A remote multi-parameter monitoring operation and maintenance method for ultrapure water preparation

Abstract

The present application relates to the technical field of ultrapure water equipment operation and maintenance, and provides a remote multi-parameter monitoring operation and maintenance method for ultrapure water preparation, comprising: obtaining various data of an ultrapure water preparation system, determining a single water pollution impact pollution impact representation based on water pollution data, determining a theoretical life folding representation of each membrane section based on membrane life data of each membrane section, constructing a membrane section response fingerprint of each membrane section based on membrane section operation data to determine a fingerprint deviation representation of each membrane section, determining a life folding conversion category based on the theoretical life folding representation and the fingerprint deviation representation, re-estimating the actual life of each membrane section based on the life folding conversion category of each membrane section, generating and executing a remote operation and maintenance strategy for the corresponding membrane section according to the re-estimated actual remaining life, and obtaining membrane section recovery data after operation and maintenance to correct the calculation weight of the theoretical life folding representation of the corresponding membrane section; the method quantifies the influence of single pollution impact on the life of the membrane section and improves the operation and maintenance effect.

Description

Technical Field

[0001] This invention relates to the field of ultrapure water equipment operation and maintenance, specifically to a remote multi-parameter monitoring and operation and maintenance method for ultrapure water preparation. Background Technology

[0002] Ultrapure water systems primarily rely on multi-stage membrane stages for deep purification, and their operational status directly impacts water quality and equipment lifespan. However, during long-term operation, membrane stages are susceptible to factors such as influent contamination, scaling, membrane fouling, and irreversible degradation, leading to decreased water production, increased pressure differential, and water quality deterioration.

[0003] Traditional membrane segment operation and maintenance mainly relies on fixed-cycle cleaning and replacement strategies, which are usually based on experience and cannot reflect the actual lifespan and performance status of the membrane segments in real time. This approach has obvious limitations: on the one hand, membrane segments may be replaced prematurely before their lifespan has significantly deteriorated, increasing operating costs; on the other hand, if membrane segments experience latent life loss or non-impact degradation, it may lead to system operation risks and water quality fluctuations if not detected in time. In addition, existing technologies lack effective means for quantifying single pollution shocks, dynamically monitoring changes in membrane segment response, and establishing a feedback loop for operation and maintenance effectiveness, making it difficult to achieve refined and intelligent remote operation and maintenance management.

[0004] Therefore, there is an urgent need for a technical solution that can acquire membrane segment operation data and influent pollution information in real time, quantify pollution impact and membrane segment life reduction status, combine historical cleaning data and membrane segment response fingerprints, dynamically re-estimate the actual remaining life, and automatically generate remote operation and maintenance strategies. This technical solution can not only formulate personalized operation and maintenance strategies for different membrane segments, but also correct life reduction calculation parameters through operation and maintenance effect feedback, improve the accuracy of membrane segment life prediction and the safety of system operation, thereby optimizing operation and maintenance costs while ensuring ultrapure water quality. Summary of the Invention

[0005] Therefore, this invention provides a remote multi-parameter monitoring and maintenance method for ultrapure water preparation, which overcomes the lack of effective means in the prior art for quantifying single contamination shocks, dynamically monitoring membrane segment response changes, and providing feedback on maintenance effects, making it difficult to achieve refined and intelligent remote maintenance of ultrapure water equipment systems.

[0006] To achieve the above objectives, this invention provides a remote multi-parameter monitoring and maintenance method for ultrapure water preparation, comprising: Step S1: Obtain the operating data of each membrane segment, influent contamination data, and membrane lifetime data of the ultrapure water preparation system; Step S2: Determine the pollution impact characterization quantity of a single water pollution shock based on the water pollution data; Step S3: Determine the theoretical lifespan characterization value of each membrane segment based on the membrane lifetime data of each membrane segment; Step S4: Construct membrane segment response fingerprints for each membrane segment based on the membrane segment operation data, and compare the membrane segment response fingerprints with the corresponding baseline response fingerprints to determine the fingerprint deviation characterization value for each membrane segment. Steps S2-S4 can be performed simultaneously; Step S5: Determine the life conversion category of each membrane segment based on the theoretical life conversion value and the fingerprint deviation value. The life conversion category includes life conversion membrane segments, life conversion non-conversion membrane segments, non-impact attenuation membrane segments, and stable non-attenuation membrane segments. Step S6: Re-estimate the actual lifetime of each membrane segment based on the lifetime conversion category of each membrane segment; Step S7: Generate and execute the remote operation and maintenance strategy for the corresponding membrane segment based on the re-estimated actual remaining lifetime; The remote operation and maintenance strategy includes a life extension observation strategy, a cleaning and maintenance strategy, and an isolation operation and maintenance strategy. Step S8: Obtain the membrane segment recovery data after maintenance to correct the theoretical lifespan characterization value of the corresponding membrane segment and calculate the weight.

[0007] As a preferred technical solution for a remote multi-parameter monitoring and maintenance method for ultrapure water preparation, in step S1, the membrane segment operation data includes the conductivity before and after the membrane segment, the membrane segment pressure difference, the product water flow rate and the treatment time; the influent contamination data includes the influent contamination intensity and the duration of contamination; and the membrane life data includes the membrane segment design life, the cumulative usage time and the historical cleaning recovery rate.

[0008] As a preferred technical solution for remote multi-parameter monitoring and maintenance methods for ultrapure water preparation, step S2, the process of determining the pollution impact characterization quantity of a single influent pollution impact based on the influent pollution intensity and the pollution duration, includes: Step S21: Obtain the influent pollution intensity at several sampling times during a single influent pollution shock. Step S22: Compare the influent pollution intensity at each sampling time with the preset influent pollution standard value to determine the pollution exceedance at each sampling time; Step S23: Construct a pollution exceedance sequence based on the pollution exceedance amount at each sampling time according to the time order; Step S24: Determine the pollution impact characterization quantity based on the area exceeding the standard formed by the pollution exceeding the standard sequence during the pollution duration.

[0009] As a preferred technical solution for a remote multi-parameter monitoring and maintenance method for ultrapure water preparation, step S3, the process of determining the theoretical lifespan characterization value of each membrane segment based on the cumulative usage time and the historical cleaning recovery rate, includes: Step S31: The ratio of the cumulative usage time of the membrane segment to the designed lifespan is determined as the usage progress coefficient; Step S32: Determine the recovery attenuation level based on the preset recovery rate range where the historical cleaning recovery rate falls; Step S33: Determine the membrane segment vulnerability ratio based on the usage process coefficient and the recovery attenuation level; Step S34: The pollution impact characterization quantity is amplified according to the membrane segment fragility ratio to determine the theoretical lifespan characterization value of the membrane segment.

[0010] As a preferred technical solution for remote multi-parameter monitoring and maintenance of ultrapure water preparation, in step S4, the membrane segment response fingerprint of the corresponding membrane segment is formed by combining the conductivity before and after the membrane segment, the membrane segment pressure difference and the product water flow rate in a preset order.

[0011] As a preferred technical solution for a remote multi-parameter monitoring and maintenance method for ultrapure water preparation, in step S4, the membrane segment response fingerprint is compared with the corresponding reference response fingerprint to determine the fingerprint deviation characterization value of each membrane segment, wherein: Step S41: Determine the deviation of each response factor in the membrane segment response fingerprint from the benchmark factor corresponding to the benchmark response fingerprint; Step S42: Determine the ratio of each deviation to the corresponding preset standard deviation as the local deviation factor; Step S43: Weighted summation of each local deviation factor to obtain the fingerprint deviation characterization value of the corresponding membrane segment.

[0012] As a preferred technical solution for remote multi-parameter monitoring and maintenance methods for ultrapure water preparation, in step S5, the lifespan conversion category of each membrane segment is determined based on the theoretical lifespan characterization value and the fingerprint deviation characterization value, including: Based on the determination results that the theoretical life reduction characterization value is greater than the life reduction threshold and the fingerprint deviation characterization value is greater than the deviation threshold, the corresponding membrane segment is determined to be the life reduction conversion membrane segment. Based on the determination that the theoretical life reduction characterization value is greater than the life reduction threshold and the fingerprint deviation characterization value is not greater than the deviation threshold, the corresponding membrane segment is identified as the life reduction unconverted membrane segment. Based on the determination that the theoretical life reduction value is not greater than the life reduction threshold and the fingerprint deviation value is greater than the deviation threshold, the corresponding membrane segment is identified as a non-impact attenuation membrane segment.

[0013] As a preferred technical solution for remote multi-parameter monitoring and maintenance of ultrapure water preparation, in step S6, the actual lifespan of each membrane segment is re-estimated based on the lifespan conversion category of each membrane segment, including: For the life-delay conversion membrane segment, the actual remaining life of the corresponding membrane segment is shortened based on the theoretical life-delay characterization value and the fingerprint deviation characterization value; For membrane segments with reduced lifespan but not converted, the reduction in actual remaining lifespan due to influent pollution shock is reduced based on theoretical lifespan characterization values ​​and fingerprint deviation characterization values. For non-impact attenuation membrane segments, the non-impact lifetime attenuation of the corresponding membrane segment is determined based on the fingerprint deviation characterization value and the historical cleaning recovery rate.

[0014] As a preferred technical solution for a remote multi-parameter monitoring and maintenance method for ultrapure water preparation, in step S7, a remote maintenance strategy for the corresponding membrane segment is generated and executed based on the re-estimated actual remaining lifetime, including: If the re-estimated actual remaining lifetime is greater than the preset safe lifetime, a lifetime observation strategy for the corresponding membrane segment is generated. If the re-estimated actual remaining lifespan is not greater than the preset safe lifespan but is greater than the preset maintenance lifespan, a cleaning and maintenance strategy for the corresponding membrane segment will be generated. If the re-estimated actual remaining lifespan is not greater than the preset maintenance lifespan, a replacement or isolation maintenance strategy will be generated for the corresponding membrane segment.

[0015] As a preferred technical solution for remote multi-parameter monitoring and maintenance methods for ultrapure water preparation, in step S8, the membrane segment recovery data after maintenance is obtained to correct the theoretical lifespan characterization value of the corresponding membrane segment and calculate the weight, including: Step S81: Obtain the membrane segment response fingerprint before and after the execution of the remote operation and maintenance strategy, and determine the operation and maintenance recovery characterization value; Step S82: Determine a correction scheme based on the operation and maintenance recovery characterization value, wherein: If the maintenance recovery characterization value is greater than the recovery threshold, the calculation weight of the theoretical life loss characterization value of the corresponding membrane segment will be reduced. If the maintenance recovery characteristic value is not greater than the recovery threshold, then the weight of the theoretical life loss characteristic value calculation for the corresponding membrane segment is increased.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides a remote multi-parameter monitoring and operation and maintenance method for ultrapure water preparation. By comprehensively acquiring membrane segment operation data, influent contamination data, and membrane life data, it assesses the membrane segment's lifespan reduction status in real time. Furthermore, by comparing the membrane segment's response fingerprint with a benchmark fingerprint, it determines the lifespan reduction transformation category, achieving dynamic re-estimation of the membrane segment's actual lifespan and generating remote operation and maintenance strategies. This method not only accurately quantifies the impact of a single contamination shock on the membrane segment's lifespan but also corrects theoretical lifespan reduction characterization values ​​or judgment thresholds based on operation and maintenance recovery data to achieve feedback on operation and maintenance effectiveness. This ensures that the membrane segment's service life is extended within a safe operating range, improves system stability and operating efficiency, and reduces maintenance costs. In particular, by comprehensively judging the membrane segment life conversion category by theoretical life conversion value and fingerprint deviation value, the membrane segment is divided into life conversion conversion membrane segment, life conversion non-conversion membrane segment, non-impact decay membrane segment, and stable non-decay membrane segment, thereby realizing a refined assessment of membrane segment life. Based on the classification results, re-evaluation can be performed to formulate personalized operation and maintenance strategies for different types of membrane segments, effectively avoiding misjudgment and over-maintenance, and improving membrane segment utilization and system reliability. In particular, based on the reassessed actual remaining lifespan, the method automatically generates lifespan extension observation, cleaning maintenance, or isolation operation and maintenance strategies to achieve remote intelligent operation and maintenance. Through hierarchical management of preset safe lifespan thresholds and maintenance lifespan thresholds, the method can dynamically adjust operation and maintenance strategies to extend the service life of membrane segments within the allowable lifespan range, while taking timely maintenance or replacement measures to reduce operational risks and optimize operation and maintenance resource allocation. In particular, by acquiring membrane segment response data after maintenance, calculating maintenance recovery characterization values, and adjusting the theoretical lifespan characterization values ​​or fingerprint deviation judgment thresholds accordingly, feedback on maintenance effectiveness is achieved. High recovery rates reduce lifespan weights, minimizing excessive reduction in lifespan; low recovery rates increase lifespan weights or lower judgment thresholds, enhancing the system's sensitivity to potential damage. This mechanism ensures the dynamic accuracy of lifespan assessment, helping to extend membrane segment lifespan, reduce maintenance costs, and improve the overall operational safety of the ultrapure water preparation system. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the steps of a remote multi-parameter monitoring and maintenance method for ultrapure water preparation according to an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0019] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0020] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0021] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; 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; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0022] Please see Figure 1 The diagram illustrates the steps of a remote multi-parameter monitoring and maintenance method for ultrapure water preparation according to an embodiment of the present invention. This embodiment provides a remote multi-parameter monitoring and maintenance method for ultrapure water preparation, comprising: Acquire operational data, feed water contamination data, and membrane lifetime data for each membrane segment of the ultrapure water preparation system. Specifically, the membrane segment operational data includes the conductivity before and after the membrane segment, the membrane segment pressure difference, the permeate flow rate, and the treatment time; the feed water contamination data includes the feed water contamination intensity and the duration of contamination; and the membrane lifetime data includes the membrane segment design lifetime, cumulative usage time, and historical cleaning recovery rate.

[0023] Based on the aforementioned influent pollution data, a pollution shock characterization quantity for a single influent pollution shock is determined. It can be understood that a single influent pollution shock refers to a complete continuous pollution fluctuation process during continuous water production where the influent pollution intensity deviates from the normal baseline state and persists for a period of time (pollution duration), before subsequently returning to the normal baseline state or entering the next significant fluctuation. In practice, when the system is running smoothly, the influent pollution intensity is within the normal range. At a certain moment, the influent pollution intensity begins to increase and remains elevated for several minutes before returning to normal. The pollution fluctuation corresponding to these few minutes is then recorded as a single influent pollution shock. Specifically, a sensor array is installed on the inlet pipeline of the ultrapure water equipment to acquire several inlet water quality parameters (including but not limited to inlet water conductivity, TOC, turbidity, hardness, silica content, and residual chlorine). A comprehensive water quality index (WQI) model is constructed. It should be understood that the WQI method standardizes and weights multiple water quality parameters (such as conductivity, TOC, turbidity, pH, residual chlorine, and hardness) to output a normalized comprehensive score (inlet pollution intensity / water quality index) to measure the overall water quality. Commonly used fusion methods include entropy weighting, weighted arithmetic index, and principal component analysis. The specific calculation methods are existing technologies and will not be elaborated further. In practice, the comprehensive water quality index is positively correlated with the degree of inlet water pollution; that is, the higher the comprehensive water quality index, the higher the inlet water pollution intensity. Understandably, the preset influent contamination standard value is the benchmark value for judging whether the influent contamination intensity has entered a contamination shock state. In practice, during the initial operation of the ultrapure water equipment (within 3 to 6 months of operation), the preset influent contamination standard value is determined by the design influent conditions of the membrane module, pretreatment unit, or ultrapure water system. After the initial operation is completed, the preset influent contamination standard value is the average influent contamination intensity during the stable operation period of the ultrapure water equipment plus 1 to 2 times the standard deviation. It should be understood that the amount of pollution exceeding the standard is the portion of the influent pollution intensity at each sampling time that exceeds the preset influent pollution standard value. That is, the amount of pollution exceeding the standard = the influent pollution intensity at the current sampling time - the preset influent pollution standard value. If the current influent pollution intensity is not greater than the preset influent pollution standard value, the amount of pollution exceeding the standard is recorded as 0. It should be understood that the pollution shock characterization quantity represents the comprehensive intensity of the potential impact of the influent pollution fluctuation on the subsequent membrane segment, and is a value that can be used for subsequent lifetime reassessment. In practice, if the pollution shock characterization quantity is low, it means that it is only a slight fluctuation and may not significantly affect the membrane lifetime. That is, it characterizes the comprehensive pollution load of a single influent pollution shock in the time dimension, which is determined by the degree of exceedance of the influent pollution parameters relative to the preset influent pollution standard value and the duration of the exceedance, and is used to characterize the possibility that the influent pollution shock will cause the theoretical lifetime consumption of the membrane segment. Understandably, a pollution exceedance sequence represents how the degree of pollution exceedance changes over time throughout the entire duration of a pollution shock; that is, it is a time-domain sequence. In practice, the exceedance area is the time integral area formed by the pollution exceedance sequence over the duration of pollution (when this sequence is plotted, the horizontal axis represents time, the vertical axis represents the amount of pollution exceeding the standard, and the area under the curve is the exceedance area). In one implementation, if the sampling process is discrete, the trapezoidal area between adjacent sampling points is used for approximate calculation. That is, the exceedance amount of pollution at two adjacent sampling times is averaged, and then multiplied by the time interval between the two sampling times to obtain the local exceedance area. The local exceedance areas are summed to obtain the exceedance area of ​​a single pollution shock. Understandably, after determining the exceedance area, it is usually necessary to normalize it, and the normalized data is used as the characterization quantity of the pollution shock. It should be understood that the pollution shock characterization quantity represents the comprehensive pollution load of a single influent pollution shock over time, reflecting the basic intensity of the theoretical lifespan consumption caused by this influent pollution shock.

[0024] The theoretical lifespan of each membrane segment is determined based on the cumulative usage time and historical cleaning recovery rate of each membrane segment. It can be understood that the theoretical lifespan is determined by combining the pollution impact assessment with the cumulative usage time and historical cleaning recovery rate of the membrane segment, reflecting how much the membrane segment will theoretically be damaged. Specifically, the ratio of the cumulative usage time of the membrane segment to its designed lifespan is determined as the usage progress coefficient; It is understandable that the same intensity of influent fouling impact will cause different lifespan losses to membrane segments in different states: the closer the membrane is to the end of its lifespan and the worse its historical cleaning recovery is, the greater the theoretical lifespan reduction caused by this fouling impact. The recovery attenuation level is determined based on the preset recovery rate range within which the historical cleaning recovery rate falls. In practice, the cleaning recovery rate typically refers to the degree to which the key operating performance of the membrane segment recovers to its historical baseline or initial state before and after cleaning. In ultrapure water systems, membrane fouling generally manifests as: increased membrane pressure differential, decreased permeate flow rate, decreased desalination rate, increased post-membrane conductivity, and longer time required to treat the same volume of water. Therefore, the cleaning recovery rate can be determined based on one or more membrane segment operating indicators before and after cleaning. It can be understood that when permeate flow rate is used as the main indicator, the cleaning recovery rate = permeate flow rate after cleaning ÷ historical baseline permeate flow rate. When pressure differential is used as the main indicator, the cleaning recovery rate can be calculated by observing whether the pressure differential after cleaning returns to the baseline level, i.e., cleaning recovery rate = 1 - (pressure differential after cleaning - baseline pressure differential) ÷ ​​(pressure differential before cleaning - baseline pressure differential). In implementation, the preset recovery rate range converts the continuous cleaning recovery rate into discrete recovery attenuation levels, including: a historical cleaning recovery rate ≥90% is recorded as Level 1 recovery attenuation (basic recovery after cleaning, membrane relatively healthy); a historical cleaning recovery rate between 75% and 90% is recorded as Level 2 recovery attenuation (partial recovery after cleaning, with some contamination accumulation); a historical cleaning recovery rate between 60% and 75% is recorded as Level 3 recovery attenuation (insufficient cleaning recovery, deep membrane fouling); and a historical cleaning recovery rate ≤60% is recorded as Level 4 recovery attenuation (poor cleaning recovery, potential irreversible fouling or membrane damage, at which point the membrane segment fragility ratio is 2.2). It is understood that the specific thresholds for each range can be set by maintenance personnel based on actual operating conditions (semiconductor ultrapure water, pharmaceutical purified water, laboratory ultrapure water). The membrane segment vulnerability ratio is determined based on the usage process coefficient and the recovery attenuation level. It is understood that the membrane segment vulnerability ratio is used to describe the current sensitivity of the membrane segment to the pollution shock. That is, for the same pollution shock, the membrane with a shorter usage time and good cleaning and recovery has a smaller lifespan, while the membrane with a longer usage time and poor cleaning and recovery has a larger lifespan. It should be understood that the higher the membrane segment vulnerability ratio, the more significant the amplification of the pollution shock characterization value to the theoretical lifespan characterization value. In implementation, the operation and maintenance platform is pre-configured with a mapping table of usage process coefficient range, recovery attenuation level and membrane segment vulnerability ratio, so as to match the membrane segment vulnerability ratio in the mapping table according to the usage process coefficient range and recovery attenuation level of the membrane segment; Table 1 is the mapping table of usage process coefficient range, recovery attenuation level and membrane segment vulnerability ratio; Table 1 Mapping Relationship Table The pollution impact characterization quantity is amplified according to the membrane segment vulnerability ratio to determine the theoretical life reduction characterization value of the membrane segment; in practice, the theoretical life reduction characterization value = pollution impact characterization quantity × membrane segment vulnerability ratio; Understandably, the theoretical life loss characterization value refers to the degree of membrane life loss estimated based on the intensity of influent pollution impact and the membrane segment's own vulnerability state before observing the actual response of the membrane segment. The larger the theoretical life loss characterization value, the higher the probability and degree of life loss caused by this influent pollution impact on the membrane segment under the current state.

[0025] In practice, the theoretical life loss characterization value is used to characterize the potential impact of pollution shock on membrane life, and it is determined during the period from the occurrence of the pollution shock to its end (at which time the actual response of the membrane segment has not yet been observed); the fingerprint deviation characterization value is used to characterize the actual performance response of the membrane during subsequent operation, and it is determined within a preset response time window after the end of the corresponding pollution shock; that is, the theoretical life loss characterization value and the fingerprint deviation characterization value are not obtained at the same time point, but are based on the same pollution shock event to establish a correspondence before and after; preferably, the preset response time window is 30 minutes after the end of the pollution shock, so as to ensure that the membrane response is fully manifested and to avoid the influence of other interfering factors.

[0026] The operating data (including conductivity before and after the membrane segment, membrane segment pressure difference, and permeate flow rate) based on the preset response time window are combined in a preset order to form the membrane segment response fingerprint of the corresponding membrane segment; Understandably, multiple operational responses of a membrane segment after an influent fouling shock are combined in a fixed order into a comparable state vector to characterize the current fouling response characteristics of that membrane segment. In other words, the conductivity before and after the membrane, the membrane segment pressure difference, and the permeate flow rate are combined into a membrane segment response fingerprint, which is then compared with the historical baseline response fingerprint to determine whether the membrane segment has experienced a real, stable, and comprehensive performance deviation. In implementation, the first parameter of the membrane segment response fingerprint is the conductivity attenuation factor determined by the conductivity before and after the membrane segment. The conductivity attenuation factor = (conductivity before membrane - conductivity after membrane) ÷ conductivity before membrane. The lower the conductivity attenuation factor, the weaker the removal capacity of the membrane segment for ionic pollutants. The second parameter is the flow resistance factor (= current membrane segment pressure difference) determined by the membrane segment pressure difference (the difference between the inlet and outlet pressures of the membrane segment). The larger the membrane segment pressure difference, the higher the risk of membrane segment blockage or fouling. The third parameter is the permeate flow rate determined based on the permeate flow rate of the reaction membrane segment. The permeate flow rate attenuation factor = (historical baseline permeate flow rate - current permeate flow rate) / historical baseline permeate flow rate. The larger the permeate flow rate attenuation factor, the more significant the membrane flux attenuation. The average permeate flow rate during the stable operation phase after the new membrane is put into operation is selected as the historical baseline permeate flow rate.

[0027] The deviation of each response factor in the membrane segment response fingerprint from the corresponding baseline factor in the baseline response fingerprint is determined. It can be understood that the baseline response fingerprint is a reference fingerprint formed when the membrane segment is in a healthy, stable, or acceptable operating state. In practice, because the treatment load, membrane type, installation location, upstream water quality, and design flow rate may differ for different membrane segments, each membrane segment establishes its own baseline response fingerprint. Based on the membrane segment conductivity before and after the membrane segment, membrane segment pressure difference, and permeate flow rate under the baseline operating state, the baseline conductivity attenuation factor, baseline flow resistance factor, and baseline permeate attenuation factor are determined and combined in the same preset order as the membrane segment response fingerprint to form the baseline response fingerprint. The ratio of each deviation to the corresponding preset standard deviation is determined as the local deviation factor. In implementation, conductivity deviation = reference conductivity attenuation factor - current conductivity attenuation factor. A positive conductivity deviation indicates a decrease in ion removal capacity; if the current conductivity attenuation factor is not lower than the reference conductivity attenuation factor, the deviation is recorded as 0. Pressure difference deviation = current flow resistance factor - reference flow resistance factor. If the current pressure difference is higher than the reference pressure difference, a positive deviation indicates an increased risk of blockage; if the current pressure difference is not higher than the reference pressure difference, the deviation is recorded as 0. Permeate deviation = current permeate attenuation factor - reference permeate attenuation factor. If the current permeate attenuation factor is higher than the reference permeate attenuation factor, it indicates an aggravated membrane flux attenuation. In one implementation, the preset conductivity deviation is 0.03 to 0.08, preferably 0.05. If the decrease reaches about 0.05, it can usually reflect a noticeable decline in the ion rejection capacity of the membrane segment. The preset differential pressure deviation can be set to 15% to 25% of the reference membrane segment differential pressure, preferably 20% of the reference membrane segment differential pressure. The increase in differential pressure is usually related to membrane fouling, blockage, and increased flow resistance. A differential pressure change of 5% to 10% may come from flow fluctuations, valve regulation, and temperature changes. 15% to 25% is more suitable as a significant deviation range. The preset permeate flow deviation can be set to 0.10 to 0.15, preferably 0.10. The permeate flow rate will be affected by temperature, inlet pressure, system recovery rate, valve status, etc. A decrease that is too small should not be directly identified as membrane flux decline. Setting it to about 10% can avoid misjudgment caused by ordinary fluctuations and can identify membrane flux decline earlier. It is understandable that the units and magnitudes of conductivity attenuation, pressure difference, and permeate flow rate are different and cannot be directly added together. Therefore, each deviation is divided by the corresponding preset standard deviation. Thus, the local deviation factor is the result of standardizing each deviation. The fingerprint deviation characterization value of the corresponding membrane segment is obtained by weighted summation of each local deviation factor. In practice, the sum of the weights of the three local deviation factors is 1, and the weights of the conductivity attenuation factor, the flow resistance factor, and the permeate attenuation factor are 0.3 to 0.35, 0.3 to 0.35, and 0.30 to 0.4, respectively. It should be understood that the fingerprint deviation characterization value is used to characterize the overall deviation of the current membrane segment response fingerprint from the reference response fingerprint, and is used to determine whether the theoretical life reduction characterization value is converted into the actual performance degradation of the membrane segment. Understandably, the fingerprint deviation characterization value is used to verify whether the theoretical lifespan reduction is actually realized through the conductivity before and after the membrane, pressure difference, permeate flow rate, and desalination rate.

[0028] The lifespan transition category is determined based on the theoretical lifespan characterization value and the fingerprint deviation characterization value of each membrane segment, including: Based on the judgment result that the theoretical life loss characterization value is greater than the life loss threshold and the fingerprint deviation characterization value is greater than the deviation threshold, in practice, this indicates that the current influent pollution shock is theoretically sufficient to cause membrane life loss and the actual operating fingerprint of the membrane segment has also deviated significantly, that is, the theoretical risk and the actual response are consistent; it means that the pollution shock has been transformed into a membrane segment with actual membrane performance degradation; therefore, during operation and maintenance, it is necessary to shorten the actual remaining life of such membrane segments, clean them in advance and increase the monitoring frequency. If the life loss transformation is serious, it is necessary to consider replacing or isolating the membrane segment.

[0029] In implementation, the actual remaining life reduction is calculated as follows: the reference life reduction unit × the theoretical life reduction value × the fingerprint deviation correction coefficient. Typically, the reference life reduction unit is set to 0.5% to 2% of the design life of the corresponding membrane segment, preferably 1%. The fingerprint deviation correction coefficient amplifies or reduces the reduction amount based on the actual deviation of the membrane segment's response. When the fingerprint deviation value is between 1 and 1.2 times the deviation threshold, the fingerprint deviation correction coefficient is 1; when the fingerprint deviation value is between 1.2 and 1.5 times the deviation threshold, the fingerprint deviation correction coefficient is 1.3; and when the fingerprint deviation value exceeds 1.5 times the deviation threshold, the fingerprint deviation correction coefficient is 1.6. That is, the more severe the fingerprint deviation, the more significant the actual membrane performance degradation, and more actual life should be deducted for the same theoretical life reduction. Based on the judgment result that the theoretical life loss characterization value is greater than the life loss threshold and the fingerprint deviation characterization value is not greater than the deviation threshold, it can be understood that this indicates that the influent pollution shock was theoretically strong, but the membrane segment response fingerprint did not show obvious abnormalities, that is, the pollution shock has not yet manifested as actual membrane performance degradation; it means that the membrane segment has been subjected to a strong pollution shock but has not experienced significant performance deviation; therefore, during operation and maintenance, the corresponding influent pollution shock should reduce the deduction of the actual remaining life, and the monitoring frequency should be increased in the short term to prevent hysteresis deviation. In implementation, the actual remaining life reduction after reduction = base life reduction unit × theoretical life reduction value × unconverted reduction factor (less than 1, usually 0.1 to 0.5, preferably 0.3). Based on the judgment result that the theoretical life reduction value is not greater than the life reduction threshold and the fingerprint deviation value is greater than the deviation threshold, it can be understood that this indicates that the current or recent influent pollution shock is theoretically not serious, but the actual response fingerprint of the membrane segment has deviated significantly. Therefore, the cause of the membrane segment abnormality is not the current influent pollution shock, but may be due to historical accumulated pollution, local blockage, aging, or incomplete cleaning. That is, the membrane segment is a membrane segment whose performance degradation is not caused by influent pollution shock. Therefore, during operation and maintenance, it is necessary to determine whether the actual performance degradation of the membrane segment is caused by historical accumulated factors and quantify the non-impact life loss. Non-impact life loss = baseline life deduction unit × fingerprint deviation value × (1 - historical cleaning recovery rate). In practice, for membrane segments with large fingerprint deviation and low historical cleaning recovery rate, the non-impact life loss is large and it is recommended to clean in time and consider replacing the membrane segment in advance. For membrane segments with large fingerprint deviation but high historical cleaning recovery rate, the non-impact life loss is small and cleaning can be delayed while maintaining observation. For membrane segments with low fingerprint deviation, routine monitoring and maintenance can be maintained. If the theoretical life reduction value is not greater than the life reduction threshold and the fingerprint deviation value is not greater than the deviation threshold, then the corresponding membrane segment is determined to be a stable, non-degraded membrane segment. At this time, the membrane segment does not show obvious life reduction and is a stable operating membrane segment. Therefore, there is no need to formulate an operation and maintenance strategy and routine monitoring is sufficient. Understandably, considering only the theoretical life loss might misjudge a situation where the influent is heavily polluted but the membrane segment is not yet damaged as membrane degradation. Considering only the fingerprint deviation might not be able to determine whether the anomaly comes from the current influent pollution shock or from long-term aging, incomplete cleaning, or local blockage. Therefore, it is necessary to combine the theoretical life loss characterization value and the fingerprint deviation characterization value to determine whether the influent pollution shock has been converted into actual membrane segment life loss, and to further distinguish the current source of membrane segment degradation and the category of operating status. In implementation, a preset life reduction threshold is used to determine whether the theoretical life consumption corresponding to the pollution shock reaches the standard life reduction level. It can be set to 1.0 to 1.5, preferably 1.2. A theoretical life reduction value less than or equal to 1.0 indicates that the pollution shock is still within an acceptable range after being amplified by the membrane segment vulnerability ratio. Taking 1.2 as the preferred value can avoid slight pollution fluctuations being misjudged as life reduction events, and at the same time can identify moderate pollution shocks amplified by the aging membrane segment. When the theoretical life reduction value is greater than the preset life reduction threshold, it indicates that the current influent pollution shock has the theoretical possibility of causing significant life reduction in the corresponding membrane segment under the current state. It is understandable that a local deviation factor of 1 indicates that the indicator has reached the standard deviation level. After weighting multiple indicators, the closer the fingerprint deviation characterization value is to 1, the more the overall deviation has reached an identifiable abnormality level. In implementation, the deviation threshold is 0.8 to 1.2, preferably 1.0. A fingerprint deviation characterization value less than 0.8 indicates that the membrane segment response deviation is weak and may be within the normal fluctuation range. A fingerprint deviation characterization value close to 1.0 indicates that the overall deviation has reached a standard deviation level. A fingerprint deviation characterization value greater than 1.2 indicates that the membrane segment response fingerprint deviation is significant and may have strong contamination or performance degradation.

[0030] The actual lifespan of each membrane segment is re-estimated based on the lifespan conversion category of each membrane segment; Remote operation and maintenance strategies for the corresponding membrane segments are generated and executed based on the reassessed actual remaining lifetime. Specifically, the reassessed actual remaining life = the original actual remaining life - the actual remaining life deduction; in practice, the preset maintenance life < the preset safety life < the design life, where the preset safety life is the minimum lifespan at which the membrane segment can still operate safely, and the preset maintenance life is the critical lifespan at which the membrane segment needs immediate maintenance or replacement; typically, the preset safety life is 40% to 60% of the design life, and the preset maintenance life is 15% to 30% of the design life; Specifically, if the re-estimated actual remaining lifespan is greater than the preset safe lifespan, a lifespan observation strategy for the corresponding membrane segment is generated. It is understood that if the membrane segment is in good condition, it does not need to be cleaned or replaced immediately; it can simply be continuously monitored to delay lifespan degradation. If the re-estimated actual remaining lifespan is not greater than the preset safe lifespan but is greater than the preset maintenance lifespan, a cleaning and maintenance strategy for the corresponding membrane segment will be generated. If the membrane segment's lifespan is subject to certain degradation, cleaning or maintenance operations should be arranged to restore performance and avoid premature lifespan reduction. If the re-estimated actual remaining lifespan is not greater than the preset maintenance lifespan, a replacement or isolation maintenance strategy will be generated for the corresponding membrane segment; if the membrane segment's lifespan is severely degraded and there is a risk of failure, the membrane segment should be replaced or isolated to ensure the safe operation of the system.

[0031] Specifically, the weighting of membrane segment recovery data after maintenance is calculated to correct the theoretical lifespan representation value of the corresponding membrane segment, including: Obtain the membrane segment response fingerprint before and after the execution of the remote operation and maintenance strategy to determine the operation and maintenance recovery characterization value; during implementation, collect the operation fingerprint data of the membrane segment before and after the execution of the remote operation and maintenance strategy, calculate the difference of each key indicator before and after operation and maintenance, normalize the difference of each indicator with the design benchmark to obtain the recovery rate of each indicator, and weight the recovery rate of each indicator to obtain the overall operation and maintenance recovery characterization value of the membrane segment. The correction scheme is determined based on the aforementioned operation and maintenance recovery characterization values, wherein: If the maintenance recovery performance value is greater than the recovery threshold, the weight of the theoretical life loss performance value calculation for the corresponding membrane segment should be reduced. If the maintenance recovery performance value is greater than the recovery threshold, it indicates that the life loss of the membrane segment after good maintenance recovery is reversible. Therefore, the value of the theoretical life loss performance value in the calculation of the actual remaining life loss should be reduced (the value of the theoretical life loss performance value in the calculation of the actual remaining life loss is defined as the weight of the theoretical life loss performance value calculation). After reducing the weight, the reduction of the theoretical life loss on the actual remaining life will be reduced, thereby reducing the sensitivity of the membrane segment's life loss and avoiding excessive life loss. If the maintenance recovery performance value is not greater than the recovery threshold, the weight of the theoretical life loss performance value of the corresponding membrane segment should be increased. If the maintenance recovery performance value is not greater than the recovery threshold, it indicates that the membrane segment recovery is limited, the life loss is irreversible, or the damage is severe. Therefore, the weight of the theoretical life loss performance value should be increased to increase the impact of this life loss on the lifespan. In practice, the recovery threshold is usually set to 0.6 to 0.8 (60% to 80%), which means that a recovery rate of 60% to 80% for the membrane segment after maintenance is considered to be good.

[0032] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0033] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A remote multi-parameter monitoring and maintenance method for ultrapure water preparation, characterized in that, include: Acquire operational data, influent contamination data, and membrane lifetime data for each membrane segment of the ultrapure water preparation system; Based on the influent pollution data, determine the pollution impact characterization quantity for a single influent pollution shock; The theoretical lifespan characterization value of each membrane segment is determined based on the membrane lifetime data of each membrane segment; Based on the membrane segment operation data, a membrane segment response fingerprint is constructed for each membrane segment, and the membrane segment response fingerprint is compared with the corresponding benchmark response fingerprint to determine the fingerprint deviation characterization value of each membrane segment. The lifespan transition category is determined based on the theoretical lifespan characterization value and the fingerprint deviation characterization value of each membrane segment, including: Based on the determination results that the theoretical life reduction characterization value is greater than the life reduction threshold and the fingerprint deviation characterization value is greater than the deviation threshold, the corresponding membrane segment is determined to be the life reduction conversion membrane segment. Based on the determination that the theoretical life reduction characterization value is greater than the life reduction threshold and the fingerprint deviation characterization value is not greater than the deviation threshold, the corresponding membrane segment is identified as the life reduction unconverted membrane segment. Based on the determination that the theoretical life reduction characterization value is not greater than the life reduction threshold and the fingerprint deviation characterization value is greater than the deviation threshold, the corresponding membrane segment is determined as a non-impact attenuation membrane segment. If the theoretical life reduction characterization value is not greater than the life reduction threshold and the fingerprint deviation characterization value is not greater than the deviation threshold, then the corresponding membrane segment is determined to be a stable, undamaged membrane segment. The actual lifetime of each membrane segment is reassessed based on its lifetime reduction conversion category, including: For the life-delay conversion membrane segment, the actual remaining life of the corresponding membrane segment is shortened based on the theoretical life-delay characterization value and the fingerprint deviation characterization value; For membrane segments with reduced lifespan but not converted, the reduction in actual remaining lifespan due to influent pollution shock is reduced based on theoretical lifespan characterization values ​​and fingerprint deviation characterization values. For non-impact attenuation membrane segments, the non-impact lifetime attenuation of the corresponding membrane segment is determined based on the fingerprint deviation characterization value and the historical cleaning recovery rate; Remote operation and maintenance strategies for the corresponding membrane segments are generated and executed based on the reassessed actual remaining lifetime. The remote operation and maintenance strategy includes a life extension observation strategy, a cleaning and maintenance strategy, and an isolation operation and maintenance strategy. Obtain the membrane segment recovery data after maintenance to correct the theoretical lifespan characterization value of the corresponding membrane segment and calculate the weight.

2. The remote multi-parameter monitoring and maintenance method for ultrapure water preparation according to claim 1, characterized in that, The membrane segment operation data includes the conductivity before and after the membrane segment, the membrane segment pressure difference, the permeate flow rate, and the treatment time. The influent contamination data includes the influent contamination intensity and the duration of contamination. The membrane life data includes the membrane segment design life, the cumulative usage time, and the historical cleaning recovery rate.

3. The remote multi-parameter monitoring and maintenance method for ultrapure water preparation according to claim 2, characterized in that, The process of determining the pollution impact characterization measure of a single influent pollution shock based on the influent pollution intensity and the pollution duration includes: Obtain the influent pollution intensity at several sampling times during a single influent pollution shock; The pollution intensity of the influent at each sampling time is compared with the preset influent pollution standard value to determine the pollution exceedance at each sampling time. Based on the time sequence, the pollution exceedance amount at each sampling time is constructed into a pollution exceedance sequence; The pollution impact characterization quantity is determined based on the area exceeding the standard formed during the duration of pollution exceeding the standard sequence.

4. The remote multi-parameter monitoring and maintenance method for ultrapure water preparation according to claim 2, characterized in that, The process of determining the theoretical lifespan characterization value of a membrane segment based on the cumulative usage time and historical cleaning recovery rate of each membrane segment includes: The ratio of the cumulative usage time of the membrane segment to its designed lifespan is determined as the usage progress coefficient; The recovery attenuation level is determined based on the preset recovery rate range within which the historical cleaning recovery rate falls; The membrane segment vulnerability ratio is determined based on the usage process coefficient and the recovery attenuation level. The pollution impact characterization value is amplified according to the membrane segment vulnerability ratio to determine the theoretical lifespan characterization value of the membrane segment.

5. The remote multi-parameter monitoring and maintenance method for ultrapure water preparation according to claim 2, characterized in that, Based on the combination of the conductivity before and after the membrane segment, the pressure difference of the membrane segment, and the permeate flow rate in a preset order, a membrane segment response fingerprint is formed for the corresponding membrane segment.

6. The remote multi-parameter monitoring and maintenance method for ultrapure water preparation according to claim 5, characterized in that, The membrane segment response fingerprint is compared with the corresponding reference response fingerprint to determine the fingerprint deviation characterization value of each membrane segment, wherein: Determine the deviation of each response factor in the membrane segment response fingerprint from the baseline factor corresponding to the baseline response fingerprint; The ratio of each deviation to the corresponding preset standard deviation is determined as the local deviation factor; The fingerprint deviation characterization value of the corresponding membrane segment is obtained by weighted summation of each local deviation factor.

7. The remote multi-parameter monitoring and maintenance method for ultrapure water preparation according to claim 1, characterized in that, Based on the reassessed actual remaining lifetime, a remote operation and maintenance strategy is generated and executed for the corresponding membrane segment, including: If the re-estimated actual remaining lifetime is greater than the preset safe lifetime, a lifetime observation strategy for the corresponding membrane segment is generated. If the re-estimated actual remaining lifespan is not greater than the preset safe lifespan but is greater than the preset maintenance lifespan, a cleaning and maintenance strategy for the corresponding membrane segment will be generated. If the re-estimated actual remaining lifespan is not greater than the preset maintenance lifespan, a replacement or isolation maintenance strategy will be generated for the corresponding membrane segment.

8. The remote multi-parameter monitoring and maintenance method for ultrapure water preparation according to claim 1, characterized in that, Obtain post-maintenance membrane segment recovery data to correct the theoretical lifespan representation value of the corresponding membrane segment and calculate the weights, including: Obtain membrane segment response fingerprints before and after the execution of remote operation and maintenance strategies to determine the operation and maintenance recovery characterization values; The correction scheme is determined based on the aforementioned operation and maintenance recovery characterization values, wherein: If the maintenance recovery characterization value is greater than the recovery threshold, the calculation weight of the theoretical life loss characterization value of the corresponding membrane segment will be reduced. If the maintenance recovery characteristic value is not greater than the recovery threshold, then the weight of the theoretical life loss characteristic value calculation for the corresponding membrane segment is increased.

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