Integrated ultrapure water intelligent production system

Abstract

The application discloses an integrated ultrapure water intelligent production system, relates to the field of ultrapure water production, and comprises a multistage reverse osmosis module, a fine treatment module, a water quality detection module and an intelligent central control module; the water quality detection module comprises a TOC detection unit and a microorganism detection unit which are configured with different detection points and are used for monitoring water quality parameters in real time; the intelligent central control module receives detection data of the TOC detection unit and the microorganism detection unit, performs real-time interactive analysis strategies on data in different points, predicts water quality change trends, and dynamically adjusts efficiency parameters of ultrapure water production in corresponding areas; the application realizes revolutionary leap from passive response to active early warning through deep integration of modular design and intelligent management, dynamically adjusts water production flow in a forward-looking manner through regional trend prediction and multi-region collaborative analysis, and finally ensures that water quality is stable and reaches the ultrapure standard while remarkably improving system operation efficiency.

Description

Technical Field

[0001] This invention relates to the field of ultrapure water production, specifically to an integrated intelligent ultrapure water production system. Background Technology

[0002] In fields such as semiconductors, photovoltaics, and high-end pharmaceuticals, ultrapure water is an indispensable "lifeblood," its purity directly determining product yield and performance. While traditional ultrapure water production systems employ multi-stage purification processes and install water quality instruments at key points, their operation and management models suffer from fundamental limitations, leading to severe challenges in terms of continuity, stability, and efficiency. Specifically, traditional systems mainly suffer from the following drawbacks:

[0003] 1. Data Isolation and Delayed Response: In traditional systems, each processing unit (such as RO, EDI, and UV) operates independently, and its monitoring data is only used for local alarms within that unit (such as high / low limit alarms). The control system lacks the ability to perform in-depth correlation analysis of multiple parameters throughout the entire process (especially microorganisms and TOC). This results in the system only issuing alarms and taking action after water quality parameters have already exceeded the standards, a passive "post-mortem" response. For example, a problem can only be detected when the TOC value of the final product exceeds the standard, but by then a large amount of substandard water has already been produced, and tracing the source of pollution requires extensive manual investigation, which is time-consuming and labor-intensive.

[0004] 2. Inability to identify early contamination patterns, especially biofilm: Biofilm contamination is a "silent killer" in ultrapure water systems. In traditional systems, microorganisms and total organic matter (TOC) are considered two independent indicators. The system cannot recognize the deeper information contained in the dynamic changes of these two (such as the "strong negative correlation" between increasing microbial numbers and decreasing TOC), and therefore cannot issue early warnings in the early stages of biofilm formation, before it affects the quality of permeate. Often, by the time obvious symptoms such as increased pressure differential and decreased permeate flow appear, the biofilm has already matured, making cleaning more difficult and causing significantly more damage.

[0005] 3. Fixed Operation and Experience-Based Reliance: The system's operating parameters (such as permeate flow rate, UV intensity, and circulation velocity) are usually preset, or can only be adjusted proportionally based on a limited number of parameters, lacking predictive dynamic optimization capabilities. Faced with fluctuations in raw water quality or the slow accumulation of internal contamination risks, the system cannot make proactive, adaptive adjustments. Operation and maintenance heavily rely on engineers' experience for regular cleaning and maintenance, which can either be too frequent, leading to resource waste and equipment wear, or not timely enough, resulting in water quality risks.

[0006] 4. Vague Fault Diagnosis and Low Maintenance Efficiency: When problems arise with the final water quality, traditional systems struggle to quickly and accurately pinpoint the faulty unit. For example, excessive microorganisms in the permeate could originate from RO membrane damage, UV lamp failure, ultrafiltration membrane permeation, or contamination of the distribution system. Troubleshooting requires checking each unit sequentially, resulting in prolonged downtime and impacting production. The lack of effective traceability tools further complicates maintenance, making it both labor-intensive and untargeted.

[0007] 5. Lack of coordination among processing units: There is a lack of intelligent linkage between pretreatment, main desalination, and fine treatment units. When the front-end unit (such as RO) shows signs of performance degradation, the subsequent units (such as mixed bed) cannot receive early warning and prepare in advance. They can only passively bear the increased pollution load, which leads to a shortened lifespan and the risk of being instantly broken down.

[0008] Traditional ultrapure water production systems are essentially "automated" but "unintelligent" systems. Their core problems lie in the fragmentation of data, the superficiality of analysis, the lag in regulation, and the isolation of units. This leads to uncontrollable risks to water quality stability, high operation and maintenance costs, and potential threats of production interruption. Summary of the Invention

[0009] The purpose of this invention is to provide an integrated intelligent ultrapure water production system to solve the problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention provides the following technical solution: an integrated intelligent ultrapure water production system, comprising a multi-stage reverse osmosis module, a fine treatment module, a water quality detection module, and an intelligent central control module. The multi-stage reverse osmosis module includes a primary reverse osmosis unit and a secondary reverse osmosis unit, which perform deep desalination and purification on the preliminarily purified raw water. The water quality detection module includes a TOC detection unit and a microbial detection unit configured with different detection points to monitor water quality parameters in real time. The detection points include mixed detection points and single detection points. The mixed detection points are set after the fine treatment module and in the circulation loop to perform TOC detection and microbial detection respectively. The single detection point includes a first microbial detection point set on the concentrate side of the reverse osmosis unit and a first TOC detection point between the primary and secondary reverse osmosis units. The intelligent central control module receives the detection data from the TOC detection unit and the microbial detection unit, and performs real-time interactive analysis strategies on the data from different points. Based on the interactive analysis results, it performs regional water quality change trend prediction, and dynamically adjusts the efficiency parameters of ultrapure water production in the corresponding region according to the predicted water quality change trend.

[0011] Preferably, the interactive analysis strategy includes calculating the microbial-TOC correlation coefficient (R_MT) based on the time series of microbial detection data and TOC detection data in the corresponding area, and determining the activity status and pollution type of microorganisms in the area and classifying them into different risk levels according to the value and trend of the correlation coefficient.

[0012] Preferably, the interactive analysis strategy further includes calculating the microbial-TOC ratio of the reverse osmosis region based on the data from the first microbial detection point and the first TOC detection point. When the ratio continues to increase and the dynamic correlation coefficient is negatively correlated, it is determined that a reverse osmosis membrane biofilm is forming.

[0013] As a preferred embodiment, the regionalized water quality change trend prediction includes establishing a spatiotemporal correlation model based on the data difference between the mixed detection points in the circulation loop and the mixed detection points after the fine treatment module. When the microbial data in the circulation loop rises first and the data after the fine treatment module rises later, it is predicted that there is a risk of biofilm shedding.

[0014] Preferably, the dynamic adjustment of the efficiency parameters for ultrapure water production includes dynamically adjusting the water production flow rate in an inverse relationship based on the predicted risk level, wherein for each increase in the risk level, the water production flow rate is reduced by a preset percentage level.

[0015] Preferably, the dynamic adjustment also includes prioritizing and significantly increasing the irradiation intensity of the ultraviolet disinfection unit serving the area where an increased risk of microbial contamination is predicted, while increasing the circulation velocity in the area and related pipelines to enhance the flushing effect.

[0016] Preferably, the interactive analysis strategy also includes fault tracing analysis:

[0017] By comparing the changes in TOC detection data before and after the first-stage reverse osmosis unit, if the removal rate decreases, it is determined that the performance of the first-stage reverse osmosis unit has deteriorated or that fouling has occurred.

[0018] By comparing the changes in microbial detection data before and after the fine treatment module, if the removal rate decreases, it is determined that the disinfection or filtration unit in the fine treatment module is ineffective.

[0019] Preferably, the interactive analysis strategy further includes establishing a time-series-based early warning model:

[0020] Identify the leading patterns of change in microbial detection data compared to TOC detection data;

[0021] When the aforementioned leading changes are detected, an early warning is issued and preventative controls are triggered before key water quality parameters exceed the standards.

[0022] Preferably, the intelligent central control module is further configured to perform multi-region collaborative analysis. When two or more regions are at risk at the same time, a weighted calculation is performed based on the severity of the risk in each region and the criticality of the process to generate a global control strategy to coordinate and adjust the efficiency parameters of the entire system.

[0023] Preferably, the intelligent central control module is configured to generate an activation command for the fine treatment module based on the output of the interactive analysis strategy, and to activate the backup fine treatment module or increase the operating load of the existing fine treatment module in advance when the water quality trend is predicted to deteriorate.

[0024] In summary, the beneficial effects of this invention are:

[0025] This invention achieves a revolutionary leap from passive response to proactive early warning through the deep integration of modular design and intelligent management. Its core lies in the real-time interactive analysis of microbial and TOC data to accurately identify the type and activity state of pollution, thereby issuing early warnings in the early stages of biofilm formation and before water quality parameters exceed the standards. Through regional trend prediction and multi-regional collaborative analysis, the system can proactively and dynamically adjust the permeate flow rate, enhance the disinfection and flushing intensity of specific areas, and activate backup fine treatment units in advance. Ultimately, while ensuring that the water quality consistently meets ultrapure standards, it significantly improves the system's operating efficiency, extends the lifespan of key membrane components, and greatly reduces operation and maintenance costs and production risks. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the invention 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 only some embodiments of the invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of the integrated ultrapure water intelligent production system process framework structure of the present invention;

[0028] Figure 2 This is a schematic diagram of the framework structure of the interactive analysis strategy in the integrated ultrapure water intelligent production system of the present invention.

[0029] Figure 3 This is a schematic diagram illustrating the content framework structure of the spatiotemporal correlation model prediction logic in the integrated ultrapure water intelligent production system of this invention.

[0030] Figure 4 This is a schematic diagram of the overall equipment of the integrated ultrapure water intelligent production system of the present invention. Detailed Implementation

[0031] The present invention will now be described in further detail with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. These drawings are simplified schematic diagrams, which are only used to illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0032] To facilitate understanding of the present invention, a more complete description of the invention will be given below with reference to the accompanying drawings, which illustrate several embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the invention will be more thorough and complete.

[0033] All features disclosed in this specification, or all steps in all disclosed methods or processes, may be combined in any way, except for mutually exclusive features and / or steps.

[0034] Any feature disclosed in this specification (including any appended claims, abstract, and drawings) may be replaced by other equivalent or similar features for a similar purpose, unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is merely one example of a series of equivalent or similar features.

[0035] The following is combined Figures 1-4 This invention provides a detailed description of one embodiment: an integrated intelligent ultrapure water production system. Through modular design and intelligent management, it achieves continuous, stable, and efficient production from raw water to ultrapure water. Its core lies in precise sensing, in-depth analysis, scientific prediction, and dynamic control, including:

[0036] Multi-stage reverse osmosis module: The main process for removing dissolved impurities (salts, ions) and most organic matter from pretreated raw water;

[0037] The first-stage reverse osmosis unit is responsible for the main tasks of desalination and pollutant retention. Under high pressure, water molecules pass through the reverse osmosis membrane, while up to 95% to 99% of ions, more than 99% of organic matter, colloids, bacteria, and pyrogens (endotoxins) are retained, forming "concentrated water" that is discharged.

[0038] Secondary reverse osmosis unit: serving as both "refining" and "safety net". It further removes residual trace ions from the primary RO permeate and specifically targets and deeply retains small-molecule neutral organic matter, as these organic substances are difficult to remove by subsequent EDI units.

[0039] The two-stage RO system in series greatly reduces the burden on subsequent fine treatment modules and ensures an extremely low TOC level in the final product water.

[0040] The purification module further enhances the already high-purity RO permeate to an "ultra-pure" level that meets the requirements of semiconductor manufacturing. Its components are as follows:

[0041] Electrodeionization: Using an electric field and ion exchange membranes, ions are continuously and deeply removed without chemical regeneration, which is a key step in the preparation of ultrapure water.

[0042] Ultraviolet Oxidizer:

[0043] 185nmUV: It directly breaks down organic molecules with high-energy photons, oxidizing them into CO2 and H2O, and is a core device for reducing TOC.

[0044] 254nm UV: Achieves continuous sterilization by destroying the DNA / RNA of microorganisms, ensuring that microbial indicators meet standards.

[0045] Terminal mixed bed: Using nuclear-grade ion exchange resin as the final guarantee, residual ions are removed to the ppt level, ensuring that the water resistivity is stable above 18.18 MΩ·cm.

[0046] Ultrafiltration: Through physical sieving, it removes tiny particles, colloids, bacterial fragments, etc. from the water, ensuring the particulate matter index.

[0047] Terminal filter: A filter with a pore size of 0.1μm or smaller installed before the point of use, serving as the last physical barrier.

[0048] Water quality testing module: includes a TOC testing unit and a microbial testing unit configured with different testing points to monitor water quality parameters in real time. The testing points include mixed testing points and single testing points.

[0049] Mixed detection points:

[0050] Location 1: After the fine treatment module: Monitor the water quality of the final produced water. This is the "final test" point for judging whether the entire system is up to standard. TOC and microbial tests are performed simultaneously, and the data is used to verify the effectiveness of the entire system.

[0051] Location 2: In the circulation loop (return point): Monitor the stability of the distribution system. Data from this point is used to determine whether secondary contamination (such as pipe leaching or biofilm growth) has occurred in the distribution pipeline, which is several kilometers long. TOC and microbial testing are also performed to correlate and analyze the types of contamination.

[0052] Single detection point:

[0053] First microbial detection point (RO concentrate side): This is a diagnostic point. The concentrate side is the area where contaminants are concentrated. Changes in the activity and quantity of microorganisms here serve as an "early warning system" reflecting the trend of biofouling on the RO membrane surface, detecting problems earlier than monitoring the permeate side.

[0054] First TOC monitoring point (between primary and secondary RO): This is a performance evaluation point. It directly reflects the removal efficiency of organic matter by the primary RO unit. If the TOC value at this point increases, it indicates that the primary RO membrane may have become clogged with organic matter or its performance has deteriorated, requiring timely intervention. It also informs the secondary RO unit of changes in its feed water load.

[0055] The intelligent central control module receives detection data from the TOC detection unit and the microbial detection unit, and performs real-time interactive analysis strategies on the data from different locations. Based on the interactive analysis results, it predicts regional water quality change trends and dynamically adjusts the efficiency parameters of ultrapure water production in the corresponding regions according to the predicted water quality change trends. The specific steps are as follows:

[0056] Step 1: Data Reception and Fusion

[0057] It receives data uploaded by all TOC and microbial detection units in real time, and aligns and correlates it with process parameters such as pressure, flow rate, temperature, and UV intensity of the system using timestamps to form a unified multidimensional data view.

[0058] Step 2: Execute real-time interactive analysis strategies

[0059] This is the key to intelligent control. The central control module not only looks at the absolute value of individual data, but also focuses on the correlation between data.

[0060] Calculate the microbial-TOC correlation coefficient: analyze whether the changes in microbial quantity and TOC value are synchronous within a specific time period and region.

[0061] A strong positive correlation may indicate the input of exogenous organic pollution and the active reproduction of microorganisms.

[0062] Strong negative correlation: This is very likely to indicate that a biofilm is forming (microorganisms attach to the surface and grow, consuming TOC in the aqueous phase).

[0063] Key Ratios and Trend Analysis:

[0064] Calculate the microbial / TOC ratio in the RO area. If the ratio continues to rise and the correlation is negative, RO membrane biofouling is highly suspected.

[0065] Compare the microbial data of the circulating loop and the finely treated water. If the loop data rises first, and the finely treated data rises later, it indicates a risk of biofilm shedding, which will soon impact the water quality at the point of use.

[0066] Step 3: Conduct regional water quality change trend prediction

[0067] The system divides the entire process into multiple functional areas (such as "RO area", "EDI area", and "distribution system area"). Based on the above interactive analysis, it performs independent yet interconnected trend predictions for each area.

[0068] For example: It is predicted that the risk of biocontamination in the "RO area" will rise to "medium risk" within the next 24 hours.

[0069] For example, it is predicted that the "distribution system" may experience biofilm shedding during the next low-flow operation.

[0070] Step 4: Dynamically adjust efficiency parameters in the corresponding region.

[0071] This is the ultimate goal of analysis—to implement precise and proactive controls.

[0072] Adjust the permeate flow rate according to the predicted risk level: the risk level is inversely proportional to the permeate flow rate. When an increase in risk is predicted in a certain area, the system permeate flow rate is appropriately reduced in advance, before the water quality deteriorates, to create conditions for implementing restorative measures (such as enhanced flushing and chemical cleaning preparation), prioritizing water quality stability.

[0073] Targeted reinforcement treatment:

[0074] If microbial risks are predicted in the RO area, the UV disinfection intensity before the RO influent is increased, and the circulation velocity within the RO unit is increased to enhance flushing.

[0075] If the risk of the allocation system is predicted, the UV intensity in the circulation pipeline and the total circulation velocity of the system will be increased.

[0076] Adjust the operating status of the fine processing module:

[0077] When the water quality at the front end is predicted to deteriorate, the backup fine treatment mixed bed or ultrafiltration unit is activated in advance to increase the buffer capacity of the overall treatment capacity and ensure that the final water production is foolproof.

[0078] It is worth mentioning that, in this embodiment, the interactive analysis strategy includes calculating the microbial-TOC correlation coefficient (R_MT) based on the time series of microbial detection data and TOC detection data in the corresponding area, and determining the activity status and pollution type of microorganisms in the area and classifying them into different risk levels according to the value and trend of the correlation coefficient.

[0079] Specifically, the essence of this strategy is to upgrade from a passive "exceedance alarm" to an active "behavioral pattern recognition" by analyzing the dynamic relationship between two key parameters, microorganisms (MP) and total organic carbon (TOC), thereby achieving early and accurate diagnosis of pollution types.

[0080] Calculation of the microbial-TOC correlation coefficient (R_MT):

[0081] Data foundation:

[0082] Within the same functional area (e.g., the "RO area"), the intelligent central control module captures microbial detection data sequences and TOC detection data sequences within a time window (e.g., the past 4 or 8 hours).

[0083] For example: MP_data=[mp1,mp2,mp3,...,mpn] and TOC_data=[toc1,toc2,toc3,...,toc_n], where each data point represents the value at a sampling time.

[0084] Calculation method:

[0085] The Pearson correlation coefficient is typically used to calculate R_MT. Its formula measures the degree and direction of the linear correlation between two variables.

[0086] To put it simply: the range of R_MT is between -1 and 1.

[0087] R_MT=1: This indicates that the two are perfectly positively correlated, increasing or decreasing together.

[0088] R_MT=-1: This indicates that the two are completely negatively correlated, with the former increasing and the latter decreasing.

[0089] R_MT=0: This indicates that there is no linear correlation between the two.

[0090] Determining microbial activity and pollution type based on R_MT values ​​and trends: Different R_MT value ranges reveal different physiological states and pollution mechanisms of aquatic microbial communities.

[0091] Strong positive correlation (R_MT>0.7)

[0092] Data characteristics: The number of microorganisms and the TOC concentration change synchronously and in the same direction.

[0093] Biological explanation: Active planktonic bacteria exist in water bodies. They reproduce freely using total organic carbon (TOC) in the water as a nutrient source. Increased TOC provides "food" for microorganisms, leading to their population growth; conversely, reduced nutrient sources limit their growth.

[0094] Pollution type diagnosis: Input of exogenous organic pollution. For example, decreased pretreatment efficiency leading to organic matter penetrating to the downstream stage; or a sudden deterioration in raw water quality.

[0095] Risk level: Medium risk. This indicates that the system is experiencing a continuous organic load that can be used for microbial growth, which, if left unchecked, will lead to widespread microbial proliferation.

[0096] Strong negative correlation (R_MT < -0.5)

[0097] Data characteristics: The number of microorganisms increased, but the TOC concentration remained stable or even decreased.

[0098] Biological explanation: This is a typical signal of biofilm formation. Microorganisms no longer exist in a planktonic state, but instead attach to the membrane surface, pipe walls, or resin, forming flocs. The attached microorganisms directly absorb organic nutrients from the interface they are attached to, leading to a decrease in TOC detected in the aqueous phase, while the number of attached microorganisms continues to increase.

[0099] Contamination type diagnosis: Biofilm fouling. This is the most challenging and harmful form of contamination in ultrapure water systems because it is difficult to flush out and becomes a persistent source of contamination.

[0100] Risk level: High risk. Once the biofilm matures, it can lead to increased pressure differential, decreased water production, reduced desalination rate, and may periodically detach, impacting the final water quality.

[0101] Weak correlation or no correlation (-0.3) <R_MT<0.3)

[0102] Data characteristics: There is no obvious correlation between the changes in microorganisms and TOC, and they each fluctuate slightly within the normal baseline range.

[0103] Biological explanation: The system is in a stable and controlled state. There is no significant biological activity in the water, and microorganisms and organic matter are effectively controlled.

[0104] Pollution type diagnosis: No specific pollution type.

[0105] Risk level: Low risk / Normal operation.

[0106] Special morphology: Microbial death and TOC release (R_MT are strongly negatively correlated, but the manifestations are different)

[0107] Data characteristics: The number of microorganisms decreased sharply, while the TOC concentration increased rapidly.

[0108] Biological explanation: When a system undergoes sudden and intensive disinfection (such as excessive addition of disinfectant) or encounters extreme conditions (such as high temperature), a large number of microorganisms are killed instantly, cells rupture, and their intracellular organic matter is released into the water, leading to an increase in TOC.

[0109] Contamination type diagnosis: disinfection shock or system disturbance.

[0110] Risk level: Medium to high risk. Although the microorganisms are killed, the released TOC and cell debris may contaminate subsequent refining units (such as clogging ultrafiltration membranes and depleting resin capacity).

[0111] Based on the above assessment, the system will classify the risk level and trigger corresponding warnings:

[0112] Low risk -0.3-0.3 System stability Continuous monitoring, no special operations required. Medium risk >0.7 External pollution / active reproduction Inspect the preprocessing unit, strengthen upstream monitoring, and prepare to adjust operating parameters. High risk <-0.5 Biofilm formation Issue an immediate biological contamination warning, increase disinfection intensity and flushing rate in the area, and prepare for chemical cleaning. Special Alert <-0.6(MP↓,TOC↑) Massive death of microorganisms Inspect the disinfectant dosing system or temperature control, and assess the impact on the filtration unit.

[0113] It should be noted that, in this embodiment, the interactive analysis strategy further includes calculating the microbial-TOC ratio of the reverse osmosis region based on the data from the first microbial detection point and the first TOC detection point. When the ratio continues to increase and the dynamic correlation coefficient is negatively correlated, it is determined that a reverse osmosis membrane biofilm is forming.

[0114] Specifically, by combining the microbial-TOC ratio and the dynamic correlation coefficient (R_MT), we can gain insight into the key transformation of microorganisms from "planktonic" to "attached".

[0115] First microbial testing point (RO concentrate side):

[0116] Unique Location: The essence of the reverse osmosis process is separation. Water molecules permeate through the membrane, while contaminants are trapped and concentrated on the concentrate side. Therefore, the concentrate side is the "enrichment zone" and "amplifier" of microorganisms, organic matter, and salts in the RO system.

[0117] Function: The microbial data MP_C at this point can reflect the trend of microbial activity on the membrane surface at an early stage. Any weak biological activity signal will be amplified here, thus issuing an early warning earlier than the data on the product water side.

[0118] First TOC detection point (between primary and secondary RO):

[0119] Special location: This point monitors the permeate from the first-stage RO system, which is about to enter the second-stage RO system.

[0120] Function: The TOC data TC_D at this point directly reflects the removal efficiency of organic matter by the primary RO membrane. When the membrane is healthy, TC_D should be stable at a very low level.

[0121] Criterion: When the MP_C / TC_D ratio continues to increase and R_MT shows a negative correlation, it is determined that a biofilm is forming.

[0122] Step 1: Calculate the microbial-TOC ratio

[0123] Formula: Ratio = MP_C / TC_D

[0124] Initial state: In a healthy and stable RO system, the microorganisms and TOC on the concentrate side will maintain a relatively stable ratio due to the concentration effect, that is, the ratio will fluctuate slightly within a baseline range.

[0125] Step Two: A "continuous increase in the ratio" can only be caused by two reasons:

[0126] Increased molecular MP_C (increased number of microorganisms);

[0127] The denominator TC_D decreases (TOC concentration decreases);

[0128] Or both may occur simultaneously;

[0129] In the RO system, this precisely reveals the initial dynamics of biofilm formation:

[0130] Microbial attachment: Planktonic microorganisms begin to attach to the surface of the RO membrane and use it as a base to reproduce. The membrane surface becomes a huge bioreactor, resulting in a significant increase in the number of microorganisms (MP_C) around it.

[0131] Consumption of Aqueous TOC: Attached microorganisms (biofilms) primarily obtain nutrients from the membrane surface and boundary layer. They actively consume organic carbon (TOC) in the aqueous phase to build their own cellular material and extracellular polymers. This results in a relative decrease or stability of detectable TOC (TC_D) in the main body of the water flow, and may even decrease slightly due to the "filtration" effect of the biofilm.

[0132] Therefore, the continuous increase in the MP_C / TC_D ratio is a strong indication that a shift is taking place within the system from "microorganisms dissolving in water" to "microorganisms attaching to the membrane".

[0133] Step 3: Perform cross-validation based on the finding that "R_MT is negatively correlated";

[0134] A negative correlation between R_MT (e.g., <-0.5) means that, in the time series, an increase in MP_C is accompanied by a stabilization or decrease in TC_D.

[0135] This perfectly illustrates the above-mentioned biofilm formation mechanism: the growth of microorganisms comes at the cost of consuming organic matter in the aqueous phase, and the two form a negative correlation of "one increasing while the other decreases".

[0136] This contrasts sharply with exogenous contamination (which is positively correlated), where microorganisms and TOC increase simultaneously, the ratio may not change much, and R_MT will be positively correlated.

[0137] This resulted in the following beneficial effects:

[0138] Very early warning: Traditional alarms are based on the product water quality exceeding the standard, while this criterion can issue an early warning when the biofilm has just begun to form and has not yet had a significant impact on the product water quality, thus buying valuable time for maintenance.

[0139] Precise diagnosis: It does not just say "the system is at risk", but precisely points to the specific fault that "a biofilm is forming on the surface of the RO membrane". This avoids misjudging biofilm contamination as general organic fouling or particulate contamination, making subsequent cleaning and maintenance measures more targeted.

[0140] Quantification and objectivity: Based on explicit mathematical calculations, it avoids the subjectivity and lag of human experience judgment.

[0141] It is also worth mentioning that, in this embodiment, the regionalized water quality change trend prediction includes establishing a spatiotemporal correlation model based on the data difference between the mixed detection points in the circulation loop and the mixed detection points after the fine treatment module. When the microbial data in the circulation loop rises first and the data after the fine treatment module rises later, it is predicted that there is a risk of biofilm shedding.

[0142] Specifically, the first is the mixed detection point in the loop (set as MP-B / TOC-B):

[0143] Location: It is usually set on the main circulation return water pipeline of the distribution system, that is, the location after the water flows through all the usage points and before returning to the storage tank or fine treatment module.

[0144] It monitors the water quality after it has been "baptized" by the entire long pipeline network, and is the first point to sense the dynamics inside the distribution system (such as the shedding of biofilm from the pipe walls and the scouring of particulate matter).

[0145] Hybrid detection points after fine processing module (set as MP-A / TOC-A):

[0146] Location: Located at the final product water outlet of the fine treatment module (such as UV, ultrafiltration, terminal mixed bed), before entering the distribution system. The water quality at this point represents the purest water produced by the entire water treatment system. Theoretically, MP-A should always be close to zero when the system is normal.

[0147] Secondly, establish the spatiotemporal correlation between MP-B and MP-A.

[0148] Spatial dimension: MP-B is located at the end of the pipeline network, and MP-A is located at the beginning of the pipeline network. Contaminants require transit time within the system.

[0149] Time dimension: The data from the two locations are a continuous sequence with high-precision timestamps.

[0150] Prediction criteria: When MP-B rises first, and MP-A rises later within a specific time window, a risk of biofilm shedding is predicted.

[0151] MP-B rises first:

[0152] This signal indicates that a biofilm that has formed and matured in a certain "dead corner" of the distribution pipeline (such as a branch with low flow rate, behind a valve, or on a rough pipe surface) has been physically detached due to some disturbance (such as sudden changes in water flow rate, pressure fluctuations, or equipment start-up and shutdown).

[0153] The detached biofilm fragments (containing a large number of bacterial clumps) are carried by the water flow, first flowing through and triggering the MP-B sensor located at the end of the loop.

[0154] MP-A increases with hysteresis:

[0155] This contaminated "water mass" needs time to flow from the point of detachment through the entire system and eventually reach the starting point of the distribution system, namely the MP-A point.

[0156] This confirms that the source of contamination is not the fine treatment module itself (otherwise MP-A would have increased first), but rather the distribution system.

[0157] The length of the lag time can help the system roughly estimate the location of the pollution source (for example, a short lag time may indicate that it is closer to MP-A).

[0158] From "passive response" to "proactive early warning": Traditional monitoring only alarms when MP-A levels exceed the standard, by which time the substandard water may have already entered critical production equipment. This strategy issues an early warning before the contaminant plume reaches MP-A, achieving true foresight.

[0159] Precisely pinpointing the type and scope of the fault: It clearly identifies the fault as a "biofilm problem within the distribution system," rather than a failure of the main water treatment system. This avoids misdirected maintenance, allowing engineers to focus their efforts on inspecting and addressing the piping system.

[0160] The system enables tiered and zoned control: it can predict the severity of an event based on the magnitude and rate of MP-B increases. It can also prioritize issuing warnings to the usage points with the highest probability of being affected, or temporarily shut off their water supply to protect the most critical production processes.

[0161] It is worth mentioning that, in this embodiment, the dynamic adjustment of the efficiency parameters of ultrapure water production includes dynamically adjusting the water production flow rate in an inverse relationship according to the predicted risk level, wherein for each level increase in risk level, the water production flow rate is reduced by a preset percentage level.

[0162] Specifically, by proactively and appropriately reducing the permeate flow rate, a "maintenance window" is created for the system to reduce the load:

[0163] Reduced pollutant load: The total amount of pollutants entering sensitive areas (such as RO membranes, EDI, and fine mixed beds) per unit time is reduced.

[0164] Increased residence time: The water stays in treatment units such as UV sterilizers for a longer period of time, which improves sterilization and oxidation efficiency.

[0165] Create conditions for physical flushing: provide operational space for simultaneous measures such as increasing flow rate, and avoid excessive system pressure.

[0166] A specific example of one of the preset percentage levels is as follows:

[0167] Level 1 (Low Risk) Parameters fluctuate slightly, but the trend remains stable. Maintain 100% The system is operating at its optimal state and is in full production. Level 2 (Medium Risk) Confirmed signs of pollution, trend forming Reduced to 85%-90% Preventative load reduction. This provides a buffer for enhanced disinfection and flushing, preventing the situation from worsening and having a minimal impact on overall production. Level 3 (High Risk) Accelerated pollution may affect water quality. Reduced to 70%-80% Protective load reduction. The primary objective is to control the situation and protect the core membrane modules and filtration unit from irreversible damage. Production output is secondary. Level 4 (Severe Risk) Water quality indicators exceeding or nearing the standard Reduce to 50% or shut down immediately Safety isolation is required. Continued production will result in a large amount of substandard wastewater and may damage the equipment. Immediate intervention, including cleaning or repair, is necessary.

[0168] When the system predicts that the risk of RO membrane biofouling is Level 3, it will immediately set the permeate flow rate to 75% of its maximum capacity. This is equivalent to the system actively "applying the brakes," sacrificing 25% of production to prevent the RO membrane from being rapidly clogged by biofilm, thus avoiding potentially longer downtime and expensive membrane replacement costs.

[0169] For areas where an increased risk of microbial contamination is predicted, the irradiation intensity of the ultraviolet disinfection units serving the area is increased preferentially and significantly, while the circulation velocity in the area and related pipelines is increased to enhance the flushing effect.

[0170] If the risk lies in the RO area: significantly increase the intensity of the UV sterilizer (especially 254nm) before the RO influent. The aim is to inactivate microorganisms to the greatest extent possible before they enter the RO unit, cutting off the "source" of biofilm formation at its origin.

[0171] If the risk lies in the distribution system: then the intensity of the UV sterilizer in the circulation pipeline will be significantly increased. The aim is to continuously kill airborne bacteria that regenerate in the pipeline and destroy the activity of shed biofilm fragments.

[0172] Instead of a slight increase of 5%-10%, the increase is 25%-100% (or even higher) depending on the risk level. For example, the power is increased from 100% to 125%-150%. For 185nm UV, the intensity is also increased to break down TOC and deprive microorganisms of nutrients.

[0173] Synergistic effect: Reducing the water production flow rate slows down the water flow velocity, naturally increasing the UV irradiation dose (mJ / cm²). Actively increasing the UV intensity at this point creates a dual benefit effect of "extending irradiation time" and "increasing irradiation intensity," resulting in an exponential increase in sterilization and TOC degradation effects.

[0174] Increase the circulation velocity in the area and related pipelines to enhance the flushing effect;

[0175] Physical removal mechanism: Increasing the flow rate means increasing the shear force of the fluid on the pipe wall and membrane surface. This has a strong physical stripping effect on microorganisms in the early stages of attachment and loose biofilms.

[0176] If the risk is in the RO area: Increase the circulation rate inside the RO unit or the concentrate side flow rate. This can directly flush the membrane surface, interfering with and preventing the attachment and colonization of microorganisms.

[0177] If the risk lies in the distribution system: Increase the overall circulation rate of the entire distribution system. This effectively cleans up biofilm precursors in the dead corners of the pipes and quickly removes any biofilm fragments that may detach, sending them to the UV and ultrafiltration units for treatment, thus preventing them from adhering at the point of use.

[0178] A "pulse-type" flushing method can be adopted: the flow rate is increased to the design maximum value in a short period of time to form turbulence and perform "impact therapy" on the biofilm.

[0179] Synergistic with flow reduction strategies: Although the total permeate flow rate is reduced, a high-velocity flushing loop can still be formed within a specific area through valve control.

[0180] In another embodiment, the interactive analysis strategy further includes fault tracing analysis:

[0181] By comparing the changes in TOC detection data before and after the first-stage reverse osmosis unit, if the removal rate decreases, it is determined that the performance of the first-stage reverse osmosis unit has deteriorated or that fouling has occurred.

[0182] By comparing the changes in microbial detection data before and after the fine treatment module, if the removal rate decreases, it is determined that the disinfection or filtration unit in the fine treatment module is ineffective.

[0183] Specifically, the core value of fault tracing analysis lies in the fact that when the final water quality fluctuates or only shows a fluctuating trend, the system can accurately locate the faulty unit, greatly shorten the troubleshooting time, and achieve precise maintenance.

[0184] First-stage reverse osmosis unit performance diagnosis

[0185] Compare the TOC data of the influent and effluent of the first-stage RO unit (i.e., the first TOC detection point).

[0186] Key metric: TOC removal rate

[0187] TOC removal rate = [(TOC influent - TOC in permeate) / TOC influent] × 100%

[0188] Normal state: The removal rate is stable at a high level (e.g., >98%), which indicates that the RO membrane is intact and has good desalination and organic matter retention performance.

[0189] Abnormal state: The removal rate continues to decrease or decreases significantly.

[0190] Judgment: The performance of the first-stage reverse osmosis unit has deteriorated or become clogged.

[0191] Source tracing analysis:

[0192] Organic fouling: If the TOC of the influent is not high, but the removal rate decreases, it may be because the membrane surface is fouled by organic matter, which clogs the membrane pores.

[0193] Membrane damage: If the removal rate suddenly drops sharply, accompanied by an abnormal increase in permeate flow, it may be due to physical damage to the membrane element or sealing ring, resulting in channeling.

[0194] Scaling or biofouling: Although it has a more direct impact on desalination rate, severe scaling can also change the membrane surface properties, indirectly affecting the removal of organic matter.

[0195] Intelligent Response: Issues an alert: "TOC removal rate of the primary RO unit has dropped to X%, suspected of being clogged / performance degradation."

[0196] Automatically suggest or trigger cleaning procedures (for fouling) or schedule integrity checks (for membrane damage).

[0197] At the same time, the system will dynamically adjust the operating parameters of the secondary RO to cope with the possible increase in influent load.

[0198] Fine treatment module performance diagnosis: Compare the microbial data of the influent and effluent of the fine treatment module (mixing detection point after the fine treatment module), with the key indicator being the microbial removal rate;

[0199] Microbial removal rate = [(MP influent - MP permeate) / MP influent] * 100%

[0200] Normal state: The removal rate is close to 100% (i.e., the microorganisms produced in the water are <1 CFU / 100mL), which indicates that the disinfection and filtration units such as UV and ultrafiltration are working normally.

[0201] Abnormal state: Removal rate decreases, microorganisms detected or increased in the produced water.

[0202] Judgment: The disinfection or filtration unit in the fine treatment module is inefficient.

[0203] Source tracing analysis:

[0204] UV sterilizer failure: Aging of the 254nm UV lamp tube, decrease in light intensity, and scaling of the quartz sleeve lead to a decrease in sterilization ability. R_MT may show a positive correlation, indicating active microorganisms.

[0205] Ultrafiltration membrane integrity issues: membrane fiber breakage, seal leakage, leading to microbial penetration.

[0206] Microbial growth in resin beds: Mixed-bed ion exchange resins become a breeding ground for microorganisms, thus becoming a source of pollution.

[0207] Intelligent Response: Issues an alarm: "The microbial removal rate of the fine treatment module has decreased, increasing the risk of excessive microbial levels in the final product water."

[0208] It automatically checks the UV lamp's running time and intensity, prompting for replacement or cleaning.

[0209] Increase the frequency of integrity testing for ultrafiltration permeate.

[0210] Activate the backup fine treatment unit in advance to ensure the safety of the final water quality.

[0211] Simultaneously, establish a time-series-based early warning model:

[0212] Identify the leading patterns of change in microbial detection data compared to TOC detection data;

[0213] When the aforementioned leading changes are detected, an early warning is issued and preventative controls are triggered before key water quality parameters exceed the standards.

[0214] Specifically, identifying the leading patterns in microbial data is crucial. In most biocontamination events, the abnormal increase in the number of microorganisms triggers the release of metabolic products, organic matter from cell lysis, and the resulting deterioration of water quality (such as increased TOC and decreased resistivity). Therefore, microbial indicators are often more forward-looking than TOC indicators.

[0215] Rising trend: Microbial data at a certain location shows a stable, slight upward slope over multiple sampling periods, even if its absolute value is still within the "acceptable" range.

[0216] Mutation spike: A brief, unusual peak in microbial data, followed by a drop. This could be an early signal of biofilm shedding.

[0217] Correlation shift: R_MT changes from no correlation to a stable negative correlation, which is a landmark dynamic signal of the beginning of biofilm formation.

[0218] Triggering early warning and preventive control: issuing warnings and taking action before key water quality parameters (such as product water TOC and resistivity) exceed the standard.

[0219] One example of the execution flow:

[0220] Data monitoring: The intelligent central control module continuously monitors the microbial MP_C on the RO concentrate side and the TOCTC_D of the primary RO permeate.

[0221] Pattern recognition:

[0222] Days 1-3: MPC slowly but steadily increased from 50 to 80 CFU / 100 mL (still within acceptable limits). TCD stabilized at 3 ppb (excellent). RMT changed from 0.1 to -0.4.

[0223] Model analysis: The system identified a leading change pattern of "MP_C trending upward + R_MT turning negatively correlated".

[0224] Issue early warnings:

[0225] The system will not wait for TC_D to exceed the limit or MP_C to surpass 100, but will issue the result directly on the third day:

[0226] Warning level: Medium

[0227] Warning message: "Enhanced biological activity and early signs of biofilm formation have been detected in the RO area. It is predicted that the TOC of the primary RO permeate will increase in the next 72 hours."

[0228] At this point, all key water quality parameters were still within acceptable limits.

[0229] Triggering preventative regulation:

[0230] The system no longer waits, but immediately and automatically executes the preset preventative measures:

[0231] Increase the UV intensity of the RO feed water by 30% to inactivate airborne bacteria at the source.

[0232] Increasing the circulation rate of the RO unit by 15% enhances membrane scouring and interferes with biofilm adhesion.

[0233] Prepare the RO chemical cleaning procedure and remind maintenance personnel that "it is recommended to perform a protective cleaning within 48 hours".

[0234] Through this time-series-based early warning model, the system has achieved a revolutionary transformation from "delayed alarm" to "advanced early warning".

[0235] In another embodiment, the intelligent central control module is configured to perform multi-region collaborative analysis. When two or more regions are at risk at the same time, a weighted calculation is performed based on the severity of the risk in each region and the criticality of the process to generate a global control strategy to coordinate and adjust the efficiency parameters of the entire system.

[0236] Specifically, when risk signals appear simultaneously in different areas of the system, the intelligent central control module will initiate a complex decision-making process, the core of which lies in weighted calculation and strategy coordination.

[0237] This is a level determined based on the results of independent interactive analysis of each region (such as R_MT, ratio, trend slope), for example, Level 1 to Level 4, which reflects "how serious the problem is in this region itself".

[0238] Process criticality: This is a preset weighting factor that represents the strategic importance of the region in the entire ultrapure water production process and its direct impact on the final water quality.

[0239] For example, it can be set as follows:

[0240] Fine treatment module area: weight factor 1.5 (highest). Because it is the "gatekeeper" of the final produced water, its failure will directly lead to substandard water entering the production line, causing huge losses.

[0241] First-stage reverse osmosis zone: weighting factor 1.2 (high). It is the main desalination barrier, and its failure will rapidly increase the burden on all subsequent units.

[0242] System allocation region: weighting factor 1.0 (medium). Although critical, its effects can sometimes be temporarily controlled with the help of circulating UV and ultrafiltration.

[0243] Pretreatment area: weighting factor 0.8 (low). Its problems typically have a long buffer period, and its impact on final water quality is indirect and slow.

[0244] Weighted calculation and global control strategy generation:

[0245] When calculating the overall risk score, if two or more areas are at risk, the central control module will not simply take the highest risk level, but will calculate a weighted overall score.

[0246] Overall risk score = (Σ(regional risk level × regional process criticality weight)) / Σ(regional process criticality weight)

[0247] Generate a comprehensive overall control strategy:

[0248] Based on the overall risk score and the risk characteristics of each region, the central control module generates a unified and coordinated set of control instructions, the principle of which is:

[0249] Bottleneck priority principle: Resources (such as traffic allocation and disinfection intensity) should be prioritized for areas with the highest weight and the most urgent risks.

[0250] Efficiency maximization principle: Control the core risks with minimal overall water production flow loss.

[0251] The principle of avoiding conflicting measures: ensuring that regulation in one region does not have a negative impact on other regions.

[0252] In another embodiment, the intelligent central control module is configured to generate an activation command for the fine treatment module based on the output of the interactive analysis strategy, and to activate the backup fine treatment module or increase the operating load of the existing fine treatment module in advance when the water quality trend is predicted to deteriorate.

[0253] Specifically, the intelligent central control module generates activation commands not based on a single, instantaneous exceeding signal, but rather on multi-dimensional predictive analysis results from upstream sources:

[0254] When the TOC removal rate of the primary RO unit shows a continuous downward trend, it indicates that the TOC content of the primary RO permeate (i.e., the influent to the fine treatment module) may increase in the near future.

[0255] When the microbial-TOC correlation coefficient (R_MT) in the EDI region turns into a strong positive correlation, it indicates that live bacteria have penetrated and may impact the fine treatment module.

[0256] Early warning of pollution incidents:

[0257] When the system predicts risks such as "RO membrane biofouling" or "biofilm shedding from the distribution system", even if the current influent water quality of the fine treatment module is normal, the central control module anticipates that a wave of pollutants (such as biofilm fragments, bacterial clusters, and released TOC) is about to arrive.

[0258] When the overall risk score reaches "medium risk" or higher, regardless of the specific area where the problem originates, the system will consider that the final water quality faces a sufficiently large potential threat, thereby triggering an enhanced instruction for the fine treatment module.

[0259] Specific regulatory directives: Two core response strategies

[0260] Based on the above decision-making criteria, the central control module will generate two different levels of instructions:

[0261] Strategy 1: Increase the operating load of existing fine processing modules

[0262] This strategy is suitable for low to medium risk levels, or as an initial response in high-risk situations.

[0263] Objective: To maximize the processing potential of existing equipment by optimizing operating parameters without enabling backup hardware.

[0264] Specific actions:

[0265] Increase the operating load of ultrafiltration:

[0266] Increase backwashing frequency: For example, reduce backwashing from once every 60 minutes to once every 30 minutes to remove trapped particulate matter and microbial aggregates more frequently.

[0267] Increase backwash intensity: During backwashing, increase the backwash water flow rate or extend the backwash time for a more thorough cleaning.

[0268] Maximize the efficiency of UV sterilizers:

[0269] Increase UV lamp power: Increase the irradiation intensity of 185nm and 254nm UV lamps from the standard operating power to the maximum safe power (e.g., by 20%-30%) to enhance the photo-oxidation capacity for TOC and the instantaneous elimination capacity for microorganisms.

[0270] Optimize the operation of the fine mixed bed:

[0271] If the system has multiple mixed beds in parallel, the valves can be adjusted to make the water flow more evenly through all the online mixed beds, avoiding premature penetration of a single mixed bed.

[0272] Activate the "recirculation mode" of the mixed bed to return some of the permeate to the mixed bed feed water for further polishing and to ensure the purity of the effluent ions.

[0273] Strategy 2: Activate the backup fine processing module in advance

[0274] This strategy is applicable to high-risk or severe-risk levels, or when a large-volume, high-concentration pollution surge is predicted.

[0275] Objective: To directly expand the system's total processing capacity by adding parallel processing units, thus building a robust "buffer zone".

[0276] Specific actions:

[0277] Activate the backup polishing mixing bed:

[0278] The system automatically opens the inlet and outlet valves of the standby mixed bed, allowing it to operate in parallel with the running mixed bed. This instantly doubles the total capacity of the ion exchange resin, greatly extending the system's "endurance" in dealing with TOC and ion load shocks.

[0279] Activate the backup ultrafiltration unit:

[0280] Similarly, the backup ultrafiltration unit is put into operation in parallel. This not only increases the total filtration area and reduces the fouling rate of individual ultrafiltration membranes, but also provides redundancy—even if the membrane fibers in one unit break, the other unit can still ensure water quality.

[0281] Forming a processing sequence:

[0282] In extreme cases, the system can configure the backup unit in series. For example, after the water flows through the first stage of fine treatment, it enters the backup second stage of fine treatment for deep polishing, forming a multi-level barrier.

[0283] In summary, this invention achieves a revolutionary leap from passive response to proactive early warning through the deep integration of modular design and intelligent management. Its core lies in utilizing real-time interactive analysis of microbial and TOC data (such as correlation coefficient and ratio calculations) to accurately identify pollution types and activity states, thereby issuing early warnings in the early stages of biofilm formation and before water quality parameters exceed standards. Through regional trend prediction and multi-regional collaborative analysis, the system can proactively and dynamically adjust permeate flow, enhance disinfection and flushing intensity in specific areas, and activate backup purification units in advance. Ultimately, while ensuring stable ultrapure water quality, it significantly improves system operating efficiency, extends the lifespan of key membrane components, and substantially reduces operation and maintenance costs and production risks.

[0284] The above description is merely a specific embodiment of the invention, but the scope of protection of the invention is not limited thereto. Any variations or substitutions conceived without inventive effort should be included within the scope of protection of the invention. Therefore, the scope of protection of the invention should be determined by the scope defined in the claims.

Claims

1. An integrated intelligent ultrapure water production system, comprising a multi-stage reverse osmosis module, a fine treatment module, a water quality testing module, and an intelligent central control module, characterized in that: The multi-stage reverse osmosis module includes a primary reverse osmosis unit and a secondary reverse osmosis unit, which perform deep desalination and purification on the initially purified raw water. The water quality monitoring module includes a TOC monitoring unit and a microbial monitoring unit configured with different monitoring points to monitor water quality parameters in real time. The monitoring points include mixed monitoring points and single monitoring points. The mixed monitoring points are set after the fine treatment module and in the circulation loop to perform TOC and microbial monitoring respectively. The single monitoring point includes a first microbial monitoring point set on the concentrate side of the reverse osmosis unit and a first TOC monitoring point set between the primary and secondary reverse osmosis units. The intelligent central control module receives detection data from the TOC detection unit and the microbial detection unit, and performs real-time interactive analysis strategies on the data at different locations. Based on the interactive analysis results, it predicts regional water quality change trends and dynamically adjusts the efficiency parameters of ultrapure water production in the corresponding region according to the predicted water quality change trends. The interactive analysis strategy includes calculating the microbial-TOC correlation coefficient based on the time series of microbial detection data and TOC detection data in the corresponding region, and determining the activity status and pollution type of microorganisms in the region and classifying different risk levels according to the value and trend of the correlation coefficient. Based on the data from the first microbial detection point and the first TOC detection point, the microbial-TOC ratio of the reverse osmosis region is calculated. When the ratio continues to increase and the dynamic correlation coefficient is negatively correlated, it is determined that a reverse osmosis membrane biofilm is forming. The regionalized water quality change trend prediction includes establishing a spatiotemporal correlation model based on the data difference between the mixed detection points in the circulation loop and the mixed detection points after the fine treatment module. When the microbial data in the circulation loop rises first and the data after the fine treatment module rises later, it is predicted that there is a risk of biofilm shedding.

2. The integrated ultrapure water intelligent production system according to claim 1, characterized in that: The efficiency parameters for dynamically adjusting ultrapure water production include dynamically adjusting the water production flow rate in an inverse relationship based on the predicted risk level, wherein for each level increase in risk, the water production flow rate is reduced by a preset percentage level.

3. The integrated ultrapure water intelligent production system according to claim 2, characterized in that: The dynamic adjustment also includes prioritizing and significantly increasing the irradiation intensity of the ultraviolet disinfection unit serving the area where an increased risk of microbial contamination is predicted, while increasing the circulation velocity in the area and related pipelines to enhance the flushing effect.

4. The integrated ultrapure water intelligent production system according to claim 3, characterized in that: The interactive analysis strategy also includes fault source tracing analysis: By comparing the changes in TOC detection data before and after the first-stage reverse osmosis unit, if the removal rate decreases, it is determined that the performance of the first-stage reverse osmosis unit has deteriorated or that fouling has occurred. By comparing the changes in microbial detection data before and after the fine treatment module, if the removal rate decreases, it is determined that the disinfection or filtration unit in the fine treatment module is ineffective.

5. The integrated ultrapure water intelligent production system according to claim 4, characterized in that: The interactive analysis strategy also includes establishing a time-series-based early warning model: Identify the leading patterns of change in microbial detection data compared to TOC detection data; When the aforementioned leading changes are detected, an early warning is issued and preventative controls are triggered before key water quality parameters exceed the standards.

6. The integrated ultrapure water intelligent production system according to claim 5, characterized in that: The intelligent central control module is further configured to perform multi-region collaborative analysis. When two or more regions are at risk at the same time, a weighted calculation is performed based on the severity of the risk in each region and the criticality of the process to generate a global control strategy to coordinate and adjust the efficiency parameters of the entire system.

7. The integrated ultrapure water intelligent production system according to claim 6, characterized in that: The intelligent central control module is configured to generate an activation command for the fine treatment module based on the output results of the interactive analysis strategy. When the water quality trend is predicted to deteriorate, the backup fine treatment module is activated in advance or the operating load of the existing fine treatment module is increased.

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