrane fiber breakage early warning model and based on the current operating condition data of the ultrafiltration membrane under test, improves monitoring efficiency and enables timely detection of abnormalities in the ultrafiltration membrane at the initial stage of membrane fiber breakage. Furthermore, by analyzing the causes of abnormalities in the ultrafiltration membrane under test, targeted guidance is provided for ultrafiltration membrane maintenance personnel to eliminate abnormal faults, which helps prevent a severe decline in the effluent quality of the ultrafiltration membrane, affecting the safe operation of subsequent desalination equipment, and avoiding the consequences of large-scale membrane fiber breakage and subsequent boiler feedwater system shutdown.

[0108] This application provides an ultrafiltration membrane health monitoring device for performing the ultrafiltration membrane health monitoring method provided in the above embodiments.

[0109] like Figure 5 The diagram shown is a structural schematic of the ultrafiltration membrane health monitoring device provided in an embodiment of this application. The ultrafiltration membrane health monitoring device 50 includes: a first acquisition module 501, a model construction module 502, a second acquisition module 503, and a monitoring module 504.

[0110] The system comprises the following modules: a first acquisition module for acquiring historical operating condition data of the ultrafiltration membrane under test, including data under conditions of membrane fiber breakage and data under normal conditions; a model building module for constructing a membrane fiber breakage early warning model for the ultrafiltration membrane under test based on the historical operating condition data; a second acquisition module for acquiring current operating condition data of the ultrafiltration membrane under test; and a monitoring module for inputting the current operating condition data into the membrane fiber breakage early warning model to obtain health monitoring results of the ultrafiltration membrane under test.

[0111] Specifically, in one embodiment, the model building module is specifically used for:

[0112] Based on historical operating condition data, a training sample set and a test sample set are constructed.

[0113] Based on the initial membrane filament rupture early warning model, the training samples are classified according to the differences in operating conditions among the training samples in the training sample set, so as to distinguish between membrane filament rupture training samples and normal training samples, and the trained membrane filament rupture early warning model is obtained.

[0114] The test samples in the test sample set are input into the trained membrane filament rupture early warning model. Based on the trained membrane filament rupture early warning model, the model test result corresponding to the test sample is determined according to the difference in operating condition indicators between the test sample and the membrane filament rupture training sample, and / or, according to the difference in operating condition indicators between the test sample and the normal training sample.

[0115] When the model test results indicate that the accuracy of the trained membrane fiber breakage early warning model reaches the preset standard, the trained membrane fiber breakage early warning model is determined as the membrane fiber breakage early warning model for the ultrafiltration membrane to be tested.

[0116] Specifically, in one embodiment, the monitoring module is specifically used for:

[0117] The current operating condition data is input into the membrane fiber breakage early warning model. Based on the membrane fiber breakage early warning model, the health monitoring result of the ultrafiltration membrane under test is determined according to the difference in operating condition indicators between the current operating condition data and the membrane fiber breakage training samples, and / or, according to the difference in operating condition indicators between the current operating condition data and the normal training samples.

[0118] Specifically, in one embodiment, the device further includes:

[0119] The cause analysis module is used to obtain the influent turbidity information of the ultrafiltration membrane under test when the health monitoring results indicate that the ultrafiltration membrane under test is currently abnormal; based on the preset membrane fiber breakage analysis model, it determines the cause of the abnormality of the ultrafiltration membrane under test according to the influent turbidity information.

[0120] Specifically, in one embodiment, the cause analysis module is specifically used for:

[0121] Based on the preset membrane fiber breakage analysis model, the current influent turbidity is characterized by the influent turbidity information of the ultrafiltration membrane under test, and it is determined whether the abnormality of the ultrafiltration membrane under test is caused by membrane fiber breakage.

[0122] Specifically, in one embodiment, the cause analysis module is specifically used for:

[0123] When the current influent turbidity, as indicated by the influent turbidity information of the ultrafiltration membrane under test, is less than the preset influent turbidity threshold, the cause of the abnormality of the ultrafiltration membrane under test is determined to be membrane fiber breakage.

[0124] When the current influent turbidity, as indicated by the influent turbidity information of the ultrafiltration membrane under test, is not less than the preset influent turbidity threshold, the cause of the abnormality of the ultrafiltration membrane under test is determined to be an abnormality of the influent filter.

[0125] Specifically, in one embodiment, the current operating condition data includes the inlet flow rate of the ultrafiltration self-cleaning filter, the inlet and outlet differential pressure of the reverse osmosis security filter, the turbidity of the feed water to the ultrafiltration membrane under test, the turbidity of the permeate water from the ultrafiltration membrane under test, the frequency feedback of the backwash water pump, and the inlet and outlet differential pressure of the ultrafiltration membrane under test.

[0126] Regarding the ultrafiltration membrane health monitoring device in this embodiment, the specific methods by which each module performs its operation have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0127] The ultrafiltration membrane health monitoring device provided in this application embodiment is used to execute the ultrafiltration membrane health monitoring method provided in the above embodiment. Its implementation method and principle are the same, and will not be described again.

[0128] This application provides an electronic device for performing the ultrafiltration membrane health monitoring method provided in the above embodiments.

[0129] like Figure 6 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this application. The electronic device 60 includes at least one processor 61 and a memory 62.

[0130] The memory stores computer-executable instructions; at least one processor executes the computer-executable instructions stored in the memory, causing the at least one processor to perform the ultrafiltration membrane health monitoring method provided in the above embodiments.

[0131] This application provides an electronic device for executing the ultrafiltration membrane health monitoring method provided in the above embodiments. Its implementation method and principle are the same, and will not be described again.

[0132] This application provides a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the ultrafiltration membrane health monitoring method provided in any of the above embodiments.

[0133] The storage medium containing computer-executable instructions in the embodiments of this application can be used to store computer-executable instructions for the ultrafiltration membrane health monitoring method provided in the foregoing embodiments. Its implementation method and principle are the same, and will not be described again.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0137] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0138] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. An ultrafiltration membrane health monitoring method, characterized by, The method comprises the following steps: acquiring historical working condition data of a to-be-tested ultrafiltration membrane; the historical working condition data comprises historical working condition data of the to-be-tested ultrafiltration membrane in a case of membrane filament rupture and historical working condition data of the to-be-tested ultrafiltration membrane in a normal case; based on the historical working condition data, a membrane filament rupture early warning model of the to-be-tested ultrafiltration membrane is constructed; acquiring current working condition data of the to-be-tested ultrafiltration membrane; the current working condition data comprises an ultrafiltration self-cleaning filter inlet flow, a reverse osmosis security filter inlet and outlet differential pressure, a to-be-tested ultrafiltration membrane inlet water turbidity, a to-be-tested ultrafiltration membrane water production turbidity, a backwashing water pump frequency feedback, and a to-be-tested ultrafiltration membrane inlet and outlet differential pressure; the current working condition data is input into the membrane filament rupture early warning model to obtain a health monitoring result of the to-be-tested ultrafiltration membrane; wherein the method further comprises: when the health monitoring result of the to-be-tested ultrafiltration membrane indicates that the to-be-tested ultrafiltration membrane is currently abnormal, acquiring inlet water turbidity information of the to-be-tested ultrafiltration membrane; based on a preset membrane filament rupture analysis model, determining a cause of the abnormality of the to-be-tested ultrafiltration membrane according to the inlet water turbidity information of the to-be-tested ultrafiltration membrane; the method of determining a cause of the abnormality of the to-be-tested ultrafiltration membrane according to the inlet water turbidity information of the to-be-tested ultrafiltration membrane based on the preset membrane filament rupture analysis model comprises: based on the preset membrane filament rupture analysis model, determining whether the cause of the abnormality of the to-be-tested ultrafiltration membrane is membrane filament rupture according to a current inlet water turbidity represented by the inlet water turbidity information of the to-be-tested ultrafiltration membrane; the method of determining whether the cause of the abnormality of the to-be-tested ultrafiltration membrane is membrane filament rupture according to a current inlet water turbidity represented by the inlet water turbidity information of the to-be-tested ultrafiltration membrane comprises: when the current inlet water turbidity represented by the inlet water turbidity information of the to-be-tested ultrafiltration membrane is less than a preset inlet water turbidity threshold, determining that the cause of the abnormality of the to-be-tested ultrafiltration membrane is membrane filament rupture; when the current inlet water turbidity represented by the inlet water turbidity information of the to-be-tested ultrafiltration membrane is not less than the preset inlet water turbidity threshold, determining that the cause of the abnormality of the to-be-tested ultrafiltration membrane is an inlet water filter abnormality.

2. The method of claim 1, wherein, the method of constructing the membrane filament rupture early warning model of the to-be-tested ultrafiltration membrane based on the historical working condition data comprises: based on the historical working condition data, constructing a training sample set and a test sample set; based on an initial membrane filament rupture early warning model, classifying the training samples in the training sample set according to differences in working condition indexes between the training samples to distinguish membrane filament rupture training samples and normal training samples, thereby obtaining a trained membrane filament rupture early warning model; inputting test samples in the test sample set into the trained membrane filament rupture early warning model to determine model test results corresponding to the test samples based on the trained membrane filament rupture early warning model according to differences in working condition indexes between the test samples and the membrane filament rupture training samples and / or according to differences in working condition indexes between the test samples and the normal training samples; when the model test results indicate that the accuracy of the trained membrane filament rupture early warning model reaches a preset standard, determining the trained membrane filament rupture early warning model as the membrane filament rupture early warning model of the to-be-tested ultrafiltration membrane.

3. The method of claim 2, wherein, The current working condition data is input into the membrane filament rupture early warning model to obtain a health monitoring result of the ultrafiltration membrane to be tested. The current working condition data is input into the membrane filament rupture early warning model to obtain a health monitoring result of the ultrafiltration membrane to be tested.

4. An ultrafiltration membrane health monitoring device, characterized by, Comprise: The first acquisition module is used for acquiring historical working condition data of the ultrafiltration membrane to be tested; the historical working condition data comprises historical working condition data of the ultrafiltration membrane to be tested in a case of membrane filament rupture and historical working condition data of the ultrafiltration membrane to be tested in a normal case; The model construction module is used for constructing a membrane filament rupture early warning model of the ultrafiltration membrane to be tested based on the historical working condition data; The second acquisition module is used for acquiring current working condition data of the ultrafiltration membrane to be tested; the current working condition data comprises an ultrafiltration self-cleaning filter inlet flow, an reverse osmosis security filter inlet and outlet differential pressure, an ultrafiltration membrane to be tested inlet water turbidity, an ultrafiltration membrane to be tested water turbidity, a backwash water pump frequency feedback and an ultrafiltration membrane to be tested inlet and outlet differential pressure; The monitoring module is used for inputting the current working condition data into the membrane filament rupture early warning model to obtain a health monitoring result of the ultrafiltration membrane to be tested; The device further comprises: The cause analysis module is used for acquiring inlet water turbidity information of the ultrafiltration membrane to be tested when the health monitoring result of the ultrafiltration membrane to be tested indicates that the ultrafiltration membrane to be tested is currently abnormal; Based on a preset membrane filament rupture analysis model, the cause analysis module determines an abnormality occurrence cause of the ultrafiltration membrane to be tested according to the inlet water turbidity information of the ultrafiltration membrane to be tested; The cause analysis module is specifically used for: Based on a preset membrane filament rupture analysis model, the cause analysis module determines whether the abnormality occurrence cause of the ultrafiltration membrane to be tested is membrane filament rupture according to a current inlet water turbidity represented by the inlet water turbidity information of the ultrafiltration membrane to be tested; The cause analysis module is specifically used for: When the current inlet water turbidity represented by the inlet water turbidity information of the ultrafiltration membrane to be tested is less than a preset inlet water turbidity threshold, it is determined that the abnormality occurrence cause of the ultrafiltration membrane to be tested is membrane filament rupture; When the current inlet water turbidity represented by the inlet water turbidity information of the ultrafiltration membrane to be tested is not less than the preset inlet water turbidity threshold, it is determined that the abnormality occurrence cause of the ultrafiltration membrane to be tested is an inlet water filter abnormality.

5. An electronic device, comprising: Comprise: At least one processor and a memory; The memory stores computer execution instructions; The at least one processor executes the computer execution instructions stored in the memory, so that the at least one processor executes the method in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and when the processor executes the computer execution instructions, the method in any one of claims 1 to 3 is realized.

Images