Intelligent control system and method for chemical water ultrafiltration process

Abstract

The application relates to the technical field of ultrafiltration control, in particular to an intelligent control system and method for a chemical water ultrafiltration process, which comprises an ultrafiltration data acquisition module, a pollutant accumulation analysis module, a fluid dynamic adjustment module, a membrane blockage early warning module, a performance monitoring module and an ultrafiltration parameter optimization module.In the application, through fine-grained adjustment and continuous monitoring of fluid dynamics, accurate control of key variables in the ultrafiltration process is realized, and the response speed and processing quality of the ultrafiltration system to environmental changes are significantly improved through adaptive adjustment; through integration of acoustic and vibration monitoring data, early detection of slight abnormalities of equipment is realized, early warning of potential faults is realized, and the risk of accidental shutdown caused by equipment failure is greatly reduced; a real-time feedback mechanism makes parameter adjustment not only based on actual operation data, but also more in line with real-time and accuracy requirements, so that the efficiency and stability of the ultrafiltration process are substantially improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ultrafiltration control, in particular to an intelligent control system and method for chemical water ultrafiltration process. BACKGROUND

[0002] The technical field of ultrafiltration control involves process control systems for fluid separation using membrane technology. Ultrafiltration is a process that utilizes semi-permeable membranes to remove suspended particles and solute molecules from water, particularly for removing proteins, viruses, bacteria and other macromolecules, aiming to optimize filtration efficiency, reduce energy consumption, and ensure process stability and reliability, using sensors, real-time data processing and automatic adjustment technology to achieve optimization of process parameters such as flow rate, pressure and temperature adjustment, not only improving the performance of ultrafiltration systems, but also helping to extend the service life of membranes and reduce maintenance requirements.

[0003] Among them, the main purpose of the intelligent control system for chemical water ultrafiltration process is to automatically adjust and control the ultrafiltration operation in the chemical water treatment process, to ensure the treatment effect and efficiency. Through intelligent control technology, the system can monitor and adjust filtration parameters such as pressure, flow and temperature in real time to adapt to different processing needs and environmental conditions, not only improving the stability and reliability of the ultrafiltration process, but also significantly improving the quality and efficiency of water treatment, suitable for various scenarios such as industrial wastewater treatment and drinking water purification.

[0004] Although existing ultrafiltration control technology includes sensors and real-time data processing, it generally performs poorly in real-time dynamic adaptability, especially when dealing with highly variable input conditions. In rapidly changing industrial applications, traditional systems often cannot adjust parameters in real time, resulting in fluctuations in processing efficiency and water quality. For example, in the event of a sudden high pollution event, a system lacking an immediate response mechanism may not be able to effectively adapt to the sudden increase in pollution load, thereby affecting the overall filtration performance. In addition, existing technology also has limitations in early fault detection, as minor equipment abnormalities often go unnoticed, leading to greater maintenance problems and costs. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art and to propose an intelligent control system and method for chemical water ultrafiltration process.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme: an intelligent control system for chemical water ultrafiltration process includes:

[0007] The ultrafiltration data acquisition module continuously monitors and collects pressure, temperature and flow rate data through sensors, and performs time series processing on the data to generate a monitoring data set;

[0008] The pollutant accumulation analysis module performs fluid distribution mapping, analyzes pollutant accumulation trends and areas, determines key areas, and generates a pollution distribution map through the monitoring data set;

[0009] The fluid dynamic adjustment module adjusts fluid flow direction and speed in real time according to the pollution distribution map, performs local adjustment in key areas, optimizes fluid distribution to avoid pollution accumulation, and generates a fluid dynamic adjustment record;

[0010] The membrane blockage warning module continuously monitors flow rate and pressure based on the fluid dynamic adjustment record, compares with the set threshold, analyzes and predicts potential membrane blockage points, and executes a warning mechanism to issue a blockage warning signal;

[0011] The performance monitoring module analyzes the blockage warning signal, identifies early indicators of membrane damage and seal failure in combination with acoustic and vibration data, and performs stability analysis to generate a performance stability monitoring record;

[0012] The ultrafiltration parameter optimization module adjusts pressure, optimizes flow rate and controls temperature based on the performance stability monitoring record, dynamically adjusts parameters through real-time feedback data, and generates an intelligent control parameter optimization scheme.

[0013] As a further scheme of the present application, the monitoring data set includes fluid pressure data, environmental temperature data and dynamic flow rate data, the pollution distribution map includes pollution density level and influence area range, the fluid dynamic adjustment record includes regional adjustment detail record, fluid behavior change analysis result and adjustment feedback effect, the blockage warning signal includes alarm trigger condition, blockage probability evaluation record and warning issuing time point, the performance stability monitoring record includes device abnormality indicator, operation deviation record and maintenance warning information, and the intelligent control parameter optimization scheme includes pressure adjustment guide, flow rate adjustment measure and temperature management strategy.

[0014] As a further scheme of the present application, the ultrafiltration data acquisition module includes a data monitoring sub-module, a data processing sub-module and a data set generation sub-module,

[0015] The data monitoring sub-module collects real-time data of parameters regularly by accessing pressure, temperature and flow rate sensors, records the time stamp information of the data, constructs and outputs real-time data stream;

[0016] The data processing sub-module sorts data according to time sequence, performs data cleaning, synchronously calibrates sensor data, and obtains serialized processing data according to the real-time data stream;

[0017] The data set generation sub-module aggregates and analyzes data based on the serialized processing data, integrates data information at all time points, and generates a monitoring data set.

[0018] As a further scheme of the present application, the pollutant accumulation analysis module comprises a data mapping submodule, a trend analysis submodule, a key area determination submodule,

[0019] The data mapping submodule performs fluid distribution mapping through the monitoring data set, locates the spatial coordinates of each data point in combination with GIS technology, and generates a fluid distribution map;

[0020] The trend analysis submodule performs statistical analysis based on the fluid distribution map, identifies the accumulation trend of pollutants in the difference area, calculates the pollution growth rate, analyzes the change pattern, and obtains an accumulation trend map;

[0021] The key area determination submodule adopts the accumulation trend map, combines environmental standards and public health data, performs multi-factor analysis, identifies the most seriously polluted key area, and generates a pollution distribution map.

[0022] As a further scheme of the present application, the pollution growth rate is calculated according to the formula,

[0023]

[0024] The calculation is performed, wherein y represents the growth rate value, X1 represents the accumulated pollutant concentration of the fluid in the target area, X2 represents the area of the region, X3 represents the fluid velocity, X4 represents the time span, and X5 represents the fluid temperature.

[0025] As a further scheme of the present application, the fluid dynamic adjustment module comprises a real-time monitoring submodule, an adjustment strategy submodule, a record generation submodule,

[0026] The real-time monitoring submodule receives real-time data in combination with the pollution distribution map, collects the flow direction and velocity information of the current fluid in the key area, captures the fluid state change using a sensor network, determines the current fluid distribution state, and generates a fluid state map;

[0027] The adjustment strategy submodule analyzes the fluid dynamics of the pollution area based on the fluid state map, optimizes the flow direction and adjusts the velocity, simulates the adjustment effect, predicts the fluid path after adjustment, and obtains an adjustment strategy record;

[0028] The record generation submodule adopts the adjustment strategy record, records the data of the adjustment time point, actual position, flow direction change, and velocity adjustment, sorts and outputs the fluid dynamic adjustment record.

[0029] As a further scheme of the present application, the membrane blockage early warning module comprises a data monitoring submodule, a blockage analysis submodule, and a warning signal issuing submodule,

[0030] The data monitoring submodule continuously monitors the flow rate and pressure data on the membrane surface by accessing the fluid dynamic adjustment record, analyzes the fluid behavior in real time, detects whether the flow rate and pressure exceed the preset safety threshold, and generates real-time monitoring data;

[0031] The blockage analysis submodule performs in-depth analysis on the flow rate and pressure data exceeding the threshold based on the real-time monitoring data, identifies potential membrane blockage points, evaluates the severity and potential location of the blockage, and obtains potential membrane blockage analysis results;

[0032] The early warning signal issuing submodule determines the high-risk area of the blockage according to the potential membrane blockage analysis results, immediately starts the early warning mechanism, issues a blockage warning through sound and light signals, and generates a blockage early warning signal.

[0033] As a further scheme of the application, the performance monitoring module includes a signal analysis submodule, an early indicator identification submodule, and a stability analysis submodule,

[0034] The signal analysis submodule performs frequency spectrum analysis using the sound and vibration frequency generated by the sensor recording device during operation based on the blockage early warning signal, identifies abnormal patterns, and generates acoustic vibration signal analysis results;

[0035] The early indicator identification submodule compares the acoustic vibration signal analysis results with historical data to identify signal patterns associated with membrane rupture and seal failure, confirms early indicators before failure occurs, and obtains early risk indicator records;

[0036] The stability analysis submodule performs comprehensive device performance stability analysis based on the early risk indicator records, evaluates the relationship between the indicators and the device operating state, determines the health status of the device, and generates performance stability monitoring records.

[0037] As a further scheme of the application, the ultrafiltration parameter optimization module includes a pressure adjustment submodule, a flow rate optimization submodule, and a temperature control submodule,

[0038] The pressure adjustment submodule analyzes historical pressure data and current operating conditions based on the performance stability monitoring records, adjusts the pressure to an optimal level, and generates optimized pressure parameters;

[0039] The flow rate optimization submodule adjusts the flow rate setting using the optimized pressure parameters in combination with real-time feedback data to match the filtration requirements and prevent membrane blockage, and monitors the flow rate changes in real time to obtain flow rate adjustment results;

[0040] The temperature control submodule monitors temperature changes during the chemical reaction process according to the flow rate adjustment results, dynamically adjusts the process temperature, maintains the ultrafiltration efficiency and membrane life, and comprehensively outputs intelligent control parameter optimization schemes.

[0041] An intelligent control method for a chemical water ultrafiltration process, comprising the following steps:

[0042] S1: Collect real-time data of parameters through pressure, temperature and flow rate sensors, sort the data in time sequence, perform data cleaning and calibration, integrate data information at all time points, and generate a monitoring data set;

[0043] S2: Perform fluid distribution mapping through the monitoring data set, locate the spatial coordinates of each data point, identify the accumulation trend of pollutants in the difference area, calculate the pollution growth rate and analyze the change mode, and obtain an accumulation trend graph;

[0044] S3: Use the accumulation trend graph to perform multi-factor analysis, identify the key area most seriously polluted, collect the flow direction and speed information of the current fluid in the key area, determine the current fluid distribution state, and generate a fluid state graph;

[0045] S4: Based on the fluid state graph, analyze the fluid dynamics of the pollution area, optimize the flow direction and adjust the speed and perform simulation prediction, record the data of the adjustment time point, actual position, flow direction change and speed adjustment simultaneously, and obtain a fluid dynamic adjustment record;

[0046] S5: Through the fluid dynamic adjustment record, continuously monitor the membrane surface flow rate and pressure data, detect whether it exceeds the preset safety threshold, identify the potential membrane blockage point according to the threshold value data, determine the high-risk area of blockage, start the early warning mechanism and issue the blockage warning, and generate a blockage warning signal;

[0047] S6: Based on the blockage warning signal, record the sound and vibration frequency generated during the operation of the equipment, perform frequency spectrum analysis and identify abnormal patterns, compare with historical data, confirm early indicators before failure occurs, determine the health status of the equipment, and output a performance stability monitoring record.

[0048] S7: Based on the performance stability monitoring record, adjust the pressure to the optimal level, adjust the flow rate setting in combination with real-time feedback data, match the filtration demand and prevent membrane blockage, synchronously monitor the temperature change in the chemical reaction process, dynamically adjust the process temperature, and comprehensively output an intelligent control parameter optimization scheme.

[0049] Compared with the prior art, the advantages and positive effects of the present application are:

[0050] In the application, through fine-grained adjustment and continuous monitoring of fluid dynamics, accurate control of key variables in the ultrafiltration process is realized, and adaptive adjustment significantly improves the response speed and processing quality of the ultrafiltration system to environmental changes. Through the integration of acoustic and vibration monitoring data, minor abnormalities in the equipment can be detected early, and early warning of potential failures is realized, greatly reducing the risk of unexpected downtime caused by equipment failure. The real-time feedback mechanism ensures that parameter adjustment is not only based on actual operation data, but also meets the real-time and accuracy requirements, thereby ensuring that the ultrafiltration process has been substantially improved in efficiency and stability. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 The system flowchart of the application is shown in the figure;

[0052] Figure 2 The acquisition flowchart of the monitoring data set of the application is shown in the figure;

[0053] Figure 3 The acquisition flowchart of the pollution distribution map of the application is shown in the figure;

[0054] Figure 4 The acquisition flowchart of the fluid dynamic adjustment record of the application is shown in the figure;

[0055] Figure 5 The acquisition flowchart of the blockage warning signal of the application is shown in the figure;

[0056] Figure 6 The acquisition flowchart of the performance stability monitoring record of the application is shown in the figure;

[0057] Figure 7 The acquisition flowchart of the intelligent control parameter optimization scheme of the application is shown in the figure;

[0058] Figure 8 The method step flowchart of the application is shown in the figure. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical scheme and advantages of the application clearer and more understandable, the application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application and do not limit the application.

[0060] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0061] Please refer to Figure 1 An intelligent control system for a chemical water ultrafiltration process includes:

[0062] The ultrafiltration data acquisition module continuously monitors and collects pressure, temperature and flow rate data through sensors, and performs time series processing on the data to generate a monitoring data set;

[0063] The pollutant accumulation analysis module performs fluid distribution mapping, analyzes pollutant accumulation trends and areas, determines key areas, and generates a pollution distribution map based on the monitoring data set;

[0064] The fluid dynamic adjustment module adjusts the fluid flow direction and speed in real time according to the pollution distribution map, performs local adjustment in the key area, optimizes the fluid distribution to avoid pollution accumulation, and generates a fluid dynamic adjustment record;

[0065] The membrane blockage warning module performs continuous monitoring of flow rate and pressure based on the fluid dynamic adjustment record, compares with the set threshold, analyzes and predicts potential membrane blockage points, and executes a warning mechanism to issue a blockage warning signal;

[0066] The performance monitoring module analyzes the blockage warning signal, identifies early indicators of membrane damage and seal failure in combination with acoustic and vibration data, and performs stability analysis to generate a performance stability monitoring record;

[0067] The ultrafiltration parameter optimization module performs pressure adjustment, flow rate optimization and temperature control based on the performance stability monitoring record, dynamically adjusts parameters through real-time feedback data, and generates an intelligent control parameter optimization scheme.

[0068] The monitoring data set includes fluid pressure data, environmental temperature data and dynamic flow rate data, the pollution distribution map includes pollution density levels and impact area ranges, the fluid dynamic adjustment record includes regional adjustment detail records, fluid behavior change analysis results and adjustment feedback effects, the blockage early warning signal includes alarm trigger conditions, blockage probability evaluation records and early warning sending time points, the performance stability monitoring record includes device abnormality indicators, operation deviation records and maintenance early warning information, and the intelligent control parameter optimization scheme includes pressure adjustment guidelines, flow rate adjustment measures and temperature management strategies.

[0069] Please refer to Figure 2 The ultrafiltration data acquisition module includes a data monitoring submodule, a data processing submodule, a data set generation submodule,

[0070] The data monitoring submodule collects real-time data of parameters regularly by accessing pressure, temperature and flow rate sensors, records timestamp information of the data, and constructs and outputs real-time data streams;

[0071] The data monitoring submodule accesses pressure, temperature and flow rate sensors. While the sensors are monitoring environmental parameters in real time, they also generate a large amount of raw data. The data needs to be recorded and sorted by timestamp. In order to ensure data accuracy and timeliness, the sensors send data to the central processing system every fixed time interval. The data includes the current pressure value, temperature reading and flow rate information. In addition, it also includes the specific time point of data acquisition. The accuracy of the timestamp is ensured by an efficient time synchronization system. The data is sorted by timestamp to ensure that the subsequent processing module can receive structured and time-ordered data streams for subsequent data processing and analysis.

[0072] The data processing submodule sorts the data by time sequence according to the real-time data stream, performs data cleaning, and synchronously calibrates the sensor data to obtain serialized processing data;

[0073] The data processing submodule first receives the data stream sorted by time sequence and performs detailed data cleaning processing on the data stream. During the cleaning process, abnormal values and noise in the data are removed. Any reading that exceeds the set threshold will be identified as abnormal and removed. At the same time, each data point in the data stream is accurately adjusted by a calibration algorithm. The calibration process includes temperature compensation and pressure deviation adjustment to ensure data accuracy. Through this method, the processed data becomes more pure and reliable after removing irrelevant noise and potential errors, providing an accurate basis for the next data set generation.

[0074] The data set generation submodule aggregates and analyzes the data based on the serialized processing data, integrates data information at all time points, and generates a monitoring data set;

[0075] The data set generation submodule is based on the serialized processing data. First, the data is aggregated and analyzed. In the process of aggregation and analysis, the system integrates all the data at different time points to form a comprehensive data set. This data set contains all the data points from the start of monitoring to the present. In this way, not only the time sequence of the data is preserved, but also the correlation and integrity of the data are strengthened. The aggregation process includes dividing the data points by time window, calculating the average, maximum and minimum values of temperature, pressure and flow rate in each time window, and integrating the statistical data into the final monitoring data set to provide a comprehensive overview of the time series data and form a comprehensive description of the monitored environment.

[0076] Please refer to Figure 3 The pollutant accumulation analysis module includes a data mapping submodule, a trend analysis submodule, a key area determination submodule,

[0077] The data mapping submodule performs fluid distribution mapping through the monitoring data set, locates the spatial coordinates of each data point in combination with GIS technology, and generates a fluid distribution map.

[0078] The data mapping submodule realizes spatial positioning through GIS technology. The input data points are first converted into specific spatial coordinates, the latitude and longitude information of each data point is analyzed, and the data is matched with the map data. In the matching process, the system locates the exact position of each point through GIS technology. The position information is closely related to the fluid distribution data. The temperature, pressure and flow rate information of each data point is mapped to the corresponding geographical location. Subsequently, the spatially positioned data points are plotted into a fluid distribution map according to their attribute values, showing the distribution of fluid in different geographical locations. Through detailed mapping, the final fluid distribution map provides an intuitive and accurate visual basis for subsequent analysis.

[0079] The trend analysis submodule performs statistical analysis based on the fluid distribution map, identifies the accumulation trend of pollutants in the difference area, calculates the pollution growth rate, analyzes the change pattern, and obtains the accumulation trend chart.

[0080] The pollution growth rate is calculated according to the formula,

[0081]

[0082] The calculation is performed, where X1 represents the accumulated pollutant concentration of the fluid in the target area, X2 represents the area of the region, which reflects the influence of the breadth of the region on the distribution of pollutants, X3 represents the fluid velocity, which is multiplied by X2 and then takes the square root, which is used to adjust the comprehensive influence of the area and the flow rate on the pollution growth rate, and to reduce the excessive influence of a single factor, X4 represents the time span, which reflects the growth trend of the pollutants over time, X5 represents the fluid temperature, which adjusts the influence of temperature on the pollution rate, especially under extreme temperature conditions.

[0083] X1 represents the accumulated pollutant concentration of the fluid in a specific area. The average concentration of pollutants in this area is measured by environmental monitoring instruments, such as water quality monitoring sensors, and the data collected is 150 mg / L.

[0084] X2 represents the area of the region, which is 3.5 square meters.

[0085] X3 represents the fluid velocity, which is measured by a flow meter, and the flow rate in this area is determined to be 2 m / s.

[0086] X4 represents the time span, reflecting the length of time for the accumulation of pollutants. It is set to the study time interval of 6 months, which is converted to days, i.e. 180 days.

[0087] X5 represents the fluid temperature, which is measured by a temperature sensor, for example, the current measured water temperature is 12 degrees Celsius.

[0088] Substitute the formula to perform the specific calculation process:

[0089] Calculation Part, i.e.

[0090]

[0091] Calculation Part, i.e.

[0092]

[0093] Substitute the above results into the formula to calculate the value of y:

[0094] y = 150 + 2.65 + 15 = 167.65

[0095] The results show that during the study period, considering the combined effects of area and flow rate and the adjustment of time span on temperature, the pollutant accumulation growth rate of the region is 167.65 mg / L per month, indicating the trend of pollutant accumulation in this area over time, and providing data support for further environmental management.

[0096] The key area determination submodule uses the cumulative trend chart, combined with environmental standards and public health data, to perform multi-factor analysis to identify the most severely polluted key areas, and generate a pollution distribution map;

[0097] The key area determination submodule utilizes the cumulative trend map, combines environmental standards and public health data, and performs a multi-factor analysis, which includes evaluating and comparing pollution data in different areas, identifying areas that exceed the standard by setting environmental safety thresholds, and considering public health data to analyze the possible impact of pollution on residents' health in each area. Through comprehensive consideration of data, the most severely polluted key areas are identified, and the final pollution distribution map details the pollution situation in each key area, providing important information for subsequent environmental management and health protection.

[0098] Please refer to Figure 4 , the fluid dynamic adjustment module includes a real-time monitoring submodule, an adjustment strategy submodule, and a record generation submodule,

[0099] The real-time monitoring submodule receives real-time data in combination with the pollution distribution map, collects current fluid flow direction and speed information in key areas, captures fluid state changes using a sensor network, determines the current fluid distribution state, and generates a fluid state map.

[0100] The real-time monitoring submodule receives real-time data from the sensor network, which is arranged in key areas and can capture real-time fluid flow direction and speed information. The data is transmitted in real time to the central monitoring system through wireless signals. First, synchronize all data timestamps to ensure consistency. Then, clean and correct the fluid speed and flow direction data to eliminate abnormal data caused by sensor errors or environmental interference. Finally, generate a fluid state map to display the current fluid distribution state in each key area, as well as the specific flow direction and speed.

[0101] The adjustment strategy submodule analyzes the fluid dynamics in the pollution area based on the fluid state map, optimizes the flow direction and adjusts the speed, simulates the adjustment effect, and predicts the fluid path after adjustment to obtain the adjustment strategy record.

[0102] The adjustment strategy submodule analyzes the dynamic changes of fluid in the pollution area based on the fluid state map, including the current state of flow speed and direction. Through fluid dynamics models, it analyzes flow direction adjustment schemes aimed at reducing pollutant accumulation and optimizing overall fluid distribution. The module tests the effects of various adjustment schemes using simulation software to evaluate their impact on fluid paths. Once the optimal adjustment strategy is determined, the system records specific parameters such as adjusted flow direction, changed speed, and corresponding actual positions. These records form the adjustment strategy record, providing detailed guidance for actual adjustment operations.

[0103] The record generation submodule uses the adjustment strategy record to record data such as adjustment time, actual position, flow direction change, and speed adjustment, and organizes and outputs fluid dynamic adjustment records.

[0104] The record generation submodule adopts an adjustment strategy record to organize detailed information of all adjustment operations, including time point of each adjustment, actual position, flow direction change and speed adjustment. The data is classified and stored through a database management system to ensure information accessibility and traceability. Then the data is further formatted to ensure standardization for analysis and report generation. Finally, the fluid dynamic adjustment record generated by the system reflects the specific situation of each adjustment in detail, provides transparency of operation, and provides necessary data support for evaluating adjustment effect.

[0105] Please refer to Figure 5 The membrane blockage early warning module includes a data monitoring submodule, a blockage analysis submodule, and an early warning signal issuing submodule.

[0106] The data monitoring submodule continuously monitors the flow rate and pressure data on the membrane surface by accessing the fluid dynamic adjustment record, analyzes the fluid behavior in real time, detects whether the flow rate and pressure exceed the preset safety threshold, and generates real-time monitoring data.

[0107] The data monitoring submodule accesses the flow rate and pressure data on the membrane surface in real time through the fluid dynamic adjustment record. The data comes from a distributed sensor network, and the sensor sends data about the flow rate and pressure of the fluid to the central monitoring system at regular intervals. The system analyzes the data in real time and detects whether each data point exceeds the preset safety threshold, including comparing each measured value of the flow rate and pressure. If the measured value exceeds the threshold, the system immediately records the event and generates real-time monitoring data. The data is then stored in the central database to provide basic data for further analysis.

[0108] The blockage analysis submodule analyzes the flow rate and pressure data that exceed the threshold based on real-time monitoring data, identifies potential membrane blockage points, evaluates the severity and potential location of blockage, and obtains potential membrane blockage analysis results.

[0109] After receiving the real-time monitoring data, the blockage analysis submodule analyzes the flow rate and pressure data that exceed the threshold in depth, identifies abnormal patterns in the data, especially patterns indicating membrane blockage. The system compares the current data with historical data to determine potential abnormal growth trends. Through regression analysis and clustering algorithms, the system identifies data points that do not conform to the conventional data pattern. Once a potential blockage point is identified, the system further evaluates the severity and possible location of the blockage point, integrates the information, and forms potential membrane blockage analysis results to provide decision support for subsequent early warning signal issuance.

[0110] The early warning signal issuing submodule determines the high-risk area of blockage based on the potential membrane blockage analysis results, immediately starts the early warning mechanism, and issues a blockage warning through sound and light signals to generate a blockage warning signal.

[0111] The early warning signal sending sub-module determines the high-risk area of blockage according to the potential membrane blockage analysis result, and the system starts an early warning mechanism on this basis, including configuring an alarm system to send sound and light signals, the triggering of the early warning mechanism depends on the specific parameters of the high-risk blockage area determined by the analysis module, such as location and severity level, once the blockage risk is confirmed, the alarm system is activated to ensure that maintenance personnel and relevant operators are immediately notified, at the same time, the system records the specific time and location of the alarm and the type of the alarm, forming a record of the blockage early warning signal, providing detailed data for subsequent maintenance and emergency response.

[0112] Please refer to Figure 6 , the performance monitoring module includes a signal analysis sub-module, an early indicator identification sub-module, a stability analysis sub-module,

[0113] The signal analysis sub-module uses the sound and vibration frequencies generated during the operation of the equipment recorded by the sensors to perform spectral analysis based on the blockage early warning signal, identifies abnormal patterns, and generates a sound and vibration signal analysis result;

[0114] After receiving the blockage early warning signal, the signal analysis sub-module uses the sensors on the equipment to record the sound and vibration frequencies generated during operation, performs spectral analysis, and uses Fast Fourier Transform (FFT) to convert time series data into frequency domain representation during the analysis process, accurately identifies the main frequency components in the signal and any abnormal frequency fluctuations, the system identifies patterns inconsistent with normal operating frequencies according to pre-set frequency ranges and thresholds, indicating early failures of the equipment such as membrane blockage or mechanical wear, and the final sound and vibration signal analysis result details all identified abnormal patterns and their possible sources, providing key information for further fault diagnosis.

[0115] The early indicator identification sub-module compares the sound and vibration signal analysis result with historical data to identify signal patterns associated with membrane damage and seal failure, confirms early indicators before failure, and obtains an early risk indicator record;

[0116] The early indicator identification sub-module uses the sound and vibration signal analysis result to first compare with the historical data stored in the database, analyzes and identifies specific signal patterns related to membrane damage or seal failure, evaluates the statistical significance of various signal characteristics, determines the relevance to known fault types, and can accurately distinguish between normal operation and early signs of potential failure, once the signal pattern related to early failure is identified, the early risk indicator record is generated simultaneously, detailing the characteristics of each identified risk indicator and the related equipment state, providing accurate basis for equipment maintenance and preventive maintenance.

[0117] The stability analysis submodule performs comprehensive equipment performance stability analysis based on early risk indicator records, assesses the relationship between indicators and equipment operating status, determines the health status of the equipment, and generates performance stability monitoring records;

[0118] The stability analysis submodule performs comprehensive stability analysis of equipment performance based on early risk indicator records. The analysis process includes collecting and organizing various operating status parameters and performance indicators, and performing in-depth statistical analysis. Time series analysis and multivariate regression models are used in the analysis to help identify trends and patterns of equipment performance degradation. By comparing current operating data with historical performance data, the health status and potential maintenance needs of the equipment are analyzed. Based on the operating history of the equipment and known performance standards, the generated performance stability monitoring records provide detailed records of the health status and expected operating life of the equipment, providing data support for operation and maintenance decisions.

[0119] Please refer to Figure 7 , the ultrafiltration parameter optimization module includes a pressure adjustment submodule, a flow rate optimization submodule, and a temperature control submodule,

[0120] The pressure adjustment submodule analyzes historical pressure data and current operating conditions based on performance stability monitoring records, adjusts the pressure to the optimal level, and generates optimized pressure parameters.

[0121] In the pressure adjustment submodule, historical pressure data P hist and current pressure P cur are considered to be adjusted to the optimal level, using the adjustment formula:

[0122] P opt =P cur -K·(P cur -P avg )

[0123] where P cur represents the current pressure, P hist represents the historical pressure data array, and P avg is the average value of historical pressure, calculated as where n is the number of data points. The value of K depends on the effect of past adjustments and the response characteristics of the equipment, which can be dynamically adjusted by analyzing the long-term deviation of P cur and P avg .

[0124] The historical pressure data P hist is collected as [5.0, 5.2, 4.8, 5.1, 5.3], and the current pressure P cur = 5.5.

[0125] Calculate the average value of historical pressure:

[0126]

[0127] The adjustment coefficient K is selected as 0.5, and based on the adjustment formula:

[0128] P opt = 5.5 - 0.5 x (5.5 - 5.08) = 5.29

[0129] The results show that through calculation and adjustment, the pressure is optimized to 5.29 units, which is the optimized level based on the current operating conditions and historical data analysis of the equipment performance, indicating that the pressure level of the equipment is closer to the historical average level, which helps to maintain the stable operation and efficiency of the equipment.

[0130] The flow rate optimization submodule uses the optimized pressure parameter, combines real-time feedback data, adjusts the flow rate setting, matches the filtration demand and prevents membrane blockage, and monitors the flow rate change in real time to obtain the flow rate adjustment result.

[0131] In the flow rate optimization submodule, the optimized pressure parameter P opt is used, combined with real-time feedback data v real to adjust the flow rate setting v set , using the following formula:

[0132] v set = v real + β · (P opt - v real )

[0133] Where β is the adjustment factor, determined according to the deviation of real-time data and pressure optimization parameter.

[0134] In the formula, v real represents the real-time flow rate, P opt is the optimized pressure parameter calculated before, and β is the adjustment factor for adjusting the flow rate to match the filtration demand and prevent membrane blockage.

[0135] Collect the real-time flow rate v real = 3.0 units, combined with the optimized pressure parameter P opt = 5.29 units known from the previous step. Set the adjustment factor β = 0.2.

[0136] According to the adjustment formula:

[0137] v set = 3.0 + 0.2 x (5.29 - 3.0) = 3.458

[0138] The results show that through real-time feedback data and pressure adjustment parameters, the flow rate setting is optimized to 3.458 units to better match the filtration demand and prevent membrane blockage, which helps to maintain the efficient operation of the filtration system and prevent operation problems caused by inappropriate flow rate.

[0139] The temperature control submodule monitors the temperature change in the chemical reaction process according to the flow rate adjustment result, dynamically adjusts the process temperature, maintains the ultrafiltration efficiency and the membrane life, and comprehensively outputs an intelligent control parameter optimization scheme.

[0140] In the temperature control submodule, the temperature change T set in the chemical reaction process is monitored according to the flow rate adjustment result v real , and the process temperature T set is dynamically adjusted. The following formula is used:

[0141]

[0142] In the formula, T real represents the current actual temperature, T target is the target temperature set in the ultrafiltration process, v set is the adjusted flow rate obtained from the previous step, v norm is the standard flow rate set for normalizing the influence of flow rate, and γ is the temperature adjustment coefficient, representing the sensitivity of temperature adjustment.

[0143] The current actual temperature T real = 60 degrees Celsius, the target temperature T target = 65 degrees Celsius, the adjusted flow rate v set = 3.458 units obtained from the previous calculation, the standard flow rate v norm = 4.0 units, and the temperature adjustment coefficient γ = 0.3 is set.

[0144] According to the adjustment formula:

[0145]

[0146] The results show that by monitoring the actual temperature and dynamically adjusting the process temperature according to the flow rate adjustment result, the temperature is set to 61.29675 degrees Celsius to maintain the ultrafiltration efficiency and the membrane life. The precise temperature control strategy helps to optimize the chemical water ultrafiltration process and ensure the optimization of filtration efficiency under different operating conditions.

[0147] Please refer to Figure 8 , an intelligent control method for a chemical water ultrafiltration process, comprising the following steps:

[0148] S1: Collect real-time data of parameters through pressure, temperature and flow rate sensors, sort the data in time sequence, perform data cleaning and calibration, integrate all data information at different time points, and generate a monitoring data set;

[0149] S2: Through the monitoring data set, the fluid distribution mapping is performed, the spatial coordinates of each data point are located, the accumulation trend of the pollutants in the difference area is identified, the pollution growth rate is calculated and the change mode is analyzed, and an accumulation trend graph is obtained;

[0150] S3: Using the accumulation trend graph, multi-factor analysis is performed, the most seriously polluted key area is identified, the flow direction and speed information of the current fluid in the key area are collected, the current fluid distribution state is determined, and a fluid state graph is generated;

[0151] S4: Based on the fluid state graph, the fluid dynamics of the pollution area are analyzed, the flow direction and speed are optimized and simulated, the data of the adjustment time point, the actual position, the flow direction change and the speed adjustment are recorded, and a fluid dynamic adjustment record is obtained;

[0152] S5: Through the fluid dynamic adjustment record, the membrane surface flow rate and pressure data are continuously monitored, whether the preset safety threshold is exceeded is detected, the potential membrane blockage point is identified according to the threshold value data, the high-risk area of blockage is determined, the early warning mechanism is started and the blockage early warning is issued, and a blockage early warning signal is generated;

[0153] S6: Based on the blockage early warning signal, the sound and vibration frequency generated during the operation of the equipment are recorded, frequency spectrum analysis is performed and abnormal patterns are identified, the early indicators before the fault occurs are confirmed, the health status of the equipment is determined, and a performance stability monitoring record is output.

[0154] S7: Based on the performance stability monitoring record, the pressure is adjusted to the optimal level, the flow rate setting is adjusted in combination with real-time feedback data, the filtration demand is matched and the membrane blockage is prevented, the temperature change in the chemical reaction process is monitored synchronously, the process temperature is dynamically adjusted, and an intelligent control parameter optimization scheme is comprehensively output.

[0155] The above is only the preferred embodiment of the present application, and does not limit the form of the present application, any skilled person in the art can use the disclosed technical content to make changes or modifications as equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments without departing from the technical solution content of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A system for intelligent control of a chemical water ultrafiltration process, characterized by: The system comprises: The ultrafiltration data acquisition module continuously monitors and collects pressure, temperature and flow rate data through sensors, and time-series processes the data to generate a monitoring data set; The pollutant accumulation analysis module performs fluid distribution mapping based on the monitoring data set, analyzes pollutant accumulation trends and areas, determines key areas, and generates a pollution distribution map; The fluid dynamic adjustment module adjusts the fluid flow direction and speed in real time according to the pollution distribution map, makes local adjustments in key areas, optimizes fluid distribution to avoid pollution accumulation, and generates a fluid dynamic adjustment record; The membrane blockage warning module continuously monitors the flow rate and pressure based on the fluid dynamic adjustment record, compares the set threshold, analyzes and predicts potential membrane blockage points, and executes a warning mechanism to issue a blockage warning signal; The performance monitoring module analyzes the blockage warning signal, identifies early indicators of membrane damage and seal failure in combination with acoustic and vibration data, and performs stability analysis to generate a performance stability monitoring record; The ultrafiltration parameter optimization module adjusts pressure, optimizes flow rate and controls temperature based on the performance stability monitoring record, dynamically adjusts parameters through real-time feedback data, and generates an intelligent control parameter optimization scheme; The pollutant accumulation analysis module comprises a data mapping submodule, a trend analysis submodule, and a key area determination submodule, The data mapping submodule performs fluid distribution mapping based on the monitoring data set, locates the spatial coordinates of each data point in combination with GIS technology, and generates a fluid distribution map; The trend analysis submodule performs statistical analysis based on the fluid distribution map, identifies the accumulation trend of pollutants in different areas, calculates the pollution growth rate, analyzes the change pattern, and obtains an accumulation trend map; The key area determination submodule uses the accumulation trend map, combines environmental standards and public health data, performs multi-factor analysis, identifies the most seriously polluted key areas, and generates a pollution distribution map; The fluid dynamic adjustment module comprises a real-time monitoring submodule, an adjustment strategy submodule, and a record generation submodule, The real-time monitoring submodule receives real-time data in combination with the pollution distribution map, collects the flow direction and speed information of the current fluid in the key area, captures the fluid state changes using a sensor network, determines the current fluid distribution state, and generates a fluid state map; The adjustment strategy submodule analyzes the fluid dynamics of the pollution area based on the fluid state map, optimizes the flow direction and adjusts the speed, simulates the adjustment effect, predicts the fluid path after adjustment, and obtains an adjustment strategy record; The record generation submodule uses the adjustment strategy record to record the data of the adjustment time point, actual position, flow direction change and speed adjustment, sorts and outputs the fluid dynamic adjustment record; The membrane blockage warning module comprises a data monitoring submodule, a blockage analysis submodule, and a warning signal issuing submodule, The data monitoring submodule continuously monitors the flow rate and pressure data on the membrane surface by accessing the fluid dynamic adjustment record, analyzes the fluid behavior in real time, detects whether the flow rate and pressure exceed the preset safety threshold, and generates real-time monitoring data; The blockage analysis submodule analyzes the flow rate and pressure data exceeding the threshold based on the real-time monitoring data, identifies potential membrane blockage points, evaluates the severity and potential location of the blockage, and obtains potential membrane blockage analysis results; The early warning signal issuing submodule determines the high-risk area of blockage based on the potential membrane blockage analysis results, immediately starts the early warning mechanism, issues a blockage warning through sound and light signals, and generates a blockage warning signal.

2. The intelligent control system for the chemical water ultrafiltration process according to claim 1, characterized in that: The monitoring data set includes fluid pressure data, environmental temperature data, and dynamic flow rate data, the pollution distribution map includes pollution density levels and impact area ranges, the fluid dynamic adjustment record includes regional adjustment detail records, fluid behavior change analysis results, and adjustment feedback effects, the blockage warning signal includes alarm trigger conditions, blockage probability evaluation records, and early warning time points, the performance stability monitoring record includes device abnormality indicators, operation deviation records, and maintenance warning information, and the intelligent control parameter optimization scheme includes pressure adjustment guidelines, flow rate adjustment measures, and temperature management strategies.

3. The intelligent control system for chemical water ultrafiltration process according to claim 1, characterized in that: The ultrafiltration data acquisition module includes a data monitoring submodule, a data processing submodule, and a data set generation submodule, The data monitoring submodule collects real-time data of parameters by accessing pressure, temperature, and flow rate sensors, records timestamp information of the data, constructs and outputs real-time data streams, and performs data cleaning and calibration on the sensor data based on the real-time data streams to obtain serialized processing data; The data set generation submodule aggregates and analyzes the data based on the serialized processing data, integrates data information at all time points, and generates a monitoring data set. The performance monitoring module includes a signal analysis submodule, an early indicator identification submodule, and a stability analysis submodule, 4. The intelligent control system for chemical water ultrafiltration process according to claim 1, characterized in that: The signal analysis submodule uses the sound and vibration frequencies generated by the sensor recording device during operation to perform frequency spectrum analysis, identify abnormal patterns, and generate acoustic vibration signal analysis results based on the blockage warning signal; The early indicator identification submodule compares the acoustic vibration signal analysis results with historical data to identify signal patterns associated with membrane damage and seal failure, confirms early indicators before failure, and obtains early risk indicator records; The stability analysis submodule performs comprehensive device performance stability analysis based on the early risk indicator records, evaluates the relationship between indicators and device operating conditions, determines the health status of the device, and generates performance stability monitoring records. The ultrafiltration parameter optimization module includes a pressure adjustment submodule, a flow rate optimization submodule, and a temperature control submodule, 5. The intelligent control system for chemical water ultrafiltration process according to claim 1, characterized in that: The pressure adjustment submodule analyzes historical pressure data and current operating conditions based on the performance stability monitoring records, adjusts the pressure to an optimal level, and generates optimized pressure parameters; The flow rate optimization submodule adjusts the flow rate setting using the optimized pressure parameters in combination with real-time feedback data to match the filtration requirements and prevent membrane blockage, and monitors the flow rate changes in real time to obtain flow rate adjustment results; ​ The temperature control submodule monitors the temperature change in the chemical reaction process according to the flow rate adjustment result, dynamically adjusts the process temperature, maintains the ultrafiltration efficiency and the membrane life, and comprehensively outputs the intelligent control parameter optimization scheme.

6. A method for intelligent control of a chemical water ultrafiltration process, characterized by, The intelligent control system for the chemical water ultrafiltration process according to any one of claims 1-5 comprises the following steps: Real-time data of parameters are collected through pressure, temperature and flow rate sensors, the data are sorted according to time sequence, data cleaning and calibration are performed, data information at all time points are integrated, and a monitoring data set is generated; Through the monitoring data set, fluid distribution mapping is performed, the spatial coordinates of each data point are located, the accumulation trend of pollutants in the difference area is identified, the pollution growth rate is calculated and the change mode is analyzed, and an accumulation trend chart is obtained; Using the accumulation trend chart, multi-factor analysis is performed, the most seriously polluted key area is identified, the flow direction and speed information of the current fluid in the key area are collected, the current fluid distribution state is determined, and a fluid state chart is generated; Based on the fluid state chart, the fluid dynamics of the pollution area are analyzed, the flow direction and speed are optimized and simulated, the data of adjustment time point, actual position, flow direction change and speed adjustment are recorded synchronously, and a fluid dynamic adjustment record is obtained; Through the fluid dynamic adjustment record, the membrane surface flow rate and pressure data are continuously monitored, whether the preset safety threshold is exceeded is detected, potential membrane blockage points are identified according to the threshold exceeding data, high-risk blockage areas are determined, a warning mechanism is started and a blockage warning is issued, and a blockage warning signal is generated; Based on the blockage warning signal, the sound and vibration frequency generated during the operation of the equipment are recorded, frequency spectrum analysis is performed and abnormal patterns are identified, early indicators before the occurrence of faults are confirmed by comparing with historical data, the health status of the equipment is determined, and a performance stability monitoring record is outputted; Based on the performance stability monitoring record, the pressure is adjusted to the optimal level, the flow rate setting is adjusted in combination with real-time feedback data, the filtration demand is matched and membrane blockage is prevented, the temperature change in the chemical reaction process is monitored synchronously, the process temperature is dynamically adjusted, and an intelligent control parameter optimization scheme is comprehensively outputted.

Images