Sea water desalination treatment system and method based on permeation method

Abstract

The invention discloses a seawater desalination treatment system and method based on a penetration method, and particularly relates to the technical field of seawater desalination treatment.The seawater desalination treatment system is provided with an online monitoring module, and various indexes in the seawater desalination treatment process are monitored through a multi-source sensor; a seawater pollution treatment module is arranged to remove suspended particles and pollutants in seawater; a seawater desalination treatment module is arranged for deep desalination; setting an equipment management module to judge whether equipment maintenance is needed or not based on the real-time operation data of the equipment; a semi-permeable membrane loss prediction module is arranged for semi-permeable membrane loss prediction; setting a semi-permeable membrane maintenance control module to automatically match a corresponding maintenance strategy according to the predicted membrane loss degree; a forward osmosis and reverse osmosis coupling process is combined, so that the desalination rate and the energy consumption efficiency ratio are improved, and the service life of the semipermeable membrane is effectively prolonged; an intelligent monitoring-prediction-control system is constructed, and the stability and reliability of the technological process are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of seawater desalination technology, and more specifically, to a seawater desalination system and method based on the permeation method. Background Technology

[0002] Currently, the main seawater desalination technologies include distillation, electrodialysis, and osmosis. Distillation uses heating to vaporize seawater and then condense it into fresh water; its principle is straightforward and it can handle large-scale seawater treatment. However, it consumes extremely high energy, and the equipment is prone to scaling due to impurities in the water, resulting in significant long-term operating costs. Electrodialysis uses ion-exchange membranes to separate ions for desalination; it consumes relatively less energy and is easy to operate, but it has stringent requirements for membrane performance, is susceptible to fouling, and struggles to handle non-ionic impurities, limiting its applicability. Osmosis, through a semi-permeable membrane, allows water molecules to pass through while retaining salt under pressure. It overcomes the high energy consumption of distillation and the membrane dependence and weak impurity treatment of electrodialysis. With its advantages of high-efficiency desalination and wide applicability, it has become an important technological pathway for seawater desalination. Therefore, osmosis is used for related research and application.

[0003] Existing seawater desalination methods based on osmosis rely on the synergistic effect of semi-permeable membranes and pressure to efficiently retain salt, achieve high desalination rates, and stably produce fresh water. They are adaptable to various scales of demand, from water supply for small islands to water security for large cities, significantly improving seawater desalination efficiency and water quality control.

[0004] However, existing methods still have shortcomings: semi-permeable membranes are susceptible to seawater fouling, with frequent microbial attachment and calcium and magnesium ion scaling, requiring frequent cleaning or replacement of membrane modules. Traditional maintenance uses fixed-cycle cleaning, which can lead to over-maintenance or under-maintenance. The former increases operation and maintenance costs, while the latter reduces the service life of membrane modules. High-pressure pumps consume a high proportion of energy to maintain pressure, raising operating costs. When faced with complex seawater with high turbidity and high organic content, the treatment capacity is limited, which can easily affect the desalination effect. Summary of the Invention

[0005] To overcome the problems of existing methods, such as the susceptibility of semipermeable membranes to seawater pollution, high operation and maintenance costs, high energy consumption, and poor desalination effect, this invention provides a seawater desalination system and method based on permeation.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a seawater desalination system based on permeation, comprising:

[0007] Online monitoring module: Uses multi-source sensors to monitor seawater pollution treatment indicators, seawater desalination treatment indicators, equipment operation status indicators, and semi-permeable membrane operation status indicators;

[0008] Seawater pollution treatment module: This module treats suspended particles and pollutants in seawater through a process flow, including a pollution treatment target setting unit, a physical treatment unit, a chemical treatment unit, a biological treatment unit, and a control unit.

[0009] Seawater desalination module: Deep desalination through process flow, including seawater desalination target setting unit, forward osmosis treatment unit, draw solution recovery unit, reverse osmosis treatment unit and control unit;

[0010] Equipment Management Module: Real-time monitoring of the operating status of key equipment in the seawater desalination process; analysis of abnormal equipment operation based on the monitored operating status parameters; and determination of whether to trigger maintenance based on the feedback results.

[0011] Semipermeable membrane loss prediction module: The monitored semipermeable membrane operating status indicators are input into the semipermeable membrane loss prediction model to obtain the semipermeable membrane loss degree. The reasoning result is matched with the preset rules to obtain the semipermeable membrane loss prediction result.

[0012] Semi-permeable membrane maintenance control module: Automatically matches the corresponding maintenance strategy based on the predicted membrane wear level;

[0013] Seawater desalination data integration module: Automatically integrates data from each seawater desalination process within a preset cycle;

[0014] Seawater desalination control effect evaluation module: Based on integrated data, calculate the proportion coefficient of compliant pollutants, desalination rate, and energy efficiency ratio of each seawater desalination process within a preset period, and evaluate the seawater desalination control effect based on the proportion coefficient of compliant pollutants, desalination rate, and energy efficiency ratio.

[0015] Intelligent decision-making module: It compares the evaluation results of seawater desalination control effect with the expected value. If the evaluation result is greater than or equal to the expected value, it is determined that no optimization is needed; otherwise, it is determined that optimization is needed.

[0016] To achieve the above objectives, the present invention provides the following technical solution: a seawater desalination method based on permeation, comprising the following steps:

[0017] S1: Use multi-source sensors to monitor seawater pollution treatment indicators, seawater desalination treatment indicators, equipment operation status indicators, and semi-permeable membrane operation status indicators.

[0018] S2: The seawater is subjected to physical, chemical and biological treatment in sequence. The set control determines whether the treatment effect of suspended particles and pollutants in the seawater meets the expectations. If the effect does not meet the expectations, the seawater is returned to the corresponding process for re-treatment.

[0019] S3: The decontaminated seawater is treated using a three-stage process of forward osmosis, extractant separation and reverse osmosis. The conductivity feedback results determine whether it is necessary to return the seawater to the corresponding treatment process.

[0020] S4: Real-time monitoring of critical equipment operation data; automatic generation and execution of maintenance plans in case of anomalies, while recording standardized maintenance logs.

[0021] S5: Construct a semipermeable membrane loss prediction model, input the monitored semipermeable membrane operating status indicators into the semipermeable membrane loss prediction model for reasoning to obtain the semipermeable membrane loss degree, and match the reasoning results with preset rules to obtain the semipermeable membrane loss prediction result.

[0022] S6: Automatically match the corresponding maintenance strategy based on the predicted membrane loss level;

[0023] S7: Automatically integrates data from each seawater desalination process within a preset cycle;

[0024] S8: Calculate the compliance pollutant ratio coefficient, desalination rate, and energy efficiency ratio. Then calculate the standard deviation and compliance rate of each indicator. Calculate the process stability coefficient based on the standard deviation of the desalination rate, the standard deviation of the compliance pollutant ratio coefficient, and the standard deviation of the energy efficiency ratio. Calculate the process reliability coefficient based on the compliance rates of the compliance pollutant ratio coefficient, the desalination rate, and the energy efficiency ratio. Calculate the process control quality index based on the process stability coefficient and the process reliability coefficient.

[0025] S9: Compare the process control quality index with the expected threshold. If the evaluation result is greater than or equal to the expected value, it is determined that no optimization is needed; otherwise, it is determined that optimization is needed.

[0026] The technical effects and advantages of this invention are as follows:

[0027] 1. This invention uses a semi-permeable membrane loss prediction module to input real-time monitored pressure differences across the semi-permeable membrane, seawater inflow, freshwater outflow, and cumulative pollutant concentration into a semi-permeable membrane loss prediction model for inference, outputting prediction results. When the prediction result indicates mild loss, operating parameters are automatically adjusted; when the prediction result indicates moderate loss, a cleaning plan is matched according to the type of pollution; and when the prediction result indicates severe loss, an emergency switching mechanism is activated, realizing the transformation from fixed-cycle maintenance to predictive maintenance, which can extend the service life of membrane modules. By monitoring the real-time operating data of the equipment, abnormal operating conditions of the equipment are determined, and maintenance plans are quickly generated, effectively improving the energy efficiency ratio. In terms of seawater pollution treatment, a three-stage treatment method of physical treatment – ​​chemical treatment – ​​biological treatment is adopted, combined with real-time monitoring by multi-source sensors and multi-dimensional pollution index control, which greatly reduces the concentration of various pollutants in seawater.

[0028] 2. This invention features a seawater desalination module that employs a three-stage process: preliminary concentration via forward osmosis, drawdown recovery, and deep desalination via reverse osmosis. Combined with real-time conductivity feedback control, this effectively improves the desalination rate. The invention constructs a complete intelligent monitoring-prediction-control system. An online monitoring module uses high-precision conductivity meters, atomic absorption spectrometers, and other equipment to monitor seawater pollution indicators in real-time from multiple dimensions. Based on a semi-permeable membrane loss model, it predicts the degree of semi-permeable membrane loss. Through control, it achieves seawater pollution treatment, seawater desalination treatment, equipment anomaly maintenance, and semi-permeable membrane maintenance. The invention also includes a seawater desalination control effect evaluation module. By calculating the process stability coefficient and process reliability coefficient, and based on these coefficients, it obtains a process control quality index, accurately and intuitively reflecting the control effect of the seawater desalination treatment. This effectively reduces fluctuations in product water quality and significantly improves the stability and reliability of the treatment process. Attached Figure Description

[0029] Figure 1 This is a system structure block diagram of the present invention.

[0030] Figure 2 This is a diagram illustrating the method steps of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] like Figure 1 This embodiment provides a seawater desalination system based on the osmosis method, including an online monitoring module, a seawater pollution treatment module, a seawater desalination treatment module, an equipment management module, a semi-permeable membrane loss prediction module, a semi-permeable membrane maintenance control module, a seawater desalination treatment data integration module, a seawater desalination control effect evaluation module, an intelligent decision-making module, and a database. The online monitoring module is connected to the seawater pollution treatment module, the seawater desalination treatment module, the equipment management module, and the semi-permeable membrane loss prediction module. The seawater pollution treatment module is connected to the seawater desalination treatment module. The semi-permeable membrane loss prediction module is connected to the semi-permeable membrane maintenance control module. The seawater pollution treatment module, the seawater desalination treatment module, the equipment management module, and the semi-permeable membrane maintenance control module are all connected to the seawater desalination treatment data integration module. The seawater desalination treatment data integration module, the seawater desalination control effect evaluation module, and the intelligent decision-making module are connected sequentially. All modules in the system are connected to the database.

[0033] The online monitoring module uses multi-source sensors to monitor seawater pollution treatment indicators, seawater desalination treatment indicators, equipment operation status indicators, and semi-permeable membrane operation status indicators.

[0034] Furthermore, the online monitoring module utilizes multi-source sensors including a high-precision turbidimeter, pressure sensor, flow sensor, temperature sensor, smart energy meter, high-precision conductivity meter, atomic absorption spectrometer, gas chromatography-mass spectrometry (GC-MS), and online microbial sensor. The high-precision turbidimeter monitors the concentration of suspended particles in seawater; the pressure sensor collects the pressure values ​​during equipment operation and the pressure difference across the semi-permeable membrane; the flow sensor measures the seawater flow rate entering each device and the freshwater output flow rate; the temperature sensor collects the temperature data during equipment operation; the smart energy meter measures the current, voltage, power, and cumulative power consumption in real time; the high-precision conductivity meter measures the conductivity of seawater; and the atomic absorption spectrometer... The spectrometer accurately detects the concentrations of dissolved ions and heavy metals in seawater, while gas chromatography-mass spectrometry (GC-MS) detects VOCs concentrations. An online microbial sensor monitors the concentration of microorganisms in seawater. Seawater pollution treatment indicators include suspended particulate concentration, dissolved ion concentration, heavy metal concentration, VOCs concentration, and microbial concentration. The seawater desalination treatment indicator is conductivity. Equipment operating status indicators include the pressure difference across the membrane, seawater flow rate into each device, freshwater output flow rate, operating temperature data, real-time metering of current, voltage, power, and cumulative power consumption. Semi-permeable membrane operating status indicators include the pressure difference across the semi-permeable membrane, seawater inflow rate, freshwater output flow rate, and cumulative pollutant concentration.

[0035] In this embodiment, it should be specifically noted that the sensor array arrangement needs to be able to monitor the incoming seawater in real time, and the sensors should also be inspected and maintained regularly to ensure their operational accuracy.

[0036] The seawater pollution treatment module treats suspended particles and pollutants in seawater through a process flow, including a pollution treatment target setting unit, a physical treatment unit, a chemical treatment unit, a biological treatment unit, and a control unit.

[0037] Furthermore, the seawater pollution treatment module includes a pollution treatment target setting unit, a physical treatment unit, a chemical treatment unit, a biological treatment unit, a control unit, and an output unit. The pollution treatment target setting unit sets the comprehensive indicators, physical treatment indicators, chemical treatment indicators, and biological treatment indicators of pollutants in the final output water quality. The physical treatment unit sequentially performs screening, sedimentation, and membrane filtration on the seawater to remove suspended particulate concentration. The chemical treatment unit removes dissolved ions, heavy metals, and VOCs. The biological treatment unit removes microorganisms from the seawater through a biofilm. The control unit determines whether the physical treatment meets expectations based on the monitoring results of the physical treatment indicators. If it meets expectations, it allows the water to proceed to chemical treatment; otherwise, it returns to the previous stage. The process returns to physical treatment. Based on the monitoring results of chemical treatment indicators, it is determined whether the physical treatment meets expectations. If it does, it is allowed to proceed to biological treatment; otherwise, it returns to chemical treatment. Based on the monitoring results of biological treatment indicators, it is determined whether the physical treatment meets expectations. If it does, a comprehensive seawater quality assessment is conducted; otherwise, it returns to biological treatment. After the physical, chemical, and biological treatments are completed, the comprehensive monitoring results of pollutants in the water quality determine whether the seawater pollution treatment meets expectations. If it does, the seawater pollution treatment ends and enters the seawater desalination process; otherwise, it returns to the corresponding treatment unit based on the type of indicator that does not meet expectations. The output unit delivers seawater with comprehensive monitoring results of pollutants in the water quality that meet expectations to the seawater desalination module.

[0038] In this embodiment, it should be specifically noted that the pollution treatment targets are set as follows: suspended particulate concentration ≤ 0.1 NTU, dissolved ion concentration ≤ 250 mg / L, heavy metal ion concentration ≤ 1 ppb, VOCs concentration ≤ 0.01 ppm, and microbial concentration ≤ 100 CFU / mL.

[0039] The seawater desalination module performs deep desalination through a process flow, including a seawater desalination target setting unit, a forward osmosis treatment unit, a draw solution recovery unit, a reverse osmosis treatment unit, and a control unit.

[0040] Furthermore, the seawater desalination module includes a seawater desalination target setting unit, a forward osmosis treatment unit, a draw liquid separation unit, a reverse osmosis treatment unit, and a control unit. The seawater desalination target setting unit sets the conductivity after forward osmosis treatment, the conductivity after draw liquid separation treatment, and the conductivity after reverse osmosis treatment. The forward osmosis unit initially concentrates seawater through osmotic pressure across the membrane. The draw liquid separation unit separates the draw liquid from the water through membrane filtration. The reverse osmosis treatment unit separates the remaining trace amounts of salt in the water by pressurizing and removing them. The control unit determines whether the forward osmosis treatment meets expectations based on the conductivity monitoring results. If it does, it allows the water to enter the draw liquid separation unit; otherwise, it returns to the forward osmosis unit for processing. The control unit also determines whether the draw liquid separation treatment meets expectations based on the conductivity monitoring results. If it does, it allows the water to enter the reverse osmosis treatment unit; otherwise, it returns to the draw liquid separation unit. Finally, the control unit determines whether the reverse osmosis treatment meets expectations based on the conductivity monitoring results. If it does, it allows output; otherwise, it returns to the reverse osmosis treatment unit.

[0041] In this embodiment, it is necessary to explain the set conductivity standards: ≤15mS / cm after forward osmosis treatment, ≤8mS / cm after extract liquid separation, and ≤0.5mS / cm after reverse osmosis treatment.

[0042] The equipment management module monitors the operating status of key equipment in the seawater desalination process in real time, analyzes abnormal equipment operation based on the monitored operating status parameters, and determines whether to trigger maintenance based on the feedback results.

[0043] Furthermore, the equipment management module includes an operation monitoring information receiving unit, an anomaly determination unit, a maintenance plan generation unit, an anomaly maintenance unit, and an automatic maintenance log generation unit. The operation monitoring information receiving unit receives pressure values ​​and pressure differences across the membrane, seawater flow rate entering each device, freshwater production flow rate, temperature data during device operation, real-time metering of current, voltage, power, and cumulative power consumption. The anomaly determination unit compares the monitoring data with preset thresholds. When the measured data deviates from the threshold, it is recorded as an anomaly. When multiple consecutive anomalies occur, an early warning signal is issued. When the data is within the normal threshold range, the equipment is determined to be normal. The maintenance plan generation unit automatically generates a maintenance plan based on the issued early warning signal. The anomaly maintenance unit performs equipment maintenance based on the generated maintenance plan. The automatic maintenance log generation unit records the entire equipment maintenance process information and automatically organizes it into a standardized log for storage.

[0044] In this embodiment, it should be specifically noted that the sensor needs to be calibrated daily to ensure that the data deviation is less than ±1%.

[0045] The semipermeable membrane loss prediction module inputs the monitored semipermeable membrane operating status indicators into the semipermeable membrane loss prediction model to infer the semipermeable membrane loss degree, and matches the inference result with preset rules to obtain the semipermeable membrane loss prediction result.

[0046] Furthermore, the semipermeable membrane loss prediction module includes a semipermeable membrane monitoring information receiving unit, a semipermeable membrane loss calculation unit, a level identification unit, and a prediction result output unit. The semipermeable membrane monitoring information receiving unit is used to receive the pressure difference across the semipermeable membrane, seawater inflow, freshwater outflow, and cumulative pollutant concentration. The semipermeable membrane loss calculation unit inputs the semipermeable membrane operation monitoring data into the semipermeable membrane loss algorithm model for inference and outputs the semipermeable membrane loss degree. The specific algorithm model is as follows: In the formula, TMP n Q represents the pressure difference across the semipermeable membrane, TMP1 represents the ultimate pressure difference across the semipermeable membrane, and Q represents the pressure difference across the membrane. n The actual water production efficiency of the semi-permeable membrane is currently calculated as the ratio of freshwater output to seawater inflow. Q1 represents the limiting water production efficiency of the semi-permeable membrane, and B... c The cumulative concentration coefficient of pollutants is calculated using the following formula: The level identification unit compares the semipermeable membrane loss degree with a preset threshold to identify the loss level; the prediction result output unit is used to output the loss level.

[0047] This embodiment specifically focuses on collecting key data during the operation of the semi-permeable membrane, including real-time parameters such as the pressure difference across the membrane, influent and effluent flow rates, salt concentration, influent temperature, turbidity, and microbial concentration, as well as historical data such as membrane cleaning records and replacement times. The data is filtered to remove outliers and missing values, and then formatted into a dataset. Features related to membrane loss are extracted from the preprocessed data, such as the pressure difference across the membrane, permeate efficiency, and cumulative contaminant concentration. These features reflect membrane fouling, aging, and other loss states. Using the loss rate (P) as the prediction target, a calculation formula is constructed based on key parameters. Input parameters are set, selecting three core parameters: the pressure difference across the semi-permeable membrane, permeate efficiency, and the cumulative contaminant concentration coefficient. The pollution rate is quantitatively calculated, and the loss level is classified according to the P value: 0 ≤ P < 0.18 indicates normal equipment, 0.18 ≤ P < 0.35 indicates mild loss, 0.35 ≤ P < 0.6 indicates moderate loss, and P ≥ 0.6 indicates severe loss. Using the actual membrane wear level as the output, the model is trained with historical data. The model's accuracy is continuously optimized by adjusting parameters, enabling it to accurately identify wear patterns. Further testing of the model's prediction performance with new operational data is then conducted, comparing the predicted results with actual wear. If the deviation is significant, the model parameters are readjusted or supplemented with data for retraining until the model can reliably predict membrane wear trends. During training, historical data is divided into training and validation sets. The training set is used to input features (such as the pressure difference across the semi-permeable membrane and permeate efficiency) and corresponding actual wear results, such as membrane fouling, allowing the model to learn the correlation between "features" and "wear." The validation set is used to test the model's prediction accuracy, continuously adjusting algorithm parameters to reduce prediction errors and control the error within 5%.

[0048] The semipermeable membrane maintenance control module automatically matches the corresponding maintenance strategy based on the predicted degree of membrane loss.

[0049] This embodiment specifically describes the output results of the semi-permeable membrane loss prediction model. Through intelligent control logic, precise maintenance and dynamic management of the semi-permeable membrane are achieved, preventing efficiency decline or even shutdown due to membrane loss. When the prediction model outputs mild loss, the module automatically initiates a preventative maintenance strategy—adjusting process parameters such as influent flow rate and pressure to reduce impurity adhesion on the membrane surface through the flushing effect of water flow; simultaneously, combined with temperature sensor data, low-pressure backwashing is performed when the water temperature is suitable, using only a small amount of fresh water to remove surface contaminants and prevent further loss. If the prediction model outputs moderate loss, the module triggers proactive intervention measures: based on the type of pollution analyzed by the prediction model (such as inorganic scale, biofilm, or organic pollution), an appropriate cleaning solution is automatically selected—for inorganic scale (such as calcium and magnesium precipitation), the chemical treatment unit precisely adds chelating agents such as citric acid, and performs cyclic cleaning according to preset concentrations and durations; for biological pollution, a low concentration of sodium hypochlorite is added to kill microorganisms, and the seawater pollution treatment module is linked to enhance influent sterilization, reducing pollution load from the source. During the cleaning process, the module monitors the permeate quality and membrane pressure differential in real time using a conductivity meter and pressure sensor to ensure that the cleaning effect meets the standards before resuming normal operation. When the predictive model outputs a severe loss warning, the module immediately activates the emergency mechanism: on the one hand, it automatically switches to the backup membrane module and quickly isolates the faulty membrane unit through the control unit to ensure continuous water production; on the other hand, it synchronizes the loss data to the equipment management module, triggers a maintenance alarm, and generates a detailed replacement plan (such as membrane model and replacement steps), while recording the causes of this loss (such as excessively high influent turbidity or excessively long maintenance cycle) to provide a basis for subsequent optimization of maintenance strategies.

[0050] The seawater desalination data integration module automatically integrates data from each seawater desalination process within a preset period.

[0051] Furthermore, the data automatically integrated by the seawater desalination treatment data integration module includes the number of compliant pollutants, the total number of pollutants, the salt concentration of the produced freshwater, the salt concentration of the original seawater, the volume of the produced freshwater, the total energy consumption of the equipment, the batches of seawater pollution treatment that meet all standards, the batches of seawater desalination that meet the desalination rate standards, the batches of seawater desalination that meet the energy efficiency ratio standards, and the total number of seawater desalination batches.

[0052] The seawater desalination control effect evaluation module calculates the proportion coefficient of compliant pollutants, desalination rate, and energy efficiency ratio for each seawater desalination process within a preset period based on the integrated data, and evaluates the seawater desalination control effect based on the proportion coefficient of compliant pollutants, desalination rate, and energy efficiency ratio.

[0053] Furthermore, the specific calculation process of the seawater desalination control effect evaluation module is as follows:

[0054] A1. Calculate the percentage coefficient Ri of compliant pollutants in the i-th seawater pollution treatment within the preset cycle. The specific formula is as follows: In the formula, mai represents the number of pollutants that meet the standards in the i-th seawater pollution treatment within the preset period, and mbi represents the total number of pollutants in the i-th seawater pollution treatment within the preset period.

[0055] A2. Calculate the desalination rate Di of the i-th seawater desalination treatment within the preset cycle. The specific formula is as follows: In the formula, Cci is the salt concentration of the produced freshwater, and Cdi is the salt concentration of the original seawater.

[0056] A3. Calculate the energy efficiency ratio Ei of the i-th seawater desalination treatment within the preset cycle. The specific formula is as follows: In the formula, Vai is the volume of fresh water produced by the i-th seawater desalination treatment within the preset cycle, and Eai is the total energy consumption of the seawater desalination equipment for the i-th seawater desalination treatment within the preset cycle.

[0057] A4. Calculate the compliance rate Ka, which is the percentage of compliant pollutants treated in the seawater pollution treatment within the preset period. The specific formula is as follows: In the formula, na represents the number of batches treated to meet the seawater pollution treatment standards, nb represents the total number of batches treated by seawater desalination, and the specific formula for calculating the seawater desalination rate compliance rate Kb within the preset period is as follows: In the formula, nc represents the number of seawater desalination batches that meet the standards, nb represents the total number of seawater desalination batches, and the energy efficiency ratio compliance rate Kc within the preset period is calculated using the following formula: In the formula, ne represents the number of batches that meet the energy efficiency ratio index, and nb represents the total number of batches of seawater desalination.

[0058] A5. Calculate the process stability coefficient S of seawater desalination within the preset period. The specific formula is as follows: In the formula, σD is the standard deviation of the desalination rate, σR is the standard deviation of the coefficient of the proportion of pollutants that meet the standards, and σE is the standard deviation of the energy efficiency ratio.

[0059] A6. Calculate the process reliability coefficient F of seawater desalination within the preset period. Specific formula:

[0060] A7. Calculate the process control quality index W for seawater desalination within the preset period. The specific formula is as follows:

[0061] In this embodiment, it is specifically noted that the compliance standards for the percentage of compliant pollutants are set as follows: ≥95%; the compliance standards for the desalination rate are set as: ≥99%; and the compliance standards for the energy efficiency ratio are set as: ≥3.0m. 3 / kWh, the closer the stability value of the quantitative process operation is to 1, the smaller the parameter fluctuation and the more stable the process. When the process reliability index is closer to 100%, it means that the reliability of each link in the whole process is higher.

[0062] The intelligent decision-making module compares the evaluation results of the seawater desalination control effect with the expected value. If the evaluation result is greater than or equal to the expected value, it is determined that no optimization is needed; otherwise, it is determined that optimization is needed.

[0063] In this embodiment, it should be specifically noted that when W≥0.85, the process control effect is excellent; when 0.7≤W<0.85, the process control effect is good; and when W<0.7, the process control process needs to be optimized.

[0064] The database is used to store data information for all modules in the system.

[0065] like Figure 2 This embodiment provides a seawater desalination method based on osmosis, including the following steps:

[0066] S1: Use multi-source sensors to monitor seawater pollution treatment indicators, seawater desalination treatment indicators, equipment operation status indicators, and semi-permeable membrane operation status indicators.

[0067] S2: The seawater is subjected to physical, chemical and biological treatment in sequence. The set control determines whether the treatment effect of suspended particles and pollutants in the seawater meets the expectations. If the effect does not meet the expectations, the seawater is returned to the corresponding process for re-treatment.

[0068] S3: The decontaminated seawater is treated using a three-stage process of forward osmosis, extractant separation and reverse osmosis. The conductivity feedback results determine whether it is necessary to return the seawater to the corresponding treatment process.

[0069] S4: Real-time monitoring of critical equipment operation data; automatic generation and execution of maintenance plans in case of anomalies, while recording standardized maintenance logs.

[0070] S5: Construct a semipermeable membrane loss prediction model, input the monitored semipermeable membrane operating status indicators into the semipermeable membrane loss prediction model for reasoning to obtain the semipermeable membrane loss degree, and match the reasoning results with preset rules to obtain the semipermeable membrane loss prediction result.

[0071] S6: Automatically match the corresponding maintenance strategy based on the predicted membrane loss level;

[0072] S7: Automatically integrates data from each seawater desalination process within a preset cycle;

[0073] S8: Calculate the compliance pollutant ratio coefficient, desalination rate, and energy efficiency ratio. Then calculate the standard deviation and compliance rate of each indicator. Calculate the process stability coefficient based on the standard deviation of the desalination rate, the standard deviation of the compliance pollutant ratio coefficient, and the standard deviation of the energy efficiency ratio. Calculate the process reliability coefficient based on the compliance rates of the compliance pollutant ratio coefficient, the desalination rate, and the energy efficiency ratio. Calculate the process control quality index based on the process stability coefficient and the process reliability coefficient.

[0074] S9: Compare the process control quality index with the expected threshold. If the evaluation result is greater than or equal to the expected value, it is determined that no optimization is needed; otherwise, it is determined that optimization is needed.

[0075] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A seawater desalination system based on permeation, characterized in that: include: Online monitoring module: Uses multi-source sensors to monitor seawater pollution treatment indicators, seawater desalination treatment indicators, equipment operation status indicators, and semi-permeable membrane operation status indicators; Seawater pollution treatment module: This module treats suspended particles and pollutants in seawater through a process flow, including a pollution treatment target setting unit, a physical treatment unit, a chemical treatment unit, a biological treatment unit, and a control unit. Seawater desalination module: Deep desalination through process flow, including seawater desalination target setting unit, forward osmosis treatment unit, draw solution recovery unit, reverse osmosis treatment unit and control unit; Equipment Management Module: Real-time monitoring of the operating status of key equipment in the seawater desalination process; analysis of abnormal equipment operation based on the monitored operating status parameters; and determination of whether to trigger maintenance based on the feedback results. Semipermeable membrane loss prediction module: The monitored semipermeable membrane operating status indicators are input into the semipermeable membrane loss prediction model to obtain the semipermeable membrane loss degree. The reasoning result is matched with the preset rules to obtain the semipermeable membrane loss prediction result. Semi-permeable membrane maintenance control module: Automatically matches the corresponding maintenance strategy based on the predicted membrane wear level; Seawater desalination data integration module: Automatically integrates data from each seawater desalination process within a preset cycle; Seawater desalination control effect evaluation module: Based on integrated data, calculate the proportion coefficient of compliant pollutants, desalination rate, and energy efficiency ratio of each seawater desalination process within a preset period, and evaluate the seawater desalination control effect based on the proportion coefficient of compliant pollutants, desalination rate, and energy efficiency ratio. Intelligent decision-making module: It compares the evaluation results of seawater desalination control effect with the expected value. If the evaluation result is greater than or equal to the expected value, it is determined that no optimization is needed; otherwise, it is determined that optimization is needed.

2. The seawater desalination system based on permeation according to claim 1, characterized in that: The online monitoring module utilizes a multi-source sensor suite including a high-precision turbidity meter, pressure sensor, flow sensor, temperature sensor, smart meter, high-precision conductivity meter, atomic absorption spectrometer, gas chromatography-mass spectrometry (GC-MS), and an online microbial sensor. The high-precision turbidity meter monitors the concentration of suspended particles in seawater; the pressure sensor collects the pressure values ​​during equipment operation and the pressure difference across the semi-permeable membrane; the flow sensor measures the seawater flow rate entering each device and the freshwater output flow rate; the temperature sensor collects the temperature data during equipment operation; the smart meter measures the current, voltage, power, and cumulative power consumption in real time; the high-precision conductivity meter measures the conductivity of seawater; and the atomic absorption spectrometer... The instrument accurately detects the concentrations of dissolved ions and heavy metals in seawater, detects VOCs concentration using gas chromatography-mass spectrometry, and monitors the concentration of microorganisms in seawater using an online microbial sensor. Seawater pollution treatment indicators include suspended particulate concentration, dissolved ion concentration, heavy metal concentration, VOCs concentration, and microbial concentration. The seawater desalination treatment indicator is conductivity. Equipment operating status indicators include the pressure difference across the membrane, seawater flow rate into each device, freshwater output flow rate, temperature data during equipment operation, real-time metering of current, voltage, power, and cumulative power consumption. Semi-permeable membrane operating status indicators include the pressure difference across the semi-permeable membrane, seawater inflow rate, freshwater output flow rate, and cumulative pollutant concentration.

3. The seawater desalination system based on permeation according to claim 1, characterized in that: The seawater pollution treatment module includes a pollution treatment target setting unit, a physical treatment unit, a chemical treatment unit, a biological treatment unit, a control unit, and an output unit. The pollution treatment target setting unit sets comprehensive indicators, physical treatment indicators, chemical treatment indicators, and biological treatment indicators for pollutants in the final output water quality. The physical treatment unit sequentially performs screening, sedimentation, and membrane filtration on the seawater to remove suspended particulate matter. The chemical treatment unit removes dissolved ions, heavy metals, and VOCs. The biological treatment unit removes microorganisms from the seawater through a biofilm. The control unit determines whether the physical treatment meets expectations based on the monitoring results of the physical treatment indicators. If it meets expectations, it allows the water to proceed to chemical treatment; otherwise, it returns to the physical treatment unit. The process involves three stages: physical treatment, chemical treatment, and biological treatment. The physical treatment is assessed based on monitoring results of chemical treatment indicators. If it meets expectations, the process proceeds to biological treatment; otherwise, it returns to chemical treatment. Similarly, the biological treatment is assessed based on monitoring results of biological treatment indicators. If it meets expectations, a comprehensive assessment of seawater quality is conducted; otherwise, it returns to biological treatment. After physical, chemical, and biological treatments, the comprehensive monitoring results of pollutants in the water are used to determine if the seawater pollution treatment meets expectations. If it meets expectations, the seawater pollution treatment ends, and the process proceeds to seawater desalination; otherwise, it returns to the corresponding treatment unit based on the type of indicator that does not meet expectations. The output unit delivers seawater with satisfactory comprehensive monitoring results of pollutants in the water to the seawater desalination module.

4. The seawater desalination system based on permeation according to claim 1, characterized in that: The seawater desalination module includes a seawater desalination target setting unit, a forward osmosis unit, a draw solution separation unit, a reverse osmosis unit, and a control unit. The seawater desalination target setting unit sets the conductivity after forward osmosis, the conductivity after draw solution separation, and the conductivity after reverse osmosis. The forward osmosis unit initially concentrates seawater by utilizing osmotic pressure across the membrane. The draw solution separation unit separates the draw solution from the water through membrane filtration. The reverse osmosis unit separates the remaining trace amounts of salt in the water by pressurizing and removing them. The control unit determines whether the forward osmosis treatment meets expectations based on conductivity monitoring results. If it does, the water is allowed to enter the draw solution separation unit; otherwise, it returns to the forward osmosis unit for processing. Similarly, the control unit determines whether the reverse osmosis treatment meets expectations based on conductivity monitoring results. If it does, the water is allowed to enter the reverse osmosis unit; otherwise, it returns to the draw solution separation unit. Finally, the control unit determines whether the reverse osmosis treatment meets expectations based on conductivity monitoring results. If it does, the water is allowed to be output; otherwise, it returns to the reverse osmosis unit.

5. A seawater desalination system based on permeation according to claim 1, characterized in that: The equipment management module includes an operation monitoring information receiving unit, an anomaly judgment unit, a maintenance plan generation unit, an anomaly maintenance unit, and an automatic maintenance log generation unit. The operation monitoring information receiving unit is used to receive pressure values ​​and pressure differences across the membrane, seawater flow rate entering each device, freshwater output flow rate, temperature data during device operation, real-time metering current, voltage, power, and cumulative power consumption. The anomaly detection unit compares the monitoring data with a preset threshold. When the measured data deviates from the threshold, it is recorded as an anomaly. When multiple consecutive abnormal data occur, an early warning signal is issued. When the data is within the normal threshold range, the device is determined to be normal. The maintenance plan generation unit automatically generates maintenance plans based on the issued early warning signals; the abnormal maintenance unit performs equipment maintenance based on the generated maintenance plans; and the maintenance log automatic generation unit records the entire equipment maintenance process information and automatically organizes it into standardized logs for storage.

6. A seawater desalination system based on permeation according to claim 1, characterized in that: The semipermeable membrane loss prediction module includes a semipermeable membrane monitoring information receiving unit, a semipermeable membrane loss calculation unit, a level identification unit, and a prediction result output unit. The semipermeable membrane monitoring information receiving unit receives the pressure difference across the semipermeable membrane, seawater inflow rate, freshwater outflow rate, and cumulative pollutant concentration. The semipermeable membrane loss calculation unit inputs the semipermeable membrane operation monitoring data into the semipermeable membrane loss algorithm model for inference and outputs the semipermeable membrane loss degree. The specific algorithm model is as follows: In the formula, TMP n Q represents the pressure difference across the semipermeable membrane, TMP1 represents the ultimate pressure difference across the semipermeable membrane, and Q represents the pressure difference across the membrane. n The actual water production efficiency of the semi-permeable membrane is currently calculated as the ratio of freshwater output to seawater inflow. Q1 represents the limiting water production efficiency of the semi-permeable membrane, and B... c The cumulative concentration coefficient of pollutants is calculated using the following formula: The level identification unit compares the semipermeable membrane loss degree with a preset threshold to identify the loss level; the prediction result output unit is used to output the loss level.

7. A seawater desalination system based on permeation according to claim 1, characterized in that: The data automatically integrated by the seawater desalination treatment data integration module includes the number of compliant pollutants, the total number of pollutants, the salt concentration of the produced freshwater, the salt concentration of the original seawater, the volume of the produced freshwater, the total energy consumption of the equipment, the batches of seawater pollution treatment that meet all standards, the batches of seawater desalination that meet the desalination rate standards, the batches of seawater desalination that meet the energy efficiency ratio standards, and the total number of seawater desalination batches.

8. A seawater desalination system based on permeation according to claim 1, characterized in that: The specific calculation process of the seawater desalination control effect evaluation module is as follows: A1. Calculate the percentage coefficient Ri of compliant pollutants in the i-th seawater pollution treatment within the preset cycle. The specific formula is as follows: In the formula, mai represents the number of pollutants that meet the standards in the i-th seawater pollution treatment within the preset period, and mbi represents the total number of pollutants in the i-th seawater pollution treatment within the preset period. A2. Calculate the desalination rate Di of the i-th seawater desalination treatment within the preset cycle. The specific formula is as follows: In the formula, Cci is the salt concentration of the produced freshwater, and Cdi is the salt concentration of the original seawater. A3. Calculate the energy efficiency ratio Ei of the i-th seawater desalination treatment within the preset cycle. The specific formula is as follows: In the formula, Vai is the volume of fresh water produced by the i-th seawater desalination treatment within the preset cycle, and Eai is the total energy consumption of the seawater desalination equipment for the i-th seawater desalination treatment within the preset cycle. A4. Calculate the compliance rate Ka, which is the percentage of compliant pollutants treated in the seawater pollution treatment within the preset period. The specific formula is as follows: In the formula, na represents the number of batches treated to meet the seawater pollution treatment standards, nb represents the total number of batches treated by seawater desalination, and the specific formula for calculating the seawater desalination rate compliance rate Kb within the preset period is as follows: In the formula, nc represents the number of seawater desalination batches that meet the standards, nb represents the total number of seawater desalination batches, and the energy efficiency ratio compliance rate Kc within the preset period is calculated using the following formula: In the formula, ne represents the number of batches that meet the energy efficiency ratio target, and nb represents the total number of batches of seawater desalination. A5. Calculate the process stability coefficient S of seawater desalination within the preset period. The specific formula is as follows: In the formula, σD is the standard deviation of the desalination rate, σR is the standard deviation of the coefficient of the proportion of pollutants that meet the standards, and σE is the standard deviation of the energy efficiency ratio. A6. Calculate the process reliability coefficient F of seawater desalination within the preset period. Specific formula: A7. Calculate the process control quality index W for seawater desalination within the preset period. The specific formula is as follows:

9. A seawater desalination method based on osmosis, using a seawater desalination system based on osmosis as described in any one of claims 1-8, characterized in that: Includes the following steps: S1: Use multi-source sensors to monitor seawater pollution treatment indicators, seawater desalination treatment indicators, equipment operation status indicators, and semi-permeable membrane operation status indicators. S2: The seawater is subjected to physical, chemical and biological treatment in sequence. The set control determines whether the treatment effect of suspended particles and pollutants in the seawater meets the expectations. If the effect does not meet the expectations, the seawater is returned to the corresponding process for re-treatment. S3: The decontaminated seawater is treated using a three-stage process of forward osmosis, extractant separation and reverse osmosis. The conductivity feedback results determine whether it is necessary to return the seawater to the corresponding treatment process. S4: Real-time monitoring of critical equipment operation data; automatic generation and execution of maintenance plans in case of anomalies, while recording standardized maintenance logs. S5: Construct a semipermeable membrane loss prediction model, input the monitored semipermeable membrane operating status indicators into the semipermeable membrane loss prediction model for reasoning to obtain the semipermeable membrane loss degree, and match the reasoning results with preset rules to obtain the semipermeable membrane loss prediction result. S6: Automatically match the corresponding maintenance strategy based on the predicted membrane loss level; S7: Automatically integrates data from each seawater desalination process within a preset cycle; S8: Calculate the compliance pollutant ratio coefficient, desalination rate, and energy efficiency ratio. Then calculate the standard deviation and compliance rate of each indicator. Calculate the process stability coefficient based on the standard deviation of the desalination rate, the standard deviation of the compliance pollutant ratio coefficient, and the standard deviation of the energy efficiency ratio. Calculate the process reliability coefficient based on the compliance rates of the compliance pollutant ratio coefficient, the desalination rate, and the energy efficiency ratio. Calculate the process control quality index based on the process stability coefficient and the process reliability coefficient. S9: Compare the process control quality index with the expected threshold. If the evaluation result is greater than or equal to the expected value, it is determined that no optimization is needed; otherwise, it is determined that optimization is needed.

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