551 results about "Membrane fouling" patented technology

The invention discloses a modularized full-process high-quality direct drinking water treatment system and a control method, and belongs to the technical field of direct drinking water treatment.The modularized full-process high-quality direct drinking water treatment system is characterized in that an online sensor is deployed at a water inlet of a flocculation basin, raw water turbidity, temperature and flow parameters are collected in real time, and qualified samples are screened to construct a standardized water quality characteristic data set in combination with historical water quality and agent adding records; based on the data set, performing multi-dimensional clustering on historical water quality parameters, generating a characteristic working condition cluster, calculating a three-dimensional efficiency index, generating a dynamic dosage reference interval by using a sliding window confidence interval and a time decay factor, establishing a mapping relation between water quality characteristics and medicament dosage, and generating a graded dosage strategy; the transmembrane pressure difference and the membrane flux change rate are continuously monitored at the water inlet end in the membrane treatment stage, the membrane pollution trend is predicted through time sequence analysis, the prediction result is corrected according to the agent adding deviation, the precipitation effluent turbidity and the membrane inflow COD data, a cleaning early warning signal is generated, and graded response is performed according to the pollution degree.

The invention provides an intelligent sewage treatment process optimization method and system. The method comprises the following steps: continuously collecting the flux attenuation rate, transmembrane pressure difference and pollutant spectrum data of a ceramic membrane, carrying out time sequence analysis, constructing a pollution accumulation dynamic model, and predicting a critical threshold value; when a threshold value is reached, triggering an ultrasonic cavitation device, adjusting high-frequency vibration to generate a microbubble group, stripping pollutants by utilizing shock waves, and calculating membrane flux recovery data; fusing the spectral data and the recovery data to extract an actual pollution stripping efficiency value, and comparing a theoretical value to obtain a difference proportion; if the actual value is lower than the theoretical value, the cleaning period is shortened, the fluid pressure is increased to strengthen the stripping effect, and meanwhile, dynamic model parameters are reversely corrected based on the pressure adjustment amplitude to form closed-loop regulation and control. According to the invention, intelligent prediction, self-adaptive cleaning and closed-loop optimization of ceramic membrane pollution are realized, the sewage filtering efficiency is improved, and the energy consumption and cleaning cost are reduced.

The invention relates to the field of wastewater treatment, and provides an industrial wastewater membrane process intelligent optimization method and system based on multi-dimensional data modeling. The method comprises the following steps: acquiring wastewater quality parameters of a target scene and a production line equipment state in real time through a multi-dimensional sensor network, and constructing a digital twinborn model of a wastewater treatment system in the target scene; the membrane surface pollution type of each node in the wastewater treatment system is identified through a digital twin model, and the inner membrane pollution evolution condition in the future preset duration is predicted; a multi-strategy evolutionary reinforcement learning algorithm is adopted, the wastewater quality parameters, the production line equipment state and the membrane pollution prediction result are combined, multi-target dynamic optimization is conducted on a wastewater treatment system through a multi-agent collaborative decision-making algorithm, an optimization strategy is generated based on the multi-target dynamic optimization result, and membrane process operation parameters and production control parameters are dynamically adjusted. According to the method, real-time intelligent adjustment of membrane process operation can be realized, the stability of membrane process operation is guaranteed, and the treatment efficiency of membrane process operation is improved.

The invention provides a zero-discharge treatment process for recycling production wastewater, and relates to the technical field of water treatment and recycling, and the zero-discharge treatment process comprises the following steps: S1, oxidizing and degrading COD through ozone and a Fenton reagent, and generating a flocculatable intermediate; s2, intercepting suspended matters by using a gradient aperture filter bed in a grading manner, and monitoring a dynamic membrane resistance coefficient delta Rm in real time to automatically trigger backwashing; s3, coupling an ultrafiltration reverse osmosis membrane to realize that the organic matter rejection rate eta is greater than or equal to 98%, and synchronously regulating and controlling flux and operation pressure by an online spectrometry; s4, regulating and controlling the current density by adopting a rotational flow electric flocculation reactor for directional crystallization to produce industrial salt with Cl <-> less than or equal to 50mg / L; and S5, based on the total dissolved solid recovery rate TR and the comprehensive energy consumption, establishing a multi-target optimization model for real-time parameter adjustment. According to the method, a membrane pollution early warning and backwashing dynamic response mechanism is constructed through cross-scale cooperative regulation and control of delta Rm and the retention rate eta, and a catalytic oxidation effect strengthening and electric crystallization parameter self-tuning technology is combined, so that the problem of cooperative optimization of high-salinity wastewater salt gradient separation and pollutant deep removal is solved.

The invention relates to the technical field of water resource purification, in particular to a control method and equipment of a reverse osmosis water purification system.The control method comprises the steps that firstly, on the fluctuation dimension of the pre-membrane pressure, the adjusting requirement of the frequency of a high-pressure pump is preliminarily determined on the dimension of the pre-membrane pressure according to an obtained pressure fluctuation index; determining a membrane resistance increase factor representing the pollution state of the reverse osmosis membrane on the basis of the transmembrane pressure difference on the dimension of separation function failure of the reverse osmosis membrane; finally, a membrane separation abnormal value is comprehensively represented by combining a pressure fluctuation index and a membrane resistance increase factor, and a purified water recovery rate is introduced to a membrane separation result end, so that the finally obtained frequency reduction demand degree is more accurate, and the control effect of the reverse osmosis water purification system is improved; therefore, the stable, efficient and sustainable operation of the reverse osmosis water purification system can be ensured while the membrane surface pollutant deposition driving force can be effectively reduced and the membrane pollution speed can be slowed down.

The invention discloses an energy-saving and consumption-reducing method for a reverse osmosis membrane system, which belongs to the technical field of water treatment and comprises the following steps: step 1, acquiring various parameter data of the reverse osmosis membrane system in real time; 2, establishing an energy consumption prediction mathematical model of the reverse osmosis membrane system; 3, inputting the real-time parameter data into the energy consumption prediction mathematical model, and judging whether the operation power of the high-pressure pump is reasonable or not according to an output result; 4, when the operation power is not reasonable, the operation power of the high-pressure pump is adjusted in real time; and 5, establishing a membrane pollution concentration early warning model, and carrying out alarm response. According to the method, comprehensive analysis can be carried out in real time according to various parameters of inflow water quality to accurately predict the predicted power, so that whether the set operation power of the high-pressure pump is reasonable or not is judged, the operation power of the high-pressure pump can be dynamically adjusted, the output of energy consumption can be reduced as much as possible under the condition that treatment is qualified, and the operation efficiency of the high-pressure pump is improved. And the cost is saved.

The invention discloses a machine learning-based membrane pollution pretreatment optimization control method and system, and the method comprises the steps: constructing a high-dimensional feature data set for Fenton pretreatment prevention and control of membrane pollution, and carrying out the comprehensive pretreatment of data; training and optimizing the candidate machine learning model based on the decision tree; evaluating the trained machine learning model, and selecting the model with the most generalization ability as a Fenton pretreatment control membrane pollution prediction model; based on the prediction model, reversely searching the optimal reagent dosage under a preset environment variable by adopting a genetic algorithm; and carrying out membrane pollution Fenton pretreatment under the corresponding environment variable condition by utilizing the optimal reagent adding amount. According to the technical scheme, the reagent adding amount of Fenton pretreatment with the optimal membrane pollution retarding capacity of different pollutants under different membrane filtration conditions can be optimized and directionally controlled, and the Fenton pretreatment device can be suitable for different types of pollutants and membrane assemblies and has the characteristics of low cost, high efficiency, accuracy, reliability and the like.

The invention relates to the technical field of water treatment, in particular to a high-recovery-rate short-process salt-tolerant reverse osmosis system and treatment method.The system comprises a pretreatment unit, a sensor monitoring unit, a self-adaptive pressure-flow coupling control unit, an intelligent operation and maintenance unit and a gradient boosting reverse osmosis membrane set. According to the invention, ultrahigh recovery rate and desalination performance are realized, gradient boost membrane module design is combined with concentration polarization inhibition technology, membrane surface flow state distribution is optimized, osmotic pressure influence is significantly reduced, pressure-flow coupling control and concentrated water excess pressure energy recovery technology are integrated, energy dynamic cyclic utilization is realized, energy consumption is reduced, and the method is suitable for industrial production. By monitoring the membrane flux attenuation rate and the transmembrane pressure difference in real time and dynamically optimizing a pulse cleaning strategy, dynamic optimization of high-salinity wastewater treatment is realized, and the problems of low recovery rate, high energy consumption and quick membrane pollution in high-salinity wastewater treatment are solved.

The invention provides a hollow fiber membrane cleaning pressure self-adaptive adjustment control method and system, and relates to the technical field of control, and the method comprises the following steps: in a set temperature range, washing a water inlet side and a water production side through a constant flow, and determining a target transmembrane pressure difference interval; according to the transmembrane pressure difference deviation and the change rate, the rotating speed of a water inlet pump and the opening degree of a concentrated water discharge valve are cooperatively regulated and controlled according to a valve-first and pump-second sequence; when the transmembrane pressure difference reaches the upper limit and pollution is reversible, an impact cleaning sequence is generated, the opening of a concentrated water valve is increased, and the rotating speed of a water inlet pump is increased to form shear scouring. According to the invention, the self-adaptive adjustment of the cleaning pressure in the membrane pollution process is realized, and the membrane cleaning efficiency is improved.

In order to solve the problems of frequent pollution events, relatively high energy consumption and the like in the technical field of membranes in the water treatment process, the invention discloses a real-time monitoring-based membrane pollution dynamic prediction and early warning method, which comprises the following steps of: acquiring various parameter data influencing membrane pollution in real time; inputting the obtained parameter data into a pre-trained LSTM neural network model; the membrane pollution degree changing along with time is output, and real-time membrane pollution monitoring is completed; the membrane pollution degree prediction result and the early warning system are combined, automatic early warning of membrane pollution is achieved, the hysteresis quality in the membrane pollution treatment process is overcome, and the treatment capacity of the membrane pollution problem is improved.

The invention relates to the technical field of wastewater treatment in the water treatment technology, in particular to a wastewater treatment multistage dynamic quantification control system based on result feedback, which comprises a storage module, an identification module, a quantification module and an execution module, the storage module stores a causal chain in a reaction process, the identification module identifies a matching state of a current operation parameter and the causal chain and enables the quantification module to determine a risk index, and the execution module performs control optimization to execute corresponding measures based on the determined risk index. Tracking intervention from a pollution source to a derivation phenomenon is realized, a pollution migration problem implied in wastewater treatment is converted into an identification item of quantitative operation, and limitation of traditional single-point identification control is broken through through sequential logic verification and a multi-dimensional matching degree fusion mechanism. The accuracy of membrane pollution prevention and control and the resource utilization efficiency are improved while the process continuity is guaranteed, and a closed-loop control normal form of traceability pre-judgment dynamic quantification grading interception is formed.

The invention belongs to the technical field of sewage treatment and membrane separation, and provides an in-situ nondestructive membrane pollution detection system and method based on an ultrasonic transmission signal, and the system comprises an ultrasonic excitation module which is used for generating a sine wave excitation signal which penetrates through a membrane body to excite the ultrasonic transmission signal for pollution detection; the signal processing module is used for continuously collecting and preprocessing ultrasonic transmission signals of the clean membrane body and the membrane bodies with different pollution degrees, extracting ultrasonic transmission signal characteristics under different pollution degrees, and establishing a pollution thickness-signal characteristic mapping model; wherein the ultrasonic transmission signal features comprise a time-frequency domain energy gravity center feature and a frequency attenuation spectrum slope feature; and the pollution detection module is used for judging the pollution stage of the membrane body to be detected based on the pollution thickness-signal feature mapping model, and completing in-situ nondestructive membrane pollution detection. According to the technical scheme, the sludge pollution state in the ceramic membrane and flat membrane filtering process can be monitored in real time, and early warning and pollution dynamic tracking are achieved.

The invention discloses an advanced treatment and recycling method for cold-rolled silicon steel magnesium oxide wastewater, and particularly relates to the technical field of wastewater treatment. Through pH adjustment, coagulation pretreatment, pre-membrane fine filtration and medicament stabilization of wastewater, stable control of inlet water quality is realized; a two-stage reverse osmosis system is combined with a dynamic flow and cross flow structure, so that the risks of concentration polarization and local stress corrosion are remarkably inhibited; by combining real-time monitoring of membrane surface pressure difference and recovery rate and an intelligent algorithm, self-adaptive optimization regulation and control of operation parameters of the system are realized; according to the method, the problems of serious membrane pollution, poor system stability, low recycling efficiency and the like in the prior art are effectively solved, and the wastewater resource utilization level and the system operation safety are remarkably improved.

The invention provides a method for calibrating a rolling friction coefficient and surface energy in a process of simulating a ceramic membrane to filter lignin by DEM-CFD, develops a semi-analytical DEM-CFD coupling interface, and on the basis, takes movement and deposition behaviors of lignin particles in a process of filtering black liquor lignin by a single ceramic membrane pore as research objects, and provides a method for calibrating the rolling friction coefficient and the surface energy in the process of simulating the ceramic membrane to filter the black liquor lignin. The capturing mechanism of lignin particles in a ceramic membrane is researched, and it is determined that key factors influencing lignin particle deposition morphology, average coordination number, coordination number distribution and lignin particle deposition structure porosity are rolling friction coefficients and surface energy among lignin particles; the rolling friction coefficient and the surface energy between the lignin particles and the ceramic membrane have little influence on the ceramic membrane; the influence of the rolling friction coefficient among lignin particles and the surface energy parameters on the deposition morphology, coordination number and porosity of the lignin particles is comprehensively considered, calibration work of the rolling friction coefficient and the surface energy parameters is completed, and a powerful tool and accurate simulation parameters are provided for subsequent research of a membrane pollution mechanism.

The invention discloses a heavy metal sewage deep purification system integrating membrane separation and chemical precipitation, and relates to the technical field of sewage treatment.The system comprises a sewage primary treatment module, a water quality parameter acquisition module, a process parameter decision module, a precipitation reaction regulation and control module and a deep separation and purification module; through a combined structure of coarse and fine grids and a horizontal flow sedimentation tank in the sewage primary treatment module, large-particle impurities and suspended solids in sewage are removed, a stable water quality basis is provided for subsequent treatment, the water quality parameter acquisition module acquires parameter data in real time through a multi-sensor fusion array, and the water quality parameter acquisition module acquires the water quality parameter data in real time. Accurate data support is provided for technological parameter decision making, the technological parameter decision making module generates a control instruction of precipitant adding and membrane operation pressure through calculation based on real-time data and historical operation data, the accuracy and adaptability of the treatment process are ensured, and the accurate control strategy not only improves the removal efficiency of heavy metal ions, but also improves the removal efficiency of the membrane operation pressure. And the risk of membrane pollution is also reduced.

The invention discloses a double-layer aeration system for preventing wire breakage and sludge deposition of an MBR double-layer component membrane pool, which comprises a raw water pool, a raw water inlet peristaltic pump, an MBR double-layer component membrane pool, a perforated aeration pipe, a pulse aeration box, an aeration pump, a gas meter, a water outlet gear pump, an ultrafiltration membrane component, a pressure sensor, a transmembrane pressure difference display, a sludge discharge peristaltic pump and a water outlet pool, the MBR double-layer assembly membrane pool comprises a first MBR membrane assembly and a second MBR membrane assembly, the first MBR membrane assembly and the second MBR membrane assembly are arranged in parallel, and the perforated aeration pipe and the pulse aeration box are configured to be used as aeration assemblies of the first MBR membrane assembly and the second MBR membrane assembly. According to the invention, the membrane is physically scrubbed by utilizing different aeration modes and a double-layer aeration mode, so that the phenomena of membrane pollution, sludge accumulation and wire breakage are effectively relieved, a reliable theoretical basis is provided for transforming an MBR membrane tank into a double-layer aeration mode in a water purification plant using the MBR hollow fiber membrane, and meanwhile, the method has important significance on reducing pollution and carbon.

The invention discloses a method for slowing down membrane scaling and improving membrane treatment capacity in saline-alkali water treatment, and belongs to the technical field of agricultural saline-alkali water treatment and membrane separation, and the method comprises the following steps: (1) for hardness ions with the highest content in saline-alkali water to be treated, calculating the theoretical ion concentration # imgabs0 # of the hardness ions on the surface of a separation membrane and the inorganic scaling supersaturation index SI under a set working condition, predicting the membrane scaling trend in the separation process, and determining the target removal rate of the hardness ions in the agent softening process; (2) calculating the target removal rate of other hardness ions in the saline-alkali water to be treated; (3) optimizing the process parameters of the agent softening process so as to achieve the target removal rate of the hardness ions calculated in the above steps; and (4) carrying out agent softening on the saline-alkali water to be treated under the optimized process parameters, and filtering the saline-alkali water softened by the agent by using a separation membrane, thereby retarding membrane scaling and improving the membrane treatment capacity.

The embodiment of the invention discloses a leachate concentrated water softening control method based on double closed-loop predictive control, and relates to the technical field of automatic control of environmental engineering. The method comprises the following steps: acquiring multi-dimensional data acquired in real time; a dynamic mass balance equation of a magnesium removal / calcium removal reaction is constructed, the dosage of NaOH / Na2CO3 is optimized in a rolling mode on the basis of a model prediction control algorithm, and accurate control over the pH value and the hardness removal rate is achieved; a membrane pollution state space equation is constructed, a membrane type switching and cleaning strategy is optimized in combination with a dynamic programming algorithm, and Pareto optimal control of flux maintenance and cost minimization is achieved. The industrial problems of low control precision, high operation cost and poor system robustness in high-hardness wastewater softening treatment are solved, and the method has remarkable technical advancement and engineering application value.

According to the efficient water treatment system based on the reverse osmosis membrane and the control method provided by the invention, gradient anti-pollution membrane arrangement and reconfigurable intelligent waterway design are creatively combined, so that a foundation is laid for high-flux and low-pollution operation from a physical structure; meanwhile, a full-closed-loop intelligent control system is constructed, and by fusing fuzzy logic, an optimization algorithm and a neural network prediction model, the system can integrate multi-dimensional information such as inlet water quality, membrane pressure difference and water yield, judge pollution risks in real time and adaptively and seamlessly switch among a high recovery mode, a segmented flushing mode and a prediction cleaning mode, so that the system is high in recovery efficiency and high in reliability. Meanwhile, parameters such as pump frequency and valves are dynamically optimized to find an optimal balance point of recovery rate and energy consumption; according to the invention, the recovery rate can be stably improved, the membrane pollution rate is greatly reduced, the chemical cleaning period and the service life of the membrane element are prolonged, and finally, the comprehensive operation and maintenance cost of the system is remarkably reduced while the water treatment efficiency and the water quality safety are improved.

The direct drinking water system comprises a pretreatment device, a membrane treatment device, a finished product water tank, a cleaning device, a water supply branch and a water return branch, the cleaning device comprises a concentrated water tank, a gas-liquid mixing branch, a bypass pipeline, a pulse water supply branch, a cleaning main pipe, a forward washing branch, a backwashing branch and a lower opening sewage branch. According to the utility model, quick switching in the cleaning process of the hollow fiber membrane is realized, the highest and lowest pressurization values in each period are accurately controlled, and meanwhile, adverse effects on membrane water production caused by water production of a water hammer and an air hammer are avoided; the pulse type positive and back washing is combined, so that the membrane pollutants are removed more thoroughly; according to the device, ozone microbubbles of a cleaning medium are exploded, so that the pollutant removal capacity is also improved; and when the filtered water in the finished water tank is stored for a long time, the ozone aeration device can also be used for sterilizing the filtered water, so that the application range of the device is expanded, and the safety of direct drinking water is guaranteed.

The present invention relates to a method for automated data processing to assess the state of multiple filtration membranes used in liquid filtration. The method involves receiving data from state sensors positioned within or near a set of membranes, which process incoming water into permeate and concentrate flows. This data, collected as time series at predefined frequencies, pertains to external physical parameters. An operating indicator is determined from this data, forming a second time series. Both the first and second time series are recorded as point clouds over a specified acquisition period. An intermediate operating indicator is generated, representing the state of new, clean, or cleaned membranes, using a learned normalization model. Finally, a normalized operating indicator is produced, characterizing membrane fouling and aging, independent of environmental variations, and forming a third time series. This method enhances the accuracy of membrane state assessment in filtration systems.

This invention relates to the field of water treatment technology, specifically to a reverse osmosis membrane cleaning agent composed of a non-oxidizing bactericide and an enzyme, and its application. Its main purpose is to remove biofouling from reverse osmosis membranes and restore membrane flux. This patent discloses a novel reverse osmosis membrane cleaning agent formulation. Considering the different pH requirements of the cleaning agent in actual production, different types of bioactive hydrolytic enzymes are selected to disrupt the structure of bacterial cell walls and their extracellular polymers. A reducing agent and a non-oxidizing bactericide are combined to enhance the cleaning effect of the reverse osmosis membrane. For different cleaning purposes, non-oxidizing bactericides suitable for acidic and alkaline cleaning are formulated. Sodium benzoate and benzalkonium chloride show good application prospects in the removal of reverse osmosis membrane fouling.

The invention relates to the field of direct drinking water of a fusion calculation model, and discloses a double-membrane mixing direct drinking water system and an operation method.The method comprises the steps that online data of a water supply end and a water quality monitoring end are collected for cleaning and time alignment, and a basic working condition data set is formed; the method comprises the following steps: establishing an operation characteristic model comprising water demand prediction, raw water quality prediction and membrane flux characteristic sub-models, calculating double-membrane target water yield and water quality indexes based on the model to generate a control parameter set, evaluating membrane pollution to generate an online cleaning task queue, performing joint simulation to generate a pressure stability control instruction set, and dynamically correcting the model. The conversion from passive response to active pre-judgment of the double-membrane mixing direct drinking water system is realized, and the operation stability, the water quality guarantee capability and the water-saving and energy-saving effects are improved.

The invention discloses an efficient anti-crystallization urea solution for vehicles and a preparation method thereof, and belongs to the technical field of preparation of urea solutions for vehicles, the preparation method comprises the following steps: preparing an inclusion compound, hydrolyzing and crystallizing, dissolving, filtering, finely filtering and ultra-filtering; the hydrolytic crystallization method comprises the following steps: mixing industrial urea and three-stage water, stirring at 70-75 DEG C for 30-40 minutes, carrying out first gradient cooling and second gradient cooling, adding an inclusion compound, carrying out third gradient cooling and fourth gradient cooling, preserving heat, centrifuging, and taking a precipitate. During preparation, the industrial urea is used as a raw material, the prepared efficient anti-crystallization urea solution for the vehicle is low in impurity content, free of precipitates and high in urea yield, the urea yield is slightly influenced by the impurity content in the industrial urea, and when precipitates in the urea solution are removed through an ultrafiltration method, the membrane flux is slightly influenced by the precipitate content in the urea solution; the problem of membrane pollution can also be avoided.

The invention relates to the technical field of data processing, and discloses an automatic control method and system for wastewater desalination treatment based on the Internet of Things. The method comprises the following steps: acquiring a membrane surface image, an interface potential, an infrared spectrum, a transmembrane pressure difference, a feeding flow rate and a flocculation image; calculating a salt gradient attention feature vector according to the salt concentration difference value and the Beckey number; inputting the multi-modal data into a membrane pollution time sequence memory network to obtain a pollution development rate and a flocculant adding proportion; extracting a floc particle size parameter to obtain a flocculation effect score; and constructing a multi-objective function optimization solution to obtain a control instruction. The salt gradient attention mechanism and the membrane pollution time sequence memory network are introduced, and visual image recognition and multi-modal data fusion are combined, so that the problems of inaccurate membrane pollution state evaluation, low intelligent level of a control algorithm and insufficient multi-target collaborative optimization capability in the prior art are solved; the automation level and the treatment efficiency of wastewater desalination treatment are improved.

The invention discloses a membrane filtration sewage treatment system based on fine control, relates to the technical field of sewage treatment, and solves the technical problems of delayed response to water quality and water quantity fluctuation and extensive biochemical reaction control caused by water inlet load impact in an existing sewage treatment system. The system comprises an inflow water pretreatment module used for predicting inflow water quality in real time by using a water quality prediction model based on a neural network, and carrying out water quantity balance and water quality adjustment on inflow water according to a prediction result to obtain a first water body; the biochemical reaction module is used for cooperatively controlling external carbon source addition, nitrification liquid backflow and aeration intensity in the biological reaction process of the first water body by utilizing a multivariable model predictive control method to obtain a second water body after biochemical reaction; and the deep purification module is used for carrying out solid-liquid separation and deep purification on the second water body to obtain an effluent water body, and triggering cleaning and maintenance operation of the membrane based on a prediction result of the membrane pollution characteristics.

The invention discloses a membrane bioreactor membrane pollution prediction method and system, and relates to the technical field of pollution detection. The method comprises the following steps: setting a frequency detection strategy, and connecting a membrane bioreactor to obtain a membrane feedback signal based on the frequency detection strategy; performing feedback signal fluctuation time sequence characteristic analysis based on the membrane feedback signal to obtain a feedback signal characteristic; the feedback signal features are combined with reactor operation parameters to be imported into a mechanism hybrid prediction model, mechanism constraint is carried out through a mechanism constraint layer according to the feedback signal features, data analysis is carried out on time series data features on the basis of the mechanism constraint through a data driving prediction layer, and a membrane state prediction result is obtained; and performing membrane pollution state analysis and progress prediction according to the membrane state prediction result, and generating a membrane pollution prediction data chain. The technical problems that in the prior art, membrane pollution prediction depends on an empirical threshold, so that recognition lags and precision is insufficient are solved, and the technical effect of improving membrane pollution prediction accuracy and real-time performance is achieved.

The invention provides an intelligent maintenance cleaning device for nanofiltration membrane pollution control and a method thereof, and solves the problems of low maintenance cleaning efficiency, poor adaptability of cleaning parameters and pollution conditions and frequent chemical cleaning of an existing nanofiltration system. Belongs to the technical field of membrane pollution control. According to the invention, the filtering system, the cleaning system and the intelligent decision-making module are integrally designed, so that the closed-loop control of the whole process of real-time monitoring, pollution identification, intelligent decision-making and automatic cleaning of the running state of the nanofiltration membrane is realized. The cleaning effect gating memory neural network model is used as a membrane pollution prediction model, membrane pollution can be accurately predicted, and key cleaning nodes can be identified. The optimal membrane cleaning parameters are generated by adopting a non-dominated sorting genetic algorithm, the usage amount of chemical agents and the cleaning cost are reduced, and the membrane pollution control effect is improved. The invention is applicable to the field of drinking water treatment.

The invention relates to the technical field of water treatment, and discloses a water treatment recycling system which comprises a pretreatment unit, a membrane treatment unit, a deep treatment unit and a recycling unit. The pretreatment unit is used for removing large-particle impurities through a grating, a grit chamber and a coagulative precipitation tank; the membrane treatment unit comprises an ultrafiltration and reverse osmosis membrane assembly and a matched membrane cleaning system; the advanced treatment unit purifies water by means of ozone-ultraviolet combined oxidation and mixed ion exchange resin; and the recycling unit is provided with a monitoring and flow control device. In addition, the system is provided with an intelligent control system, real-time monitoring can be achieved, parameters can be automatically adjusted according to the water quality, and efficient and stable water treatment and recycling are achieved. The system has the advantages that the structure and function of each treatment unit are optimized, the water treatment efficiency and quality are improved, the operation cost is reduced, efficient recycling of water resources is realized, and meanwhile, the problems of membrane pollution and difficulty in removal of refractory organic matters in the prior art are solved.

The invention discloses a PINN algorithm-based coordinated regulation and control method and system for an electrochemical carbon capture membrane distillation process, and the method comprises the steps: collecting multi-mode operation data in the electrochemical carbon capture membrane distillation process, constructing a physical model in the electrochemical carbon capture membrane distillation process, constructing a physical information neural network (PINN), and carrying out the PINN algorithm-based coordinated regulation and control of the electrochemical carbon capture membrane distillation process. Taking the physical model as a physical constraint of a physical information neural network (PINN); inputting the multi-mode operation data into a physical information neural network (PINN), predicting membrane flux, Li2CO3 yield and membrane scaling rate, and calculating a physical constraint residual error; and according to an error between a predicted result and a true value and a physical constraint residual error, carrying out dual-time scale optimization strategy adjustment on the multi-modal operation data. According to the invention, dynamic decoupling regulation and control of multiple operation parameters in the electrochemical reaction process are realized, and the CO2 capture efficiency and the membrane distillation performance are improved.