Intelligent control system and method for chemical water ultrafiltration process

Abstract

The invention relates to the technical field of ultrafiltration control, in particular to an intelligent control system and method for a chemical water ultrafiltration process, and the system comprises an ultrafiltration data acquisition module, a pollutant accumulation analysis module, a fluid dynamic regulation module, a membrane blockage early warning module, a performance monitoring module and an ultrafiltration parameter optimization module. According to the invention, through dynamic fine granularity adjustment and continuous monitoring of the fluid, accurate control of key variables in the ultrafiltration process is realized, the response speed and processing quality of the ultrafiltration system to environmental change are remarkably improved through adaptive adjustment, and through integration of acoustics and vibration monitoring data, tiny abnormities of equipment can be detected as soon as possible. According to the technical scheme, early warning of potential faults is achieved, the risk of accidental shutdown caused by equipment faults is greatly reduced, a real-time feedback mechanism enables parameter adjustment not only to be based on actual operation data, but also to better meet the requirements for real-time performance and accuracy, and therefore it is ensured that the efficiency and stability of the ultrafiltration process are both substantially improved.

Description

Technical Field

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

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

[0003] Among them, the main purpose of the intelligent control system of the 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 treatment requirements and environmental conditions, which not only improves the stability and reliability of the ultrafiltration process, but also significantly improves the quality and efficiency of water treatment. It is suitable for a variety of scenarios such as industrial wastewater treatment and drinking water purification.

[0004] Although existing ultrafiltration control technologies include sensors and real-time data processing, they are generally insufficient in terms of 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 treatment efficiency and water quality. For example, in the event of a sudden high pollution event, a system that lacks 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 technologies also have limitations in early fault detection, and smaller equipment anomalies are often not identified in a timely manner, resulting in greater maintenance issues and costs. Summary of the invention

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

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: an intelligent control system for chemical water ultrafiltration process comprises:

[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 through the monitoring data set, analyzes pollutant accumulation trends and areas, determines key areas, and generates a pollution distribution map;

[0009] 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 key areas, optimizes fluid distribution to avoid pollution accumulation, and generates fluid dynamic adjustment records;

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

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

[0012] 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 plan.

[0013] As a further solution of the present invention, the monitoring data set includes fluid pressure data, ambient temperature data and dynamic flow rate data, the pollution distribution map includes the pollution density level and the affected area range, the fluid dynamic adjustment record includes the regional adjustment detail record, the fluid behavior change analysis result and the adjustment feedback effect, the blockage warning signal includes the alarm triggering condition, the blockage probability assessment record and the warning issuance time point, the performance stability monitoring record includes equipment abnormality indicators, operation deviation records and maintenance warning information, and the intelligent control parameter optimization plan includes pressure adjustment guidelines, flow rate adjustment measures and temperature management strategies.

[0014] As a further solution of the present invention, the ultrafiltration data acquisition module includes a data monitoring submodule, a data processing submodule, and a data set generation submodule.

[0015] The data monitoring submodule collects real-time data of parameters regularly by connecting to pressure, temperature and flow rate sensors, records the timestamp information of the data, and constructs and outputs real-time data streams;

[0016] The data processing submodule sorts the data in time series according to the real-time data stream, performs data cleaning, and synchronously calibrates the sensor data to obtain serialized processed data;

[0017] The data set generation submodule performs aggregate analysis on the data based on the serialized processing data, integrates the data information of all time points, and generates a monitoring data set.

[0018] As a further solution of the present invention, the pollutant accumulation analysis module includes a data mapping submodule, a trend analysis submodule, and 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 different areas, calculates the pollution growth rate, analyzes the change pattern, and obtains a cumulative trend map;

[0021] The key area determination submodule uses the cumulative trend map, combined with environmental standards and public health data, to conduct multi-factor analysis, identify the most polluted key areas, and generate a pollution distribution map.

[0022] As a further embodiment of the present invention, the pollution growth rate is calculated according to the formula:

[0023]

[0024] Calculate, where y represents the growth rate value, X 1 represents the accumulated pollutant concentration of the fluid in the target area, X 2 Represents the area of ​​the region, X 3 represents the fluid velocity, X 4 Represents the time span, X 5 Represents the fluid temperature.

[0025] As a further solution of the present invention, the fluid dynamic adjustment module includes a real-time monitoring submodule, an adjustment strategy submodule, and 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 speed information of the current fluid in the key area, uses the sensor network to capture the fluid state change, determines the current fluid distribution state, and generates a fluid state map;

[0027] The regulation strategy submodule analyzes the fluid dynamics in the contaminated area based on the fluid state diagram, optimizes the flow direction and adjusts the speed, simulates the regulation effect, predicts the fluid path after regulation, and obtains the regulation strategy record;

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

[0029] As a further solution of the present invention, the membrane blocking early warning module includes a data monitoring submodule, a blocking analysis submodule, and an early 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 value 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 ​​blockage according to the potential membrane blockage analysis result, immediately activates the early warning mechanism, issues a blockage warning through sound and light signals, and generates a blockage warning signal.

[0033] As a further solution of the present invention, the performance monitoring module includes a signal analysis submodule, an early indicator identification submodule, and a stability analysis submodule.

[0034] Based on the blocking warning signal, the signal analysis submodule uses sensors to record the sound and vibration frequency generated when the equipment is running, performs spectrum analysis, identifies abnormal patterns, and generates sound and vibration signal analysis results;

[0035] The early indicator identification submodule uses the acoustic vibration signal analysis results to compare with historical data, identifies signal patterns associated with membrane damage and sealing failure, confirms early indicators before the failure occurs, and obtains early risk indicator records;

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

[0037] As a further solution of the present invention, 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 uses the optimized pressure parameters in combination with real-time feedback data to adjust the flow rate setting to match the filtration requirements and prevent membrane clogging, and monitors the flow rate changes in real time to obtain the flow rate adjustment results;

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

[0041] A chemical water ultrafiltration process intelligent control method comprises the following steps:

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

[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 different areas, calculate the pollution growth rate and analyze the change pattern to obtain a cumulative trend graph;

[0044] S3: using the cumulative trend graph, performing multi-factor analysis, identifying the most seriously polluted key areas, collecting the flow direction and velocity information of the current fluid in the key areas, determining the current fluid distribution state, and generating a fluid state graph;

[0045] S4: Based on the fluid state diagram, analyzing the fluid dynamics in the contaminated area, optimizing the flow direction and adjusting the speed, and performing simulation prediction, synchronously recording the adjustment time point, actual position, flow direction change and speed adjustment data, and obtaining the fluid dynamic adjustment record;

[0046] S5: Continuously monitor the flow rate and pressure data on the membrane surface through the fluid dynamic adjustment record to detect whether it exceeds the preset safety threshold, identify potential membrane blocking points based on the over-threshold data, determine the high-risk area for blocking, start the early warning mechanism and issue a blocking warning, and generate a blocking warning signal;

[0047] S6: Based on the blocking warning signal, record the sound and vibration frequency generated by the equipment during operation, perform 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 performance stability monitoring records.

[0048] S7: Based on the performance stability monitoring records, adjust the pressure to the optimal level, adjust the flow rate setting in combination with real-time feedback data, match the filtration requirements and prevent membrane clogging, simultaneously monitor the temperature changes during the chemical reaction, dynamically adjust the process temperature, and comprehensively output the intelligent control parameter optimization plan.

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

[0050] In the present invention, accurate control of key variables in the ultrafiltration process is achieved through fine-grained adjustment and continuous monitoring of fluid dynamics. Adaptive adjustment significantly improves the response speed and processing quality of the ultrafiltration system to environmental changes. By integrating acoustic and vibration monitoring data, it is possible to detect slight abnormalities of the equipment early, achieve early warning of potential failures, and greatly reduce the risk of unexpected downtime due to equipment failure. The real-time feedback mechanism makes parameter adjustment not only based on actual operating data, but also more in line with real-time and accuracy requirements, thereby ensuring that the ultrafiltration process is substantially improved in efficiency and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a system flow chart of the present invention;

[0052] Figure 2 A flowchart for obtaining a monitoring data set of the present invention;

[0053] Figure 3 A flow chart for obtaining a pollution distribution map of the present invention;

[0054] Figure 4 A flowchart for obtaining a fluid dynamic adjustment record of the present invention;

[0055] Figure 5 This is a flow chart of obtaining a blocking warning signal of the present invention;

[0056] Figure 6 A flowchart for obtaining performance stability monitoring records of the present invention;

[0057] Figure 7 This is a flow chart for obtaining the intelligent control parameter optimization solution of the present invention;

[0058] Figure 8 The figure is a flow chart of the steps of the method of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0060] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0061] See also Figure 1 , an intelligent control system for 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 by monitoring data sets, analyzing pollutant accumulation trends and areas, identifying key areas, and generating pollution distribution maps;

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

[0065] The membrane blockage warning module continuously monitors the flow rate and pressure based on the fluid dynamic regulation record, compares the set threshold, analyzes and predicts the potential membrane blockage point, and executes the warning mechanism to issue a blockage warning signal;

[0066] The performance monitoring module analyzes blockage warning signals, combines acoustic and vibration data to identify early indicators of membrane damage and seal failure, and performs stability analysis to generate performance stability monitoring records;

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

[0068] The monitoring data set includes fluid pressure data, ambient temperature data and dynamic flow rate data. The pollution distribution map includes the pollution density level and the scope of the affected area. The fluid dynamic adjustment record includes the regional adjustment detail record, fluid behavior change analysis results and adjustment feedback effect. The blockage warning signal includes the alarm triggering conditions, blockage probability assessment records and warning issuance time point. The performance stability monitoring record includes equipment abnormality indicators, operation deviation records and maintenance warning information. The intelligent control parameter optimization plan includes pressure adjustment guidelines, flow rate adjustment measures and temperature management strategies.

[0069] See also Figure 2 The ultrafiltration data acquisition module includes a data monitoring submodule, a data processing submodule, and a data set generation submodule.

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

[0071] The data monitoring submodule is connected to the pressure, temperature and flow rate sensors. While the sensors monitor the environmental parameters in real time, they also generate a large amount of raw data. The data needs to be recorded and sorted by timestamps. In order to ensure the accuracy and timeliness of the data, the sensor sends data to the central processing system every fixed time period. The data includes the current pressure value, temperature reading and flow rate information. In addition, it also includes the specific time point of data collection. The accuracy of the timestamp is guaranteed by an efficient time synchronization system. The data is sorted according to the timestamp to ensure that the subsequent processing module can receive a structured and chronologically arranged data stream, which will be used for subsequent data processing and analysis.

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

[0073] The data processing submodule first receives the data stream sorted in time series and performs detailed data cleaning on the data stream. During the cleaning process, outliers and noise in the data are eliminated. Any reading that exceeds the set threshold will be identified as an abnormality and eliminated. At the same time, each data point in the data stream will be precisely adjusted through a calibration algorithm. The calibration process includes temperature compensation and pressure deviation adjustment to ensure the accuracy of the data. In this way, the processed data becomes purer and more reliable serialized data after removing irrelevant noise and potential errors, which will provide an accurate basis for the next step of data set generation.

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

[0075] The dataset generation submodule processes data based on serialization. It first performs aggregation analysis on the data. During the aggregation analysis, the system integrates the data at all time points to form a comprehensive dataset, which contains all data points from the beginning of monitoring to the present. In this way, not only the temporal sequence of the data is retained, but also the correlation and integrity between the data are strengthened. The aggregation process includes dividing the data points by time windows and counting the data changes in each time window, such as the average, maximum and minimum values ​​of temperature, pressure and flow rate. The statistical data are integrated into the final monitoring dataset, providing a comprehensive overview of the time series data and forming a comprehensive description of the monitoring environment.

[0076] See also Figure 3 The pollutant accumulation analysis module includes a data mapping submodule, a trend analysis submodule, and a key area determination submodule.

[0077] The data mapping submodule performs fluid distribution mapping by monitoring data sets, 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 incoming data points are first converted into specific spatial coordinates, and the longitude and latitude information of each data point is parsed and matched with the map data. During the matching process, the system locates the precise 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 located data points are drawn into a fluid distribution map according to their attribute values, showing the distribution of the fluid in different geographical locations. Through detailed mapping, the final generated 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 different areas, calculates the pollution growth rate, analyzes the change pattern, and obtains the cumulative trend map;

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

[0081]

[0082] Calculate, where X 1 represents the accumulated pollutant concentration of the fluid in the target area, X 2 Represents the area of ​​the region, which is used to reflect the impact of the breadth of the region on the distribution of pollutants. 3 represents the fluid velocity, and X 2 The square root is taken after multiplication to adjust the combined effect of regional area and flow velocity on the pollution growth rate and reduce the excessive impact of a single factor. 4Represents the time span, reflecting the growth trend of pollutants over time, X 5 Represents fluid temperature and regulates the effect of temperature on contamination rate, especially under extreme temperature conditions.

[0083] X 1 It represents the cumulative pollutant concentration of the fluid in a specific area. The average pollutant concentration in the area is measured by environmental monitoring instruments, such as water quality monitoring sensors, and the collected data is 150 mg / L.

[0084] X 2 Indicates the area of ​​the region, which is 3.5 square meters.

[0085] X 3 It represents the fluid velocity, which is measured by a flow meter and determined to be 2m / s.

[0086] X 4 Indicates the time span, reflecting the length of time that pollutants accumulate. The research time interval is set to 6 months, and the months are converted into days, that is, 180 days.

[0087] X 5 Indicates the fluid temperature, measured by a temperature sensor. For example, the current measured water temperature is 12 degrees Celsius.

[0088] Substitute the formula for the specific calculation process:

[0089] calculate Part, that is

[0090]

[0091] calculate Part, that is

[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, after considering the combined effects of regional area and flow velocity and the adjustment of temperature by time span, the cumulative growth rate of pollutants in the region was 167.65 mg / L per month, indicating a trend of pollutants accumulating in this area over time and providing data support for further environmental management.

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

[0097] The key area identification submodule uses cumulative trend charts, combined with environmental standards and public health data, to perform multi-factor analysis. This analysis process includes evaluating and comparing pollution data from different regions, identifying areas that exceed the standards through set environmental safety thresholds, and analyzing the possible impact of pollution in each region on residents' health by taking into account public health data. Through comprehensive consideration of the data, the most polluted key areas are identified. The final pollution distribution map shows the pollution situation in each key area in detail, providing important information for subsequent environmental governance and health protection.

[0098] See also Figure 4 The fluid dynamic regulation module includes a real-time monitoring submodule, a regulation strategy submodule, and a record generation submodule.

[0099] The real-time monitoring submodule combines the pollution distribution map, receives real-time data, collects the flow direction and speed information of the current fluid in the key area, uses the sensor network to capture the fluid state changes, 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. The sensor network is arranged in key areas and can capture the instant flow direction and speed information of the fluid. The data is transmitted to the central monitoring system in real time via wireless signals. First, data synchronization is performed to ensure that the timestamps of all data are consistent. Then the fluid speed and flow direction data are cleaned and corrected to eliminate abnormal data caused by sensor errors or environmental interference. Finally, a fluid state diagram is generated to show the current distribution status of the fluid in each key area, as well as the specific conditions of the flow direction and speed.

[0101] The regulation strategy submodule analyzes the fluid dynamics in the contaminated area based on the fluid state diagram, optimizes the flow direction and adjusts the speed, simulates the regulation effect, predicts the fluid path after regulation, and obtains the regulation strategy record;

[0102] The regulation strategy submodule analyzes the dynamic changes of the fluid in the contaminated area based on the fluid state diagram, including the current state of the flow velocity and flow direction. Through the fluid dynamics model, it analyzes the flow direction adjustment plan, which aims to reduce the accumulation of pollutants and optimize the overall distribution of the fluid. Through the simulation software, the module tests the effects of various regulation plans and evaluates the impact on the fluid path. Once the optimal regulation strategy is determined, the system records the specific parameters of the adjustment, such as the adjusted flow direction, the speed of change and its corresponding actual position. These records form the regulation strategy record, which provides detailed guidance for the actual implementation of the regulation operation.

[0103] The record generation submodule adopts the regulation strategy record to record the adjustment time point, actual position, flow direction change and speed adjustment data, and organizes and outputs the fluid dynamic regulation record;

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

[0105] See also Figure 5 The membrane blocking warning module includes a data monitoring submodule, a blocking analysis submodule, and a 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 records the flow rate and pressure data of the membrane surface in real time through fluid dynamic adjustment. The data comes from a distributed sensor network. The sensor periodically sends data on the flow rate and pressure of the fluid to the central monitoring system and performs real-time analysis to detect whether each data point exceeds the preset safety threshold, including comparing each measurement of the flow rate and pressure. If the measurement 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 conducts in-depth analysis of the super-threshold flow rate and pressure data based on 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;

[0109] After receiving the real-time monitoring data, the blockage analysis submodule conducts in-depth analysis of the flow rate and pressure data that exceed the threshold value to identify abnormal patterns in the data, especially patterns that indicate membrane blockage. The system determines potential abnormal growth trends by comparing current data with historical data. It uses regression analysis and clustering algorithms to identify data points that do not conform to conventional data patterns. Once the potential blockage point is determined, the severity and possible location of the blockage point are further evaluated, and the information is integrated to form a potential membrane blockage analysis result to provide decision support for the subsequent issuance of early warning signals.

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

[0111] The early warning signal issuing submodule determines the high-risk area of ​​blockage based on the potential membrane blockage analysis results. The system activates the early warning mechanism on this basis, including configuring the alarm system to emit 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 notified immediately. At the same time, the system records the specific time and location of the alarm and the type of alarm, forming a record of the blockage warning signal, providing detailed data for subsequent maintenance and emergency response.

[0112] See also Figure 6 The performance monitoring module includes a signal analysis submodule, an early indicator identification submodule, and a stability analysis submodule.

[0113] Based on the blocking warning signal, the signal analysis submodule uses sensors to record the sound and vibration frequency generated when the equipment is running, performs spectrum analysis, identifies abnormal patterns, and generates sound and vibration signal analysis results;

[0114] After receiving the blockage warning signal, the signal analysis submodule uses the sensors on the equipment to record the sound and vibration frequencies generated during operation and performs spectrum analysis. During the analysis process, the fast Fourier transform (FFT) is used to convert the time series data into frequency domain representation to accurately identify the main frequency components in the signal and any abnormal frequency fluctuations. The system identifies patterns that are inconsistent with the normal operating frequency based on the preset frequency range and threshold, indicating early equipment failures such as membrane blockage or mechanical wear. The final acoustic and vibration signal analysis results record in detail all identified abnormal patterns and their possible sources, providing key information for further fault diagnosis.

[0115] The early indicator identification submodule uses the acoustic vibration signal analysis results to compare with historical data to identify the signal patterns associated with membrane damage and sealing failure, confirm the early indicators before the failure occurs, and obtain early risk indicator records;

[0116] The early indicator identification submodule uses the acoustic vibration signal analysis results to first compare with the historical data stored in the database, analyzes and identifies specific signal patterns associated with membrane damage or sealing failure, evaluates the statistical significance of various signal characteristics, and determines the correlation with known fault types. It can accurately distinguish between normal operation and early signs of potential failures. Once the signal pattern related to early failures is identified, an early risk indicator record is generated synchronously, which describes in detail the characteristics of each identified risk indicator and the related equipment status, providing an accurate basis for equipment maintenance and preventive maintenance.

[0117] The stability analysis submodule conducts comprehensive equipment performance stability analysis based on early risk indicator records, evaluates 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 conducts a 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 conducting in-depth statistical analysis. Time series analysis and multivariate regression models are used in the analysis to help identify trends and patterns in equipment performance degradation. By comparing current operating data with historical performance data, the health status of the equipment and possible maintenance needs are analyzed. Based on the equipment's operating history and known performance standards, the generated performance stability monitoring records comprehensively record the health status of the equipment and its expected operating life, providing data support for operation and maintenance decisions.

[0119] See also 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 adjusts the pressure to the optimal level and generates optimized pressure parameters based on the performance stability monitoring records, analyzes the historical pressure data and current operating conditions;

[0121] In the pressure adjustment submodule, the historical pressure data P is considered hist and the current pressure P cur To adjust to the optimal level, use 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, P avg is the historical pressure average, 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 device, which can be obtained by analyzing P cur With P avg Dynamically adjust based on long-term deviations.

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

[0125] Calculate the historical pressure average:

[0126]

[0127] Select adjustment coefficient K = 0.5, based on the adjustment formula:

[0128] P opt =5.5-0.5×(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 optimization level based on the equipment performance and historical data analysis under the current operating conditions. It shows 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 optimized pressure parameters and real-time feedback data to adjust the flow rate settings to match filtration requirements and prevent membrane clogging, and monitors flow rate changes in real time to obtain flow rate adjustment results;

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

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

[0133] Among them, β is the adjustment factor, which is determined based on the deviation between real-time data and pressure optimization parameters.

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

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

[0136] According to the adjustment formula:

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

[0138] The results showed that through real-time feedback data and pressure adjustment parameters, the flow rate setting was optimized to 3.458 units to better match the filtration demand and prevent membrane clogging, helping to maintain the efficient operation of the filtration system and prevent operational problems caused by inappropriate flow rates.

[0139] The temperature control submodule monitors the temperature changes during the chemical reaction according to the flow rate adjustment results, dynamically adjusts the process temperature, maintains the ultrafiltration efficiency and membrane life, and comprehensively outputs the intelligent control parameter optimization plan;

[0140] In the temperature control submodule, the result v is adjusted according to the flow rate set , monitor the temperature change T during the chemical reaction real , and dynamically adjust the process temperature T set . Using the following formula:

[0141]

[0142] Where, T real Represents the current actual temperature, T target is the target temperature set during ultrafiltration, v set is the adjusted flow rate obtained from the previous step, v norm is the set standard flow rate, which is used to normalize the influence of flow rate, and γ is the temperature adjustment coefficient, which expresses the sensitivity of temperature adjustment.

[0143] Collect the current actual temperature T real =60 degrees Celsius, target temperature T target = 65 degrees Celsius, the adjusted flow rate v calculated previously set =3.458 units, standard flow rate v norm =4.0 units. Set the temperature adjustment coefficient γ = 0.3.

[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 results, the temperature is set to 61.29675 degrees Celsius to maintain ultrafiltration efficiency and membrane life. Accurate temperature control strategy helps optimize the chemical water ultrafiltration process and ensure the optimization of filtration efficiency under different operating conditions.

[0147] See also Figure 8 , a chemical water ultrafiltration process intelligent control method, comprising the following steps:

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

[0149] S2: Perform fluid distribution mapping through monitoring data sets, locate the spatial coordinates of each data point, identify the accumulation trend of pollutants in different areas, calculate the pollution growth rate and analyze the change pattern to obtain the cumulative trend graph;

[0150] S3: Use cumulative trend charts to conduct multi-factor analysis, identify the most seriously polluted key areas, collect the current flow direction and velocity information of the fluid in the key areas, determine the current fluid distribution state, and generate a fluid state diagram;

[0151] S4: Based on the fluid state diagram, analyze the fluid dynamics in the contaminated area, optimize the flow direction and adjust the speed, and perform simulation predictions. Simultaneously record the adjustment time point, actual position, flow direction change, and speed adjustment data to obtain the fluid dynamic adjustment record;

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

[0153] S6: Based on the blocking warning signal, record the sound and vibration frequency generated by the equipment during operation, perform 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 performance stability monitoring records.

[0154] S7: Based on the performance stability monitoring record, adjust the pressure to the optimal level, adjust the flow rate setting based on the real-time feedback data, match the filtration needs and prevent membrane clogging, simultaneously monitor the temperature changes during the chemical reaction, dynamically adjust the process temperature, and comprehensively output the intelligent control parameter optimization plan.

[0155] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent control system for chemical water ultrafiltration process, characterized in that: The system comprises: 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; The pollutant accumulation analysis module performs fluid distribution mapping through 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, performs local adjustment in key areas, optimizes fluid distribution to avoid pollution accumulation, and generates fluid dynamic adjustment records; The membrane blocking warning module continuously monitors the flow rate and pressure based on the fluid dynamic adjustment record, compares the set threshold, analyzes and predicts the potential membrane blocking point, and executes the warning mechanism to issue a blocking warning signal; The performance monitoring module analyzes the blockage warning signal, combines acoustic and vibration data, identifies early indicators of membrane damage and seal failure, and performs stability analysis to generate performance stability monitoring records; 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 plan.

2. The intelligent control system for chemical water ultrafiltration process according to claim 1 is characterized in that: The monitoring data set includes fluid pressure data, ambient temperature data and dynamic flow rate data, the pollution distribution map includes the pollution density level and the scope of the affected area, the fluid dynamic adjustment record includes the regional adjustment detail record, the fluid behavior change analysis result and the adjustment feedback effect, the blockage warning signal includes the alarm triggering condition, the blockage probability assessment record and the warning issuance time point, the performance stability monitoring record includes equipment abnormality indicators, operation deviation records and maintenance warning information, and the intelligent control parameter optimization plan 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 is 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 regularly by connecting to pressure, temperature and flow rate sensors, records the timestamp information of the data, and constructs and outputs real-time data streams; The data processing submodule sorts the data in time series according to the real-time data stream, performs data cleaning, and synchronously calibrates the sensor data to obtain serialized processed data; The data set generation submodule performs aggregate analysis on the data based on the serialized processing data, integrates the data information of all time points, and generates a monitoring data set.

4. The intelligent control system for chemical water ultrafiltration process according to claim 1 is characterized in that: The pollutant accumulation analysis module includes a data mapping submodule, a trend analysis submodule, and a key area determination submodule. 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; 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 a cumulative trend map; The key area determination submodule uses the cumulative trend map, combined with environmental standards and public health data, to conduct multi-factor analysis, identify the most polluted key areas, and generate a pollution distribution map.

5. The intelligent control system for chemical water ultrafiltration process according to claim 4 is characterized in that: The pollution growth rate, according to the formula, Calculation is performed, where y represents the growth rate value, X1 represents the cumulative 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.

6. The intelligent control system for chemical water ultrafiltration process according to claim 1 is characterized by: The fluid dynamic adjustment module includes 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, uses the sensor network to capture the fluid state change, determines the current fluid distribution state, and generates a fluid state map; The regulation strategy submodule analyzes the fluid dynamics in the contaminated area based on the fluid state diagram, optimizes the flow direction and adjusts the speed, simulates the regulation effect, predicts the fluid path after regulation, and obtains the regulation strategy record; The record generation submodule adopts the adjustment strategy record, records the adjustment time point, actual position, flow direction change and speed adjustment data, and organizes and outputs the fluid dynamic adjustment record.

7. The intelligent control system for chemical water ultrafiltration process according to claim 1 is characterized in that: The membrane blockage warning module includes 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 performs in-depth analysis on the flow rate and pressure data exceeding the threshold value 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 according to the potential membrane blockage analysis result, immediately activates the early warning mechanism, issues a blockage warning through sound and light signals, and generates a blockage warning signal.

8. The intelligent control system for chemical water ultrafiltration process according to claim 1 is characterized by: The performance monitoring module includes a signal analysis submodule, an early indicator identification submodule, and a stability analysis submodule. Based on the blocking warning signal, the signal analysis submodule uses sensors to record the sound and vibration frequency generated when the equipment is running, performs spectrum analysis, identifies abnormal patterns, and generates sound and vibration signal analysis results; The early indicator identification submodule uses the acoustic vibration signal analysis results to compare with historical data, identifies signal patterns associated with membrane damage and sealing failure, confirms early indicators before the failure occurs, and obtains early risk indicator records; The stability analysis submodule performs a comprehensive equipment performance stability analysis based on the early risk indicator records, evaluates the relationship between the indicators and the equipment operating status, determines the health status of the equipment, and generates performance stability monitoring records.

9. The intelligent control system for chemical water ultrafiltration process according to claim 1 is characterized in that: The ultrafiltration parameter optimization module includes a pressure adjustment submodule, a flow rate optimization submodule, and a temperature control submodule. 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 uses the optimized pressure parameters in combination with real-time feedback data to adjust the flow rate setting to match the filtration requirements and prevent membrane clogging, and monitors the flow rate changes in real time to obtain the flow rate adjustment results; The temperature control submodule monitors the temperature change during the chemical reaction according to the flow rate adjustment result, dynamically adjusts the process temperature, maintains the ultrafiltration efficiency and membrane life, and comprehensively outputs an intelligent control parameter optimization plan.

10. An intelligent control method for chemical water ultrafiltration process, characterized in that: According to any one of claims 1 to 9, the intelligent control system for the chemical water ultrafiltration process comprises the following steps: Through pressure, temperature and flow rate sensors, real-time data of parameters are collected, data is sorted in time series, data cleaning and calibration are performed, data information at all time points is integrated, and monitoring data sets are generated; Through the monitoring data set, fluid distribution mapping is performed to locate the spatial coordinates of each data point, identify the accumulation trend of pollutants in different areas, calculate the pollution growth rate and analyze the change pattern to obtain a cumulative trend graph; Using the cumulative trend graph, multi-factor analysis is performed to identify the most seriously polluted key areas, collect the flow direction and velocity information of the current fluid in the key areas, determine the current fluid distribution state, and generate a fluid state graph; Based on the fluid state diagram, the fluid dynamics in the contaminated area are analyzed, the flow direction and speed are optimized and simulated and predicted, and the adjustment time point, actual position, flow direction change and speed adjustment data are simultaneously recorded to obtain the fluid dynamic adjustment record; Through the fluid dynamic adjustment record, the membrane surface flow rate and pressure data are continuously monitored to detect whether they exceed the preset safety threshold, identify potential membrane blockage points based on the over-threshold data, determine the high-risk area of ​​blockage, start the early warning mechanism and issue a blockage warning, and generate a blockage warning signal; Based on the blocking warning signal, the sound and vibration frequency generated by the equipment during operation are recorded, spectrum analysis is performed and abnormal patterns are identified, compared with historical data, early indicators before failure occur are confirmed, the health status of the equipment is determined, and output performance stability monitoring records are recorded. Based on the performance stability monitoring records, the pressure is adjusted to the optimal level, and the flow rate setting is adjusted in combination with the real-time feedback data to match the filtration requirements and prevent membrane clogging. The temperature changes during the chemical reaction process are simultaneously monitored, the process temperature is dynamically adjusted, and the intelligent control parameter optimization plan is comprehensively output.

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